Reviewerly/exHarmony huggingface dataset:
Graph-based RGB-D Image Segmentation Using Color-directional-region Merging

Color and depth information provided simultaneously in RGB-D images can be used to segment scenes into disjoint regions. In this paper, a graph-based segmentation method for RGB-D image is proposed, in which an adaptive data-driven combination of color- and normal-variation is presented to construct dissimilarity between two adjacent pixels and a novel region merging threshold exploiting normal information in adjacent regions is proposed to control the proceeding of the region merging. We evaluate our method on the NYU-v2 depth database and compare it with several published RGB-D partition methods. The experimental results show that our method is comparable with the state-of-the-art methods and provides more details of structures in the scene.
Beyond Small Chunks

Although open educational resources (OERs) can help bridge high school-university vocabulary gaps, central questions remain unanswered regarding effective design that enables students to go beyond the “small chunks” that typically characterize vocabulary learning in mobile learning environments (MLEs). This bi-national study adopted a design-based research paradigm to collect input of students at two Chinese universities regarding design factors to overcome potential barriers to learning vocabulary in MLEs. The study iterated through a cycle of analysis, development, implementation, and evaluation with two cohorts of 222 and 136 students, respectively, over two years. Results showed that L1 translations correlated with significantly improved performance on basic-level vocabulary exercises and pop vocabulary quizzes. However, results suggest that moving mobile learning into the mainstream will require designing more effective scaffolding for complex vocabulary learning as well as clearer guidelines for optimal integration of PC and MLEs for supporting self-directed vocabulary study.
Research and implementation of power distribution systyem security protection technology based on blockchain

Smart grids are gradually becoming popular, but itself, especially its power distribution grids, faces many security issues, and false data injection is one of the most attack that happens. Blockchain is considered a key technology for protecting IoT due to its decentralization, non-deformable and high scalability. However, the current blockchain technology costs lots of resources storing and producing blocks, it cannot be directly applied to the power distribution grids with a large number of resource-limited nodes. Therefore, this paper proposes historical chain compression, and then modifys the DPOS and applies it to the blockchain, which help reduce the storeage burden and improve the effiiciency of block producing.
Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads

Inexpensive DRAMs have created new opportunities for in-memory data analytics. However, the major bottleneck in such systems is high memory access latency. Traditionally, this problem is solved with large cache hierarchies that only benefit regular applications. Alternatively, many data-intensive applications exhibit irregular behavior. Hardware multithreading can better cope with high latency seen in such applications. This article implements a multithreaded prototype (MTP) on FPGAs for the relational selection operator that exhibits control flow irregularity. On a standard TPC-H query evaluation, MTP achieves a bandwidth utilization of 83%, while the CPU and the GPU implementations achieve 61% and 64%, respectively. Besides being bandwidth efficient, MTP is also 14.2× and 4.2× more power efficient than CPU and GPU, respectively.
A Bibliographic Review of Software Metrics: Applying the Consolidated Meta-Analytic Approach

This research aimed to provide an integrating model for the principle contributions of the scientific literature on software metrics with an impact on that field. Software metrics has emerged as a crucially important aspect of software development planning insofar as indicators representing development costs and the effort involved are essential for the formulation of new systems proposals and the analysis of the systems already in use inside organizations. To that end an exploratory, quantitative, bibliometric study was carried out using the Theory of the Consolidated Meta-analytic Approach. The study retrieved 658 relevant registrations from the Web of Science database for the period 2010–2018. The main contributions and most important approaches are presented together with an integrating model with three main classifications (a) Metrics for Quality in Cloud (40.93%), (b) Software Metrics as Technique (29.30%), and (c) Current Uses of Software metrics (29.77%). In addition the taxonomy of the most cited articles was established and comparisons were made with the results from multi-language databases like Scopus and Google scholar.
Pruning SIFT &amp; SURF for Efficient Clustering of Near-duplicate Images

Clustering and categorization of similar images using SIFT and SURF require a high computational cost. In this paper, a simple approach to reduce the cardinality of keypoint set and prune the dimension of SIFT and SURF feature descriptors for efficient image clustering is proposed. For this purpose, sparsely spaced (uniformly distributed) important keypoints are chosen. In addition, multiple reduced dimensional variants of SIFT and SURF descriptors are presented. Moreover, clustering time complexity is also improved by proposed contextual bag-of-features approach for partitioned keypoint set. The F-measure statistic is used to evaluate clustering performance on a California-ND dataset containing near-duplicate images. Clustering accuracy of the proposed pruned SIFT and SURF is found to be at par with traditional SIFT and SURF with a significant reduction in computational cost.
Image Reflection Removal Using the Wasserstein Generative Adversarial Network

