July 29 ~ 30, 2023, London, United Kingdom
Xu Lin1,*, Heng Li1,*, Yukun Qian1, Yun Lu2 and Mingjiang Wang1, 1College of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China, *These authors contributed to the work equally and should be regarded as co-first authors and 2School of Computer Science and Engineering, Huizhou University, Huizhou, China
Sleep apnea syndrome (SAS) is a dangerous and high incidence sleep disorder. As more and more people are affected by SAS, monitoring SAS in family life becomes increasingly important. It is very meaningful to design an automatic SAS monitoring device. We designed a SAS detection model based on neural network, and transplanted the model to an application specific integrated circuit (ASIC). There are many problems in the process of transplanting neural network to ASIC. One of the most serious problems is the transplantation of nonlinear activation functions. We proposed a software-hardware joint optimization method to solve the problem of activation function in the SAS model transplantation. In the process of building the neural network model, our research modified the activation function in the traditional LSTM model and the attention mechanism, and adopted hard-sigmoid and Leaky-ReLU activation function for digital circuits. In the hardware transplantation, the activation function was constructed using the binary shift-based division, three-segment and two-segment function. A digital circuit without migration errors can be obtained with a small area and time consumption. We jointly optimized the structure of the model design and the digital circuit implementation. The model was more suitable for the digital circuit structure, so that the neural network can be transplanted more smoothly.
Sleep apnea detection, Neural network, Hardware transplantation, Activation function.
Victoria Andrea Cotella, Department of Architecture, University of Naples Federico II, Naples, Italy
In recent years, interest in the automatic semantic segmentation of 3D point clouds using machine and deep learning (ML/DL) has grown due to its fundamental role in scene understanding in various computer vision, robotics and remote sensing applications. In the architecture, engineering and construction (AECO) sector, Building Information Modelling (BIM) has become a standard approach to design and the use of 3D point clouds is currently the basis for the creation of as-built BIM models. Today, there is a research gap concerning the interface between point cloud segmentation and the Historical BIM process: there are no consistent studies demonstrating the possibility of automating the modelling of BIM families from the result obtained in the segmentation process in terms of geometry and semantic labels. Based on these assumptions, the present research aims to conduct a systematic review of the state of the art, including both empirical and conceptual studies, with the goal of offering a constructive synthesis that will provide a starting point for the development of innovative approaches in the field of BIM and AI.
Artificial Intelligence, 3D Point cloud, HBIM, Cultural Heritage, Digitalisation.
Simisani Ndaba, Department of Computer Science, Faculty of Science, University of Botswana
Depression is a prevailing mental disturbance affecting an individual’s thinking and mental development. There have been many researches demonstrating effective automated prediction and detection of Depression. The majority of datasets used suffer from class imbalance where samples of a dominant class outnumber the minority class that is to be detected. This review paper uses the PRISMA review methodology to enlist different class imbalance handling techniques used in Depression prediction and detection research. The articles were taken from information technology databases. The results revealed that the common data level technique is SMOTE as a single method and the common ensemble method is SMOTE, oversampling and under sampling techniques. The model level consists of various algorithms that can be used to tackle the class imbalance problem. The research gap was found that under sampling methods were few for predicting and detecting Depression and regression modelling could be considered for future research.
Depression prediction, Depression detection, Class Imbalance, Sampling, Data Level and Model Level.
Ziqi Liu1, Yujia Zhang2, 1The Governor’s Academy, 1 Elm St, Byfield, MA 01922, 2Computer Science Department, California State Polytechnic University, Pomona, CA91768
Online schooling has become more and more popular during recent years due to COVID-19 [1][15]. It allowsteaching to continue without in-person contacts. A prominent issue with online schooling is teachers are unabletooversee students’ behavior during class as they would in-person. It has been known that many students tend toloseattention. This can make online schooling less ef ective, causing itci to yield worse results than in-person schooling[2]. In order to tackle this issue, this paper outlines a tool that has been developed to monitor children’s mouse andkeyboard movements during online classes and analyze the data with artificial intelligence to ensure students arefocused in class [3]. For example, if the students are typing and clicking their mouse frequently, then there is ahigher possibility the student is not focused because frequent keyboard and mouse movements might indicate theyare chatting with friends or playing games; on the other hand, if they are attentive in class, there would be lesskeyboard and mouse movements, as they should be taking notes.
