Reviewerly/exHarmony huggingface dataset:
Helping software developers through live software metrics visualization

With the increasing complexity of software systems, software developers would benefit from instant and continuous feedback about the system they are maintaining and evolving. Despite existing several solutions providing feedback and suggesting improvements, many tools require explicit invocations, leading developers to miss some improvement opportunities, such as important refactorings, due to the loss of their train of thought. Therefore, to address these limitations, we developed a Visual Studio Code plugin providing real-time feedback --- and also information about each commit made to the version control system. This tool is also capable of recommending two types of refactorings. To validate this approach, we did a preliminary controlled experiment using hypothesis-tests to check specific results. However, in this initial stage, we didn't have enough data to confirm our research questions, and we weren't able yet to confirm the main hypothesis.
Research on pre-competition emotion recognition of student athletes based on improved machine learning

There are currently few studies on the stress of athletes, so it is impossible to provide effective stadium guidance for athletes. Based on this, this study combines machine learning algorithms to identify athletes’ pre-game emotions. At the same time, this study obtains the data related to the research through the survey access form and obtains the physiological parameters of the athletes under stress in the experimental way and processes the physiological parameters of the athletes with the machine learning algorithm. In order to improve the efficiency of data processing, this study improves the traditional machine learning algorithm, and combines the particle optimization algorithm with the support vector machine to realize the effective recognition of the athlete’s physiological state. In addition, through the experimental method combined with the contrast method, this paper compares the performance of the improved algorithm with the traditional algorithm and combines the data analysis to analyze the test results. Finally, this study analyzes the effectiveness of the proposed algorithm by example analysis. The research shows that the proposed algorithm has better performance than the traditional algorithm and has certain practical significance and can provide theoretical reference for subsequent related research.
LEA: 128 High Frequency Architecture with Image Analysis

A High throughput architecture for IoT applications and security is the necessity of today. To secure real time applications, there is a need of fast encryption process. LEA is lightweight block cipher uses any one size of key out of three key size of 128-bit, 192-bit and 256-bit based on different level of security. LEA block size is of 128-bit. The work of this paper mainly focused on LEA-128 bit key size and have got the better results as compared to other papers available in literature. Method used to get high performance results in this work, consist of pipelined architecture which provides better throughput. Proposed work mainly focused on real time application and hence throughput is chosen as main cretaria. Proposed architecture is showing 36% more frequency and 49% more throughput. In this paper reduced clock cycle method have been used to get better performance for image encryption.
Virtual Reality in Professional Training: An Example from the Field of Bank Counselling

Virtual Reality has become a mature and widespread technology in many application fields, including design, industry, defense, health care, and gaming. Also, in education, the potential of immersive technologies is acknowledged more commonly. Evidence-based insights in the mechanisms and impacts of VR trainings are still sparse, however. This paper introduces a VR-based professional training solution for bank counsellors, where soft skills, communication skills, attention, and awareness are in the focus. In a study, we compared a VR-based training solution with a face-to-face presence training covering the same domain. The results show that the VR solutions excelled the expectations of participants and that learning motivation was higher as opposed to the conventional presence training. In the perceived effectiveness, the VR condition achieved equal results than the face-to-face training condition. The results provide some evidence that VR solutions are an appropriate and effective solution in professional training. Moreover, the study revealed distinct characteristics that make VR-based training more accepted and satisfying than conventional trainings.
SGD-Rec: A Matrix Decomposition Based Model for Personalized Movie Recommendation

A personalized recommendation has been an active area of research. Many companies such as Facebook, Amazon, and eBay have incorporated such functionality to enhance user experience and engagement. In today's market, streaming digital contents (e.g., online movies) have become ubiquitous and accessible from anywhere and anytime. The rapid growth of streaming market urges many providers to offer a personalized experience to capture customer loyalty. In this paper, we present a movie recommending system based on our proposed rating prediction algorithm using singular value decomposition (SVD). Empirical evaluation is conducted on two tasks: rating prediction and movie recommendation, using two case studies from MovieLens and Thaiware Movie.
Time‐resolved coded structured light for <scp>3D</scp> measurement

