Reviewerly/exHarmony huggingface dataset:
Case Study: Checking a Serious Security-Awareness Game for its Legal Adequacy

It is generally accepted that the management of a company has a legal obligation to maintain and operate IT security measures as part of the company’s own compliance – this includes training employees with regard to social engineering attacks. On the other hand, the question arises whether and how the employee must tolerate associated measures, as for example social engineering penetration testing can be very intrusive.
Analysis and Application: Sports Education Information Management under the Background of Big data

The new environment of education created by big data has brought a great impact on college physical education. To accelerate the modernization of physical education in colleges and universities, this paper points out the great value and application characteristics of big data in sports education information management. Combined with the characteristics of education big data, the application of data mining technology in education field is analyzed, and an Apriori algorithm based on MapReduce model is proposed. The scheme makes full use of the high-performance characteristics of the distributed computing framework, and improves the management data calculation and storage capacity. The system also provides various data API interfaces, which can effectively integrate all kinds of sports resources in colleges and universities, so as to improve the accuracy of association rule mining. The improvement strategy can then be used to build sports big data environment and improve the decision-making ability and intelligent level of sports management information system.
Tweet Sentiment Analysis for 2019 Indonesia Presidential Election Results using Various Classification Algorithms

The Presidential general election on 2019 became one of the most popular topics on twitter nowdays. Sentiment analysis over Twitter offers people a effective way to measure the public's feelings towards their party and politicians. The society give their opinion about the pair of candidates that they are support through the social media. This paper addresses the sentiment analysis on Twitter data, The dataset was used based on the tweet on the @jokowi twitter account. The retrieval of data by using the Tweepy library with the Python 2.7 programming language. We utilized few of machine learning algorithm to build our classifier and classified the test data as positive and negative sentiment for the training dataset. This research proposes the weighting word method Unigram, Bigram, Trigram, N-Gram (1–2) and N -Gram (1–3) combined with several machine learning algorithms to compare the best algorithm.
Measuring Agile teamwork

Background: The literature presents distinct models to assess the Teamwork Quality (TWQ) for agile teams. These models have different constructs and, consequently, measures. Unfortunately, there are no results of empirical studies contrasting the existing models. Goal: To fill this gap, this study aims to provide a deep insight into how two state-of-the-art TWQ models (i.e., instruments) compare to each other with respect to the calculated results for practical use. Method: We performed an empirical study to compare both models in terms of their constructs and measures. For comparing their constructs, we ranked the variables from both models, given their relative impact on teamwork quality, and compared the ranks. For comparing their measures, we collected data using both instruments by interviewing 158 team members from two software development companies. First, we theoretically mapped the variables from both models, given their definition. Afterward, from the collected data, we calculated each of the models' variables using the procedures proposed by their creators. Then, we analyzed the level of agreement between the models for each variable, using the Mean Relative Error (MRE). Results: In terms of the constructs, for practical purposes, both models are equivalent, except for including a variable for Team Autonomy. For the measures, the instruments produced similar results for five variables (i.e., Communication, Coordination, Balance of Member Contribution, Effort, and Cohesion). For the sixth variable (Mutual Support), we presented evidence that the results are similar in the models. Still, we believe that more research is needed to analyze it. Conclusions: We believe that we have enough evidence to claim that, for practical purposes, both models yield similar results. Thus, the study indicates that, it is up to the teams to choose which model best suits their context. Still, we concluded that there is a need for more research regarding how to assess TWQ for agile teams.
Innovative development of socio-technical systems

Modern management of socio-technical systems is based on the principles of the classical ideal bureaucracy of Max Weber, formed many decades ago. Among the historically inherited problems of modern management, corresponding to the traditional development of socio-technical systems, the decisive factor is the bias towards the formalization of management. A person with untapped potential is pushed out of the management system. The subject is depersonalized and placed on a par with other elements of the model. This approach underlies the development of socio-technical systems, which can be called traditional. Innovative development requires the use of new management methods to unleash the potential of people. Among the subject-oriented approaches to management, we should underline the theory of intersubjective management, proposed by V.A. Vittikh. It could complement traditional management.
Sounding the Grind

