Reviewerly/exHarmony huggingface dataset:
Development of a 3D User Interface based on Kinect Sensor and Bend-Sensing Data Glove for Controlling Software in the Mining Industry

In this study, we developed a 3D user interface (UI) using the Microsoft’s Kinect sensor and bend-sensing data glove to control software in the mining industry. The developed UI controls the mouse cursor in Windows according to the user’s hand position tracked by the Kinect sensor. In addition, it controls the mouse click events in Windows by measuring the user’s finger flexion using the bend-sensing data glove. An open-source hardware, Arduino Lilypad Board, and National Instrument’s LabVIEW software were used to develop the UI. Applications of the 3D UI to several 3D software utilized in the mining industry revealed that it can control the 3D software in more intuitive way than traditional UIs such as mouse and keyboard.
Dynamic Path Planning with Stable Growing Neural Gas

This paper considers the problem of path planning under dynamic aspects. We propose ”Neural Gas Dynamic Path Planning” (NGDPP), a novel algorithm that continuously provides a valid path between two points inside an environment that transforms in an unpredictable manner. These transformations can occur due to both, changes in the environment’s shape and moving collision objects. The algorithm incorporates several techniques: Neural Gas, a dynamic discretization method; the A* Algorithm, a path planning algorithm for graphs; and the Potential Field method, which facilitates the avoidance of collisions. We empirically evaluate the proposed algorithm under various aspects providing performance information and guidance about situations and applications benefiting from the algorithm. The evaluation reveals that NGDPP is a solid algorithm for path planning in dynamic environments. Yet, the algorithm is based on heuristic information, i.e. a optimal result in term of the path length cannot be guaranteed.
Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition

In this paper, a kernel-based robust disturbance dictionary (KRDD) is proposed for face recognition that solves the problem in modern dictionary learning in which significant components of signal representation cannot be entirely covered. KRDD can effectively extract the principal components of the kernel by dimensionality reduction. KRDD not only performs well with occluded face data, but is also good at suppressing intraclass variation. KRDD learns the robust disturbance dictionaries by extracting and generating the diversity of comprehensive training samples generated by facial changes. In particular, a basic dictionary, a real disturbance dictionary, and a simulated disturbance dictionary are acquired to represent data from distinct subjects to fully represent commonality and disturbance. Two of the disturbance dictionaries are modeled by learning few kernel principal components of the disturbance changes, and then the corresponding dictionaries are obtained by kernel discriminant analysis (KDA) projection modeling. Finally, extended sparse representation classifier (SRC) is used for classification. In the experimental results, KRDD performance displays great advantages in recognition rate and computation time compared with many of the most advanced dictionary learning methods for face recognition.
Implementation of Sobel Method Based Edge Detection for Flower Image Segmentation

Edge detection is the first step to cover information in the image. The edges characterize the boundaries of objects and therefore edges are useful for the process of segmentation and identification in the image. The purpose of edge detection is to increase the appearance of the boundary line of the object in the image. The sobel method is a method that uses two kernels measuring 3x3 pixels for gradient calculations so that the estimate gradient is right in the middle of the window. Digital image processing aims to manipulate image data and analyze an image with the help of a computer. Matlab is made to facilitate the use of two collections of subroutines in the fortran library, linpack and eispack, in handling matrix computing, and develops into an interactive system as a programming language. Experimental results from the input image research, namely the flower image have different MSE values because each input image has a different pixel value
Using Network Emulation to Study Blockchain Distributed Systems: The Ethereum Case

