Reviewerly/exHarmony huggingface dataset:
A qualitative analysis of themes in instant messaging communication of software developers

Software developers use instant messaging (e.g., Slack, Gitter) to collaboratively discuss software engineering problems and solutions. This communication takes place in chat rooms that generally contain a description of the main topic of discussion and the messages exchanged. To analyze whether and how the knowledge accumulated in these chat rooms is relevant to other developers, we first need to understand the themes discussed in these chat rooms. In this paper, we used thematic analysis to manually identify software engineering themes in the description of 87 chat rooms of Gitter, an instant messaging tool for software developers. Then, we checked whether these themes also occur in 184 public chat rooms of Slack, another instant messaging tool. We identified 47 themes in Gitter chat rooms, and regarding the applicability of themes, we could relate 36 of our themes to 173 Slack chat rooms. Our results indicate that, in the context of our study, chat rooms in developer instant messaging communication are mostly about software development technologies and practices rather than development processes. Furthermore, most chat rooms are topic- rather than project-related (e.g., a chat room used by developers of a particular software development project).
Balanced Dual-Mask Protection Scheme for GIFT Cipher Against Power Attacks

Currently NIST is working towards the standardization of lightweight cryptography (LWC).Although the cryptanalytic strength of LWC is currently under deep scrutiny, the LWC implementation security has not been yet widely explored.GIFT block cipher is the main building block of many of the LWC NIST candidates and therefore has the potential to be part of the next lightweight crypto-standard.Hence it is important to understand its implementation vulnerabilities such as side-channel attacks (SCAs).Although SCAs have been evaluated for hardware implementations, no analysis or countermeasures have been proposed yet for software implementations.This work evaluates GIFT 128bit software implementations (protected and unprotected) against power-based SCAs.Our protected implementation is based on a new lightweight countermeasure consisting of two balanced and masked SBoxes.Our results show that GIFT's SBox (or SubCell function) is vulnerable against profiled and non-profiled attacks when unprotected or protected implementations based on existing balancing or masking techniques are used.On the other hand, our proposed countermeasure that smartly combines balancing and masking offers full protection with negligible overhead.
Smart Wheelchair with Remote Patient Health Monitoring System

According to the United Nations World Travel Organization (UNWTO), people with disabilities have increased by 2% per year in the decade since 2001 and are estimated at about 26.8 million. Requiring constant monitoring, the elderly people as well as people with disabilities, have the urge for daily motion. Most of the smart wheelchair systems focus on the mobility aspect of the wheelchair. This paper presents a smart wheelchair system, which focuses on the health monitoring as well as the accident detection aspect and provides robust and quick alerts in case of any health-related anomaly or if a fall is detected. The proposed research study intends to bring mobility to the health monitoring system so that the user can be monitored remotely even on a wheelchair.
Securing cash withdrawal from ATM with the help of Smart Mobile Banking Application

Performing cash withdrawal transaction with ATM is increasing day by day. ATM is very important device throughout the world. Each one of us uses ATM for our different banking needs. We are suggesting a method to withdraw money from ATM which uses a Smart Mobile Banking Application (SMBA). The customer has to first register the smart phone with bank server and install smart mobile banking application from bank portal. Customer can set a password as a security authentication for initiating a cash withdrawal from SMBA. After initiating the cash withdrawal and successful password authentication by bank server, cash withdrawal is partially committed to the database of bank, customer can visit the nearest ATM and collect the cash by transferring personal information by Quick Response (QR) code.
Stochastic Event-triggered Variational Bayesian Filtering

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process noise covariances and an initial measurement noise covariance, a variational Bayesian method and a fixed-point iteration method are utilized to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The proposed algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. Finally, the performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle.
A Conceptual Model for Development of Small Farm Management Information System: A Case of Indonesian Smallholder Chili Farmers

Farm Management Information Systems (FMIS) assists farmers in managing their farms more effectively and efficiently. However, the use of FMIS to support crop cultivation is, at the present time, relatively expensive for smallholder farmers. Due to some handicaps, providing an FMIS that is suitable for small-holder farmers is a challenge. To analyze this gap, this study followed 3 steps, namely: (1) identified commodity and research area, (2) performed Farmers’ Information Needs Assessment (FINA), and (3) developed the conceptual model using the Soft System Methodology. Indonesian smallholder chili farmers are used as a case study. The most required information of smallholder’ farmers was identified through a qualitative questionnaire. Despite this, not all identified information needs could be accurately mapped. Thus, this indicates the need for a new FMIS conceptual model that is suitable for smallholder farmers. This study proposes an FMIS conceptual model for farm efficiency that incorporates five layers, namely farmers’ information needs, data quality assessment, data extraction, SMM (split, match and merge), and presentation layer. SMM layer also provides a method to comprehensively tackle three main problems in data interoperability problems, namely schema heterogeneity, schema granularity, and mismatch entity naming.
Adoption of LibGuides as a reference service in academic libraries: Insights from Aga Khan University, Uganda