Depression prediction, Depression detection, Class Imbalance, Sampling, Data Level and Model Level.
Mahfoudh Batarfi and Manohar Mareboyana, Department Of Computer Science, Bowie State University, Bowie, MD, USA
Features that can be extracted from a single image are very important in 3D face reconstruction neural networks because they provide additional information beyond the image’s size and quality. These features can be used to compensate for the lack of prior knowledge provided by a single 2D image and to overcome the dimensional differences between 2D and 3D. This paper collects features that can be extracted from a single image include facial landmarks, which provide information about the geometric structure of a face, and texture maps, which provide information about the surface properties of a face. Additionally, depth maps, shading information, and albedo maps can be used to understand the 3D structure of the face and how light interacts with it. By using these features, 3D face reconstruction neural networks can create more detailed and accurate 3D models of faces, even when the input image is of low quality or has extreme poses or occlusions.
Landmarks, Depth, Texture, UV, Shading, Albedo,Face Parsing.
Ebtehal T. Alotaibi1 and Michael Herrmann2, 1, 2Institute of Perception, Action and Behaviour, Edinburgh, UK, 1Computer Science Department, Imam Mohammad Ibn Saud University, Riyadh, Saudi Arabia
Autonomous Mobility on Demand (AMoD) systems have the potential to revolutionize urban transportation by offering customers mobility as a service without the need for car ownership. However, optimizing the performance of AMoD systems presents a challenge due to competing objectives of reducing customer wait times and increasing system utilization while minimizing empty miles. To address this challenge, this study compares the performance of max-policy sharing agents and independent learners in an AMoD system using reinforcement learning. The results demonstrate the advantages of the max-policy sharing approach in improving Quality of Service (QoS) indicators such as completed orders, empty miles, lost customers due to competition, and out-of-charge events. The study identifies the importance of striking a balance between competition and cooperation among individual autonomous vehicles (AVs) and tuning the frequency of policy sharing to avoid suboptimal policies. The findings suggest that the max-policy sharing approach has the potential to accelerate learning in multi-agent reinforcement learning systems, particularly under conditions of low exploration.
reinforcement learning, multi-agent, consensus learner, max-policy sharing, autonomous mobility on demand.
Jianqiao Cui and Stefan Bleeck, Institute of Sound and Vibration Research, University of Southampton, Southampton, UK
Generally, speech enhancement (SE) models based on supervised deep learning technology use input features from both noisy and clean speech, but not from the noise itself. In this paper, we propose that the clean background noise, before mixing with speech, can also be utilized to improve SE, which has not been described in previous literature to our knowledge. Our proposed model initially enhances not only the speech but also the noise, which is later combined for improved intelligibility and quality. We also present a second innovation to capture better contextual information that traditional networks often struggle with. To leverage speech and background noise information, as well as long-term context information, we describe a sequence-to-sequence (S2S) mapping structure using a novel two-path speech enhancement system consisting of two parallel paths: a Noise Enhancement Path (NEP) and a Speech Enhancement Path (SEP). In the NEP, the encoder-decoder structure is used to enhance only the clean noise, while the SEP is used to suppress the background noise in the clean speech. In the SEP, we adopt a Hierarchical Attention (HA) mechanism to capture long-range sequences more effectively. In the NEP, we utilize a traditional gated controlled mechanism from ConvTasnet but improve it by adding dilated convolution to increase receptive fields. We conducted experiments on the Librispeech dataset, and the results show that our proposed model outperforms recent models in various measures, including ESTOI and PESQ scores. We conclude that the simple speech plus noise paradigm often adopted for training such models is not optimal.
Supervised speech enhancement, separate paths, hierarchical attention mechanism, gated control, magnitude.
Omaimah Al Hosni, Andrew Starkey, School of Engineering, University of Aberdeen, Scotland/UK
This study aims to examine the performance of Complexity Measures andInstance Hardness Measures in Microarray dataset properties. The study assumes that since these measures are data dependent, they might also be negatively affected by complex datacharacteristics in not reflecting the actual data complexity.In addition, the study argues that the experiment strategy adopted mainly by others in examining only the correlation between the classification algorithm and measures performance is not a good independent indicator to validate the measures performance in estimating the actual data difficulty nor for showing the causes of the poor prediction of the learning algorithms performance as both are data dependant. Therefore, the study adopted a different experiment strategy than other works undertaken in this context. The outcomes indicated thatAmong 35 measures covered in this study, the measures had responded differently against each data challenge due to the different assumptions they adopted and their sensitivity to the different data challenges.
