Reviewerly/exHarmony huggingface dataset:
Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project

Software engineering repositories have been attracted by researchers to mine useful information about the different quality attributes of the software. These repositories have been helpful to software professionals to efficiently allocate various resources in the life cycle of software development. Software fault prediction is a quality assurance activity. In fault prediction, software faults are predicted before actual software testing. As exhaustive software testing is impossible, the use of software fault prediction models can help the proper allocation of testing resources. Various machine learning techniques have been applied to create software fault prediction models. In this study, ensemble models are used for software fault prediction. Change metrics-based data are collected for an open-source android project from GIT repository and code-based metrics data are obtained from PROMISE data repository and datasets kc1, kc2, cm1, and pc1 are used for experimental purpose. Results showed that ensemble models performed better compared to machine learning and hybrid search-based algorithms. Bagging ensemble was found to be more effective in the prediction of faults in comparison to soft and hard voting.
Mobile Pictogramming

We have been developing a new programming learning environment called “Pictogramming”. Pictogramming is coined from two words, “pictogram” and “programming”. The basis of this application is using a human shaped pictogram (e.g., “human pictogram”). Pictograms have the effect of reminding oneself of some person or some object because of an abstract representation. The Human pictogram is associated with syntonic learning. This application moves these parts using a command sequence called the “Pictogram Animation Command”. The application can draw movement history (similar to turtle graphics) called “Pictogram graphics”. The combination of both functions enables learning using small steps in a short period of time using a small command set. This paper proposes a smartphone version of Pictogramming. We compared with other smartphone enabled programming language environments and show advantages and merit of our proposed application.
The influence of ant fragility and cross-cultural adaptability on employee engagement: The moderated mediation role of perceived insider status

The purpose of the study was to examine the inluence of antifragility and cross-cultural adaptability on employee engagement, directly and in the moderated mediation role of perceived insider status.A self-designed questionnaire was utilized to collect data from 207 employees from Land Record Department, Punjab, Pakistan.The collected data was analysed through conirmatory factor analysis and structural equation modelling technique by utilizing AMOS as well as the moderating effects were evaluated through using Process Macro.Findings revealed that antifragility and cross-cultural adaptability has positive direct as well as indirect effect on employee engagement.Also, the moderating role of perceived organizational support is proved.This study has both theoretical and practical contributions, especially with reference to new model in Land Record Department of Punjab, Pakistan.This study will be valuable for Government of Punjab as well as other provincial Government for proper management of employees at Tehsil Land Records Centre level moreover its helpful for policy makers and administrator to appropriately deal with employees' engagement and perceived insider status in consideration of antifragility and cross cultural adaptability.These indings cannot be generalized as study is conducted in Land Record Department of Punjab, Pakistan.Organizations can improve the level of employees' engagement by focusing on antifragility, cultural adaptability, and perceived insider status at tehsil level.
Communication Rights for Social Bots?: Options for the Governance of Automated Computer-Generated Online Identities

Abstract Social bots, automated agents operating in social networks, are suspected of influencing online debates, opinion-formation processes and thus, the outcome of elections and votes. They do so by contributing to the dissemination of illegal content and disinformation and by jeopardizing an accurate perception of the relevance and popularity of persons, topics, or positions, through their potentially unlimited communication and networking activities, all under the false pretense of human identity. This paper identifies and discusses preventive and repressive governance options for dealing with social bots on state, organizational, and individual levels respecting the constitutional provisions on free expression and opinion-formation.
Anti-Tailgating Solution Using Biometric Authentication, Motion Sensors and Image Recognition

Tailgating is a social engineering attack challenging physical security within organizations. It gained public traction in the year 1999 and has since remained a major concern in the field of security leading to the development of several anti-tailgating solutions. These solutions began with simple mechanisms like mechanical turnstiles, revolving doors, and man-trap systems and evolved into more modern technologies using infrared beams, 3D machine vision, face detection, BMI and face recognition combination, and an embedded solution using IP camera and video analytics. A critical analysis of these solutions uncovered certain weaknesses which run through most of them. These are the inability to detect two people side by side and the incapability of detecting multiple entries after a single access authorization. These shortfalls led to the development of the solution in this paper which aims to eliminate the shortcomings of existing technologies and boost security, by using a three-step anti-tailgating solution. The design science research methodology and aspects of qualitative and quantitative research are employed in designing a three-step anti-tailgating solution that combines face detection, palm recognition, and motion sensors, to eliminate the loopholes of existing technologies. The results from experimentation indicated that the face detection tool could detect two faces present. The motion sensors were shown to be efficient in performing people counting and detection, to eliminate tailgating and discrepancies in the number of entries against the number of authorized personnel. Integrated with palm recognition the overall system will function effectively because the three technologies complement each other's shortfalls, therefore preventing tailgating. It is concluded that this system will be an improved and more effective anti-tailgating solution.
Guided thinning