Battles in Japanese role-playing games (JRPGs) present special problems for game immersion because of their sheer ubiquity and repetitiveness. This is particularly true of games that require the player to “grind”—that is, to engage in repetitive fights in order to level-up characters and thereby gain tactical advantages later. This paper argues that battle music is first and foremost a variety of functional music, a genre that fans measure not by its sonic beauty but by its psychological effectiveness. This point leads directly to a number of pressing questions: How do battle themes function? How do these functions relate to the all-important concerns of immersion and interactivity? How do we evaluate the effectiveness of battle music? And finally, what would a preliminary theory of battle music composition look like? This article examines how the grind of JRPG fighting manifests as an integral part of battle music composition, analyzing three standard conventions in detail: (1) a clear opening audiovisual rupture, (2) a fanfare as cadence, and (3) a sustained period of harmonic stasis underpinning busy surface textures. This last phenomenon creates a sense of “musicospatial stasis”—that is, a musically induced sense of stasis that intermingles with and projects itself onto the visual and narrative fields. Because the repetitive grind of JRPG battles interrupts movement through the overworld, battle themes should be understood as ruptures in the sonic environment, just as the battle stage is a spatial rupture in the overworld. Battle themes therefore make little sense as analytical objects out of context: by definition, they signal a break that impedes the player’s movement throughout a larger environment.
Unit groups of finite group algebras of Abelian groups of order 17 to 20

<abstract> Let $ F $ be a finite field of characteristic $ p $ having $ q = p^n $ elements and $ G $ be an abelian group. In this paper, we determine the structure of the group of units of the group algebra $ FG $, where $ G $ is an abelian group of order $ 17\leq |G|\leq 20 $. </abstract>
A Two-Stage Graph Computation Model with Communication Equilibrium

Distributed graph computing aims at performing in-depth analysis on large networks in a parallel manner. Iterative communication computation is an important model to perform graph analysis. Moreover, high-efficiency iterative communication computation is necessary for assuring the quality of graph partitioning. Many strategies to improve graph computing are usually based on the hypothesis of single communication iteration which focuses on the optimization of load balance in a single graph partitioning and the improvement of parallel granularity or communication manners. However, a graph in the real-world usually requires complex communication iterations to achieve good analysis results, which often have the problems of data and communication tilting and low analysis efficiency. In this paper, we propose a two-stage graph computing model with communication equilibrium (TSMCE). The model employs a communication-equilibrated graph partitioning strategy (CEGP) for load balancing and a two-stage graph computing mode (TS) to reduce the crossing-partition communication. With the change of graph density, various factors such as load balancing, communication balancing, cross-partition transmission traffic, communication delay, and convergence speed can always maintain an efficient balance. The experiments conducted on the real-world datasets show that the communication-equilibrated graph partitioning strategy can divide a graph with high quality compared with Hash and Metis. Besides, the overall performance of our two-stage graph computing model with communication equilibrium is higher than that of the BSP model.
Color, Edge, and Pixel-wise Explanation of Predictions Based on Interpretable Neural Network Model

We design an interpretable network model by introducing explainable components into a Deep Neural Network (DNN). We substituted the first kernels of a Convolutional Neural Network (CNN) and a ResNet-50 with the well-known edge detecting filters such as Sobel, Prewitt, and other filters. Each filters' relative importance scores are measured with a variant of Layer-wise Relevance Propagation (LRP) method proposed by [1]. Since the effects of the edge detecting filters are well understood, our model provides three different scores to explain individual predictions: the scores with respect to (1) colors, (2) edge filters, and (3) pixels of the image. Our method provides more tools to analyze the predictions by highlighting the location of important edges and colors in the images. Furthermore, the general features of a category can be shown in our scores as well as individual predictions. At the same time, the model does not degrade performances on MNIST, Fruit360 and ImageNet datasets.
Experimental Demonstration of Programmable 100 Gb/s SDN-Enabled Encryptors/Decryptors for QKD Networks

We successfully demonstrated on-demand and programmable encryption/decryption using SDN-enabled FPGA-based technology over a QKD network. Data rate over 90 Gb/s with maximum data encryption of 11.25 GB/s for different encryption schemes were achieved.
Convolution Neural Network-Based CAPTCHA Recognition for Indic Languages