Complexity Measures, Instance Hardness Measures, Small Sample size, High Dimensionality, Imbalanced Classes, Microarray dataset.
Tingyu Zhang1, Jonathan Sahagun2, 1Materdei High School, 1202 W Edinger Ave, Santa Ana, CA 92707, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
The background to the problem we are trying to solve is the need for a reliable and affordable baby monitor that provides parents with real-time information on their babys condition and wellbeing [1]. While there are various baby monitors on the market, many of them have limitations such as limited range, poor connectivity, and lack of features [2]. Our proposal is to develop a smart baby monitor that incorporates a range of technologies such as Wi-Fi connectivity, temperature and humidity sensors, audio and video monitoring, and a smartphone app for remote monitoring and control [3]. Our device also includes features such as a nightlight, lullabies, and two-way communication, making it a comprehensive solution for parents. One of the main challenges we faced was ensuring that the devices connectivity was reliable and stable, particularly when transmitting data wirelessly over Wi-Fi. We addressed this challenge by using high-quality components and optimizing the devices firmware to ensure optimal performance [4]. During experimentation, we tested the device in various scenarios such as different room sizes, WiFi network setups, and environmental conditions. The results showed that the device performed reliably and accurately in all scenarios, providing parents with real-time updates on their babys condition and wellbeing. The most important results we found were that our device provided parents with a comprehensive and reliable solution for monitoring their babys condition and wellbeing. Our devices features, such as video monitoring and smartphone control, made it easier for parents to stay connected with their baby, even when they were not in the same room [5]. Our idea is ultimately something that people should use because it provides parents with peace of mind, knowing that they can monitor their babys condition and wellbeing in real-time, even when they are not in the same room. Additionally, our devices comprehensive features, such as video monitoring and two-way communication, make it a valuable tool for parents to use as their baby grows and develops [6].
Monitor, AI, Senser, baby.
Djalal Merad Boudia, Kheira Ziadi and Assia Touati, Department of Computer Science, Ain-Témouchent University, Algeria
Nowadays, in addition to the flexibility, efficiency, speed and comfort of the private car, the spatial dispersion of the habitat and their activities contribute to a considerable growth in traffic and the use of cars. This means of transport becomes the most popular and most preferred by users. For this, the automation of parking management is necessary because the human being is unable to identify, in real time and without mistakes, the cars that enter a safe place. Today, there are many systems of recognition of license plates, these systems have two major axes, which are the detection of the license plate and the recognition of its characters. Our system makes it possible to identify cars in a car park by reading the license plates. It relies on a camera associated with a plate recognition software and a database that contains the list of incoming and outgoing cars. First, pretreatments are applied to facilitate subsequent image analysis. We start with the detection of all areas that could be plates and then a procedure of recognition is applied in order to obtain the registration of the car.
Smart Parking, wireless sensor network, character recognition, image processing, license plate.
Sri Gayathri Devi I, Sowmiya Sree S, Jerrick Gerald, Geetha Palanisamy, College of Engineering, Anna University, Chennai, India
View synthesis allows the generation of new views of a scene given one or more images. Current methods rely on multiple input images which are practically not feasible for such applications. Whereas utilizing a single image to generate the 3D scene is challenging as it requires comprehensive understanding of 3D scenes. To facilitate this, a complete scene understanding of a single-view image is performed using spatial feature extraction and depth map prediction. This work proposes a novel end-to end model, trained on real images without any ground-truth 3D information. The learned 3D features are exploited to render the 3D view. Further, on querying, the target view is generated using the Query network. The refinement network decodes the projected features to in-paint missing regions and generates a realistic output image. The model was trained on two datasets namely RealEstate10K and KITTI containing an indoor and outdoor scene.
3D Scene Rendering, Dif erentiable Renderer, Scene Understanding, Quantized Variational Auto Encoder.
