Reviewerly/exHarmony huggingface dataset:
Implementation of Lightweight Ciphers and Their Integration into Entity Authentication with IEEE 802.11 Physical Layer Transmission

This paper investigates the performance of three lightweight authenticated ciphers namely ACE, SPIX and WAGE in the WiFi and CoAP handshaking authentication protocols. We implement the WiFi and CoAP handshake protocols and the IEEE802.11a physical layer communication protocol in software defined radio (SDR) and embed these two handshaking protocols into the IEEE802.11a OFDM communication protocol to measure the performance of three ciphers. We present the construction of KDF and MIC used in the handshaking authentication protocols and provide optimized implementations of ACE, SPIX and WAGE including KDF and MIC on three different (low-power) microcontrollers. The performance results of these three ciphers when adopted in WiFi and CoAP protocols are presented. Our experimental results show that the cryptographic functionalities are the bottleneck in the handshaking and data protection protocols.
YOLOv4-MobileNetV2-DW-LCARM: A Real-Time Ship Detection Network

Ship detection is a key research task for ship identification, monitoring and management to ensure the safety of shipping lanes and harbour. Object detection methods based on computer vision and deep learning has the potential for real-time ship detection, but has the challenges of accuracy, real-time, and lack of high-quality ship datasets. This paper proposes an object detection network named YOLOv4-MobileNetV2-DW-LCARM for ship detection, which is a hybrid application of YOLOv4, MobileNetV2, Depthwise separable convolution and a proposed Lightweight Channel Attention Residual Module (LCARM). To verify the effectiveness of the network, we built a ship dataset with 10 categories and 20216 samples for model training, ablation experiments, and comparative experiments. Compared with YOLOv4, the results show that our network reduces the number of parameters by 82.58% and improves the forward inference speed by 53.6%, reaching the accuracy of 62.75% ArP@0.75 and 94% AP@0.5. The proposed network can be deployed on edge devices for real-time ship detection because it has 24.82 FPS of processing speed. The proposed dataset construction methods also contribute to similar object detection tasks.
AttenQ- Attention Span Detection Tool for Online Learning

The global pandemic of Covid-19 has caused educational institutes all over the world to shut down and switch to digital versions of the classroom. As per research conducted, it has been uncovered that the attention span of students has gradually reduced due to online learning. This can have ill-effects on their mental capability. In order to address the problem, we aim to build a system that would monitor the activities of students throughout the session and provide the teacher with the information regarding the attention span of students with the help of a dashboard. The system will make use of computer vision and feature detection methods in order to provide precise information about the student activity. The factors taken into consideration are the face detection, eye position estimation, head pose estimation, and drowsiness detection of the student to determine whether a student is attentive in class or not.
Identifying Desired Timestamps in Carved Digital Video Recorder Footage

Closed-circuit television cameras conduct round-the-clock surveillance to capture video evidence of illegal activities and suspects. The camera outputs are processed and stored on hard drives in standalone digital video recorders. As closed-circuit television cameras operate continuously, video files are constantly stored on the hard drives, with the new files periodically overwriting older files. The partially-overwritten files, which are present in slack space, can be recovered via data carving. However, the majority of the recovered video files are often fragmented. Unlike a normal video stream, a recovered video stream comprises frames that are not in chronological order. As a result, it is very tedious for a digital forensic professional to manually locate frames of interest. This chapter describes a method for identifying timestamps in carved digital video recorder footage. Experimental results using a Python implementation demonstrate the efficiency of timestamp identification and its effectiveness at recreating video footage.
FastSlowMo: Federated Learning With Combined Worker and Aggregator Momenta

In this article, we propose and analyze FastSlowMo, a combined Nesterov accelerated gradient (NAG) style worker momentum and aggregator momentum algorithm for federated learning (FL). Existing NAG momentum-based FL algorithms depend on either worker momentum only or aggregator momentum only, which may be inefficient due to infrequent usage of momentum, data heterogeneity, and out-of-date momentum. FastSlowMo combines the advantages of worker momentum and aggregator momentum to address these issues. We then provide mathematical proof for the convergence of FastSlowMo. Finally, extensive experiments based on real-world datasets and trace-driven simulation are conducted, verifying that FastSlowMo decreases the total training time by 3–61%, and outperforms existing mainstream benchmarks under a wide range of settings.
A Survey for News Credibility in Social Networks

