Reviewerly/exHarmony huggingface dataset:
Biangular Lines Revisited

Abstract Line systems passing through the origin of the d -dimensional Euclidean space admitting exactly two distinct angles are called biangular. It is shown that the maximum cardinality of biangular lines is at least $$2(d-1)(d-2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>(</mml:mo> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> <mml:mo>(</mml:mo> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , and this result is sharp for $$d\in \{4,5,6\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>∈</mml:mo> <mml:mo>{</mml:mo> <mml:mn>4</mml:mn> <mml:mo>,</mml:mo> <mml:mn>5</mml:mn> <mml:mo>,</mml:mo> <mml:mn>6</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> . Connections to binary codes, few-distance sets, and association schemes are explored, along with their multiangular generalization.
Machine Learning Based Decision Support System for High-School Study

The objectives of this study are to investigate the correlations between personal factors, learning factors family, and economic factors affecting high-school study program selection and also to create and compare models of high-school study program selection with data mining techniques and to develop a decision support system for high-school study program selection with a data mining technique. Data were analyzed by five data mining techniques, and models of high-school study program selection were constructed. These models were then used to construct a decision support system from data mining software called RapidMiner Studio 9. The research findings were as follows personal factors, learning factors family, and economic factors affecting high-school study program selection, and from the result of high-school study program selection, the Decision Tree method, C4.5 algorithm provided the highest accuracy. Therefore, the researcher selected the forecasting model with the Decision Tree method, C4.5 algorithm together with the selection of features with the backward elimination method to create a decision support system.
RNS Comparison via Shortcut Mixed Radix Conversion: The Case of Three 4-Moduli Sets {2<sup><i>n</i> + <i>k</i></sup>, 2<sup><i>n</i></sup> ± 1, <i>m</i>} (<i>m</i> ϵ {2<sup><i>n</i> + 1</sup> ± 1, 2<sup><i>n</i> − 1</sup> − 1})

Comparison of numbers represented in residue number systems (RNS) is considered as a difficult RNS operation. However, RNS comparators faster than the straightforward “convert-to-binary and compare” alternative have been recently reported. Nevertheless, we present improved RNS comparators for three balanced 4-moduli sets (listed in the title, above) based on shortcut mixed radix conversion. Synthesis results show considerable improvements regarding some figures of merit. For example, 29% (24%) speedup (cost reduction) for the first moduli set, 18% cost reduction for the second, 28% lower power dissipation and 64% energy saving regarding the third one in comparison to previous works are achieved.
Clustering-Augmented Multi-instance Learning for Neural Relation Extraction

Despite its efficiency in generating training data, distant supervision for sentential relation extraction assigns labels to instances in a context-agnostic manner—a process that may introduce false labels and confuse sentential model learning. In this paper, we propose to integrate instance clustering with distant training, and develop a novel clustering-augmented multi-instance training framework. Specifically, for sentences labeled with the same relation type, we jointly perform clustering based on their semantic representations, and treat each cluster as a training unit for multi-instance training. Comparing to existing bag-level attention models, our proposed method does not restrict the training unit to be sentences with the same entity pair, as it may cause the selective attention to focus on instances with simple sentence context, and thus fail to provide informative supervision. Experiments on two popular datasets demonstrate the effectiveness of augmenting multi-instance learning with clustering.
Трансформації комедії дель арте в контексті сучасного стильового синтезу (на матеріалі п`єси Л. Петрушевської «Квартира Коломбіни»)

In the article, the author analyzes the features of comedy dell’arte, the laws of poetic techniques and stylistic synthesis. Attention is drawn to the style and genre of the play, a synthesis of the principles of postmodernism, modernism, neo-baroque. The main artistic means of comedy dell’arte are analyzed. The study showed that the basic aesthetic principles of commedia dell’arte are reduced to an aesthetic system that contains three essential elements: first, the theater is held by a professional actor who is fully committed to the theater; secondly, the influence of theater is stronger through the synthesis of arts: plastic, music, dance and words; third, the soul of the play is action.
Pixel Value Differencing of Image Steganography in Substitution Technique: A Survey

The main concern is security for everyone in today’s world. Data are embedded in digital media for hiding. Cryptography and steganography are mostly used for security of data. By seeing an image, one cannot come to know about hidden data inside. This process of hiding data in digital media for security is called image steganography. Many algorithms are available in spatial domain of image steganography for embedding data in cover image. Many criteria are there for evaluating security against detection of particular algorithm. Criteria are like capacity, quality, detection, etc. Pixel value differencing algorithm is well known for its payload capacity, detection and quality. Here different PVD methods and its comparisons are given.
Requirement analysis for autograder system using use case model