Fatimah Alanazi, Richard Davison, Gary Ushaw, and Graham Morgan, School of Computing, Newcastle University, Newcastle upon Tyne, UK
The detection of deep fakes simulating human faces for potentially nefarious purposes is an ongoing and evolving topic of interest. Research in prosopagnosia, or face-blindness, has indicated that specific parts of the face, and their movement, provide clues for identification to subjects with the condition. This paper outlines studies in the area of detecting and addressing the effects of prosopagnosia. For the first time, we suggest that the findings of these studies could be applied to the detection of deep fake faces, drawing a link between the facial features and movements most useful in combating the effects of prosopagnosia, with the features most productive for analysis in deep fake facial detection.
Deep fake detection, Facial recognition, Prosopagnosia, Deep learning & Biometric
Jana Skirnewskaja and Timothy D. Wilkinson, Electrical Engineering Division, Department of Engineering, University of Cambridge, 9 JJ Thomson Avenue, Cambridge, CB3 0FA, UK
Systems engineering based real-time head-up displays can increase safety and promote inclusivity in transportation. This works utilizes accelerated algorithms, a 4k spatial light modulator, virtual Fresnel lenses, a He-Ne laser resulting in the augmented reality floating holographic projections to appear within 0.9 seconds. Additionally, the personalised layout of the 3D head-up display is a paramount to this work. A robustness analysis based on Failure Mode and Effects Analysis (FMEA) was carried out. The study consisted of optical system architecture design and a failure-cause correlation. In addition, in-depth analyses of each system component were created within the optical setup boundary. The 3D floating holographic projections were assessed based on consumer demand, safety and comfort, and a cost/benefit analysis.
Augmented Reality, Systems Engineering, FMEA, 3D Computer-Generated Holography, Head-Up Displays, Automotive Applications.
Derya UYSAL and Ulug Bayazit, Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
In this work, we improve the state-of-the-art in crowd counting by further developing a recently proposed multi-scale, and multi-task crowd counting approach. While most of the studies treat density-based architectures, this study proposed a point-based method for crowd analysis. We propose automatic, and optimal weight assignment to constituents of the loss function. This approach, which is applied to each patch, ensures that the weight parameters are updated in each epoch, and added to the optimizer with model parameters rather than remaining constant. For validation of our proposed approach, we use three popular crowd counting datasets, ShanghaiTech A, ShanghaiTech B, and UCF_CC_50. The performance of our approach exceeds the performances of the other studies on the ShanghaiTech dataset, and is highly competitive with the performances of the other studies on the UCF CC 50 dataset.
Crowd Counting, Multi Scale, Automatic Weighted Loss, Point Supervision.
Matthew King1, Jonathan Sahagun2, 1University high school, 4771 Campus Drive, Irvine 92612, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
Heated jackets are a popular clothing item among outdoor enthusiasts, athletes, and workers who need to work outside in cold weather [1]. They offer a convenient and effective way to stay warm in cold temperatures, while also providing therapeutic benefits for those who suffer from certain medical conditions [2]. However, existing methods and tools can be expensive and bulky, and some may not provide even heat distribution. A new approach to creating a heater that can be added to any existing jacket and powered by plugging into a wall socket or connecting to a battery source offers a practical, flexible, and cost-effective solution to the need for warmth in cold weather [3]. This approach allows users to easily add heating elements to their existing clothing, without the need to purchase a new jacket or invest in expensive heated clothing. The option to power the heater through a wall socket or battery source offers flexibility and convenience for users who may not have access to a power source when they need it [4]. Overall, the innovative approach to adding heating elements to existing jackets offers a practical, flexible, and cost- effective solution to the need for warmth in cold weather.
Heat generation, Battery efficiency, Customization, Optimization
In Cho Cho1, Jae-Kwang Kim2, Yicheng Yang3, Yonghyun Kwon4, and Ashish Chapagain5, 1Department of Civil, Construction, and Environmental Engineering (CCEE), Iowa State University (ISU), Ames, USA, 2Department of Statistics (STAT), ISU, Ames, USA, 3CCEE, ISU, Ames, USA, 4STAT, ISU, Ames, USA, 5CCEE, ISU, Ames, USA
Advancements in machine learning (ML) hinges upon data - the vital ingredient for training. Statistically curing the missing data is called imputation, and there are many imputation theories and tools.Butthey often require difficult statistical and/or discipline-specific assumptions, lacking general toolscapable ofcuring large data. Fractional hot deck imputation (FHDI) can cure data by filling nonresponses with observed values (thus, “hot-deck”) without resorting to assumptions. The review paper summarizes how FHDI evolves to ultra data-oriented parallel version (UP-FHDI).Here, “ultra” data have concurrentlylarge instances (big-n) and high dimensionality (big-p). The evolution is made possible with specialized parallelism and fast variance estimation technique. Validations with scientific and engineering data confirm thatUP-FHDI can cure ultra data(p >10,000& n > 1M) andthe cured data sets can improve the prediction accuracy of subsequent ML. The evolved FHDIwill help promote reliable ML with “cured” big data.
