Reviewerly/exHarmony huggingface dataset:
A Low-Cost Approach to Fish Tank Virtual Reality with Semi-Automatic Calibration Support

We describe the components and implementation of a cost-effective fish tank virtual reality system. It is based on commodity hardware and provides accurate view tracking combined with high resolution stereoscopic rendering. The system is calibrated very quickly in a semi-automatic step using computer vision. By avoiding the resolution disadvantages of current VR headsets, our prototype is suitable for a wide range of perceptual VR studies.
Using Technology in English Teaching of Pre-Service Teachers in 4.0 Industrial Revolution Era

The study aims at knowing the use of technology in teaching learning process of pre-service teachers of English Study Program of Universitas PGRI Semarang. The subjects of the study are the eight semester students which are Pre-service teachers of the English Study Program of Universitas PGRI Semarang in the academic year 2018/2019. The instrument used in the study is questionnaire. The techniques used in the study are sharing the questionnaire to the eight semester students through WhatsApp to know what kinds of technology used by them are and interview to know how their perception of the use of technology in teaching learning process is. The qualitative data are analyzed by reducing the data, classifying the data and drawing conclusion. The result of the study shows that Pre-service teachers tend to use same kinds of technology in teaching learning process, and they have an opinion that technology is really important for the teachers and for students.
Parallelized Training of Restricted Boltzmann Machines Using Markov-Chain Monte Carlo Methods

Restricted Boltzmann machine (RBM) is a generative stochastic neural network that can be applied to collaborative filtering technique used by recommendation systems. Prediction accuracy of the RBM model is usually better than that of other models for recommendation systems. However, training the RBM model involves Markov-Chain Monte Carlo method, which is computationally expensive. In this paper, we have successfully applied distributed parallel training using Horovod framework to improve the training time of the RBM model. Our tests show that the distributed training approach of the RBM model has a good scaling efficiency. We also show that this approach effectively reduces the training time to little over 12 min on 64 CPU nodes compared to 5 h on a single CPU node. This will make RBM models more practically applicable in recommendation systems.
The Role of Means of Modern Technology in the Criminal Procedures in Jordan with a Special Focus on Electronic Trial

In recent times, the legal field has not been exempted from the benefits of technology which has surged significantly.Countries have tended to benefit from using the means of modern technology in the legal field.It is noteworthy that the notion of regulating the use of means of modern technology in the criminal procedures in Jordan is relatively new despite its importance.The recent outbreak of coronavirus (Covid-19) has brought about a shift in the way justice can still be carried out, by way of remote court appearances in order to observe social distancing rules.Consequently, the use of means of modern technology in criminal procedures especially conducting electronic trial could be illustrated as an effective alternative under the current situation.This article aims to examine the extent the Jordanian legislations welcome the use of means of modern technology in criminal procedures.The advantages of using the means of modern technology in the Jordanian criminal justice system will also be examined.A combination of various approaches is used for the purposes of this research.This includes a critical analysis approach by analysing the current primary and secondary available sources related to the use of means of modern technology in the criminal procedures in Jordan.The critical analysis approach is also used to identify how the Jordanian legislations have dealt with the use of means of modern technology in the criminal procedures especially the Jordanian Regulation of the Use of Means of Modern Technology in Criminal Procedures.A comparative approach is utilised to compare traditional criminal procedures with criminal procedures that use the means of modern technology.A descriptive approach is engaged to describe the laws and practices in Jordan with regards to the use of means of modern technology.This article concludes that Jordan has made significant efforts to facilitate the use of means of modern technology in criminal procedures.This could be represented by enacting the Jordanian Regulation of the Use of Means of Modern Technology in Criminal Procedures.This article also concludes that the advantages of the use of means of modern technology in the criminal procedures supersede their disadvantages.
Batik Motifs Detection Using Pattern Recognition Method