Creative ideas and technology across the world for utilization have influenced the ways academic libraries present themselves to their users. Before digital era, librarians developed print subject guides and pathfinders to highlight important information resources in different fields, and the guides were brought online as libraries created web sites and started to depend on web resources. With the emergence of Web 2.0 technologies, librarians across the globe saw an opportunity to create more appealing and interactive online guides for the millennial patrons. These technologies simplified the creation and maintenance of guides such as wikis and FAQ's and subject guides to provide access to electronic resources. Academic librarians in the Sub-Saharan Africa as their counterparts in the rest of the world, are under pressure to offer relevant information services to satisfy varied client needs. Studies have shown that librarians believe that creating LibGuides enhances their knowledge of subject resources as reference service. According to Springshare (2020), 6100 libraries in 82 countries have implemented the LibGuides platform. There is however no clear indication of how many institutions in low middle-income countries have implemented libguides. This paper gives insight into the context of Sub Saharan Africa and lists the specific institutions that have adopted libguides. The Aga Khan University in Uganda prides itself as the pioneer of libguides implementation as a reference service. Since implementation in 2014, usage statistics have shown a gradual and steady improvement in access and use of e-resources such as journal and ebooks. This is largely attributed to the ease of reference and awareness aided by libguided. Many universities in Uganda have benchmarked with the Aga Khan University library and it is hoped that in the near future a number of other libraries will also implement libguides as reference service tools.
Accelerometer Based Hand Gesture Recognition and Control of Motor Powered Trolley

The passengers and workers always prefer the wheeled trolley due to its ease of transporting goods. It is still challenging for the old people to manage themselves and the trolley. This paper presents the insight of the hand gesture-based smart control of trolley with speed variation to match the user requirements. There is a lot of scope in this area to improve the smartness of control, but this work gives a complete description of the hardware equipment's design idea and experimental values. It uses an accelerometer (ADXL-335) to sense the hand gesture and sends them to the receiver through a wireless RF transmitter. An L293D driver drives the motors in the receiver with proper motor wiring to ensure the progression of the trolley as per the command received by the RF receiver.
Research into Collision Avoidance Models for Unmanned Aerial Vehicles

This paper presents an investigation into models that can be used to train unmanned aerial vehicles (UAVs or drones) to avoid obstacles and to keep a safe distance from such. The paper describes the key components of the MATLAB/Simulink model and covers the subsystem of blocks for the UA V flight scenario. The authors have tested two scenarios based on vector field histograms (VFH) using different parameters, whereby the accuracy of tracing flight waypoints varied. Simulation results are shown in graphs. The paper further shows how the ROS simulator can use learning algorithms that could be tested on real aircraft.
Efficient index-free SimRank similarity search in large graphs by discounting path lengths

Link-based similarity search aims to find similar nodes for a given query node in a graph, which arises in numerous applications, including web spam detection, social network analysis and web search. Among existing methods, SimRank is a well-known similarity model, which provides an effective and trustful function for similarity search. A large amount of techniques on SimRank similarity search are devoted recently, which compute the similarity scores by traversing the paths between query and candidate nodes. However, the number of paths increases exponentially as path length increases, which makes the computation expensive and cannot support fast similarity search over large graphs. In this paper, we propose an efficient index-free SimRank similarity search approach, namely DisSim, which reduces the computational cost by discounting path length. We observe that SimRank could rapidly converge at a stable state and the results change little after a few of iterations. Based on the fast convergence, the similarity between nodes is defined as the SimRank score at the second iteration. For the computation of DisSim, we divide the similarity into one-step and two-step first-meeting probabilities. The one-step first-meeting probabilities are computed by path traverses from query to candidate nodes, which reduces computational cost by skipping unnecessary nodes. And the two-step first-meeting probabilities are computed by integrating the repeated parts of the paths. For further speeding up query processing, we develop a pruning algorithm, which prunes unpromising path traverses by setting a threshold, and the accuracy loss under threshold is given through mathematical analysis. Extensive experiments on real graphs demonstrate the performance of DisSim through comparing with the state-of-the-art algorithms.
Risk Assessment and Management using Machine Learning Approaches