We present a generic improvement for parallelizable iterative thinning algorithms. The improvement achieves a significant better approximation of the medial axis. The values of a previously computed distance map guide the thinning algorithms when to remove a point. We define precise metrics that allows us to make unbiased comparisons among algorithms and to quantify their properties. With the new method, the skeletons are more centered and the reconstruction is more precise, i.e., they characterize the shapes in a more representative way without altering other properties of the underlying thinning algorithm. A surprising result is the fact that some older classical thinning algorithms (and a modification of these as available in the ImageJ software suite) now achieve better skeletons with a smaller number of iterations than more recent algorithms.
Working with parallel corpora

Although parallel corpora are vital for cross-linguistic and natural language processing (NLP) research, most have been designed for just one particular purpose, which may unnecessarily restrict their usefulness and usability. My argument is that the usefulness of existing parallel corpora increases exponentially when data so obtained are combined with those yielded by comparable and/or monolingual corpora. Usability criteria such as the choice of processing tools and adherence to international standards, among others, also have an impact on corpus usefulness. This chapter proposes courses of action that serve to improve the recycling and reprocessing of available resources. It also presents a corpus-based, post-editing and quality assessment application as an illustration of the multifarious uses parallel corpora may serve.
Research on Multi-robot Path Planning Method Based on Improved MADDPG Algorithm

An improved multi-robot path planning method is proposed, aiming at the problems of slow training speed and low success rate of multi-robot path planning algorithm based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). First of all, a form of experience storage and replay called partitioned experience pool is designed to distinguish positive and negative experience by dividing the state space, and then some experience data are extracted by stratified random sampling. Secondly, in view of the fact that it is easy to ignore the value of different experience samples by using random sampling method, the samples in each experience pool are sorted according to the priority, so as to give priority to learning high quality experience. Thirdly, in order to solve the problem that choosing the experience with high priority will increase the training time, the storage form of the experience pool is changed to binary tree structure. Finally, two kinds of obstacle simulation environments with increasing difficulty are built on the three-dimensional simulation platform named Gazebo, and comparative experiments are set up for the improved algorithm and the original algorithm. Simulation results show that compared with the original algorithm, the training time of improved path planning algorithm is obviously reduced, the success rate in the two simulation environments is increased by 18% and 22% respectively, and the average path length in the two simulation environments is shortened by 12% and 17% respectively. This shows that the introduction of partitioned experience pool and priority experience replay mechanism makes the algorithm learn the optimal strategy faster and more effectively in the two environments established in this paper.
Bitcoin

Abstract Bitcoin was proposed by Nakamoto (2008) as the first electronic payment system, which fully relies on cryptographic primitives in order to work over a purely peer-to-peer system, where everyone can participate in spending funds to other users without the need for a trusted third party. This chapter first introduces the basic ideas of Satoshi Nakamoto, who defined an electronic coin as a chain of digital signatures. It explains how the addresses in Bitcoin are derived, and how the elliptic curve cryptography (ECC) key pair is used in order to transact funds from one user to another. For this, it shows how the transactions are constructed in Bitcoin, based on the most common transaction, which is the Pay-to-Public-Key-Hash transaction. The last section then shows how the transactions are permanently stored in the public ledger, the blockchain, and how the miners solve the Proof-of-Work in order to safeguard the records.
An overview of Basque corpora and the extraction of certain multi-word expressions from a translational corpus

Since the 1980s, considerable efforts have been made to create different types of Basque corpora. However, to systematically analyse the Basque translations of German literary texts, it was necessary to create a corpus from the ground up. Intermediary versions were included in this corpus whenever the Basque target text was not a translation from the German original but came instead from a translation into another language (Spanish in most cases). A tool called TAligner was used to align the bitexts and the tritexts. The aim of this chapter is, firstly, to provide the reader with an overview of the main Basque corpora. Secondly, I will describe the design and compilation process of a parallel and multilingual corpus using TAligner 3.0. Thirdly, I will present how the corpus has been lemmatized and annotated at the level of part-of-speech. Finally, the process of extracting potential Basque multi-word expressions will be shown.
Streaming Remote Sensing Data Processing for the Future Smart Cities