Imaging through a semi-transparent material such as glass often suffers from the reflection problem, which degrades the image quality. Reflection removal is a challenging task since it is severely ill-posed. Traditional methods, while all require long computation time on minimizing different objective functions with huge matrices, do not necessarily give satisfactory performance. In this paper, we propose a novel deep-learning based method to allow fast removal of reflection. Similar to the traditional multiple-image approaches, the proposed algorithm first captures the multi-view images of a scene. Then the images are fed to a convolutional neural network to obtain the depth information along the edges of the image. It is sent to a Wasserstein generative adversarial networks (WGAN) for estimating the edges of the background. Finally, the background edges are used in another WGAN to reconstruct the background image. Experimental results show that the proposed method can achieve state-of-the-art performance, and is significantly faster than the traditional methods due to the use of the deep learning methods.
CT lesion recognition algorithm based on improved particle reseeding method

In order to improve the performance of CT image's lesion recognition algorithm and improve the diagnosis accuracy of doctors, a CT lesion recognition algorithm based on improved particle reseeding method is proposed. First of all, aiming at the non- uniformity of topography, the Lagrangian labeled particles are calculated before the level set formula is calculated to reconstruct the embedded interface, thus improving the quality conservation characteristics of the level set algorithm. Secondly, in view of the uncertainty of the traditional particle method in dealing with interface singularity and complex geometry- related problems, the convergence of velocity fields at singular points and topological change points is promoted by adding velocity vectors and unit normal vectors. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.
Agile Practices and Motivation

The motivation of software engineers has been a recurring object of study with the aim of seeking a greater and healthier performance in software development teams. Although few studies associate the use of agile methods with motivation, they have not been able to explain the reasons beyond this connection. Oriented by a theory of work motivation of software engineers, this paper aims to investigate how agile practices are related to the motivation of these individuals. A research questionnaire was designed to evaluate the correlation between the use of agile attributes and job characteristics associated to work motivation. A total of 90 software engineers in 12 Brazilian states and 48 different companies participated in the survey, and results indicate that 24 of 25 of the agile attributes have significant correlations with at least one of the characteristics of the work that ignites motivation. Following agile requirements management process and Delivering valuable functionalities earlier are among the most impacting practices.
On accommodating the semantic-based interpretability in evolutionary linguistic fuzzy controller

In this paper, we propose the accommodation of the semantic-based interpretability in evolutionary linguistic fuzzy controller through an overlap-based encoding strategy for the triangular membership function (MF) parameters. With this strategy, the coverage and the distinguishability aspect of the fuzzy partitions are guaranteed during the evolution. The idea behind this proposal is that the set of the membership functions forming the fuzzy partition are defined as overlapped functions instead of separate functions subjected to some constraints as the most previous works do. To test the design technique, simulations were performed to optimize direct-drive DC motor fuzzy controller. Simulation results show that fuzzy partition interpretability is guaranteed and the evolved FLC exhibits high performances in slow and fast tracking, as compared with the conventional PD controller.
An audio attention computational model based on information entropy of two channels and exponential moving average

Abstract The main down-top attention model usually uses some characteristics of audio signal to extract the auditory saliency map at present. But existing audio attention computational model based image saliency mostly doesn’t consider the continuity and attenuation mechanism of the human brain on paying attention to some occurred events in our real environment. To address these issues, we propose a model based on information entropy of two channels and exponential moving average (EMA) to simulate the processing of sound signals from the basilar membrane to the cochlear nucleus, and get the local information entropy of image and audio channel, finally we use the relevant EMA methods to calculate the auditory attention map. Some experimental results on artificial audio signal and real-world audio signal belonging to a public corpus show that the proposed model not only can detect the attention event, but also reflect the mechanism of human brain, and the experimental results of talk show in real environment are better.
Juno: An Intelligent Chat Service for IT Service Automation