Text mining has been a vital area that has been linked to some fields of research such as machine learning, data analysis and gathering, and information recovery. To extract knowledge and information, Natural Language Processing (NLP) was used alternative techniques. Text mining analyses unstructured data to provide critical data and information plans in a timely manner. Nowadays everyone uses online communication activities to keep in touch with others in their daily life. As a result, they're a great way to connect. Not sorting in a paragraph in a format suitable for word recognition has become a point of contention. intensity can cause a variety of inconsistencies, such as lexical, semantic, linguistic, and syntactic ambiguities, determining the proper data arrangement. Information and data are required for learning things and reaching knowledge. This paper covered how to use text mining to determine the credibility of news on social media. The findings of this study could be used as the basis for future text mining research.
Distributed Ledger Technology (DLT) and Byzantine Fault Tolerance in Blockchain

With the advent of technology, the world saw the rise of blockchain technology, because of its accessibility, and efficiency in managing transactions and the related records. According to IBM, because it delivers immediate, shareable and entirely transparent information kept on an immutable ledger that can only be viewed by permissioned network users, blockchain is excellent for delivering that information. The most important aspect of blockchain is its distributed ledger technology. The Byzantine Fault Tolerance which is largely associated with distributed systems is a feature that allows a decentralised, trustless network to function even when some nodes are broken or malevolent. This paper explains the use of proof-of-work algorithms and Byzantine Fault Tolerance (BFT) to handle the Byzantine Faults in blockchain technology, its effects and the solutions to this problem.
Toward Efficient Computing for Robotics: From a Circuit and System View

As more and more robots enter our life and society, a new era of information technology has begun. The tutorial discusses how to design an efficient circuit and system for Robotics. This brief presents the kinematics, motion planning, perception algorithms and accelerators in robotics and analyzes their advantages, disadvantages, and bottlenecks. In view of the shortcomings of the existing computing system for robotics, we present the potential technique to achieve much higher performance and energy-efficient hardware.
E-learning Recommendation Systems: A Literature Review

Lately, the problem of choosing and searching data is becoming more and more difficult to solve, especially with the significant increase in the mass of information that exists on the internet. Computer techniques exist to facilitate research and allow the relevant extraction of information, one of these techniques is the recommended process that guides the user during his exploration, seeking for him the information that seems relevant. Different systems are capable of providing personalized recommendations to guide the user to interesting and useful resources within a large data space. Their goal is to reduce information overload through a process of collecting, filtering, and proactively recommending information. Recommendation systems introduce the notions inherent in the recommendation, based, among other things, on information retrieval, filtering, machine learning, and collaborative approaches. It also deals with the evaluation of such systems and presents different application cases. This article aims to provide a review of recommendation systems used in an e-learning environment. In the first part, we will present the concept of recommendation systems, their operating principle, its different approaches with their advantages and disadvantages, the second one, concerned this type of system in an educational environment and especially e-learning with a precision of its different approaches used and adopted in the same field.
Experimental Teaching of Robotics in the Context of Manufacturing 4.0: Effective Use of Modules of the Model Program of Environmental Research Teaching in the Working Process of the Centers “Clever”

Chemical kinetics have a huge practical solution, as it allows to determine the possibility or impossibility of a process, as well as to determine the conditions in which it occurs. This, ultimately, allows to develop new methods of synthesis of various substances, including drugs, and to solve environmental problems. It is worth noting that because of the complex tasks the requirements for methods of studying the kinetics of reactions are significantly increasing. The use of high-tech equipment allows for more in-depth research in theoretical physics, methods for measuring plasma parameters, kinetics of chemical reactions, synthesis of single and polycrystals of ultrapure ceramic materials, which confirms the relevance of this study. The subject of the study is nebulous computing and their balanced use. The object of research is the climate control system with the use of the concept of the Internet of Things. The purpose of the experimental study was to develop a hardware-software system with a balanced use of nebulous computing, which allowed to monitor the state of the environment. As part of the experimental study a device for recording changes in temperature of the substance, and the corresponding software for processing data from the microcontroller were created. A system for data analysis and determination of the amount of heat Q for different substances was developed in the table processor. The paper shows the development of a device that will investigate the course of a chemical reaction. The developed device can be used by installing software on a personal computer, which allows not to spend money on the purchase of valuable equipment, manual calculations and learning new software.
: A Tool to Transform Feature Models with Numerical Features and Arithmetic Constraints