The explosion of data and the increase in processing complexity, together with the increasing needs of real-time processing and concurrent data access, make remote sensing data streaming processing a wide research area to study. This paper introduces current situation of remote sensing data processing and how timely remote sensing data processing can help build future smart cities. Current research on remote sensing data streaming is also introduced where the three typical and open-source stream processing frameworks are introduced. This paper also discusses some design concerns for remote sensing data streaming processing systems, such as data model and transmission, system model, programming interfaces, storage management, availability, etc. Finally, this research specifically addresses some of the challenges of remote sensing data streaming processing, such as scalability, fault tolerance, consistency, load balancing and throughput.
Alternative Constraint Handling Technique for Four-Bar Linkage Motion Generation

This paper proposes an extension of a new concept for four-bar path generation and a new penalty constraint handling technique from our previous work to a motion generation problem. The proposed technique could neglect the timing constraint for problems without prescribed timing, which is found to be an efficient technique for a four-bar path generation problem. Later, the remaining constraints are solved with a new constraint handling technique that also gives high performance. The technique is one kind of penalty function techniques. The present work proposes to combine the previous two methods for solving the four-bar motion generation problems. The comparative study, optimisation of motion generation problems with a new and a traditional penalty techniqueare solved by using self-adaptive population size teaching-learning based optimization (SAP-TLBO), is presented. In this study, three new motion generation test problems are used to test the proposed technique. The results show that the new technique gives acceptable results and can be applied with the motion generation problems without prescribed timing.
A Structural Result for Personalized PageRank and its Algorithmic Consequences

Many systems, such as the Internet, social networks, and the power grid, can be represented as graphs. When analyzing graphs, it is often useful to compute scores describing the relative importance or distance between nodes. One example is Personalized PageRank (PPR), which assigns to each node v a vector whose i-th entry describes the importance of the i-th node from the perspective of v. PPR has proven useful in many applications, such as recommending who users should follow on social networks (if this i-th entry is large, v may be interested in following the i-th user). Unfortunately, computing n PPR vectors exactly for a graph of n nodes has complexity O(n^3), which is infeasible for many graphs of interest. In this work, we devise a scheme to estimate all n PPR vectors with bounded l_1 error and complexity O(n c ), where c &lt; 2 depends on the degrees of the graph at hand, the desired error tolerance, and a parameter that defines PPR. This improves upon existing methods, the best of which have complexity O(n 2 łog n) in our setting. Our complexity guarantee holds with high probability, for certain choices of the PPR parameter, and for a certain class of random graphs (roughly speaking, the sparse directed configuration model with heavy-tailed in-degrees); our accuracy guarantee holds with probability 1 and for arbitrary graphs and PPR parameters. The complexity result arises as a consequence of our main (structural) result, which shows that the dimensionality of the set of PPR vectors scales sublinearly in n with high probability, for the same class of random graphs and for a notion of dimensionality similar to matrix rank. It is this coupling of the PPR vectors for the nodes on a common underlying graph that allows for estimating them faster. Hence, at a high level, our scheme is analogous to (but distinct from) low-rank matrix approximation. We also note that our scheme is similar to one that was proposed in [Jeh and Widom 2003] but lacked accuracy and complexity guarantees, so another contribution of our paper is to address this gap in the literature.
Cyber Crimes and Hackers

This chapter surveys the history and examples of cybercrimes, their types, costs to society, and strategies of detection and prevention. In the discussion, it is noted that a great number of computer attacks fall into two categories: penetration and denial of service attacks. And these are discussed in depth. Attack motives are also discussed. Although it is difficult to estimate the actual costs of e-attacks on physical system resources, progress is being made for better and more accurate estimates. An in-depth discussion of the social and ethical consequences that include psychological effects, moral decay, loss of privacy, and loss of trust follows. Hackers, hacker types, communities, and the history of hacking are also discussed. The chapter ends with a discussion of ways to deal with the rising tide of cybercrimes.
Analog-Inspired Hardware Security: A Low-Energy Solution for IoT Trusted Communications

With the proliferation of connected internet of things (IoT) devices, trusted communications between such de-vices is an increasing concern. While researchers have spent significant resources to address this challenge, most solutions impose significant energy, delay, and complexity overhead on energy-constrained IoT devices. In this paper, we first provide an overview of some of the techniques used to incorporate security and trust features into IoT devices. Then, we propose and demonstrate an innovative encryption approach for wireless IoT communications which is low-energy, low-complexity, and low-latency. The proposed cryptography integrates the encryption into the RF front-end of a wireless transceiver and is energy-efficient, making it suitable for real-time and energy-limited IoT connectivity applications.
Diversifying Image Retrieval by Affinity Propagation Clustering on Visual Manifolds

