Reviewerly/exHarmony huggingface dataset:
Determination of the Optimal Ratio of Normal to Anomalous Points in the Problem of Detecting Anomalies in the Work of Industrial Control Systems

Algorithms for unsupervised anomaly detection have proven their effectiveness and flexibility, however, first it is necessary to calculate with what ratio a certain class begins to be considered anomalous by the autoencoder. For this reason, we propose to conduct a study of the efficiency of autoencoders depending on the ratio of anomalous and non-anomalous classes. The emergence of high-speed networks in electric power systems creates a tight interaction of cyberinfrastructure with the physical infrastructure and makes the power system susceptible to cyber penetration and attacks. To address this problem, this paper proposes an innovative approach to develop a specification-based intrusion detection framework that leverages available information provided by components in a contemporary power system. An autoencoder is used to encode the causal relations among the available information to create patterns with temporal state transitions, which are used as features in the proposed intrusion detection. This allows the proposed method to detect anomalies and cyber attacks.
Processes, Paradoxes and Illusions: Compositional Strategies in the Music of Hans Abrahamsen

Abstract Hans Abrahamsen’s recent music has been the subject of much critical and public acclaim, with his output of the last decade finding a new directness of expression even as it incorporates and develops elements of his musical language that have remained consistent over many years. This article examines the use of compositional processes within a number of these large-scale works – Schnee (2008), Wald (2009), the Double Concerto (2011) and Let me tell you (2013) – and explores the ways in which Abrahamsen generates and controls material through a variety of techniques of transformation and repetition. How smaller-scale systems interact to create music of great allusive complexity is considered through discussion of the variation form of Wald ; finally, Abrahamsen’s embracing of types of paradox and illusion are presented as strategies to unlock broader aesthetic issues within his music.
A Quasiconvex Formulation for Radial Cameras

In this paper we study structure from motion problems for 1D radial cameras. Under this model the projection of a 3D point is a line in the image plane going through the principal point, which makes the model invariant to radial distortion and changes in focal length. It can therefore effectively be applied to uncalibrated image collections without the need for explicit estimation of camera intrinsics.We show that the reprojection errors of 1D radial cameras are examples of quasiconvex functions. This opens up the possibility to solve a general class of relevant reconstruction problems globally optimally using tools from convex optimization. In fact, our resulting algorithm is based on solving a series of LP problems. We perform an extensive experimental evaluation, on both synthetic and real data, showing that a whole class of multiview geometry problems across a range of different cameras models with varying and unknown intrinsic calibration can be reliably and accurately solved within the same framework. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Research on Library Personalized Service Mode Based on Big Data Application

This paper explores the concept and technology of the big data, analyzes the big data environment in the library, and analyzes the source, classification and characteristics of library big data from the relationship between readers, collections and librarians. On this basis, this article constructs a library personalized information service model based on big data applications, and analyzes the current problems and countermeasures faced by libraries.
Weighted parsing for grammar-based language models over multioperator monoids

We develop a general framework for weighted parsing which is built on top of grammar-based language models and employs multioperator monoids (M-monoids) as weight algebras. It generalizes previous work in that area, e.g., semiring parsing and weighted deductive parsing, and also covers applications outside the classical scope of parsing, e.g., algebraic dynamic programming. More specifically, we introduce weighted RTG-based language models (where RTG stands for regular tree grammar) and define the M-monoid parsing problem. We show an algorithm which is supposed to solve this problem and prove in detail that, for a large class of weighted RTG-based language models, the algorithm terminates and, indeed, solves this problem. We compare our algorithm with semiring parsing and weighted deductive parsing regarding applicability and complexity.
How Safely Do We Behave Online? An Explanatory Study into the Cybersecurity Behaviors of Dutch Citizens

The Capability-Opportunity-Motivation-Behavior (COM-B) model states that people’s behavior can be explained by their capabilities, opportunity, motivation, and the interaction between these components. This research focuses on applying the COM-B model to Dutch citizens’ cybersecurity behavior. Data are used from a Dutch cross-sectional online survey (N = 2,426). Multivariate analysis was performed with multivariate linear regression models. The results suggest, in line with the literature, that people that have relevant knowledge, and are motivated to protect themselves, report more cyber secure behavior. The opportunity people have to protect themselves was found to be only partly related to self-reported cybersecurity behavior. Explanations are provided for these findings, as well as opportunities for future research.
Implementation of tensor flow-based deep learning in the learning application of around things in English

