Reviewerly/exHarmony huggingface dataset:
Collision Avoidance at Swarm Regrouping Using Modified Network Operator Method with Various Number of Arguments

In this paper we consider collision avoidance at swarm regrouping, one of the fundamental problems in the field of mult-agent systems. We give each mobile robot its own optimizer which is able to search in the space of functions from variable number of arguments. Moving towards the target without outside interference a modified network operator of a particular robot represents a control function from discrepancy between current and terminal states. If some another robot arises on his way to the target, the argument base of the control function extends to the discrepancy between considering and neighboring robot's states. We use modified network operator to synthesize the control function which encoding scheme of mathematical expression is more concise compared with classical version of the method. The experimental part demonstrates strong result for a swarm of 18 robots.
THE CORRELATION BETWEEN READING STRATEGIES AND READING COMPREHENSION ACHIEVEMENT OF THE SIXTH SEMESTER IN ENGLISH EDUCATION STUDY PROGRAM OF BENGKULU UNIVERSITY

This research is aimed at identifying student’s reading strategies used and students’s reading comprehension achievement. The research would like to find out whether or not there is any significant correlation between reading strategies and reading comprehension achievement at the sixth-semester students of English Education Study Program at Universitas BengkuluAcademic Year 2018/2019. The research was designed as a quantitative correlation method, the subject were 78 students and the instrument used for collecting the data were reading strategies questionnaire and reading test. The collected data were analyzed by SPSS 23. The result of this research revealed that there is a significant correlation between reading strategies and reading comprehension achievement because the correlation coefficient is 0.033 which is very weak correlation category. It seems that most of the students were not familiar to the strategies in reading comprehension therefore more understanding of strategies is needed until a reading comprehension problem is solved.Keywords: Correlation, Reading Strategies, Reading comprehension Achievement
Ghost Trace on the Wire? Using Key Evidence for Informed Decisions

Modern smartphone messaging apps now use end-to-end encryption to provide authenticity, integrity and confidentiality. Consequently, the preferred strategy for wiretapping such apps is to insert a ghost user by compromising the platform’s public key infrastructure. The use of warning messages alone is not a good defence against a ghost user attack since users change smartphones, and therefore keys, regularly, leading to a multitude of warning messages which are overwhelmingly false positives. Consequently, these false positives discourage users from viewing warning messages as evidence of a ghost user attack. To address this problem, we propose collecting evidence from a variety of sources, including direct communication between smartphones over local networks and CONIKS, to reduce the number of false positives and increase confidence in key validity. When there is enough confidence to suggest a ghost user attack has taken place, we can then supply the user with evidence to help them make a more informed decision.
Efficient Corpus Creation Method for NLU Using Interview with Probing Questions

This paper presents an efficient method to build a corpus to train natural language understanding (NLU) modules. Conventional corpus creation methods involve a common cycle: a subject is given a specific situation where the subject operates a device by voice, and then the subject speaks one utterance to execute the task. In these methods, many subjects are required in order to build a large-scale corpus, which causes a problem of increasing lead time and financial cost. To solve this problem, we propose to incorporate a “probing question” into the cycle. Specifically, after a subject speaks one utterance, the subject is asked to think of alternative utterances to execute the same task. In this way, we obtain many utterances from a small number of subjects. An evaluation of the proposed method applied to interview-based corpus creation shows that the proposed method reduces the number of subjects by 41% while maintaining morphological diversity in a corpus and morphological coverage for user utterances spoken to commercial devices. It also shows that the proposed method reduces the total time for interviewing subjects by 36% compared with the conventional method. We conclude that the proposed method can be used to build a useful corpus while reducing lead time and financial cost.
Mineralogy and prospect of noble metals of gold-bearing ore breccias from ore fields of polymetallic deposits of Ore Altai

Abstract The object of the present study are the ore-bearing breccias of the worked-out lower-middle Devonian gold-silver barite-polymetallic Zmeinogorsky deposit belonging to the cognominal ore district and the conserved massive sulfide Chekmar deposit belonging to the Leninogorsk ore district. Both ore districts occupy an adjacent position within the northeastern depression zone of the Ore Altai megatrough. A brief structural and geological description of near-fault ore-bearing breccias, which developed within the peripheral parts of the deposits, is given. Analysis of petrographic composition of breccia fragments and their cementing material allowed to define barite-quartzite breccias of the Zmeinogorsky deposit and pyritized adularia-quartz metasomatites formed upon acidic igneous rocks and greenschists of the Chekmar deposit. Results of mineralogical studies with use of raster electron microscope show that there are ore minerals microinclusions in the detrital material and cement of breccias. Barite-quartzite breccias of Zmeinogorsk deposit contain barite, galena, Hg and Sb containing polymineral formations, tetrahedrite-boulangerite, bromargyrite, leucoxene. Microquartzites of these breccias mostly consist of quartz. Chekmar deposit ore breccias detrital material contains pyrite, barite, arsenopyrite, galena, sphalerite, zircon, titanite, leucoxene and REE minerals. Quartzitic compound of these breccias is cryptocrystalline and consists of quartz and adularia with admixtures of sericite and pyrite. Due to results obtained by use of SEM it was stated that barite, sulfides and sulfosalts of breccias contain noticeable amounts of dissipated Au, Pd, Pt, Ir. ICP-MS results confirm that studied ore breccias contain such noble metals as Au, Ag, Pd, Pt. Moreover, noble metals contents in Zmeinogorsk deposit breccias reach industrial standards while noble metals of Chekmar deposit ores can be extracted along with the processing of basic massive sulfide ores. Further study of ore breccias is important as these breccias can be considered as a noble metals containing subformation among polymetallic ores of Ore Altai.
Application of Agent in Security Platform