Juno is a chat-based service that interacts with user through natural language, in order to understand, assist and execute the user's service request with a IT Service Management (ISM) system.
Advanced wang–landau monte carlo‐based tracker for abrupt motions

Conventional tracking methods rarely consider abrupt motions and easily fail to track the abrupt motion of an object because they are based on the assumption of smooth motion. To assuage this problem, we propose a novel tracking algorithm combining the background subtraction method with the Wang–Landau Monte Carlo (WLMC) sampling method for dealing with abrupt motions effectively. First, the visual background extractor (ViBe), a background subtraction technique, is introduced to detect the object roughly. Second, we integrate the density‐of‐states term and ViBe information into a WLMC‐based tracking framework. By employing the density‐of states term and ViBe information, the Wang–Landau sampling scheme efficiently tracks the target in the whole state space in a short time. Third, in order to obtain good performance in terms of scalability, an independent fast scale estimation method is applied. Experimental results in both quantitative and qualitative measures demonstrate that our algorithm efficiently samples and tracks the target not only in position but also in scale variance accurately and robustly. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Score-specific Non-maximum Suppression and Coexistence Prior for Multi-scale Face Detection

Face detection is an ultimate component to support various visual facial related tasks. However, detecting faces with extremely low resolution or high occlusion is still an open problem. In this paper, we propose a two-step general approach to refine the performance of modern face detectors according to human's high-level context-aware ability. First, we propose Score-specific Non-Maximum Suppression (SNMS) to preserve overlapped faces. Second, we consider the coexistence prior among faces in the scene, which could raise the sensitivity of face detection in the crowd. When integrating our approach to the existing face detectors, most of them have better results on a challenging benchmark (WIDER FACE) and a newly proposed dataset (Faces in Crowd, FIC) made by us. Codes are available on https://github.com/AIoTP/SNMSandCoexistence.
Structural and Statistical Feature Extraction Methodology for the Recognition of Handwritten Arabic Words

Knowledge concerning the topography of Arabic letters, as well as the structural characteristics between background regions and character components is investigated as a novel approach for Arabic recognition. The suggested feature extraction method reduces the classifier input data to only the most significant and essential. First, connected components consisting of more than one character are segmented into characters. Secondly, the primitives are extracted according to the knowledge of character structures and some statistical characteristics. Finally a hybrid model based on the combination of support vector machines (SVM) classifier and particle swarm optimization (PSO) is used to evaluate the performance of features extracted.
Developing Precautionary Reference Points for Fishery Management Using Robust Control Theory: Application to the Chesapeake Bay Blue CrabCallinectes sapidusFishery

Abstract Most efforts to establish precautionary guidelines for fisheries management use essentially ad hoc impressions as to what constitutes conservative management. Such approaches, however, fail to take into account the magnitude of the uncertainty about particular systems. One alternative approach to precautionary management is robust control, in which decision makers attempt to maximize an outcome under the assumption that the conditions will be worse than expected. In this paper, we apply a robust optimization approach to estimate the maximum sustainable yield and other reference points for the Chesapeake Bay blue crab Callinectes sapidus fishery. This approach is relatively easy to implement in standard stock assessment models that use a maximum‐likelihood approach to estimate model parameters. In addition, it has the advantage that a standard level of precaution can be chosen by decision makers and then applied to different fisheries with vastly different levels of data and analysis.
Software visual specification for requirement engineering education

Requirement engineering course is perceived as an important course that become one of compulsory courses to be thought to Software Engineering graduates. However, Requirement Engineering Education (REE) is suffering from difficulty in content delivery (lecturer) and learning (student) due to the nature of Requirement Engineering theoretical and diversity of knowledge especially in teaching the student how to specify the requirement. In order to overcome this problem, software visualization approach is introduced in learning requirement engineering course focus on specifying the requirements. A control experiment was conducted to study the applicability of the approach. Based on the analysis of the survey data, the proposed approach seems able to give positive impact to the quality of overall requirement specification.
Fragile Watermarking Techniques for 3D Model Authentication: Review

The need for multimedia data in many fields such as industry, medicine, and entertainment has become a common need nowadays. 3D graphics, as a type of multimedia, attracts the attention of many researchers. The spread of programs that are used to modify and duplicate this data easily leads to increase the necessity to develop a variety of watermark technologies for various protection purposes such as copyrights protection and authentication. In this study, we provide a review study of fragile watermarking techniques for 3D models over the last decade and clarify what are the criteria and requirements for design such systems. We briefly show the different types of 3D models representation and what are the most common kinds of attacks could be applied to them, and finally discuss the challenges encountered at designing such techniques.
The Evolution of Trends and Techniques used for Data Mining

