Reviewerly/exHarmony huggingface dataset:
Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects.

Abstract Context Unit and integration testing are popular testing techniques. However, while the software development context evolved over time, the definitions remained unchanged. There is no empirical evidence, if these commonly used definitions still fit to modern software development. Objective We analyze, if the existing standard definitions of unit and integration tests are still valid in modern software development contexts. Hence, we analyze if unit and integration tests detect different types of defects, as expected from the standard literature. Method We classify 38,782 test cases into unit and integration tests according to the definition of the IEEE and use mutation testing to assess their defect detection capabilities. All integrated mutations are classified into five different defect types. Afterwards, we evaluate if there are any statistically significant differences in the results between unit and integration tests. Results We could not find any evidence that one test type is more capable of detecting certain defect types than the other one. Our results suggest that the currently used definitions do not fit modern software development contexts. Conclusions This finding implies that we need to reconsider the definitions of unit and integration tests and suggest that the current property-based definitions may be exchanged with usage-based definitions.
Dukungan Sosial Orang Tua Dan Motivasi Berprestasi Dengan Strategi Pembelajaran Mandiri Dengan Minat Sebagai Variabel Mediator

Learning is the main obligation that must be carried out by students as a student. However, not all students have good learning management or have the initiative to carry out the learning process. Social cognitive theory explains how the individual's environment can influence the emergence of intrinsic motivation which can encourage students to be able to learn independently and be able to achieve learning goals. The purpose of this study was to determine the effect of parental social support and achievement motivation with self-regulated learning with interest as a mediator variable. This research was conducted in the city of Surakarta by involving 3 different schools, namely SMP N 10 Surakarta, SMP N 24 Surakarta, and SMP N 9 Surakarta. The number of the population in this study amounted to 730 students. While the number of samples used was 288 students. The sampling technique in this study used proponate stratified random sampling. The data analysis technique used is the Structural Equation Modeling (SEM) model with the AMOS program. The results of this study indicate that there is a significant influence between social support and achievement motivation on interest. There is a significant influence between social support, achievement motivation and interest in self regulated learning. There is an influence between social support and achievement motivation on self-regulated learning through interest variables. The highest direct effect is found in the interest variable on self regulated learning which is 0.693. Meanwhile, the highest indirect effect was found in the variable of achievement motivation on self-regulated learning through interest with the amount of influence of 0.121.Keywords : Parents' social support, achievement motivation, learning strategiesindependent, interest.
Applying Meta-Heuristics Algorithms in Model-Driven Approaches for Solving the CRA Problem

The Class Responsibility Assignment (CRA) problem is one of the most important problems in Object-Oriented Software Engineering. It is a Search-based optimization problem to assign attributes and methods to a set of classes such that the related class diagram has maximum cohesion and minimum coupling. Due to the large and complex search space of the problem, finding an optimal solution is a costly and challenging task. In this regard, the use of optimization approaches can be promising. In this paper, the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms are implemented using Model-Driven Engineering (MDE) techniques for solving the CRA problem. To evaluate the proposed approach, the effectiveness of provided algorithms is presented using models with different scales. Additionally, the results are compared with existing solutions for the CRA problem in the community. The results indicated that for large-scale models the ACO algorithm could find a much better solution in less time compared to the PSO algorithm.
Digital apocalypses: visual or verbal decline?

This paper addresses the question as to whether new digital forms of interaction instantiate a decline of verbal language or of visual communication. Many studies claim that web-based interactions are more based on verbal language, as they involve live, spoken interactions, which are by definition shaped by conversation, whereas others argue that image is dominant in the digital domain, as it deploys affordances typical of multimodal environments. It provides a theoretical introduction to the study of videochats, seen as dynamic texts that enact a new form of interaction. Interactional resources are arranged in such innovative ways in videochats that they produce a new field investigation for researchers in the field of linguistics, communication, media studies, visual ethnography and digital literacy. The paper singles out relevant aspects, such as the alternation of speech and writing, new patterns in proxemics and kinesics, impossibility of eye contact, which are so striking as to warrant special investigation. In particular, speech and writing are technologically integrated, allowing participants to modeswitch, i.e. alternate between spoken and written discourse. New arrangements of verbal and non verbal resources attempt to simulate faceto-face conversations. However, the illusion of a face-to-face conversation dissolves as soon as videochat-specific resources are unpacked. Despite the growing research into non verbal behavior, videochat data challenge visual analysts and researchers for a variety of reasons illustrated in this paper.
Scalable and parallel tweezer gates for quantum computing with long ion strings