Abstract Corresponding points matching is the fundamental computational task underlying stereo vision, and the key issue in making stereo vision practical is to find the most suitable algorithm to quickly match corresponding points, which leads to 3D reconstruction. In this paper, we proposed a new encoding strategy of dot‐pattern structured light based on temporal modulated vertical cavity surface emitting laser (VCSEL) array, which is especially suitable for the dot projector in miniaturized devices for stereo vision. Although VCSEL has been used in Apple's iPhone X for dot projector, the time dimension information is not used. VCSEL array has the capability of controlling each light source separately, so we can encode the information of time dimension into the structured light, which improves the stereo‐matching accuracy and simplifies the algorithm. In addition, compared to the temporal random speckle structured light method, this time‐resolved coded dot‐pattern structured light method greatly reduces the number of frames of projecting images required, which has great significance for real‐time 3D reconstruction in practical applications. Therefore, the method proposed here could be of high interest for its potentials in 3D measurement in mobile phones and other miniaturized devices. The proof‐of‐principle experiment results verify the computational efficiency and matching accuracy of the method.
Model-Based Testing for MAPE-K adaptation control loops

Self-adaptation is an effective solution for complex systems that need to adapt themselves to external changes while keeping internal goals; it can be realized in terms of MAPE-K feedback control loops, organized according to specific MAPE patterns in case of decentralized and distributed control. The MSL-centric framework allows the definition of the structure of MAPE-K loops using the MAPE Specification Language (MSL), and the description of its adaptation logic in terms of self-adaptive Abstract State Machines (ASMs). These models can be tested by exploiting classical model-based testing for ASMs that allows structural coverage of ASM models, but which is not adequate to test executions that are peculiar of the structure of MAPE-K loops. To overcome this limitation, in this paper, we propose a MAPE-based testing approach, which generates, from the MSL model, test goals specifying requirements on the control loop executions, translates these into concrete test predicates for the corresponding self-adaptive ASM, and then generates (by using a model checking based technique) abstract tests covering these test predicates. Such abstract tests are suitable to test the desired executions of MAPE control loops. The application of the approach is demonstrated on a smart home case study.
A Machine Learning Platform for Multirotor Activity Training and Recognition

Machine learning is a new paradigm of problem solving. Teaching machine learning in schools and colleges to prepare the industry's needs becomes imminent, not only in computing majors, but also in all engineering disciplines. This paper develops a new, hands-on approach to teaching machine learning by training a linear classifier and applying that classifier to solve Multirotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. This work extends Arizona State University's Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development.
Adversarial Classification: Impact of Agents’ Faking Cost on Firms and Agents

Classification of agents such as suppliers and customers into good and bad types based on their attributes is ubiquitous in business. In adversarial classification contexts, firms face agents who fake their attributes to receive favorable decisions from the classifier; for instance, suppliers could misrepresent their safety or quality features in order to qualify as a firm’s accredited vendor. Anticipating such faking by agents, firms may strategically design their classifiers and may also verify the information presented by agents. We examine the impact of agents’ faking on the firm and on agents in a context in which agents of two types—good‐risk and bad‐risk—have possibly different faking costs. We find that the firm is hurt when bad‐risk agents’ faking costs decrease, and the firm benefits when good‐risk agents’ faking costs decrease. We find that agents do not always benefit when faking becomes easier. Further, a decrease in faking costs of one type will sometimes benefit the other type. We show that when the faking costs decrease the firm may be better off using some attributes that do not offer a positive value to the firm at the higher faking cost. Though supplementing a strategic classifier with verification of the agents’ information could benefit the firm, 100% verification may not be optimal even if there was no cost incurred in conducting verification. Experiments conducted using a Naive Bayes classifier on a real‐world dataset demonstrate that our findings hold for extant (and potentially non‐optimal) commercial classifiers.
Provably secure certificateless deniable authenticated encryption scheme

Deniable Authenticated Encryption (DAE) scheme ensures the security of deniable authentication and confidentiality by performing both cryptographic functions of deniable authentication and public key encryption in one single logical step at a cost significantly lower than that required for performing these two functions separately. In this paper, we have designed a deniable authenticated encryption scheme CL-DAE on certificateless setting. We formalize the security and adversary model for deniable authentication and confidentiality and have proven the security in random oracle model. The proposed scheme can be applied to a network or system model where two communicating parties or devices exchanging their confidential messages and the recipient is not allowed to disclose the origin of message to any third party. Since our proposed protocol consumes very low cost, it is suited to implement on the framework where the constraints devices such as sensors, cell-phone, PDA, tablets etc. are involved in the communicating system. This protocols can be used for smart grid security, designing of secure exchange of e-mail, e-voting system etc.
Provably secure lattice based identity based unidirectional PRE and PRE<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:msup><mml:mrow /><mml:mo>+</mml:mo></mml:msup></mml:math> schemes