CAPTCHAs (completely automated public turing test to tell computers and humans apart) are automated turing tests to identify whether the user is a human or not. There are several types of CAPTCHAs available; among them, most of the CAPTCHAs are based on characters. These are some distortions of character images and symbols identifiable by most human beings but are difficult for machines like a computer to recognize. CAPTCHAs are used for many security reasons in various sectors. We can see that existing alphanumeric CAPTCHAs are still difficult to recognize by humans also. In comparison with the traditional English CAPTCHAs, Indic languages like Hindi and Bengali are difficult to recognize. For this purpose, we use deep learning models like convolution neural networks to recognize CAPTCHAs automatically. In this paper, we have used three Indic languages, and two of them are northern language, one is Hindi, the other is Bengali, and one southern language Tamil as Indic CAPTCHAs. We generated the dataset with the help of the Pillow library for all three languages. After experiments, we observed that the model has reasonable accuracy for the Bengali language, Hindi language, and for the Tamil language.
Anomaly detection method of power dispatching data based on cloud computing platform

The traditional power dispatching data anomaly detection method USES the principle of template matching, which not only has large calculation amount and long processing time, but also the accuracy of the detection method is greatly affected by the template. Aiming at the above problems, the anomaly detection method of power dispatching data based on cloud computing platform is studied. The power demand of residents in the grid is analyzed to determine the load response of residential electricity. To process the detected power dispatching data, the cloud computing platform is used to cluster the processed data and the electricity demand response data to complete the detection of abnormal data. Through the comparison experiment with the traditional power dispatching data anomaly detection method, it is verified that the detection method based on the cloud platform saves about 1.3 times of the detection time, and the detection accuracy of abnormal data is higher.
Digital Literacy Through Citizenship Education Learning An Effort To Address The Spread Of False News (HOAX)

This research aims to find out: 1) Digital literacy in Pancasila Education and Citizenship subjects to address the spread of fake news or HOAX; 2) Barriers and solutions in the application of digital literacy in the learning of Pancasila Education and Citizenship. This research uses qualitative research methods with purposive sampling techniques. Data collection using observations, interviews, and documentation. This research was conducted at Sma Batik 2 Surakarta and interviewed with expert speakers. The results showed that: 1) The application of digital literacy in Pancasila education and citizenship learning is with 3 stages namely planning, implementation, and assessment. Planning comes from a learning implementation plan that is integrated with the concept of digital literacy. The implementation phase is to implement various innovative learning models that can foster digital literacy capabilities in students to reduce the spread of hoax news. The assessment stage is that the teacher assesses the knowledge and attitude of the student. 2) The obstacles in this study are the lack of student ability and the lack of creativity of teachers in learning. The solution to overcome these barriers is to provide learning innovations that can further foster student stimulus to meet the ability in digital literacy and provide workshops and training to teachers to master digital media properly.
Truncated simulation and inference in edge-exchangeable networks

Edge-exchangeable probabilistic network models generate edges as an i.i.d. sequence from a discrete measure, providing a simple means for statistical inference of latent network properties. The measure is often constructed using the self-product of a realization from a Bayesian nonparametric (BNP) discrete prior; but unlike in standard BNP models, the self-product measure prior is not conjugate the likelihood, hindering the development of exact simulation and inference algorithms. Approximation via finite truncation of the discrete measure is a straightforward alternative, but incurs an unknown approximation error. In this paper, we develop methods for forward simulation and posterior inference in random self-product-measure models based on truncation, and provide theoretical guarantees on the quality of the results as a function of the truncation level. The techniques we present are general and extend to the broader class of discrete Bayesian nonparametric models.
TruckTrix<sup>®</sup> Path-Planning in the helyOS Operating System for Yard Automation

In this paper we briefly introduce the helyOS operating system for remote operation of autonomous mobile machines and more precisely the TruckTrix <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> -Path Service, which is used for path planning for yard automation solutions, introduced by Fraunhofer Institute for Transportation and Infrastructure Systems. TruckTrix <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> -Path provides the path planning for complex vehicle structures through critical narrows. Its extensions, developed specially for yard automation problems, adopt calculated trajectories for to be driven by a real automated truck. Alongside with other products of the TruckTrix <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> -Family: TruckTrix <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> -Coop, TruckTrix <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> -Control and TruckTrix <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> -Field it provides a fast and simple solution to control several automated vehicles on closed yards or fields.
Boundary Optimised Samples Training for Detecting Out-of-Distribution Images