Abstract English is the most widely used international language in communication and is the second language after Indonesian. English will be taught more easily and effectively to children from an early age. But it needs learning media that suits the needs of children’s growth. Learning media should be fun and able to get the child moving to explore the surrounding environment. This research aims to create an English learning application by applying Tensor flow machine learning that is able to recognize objects around us and package them in a learning application in which there is a game that is able to attract the attention of children to learn English. This research was built using the RUP (Rational Unified Process) system development method which consists of 2 dimensions. The first dimension consists of dynamic aspects in development consisting of inception, elaboration, construction, and transition phases. The second dimension represents static aspects which are grouped into 4 important elements, namely who is doing, what, how, and when. The machine learning process consists of 3 stages, namely data input (object image capture), preprocessing, and the training process. The results of this study are in the form of a learning media application that can recognize surrounding objects by applying Tensor Flow-based deep learning. The application consists of 2 parts, namely vocabulary learning and a quiz for the introduction of surrounding objects. Parents have a role to be able to add names and objects to the quiz in the application. The results of the research can be used as a learning medium according to the needs of the child.
Non-Formal Education for the Older Generation Representatives as Factor of Their Active Ageing

The study aims to determine psychological and pedagogical conditions for organising non-formal education for the older generation representatives, which contributes to their active ageing. The authors have presented the main approaches to defining the concepts “non-formal education”, “third age”, “active ageing”, “andragogical interaction”, “tertiary socialisation”, “higher public school”. The article substantiates relevance of organising non-formal education to promote the older people’s active ageing, determines learning process specificity in the third age, proves andragogical interaction effectiveness for the older people’s tertiary socialisation as a basis for their active ageing. The authors have determined psychological and pedagogical conditions for organising non-formal education for the older generation representatives. Scientific novelty lies in developing an organisational and methodological system of education for the elderly in the context of trends in modern education development and constructing new educational reality on this basis as an implementable authors’ project. As a result of the study, non-formal education importance for longevity of the elderly is confirmed, psychological and pedagogical conditions for organising this activity are identified. Using quantitative and qualitative diagnostic methods, the authors have proved non-formal education effectiveness for the older people’s tertiary socialisation as a basis for their active ageing.
Intelligent Information extraction algorithm of Agricultural text based on Machine Learning method

Abstract The Internet agricultural technology question and answer platform now only relies on manual to provide answer service, the response speed is slow, and the answer quality is difficult to be guaranteed. In order to realize the intelligent question and answer of agricultural technology and construct the knowledge base of agricultural technology, it is necessary to extract the named entity triple of “crop-pest-pesticide” from the existing question and answer data. There are few researches on agricultural Chinese named entity recognition, and the accuracy is low. According to the characteristics of named entities of crops, diseases and insect pests and pesticides, and according to the question and answer data of agricultural technology, a method of identifying named entities of crops, diseases and pests and pesticides based on conditional random field was proposed. The data set is formatted and segmented automatically, and the corpus after word segmentation is automatically tagged according to whether it contains a specific definition word, whether it contains a specific partial radical, whether it is a quantifier, whether it is a specific left and right definition word and part of speech. Using the tagged data to train the CRF model, we can classify the corpus, including judging whether the corpus belongs to crop, pest and pesticide named entities and identifying the position of the corpus in the compound named entity, thus realizing the recognition of the three kinds of named entities and automatically constructing the associated triple. Through the experiment to select the feature combination and adjust the context window size, the recognition accuracy of this method is improved, the model training time is reduced, and the accuracy of crop, pest and pesticide named entity recognition is 97.72%, 87.63% and 98.05%, respectively, which is significantly higher than the existing methods.
Using Real-World Problems to Explore and Improve Students' Understanding of Parallelism Concepts