Trapped-ion quantum computers have demonstrated high-performance gate operations in registers of about ten qubits. However, scaling up and parallelizing quantum computations with long 1D ion strings is an outstanding challenge due to the global nature of the motional modes of the ions which mediate qubit-qubit couplings. We devise methods to implement scalable and parallel entangling gates by using engineered localized phonon modes. We propose to tailor these modes by tuning the local potential of individual ions with programmable optical tweezers. Localized modes of small subsets of qubits enable to perform entangling gates on these subsets in parallel.
Unscented Kalman Filter Based Attitude Estimation of a Quadrotor

Quadrotors are well - known unmanned aerial vehicle structures that have some advantages such as hovering, vertical take – off and landing, and low – speed flight. On the other hand, quadrotors are subjected to modeling and sensor uncertainties that lead to erroneous state estimation. Kalman filter has been proven to be the optimal estimator for the Gaussian distributed noise for linear processes. However, linear dynamical models of the quadrotors are not accurate representations of the systems due to nonlinearities, and coupling between the states. Extended Kalman filter (EKF) is proposed to solve the above issue. But, first order Taylor series approximation for the nonlinear state model may lead inefficiencies. For this reason, another Kalman filter framework is proposed that employs unscented transformation (UT). Unscented Kalman filter (UKF), can model the state distribution as Gaussian random variable to the third degree for arbitrary nonlinearities.So, in this study, unscented Kalman filter based estimation scheme is presented to overcome the sensor and model noises for nonlinear quadrotor attitude dynamics. According to the statistical analysis, the approach can estimate and reduce the mean absolute error, root mean square error and also variance of the noise for all attitude states.
Enhancing Bi-directional English-Tigrigna Machine Translation Using Hybrid Approach

Machine Translation (MT) is an application area of NLP where automatic systems are used to translate text or speech from one language to another while preserving the meaning of the source language. Although there exists a large volume of literature in automatic machine translation of documents in many languages, the translation between English and Tigrigna is less explored. Therefore, we proposed the hybrid approach to address the challenges of applying syntactic reordering rules which align and capture the structural arrangement of words in the source sentence to become more like the target sentences. Two language models were developed- one for English and another for Tigrigna and about 12,000 parallel sentences in four domains and 32,000 bilingual dictionaries were collected for our experiment. The parallel collected corpus was split randomly to 10,800 sentences for training set and 1,200 sentences for testing. Moses open source statistical machine translation system has been used for the experiment to train, tune and decode. The parallel corpus was aligned using the Giza++ toolkit and SRILM was used for building the language model. Three main experiments were conducted using statistical approach, hybrid approach and post-processing technique. According to our experimental result showed good translation output as high as 32.64 BLEU points Google translator and the hybrid approach was found most promising for English-Tigrigna bi-directional translation.
Artificial Intelligence and Intellectual Property: The View of The British and Irish Law, Education and Technology Association (BILETA)

Between September and November 2020, the UK Intellectual Property Office (UKIPO) ran a consultation about the implications Artificial Intelligence (AI) may have for Intellectual Property (IP) policy, as well as the impact IP may have for AI.This response was prepared on behalf of the British and Irish Law Education and Technology Association (BILETA) by Prof Dinusha Mendis, Associate Prof Guido Noto La Diega, Dr Felipe Romero-Moreno, and Dr Hiroko Onishi.BILETA was formed in April 1986 to promote, develop and communicate high-quality research and knowledge on technology law and policy to organisations, governments, professionals, students and the public.This submission focuses on the maincopyright and trade mark issues in AI.
On the Feasibility of Automated Prediction of Bug and Non-Bug Issues.