Based on Belief-Desire-Intention(BDI) architecture,the Agent built in Answer set programming (ASP) is applied to a security platform.Firstly, the task planning is described in BDI models for different autonomous agents,which corresponding structure and cooperation mechanism are explained in detail; Secondly, the symbol language Lp including predicate set is created for the security task, which defines the unified abstract symbols for representation, interaction and state machine.The paper describes the system as a dynamic application program in ASP to solve multi-agents problem. In addition, the method to solve the conflict of beliefs in multi-agent collaboration process is given with a technical framework called Equibel, which shares beliefs among agents and the renewal of their own beliefs. Finally, the theoretical method is simulated and validated with the security application background.The simulation results prove the effectiveness of Agent method, which can upgrade the traditional security system conveniently.
L 2, 1‐Norm Regularized Matrix Completion for Attack Detection in Collaborative Filtering Recommender Systems

Collaborative filtering recommender systems (CFRSs) are known to be highly vulnerable to profile injection attacks, in which malicious users insert fake profiles into the rating database in order to bias the systems' output, since their openness, and attack detection is still a challenging problem in CFRSs. In order to provide more accurate recommendations, many schemas have been proposed to detect such shilling attacks. However, almost all of them are proposed to detect one or several specific attack types, and few of them can handle hybrid attack types, which usually happen in practice. With this problem in mind, we propose a novel L2, 1-norm regularized matrix completion incorporating prior information (LRMCPI) model to detect shilling attacks by combining matrix completion and L2, 1-norm. The proposed LRMCPI formalizes the attack detection problem as a missing value estimation problem, and it is appropriate because the user-item rating matrix is approximately low-rank and attack profiles could be considered as structural noise. The proposed LRMCPI model not only can better recover the rating matrix using correct rating value but also can detect the positions where the attackers are injected. We evaluate our model on three well-known data sets with different density and the experimental results show that our model outperforms baseline algorithms in both single and hybrid attack types.
8. Towards personalized virtual reality touring through cross-object user interfaces

Real-time adaptation is one of the most important problems that currently require a solution in the field of personalized human-computer interaction. For conventional desktop system interactions, user behaviors are acquired to develop models that support context-aware interactions. In virtual reality interactions, however, users operate tools in the physical world but view virtual objects in the virtual world. This dichotomy constrains the use of conventional behavioral models and presents difficulties to personalizing interactions in virtual environments. To address this problem, we propose the cross-object user interfaces (COUIs) for personalized virtual reality touring. COUIs consist of two components: a Deep Learning algorithm-based model using convolutional neural networks (CNNs) to predict the user’s visual attention from the past eye movement patterns to determine which virtual objects are likely to be viewed next, and delivery mechanisms that determine what should when and where be displayed on the user interface. In this chapter, we elaborate on the training and testing of the prediction model and evaluate the delivery mechanisms of COUIs through a cognitive walk-through approach. Furthermore, the implications for using COUIs to personalize interactions in virtual reality (and other environments such as augmented reality and mixed reality) are discussed.
Ensemble Decision Systems for General Video Game Playing

Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem. Each individual algorithm has its own inherent strengths and weaknesses, and often it is difficult to overcome the weaknesses, while retaining the strengths. Instead of altering the properties of the algorithm, the Ensemble Decision System augments the performance with other algorithms that have complementing strengths. This work outlines different options for building an Ensemble Decision System as well as providing analysis on its performance compared to the individual components of the system with interesting results, showing an increase in the generality of the algorithms without significantly impeding performance.
Investigation of Biomarkers in Triassic Coprolites from India

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PENGEMBANGAN MEDIA E-BOOKLET MATERI ZAT UNTUK MENINGKATKAN KARAKTER SISWA SD ISLAMIC  GLOBAL SCHOOL MALANG

