Reviewerly/exHarmony huggingface dataset:
On the Evolution of Keyword-Driven Test Suites

Many companies rely on software testing to verify that their software products meet their requirements. However, test quality and, in particular, the quality of end-to-end testing is relatively hard to achieve. The problem becomes challenging when software evolves, as end-to-end test suites need to adapt and conform to the evolved software. Unfortunately, end-to-end tests are particularly fragile as any change in the application interface, e.g., application flow, location or name of graphical user interface elements, necessitates a change in the tests. This paper presents an industrial case study on the evolution of Keyword-Driven test suites, also known as Keyword-Driven Testing (KDT). Our aim is to demonstrate the problem of test maintenance, identify the benefits of Keyword-Driven Testing and overall improve the understanding of test code evolution (at the acceptance testing level). This information will support the development of automatic techniques, such as test refactoring and repair, and will motivate future research. To this end, we identify, collect and analyze test code changes across the evolution of industrial KDT test suites for a period of eight months. We show that the problem of test maintenance is largely due to test fragility (most commonly-performed changes are due to locator and synchronization issues) and test clones (over 30% of keywords are duplicated). We also show that the better test design of KDT test suites has the potential for drastically reducing (approximately 70%) the number of test code changes required to support software evolution. To further validate our results, we interview testers from BGL BNP Paribas and report their perceptions on the advantages and challenges of keyword-driven testing.
Multi-person pose estimation based on a deep convolutional neural network

Human motion recognition based on computer vision plays an important role in many fields, such as video surveillance, virtual reality, and medical care. To solve the inaccurate multi-person pose estimation problem and improve the generalizability of the extracted features, this paper proposes a multi-person pose estimation method based on a deep convolutional neural network. This method mainly relies on a top-down structure which includes two stages. In the first stage, the bounding boxes that are likely to contain people are first detected by an improved faster R-CNN. Individuals in the complex scenario are then tailored by box cropping. In the second stage, we combine heatmap detection with coordinate regression to address the single person pose estimation problem. Specially, a deep convolutional ResNet is employed to produce heatmaps of human body. The precise location of each joint is achieved by the fully connected conditional random field. Experimental results demonstrate our method achieves comparable performance with the state-of-the-art ones.
Situation-aware model for multi-objective decision making in ambient intelligence

The emergence of automated environments to add intelligence to the decision making process on real world problems with multiple and conflicting objectives is an actual issue. In some cases, these objectives have the same importance and no easy prioritization can be done. For dealing with reasoning on every-day activities, systems for ambient intelligence (AmI) often deal with multi-objective problems. This paper presents a situation-aware model to support multi-objective decision making for objectives with equal importance in AmI. By using contextual data from the environment, a system based on this model identifies the situation of interest; performs multi-objective decision without assigning weights to the objectives, and performs an action to control the environment. To verify the model, a system was developed aiming to manage the multi-objective problem of thermal comfort and energy consumption of an office. As results, this work developed an L-fuzzy library (used in the decision module) and shows that the inclusion of this intelligence in AmI systems allows the achievement of both objectives, without the need of giving priority to one or to the other aspect.
An ontology for reasoning on body-based gestures

Body-based gestures, such as acquired by Kinect sensor, today benefit from efficient tools for their recognition and development, but less for automated reasoning. To facilitate this activity, an ontology for structuring body-based gestures, based on user, body and body parts, gestures, and environment, is designed and encoded in Ontology Web Language according to modelling triples (subject, predicate, object). As a proof-of-concept and to feed this ontology, a gesture elicitation study collected 24 participants X 19 referents for IoT tasks = 456 elicited body-based gestures, which were classified and expressed according to the ontology.
Trolls maintained: baiting technological infrastructures of informational justice

In this paper, we use trolling to illuminate the entangled and complex relationship between online debate, technological infrastructure, and justice. While a great deal of research has investigated the harmful effects of trolling in the form of cyberbullying and online harassment, attention to the infrastructure of trolling can provide new insights into information flows within digital infrastructure, and consequently, bears on questions such as access to, and quality of, information; the status and credibility of knowledge claims and claimants; and about the gatekeepers of knowledge and information. We show how trolling takes advantage of the rapidity of information transmission and reproduction; technical illiteracy; automation; and ‘soft’ infrastructure such as conventions, protocols, etiquette, and rules governing online communities to affect informational justice using a methodologically symmetrical approach.
Balanced Odd-Variable RSBFs with Optimum AI, High Nonlinearity and Good Behavior against FAAs