Appropriate estimation of risk and its management is a significant part in the lifecycle of software engineering. Risks are the crucial factors that affect the project's growth and work. The unidentified risk may damage the crucial components of the software. If the risk is underestimated, it may compromise the development and testing of the software. Looking at the facts, risk management stands as the crucial phase of any software development lifecycle. Risk management and assessment include various activities like recognizing, analyzing, planning, and controlling events that threaten project development. Machine learning, a booming area of research, has also shown its dominance in the field of risk assessment, however, it is still in the preliminary phase and has limited applicability. This paper considers various machine learning classifiers, such as Naive Bayesian, Decision Tree, Artificial Neural Network, and K Nearest Neighbor, and shows their comparative analysis and usage in developing the software risk assessment estimator. In order to boost the usage of machine learning models in the field of risk assessment, based on the findings, future directions are provided for researchers and stakeholders.
Loitering Detection in Home Surveillance System

An automated system which recognizes human action from continuous videos, though seem like a work of fiction, is possible now due to the magnificent increase in processing power and improvements in the field of automated analysis. Human Action Recognition (HAR) has a wide range of applications in fields of Gaming, Animation, Assisted Living and Surveillance etc. The home surveillance systems are easily available for anyone to use, the surveillance camera records video 24/7, reviewing the same to find out malicious activity will be a time critical work, the proposed method focuses on creating a system which looks for the loitering action in home surveillance videos and records the loiterer information in a file with the photo identity of the loiter. Loitering is an action that is defined as walking around for no obvious reason. In a home environment, if a random person seems to be walking around the house, then it is a suspicious activity, which needs to be addressed. The proposed system uses YOLOv3 (You Only Look Once) for identifying the person of interest and loitering is tracked using DeepSORT (Simple Online Real Time Tracking), Once the loiterer is identified and tracked, his face is captured by using the Haar Cascade algorithm and stored in for future reference. People who have installed home surveillance CCTV system can use this to system to identify loiterers.
BPCNN: Bi-Point Input for Convolutional Neural Networks in Speaker Spoofing Detection

We propose a method, called bi-point input, for convolutional neural networks (CNNs) that handle variable-length input features (e.g., speech utterances). Feeding input features into a CNN in a mini-batch unit requires that all features in each mini-batch have the same shape. A set of variable-length features cannot be directly fed into a CNN because they commonly have different lengths. Feature segmentation is a dominant method for CNNs to handle variable-length features, where each feature is decomposed into fixed-length segments. A CNN receives one segment as an input at one time. However, a CNN can consider only the information of one segment at one time, not the entire feature. This drawback limits the amount of information available at one time and consequently results in suboptimal solutions. Our proposed method alleviates this problem by increasing the amount of information available at one time. With the proposed method, a CNN receives a pair of two segments obtained from a feature as an input at one time. Each of the two segments generally covers different time ranges and therefore has different information. We also propose various combination methods and provide a rough guidance to set a proper segment length without evaluation. We evaluate the proposed method on the spoofing detection tasks using the ASVspoof 2019 database under various conditions. The experimental results reveal that the proposed method reduces the relative equal error rate (EER) by approximately 17.2% and 43.8% on average for the logical access (LA) and physical access (PA) tasks, respectively.
Discriminative Learning Based Dual Channel Denoising Network For Removal of Noise from MR Images

The presented manuscript proposes a dual path denoising network for removal of Rician noise from Magnetic Resonance Images (MRI). Noise is an undesired property which gets induced in the MRI or any other imaging modality during the image acquisition process. In this paper we introduced a dual path denoiser incorporated using different normalization techniques and activations in both paths. The medical images are needed to be free from spurious variations to plan the correct diagnosis of the disease. The diverse features learnt by the denoiser enables the network to produce the results which are closer to the expectations in medical imaging domain. The evaluation metrics showed the enhanced capability of the denoising network for removal of Rician noise from MR Images. The network trained once produced exceedingly good results for other datasets without retraining. The SSIM and PSNR metrics showed an improvement of about 4% and 5%.
Drone Detection using YOLOv4 and Amazon Rekognition

Creating effective tools and techniques for the automatic detection of drones is the need of the hour. Several detection technologies are under development, with a variety in complexity, range, and capabilities. There are some gaps in the present surveillance models, as the compact size and fast maneuvers result in drones being a difficult target to be detected when compared to other aircraft. Effective usage of video analytics does solve the problem, but powerful algorithms are needed to operate also under unfriendly conditions such as long-distance, low visibility, etc. This study aims to detect one or multiple drones in video sequences where drones appear at the same point when other distractor objects may also be present, whereas, algorithms should provide analysis only when the drone is present and not resulting alarms on other distractors, nor rambling by the rest of the frame. In particular, two approaches based on different deep learning strategies are put-forward and compared on a substantial dataset. Experimental analysis, as well as visual analysis of the proposed technologies, are discussed in the paper.
Vronicle