Abstrak Penelitian ini bertujuan untuk menghasilkan media pembelajaran berbasis elektronik yaitu e-booklet sebagai penunjang proses pembelajaran sertamengetahui kelayakan, kepraktisan, efektivitas media pembelajaran e-booklet dalam meningkatkan karakter siswa. Penelitian menggunakan metode penelitian dan pengembangan ( R&D) dengan model ADDIE. Subjek uji coba produk terdiri dari ahli media, ahli materi, ahli bahasa, guru dan siswa. Instrumen pengumpulan data menggunakan observasi, wawancara, angket, serta dokumentasi. Teknik analisis data menggunakan data kuantitatif dan kualitatif. Hasil penilaian media e-booklet oleh ahli menunjukkan layak digunakan dengan kriteria kelayakan 94% dengan kategori sangat layak. Kepraktisan media e-booklet diketahui dari hasil uji coba oleh guru dan siswa menunjukkan 78% dengan kategori praktis. Keefektifan ditinjau dari hasil lembar observasi siswa aspek motivasi, aspek keaktifan, dan efektivitas pembelajaran menunjukkan rata-rata 81% dengan kategori sangat efektif danpeningkatan karakter siswa diketahui dari perolehan hasil belajar setelah menggunakan media memperoleh 86% yang telah mencapai KKM sekolah. Kata Kunci : media pembelajaran, e-booklet , IPA SD Abstract This research aimed to produce electronic-based is a e-booklet forsupporting the learning process, knowing the feasibility, the practicality, the effectiveness of learning media e-booklet toincrease students character. This research methodsused research and development (R&D) with the ADDIE model. The subjects of the trial products is media experts, material experts, linguist,teacher and students. The instrument of data usedobservation, interview, questionnaires, and documentation. The data analysis technique used quantitative and qualitative. The results of the assessment e-booklet media by the experts show 94% is very worth it. The practicality of e-booklet media is known from by teachers and students showing 78% with practical categories. Effectiveness viewed from the results of the student observation sheets aspects of motivation, aspects of activity, and effectiveness of learning showed an average 81% with a very effective category and the increase of students character known from result learning outcomes after using the media obtained 86% who had reached the school criteria minimal. Keywords: learning media, e-booklet, elementary school science
Dictionary Learning and Sparse Coding for Digital Image Forgery Detection

Nowadays, digital images are used as critical evidence for judgment, but they can be forged using image processing tools with invisible traces and little effort. Hence, it is very important to determine the authenticity of these digital images. In this paper, we propose a novel approach that uses dictionary learning and sparse coding to detect digital image forgery. We experimented with two popular data sets to determine how effectively and efficiently our approach detects digital image forgery compared to previous approaches. The results show that our approach not only outperforms these approaches in terms of Precision, Recall, and F1 score, but it is also more robust against compression and rotation attacks. Also, our approach detects forgery significantly faster than previous approaches since it uses a sparse representation that dramatically reduces the feature dimensionality by a factor of more than 20.
Sistem Informasi Penjualan Beras Dari Produsen Ke Konsumen Berbasis Aplikasi Mobile

Indonesian mainly consume rice as their primary food. However, along with the sales chain that exist in intense level, it makes the price of rice increases. This application made to simpifly the trade distribution channel from producer to customer. The aim of this research is to reducing the prices for customers by create more eficiency of distribution channel. The making of this application was implemented to a website form in which can be accessed through smartphone or computer. This research method uses the waterfall method, and created use the HTML, bootstrap, and CSS. The testing technique uses the Black Box and UAT (User Acceptance Test) methods for 30 potential users. The results of the Black Box trial show results that are in accordance with the features designed, while the results from the UAT show an average value of 83.9% so this application can be accepted by users and has succeeded in reducing the distribution channel of rice sales.
System Test Effort Estimation Using Class Model: A Case Study

The success of a software product depends on its functionality and quality compliance. Customer satisfaction, which is vital for an IT organization’s business, is greatly influenced by the quality of the delivered product. Quality is ensured through thorough testing based on accurate test planning. Accurate test planning requires a reliable estimation of the test effort. Our work proposes an innovative method for estimating system test effort for object-oriented systems (OOS) using the highly popular UML Class models. As part of the experimental study, we have presented the test effort estimation steps for a recently completed Web-based project executed in a well-established IT organization. The results obtained confirmed that the proposed approach has the potential to estimate the test effort with reasonable accuracy.
A CONVOLUTIONAL NEURAL NETWORK (CNN) APPROACH TO DETECT FACE USING TENSORFLOW AND KERAS

Face recognition is used in a variety of aspects in the modern world. Face detection means to identify the face from a digital image. The deep neural network is considered a powerful tool as it can handle huge amounts of data .conventional neural network is one most popular tool to detect face detection. In this paper, a deep convolutional neural network (CNN) to extract features from input images. Keras is used for implementing CNN also D’lib and OpenCV for aligning faces on input images. Face recognition performance is evaluated using a custom dataset.
Mapping detected periodic dance movements to control tempo in the music playback of Electronic Dance Music