Abstract At the beginning of software development, requirement analysis is needed to determine what features and components must be applied in the software to meet its purpose. Autograder have the purpose to grade student submission so the lecturer and student can know the grade immediately. To fulfil it, an analysis of requirements for autograder should be done so we can determine what features and components needed to be applied in autograder. This requirement also can be used to determine the future test plan. In this paper, we analyse the needed requirement for the autograder system and present it in the use case model.
An Algorithmic Approach to Solve Continuum Hypothesis

The continuum hypothesis has been unsolved for hundreds of years. That said, can it be completely answered? With disproving the cultural competent continuum (Lam, 2014), one can link it with the mathematical continuum and possibly disprove the continuum hypothesis problem (Lam, 2018). It may also be possible to find out the conditions of its independence to ZF(C). To move forward, one may extend our mathematical system (by employing a stronger set theory) and solve the continuum problem. This is similar to three cases in the discriminant of solving quadratic equations. Hence, an algorithmic flowchart can completely solve and depict the problem. From the above case, one may state the following: when mathematics is extended into a new system, important mathematical issues can be solved. Indeed, there are different types of such mathematical systems — similar to ancient mathematical numeral symbols. Hence, different cultures have different ways of representation, which is similar to a Chinese saying: “different villages have different laws”. However, the primary purpose of mathematical symbols was initially for memorisation and communication. This implies that the basic purpose for developing any new mathematical system is to help solve natural phenomenon in our universe.
An Improved Visual Tracking Approach Based on Hierarchical Convolutional Features

In recent years, visual tracking faces numerous challenges, and convolutional neural networks are used more and more frequently to extract features. The Hierarchical Convolutional Features method (HCF for short) is one of the classic applications of Convolutional Neural Network in correlation filter tracking algorithms. But it is a problem that the speed of HCF method is slow. To tackle this problem, this paper optimizes the model update strategy of the baseline (HCF). In order to reduce the model update frequency, we set an interval parameter, which not only saves time, but also avoids the problem of model drift and improves the tracking effects to a certain extent. The proposed method is compared with 10 excellent trackers on the OTB2013 data set. Experimental results indicate that our approach has satisfactory results. In addition, compared with baseline, the tracking speed of the proposed approach is also slightly faster.
Clustering Count Data with Stochastic Expectation Propagation

Clustering count vectors is a challenging task given their sparsity and high-dimensionality. An efficient generative model called EMSD has been recently proposed, as an exponential-family approximation to the Multinomial Scaled Dirichlet distribution, and has shown to offer excellent modeling capabilities in the case of sparse count data and to overcome some limitations of the frameworks based on the Dirichlet distribution. In this work, we develop an approximate Bayesian learning framework for the parameters of a finite mixture of EMSD using the Stochastic Expectation Propagation approach. In this approach, we maintain a global posterior approximation that is being updated in a local way, which reduces the memory consumption, important when making inference in large datasets. Experiments on both synthetic and real count data have been conducted to validate the effectiveness of the proposed algorithm in comparison to other traditional learning approaches. Results show that SEP produces comparable estimates with traditional approaches.
Effects of Physical Programming Workshops Using Electronic Musical Instrument “Photomin” for Education

We have been continuously engaged in scientific awareness activities related to scientific manufacturing. Based on our experience and knowledge, we have created a series of electronic instruments for STEAM education called “Photomin” so that people can learn to make things and program with pleasure. We have developed a new version of Photomin using educational microcomputer, micro : bit to make electronic crafting and programming easier and more familiar for children, especially for those who are not skilled in computers. This paper describes the structure of the new type of Photomin, the fabrication method, and the outline of the program. In addition, we also implemented a workshop for elementary school students, and we report on the design of the workshop, the issues found in its implementation, and the children’s efforts.
EXPANDING THE CIRCULATION OF FOREIGN STUDENTS IN THE ASPECT OF SOCIO-CULTURAL EDUCATION

The article provides a methodological description of the lesson on the topic "Music and We: Famous Russian Composers" using regional material of a socio-cultural nature. At the beginning of the lesson, a system of goals and objectives is given, aimed at the formation and development of knowledge, abilities and skills. Then the structure of the lesson is given, indicating the test material on the stated topic and comments on each part. The lesson is based on a mini-lecture by the teacher about famous Russian composers (P.I.Tchaikovsky, M.I. Glinka, I.P. Mussorgsky, S.V. Rachmaninov, G.V. composers, fragments of their musical works. The teacher can choose the composer himself, about whose life and work the text will be read. The indispensable condition is the use of regional material. So, in this particular lesson, the author proposes to include information about G.V. Sviridov, who is directly related to the Kursk province, where he was born and received his primary musical education. In addition, folk musical traditions characteristic of this region are reflected in his work. The Kursk School of Music bears the name of the famous Kurian composer. The story about the Kursk composer can be accompanied by a presentation depicting streets, buildings, monuments associated with the name of the composer and familiar to students.
SISTEM INFORMASI PELAYANAN KESEHATAN GIGI PASIEN UMUM DAN PESERTA BPJS PADA KLINIK PRAKTEK drg. FARAH CHAIRANI