Big Incomplete Data, Fractional Hot-Deck Imputation,Machine Learning, High-Dimensional Missing Data.
Richard Zhang1 and Ang Li2, 1Oakton High School, 2900 Sutton Rd, Vienna, VA 22181 and 2Computer Science Department, California State Polytechnic University, Pomona, CA91768
Oftentimes we lose track of the time we take to skim over a website or article online or we are simply curious about the time it might take for us to read over some text. We might also be curious about our attention span based onthelength or dif iculty of an article. This paper details the development process of an intelligent google chromeextension capable of gathering data from specific articles and processing the data to estimate the amount of timeneeded to read over an article based on the time it took to read similar or dissimilar articles [10]. This applicationtakes into account the length, readability, average word size, and comparisons to other reading times in order toreturn the most accurate time predictions. The benefit of this application is improved time management as anaccurate prediction of time will be provided.
Chrome-extension, Time management, Machine learning, Web scraping.
Abdelghani ZOUADI, Laboratory of Studies and Research in the Sciences of Education, Didactics and Management, Regional Center for Education and Training Careers Daraa-Tafilalet, Errachidia, Morocco
The issue of distance education is of great importance due to the development of mass media and information communication technologies. This importance has grown greater and greater, especially in the period of the coronavirus pandemic (Covid19) which obliged people to stay at home and continue studying online and via different tools. This study aimsis to investigate the trainees perceptions related to distance education at the Regional Center for Education and Training Careers – Daraa Tafilalet (RCETC-DT) during the period of coronavirus (Covid19). The research method included the quantitative approach. The data was collected through a questionnaire from a sample of 41 participants at the department of educational administration and preservice teachers in the RCETC-DT in Errachidia and Ouarzazate, Morocco. The findings indicated that distance education has strengths and weaknesses. They confirmed also that distance education can be more successful if more attention is given to from the official educational authorities.
Distance Education, E-Learning, Information Communication Technology, Education &Training, Educational Administration.
Maruf Farhan, Dr. Abdul Salih, Northumbria University, United Kingdom
Internet of Things (IoT) devices play bigger roles in providing smart, intelligent, and efficient industry solutions. IoT devices typically communicate over the network to perform a variety of activities, for example sharing collected information from the sensors and receiving instructions to perform a specific task. These activities require IoT devices to identify over the network. Verifying identity of IoT devices using traditional methods expose too much information. In case of security breach, malicious actors can use this information to perform impersonation attacks. A better approach to identify IoT devices over the network is required such as Self-sovereign identity (SSI). SSI is typically a decentralized digital identity framework which uses digital identity and verifiable credentials. This paper will explore the use of SSI solution with IoT devices to empower privacy of IoT devices.
IoT, Internet of Things, Self-Sovereign identity, SSI, Decentralized Identifiers, Privacy, Security
Matthew Ciolino and Grant Rosario and David Noever, PeopleTec, Inc, USA
Soft labels in image classification are vector representations of an images true classification. In this paper, we investigate soft labels in the context of satellite object detection. We propose using detections as the basis for a new dataset of soft labels. Much of the effort in creating a high-quality model is gathering and annotating the training data. If we could use a model to generate a dataset for us, we could not only rapidly create datasets, but also supplement existing open-source datasets. Using a subset of the xView dataset, we train a YOLOv5 model to detect cars, planes, and ships. We then use that model to generate soft labels for the second training set which we then train and compare to the original model. We show that soft labels can be used to train a model that is almost as accurate as a model trained on the original data.