Issue tracking systems are used to track and describe tasks in the development process, e.g., requested feature improvements or reported bugs. However, past research has shown that the reported issue types often do not match the description of the issue. We want to understand the overall maturity of the state of the art of issue type prediction with the goal to predict if issues are bugs and evaluate if we can improve existing models by incorporating manually specified knowledge about issues. We train different models for the title and description of the issue to account for the difference in structure between these fields, e.g., the length. Moreover, we manually detect issues whose description contains a null pointer exception, as these are strong indicators that issues are bugs. Our approach performs best overall, but not significantly different from an approach from the literature based on the fastText classifier from Facebook AI Research. The small improvements in prediction performance are due to structural information about the issues we used. We found that using information about the content of issues in form of null pointer exceptions is not useful. We demonstrate the usefulness of issue type prediction through the example of labelling bugfixing commits. Issue type prediction can be a useful tool if the use case allows either for a certain amount of missed bug reports or the prediction of too many issues as bug is acceptable.
English Learning through the Group Work for Learner

Language have several ways to study, especially English. There is a special way to study English, for example Group Work learner. Like the other activities, group work for learner in language is more likely to go well if it is properly planned. Group work as a media to learn language so group work must plan with properly guided. Planning requires an understanding of the principle that lies behind successful group work. After planned the group work for learner, we can apply the principle to a good preparation. There are the typical question for the teacher “How many people should there be in a group?”, “Is the best to have people/students of mixed ability/proficiency or equal ability/proficiency?” etc. the best answered is applying the principles. There are the 5 types of principles of group work: the combining arrangement, the cooperative arrangement, the superior-inferior arrangement, and the individual arrangement. If the principle is not applied, then the group work for learner will probably not go smoothly. Each the principle have advantage and disadvantage. So the goal of group work for learner help learning in the following ways: negotiation of input, new language item, fluency, communication strategies, and content.
Eve as Cyborg

The essay argues that the figure of the female cyborg introduced by Donna Haraway has yet to live up to its post-gender emancipatory promise. Both Haraway and Jack Halberstam contrast the female cyborg with the biblical Eve and her mythic origins in the garden of Eden. I argue that the figure of Eve is actually an ancient precursor to the female cyborg. Even more than the Greek myth of Pygmalion, the Eden myth shares common elements with later stories of man-made women. Like them, Eve was made to satisfy male desire, but then followed her own desires. Like later stories of female cyborgs, the representation of Eve reflects both male desire and male fear. After analyzing both Ovid’s Pygmalion and Genesis 2-3, I trace common elements through the nineteenth century science fiction novel L’Eve future and the twenty-first century film Ex Machina. Like Eve, the female cyborgs in these works are built in a garden and their “emancipation” occurs only within the confines of androcentric heteronormative gender patterns.
Explaining Pair Programming Session Dynamics from Knowledge Gaps.

Background: Despite a lot of research on the effectiveness of Pair Programming (PP), the question when it is useful or less useful remains unsettled. Method: We analyze recordings of many industrial PP sessions with Grounded Theory Methodology and build on prior work that identified various phenomena related to within-session knowledge build-up and transfer. We validate our findings with practitioners. Result: We identify two fundamentally different types of required knowledge and explain how different constellations of knowledge gaps in these two respects lead to different session dynamics. Gaps in project-specific systems knowledge are more hampering than gaps in general programming knowledge and are dealt with first and foremost in a PP session. Conclusion: Partner constellations with complementary knowledge make PP a particularly effective practice. In PP sessions, differences in system understanding are more important than differences in general software development knowledge.
Automated Cause-of-Death Tagging from 1918 Ohio Death Records

Historical documents contain huge amounts of information, but their handwritten nature has made it diffcult and costly to digitize their information in an feffective way. Extraction of information from these sources has relied primarily on volunteer human indexers that manually transcribe the information into databases. Meanwhile, deep neural networks are becoming increasingly prevalent in most elds and have proven to be especially effective at image segmentation tasks and natural language processing. We take advantage of these strengths and create an end-to-end pipeline that automatically extracts and indexes causes of death from handwritten death records. This sequence of networks allows us to digitize information from death records more quickly, accurately, and cheaply than previously possible, providing access to future research opportunities previously untouched.
Coherent Ising machines based on optical parametric oscillators