Engaging in the music set of one’s favorite artist or DJ is oftentimes leading to the result of a powerful and euphoric felt experience, a sensation partly also induced from dancing in beat to the ...
Tailoring virtual environments of an exergame for physiotherapy: the role of positive distractions and sensation-seeking (Adaptando entornos virtuales para un exergame para la fisioterapia: el papel de las distracciones positivas y la búsqueda de sensaciones)

Recent findings have shown that exergames can facilitate physiotherapy. Environmental conditions, such as the inclusion of positive distractions, also seem to play a role in health recovery, but prior studies have never analysed their role in virtual environments. In this study we developed an exergame for the exercise of upper limbs by designing virtual environments with nature elements and testing the impact of including additional positive distractions. Participants (n = 124, 81 females) were randomly assigned to one of two virtual environments: positive distractions vs. no distractions. To test whether these two environments matched the users’ characteristics, sensation-seeking was examined as a moderator. Measures of affect, sense of presence, intrinsic motivation and vitality were applied after the sessions, and game performance was assessed. Results showed that both environments were positively evaluated for all the dependent variables, regardless of the positive distractions and the sensation-seeking traits. However, game performance was affected by the environment and the participants’ sensation-seeking traits, suggesting that the additional distractions can reduce performance, and that individual differences also seem to impact performance.
StarCAM - A 16K stereo panoramic video camera with a novel parallel interleaved arrangement of sensors

The most common sensor arrangement of 360 panoramic video cameras is a radial design where a number of sensors are outward looking as in spokes on a wheel. The cameras are typically spaced at approximately human interocular distance with high overlap. We present a novel method of leveraging small form-factor camera units arranged in stereo pairs and interleaved to achieve a fully panoramic view with fully parallel sensor pairs. This arrangement requires less keystone correction to get depth information and the discontinuity between images that have to be stitched together is smaller than in the radial design. The primary benefit for this arrangement is the small form factor of the system with the large number of sensors enabling a high resolving power. We highlight mechanical considerations, system performance and software capabilities of these manufactured and tested imaging units. One is based on the Raspberry Pi cameras and a second based on a 16 camera system leveraging 8 pairs of 13 megapixel AR1335 cell phone sensors. In addition several different variations on the conceptual design were simulated with synthetic projections to compare stitching difficulty of the rendered scenes.
Exploration Driven by an Optimistic Bellman Equation

Exploring high-dimensional state spaces and finding sparse rewards are central problems in reinforcement learning. Exploration strategies are frequently either naive (e.g., simplistic-greedy or Boltzmann policies), intractable (i.e., full Bayesian treatment of reinforcement learning) or rely heavily on heuristics. The lack of a tractable but principled exploration approach unnecessarily complicates the application of reinforcement learning to a broader range of problems. Efficient exploration can be accomplished by relying on the uncertainty of the state-action value function. To obtain the uncertainty, we maintain an ensemble of value function estimates and present an optimistic Bellman equation (OBE) for such ensembles. This OBE is derived from a relative entropy maximization principle and yields an implicit exploration bonus resulting in improved exploration during action selection. The implied exploration bonus can be seen as a well-principled type of intrinsic motivation and exhibits favorable theoretical properties. OBE can be applied to a wide range of algorithms. We propose two algorithms as an application of the principle: Optimistic Q-learning and Optimistic DQN which outperform comparison methods on standard benchmarks.
Construction of Consistency Judgment System of Diploma Policy and Curriculum Policy using Character-level CNN

Higher education institutions or universities secure the quality of their education in order to build the confidence of stakeholders. In the mechanism of quality assurance, it is necessary to handle an enormous amount of text data related to their education activities. National Institution for Academic Degrees and Quality Enhancement of Higher Education conducts a research for quality assurance to analyze the text data of their admission, curriculum and diploma policies. An effective support system for text information processing by using information technology is desired. In this paper, we will consider the mechanism of preliminary system using character-level convolutional neural networks to judge the consistency between diploma policy and curriculum policy.
Expert Evaluation of the Usability of HeloVis: A 3D Immersive Helical Visualization for SIGINT Analysis

This paper presents an evaluation of HeloVis: a 3D interactive visualization that relies on immersive properties to improve user performance during SIGnal INTelligence (SIGINT) analysis. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained in the data. In this paper, we briefly recall what is SIGINT, the challenges that it brings to visual analytics, and the limitations of state of the art SIGINT tools. Then, we present HeloVis, and we evaluate its efficiency through the results of an evaluation that we have made with civil and military operators who are the expert end-users of SIGINT analysis.