Students' conceptual knowledge about parallelism continues to grow because of the increasing infusion of parallelism topics in all courses, even if they are not dedicated to those topics. Most times, such infusion is through high-level theoretical problems, as listed in the textbooks. However, without providing corresponding real-life instances, such problems might not engage students with the content in such courses. Does providing real-life activities along with theoretical constructs help students to grasp such topics better? As an attempt to explore student comprehension of parallelism in such course scenarios, this paper describes an unplugged assignment, where students were asked to categorize their daily activities into concurrent and parallel tasks. The paper then presents a thematic analysis of student experience using three semesters' worth of student data. The analysis shows that, although students exhibit limited knowledge of parallelism in real-life situations and have numerous misconceptions about parallelism; their participation in such assignments are deliberate, their thinking processes are analytical, their writing skills in expressing such concept is limited and their impressions about such assignments are positive. The paper then presents an effort to enhance student's comprehension and shows how the intervention has improved students' understanding of this critical concept. This study confirms that it is essential to provide real-life analogies and examples to facilitate a greater understanding of parallelism. Because providing only theoretical examples might not engage a diverse body of students in courses that are just trying to expose students to this critical topic.
SNPE-SRGAN: Lightweight Generative Adversarial Networks for Single-Image Super-Resolution on Mobile Using SNPE Framework

Abstract An image resulting from a low-resolution (LR) camera on the mobile phone has lower quality than a high-resolution(HR) camera on a DSLR. Meanwhile, the HR camera is pricing if compared with the LR camera. How to achieve a single-image quality on LR camera likewise on HR camera becomes essential research in the past years. Addressing this issue can be done by upscaling a single LR image. Recently, the super-resolution generative adversarial network (SRGAN) model is one of the state-of-the-art super-resolution(SR)models employed on single-image SR. However, implementing a deep learning model like SRGAN on a mobile device is challenging in computation power and resources. This study aims to develop a smaller and lower resources model while preserving single-image SR quality on mobile devices. To meet these objectives, we convert, quantize, and compress the SRGAN model on Snapdragon Neural Processing Engine (SNPE) as an example. We then validate the SRGAN on the DIV2K dataset on which improves the model performances. Besides, we conduct experiments on GPU, DSP environment. The experimental result confirmed that SNPE-SRGAN capable of achieves not only HR images’ quality but also low latency by 0.06 second and smaller model by 1.7 Mb size running on DSP. Also, the SRGAN-DLC-Quantized running on GPU has a smaller size by 1.7 Mb and lower latency by 1.151 seconds compared with Non-quantized SRGAN-TensorFlow by 9.1 Mb and 1.608 seconds latency.
Research on Data Visualization Design for Police System

The increasingly complicated police work requires increasing information collection, analysis and transmission of the daily police system. From the design point of view, this paper intends to study how to improve the effectiveness and efficiency of data information transmission in police system. By analyzing the objectives and requirements of data information transmission in police system, from the perspective of user experience, the data visualization design model is optimized, the data visualization design principles are deduced, and the laws of human vision and cognition are combined to study the data visualization design strategy of police system to guide design practice. The verification results show that from the perspective of user experience, data visualization design practice can be effectively guided by the design scheme combining human vision and cognitive laws. The design practice process and results show that police data information can be efficiently conveyed, the user experience of police staff can be enhanced, and the efficiency of police work can be improved, based on the user's visual and cognitive laws of data visualization design.
VAR based (Virtual Augmented Reality) Education Game Dissemination in Geometry Learning at Unissula Semarang

One of the problems that students have is difficulty in understanding the concept of Geometry. However, the current technological developments support teachers to try to create more interesting and fun learning. One alternative is through a VAR (Virtual Augmented Reality) based educational game. The purpose of this study was to determine the feasibility of VAR-based educational game products in geometry courses at UNISSULA Semarang. This research method uses the ADDIE model development model (Analysis, Design, Develop, Implementation, Disseminate, Evaluation). This study used a student response questionnaire and a lecturer response questionnaire through the disseminate stage. The average percentage of student responses is 89.25% which means that students generally rate this product as suitable for use in classroom learning. In comparison, the average result of the percentage of lecturers’ responses is 90%, which means that the lecturers generally consider this product to be very suitable for use as a learning medium.
Historical and pedagogical analysis of the process of global digitalization of education