Abstract. Batik motif is one of the factors that makes batik unique and attractive. There are various kinds of batik motif designs in various areas. Each of these design motifs implies symbols/illustrations that contain certain meanings.The design of the batik motif is used in different events according to the occasions. But unfortunately, not many people understand this, even though local wisdom on the design of batik motifs is one form of cultural heritage of the archipelago that must be preserved. Related to this, development of information technology and multimedia should be used as a solution. However, until now, there is no accurate and fast information system in detecting batik motifs. This study applies pattern recognition methods to find the most appropriate and accurate method for detecting and interpreting batik motifs. The method will be used to build a batik motif detection information system to help users get information quick and accurately.Keywords: pattern recognition, batik motifs, analysis and design of information systems.Abstrak. Motif batik merupakan salah satu faktor yang menjadikan batik unik dan menarik. Terdapat berbagai macam desain motif batik di berbagai area. Setiap desain motif tersebut mengisyaratkan simbol-simbol/ilustrasi yang mengandung makna tertentu. Tentu saja desain motif batik tersebut digunakan dalam acara yang berbeda-beda sesuai dengan keperluanya. Namun sayang, tidak banyak orang yang mengerti hal ini, padahal kearifan lokal pada desain motif batik tersebut merupakan salah satu bentuk warisan budaya nusantara yang wajib dilestarikan. Terkait hal tersebut, seharusnya perkembangan teknologi informatika dan multimedia dapat digunakan sebagai solusi. Namun demikian, sampai saat ini, belum ada system informasi yang akurat dan cepat dalam mendeteksi dan menginterpretasi motif batik. Penelitian ini menerapkan metode-metode pengenalan pola guna menemukan metode yang paling tepat dan akurat untuk mendeteksi dan menginterpretasi motif batik. Metode tersebut akan digunakan untuk membangun system informasi deteksi motif batik untuk membantu pengguna yang tidak mengenal motif batik mendapatkan informasi secara lebih cepat dan akurat.Kata Kunci: pattern recognition, batik motifs, analysis and design of information systems.
Sequence-level self-learning with multi-task learning framework

In this work, we develop new self-learning techniques with an attention-based sequence-to-sequence (seq2seq) model for automatic speech recognition (ASR). For untranscribed speech data, the hypothesis from an ASR system must be used as a label. However, the imperfect ASR result makes unsupervised learning difficult to consistently improve recognition performance especially in the case that multiple powerful teacher models are unavailable. In contrast to conventional unsupervised learning approaches, we adopt the multi-task learning(MTL) framework where then n-th best ASR hypothesis is used as the label of each task. The seq2seq network is updated through the MTL framework so as to find the common representation that can cover multiple hypotheses. By doing so,the effect of the hard-decision errors can be alleviated. We first demonstrate the effectiveness of our self-learning methods through ASR experiments in an accent adaptation task between the US and British English speech. Our experiment results show that our method can reduce the WER on the British speech data from 14.55% to 10.36% compared to the baseline model trained with the US English data only. Moreover, we investigate the effect of our proposed methods in a federated learning scenario.
The Chemistry and Mineralogy of Sinter Deposits From two Large Geysers in the Upper Geyser Basin, Yellowstone National Park

Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]The Chemistry and Mineralogy of Sinter Deposits From two Large Geysers in the Upper Geyser Basin, Yellowstone National ParkAuthors Dakota Churchill iD Michael Manga iD Wei Lin iD Shaul Hurwitz Sara Peek David Damby Jefferson Hungerford iD Behnaz Hosseini, John Wood See all authors Dakota ChurchilliDCorresponding AuthorUniversity of California BerkeleyiDhttps://orcid.org/0000-0003-3382-5562view email addressThe email was not providedcopy email addressMichael MangaiDUniversity of California BerkeleyiDhttps://orcid.org/0000-0003-3286-4682view email addressThe email was not providedcopy email addressWei LiniDUniversity of California BerkeleyiDhttps://orcid.org/0000-0002-8643-594Xview email addressThe email was not providedcopy email addressShaul HurwitzU.S. Geological Surveyview email addressThe email was not providedcopy email addressSara PeekUSGS Volcano Science Centerview email addressThe email was not providedcopy email addressDavid DambyUSGS California Volcano Observatoryview email addressThe email was not providedcopy email addressJefferson HungerfordiDNational Park Service, Yellowstone Center for ResourcesiDhttps://orcid.org/0000-0003-2651-2285view email addressThe email was not providedcopy email addressBehnaz Hosseini,Yellowstone Center for Resourcesview email addressThe email was not providedcopy email addressJohn WoodNPS, Geologic Resources Divisionview email addressThe email was not providedcopy email address
Efficient Generation of Atomic Singlet State with Rydberg Blockade and Lie‐Transform‐Based Pulse Design

Abstract A protocol to generate atomic singlet state of three atoms is proposed. The strong Rydberg interactions between atoms mediate the multi‐qubit operations and help to simplify the dynamics of the system. The effective Hamiltonian is constructed in the regime of Rydberg blockade and the evolution path is built up from an initial state to the target singlet state via the Lie‐transform‐based pulse design. Numerical simulations show the protocol can produce atomic singlet state with high fidelity and holds robustness against several types of experimental imperfections. Therefore, it may provide some useful results for the generation of singlet states in atomic system.
Restrictions on ordering of adjectives in Spanish