Large-scale distributed systems are becoming more widespread and, at the same time, their sizes grow day by day. In this type of systems, the adoption of blockchains is gaining particular traction for data storage in a secure and distributed manner. Nevertheless, design and testing of new protocols and features face the challenge of determining whether the proposed modifications would actually improve the system as expected. In the case of existing cryptocurrency systems, building an evaluation platform poses additional difficulties due to the resource-consuming nature of the associated processes. In this paper, we propose a novel methodology that relies on container-based network emulation to create scalable local testbeds in which Proof-of-Work-based blockchain systems can be evaluated. Using one of the mainstream Ethereum clients, we replaced the mining algorithm with a simulation model built upon the statistical characteristics of the mining process and instrumented the client to capture relevant network events. These events are used to create an offline reconstruction of the global view of the blockchain and all forking events, and to completely characterize the working conditions under arbitrary setups. Based on the versatility and scalability of our platform, we are able to test several network scenarios of increasing size in which we analyze the incidence of the target time in the generation of contradictory views of the blockchain (i.e. forks). We show that even using a limited testbed constituted by just commodity hardware, it is possible to use our platform to study the dynamics of blockchain-based systems up to hundreds of nodes.
Developing Phonics Material to Improve the Spoken English of Indonesian Tertiary Students

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Academic Leadership Through the Prism of Managerialism: The Relationship Between University Development and Rector’s Specialization

The article explores the relationship between rectors’ professional back­grounds and the development of the universities governed by them. The basic hypothesis that universities governed by rectors with managerial experience are relatively more successful in their development is tested using a pre-de­veloped classification of rectors’ background. Development of universities was assessed by exploring how their academic, research and financial indicators changed over time. The universities were grouped into clusters so that dyna­mics of indicators were compared for universities with similar performance profiles. The study uses the data on performance of Russian ( public and pri­vate) universities in 2013 and 2016 which they submitted for the Monitoring of University Effectiveness. Rectors’ biographical data was collected from a nu­mber of publicly available sources. In addition to the main analysis, the article also presents the demographic characteristics of Russian university rectors.
ANALISIS IMPLEMENTASI UJIAN NASIONAL BERBASIS KOMPUTER DENGAN UJIAN BERBASIS KERTAS DI SMPN 3 INGIN JAYA KABUPATEN ACEH BESAR

Today, information technology and computers are not only used for knowledges, but also can be a evaluation for students. This study describes the implementation of UNBK at SMP 3 Ingin Jaya which includes: (1) knowing the internal and external of the student readiness, (2) and knowing about the school readiness. The object of this study is the UNBK activities carried out at SMP 3 Ingin Jaya. The subjects of this study were included 53 students and 2 teachers. This study used the mixed/combination methodologies of the methods. The main data of this study taken from the questionnaires as the quantitative data. Then, to corrected the data of questionnaires, was carried out using qualitative methods, includes observation and interview techniques. The results of this study are the students were ready to UNBK, this was evidenced by the results of obtaining a test score of 83.14% in the questionnaires for UNBK assessment. As for the paper-based national examination assessment questionnaires, the test score was 78%. and schools are ready to implement UNBK without obstacles. But it still has to make improvements to get better results.
Ringing revivals produced by non classical fields generated by conditional measurements

Abstract We study single photon resonant transitions of a two level atom with a quantized electromagnetic field when conditional measurements take place, showing that squeezed states may be generated in the multiphoton processes that occur during evolution. We examine some field properties and show that the field not only acquires squeezing properties but also may gain or lose more than one photon. Furthermore, the squeezing properties of the generated field are put in evidence by making it to interact with a second two level atom and observing the ringing revivals characteristic of the squeezed fields.
Demographics, Design, and Free Speech

The toxicity of online interactions presents unprecedented challenges to traditional free speech norms. The scope and amplification properties of the internet give new dimension and power to hate speech, rape and death threats, and denigrating and reputation-destroying commentary. Social media companies and internet platforms, all of which regulate speech through moderation processes every day, walk the fine line between censorship and free speech with every decision they make, and they make millions a day. This chapter will explore how a lack of diversity in the tech industry affects the design and regulation of products and, in so doing, disproportionately negatively affects the free speech of traditionally marginalized people. During the past year there has been an explosion of research about, and public interest in, the tech industry’s persistent diversity problems. At the same time, the pervasiveness of online hate, harassment, and abuse has become evident. These problems come together on social media platforms that have institutionalized and automated the perspectives of privileged male experiences of speech and violence. The tech sector’s male dominance and the sex segregation and hierarchies of its workforce result in serious and harmful effects globally on women’s safety and free expression.
To Calculate or To Follow Others: How Do Information Security Managers Make Investment Decisions?