This paper presents a new approach to the problem of detecting out-of-distribution (OOD) inputs in image classifications with deep convolutional networks. We leverage so-called boundary samples to enforce low confidence (maximum softmax probabilities) for inputs far away from the training data. In particular, we propose the boundary optimised samples (named BoS) training algorithm for generating them. Unlike existing approaches, it does not require extra generative adversarial network, but achieves the goal by simply back propagating the gradient of an appropriately designed loss function to the input samples. At the end of the BoS training, all the boundary samples are in principle located on a specific level hypersurface with respect to the designed loss. Our contributions are i) the BoS training as an efficient alternative to generate boundary samples, ii) a robust algorithm therewith to enforce low confidence for OOD samples, and iii) experiments demonstrating improved OOD detection over the baseline. We show the performance using standard datasets for training and different test sets including Fashion MNIST, EMNIST, SVHN, and CIFAR-100, preceded by evaluations with a synthetic 2-dimensional dataset that provide an insight for the new procedure.
Relationship graph learning network for visual relationship detection

Visual relationship detection aims to predict the relationships between detected object pairs. It is well believed that the correlations between image components (i.e., objects and relationships between objects) are significant considerations when predicting objects' relationships. However, most current visual relationship detection methods only exploited the correlations among objects, and the correlations among objects' relationships remained underexplored. This paper proposes a relationship graph learning network (RGLN) to explore the correlations among objects' relationships for visual relationship detection. Specifically, RGLN obtains image objects using an object detector, and then, every pair of objects constitutes a relationship proposal. All relationship proposals construct a relationship graph, in which the proposals are treated as nodes. Accordingly, RGLN designs bi-stream graph attention subnetworks to detect relationship proposals, in which one graph attention subnetwork analyzes correlations among relationships based on visual and spatial information, and the other analyzes correlations based on semantic and spatial information. Besides, RGLN exploits a relationship selection subnetwork to ignore redundant information of object pairs with no relationships. We conduct extensive experiments on two public datasets: the VRD and the VG datasets. The experimental results compared with the state-of-the-art demonstrate the competitiveness of RGLN.
Application of WeChat Mini Program in Xindong Gate Project

Abstract The WeChat Mini Program is a lightweight application. It is a mobile application that does not need to be installed and can be used at any time. The application effectively reduces the difficulty for mobile phone users to use the application, and well solves the cross-platform and adaptation problems of mobile application development. After the system is designed and developed, it will be used in the water diversion project of the Xindong gate project. This project makes full use of the advantages of small programs to greatly improve the work efficiency of the construction site and ensure the safety of the construction site.
STN-enhanced message passing guided by adversarial learning for human pose estimation

In this paper, an STN (Spatial Transformer Network)-enhanced message passing module guided by adversarial learning is proposed for human pose estimation. Transformations performed on the predicted heatmaps mean to modify the raw predictions and conduct the message passing among human joints in an elegant way. Firstly, we employ STN submodule to transform the predicted heatmap of one human joint to heatmap of its neighboring joint to remove ambiguity of its neighboring predictions. STN submodule automatically learns the geometric information between adjacent joints and thus builds related neighboring associations among them. Nevertheless, it seems difficult for STN submodule to learn inherent geometric information from a single RGB image alone. Secondly, limb guidance is introduced to assist STN in predicting corresponding correlations. Since quality of limb predictions poses great significance for the guidance, we propose to exploit adversarial learning to improve the quality of limb heatmaps which are easier to learn than precise keypoint location. Hence, the precision of STN transformation improves owing to more precise prior instructions. However, STN submodule might be confused when performing the transformation due to massive noises of the heatmaps. To circumvent this dilemma, at last, we propose to utilize Weighted Mean Square Error (WMSE) loss and convolutional random walk (CRW) which improve the performance further. Our method achieves competitive results on both MPII and LSP benchmarks.
Kernel switching ridge regression in business intelligence systems

Business intelligence measurements depend on a variety of statistical advanced technologies used to enable the collection, analysis, and dissemination of internal and external business information. The key goal of business intelligence is to draw a well-informed business decision. Statistical machine learning-based business intelligence systems prioritize a comprehensive analysis to empower businesses. In kernel methods, the nonlinear feature map is given by a positive-definite kernel, which provides nonlinear methods for data analysis with efficient computation. In kernel methods, the nonlinear feature map is given by a positive-definite kernel , which provides nonlinear methods for data analysis with efficient computation. The standard industrial informatics are facing the challenges of processing complex business data. Business intelligence plays an important role to bridge the gap between enterprise systems and industrial informatics. In supervised machine learning tasks, switching regression is becoming an increasingly common component in the age of big business data.