A postprocessing system based on affinity-propagation clustering on manifolds can improve the diversity of retrieval results without reducing their relevance.
Incorporating Nesterov’s Momentum into Distributed Adaptive Gradient Method for Online Optimization

Optimization methods based on adaptive gradients, such as AdaGrad, RMSProp, and Adam, are widely used to solve large-scale machine learning problems. This work aims to improve the recently proposed and rapidly promoted distributed adaptive gradient descent optimization algorithm(DADAM). There are two main components in Adam-a momentum component and an adaptive learning rate component. However, regular momentum can be proved conceptually and empirically inferior to a similar algorithm called Nestrov’s Accelerating Gradient (NAG). We therefore incorporate Nesterov’s Momentum into Distributed Adaptive Gradient Method (DADAM) for Online Optimization and obtain our NDADAM algorithm. Experiments show that the convergent speed of the proposed NDADAM algorithm has been greatly improved.
BRR-DQN: UAV path planning method for urban remote sensing images

Unmanned aerial vehicle(UAV) urban path planning is a research hotspot in the construction of modern smart cities. We propose a novel DQN combine BRRNet which named BRR-DQN that can directly plan the urban remote sensing image path. First, the BRRNet is designed to model the environmental map, which provides a new solution for the environmental map modeling problem in the path planning field. Second, the reward function is introduced, we mainly optimizes the reward function of the DQN from two aspects: (a) Setting obstacles in the nearby dangerous area; (b) Designing the target area near the target point. Through the above optimization methods make up for the lack of building information extracted by BRRNet, the risk of UAV hitting the building is reduced, and the model convergence speed is accelerated. At the effiency of our method is demonstrated by urban remote sensing image.
ProtoSpray: Combining 3D Printing and Spraying to Create Interactive Displays with Arbitrary Shapes

ProtoSpray is a new fabrication method that combines 3D printing and spray coating, to create touch-sensitive displays of arbitrary shape. Our approach makes novel use of 3D printed conductive channels to create base electrodes and shape displays. A channelled 3D printed object is then combined with spraying active, electroluminescent, materials to produce illumination. This demonstration involves multiple different devices, created through the ProtoSpray process, showing its free-form applicability to irregular shapes such as a mobius strip and spherical surfaces. Our work provides a platform to empower makers with displays as a fabrication material.
Detection of Transmission Towers and Insulators in Remote Sensing Images with Deep Learning

Intelligent inspections of high-voltage electronic power grids with high-altitude aerial or satellite remote sensing images (RSIs) have attracted more and more attention. The detection of transmission tower and insulator in smart electronic power is of great importance. Traditional image recognition based methods have been proven to be difficult to complete this task effectively and efficiently, lots of deep learning based methods have been adopted due to their promising performance. However, collecting a large number of labeled aerial/satellite imaging data for deep learning requires a lot of manpower/financial costs and the deep models trained on small size of samples are often easy to over-fit. For this reason, it has practical significance to study the automatic detection of towers and insulators in the case of small samples. Aiming at the problem of object detection under small samples, a deep learning framework for simultaneous towers and insulators detection in RSIs based on Faster-RCNN and neural style image synthesis is proposed. Firstly, to alleviate the small sample size problem, a sample generation method based on neural style transfer and alpha channel image fusion techniques is proposed, which randomly combines the foreground towers and background images to expand the training data set. Secondly, upon the expanded training data, an object detection model for towers and insulators based on Faster-RCNN is further trained. Experiments show that the object detection model trained with the extended training data has better generalization performance and can better suppress false alarms.
Frequency Analysis of Task Evoked Pupillary Response and Eye Movement

This paper describes the influence of eye blinks on frequency analysis and power spectrum difference for task-evoked pupillography and eye movement during an experiment which consisted of target following tasks and oral calculation tasks with three levels of task difficulty: control, 1\(\times \)1, and 1\(\times \)2 digit oral calculation. The compensation model for temporal pupil size based on MLP (multilayer perceptron) was trained to detect a blink and to estimate pupil size by using blink less pupillary change and artificial blink patterns. The PSD (power spectrum density) measurements from the estimated pupillography during oral calculation tasks show significant differences, and the PSD increased with task difficulty in the area of 0.1–0.5 and 1.6–3.5 Hz, as did the average pupil size. The eye movement during blinks was corrected manually, to remove irregular eye movements such as saccades. The CSD (cross-spectrum density) was achieved from horizontal and vertical eye movement coordinates. Significant differences in CSDs among experimental conditions were examined in the area of 0.6–1.5 Hz. These differences suggest that the task difficulty affects the relationship between horizontal and vertical eye movement coordinates in the frequency domain.