Rotation symmetric Boolean functions which are invariant under the action of cyclic group have been used in many different cryptosystems. This paper presents a new construction of balanced odd-variable rotation symmetric Boolean functions with optimum algebraic immunity. It is checked that, at least for some small variables, such functions have very good behavior against fast algebraic attacks. Compared with some known rotation symmetric Boolean functions with optimum algebraic immunity, the new construction has really better nonlinearity. Further, the algebraic degree of the constructed functions is also high enough.
Unsupervised Anomaly detection of LM Guide Using Variational Autoencoder

Linear Motion (LM) is a linear motion guide that helps directional moving of machine. It is important to judge the anomaly state of LM guides because LM guides are used in various industries to support various task in industry application. In this paper, we proposed a machine learning algorithm for determining the anomaly state of LM guide. Considering that it is difficult to actually generate the anomaly signal, we trained model with only healthy state data. One of the generative models, variational autoencoder, is used for training healthy state data and the distribution of healthy state data is trained. Our trained model determines whether or not anomaly state has occurred based on a reconstruction error of the trained network.
An Efficient Method for Automatic Generation of Labanotation Based on Bi-Directional LSTM

Abstract Labanotation uses a variety of graphic symbols to analyse and record human movements accurately and flexibly, which is an important means to protect traditional dance. In this paper, we introduce an efficient method for automatic generation of Labanotation from motion capture data by identifying human movements with bidirectional LSTM network (Bi-LSTM). Up to our knowledge, this is the first time that Bi-LSTM network has been introduced to the field of Labanotation generation. Compared with previous methods, Bi-LSTM used in our human movements recognition system learns context information for sequential data from not only the past but also the future directions. Combined with a newly designed discriminative skeleton-topologic feature, our approach has the ability to generate more accurate Labanotation than previous work. Experiment results on two public motion capture datasets show that our method outperforms state-of-the-art methods, demonstrating its effectiveness.
Didactics of Historical-Cultural Heritage QR Codes and the TPACK Model: An Analytic Revision of Three Classroom Experiences in Spanish Higher Education Contexts

This study aims to establish a first reference didactic and methodological framework for the operational and effective integration of Quick Response (QR) codes in the training of Social Sciences teachers in Primary Education, paying special attention to the integrated acquisition of social, civic, and digital competences in the framework TPACK (Technological Pedagogical Content Knowledge). With this purpose, it specifies the didactic potential of these new technological resources in the teaching-learning of the Social Sciences, its eventual integration in the design and implementation of teaching innovation projects for this educational stage, and its capacity for the acquisition of competence digital teaching in Primary Education teachers in initial training. In this sense, the work explores a selection of classroom experiences and research and innovation proposals, pioneers in the field of Social Sciences Didactics in Spain, with the aim of offering an approximation and status of the didactic treatment of History and, particularly, the historical-cultural heritage in the formation of Primary Education teachers.
A Comparative Study Between Methodologies Based on the Hough Transform and Watershed Transform on the Blood Cell Count

It is increasingly common to use engineering techniques in the areas of health, in order to solve simple problems or even create new diagnostic methods. In the last decade, the Hough Transform has been widely used as a tool for segmentation of blood smear images for the purpose of counting blood cells. However, it is noted that the Watershed transform has been applied to perform the same function. Based on this, a methodology based on the Hough Transform was created, aiming to perform the detection and counting of erythrocytes and leukocytes and verify the applicability of the methodology when compared to others. The study was conducted based on the determination of accuracy and simulations performed on different hardware platforms and subsequent comparison with the WT-MO methodology. The results demonstrated that both methodologies are able to perform the task of detection and counting of blood cells in digital images of blood smear. However, the methodology based on the Watershed Transform best meets the criteria of speed and reliability (counts), which are indispensable to medical laboratory routine.
Balancing the Degree of Exploration and Exploitation of Swarm Intelligence Using Parallel Computing