Demonstrating veracity of videos is a longstanding problem that has recently become more urgent and acute. It is extremely hard to accurately detect manipulated videos using content analysis, especially in the face of subtle, yet effective, manipulations, such as frame rate changes or skin tone adjustments.
Smart Image Storage and Retrieval For Crime Scene Investigation

Crime scene investigation and initial evidence obtained from a crime scene plays a foundational role for solving any kind of criminal offense. As a result this paper proposes development of an efficient Crime scene investigation image storage and retrieval system which will emancipate law enforcement agencies from various technological and time constraints. The proposed system is developed with the help of various Transfer learning models incorporating algorithms of Residual Neural Networks such as ResNet18. Firstly the models are trained for categorization with the aim of storage by transfer learning model followed by models of ResNet18 for image retrieval. This paper proposes a system that can act as a repository of information regarding victims, suspects crime registration which can be used for comparative analysis, academic study and a tool for law enforcement policy making along with criminal investigation. Experimental Results obtained by testing the models on a custom-made crime scene investigation image data set have shown efficient performance by proposed algorithm
Gaussian Quantum-Behaved Particle Swarm with Learning Automata-Adaptive Attractor and Local Search

This paper presents Gaussian Quantum-Behaved Particle Swarm Optimization (GQPSO) with a Learning Automata- Adaptive Attractor (LAAA) and a Probabilistic Local Search Mutation Operator (PLSMO). The LAAA allows the swarm to reinforce prior experience and adapt the behavior of the algorithm to a specific environment. The PLSMO was used as the supporting search to avoid premature convergence. The authors presented the numerical results of 16 benchmark functions to demonstrate that the proposed GQPSO-LA <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LO</inf> outperforms the original QPSO and its variants in terms of global search and robustness.
Node Protection using Hiding Identity for IPv6 Based Network

Protecting an identity of IPv6 packet against Denial-of-Service (DoS) attack, depend on the proposed methods of cryptography and steganography. Reliable communication using the security aspect is the most visible issue, particularly in IPv6 network applications. Problems such as DoS attacks, IP spoofing and other kinds of passive attacks are common. This paper suggests an approach based on generating a randomly unique identities for every node. The generated identity is encrypted and hided in the transmitted packets of the sender side. In the receiver side, the received packet verified to identify the source before processed. Also, the paper involves implementing nine experiments that are used to test the proposed scheme. The scheme is based on creating the address of IPv6, then passing it to the logistics map then encrypted by RSA and authenticated by SHA2. In addition, network performance is computed by OPNET modular. The results showed better computation power consumption in case of lost packet, average events, memory and time, and the better results as total memory is 35,523 KB, average events/sec is 250,52, traffic sent is 30,324 packets/sec, traffic received is 27,227 packets/sec, and lose packets is 3,097 packets/sec.
LPLB: An approach for the design of a lightweight convolutional neural network

SUMMARY Convolutional neural network (CNN) is one of the widely used deep neural network architecture for data analytics in the Internet of Things (IoT). However, due to its severe resource requirements, deploying CNN on resource‐constrained edge devices is quite challenging. Moreover, IoT services demand fast data analytics in order to be useful in their context. Hence, ensuring the deployment of CNN models on IoT edge devices is crucial. To this purpose, this article proposes a framework LPLB (less parameters and less bits) for the design of a lightweight CNN with reduced number of parameters and lesser storage requirements while preserving the model accuracy. LPLB consists of three steps: CNN training, iterative parameter drop and post‐training quantization. The performance of LPLB is evaluated on three datasets: German traffic sign recognition dataset, Kaggle hand gesture recognition dataset, and CIFAR‐10 dataset. Experiments conducted in the study reveal that the proposed framework is able to reduce the number of parameters by 11× in case of German traffic sign recognition dataset, 12.5× in case of Kaggle hand gesture recognition dataset, and 14× in case of CIFAR‐10 dataset. Moreover, the storage requirements are reduced by 44.42× in case of German traffic sign recognition dataset, 50× in case of Kaggle hand gesture recognition dataset, and 56.7× in case of CIFAR‐10 dataset. This is achieved without a significant drop in the accuracy. The accuracy drop in case of German traffic sign recognition dataset is 0.215%, 0.18% in case of Kaggle hand gesture recognition dataset, and 0.148% in case of CIFAR‐10 dataset. The proposed framework is generic and can be used for the design of lightweight CNN models corresponding to other use‐cases as well. This will make real‐time classification/recognition possible in delay‐sensitive applications like self‐driving cars, advanced driver assistance systems, health monitoring, elderly posture recognition and so forth.