Economic models of information security investment suggest estimating cost and benefit to make an information security investment decision.However, the intangible nature of information security investment prevents managers from applying costbenefit analysis in practice.Instead, information security managers may follow experts' recommendations or the practices of other organizations.The present paper examines factors that influence information security managers' investment decisions from the reputational herding perspective.The study was conducted using survey questionnaire data collected from 106 organizations in Finland.The findings of the study reveal that the ability and reputation of the security manager and the strength of the information about the security investment significantly motivate the security manager to discount his or her own information.Herding, as a following strategy, together with mandatory requirements are significant motivations for information security investment.
ATNet: Answering Cloze-Style Questions via Intra-attention and Inter-attention

This paper proposes a novel framework, named ATNet, for answering cloze-style questions over documents. Our model, in the encoder phase, projects all contextual embeddings into multiple latent semantic spaces, with representations of each space attending to a specific aspect of semantics. Long-term dependencies among the whole document are captured via the intra-attention module. A gate is produced to control the degree to which the retrieved dependency information is fused and the previous token embedding is exposed. Then, in the interaction phase, the context is aligned with the query across different semantic spaces to achieve the information aggregation. Specifically, we compute inter-attention based on a sophisticated feature set. Experiments and ablation studies demonstrate the effectiveness of ATNet.
Geometrical features of a Jewish monument: study with a DGS

We study a couple of geometrical elements of a monumental synagogue (Dohany Synagogue in Budapest). Then we describe how undergraduate students from the Jewish orthodox population have performed a technology-based study, using a Dynamical Geometry System, notions, and tools of analytic geometry. These students come without a classical background in mathematics, and such activities relying on artefacts form their everyday life help to attract them to mathematics learning.
Mastering Software Robot Development Projects: Understanding the Association between System Attributes &amp; Design Practices

Software robots tend to increasingly take over organizational processes.However, little is known about principles of developing as opposed to using robotic systems, such as RPA robots and chatbots.Therefore, based on a comparative case study, this paper elaborates how different types of robots, due to distinguishing system attributes, relate to different design practices that arise from varying challenges of transforming existing routines into robots.
AI Agency Risks and Their Mitigation Through Business Process Management: A Conceptual Framework

After over 60 years of research and development, AI has made its way into mainstream business operations.Continuous advances in the fields of machine learning, knowledge representation, and logical reasoning are expected to result in higher autonomy of AI-enabled systems such as Distributed AI (DAI) agents that can think and act.The increased agency of the AI systems is expected to result in agency risks and the need for mitigating such risks through AI governance.In this paper, we build on agency theory and identify factors that increase the risk of an agency problem between a principal (a human or an organization) and an AI agent and propose a framework for AI agency problem analysis.The framework is illustrated through AI use cases and industry examples.Implications for AI governance research and practice are discussed.
Analyzing Trends and Topics in Internet Governance and Cybersecurity Debates Found in Twelve Years of IGF Transcripts

Internet Governance research generates substantial and innovative, interdisciplinary global scholarship.What are key topics and themes in this research area, and how do they relate to cybersecurity?This paper answers these questions by analyzing transcripts from twelve years of the UN Internet Governance Forum (IGF), asking: (1) What key themes, topics, and entities are discussed at IGF? (2) Which issues have remained consistent at IGF, and which have changed?And ( 3) to what extent is the NIST Cybersecurity Framework represented at IGF? Using the CRISP-DM approach to text mining, we find human rights as the most dominant IGF theme, followed by freedom of expression, with disability being a persistent issue.During entity extraction cybersecurity emerges prominently, as does blockchain and IoT.Topic Modeling illustrates the resilience of human rights, but also identifies the IANA transition, accessibility, and "fake news."Finally, the NIST cybersecurity framework is represented clearly in the data.
DTSW: A data transmission scheme based on weighted security partition model in industrial Internet of Things environment