Soft Labels, Object Detection, Datasets
Jevon Mao1, Marisabel Chang2, 1,Santa Margarita Catholic High School 22062 Antonio Pkwy, Rancho Santa Margarita, CA 92688, 2,Computer Science Department, California State Polytechnic University, Pomona, CA 91768
Mass shootings have emerged as a significant threat to public safety, with devastating consequences for communities and individuals affected by such events [7]. However, a lack of widespread use of new technological infrastructure poses significant risk to victims [8]. This paper proposes a system to classify and localize gunshots in reverberant indoor urban conditions, using MFCC features and a Convolutional Neural Network binary classifier [9]. The location information is further relayed to users through a mobile client in real time. We installed a prototype of the system in a high school in Orange County, California and conducted a qualitative evaluation of the approach. Preliminary results show that such a mass shooting response system can effectively improve survivability.
Machine Learning, Public Safety, Acoustics, Directioning.
Puthiyavan Udayakumar, United Arab Emirates
Several domains of our daily lives are rapidly experiencing the Internet of Things, including home appliances, vehicles, industry, education, agriculture, hospitals, environmental monitoring, etc. These domains have reached new peaks and are rapidly growing in popularity. Each aspect of the Internet of things (IoT) has its own marks and milestones, gradually increasing as the technology becomes more advanced and convenient. The Internet of things (IoT) combines various technologies and techniques to create an organized and interconnected world so communication between entities can be done in a better, more efficient, and usable manner. The main characteristics of any technology are its security, privacy, authentication, and trustworthiness for the end users. Security, trust, and confidentiality are crucial in ensuring users satisfaction, and IoT security is chiefly concerned with authentication, confidentiality, and access control. There are several ways to manage IoT security, privacy, and trust, including NFC, RFID, and WSN. However, IoT systems are hampered by a lack of comprehensive security solutions across various vertical application domains due to a lack of comprehensive security solutions. This research paper will focus on the top ten areas that must be secured from a security and privacy standard point for IOT devices to fill this gap.
Sensors/Devices, Data Processing, Network connectivity, Network Protocols, Wireless Networks, Mobile Networks, Viruses, Worms, Trojan, Hardware-based Root of Trust, Small Trusted Computing Base, Defense in Depth, Compartmentalization, Certificate-based Authentication, Renewable Security, Failure Reporting.
Bilel Ben Romdhanne, Mourad Boudia and Nicolas Bondoux, Artificial Intelligence Research, Amadeus SAS, Sophia Antipolis, France
With the development of the cloud offers, we observe a prominent trend of applications being migrated from private infrastructure to the cloud. Depending on the application’s complexity, the migration can be complex and needs to consider several dimensions, such as dependency issues, service continuity, and the service level agreement (SLA). Amadeus, the travel industry leader, had partnered with Microsoft to migrate its IT ecosystem to the Azure cloud. This work addresses the specificity of cloud-to-cloud migration and the multi-cloud constraints. In this paper, we summarise the Amadeus Migration process. The process aims to drive the migration from an initial private cloud environment to a target environment that can be a public or hybrid cloud. Further, the process focuses on a prediction phase that guides the migration process. This paper expects to provide an efficient decision-making process that guides managers and architects to optimise and secure their migration process while considering micro-services-oriented applications targeting an efficient deployment over multi-cloud or hybrid cloud. The prediction relies on the network simulation to predict applications’ behaviour in the cloud and evaluate different scenarios and deployment topologies beforehand. The objective is to predict migrated applications’ behaviour and identify any issue related to the performance, the application’s dependency on other components, or the deployment in the cloud. The migration process proposed in this paper relies on SimGrid, a toolkit developed by INRIA[52] for distributed application modelling. This framework offers a generic process to model IT infrastructure and can assist cloud-to-cloud migration. Specific attention is given to predictive and reactive optimisations. The first results show predictive optimisations impact on securing KPI and reactive optimisation to optimise the solution cost.
Cloud migration, SimGrid, system simulation, app modelling, decision support, cloud deployment strategy.
Xiyang Sun, Yue Zhao and Sheng Zhang, Key Laboratory of Advanced Sensor and Integrated System, Tsinghua Shenzhen, International Graduate School, Tsinghua University, Shenzhen, 518055, China
Convolutional neural networks have been continuously updated in the last decade, requiring more diverse floating-point formats for the supported domain specific architectures. We have presented VARFMA, a tunable-precision fused multiply-add architecture based on the Least Bit Reuse structure. VARFMA optimizes the core operation of convolutional neural networks and supports a range of precision that includes the common floating-point formats used widely in enterprises and research communities today. Compared to the latest standard baseline fused multiply-add unit, VARFMA is generally more energy-efficient in supporting multiple formats, achieving up to 28.93% improvement for LeNet with only an 8.04% increase in area. Our design meets the needs of the IoT for high energy efficiency, acceptable area, and data privacy protection for distributed networks.