Data mining has got the festivity among the most emerging fields of the current epoch. Extensive data is being generated therefore the need of the hour is to mine the interesting trends and patterns. For mining such trends, different techniques and algorithms are introduced and applied during the last decades. In this paper, we are extracting the essence from massive text corpora in the form of potential techniques used in the esteemed field of data mining. For the purpose of exploring the trends, we considered the time span from 2014 to 2018. For this purpose, a novel dataset comprising of the abstracts and metadata of 5,843 articles is procured. All the extracted contents of data mining are published in esteemed journals indexed by ScienceDirect. Text mining techniques including noun phrase mining and TF-IDF are employed to reveal the evolution of hot trends in the field of data mining during the underlying span. For better visualization, the year-wise and overall evolution of these hot trends is presented in the form of the word cloud. Machine learning algorithms are found as the shoulders of the giants, on which, data mining domain is standing.
Toward Universal Spatialization Through Wikipedia-Based Semantic Enhancement

This article introduces Cartograph, a visualization system that harnesses the vast world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. Although these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph’s map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original datasets but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered. We present findings from two user studies exploring design choices and use of the system.
Bringing Modern Machine Learning into Clinical Practice Through the Use of Intuitive Visualization and Human–Computer Interaction

The increasing trend of systematic collection of medical data (diagnoses, hospital admission emergencies, blood test results, scans, etc) by healthcare providers offers an unprecedented opportunity for the application of modern data mining, pattern recognition, and machine learning algorithms. The ultimate aim is invariably that of improving outcomes, be it directly or indirectly. Notwithstanding the successes of recent research efforts in this realm, a major obstacle of making the developed models usable by medical professionals (rather than computer scientists or statisticians) remains largely unaddressed. Yet, a mounting amount of evidence shows that the ability to understand and easily use novel technologies is a major factor governing how widely adopted by the target users (doctors, nurses, and patients, amongst others) they are likely to be. In this work we address this technical gap. In particular, we describe a portable, web-based interface that allows healthcare professionals to interact with recently developed machine learning and data driven prognostic algorithms. Our application interfaces a statistical disease progression model and displays its predictions in an intuitive and readily understandable manner. Different types of geometric primitives and their visual properties (such as size or colour) are used to represent abstract quantities such as probability density functions, the rate of change of relative probabilities, and a series of other relevant statistics which the heathcare professional can use to explore patients’ risk factors or provide personalized, evidence and data driven incentivization to the patient.
Toward a Logic of Everyday Reasoning

Logic should return its focus to valid reasoning in real-world situations. Since classical logic only covers valid reasoning in a highly idealized situation, there is a demand for a new logic for everyday reasoning that is based on more realistic assumptions, while still keeps the general, formal, and normative nature of logic. NAL (Non-Axiomatic Logic) is built for this purpose, which is based on the assumption that the reasoner has insufficient knowledge and resources with respect to the reasoning tasks to be carried out. In this situation, the notion of validity has to be re-established, and the grammar rules and inference rules of the logic need to be designed accordingly. Consequently, NAL has features very different from classical logic and other non-classical logics, and it provides a coherent solution to many problems in logic, artificial intelligence, and cognitive science.
Walk rally application development environment suitable for individual situations

The Walk Rally application is a mobile application that encourages users to walk around an area using mobile terminals. Several Walk Rally applications have been developed for use in various situations. However, depending on the area and the purpose of use, the information presented in the application and the functions required for it are different. Therefore, it is necessary to individually develop Walk Rally applications suitable for specific situations, and this of course becomes expensive. In this study, we developed a Walk Rally application development environment named Machinavi, where various functions are independently developed as business logic to enable use in various situations. Customizing the business logic to the purpose of use makes it possible to develop Walk Rally applications suitable for various situations. We used Machinavi to develop three Walk Rally applications for three specific situations: an event, a sightseeing spot, and a commercial area. The results of experiments and a questionnaire survey demonstrate the effectiveness of the Walk Rally applications developed using this development environment.