The two basic search behaviors used in metaheuristic algorithms in general and swarm intelligence in particular are exploration and exploitation. Exploration refers to searching the unexplored area of the feasible region while exploitation refers to the search of the neighborhood of a promising region. The success of these algorithms highly depends on how these two search behaviors are balanced. Increasing the number of initial solutions indeed increase the performance of the algorithm over the expense of the runtime. Different exploration and exploitation adjustments are used in different metaheuristic algorithms. To well balance the degree of exploration and exploitation, which ultimately leads to finding good solutions, needs a careful implementation and sufficient runtime. However, the runtime is another issue which has been a bottleneck for different application, especially for high dimensional, highly constrained and expensive objective function cases. Using parallel computing to overcome this limitation has been a central research issue in the study of metaheuristic algorithms. The three basic parallelization approaches used are the parallel move model which is a master slave model where solutions will be distributed to different processors for updating, parallel multi start model which uses several updating methods for the solutions in parallel and move acceleration method, which refers to a parallel implementation with parallelizing the objective function. There is no specific parallelization used towards balancing of exploration and exploitation is mentioned. Hence, in this paper a parallel implementation of swarm intelligence in general and two algorithms, namely particle swarm optimization and prey predator algorithm, in particular will be discussed. The parallelization proposed is aimed to merge the parallel move model and the parallel multi start model with the aim of adjusting the degree of exploration and exploitation. Experimental study based on ten benchmark problems from constrained as well as unconstrained problems will be used for simulation. It will be shown that the parallelization with specific distribution of exploration and exploitation runs better and efficiently compared to the serial and also the parallel version without exporation/exploitation adjustment.
Clustering right-skewed data stream via Birnbaum–Saunders mixture models: A flexible approach based on fuzzy clustering algorithm

Despite the widespread use of Gaussian mixture model for clustering datasets, practical applications show that the skewed and leptokurtic mixture models can be considered as promising alternatives. This paper proposes a finite mixture of Birnbaum–Saunders (FM-BS) distributions for analyzing and clustering right-skewed, leptokurtic, and multimodal lifetime datasets. The maximum likelihood (ML) estimates of the proposed model are obtained by developing a computationally analytical expectation–maximization (EM) type algorithm, as well as a fuzzy classification maximum likelihood (FCML) type algorithm, that combines the advantages of fuzzy clustering and robust statistical estimators. Simulation studies demonstrate the accuracy and computational efficiency of the FCML algorithm to estimate parameters of the FM-BS distributions and to cluster samples drawn from the FM-BS distributions. Finally, some real datasets have been analyzed to illustrate how well the proposed FM-BS model estimates the membership values.
Interactive IR User Study Design, Evaluation, and Reporting

Since user study design has been widely applied in search interactions and information retrieval (IR) systems evaluation studies, a deep reflection and meta-evaluation of interactive IR (IIR) user stu
Barracuda

A blockchain is a database of sequential events that is maintained by a distributed group of nodes. A key consensus problem in blockchains is that of determining the next block (data element) in the sequence. Many blockchains address this by electing a new node to propose each new block. The new block is (typically) appended to the tip of the proposer's local blockchain, and subsequently broadcast to the rest of the network. Without network delay (or adversarial behavior), this procedure would give a perfect chain, since each proposer would have the same view of the blockchain. A major challenge in practice is forking. Due to network delays, a proposer may not yet have the most recent block, and may therefore create a side chain that branches from the middle of the main chain. Forking reduces throughput, since only one a single main chain can survive, and all other blocks are discarded. We propose a new P2P protocol for blockchains called Barracuda, in which each proposer, prior to proposing a block, polls ℓ other nodes for their local blocktree information. Under a stochastic network model, we prove that this lightweight primitive improves throughput as if the entire network were a factor of ℓ faster. We provide guidelines on how to implement Barracuda in practice, guaranteeing robustness against several real-world factors.
The Prototype of Hand Gesture Recognition for Elderly People to Control Connected Home Devices

Abstract Nowadays, the technology development makes a human can create a tool which is used to recognize an object and it becomes a popular technology device. It happens because this tool has an important role for interaction between a human and a computer. One example of this technology usage is to recognize a hand gesture for controlling a home automation system. The existing of this technology creates the change related to how the human controls any tools in a house and it also reduces the complexity including an effort when it is used for controlling. This feature is very useful, especially for elderly people who stay in independent living. This study is going to develop a controller prototype by using FAST (Features from Accelerated Segment Test) algorithm to detect hand gesture for operating the connected home devices. This controller uses an embedded system to translate a command which is created by using the hand gesture of senior captured by the cam for controlling the lamps. The lamps itself are represented as several tools in the house. The observation gives a result that the hand gesture is potential to be implemented as a command for controlling the proposed system prototype in the range which is not far than 1 meter with the percentage average recognition accuracy is almost 80%.
Quantum Algorithms for Fluid Simulations