Fused Multiply-add, Tunable-precision, Distributed Network, Energy Efficiency, IoT.
Madhusudhan Rao Mulagala1 and Saketha Kusuru2, 1Department of Computer Science and Engineering, Lovely Professional University, Phagwara, India, 2Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Pillaichavady, India
Green computing is illustrated because of the examination and sees of arranging, creating, utilizing, and getting rid of PCs, servers, and related frameworks, for example, screens, printers, stock-piling gadgets, and systems administration and correspondences frameworks in a very efficient and able manner and in such a manner on accomplishing the ideal outcome with least or no effect on the climate. The objective of green distributed computing is to hack back the utilization of risky assets, augment vitality intensity all through the item’s time frame and advance the recyclability and reuse of out-of-date stock items and mechanical plant squander. Green distributed computing is regularly accomplished by utilizing the product long life Resource portion strategies or more virtualization systems or Power the board methods. Force is the bottleneck of rising the framework execution. Force utilization is delivering a major issue because of extreme warmth. As circuit speed expands, power utilization develops. The information focuses on working with computing models that have a few applications that require on-request asset provisioning and designation considering time-changing remaining burdens that square measure statically dispensed bolstered top burden qualities, to continue separation and give execution ensures while not giving much consideration to vitality utilization.
Cloud computing, Performance, Utilization, Green computing.
Jing Zhao and Qianqian Su, Department of Computer Science and Technology, Qingdao University, Qingdao, China
One of scenarios in data-sharing applications is that files are managed by multiple owners, and the list of file owners may change dynamically. However, most existing solutions to this problem rely on trusted third parties and have complicated signature permission processes, resulting in additional overhead. Therefore, we propose a verifiable data-sharing scheme (VDS-DM) that can support dynamic multi-owner scenarios. We introduce a management entity that combines linear secret-sharing technology, multi-owner signature generation, and an aggregation technique to allow multi-owner file sharing. Without the help of trusted third parties, VDS-DM can update file signatures for dynamically changing file owners, which helps save communication overhead. Moreover, users independently verify the integrity of files without resorting to a third party. We analyse the security of VDS-DM through a security game. Finally, we conduct enough simulation experiments and the outcomes of experimental demonstrate the feasibility of VDS-DM.
Security, Data Sharing, Dynamic Multi-Owner, Verification
Nick Kalsi, Fiona Carroll, Kasha Minor, Jon Platts, Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus,Western Avenue, Cardiff, CF52YB
Enhancing the sustainability of the hospitality sector with technology is essential to achieving growth whilst also reducingthe hotel’s impact on the environment. Indeed, the concept of Internet of Things (IoT) has recently gained popularity as a new research topic in a wide variety of industrial disciplines, including the hospitality industry. IoT is being seen and used to transform the hospitality industry for the newly desired sustainable growth. However, it is not all ‘smooth sailing’ as multiple challenges must be addressed by organisations in the hospitality industry when installing IoT. These challenges include cost, security, infrastructure and IoT protocols. Taking into consideration the diversity of IoT applications, the paper will examine IoT’s use in hotels whilst also highlighting the challenges that hotels face when using IoT. In particular, it will cover the effect of cyber security including IoT’s protocol layers, potential monitoring and sensor technologies.
Hotel, Internet of Things (IoT), Sensors, IoT Security, Cloud, compliance, privacy, safety, standard, communication, information.
Falak Nawaz1 and Naeem Khalid Janjua2, 1National Computational Infrastructure (NCI), Australian National University, Canberra, ACT, Australia, 2Edith Cowan University, Perth, WA, Australia
In the literature, existing approaches use runtime monitoring to detect SLA violation and prediction techniques for SLA violation prediction by using the historic QoS data to predict the future QoS values. However, these approaches do not consider the occurrence of eventsand the impact they will have on SLA violation. Moreover, existing approaches also do not provide any recommendation actions to the SLA manager to prevent violation proactively. These limitations of the current literature need the development of a comprehensive framework that can identify and capture events which impact a services quality/performance and model their effect on QoS attributes to reduce the negative consequences by avoiding SLA violation.In order to address these problems, we propose an SLA violation prevention framework. The proposed framework provides a methodology for recommending suitable actions to the SLA manager for SLA violation prevention. Moreover, the performance and the usefulness of the proposed framework is evaluated and validated by developing a proof of concept and applying it on case studies.