The Practice Clinic of drg. Farah Chairani collected data on patient data, complaint data, care data, and cost data.In patient data, patients are divided into general patients and BPJS participants.Then in the maintenance data, it is also distinguished on the current treatment and ongoing care.The Practice Clinic of drg. Farah Chairani in the process of storing patient data, complaint data, care data, and cost data are still in paper documents.So when the patient's status card runs out, and data collection is done in a different place, the data is often lost and not found. In the process of searching for patient data, drg. Farah Chairani also took a long time. Due to having to look for data that is not in alphabetical order in data storage. As well as recording their financial reports, drg. Farah Chairani is still listed in the ledger.With the above problems, a desktop-based application was made to facilitate processing patient data, searching for patient data, and making financial reports at drg. Farah Chairani's Clinical Practice. And based on the results of trials on this information system, it can store patient data, search data, and make financial reports at drg. Farah Chairani's Practice Clinic.&#x0D; Keywords: Information System, Health Service, Patient Regirstration, BPJS Participants.
Requirements Engineering in the Planning Phase of a Software Ecosystem

[Motivation] Companies are building software ecosystems to gain competitive advantage by developing digital services together for customers. The planning phase of the software ecosystem can, however, be challenging. [Question] The goal of this study was to analyze what the role of requirements engineering (RE) was in the planning phase of a small-sized software ecosystem. The case study was conducted by interviewing representatives of all six actors of the ecosystem and analyzing material from the 12 planning workshops. [Results] The paper describes the conceptualization process of digital services the actors used during the planning phase. This process contained a flow of tasks from a vision and objectives of the software ecosystem to a go/no-go decision on the development of a Minimum Viable Product (MVP). One key characteristic of the conceptualization process was to have traceability from the prioritized functionalities of the MVP to a value proposition, target customer groups and customer paths of digital services and further to the vision and objectives of the software ecosystem. [Contribution] The paper provides knowledge on how actors can start building a software ecosystem together from a business perspective. In addition, it addresses the importance of RE to link the business view to the development of the MVP of digital services in the software ecosystem.
Internet-based deep learning of English vocabulary

Vocabulary deep learning includes five levels, namely phonetics, semantics, morphology, grammar, pragmatics. Internet-based English learning resources can facilitate learners' in-depth vocabulary processing. The application of deep learning technology in five aspects of lexicon is based on the exploration of big data in vocabulary. Deep learning technology is bound to bring reformation in education.
Biodiversity studies of echinoderms in Sedati waters, Sidoarjo District, East Java Province

Abstract Sidoarjo waters is a buffer zone of Surabaya City. Sidoarjo has many industries such as the metal, textile and waste treatment industries. Many industrial wastes are discharged into Sidoarjo waters, especially Sedati. Waste entering the waters can affect water quality, which influences the living things. One of them is echinoderms. This research purpose to find out the differences of echinoderms in Sedati waters. The research was done from October to December 2019. The data obtained were analyzed descriptively quantitatively using the diversity index Shanon Winner and ANOVA. The results showed that the diversity of echinoderms species found has significant differences. Species were found like Archaster typicus, Echinothrix calamaris, and Ophiothrix sp. The average diversity of echinoderms was 0.4119 + 0.1479 (H &lt; 1) so it was classified as low criteria with polluted water quality. The low variation of echinoderms can be caused by several factors such as location, season, physical quality, chemical quality, biotic factors and the fishing gear used. One of the efforts reduces the impact of pollution is to treat waste before it transferred to the release and move the fishing gear used to be more environmentally friendly.
MLSH: Mixed Hash Function Family for Approximate Nearest Neighbor Search in Multiple Fractional Metrics

In recent years, the approximate nearest neighbor search in multiple $$\ell _p$$ metrics (MMS) has gained much attention owing to its wide applications. Currently, LazyLSH, the state-of-the-art method only supports limited values of p and cannot always achieve high accuracy. In this paper, we design a mixed hash function family consisting of two types of functions generated in different metric spaces to solve MMS problem in the external memory. In order to make the mixed hash function family work properly, we also design a novel searching strategy to ensure the theoretical guarantee on the query accuracy. Based on the given scenario, MLSH constructs the corresponding mixed hash function family automatically by determining the proportion of two types of hash functions. Experimental results show that MLSH can meet the different user-specified recall rates and outperforms other state-of-the-art methods on various datasets.
A Rapid Cu Enrichment Mechanism from Cu-Bearing Brine in Kuqa Basin, Xinjiang, China: Controlled by Crystallized Sequence of Saline Minerals