Real-world need whose features not only have boolean values satisfying boolean constraints, but also have numeric attributes satisfying arithmetic constraints. A key operation on s finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting. (Numbers, features, models) supports by , the technique that encodes arithmetic as boolean clauses. translates s to propositional formulas whose products can be counted efficiently by #SAT solvers, enabling near-optimal products to be found. We evaluate with a diverse set of real-world , complex arithmetic constraints, and counting experiments in this paper.
Issues and Challenges in Using Electronic Health Records for Smart Hospitals

In the recent years, vast developments in information technology have given rise to the concept of Smart healthcare. Smart healthcare transforms the traditional health system into more efficient, convenient, and personalized. The technical advancements have paved the way for storing patients’ medical data in the digital form known as Electronic Health Records (EHR). EHR is playing a vital role in healthcare industry and is being implemented in several countries. Using EHR has its own set of benefits. It aids the medical practitioners to provide better and more suitable treatment to the patients. In this paper, we discuss the current issues in using the electronic health records such as privacy, security, interoperability, and data quality. Securing the health records from the unauthorized users and maintaining the privacy of patient data is an essential feature that needs to be included in the EHR. Currently, sharing EHR across the healthcare organizations is not an easy task due to lack of interoperability and standards. Another issue in the EHR is the quality of patient information stored. In this paper, we also provide an insight into the blockchain technology and semantic-based storage which can be used to store and manage the EHR. Finally, discuss the possible solutions to the current issues in electronic health records using the semantic storage and blockchain technology.
VOTA: A Heterogeneous Multicore Visual Object Tracking Accelerator Using Correlation Filters

A correlation filter (CF) is a lighter and faster solution for visual object tracking (VOT) compared with a deep neural network (DNN). However, the CF-based trackers introduce diverse computational kernels and require FP16 computations. To address these challenges, we present a VOT accelerator (VOTA), a domain-specific accelerator for CF-based VOT that meets real-time performance at high efficiency, while providing flexibility and programmability. The VOTA encompasses a Winograd convolution core (WINO), a fast Fourier transformation (FFT) core (FFT), and a vector core (VEC) for diverse kernels, integrated in a high-bandwidth star-ring topology. The VOTA’s frame-based instruction set and execution enable a 537 GFLOPS performance, adapt to variances of CF trackers, and reduce the code size. An instruction-chaining mechanism permits inter-core pipelining and improves the hardware utilization up to 84.2%. A 10.2-mm 2 28-nm FP16 system on chip (SoC) prototype incorporating the VOTA, an RISC-V host CPU, and other supportive peripherals is taped out and measured to achieve 2.45 TFLOPS/W at 0.72 V. Running the oriented particle CF (OPCF), a CF tracker enhanced by adaptive boosting and particle filtering, the SoC chip achieves 1157 frames/s ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times480$ </tex-math></inline-formula> frame size) at 0.9 V and 175 MHz, consuming 296 mW.
P-ACOHONEYBEE: A Novel Load Balancer for Cloud Computing Using燤athematical Approach

Cloud computing is a collection of disparate resources or services, a web of massive infrastructures, which is aimed at achieving maximum utilization with higher availability at a minimized cost. One of the most attractive applications for cloud computing is the concept of distributed information processing. Security, privacy, energy saving, reliability and load balancing are the major challenges facing cloud computing and most information technology innovations. Load balancing is the process of redistributing workload among all nodes in a network; to improve resource utilization and job response time, while avoiding overloading some nodes when other nodes are underloaded or idle is a major challenge. Thus, this research aims to design a novel load balancing systems in a cloud computing environment. The research is based on the modification of the existing approaches, namely; particle swarm optimization (PSO), honeybee, and ant colony optimization (ACO) with mathematical expression to form a novel approach called P-ACOHONEYBEE. The experiments were conducted on response time and throughput. The results of the response time of honeybee, PSO, SASOS, round-robin, PSO-ACO, and P-ACOHONEYBEE are: 2791, 2780, 2784, 2767, 2727, and 2599 (ms) respectively. The outcome of throughput of honeybee, PSO, SASOS, round-robin, PSO-ACO, and P-ACOHONEYBEE are: 7451, 7425, 7398, 7357, 7387 and 7482 (bps) respectively. It is observed that P-ACOHONEYBEE approach produces the lowest response time, high throughput and overall improved performance for the 10 nodes. The research has helped in managing the imbalance drawback by maximizing throughput, and reducing response time with scalability and reliability.
Linking Testing Activities with Success in Agile Development of Physical Products