Cloud of Things (CoT),Service Level Agreement (SLA), SLA violation, violation prevention,proactive management, Quality of Service (QoS)
Shaikha Alhajri, Noura Aljulaidan, Zainab Alramdan, Relam Alkhaldi, Zomord Alshihab, Khaznah Alhajri, Huda Althumali, and Taghreed Balharith, Computer Science Department, College of Science and Humanities, Imam, Abdulrahman Bin Faisal University, P.O.Box 31961, Jubail, Saudi Arabia.
The Internet of Things (IoT) is one of the technology trends nowadays. In addition, the IoT is one means of developing a living life. This paper presents a smart city model based on the IoT using Cisco Packet Tracer simulation software. As a starting point, the paper explains smart city architecture that aims to improve life through three aspects. The first aspect is creating a network that allows users to control their smart devices from anywhere and at any time. The second aspect is bypassing the high budget by improving operational efficiency through the managed interconnection between smart devices within the city. Ultimately, the third aspect is increasing security in all city facilities. The simulation showed that the smart city would make life in cities more productive and interactive.
IoT, Smart City, Cisco, Packet Tracer, Networks
Nagla O. Mohamed, Computer Science and Engineering Department, Yanbu Industrial College, KSA
The combined guard channels and mobile assisted with handoff queueing call admission control is studied. Two customer types, narrowband (voice calls) and wideband (data, video and media) are considered. Matrix algorithmic techniques are used to solve the balance equations to calculate the different performance measures of the system. The results indicate that when handoff call are queued, handoff call dropping is reduced for both types of calls and there is an increase in the bandwidth utilization. There is no noticeable change in the blocking probability of new calls. Increasing the size of the queue, led to further reduction in the handoff call dropping and increase in the bandwidth utilization.
Call admission control, handoff, guard channels, mobile assisted handoff.
Shengyu Zhang1, Jonathan Sahagun2, 1Rancho Cucamonga High School, 11801 Lark Dr, Rancho Cucamonga, CA 91701, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
The project aims to develop a mobile application to increase device donation within a community and evaluate its ef ectiveness in increasing donation and user satisfaction [1]. The background of the problem is presented along with the hypothesis that the mobile application will increase donation by making it more convenient and increasing awareness of the need for donation [2]. The proposed solution involves conducting two studies: one to measure the number of devices donated by participants who have access to the mobile application and one to measure the satisfaction level of users who donate via the mobile application compared to traditional methods. The key technologies and components of the program are outlined, including a user-friendly interface and features such as device compatibility and donation tracking. Challenges encountered during development are discussed, including issues with testing and user feedback. The application is tested in various scenarios to ensure functionality and usability. The results show an increase in the number of devices donated and higher satisfaction levels among users who donate via the mobile application [3]. The projects importance is highlighted by the potential to make a positive impact on the community and improve access to technology for those in need.
Digital divide, Device donation, E-waste reduction, Community engagement.
Seyi E. Olukanni,Department of Physics, Confluence University of Science and Technology, Nigeria
This paper presents a comparative study of denoising techniques for improving 5G communication at 3.5GHz. A 5G communication system comprising a transmitter, a channel, and a receiver is simulated with MatLab and three types of noise (thermal noise, intermodulation noise, and external interference) are introduced to the generated signal. The wavelet, PCA, weiner, median filter, and the karmer filter are used to denoised the signal with only thermal noise and also denoise the signal with all the noise present.Their perfomances are measured using signal-to-noise ratio (SNR), mean square error (MSE), and peak signalto-noise ratio (PSNR).The results show that karmer filter outperforms the other techniques in terms of MSE, SNR and PSNR.These findings can be valuable for researchers and practitioners in the field of 5G communication system design and implementation, as they provide insights into the most effective denoising techniques for improving 5G communication performance.
5G, communication, Denoising, Wavelet, Wiener Filtering, PCA, Median Filtering, Kalman Filtering.