Abstract. Sequences of multiple modifying adjectives are subject to poorly understood lexical ordering restrictions. There are certain commonalities to these restrictions across languages, as well as substantive language variation. Ordering restrictions in Spanish are still under empirical debate, with some proposing strict ordering for direct modifier adjectives; others proposing broad ordering restrictions based on the contrast between intersective and non-intersective adjectives, and yet others raising the possibility that adjectival order is fully unrestricted. The goal of the present study is to examine corpus evidence for adjectival sequences. We look at both sequences of two postnominal adjectives (Noun +Adjective + Adjective, NAA sequences) as well as sequences of one prenominal, and one postnominal adjective (Adjective + Noun +Adjective, ANA sequences). The results from the NAA datasets clearly categorically confirms that relational adjectives are structurally closer to the noun. There is some evidence for an ordering bias along the line of the intersectivity hypothesis, but little else in term of hard evidence for restrictions. Additional ordering constraints appear once we incorporate the ANA datasets into the empirical picture. One interpretation is that these restrictions can be subsumed under an approach where evaluative adjectives have to occupy the prenominal restriction. In sum, the evidence is most compatible with the middle ground approach, but not with a fully articulated set of ordering restrictions.
Ontology and Cognitive Outcomes

Here we understand 'intelligence' as referring to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts. Of particular importance to the Cognitive Process Ontology is the class Representation that is Warranted. Such a representation is descriptive in nature and deserving of trust in its veridicality. The latter is because a Representation that is Warranted is always produced by a process that was vetted (or successfully designed) to reliably produce veridical representations. As such, Representations that are Warranted are what in other contexts we might refer to as 'items of knowledge'.
Chess, Artificial Intelligence, and Epistemic Opacity

In 2017 AlphaZero, a neural network-based chess engine shook the chess world by convincingly beating Stockfish, the highest-rated chess engine. In this paper, I describe the technical differences between the two chess engines and based on that, I discuss the impact of the modeling choices on the respective epistemic opacities. I argue that the success of AlphaZero’s approach with neural networks and reinforcement learning is counterbalanced by an increase in the epistemic opacity of the resulting model.
Upgrading YouTube Video Search by Generating Tags Through Semantic Analysis of Contextual Data

Nowadays, people share various information and ideas through several online video service platforms such as Facebook, Twitter, Flicker, Buzznet and Google. Among them, YouTube has gained immense popularity as a comprehensive source of dramatic, aesthetic and artistic videos due to its promising criteria like title, description and tags. Generally, tag is the major criteria which determine more specifically what the video is about. Sometimes, irrelevant videos appear on search panel due to inappropriate tags, which is one of the key problems to increase YouTube videos popularity and surfaces. To solve this issue, we proposed a semantic annotation-based tag system on YouTube video. We show that semantic technique has the potentiality to improve videos tag system and increase videos popularity according to the search. The effectiveness of the proposed system has demonstrated through the statistical and human base evaluation process. The evaluation states that the proposed approach is effective to generate appropriate YouTube video tags and explore a new way to recommend tags for video representation.
Board 144: PRIME: Engaging STEM Undergraduates in Computer Science with Intelligent Tutoring Systems

He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).His research on personalized learning technologies ranges from intelligent tutoring systems and game-based learning environments to affective computing,
The upbringing of the performance skills of the piano class students in contemporary conditions

We reveal the content of “performance skill” concept in the process of implementation of artistic and creative potential in the framework of initial piano training. Attention is paid to the ways of preparing the student for a concert performance, which are necessary for overcoming stage excitement. We analyze and highlight modern platforms and projects that affect the success rate of skill formation in children’s performance practice, taking into account different degrees of giftedness. The degree of performance success depends on the competent construction of educational work by the teacher, the complex development of individual abilities of the student. It is emphasized that the process of mastering performance skills is carried out in result of cognitive interests formation of the student depending on the creative abilities, as well as increasing the overall musical culture of the child. It is determined that the ability to convey the most effective own way of work interpretation is possible as a result of continuous public activity from the first steps of a young musician. We draw conclusion that it is necessary to create additional conditions for creative self-realization of students during the initial training in playing the instrument, in which the process of forming the studied skill can be more effective by developing separate leisure programs and including family music.
Pengembangan Aplikasi Kehadiran Mahasiswa Berbasis Web Pada Program Studi Pendidikan Teknologi Informasi dan Komputer

Through Aplikasi Kehadiran Mahasiswa Teknologi Informasi dan Komputer (AKEMATIK), Education of Technology Information and Computer Program on IKIP PGRI Pontianak can facilitate lecturers in filling teaching journals and monitoring student attendance, while also helping to prepare lecture reports that will help evaluation of teaching and learning in the Program Studi P.TIK IKIP PGRI Pontianak. The research aims to: 1) provide an overview of the application development process; and 2) find out the feasibility of the application. Application development uses the ADDIE model which consists of 5 stages: 1) Analysis; 2) Design; 3) Development; 4) Implementation; and 5) Evaluation. Based on this model it is known that the application has 3 actors namely admin, lecturer, and student. Application testing is done by validating media experts, and alpha testing. The results of expert validation were in the excellent category. The alpha test was conducted on 15 lecturers and 30 students of P.TIK, the results were in the excellent category.
Three compartment breast machine learning model for improving computer-aided detection