With the rapid development of industrial Internet of Things, the secure transmission of nodes in Internet of Things is easily attacked by malicious nodes at all times, thereby causing a great threat to network performance and security of industrial Internet of Things. To improve the security and success rate of data transmission in industrial Internet of Things environment, this work proposes a data transmission scheme based on the weighted security partition model by considering the requirements of a hostile industrial environment. The weighted security partition of the nodes determines the secret distribution model. On this basis, a secure authentication scheme for network transmission is proposed for industrial Internet of Things to prevent the data from being tampered by malicious industrial nodes. Authority access control is added to the authentication scheme. The network access authority of users is used to eliminate the access of malicious users. Finally, the simulation experiments and performance evaluation are conducted and compared with other secure transmission algorithms. Results show that, by comparing to low-energy adaptive clustering hierarchy and distributed data secure transmission, the transmission rate of the proposed algorithm has increased by 35% and 25%, and the lifetime has increased by 34.3% and 37.3%, respectively.
A Novel Deep Hashing Method with Top Similarity for Image Retrieval

Due to the advantages of retrieval speed and storage space, deep hashing methods have become a research hotspot in the field of large-scale image retrieval. Most of existing deep hashing methods pay close attention to similarity between images without images at the top of the ranking list similar to query targets. In the paper, a novel deep hashing model is proposed to preserve top images similar to the query images and optimize the quality of hash codes for image retrieval. Specifically, the optimized AlexNet is utilized to extract discriminative image representations and learn hashing functions simultaneously. The loss function based on acceleration strategy is designed to ensure similarity between returned images at the top of the ranking list and query images. In addition, we implement the model training in a batch-process fashion to low the image storage. Moreover, our extensive experiments on standard benchmarks demonstrate that our method outperforms several state-of-the-art deep hashing methods.
Indonesian online travel agent sentiment analysis using machine learning methods

Many companies use social media to support their business activities. Three leading online travel agent such as Traveloka, Tiket.com, and Agoda use Facebook for supporting their business as customer service tool. This study is to measure customer satisfaction of Traveloka, Tiket.com, and Agoda by analyzing Facebook posts and comments data from their fan pages. That data will be analyzed with three machine learning algorithms such as K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machine (SVM) to determine the sentiment. From the classification results, data will be selected with the highest f-score to be used to calculate the Net Sentiment Score used to measure customer satisfaction. The result shows that KNN result better than Naive Bayes and SVM based on f-score. Based on Net Sentiment Score shows companies that get the highest satisfaction value of Traveloka followed by Tiket.com and Agoda
Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements

The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details.
TCEC Cup 1

This is a report of the 'TCEC Cup 1' computational experiment, the first TCEC knockout event and the second major part of TCEC’s 13th season. It featured the best 32 engines of ‘TCEC 13’ and the games were held at Blitz tempo, The different format called for different optimal settings in the engines, thereby enabling a comparison with performance at slower tempi. This event notably featured the first appearance of two non-Shannon-AB, neural-network/MCTS engines, DEUS X and LEELA CHESS ZERO. LC0 notably was a quarter-finalist, having started as 21st seed. STOCKFISH beat HOUDINI in the final with the sole win, remarkably, a short and dramatic game. All decisive games in the supplementary data have been played out by FRITZ17 at search depth 24 to benchmark endgame practice.
Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection

Salient Object Detection (SOD) aims to model human visual attention system to cope with the complex natural scene which contains various objects at different scales. Over the past two decades, a wide range of saliency features have been introduced in the SOD field, however feature selection has not been widely investigated for selecting informative, non-redundant, and complementary features from the existing features. In SOD, multi-level feature extraction and feature combination are two fundamental stages to compute the final saliency map. However, designing a good feature combination framework is a challenging task and requires domain-expert intervention. In this paper, we propose a genetic programming (GP) based method that is able to automatically select the complementary saliency features and generate mathematical function to combine those features. The performance of the proposed method is evaluated using four benchmark datasets and compared to nine state-of-the-art methods. The qualitative and quantitative results show that the proposed method significantly outperformed, or achieved comparable performance to, the competitor methods.