This chapter describes results of a recent investigation aiming to assess the potential of quantum computing and suitably designed algorithms for future computational fluid dynamics applications.For quantum computers becoming available in the near future, it can be expected that applications of quantum computing follow the quantum coprocessor model, where selected parts of the computational task for which efficient quantum algorithms exist are executed on the quantum hardware.For example, in computational fluid dynamics algorithm, this hybrid quantum/classical approach is discussed, and in particular it is shown how the approximate quantum Fourier transform (AQFT) can be used in the Poisson solvers of the considered method for the incompressible-flow Navier-Stokes equations.The analysis shows that despite the inevitable errors introduced by applying AQFT, the method produces meaningful results for three-dimensional example problems.A second example of a quantum algorithm for flow simulations is then described.This method based on kinetic modeling of the flow was developed to reduce the information transfer between quantum and classical hardware in the quantum coprocessor model.It is shown that this quantum algorithm can be executed fully on quantum hardware during a simulation.The conclusion summarizes further challenges for algorithm developments and future work.
Virtual Reality for Individuals With Substance Use Disorders

Substance use disorders (SUDs) remain difficult to treat, with relapse rates averaging 50% despite available treatment. There is a need for new and novel treatment for treating SUDs. The use of virtual reality (VR) shows promising results as a strategy to help patients with SUDs manage cravings, say no to substance use, reduce stress and anxiety, treat pain, learn life skills, exercise, and use as a substitute for substance use. In VR, patients use a head-mounted device to experience computer-generated 360° simulated environments. Studies have shown that VR activates some of the same areas of the brain that substances activate. The science and technology of VR is evolving, resulting in more affordability, but the price is likely still a barrier for most treatment providers and patients. In addition, applications used to treat SUDs were developed for use in research and are not currently available for public use. More rigorous studies are needed to determine the effectiveness of VR as a treatment for SUDs. [Journal of Psychosocial Nursing and Mental Health Services, 57(6), 15-19.].
BatchNorm Decomposition for Deep Neural Network Interpretation

Layer-wise relevance propagation (LRP) has shown potential for explaining neural network classifier decisions. In this paper, we investigate how LRP is to be applied to deep neural network which makes use of batch normalization (BatchNorm), and show that despite the functional simplicity of BatchNorm, several intuitive choices of published LRP rules perform poorly for a number of frequently used state of the art networks. Also, we show that by using the $$\varepsilon $$ -rule for BatchNorm layers we are able to detect training artifacts for MobileNet and layer design artifacts for ResNet. The causes for such failures are analyzed deeply and thoroughly. We observe that some assumptions on the LRP decomposition rules are broken given specific networks, and propose a novel LRP rule tailored for BatchNorm layers. Our quantitatively evaluated results show advantage of our novel LRP rule for BatchNorm layers and its wide applicability to common deep neural network architectures. As an aside, we demonstrate that one observation made by LRP analysis serves to modify a ResNet for faster initial training convergence.
An Inception Convolutional Autoencoder Model for Chinese Healthcare Question Clustering

Healthcare question answering (HQA) system plays a vital role in encouraging patients to inquire for professional consultation. However, there are some challenging factors in learning and representing the question corpus of HQA datasets, such as high dimensionality, sparseness, noise, nonprofessional expression, etc. To address these issues, we propose an inception convolutional autoencoder model for Chinese healthcare question clustering (ICAHC). First, we select a set of kernels with different sizes using convolutional autoencoder networks to explore both the diversity and quality in the clustering ensemble. Thus, these kernels encourage to capture diverse representations. Second, we design four ensemble operators to merge representations based on whether they are independent, and input them into the encoder using different skip connections. Third, it maps features from the encoder into a lower-dimensional space, followed by clustering. We conduct comparative experiments against other clustering algorithms on a Chinese healthcare dataset. Experimental results show the effectiveness of ICAHC in discovering better clustering solutions. The results can be used in the prediction of patients’ conditions and the development of an automatic HQA system.
Deep learning: a branch of machine learning

Abstract Deep learning is a rising territory of machine learning (ML) inquire about. It includes different shrouded layers of artificial neural systems. Deep learning (DL) is a part of machine learning dependent on an arrangement of algorithm that aim to show abnormal state reflections in information. It is utilized by Google in its voice and picture recognition algorithm, by Netflix and Amazon to choose what you need to watch or purchase straightaway, and by specialists at MIT to anticipate what’s to come. Deep Learning is utilized in different fields for accomplishing various levels of deliberation like sound, content, pictures highlight extraction and so forth. The Deep learning philosophy applies nonlinear changes and model reflections of abnormal state in extensive databases. With Deep learning capacity to make forecasts and groupings taking the upside of huge information, it can be a creative answer for issues and issues that have been never thought to be understood in such a simple way. Then again, it makes numerous difficulties on the researchers who are trying to convey such another methodology. The accompanying survey sequentially shows how and in what real applications deep learning algorithms have been used. We have completed a broad writing survey and reviewed the utilization of deep learning in different fields.