Agile development methods are becoming increasingly important for mechanical engineering and the development of physical products. A key element of agile development and an integral part of the product development process is testing. This paper investigates the link between testing activities and success in agile product development. An observational study was conducted with 355 students grouped into 72 teams. Each team developed a physical product in a 12-week project. The project structure incorporated several aspects of the agile method Scrum. At the end of the project, the performance of each team’s product was evaluated with a performance score. During the project, the participants documented their invested time in development activities. To validate the result of the self-assessment, the usage of the mechatronic equipment of the individual teams was logged electronically for methodological triangulation. Statistical analysis was carried out using multiple regression models. The results show that time invested early in the project and testing activities throughout the project were statistically significant predictors of success. Furthermore, the results highlight the variety of performed testing activities and show that testing beyond the activities within Verification and Validation were performed until the very end of the project. The study results underline the importance of testing and support essential concepts discussed in the literature with quantitative empirical evidence.
Dual Axis Solar Tracker with Smart Irrigation System

In rural areas where the availability of utility grids is not possible, it is very difficult to install electric grid in those areas. The availability of plentiful sunlight is prone in those areas so the one of the best available fuel is solar energy or the electricity. In those areas solar cells are one of the most promising sources for the water supply and other agriculture use in the field. The intention of making the smart irrigation system using photo volcanic panels to provide efficient and easily available electric energy in the rural areas and agricultural fields. A humidity sensor is also installed in the dual axis solar tracker and mainly used for the purpose to sense the water requirement in the field according to the need of the soil. Pump is automatically activated when the humidity sensor senses the requirement of water in the field. This solar PV system based project is called Dual axis based solar tracker system. With the intention to monitor the movement by the sun, an LDR sensor is used which changes the direction of the PV panels according to the direction of sun. Since, it is a dual axis it can move horizontally as well as vertically Arduino microcontroller is used to make this solar tracker, it captures 40% more energy as compared to single and stationary mode solar tracker axis.
Contrastive Graph Learning with Graph Convolutional Networks

We introduce a new approach for graph representation learning, which will improve the performance of graph-based methods for the problem of key information extraction (kie) from document images. The existing methods either use a fixed graph representation or learn a graph representation for the given problem. However, the methods which learn graph representation do not consider the nodes label information. It may result in sub-optimal graph representation and also can lead to slow convergence. In this paper, we propose a novel contrastive learning framework for learning the graph representation by leveraging the information of node labels. We present a contrastive graph learning convolutional network (cglcn), where the contrastive graph learning framework is used along with the graph convolutional network (gcn) in a unified network architecture. In addition to this, we also create a labeled data set of receipt images (Receipt dataset), where we do the annotations at the word level rather than at the sentence/group of words level. The Receipt dataset is well suited for evaluating the kie models. The results on this dataset show the superiority of the proposed contrastive graph learning framework over other baseline methods.
Automated virtual staining, segmentation and classification of deep ultraviolet (UV) microscopy images for hematological analysis

We present a deep learning based automated framework to virtually stain single-channel deep-ultraviolet microscopy (UV) images of blood smears, and segment and classify white blood cells for fast, label-free hematological analysis.
Detecting Anchors’ Opinion in Hinglish News Delivery

Humans like to express their opinions and crave the opinions of others. Mining and detecting opinions from various sources are beneficial to individuals, organisations, and even governments. One such organisation is news media, where a general norm is not to showcase opinions from their side. Anchors are the face of the digital media, and it is required for them not to be opinionated. However, at times, they diverge from the accepted norm and insert their opinions into otherwise straightforward news reports, either purposefully or unintentionally. This is primarily seen in debates as it requires the anchors to be spontaneous, thus making them vulnerable to add their opinions. The consequence of such mishappening might lead to biased news or even supporting a certain agenda at the worst. To this end, we propose a novel task of anchors’ opinion detection in debates. We curate code-mixed news debates and develop the ODIN dataset. A total of 2054 anchors’ utterances in the dataset are marked as opinionated or non-opinionated. Lastly, we propose DetONADe – an interactive attention-based framework for classifying anchors’ utterances and obtain the best weighted-F1 score of 0.703. A thorough analysis and evaluation show many interesting patterns in the dataset and predictions.