In 1998, at Eurocrypt, a novel primitive, called Proxy Re-Encryption (PRE) was introduced by Blaze, Bleumer and Strauss [1]. In the given concept, a delegator computes re-encryption key and gives it to the semi trusted proxy. Then proxy can convert a message, enciphered with Alice’s public key into a message, enciphered with Bob’s public key without knowing the message. Upto best our knowledge, we have proposed the first unidirectional PRE scheme, which is noninteractive and based on lattice based identity based cryptosystem. In this scheme a trapdoor function is used for lattices, which is proposed by Micciancio and Peikert [2]. It is simple, efficient, strong and easy to implement compared to [3]. In 2013, Wang et al.[4, 5] introduced a new primitive: PRE+, which is similar to the conventional PRE except that, in PRE+ scheme encryptor computes re-encryption keys. Here encryptor is the delegator. We have also constructed a lattice identity based unidirectional PRE+ scheme.
Web Page Prediction Using Genetic Algorithm and Logistic Regression based on Weblog and Web Content Features

As website users are increasing day by day, user behaviour analysis for improving the website performance attracts many researchers. This paper introduces the web page prediction model by involving the Logistic Regression (LR), which takes web page content similarity as input in group of similar weblog rules. To get association rules, this work introduce Feed Forward Counter (FFC) model for identifying the association rule with single data iteration technique. Both regression and FFC increased the learning rate for enhancing the accuracy of page recommendation. For adopting the dynamic situation in the work, final prediction is done by Particle Swarm Optimization (PSO) algorithm, which uses learned regression value in fitness function value evaluation. Experiment is performed on instantaneous dataset attained from ProjectTunnel website, which is collection of weblog and keywords. Results shows that proposed Logistic Regression based Web Page Prediction Model (LWPPM) for next page prediction system has improved various evaluation parameters like precision, coverage, m-metric.
Cluster-Based Under-Sampling Using Farthest Neighbour Technique for Imbalanced Datasets

In domain of data mining, learning from imbalanced class distribution datasets is a challenging problem for conventional classifiers. The class imbalance exists when the number of samples of one class is much lesser than the ones of the other classes. In real-world classification problems, data samples often have unequal class distribution. This problem is represented as a class imbalance problem. However, many solutions have been proposed in the literature to improve classifier performance. But recent works entitlement that imbalanced dataset is not a problem in itself. The degradation of classifier performance is also linked with many factors like small sample size, sample overlapping, class disjunct and many more. In this work, we proposed cluster-based under-sampling based on farthest neighbors. The majority class samples are selected based on the average distance to all minority class samples in the cluster are farthest. The experimental results show that our cluster-based under-sampling approach outperform with existing techniques in the previous studies.
Synthesizing Images from Hand-Drawn Sketches using Conditional Generative Adversarial Networks

Today, Technology have remarkable charm in the area of Computer Graphics and Vision. Producing absolute images from the poor hand-drawn sketches is a very demanding and laborious task in this area. Hand-drawn sketch recognition is widely used in sketch based image and video retrieval, manipulations and reorganizations. In S ketch to image synthesis, the sketches are translated to realistic images with the use of a generative model. An image is put forward to image translation network that involves in producing a synthesized image from the input sketch via an adversarial process. A novel Conditional Generative Adversarial Network (cGANs) which is an extension of Generative Adversarial Networks (GANs) is used to produce the images with some sort of conditions or attributes. In this work, the implementation of cGANs for synthesizing the images from hand-drawn sketches gives a remarkable output. The performance of the proposed sketch to image translation network was excellent and appreciable.
Evaluation of Side-Channel Leakage Simulation by Using EMC Macro-Model of Cryptographic Devices

Side-channel (SC) leakage from a cryptographic device chip is simulated as the dynamic current flowing out of the chip. When evaluating the simulated current, an evaluation by comparison with an actual measurement is essential; however, it is difficult to compare them directly. This is because a measured waveform is typically the output voltage of probe placed at the observation position outside the chip, and the actual dynamic current is modified by several transfer impedances. Therefore, in this paper, the probe voltage is converted into the dynamic current by using an EMC macro-model of a cryptographic device being evaluated. This paper shows that both the amplitude and the SC analysis (correlation power analysis and measurements to disclosure) results of the simulated dynamic current were evaluated appropriately by using the EMC macro-model. An evaluation confirms that the shape of the simulated current matches the measured one; moreover, the SC analysis results agreed with the measured ones well. On the basis of the results, it is confirmed that a register-transfer level (RTL) simulation of the dynamic current gives a reasonable estimation of SC traces.
The Nexus Between VR Affordability, Cognition, and VR Addiction: A Gaming Perspective