Sediment-hosted copper deposit is usually related to arid climate, ancient saline lake basin, and brine. The Kuqa Basin filled with giant-thickness evaporite units is located in the northern Tarim Basin, Xinjiang, China. It is famous for sandstone-hosted Cu deposits formed by synsedimentary processes. However, our recent studies reveal that Cu enrichment is closely related to brine on the surface of clastic rocks in the basin. It is shown that green Cu mineral coexisting with halite and gypsum occurred in the fractured fault belts of sandstones or was precipitated with halite on the surface of maroon clay in the scallops of sandstone. By SEM, EDS, and geochemical analysis methods on Cu-mineralized solid samples and brines, respectively, combined with previous geological evidence, our studies demonstrate that green Cu mineral is paratacamite, and it occurred with gypsum, halite, secondary glauberite, natural copper, and probably lead chloride on the fractured fault belts of sandstones or surface of clay. Meanwhile, the precipitation of paratacamite is controlled by a crystallized sequence of saline minerals accompanying with evaporated-concentrated course of brine in which gypsum, secondary glauberite, paratacamite, and halite are crystallized in turn. The Cu-bearing brine derived from meteoric waters and ancient seawater has a powerful capacity to leach metallic ions from its surrounding rocks and can be formed in a very short time (10 days is OK) in normal pressure and temperature conditions; also, the cycle of surface-Cu enrichment (mineralization) is only a few months (no more than 5). These indicate that a rapid Cu enrichment mechanism from Cu-bearing brine occurs on the earth’s surface of the evaporite basin. The mechanism might be supposed to make an enormous amount of metal mineralization in a short time if considered from a large-scale spatial viewpoint. By contrast with the lengthy geological period, the short evolutionary cycle of Cu enrichment (mineralization) is obviously different from the previous cognition.
A Profile-Based ‘GrEvolutionary’ Hearthstone Agent

In the last few years, the Hearthstone AI international Competition has been gaining fame among the scientific community. Several different entries have been presented using varied approaches. One of the best, EVA, was based on a Greedy approach combined with an Evolutionary Algorithm. However, almost all the proposals were designed to work in a general way, i.e. for any of the possible heroes. This generalisation presents a flaw, since the exclusive cards per hero are not really exploited, nor their potential different behaviour profiles. This paper follows a similar philosophy to EVA, also hybridizing Greedy + Evolutionary algorithms, but having in mind three different, and extended among the community, archetypes or profiles: Aggro, Control and Midrange. Thus, three different behaviours have been optimized aiming to create a more specialized agent able to use an Artificial Intelligence engine depending on the hero to play with. To prove the value of the approach several experiments have been conducted, comparing the evolved agents with EVA in many different matches using three different heroes. The results show an improvement over EVA for the three profile-based agents, as well as an excellent performance when combined with a MonteCarlo Tree Search approach.
Cloud Basics

Cloud computing was made necessary by the birth of the smartphone. It created scatted data for users. Initially, smartphones used a different operating system than common computers. A letter or note might exist on your smartphone but could not be shared with your personal computer unless manufactured by the same supplier. A workaround for a single user was to email a copy to himself. The internet being device neutral, Dropbox was the first neutral service to permit document sharing between users and devices regardless of format. This chapter explores cloud basics.
Cost Management in Cloud Computing

Historically, computing costs were tied to a quarterly or yearly hardware procurement investment. In a traditional on-premises world, cost management involves refresh cycles, data center acquisitions, host renewals, and recurring maintenance issues. One of the main reasons for the companies to migrate to the cloud is cost savings. However, managing cloud costs is becoming a huge challenge for organizations, one of the reasons being the huge investment made on public cloud services. Success depends more on the maturity of cloud management and governance practices than the nature of the workload. The paper attempts to understand the various aspects cloud cost management and governance.
Content Prioritization Based on Usage Pattern Analysis

Providing appropriate help is important in smartphone development as smartphones have become increasingly complex owing to their large number of features. To determine the appropriate help content, numerous studies on contextual help systems have been conducted; however, few studies have been concerned with user manual content. Thus, to provide effective user manuals, we focused on content prioritization, considering the usage pattern. Specifically, we calculated the vector representation of each element of the usage pattern and adopted a heterogeneous embedding approach. Moreover, we embedded the entire usage pattern using RNN-SVAE to calculate a user modeling value for representing user interests. Additionally, we trained InfoGAN (a generative adversarial network) to predict the usage of the user manual, and we prioritized and re-organized its content accordingly. Experiments demonstrated that, compared with existing benchmark methods, the proposed method can achieve better content-usage prediction and more effective prioritization of the top-k contents.