The purpose of the article is to conduct a historical and pedagogical analysis of the process of global digitalization of education, to determine the stages of this phenomenon, and to highlight the features of online learning. The objectives of the study are the analysis of sources on the digitalization of education, the definition of the “global digitalization of education” concept, the classification of the stages of this digitalization, and the definition of the main aspects of online learning. The methodology is the use of a comparative analysis of theoretical and practical sources of global digitalization of education. The lack of attention in modern research to the disclosure of the historical and pedagogical subject of this phenomenon was identified. The method of theoretical analysis was used to identify new realities in the educational process. The main aspects of the global digitalization of education were identified by applying the method of deduction and historical and pedagogical analysis. These aspects were classified to positive and negative lists by their impact on the participants of the educational process The global digitalization of education is the technical and technological development of civilization, as a result of which there was a change in informational social development according to the scheme: informatization-digitalization-artificial intelligence. It led to the new forms of thinking and socialization of the individual in the digital educational environment. The phasing is from the 60s of the last century and to the present, due to the rapid technological progress.
A multi-angle shape descriptor with the distance ratio to vertical bounding rectangles

In this paper, we proposed a simple and fast matching shape descriptor, a multi-angle shape descriptor with the distance ratio from the contour point to the vertical bounding rectangle(mADR). Shape descriptors are widely used to describe shape features, especially in shape retrieval and shape classification. Contour point is an important feature of shape. The method used in this paper is to find the ratio of distance from contour points to shape vertical bounding rectangle to the average distance. Because vertical bounding rectangle can reflect the position and size information of shape, and can also be used as a feature of shape. In this method, the shape is rotated 360° according to a certain angle respectively, and the vertical bounding rectangle of the shape after each rotation is obtained, then the distance ratio between the contour of all the rotated shapes and the corresponding vertical bounding rectangle is obtained as the shape features. Compared with the traditional shape descriptor based on contour points such as the shape of the distance based on the centroid and contour descriptor (CCD) and based on the shape of the shape context descriptor (SC) has direction characteristic, can distinguish the outline or rough shape similar to the similar shape, have stronger robustness, simpler process and faster matching speed, more importantly, the experiment results show that the shape descriptor has higher retrieval accuracy.
Performance evaluation of chaotic random numbers generated from responses of integer logistic maps

One of the engineering applications of chaotic nonlinear dynamical systems is a pseudorandom number generator. Pseudorandom numbers generated from chaotic dynamical systems are called chaotic random numbers. A logistic map which exhibits a chaotic response can be used as such a chaotic random number. However, an important issue exists when we use such dynamical systems as a pseudorandom number generator by a digital computer: when the chaotic response of a logistic map is reproduced numerically, the number of iterations that the chaotic response is sustained depends on the precision of the numerical calculation, because the precision of the numerical calculation affects the size of the numerical error. In this paper, we extended the logistic map to an integer logistic map to reduce such numerical errors. We investigated the performance of chaotic random numbers obtained from the integer logistic map with varying numerical precisions and transforming them into binary random numbers. We then used NIST SP 800-22 to evaluate the performance of the random numbers. The results show that a numerical precision of 20 orders of magnitude or more is desirable to the generation of a well-performing chaotic random numbers from the response of the integer logistic map.
Related Entity Expansion and Ranking Using Knowledge Graph

Nowadays, it is possible for web search users to receive relevant answers, such as a summary and various facts of entity, quickly from a knowledge panel. Search services recommend relevant entities based on search trends. If the search engine recommends sufficient related entities, users will acquire adequate information of interest. This enhances user experience and for web service providers, increases the opportunity to attract users. In this study, we increase the number of knowledge panels that recommend related entities and optimize their order using a knowledge graph. We also introduce a production-level system that generates related entities from a massive knowledge base and search log; it achieves low latency serving. We deploy our system to the production environment and perform quantitative and qualitative estimation using A/B testing. Based on the results, we conclude that our method significantly enhances the impression based coverage of knowledge panels preventing a significant change in click-through rate.
Phishing Website Detection Based on URL

In today's era, due to the surge in the usage of the internet and other online platforms, security has been major attention. Many cyberattacks take place each day out of which website phishing is the most common issue. It is an act of imitating a legitimate website and thereby tricking the users and stealing their sensitive information. So, concerning this problem, this paper will introduce a possible solution to avoid such attacks by checking whether the provided URLs are phishing URLs or legitimate URLs. It is a Machine Learning based system especially Supervised learning where we have provided 2000 phishing and 2000 legitimate URL dataset. We have taken into consideration the Random Forest Algorithm due to its performance and accuracy. It considers 9 features and hence detects whether the URL is safe to access or a phishing URL.