Our purpose was to determine if the lipid, water, and protein lesion composition (3CB), combined with computer-aided detection (CAD) had higher biopsy malignancy specificity than CAD alone. High and low-kVp full-field digital 3CB mammograms were acquired on women with suspicious mammographic lesions (BIRADS 4) and that were to undergo biopsy. Radiologists delineated 673 lesions (98 invasive ductal cancers (IDC), 60 ductal carcinomas in situ (DCIS), 103 fibroadenomata (FA), and 412 benign (BN)) on the diagnostic mammograms using the pathology report to confirm location. The diagnostic mammograms were processed by iCAD SecondLook software using its most sensitive setting to create to further delineations and probabilities of malignancy. The iCAD delineated a total of 375 annotation agreeing regions that were classified as either masses or calcification cluster. The 3CB algorithm produced lipid, water, and protein thickness maps for all ROIs and peripheral rings from which 84 compositional input features were derived. A neural network (3CBNN) was trained with cross-validation on 80% of the data to predict the lesion type. Biopsy pathology served as the gold standard outcome. IDC and DCIS predicted probabilities were summed together to obtain a probability of malignancy which was evaluated against the iCAD probabilities using the area under the ROC curves. On a holdout test set, 20% of the data, the iCAD's output alone had an AUC of 0.61 while the 3CBNN’s AUC was 0.73. We conclude that compositional information provided by the 3CB algorithm contains important diagnostic information that can increase specificity of CAD software.
A Novel Hybrid Swarm Intelligence Algorithm Combining Modified Artificial Bee Colony and Firefly Algorithms

Nature-inspired population-based swarm intelligence (SI) algorithms can be used to obtain moderate results for optimization problems. However, by creating connections between the nature-inspired algorithms, further improved results can be achieved. Hybrid algorithms can play a noticeable role in improving the search capability of algorithms. In this paper, two swarm intelligence algorithms have been hybridized, which are—a modified version of the Artificial Bee Colony algorithm (ABC-T) and the Firefly Algorithm (FA). The proposed hybrid algorithm, named as Artificial Bee Colony-Inspired Firefly Algorithm (ABC-IFA), gave comparatively better results on a number of continuous function optimization problems. The hybridization is performed by implementing an island model where both ABC-T algorithm and FA are executed in an alternative manner, sequentially running one after another on two separate populations of candidate solutions. Then after every generation, some individuals from one population are migrated to the other to hybridize the properties of the different algorithms. As the migration policy, five different strategies are proposed, which are compared with each other. The hybrid approach is evaluated on 11 different benchmark functions, and the results are compared among the ABC-T, FA and the proposed hybrid ABC-IFA. The hybrid approach is observed to perform better on some of the benchmark functions compared to the ABC-T algorithm and FA.
Resynthesizing the GECO speech corpus with VocalTractLab

We are addressing the challenge of learning an inverse mapping betweenacoustic features and control parameters of a vocal tract simulator. As a first step,we synthesize an articulatory corpus consisting of control parameters and waveforms using VocalTractLab (VTL; [1]) as the vocal tract simulator. The basis forthe synthesis is a concatenative approach that combines gestures of VTL accordingto a SAMPA transcription. SAMPA transcriptions are taken from the GECO corpus[2], a spontaneous speech corpus of southern German. The presented approach usesthe duration of the phones and extracted pitch contours to create gesture files for theVTL. The resynthesis of the GECO corpus results in 53960 valid spliced out wordsamples totalling in 6 hours and 23 minutes of synthesized speech. The synthesisquality is mediocre. We believe that the synthesized samples resemble some of thenatural variability found in natural human speech.
THE M-ARY SECURITY DIFFERENTIAL CHAOTIC FREQUENCY SHIFT SYSTEM BASED ON THE MIMO MODEL

To improve the data security and efficiency of the multi-input multi-output (MIMO)-based differential chaotic shift keying (DCSK) system under the wireless multipath fading channel, a new non-coherent modulation solution, i.e., the M-ary security differential chaotic shift keying (MS-DCSK), is proposed in this study. The scrambling matrix and shift matrix are used to construct a scrambling matrix group in this system, enabling the scrambling matrix to enhance the security of chaotic signals while carrying the M-ary bit information. By doing so, the security, energy efficiency, and spectral efficiency of the system are improved. In addition, by taking advantage of the quasi-orthogonal characteristics of chaotic sequences, the system receiver can effectively utilize the multipath diversity and space diversity of the multi-antenna transmitter and receiver system, without requiring the channel state information (CSI).