Although some experimental research investigated the positive effect of VR games on learning, the problematic use of VR games has become increasingly severe among a large proportion of learners. One of these is learner addiction in VR games. However, there is still a lack of literature on VR game addiction in the learning context. This study examines the formation process of addiction with a particular focus on the abilities of VR technology to understand the mechanism of VR game addiction. Based on the cognitive-behavioral model, this study used empirical data to generate findings and implications. This study used multiple mediations and hierarchal regression for moderation evaluation with a multi-perspective approach, which is developing the cognition behavioral changes of users. Findings show that addictive behavior is determined by VR experiences (i.e., presence and flow experience) of VR games. Besides, these experiences are influenced by Mental Imagery (MI), VR Game Curiosity (VGC), and VR Affordance (VRF). We enhance the cognitive-behavioral model in the current context by identifying MI, VGC, and VRF as distal causes and perceived presence and flow experiences as proximal causes. The practical implications of preventing VR game addiction are also discussed in this research.
Trajectory Planning of Unmanned Aerial Vehicle using Invasive Weed Optimization

This paper presents an approach to trajectory planning of Unmanned Combat Aerial Vehicles using Invasive Weed Optimization. It proposes a system that generates trajectory for an Unmanned Combat Aerial Vehicle in a battlefield while avoiding regions where the plane risks being shot, at the same time, the system tries to minimize the path length. The threats are fully known and are static in nature. Trajectory planning is a critical problem in combat as a lot is at stake. This research work will first explore the problem and the associated issues in brief, then the problem is characterized using a simple and elegant model. Invasive Weed Optimization is used as the optimization technique for finding safe and short paths for Unmanned Combat Aerial Vehicles for going from source to destination; this technique is based on the life-cycle of weeds. Lastly, B-spline curve is formed to smoothen the resultant trajectory; the coordinates generated by our approach are used as control points for a B-spline curve. On all the runs, the trajectory never intersected the regions which were to be avoided as there was a heavy penalty inherent in the planning algorithm in case the path strays into risky regions.
SHOSVD: Secure Outsourcing of High-Order Singular Value Decomposition

Tensor decomposition is a popular tool for multi-dimensional data analysis. In particular, High-Order Singular Value Decomposition (HOSVD) is one of the most useful decomposition methods and has been adopted in many applications. Unfortunately, the computational cost of HOSVD is very high on large-scale tensor, and the desirable solution nowadays is to outsource the data to the clouds which perform the computation on behalf of the users. However, how to protect the data privacy against the possibly untrusted clouds is still a wide concern for users. In this paper, we design a new scheme called SHOSVD in the two-cloud model for secure outsourcing of tensor decomposition. At the core of our technique is the adoption of additive secret sharing. Our SHOSVD could guarantee the outsourced data privacy for users assuming no collusion between the two clouds. Moreover, it supports off-line users which means that no interaction between users and clouds is required during the computation process. We prove that our scheme is secure in the semi-honest model, and conduct the theoretical analyses regarding its computational and communicational overhead. The experiment results demonstrate that our scheme is of desirable accuracy.
TRUST.IO: Protecting Physical Interfaces on Cyber-physical Systems

Cyber-physical systems (CPSes) have been replacing their mechanical counterparts in many safety and securitycritical applications (e.g., door locks, automobiles, and critical infrastructure). However, this paradigm shift has introduced a new software-based attack vector into these historically isolated systems. Since many of these devices are networked, their physical interfaces are vulnerable to both remote and local attackers. In this work, we present TRUST. IO, a framework that automatically, and transparently, hardens these physical interfaces against all software-based exploits. More precisely, TRUST.IO ensures that the software on the device cannot access any protected general purpose input/output (GPIO) interfaces unless the command was initiated from a trusted external client (e.g., a key, phone, or centralized server). TRUST.IO exploits the fact that users rarely interact directly with these embedded devices. Instead, users interact with a remote system (e.g., a car key, smart hub, or control system) that ultimately issues commands to the single-purpose embedded device. Thus, TRUST.IO leverages modern embedded processor features to ensure that these critical physical interactions (e.g., actuating motors or reading sensors) will be performed if and only if the command was issued by an authorized external device that can satisfy a cryptographic challenge. We demonstrate that TRUST.IO can be easily applied to existing CPSes, both bare-metal and Linux-based, with minimal runtime overhead and minimal code modifications.