Coherent Ising Machines (CIMs) are an emerging class of computational architectures that embed hard combinatorial optimization problems in the continuous dynamics of a physical system with analog degrees of freedom. While crisp theoretical results on the ultimate performance and scaling of such architectures are lacking, large-scale experimental prototypes have begun to exhibit promising results in practice. Our team at Stanford has begun to study the fundamental properties of CIM dynamics using a combination of techniques from statistical physics, random matrices, and dynamical systems theory. Many connections to recent work in neuroscience and deep learning are noted. Our work focuses specifically on CIMs that utilize the nonlinear threshold behavior of optical parametric oscillators to effect a soft (potentially glassy) transition between linear and binary dynamical regimes.
Community structure-aware fairness and goodness algorithm for link weight prediction

Abstract In this research, the problem of predicting the edge weight in the Bitcoin network has been resolved by utilizing the structure of the community based Fairness and Goodness. Community detection using Newman-Girvan algorithm has been applied to obtaine the trusted communities which depends on the features representation generally. The former may represent the trusted transactions that have trust value more that certain threshold. Concering the missing edge weight, the prediction is based on the fairness and goodness that supported by the above communities. In fairness measure, can capture how fair the node is in rating other nodes’ trust that has transactions (trusted transactions) with. While the goodness of a node shows how much this node is liked or trusted by other nodes that have transactions (trusted transactions) with. In both cases, only the nodes that are within community of the target node contribute with setting the fairness or the goodness of the target. The model was evaluated in practice using two real-world datasets; the Bitcoin-OTC and the Bitcoin-Alpha datasets. The experimental findings explain the effectiveness of the proposed comparable with other methods. The percentage error minimization is 18% for the Alpha dataset and 26% for OTC dataset.
A Novel Hybrid Fall Detection Technique Using Body Part Tracking and Acceleration

Falls by elderly individuals are a major issue in modern health care. A significant amount of research has been done in this domain. In this paper, we have proposed a hybrid solution for fall detection by using body part tracking and human body acceleration. The paper finds that in most cases vision-based fall detection systems work better and give a more accurate result when compared to non-vision-based systems because of the limitations of non-vision based systems (e.g., people forget to wear the wearable detection devices). The proposed system improves the accuracy of the state-of-the-art solution and reduces its computation cost. The vertical distances between head and body center, and human body acceleration are the features used in the proposed method and a Support Vector Machine (SVM) classifier is used to classify the outcome into two classes. The depth image from a Kinect Camera was used as an input to avoid any privacy issues that may occur by using RGB-based texture images, and the events were classified as an activity of daily living (ADL) or a fall.
Shape-constrained Gaussian process regression for surface reconstruction and multimodal, non-rigid image registration

We present a new statistical framework for landmark ?>curve-based image registration and surface reconstruction. The proposed method first elastically aligns geometric features (continuous, parameterized curves) to compute local deformations, and then uses a Gaussian random field model to estimate the full deformation vector field as a spatial stochastic process on the entire surface or image domain. The statistical estimation is performed using two different methods: maximum likelihood and Bayesian inference via Markov Chain Monte Carlo sampling. The resulting deformations accurately match corresponding curve regions while also being sufficiently smooth over the entire domain. We present several qualitative and quantitative evaluations of the proposed method on both synthetic and real data. We apply our approach to two different tasks on real data: (1) multimodal medical image registration, and (2) anatomical and pottery surface reconstruction.
ANALISIS DAN KOMPARASI ALGORITMA NAÏVE BAYES DAN C4.5 UNTUK KLASIFIKASI LOYALITAS PELANGGAN MNC PLAY KOTA SEMARANG

Customer loyalty is an important factor to the survival of a company. The problem facting today is related to the descreasing number of customer loyalty that happened to PT MNC Play. If this is allowed to continue it is not impossible this will endanger the continuity of the company’s business. Keep in mind the factors that cause customers to have loyal and disloyal status, data mining classification techniques can be used to classify loyal and disloyal customers. Many data mining classification algorithms can be used, so it needs to be comparative to know the accuracy of each algorithm, the algorithem used is C4.5 and Naïve Bayes. The data used were 28,899MNC Play customer of Semarang City. The results of the classification process were evaluated using cross validation, confusion matrix, ROC Curve to find the more accurate data mining classification algorithm to determine loyal and disloyal customers. Keywords: Classification, Customers Loyalty, Naïve Bayes, C4.5.