Reviewerly/exHarmony huggingface dataset:
Spectral collapse in two-mode two-photon Rabi model

We have investigated the eigenenergy spectrum of the two-mode two-photon Rabi model at the critical coupling, particularly the special feature “spectral collapse”. The eigenenergy spectrum is found to consist of both a set of discrete energy levels and a continuous energy spectrum. Each of these eigenenergies has a two-fold degeneracy corresponding to the spin degree of freedom. The discrete eigenenergy spectrum has a one-to-one mapping with that of a particle in an isotropic two-dimensional ”Lorentzian function” potential well, and the continuous energy spectrum can be derived from the scattering problem associated with an isotropic two-dimensional potential barrier. The number of bound states available at the critical coupling is determined by the energy difference between the two atomic levels so that the extent of the “spectral collapse” can be monitored straightforwardly.
Methods for Verification of Sentiment Frames

The paper describes the approaches to verification of sentiment frames in the RuSentiFrames lexicon describing sentiment connotations related to specific Russian predicates. Two approaches for verification were used: 1) analysis of specific sentences from Russian National Corpus, 2) via crowdsourcing platform Yandex.Toloka. The idea was to find similarities and differences between the annotations made by the experts in RuSentiFrames and by non-experts from Yandex.Toloka, thus verifying the RuSentiFrames descriptions. The first approach showed that implicit information influences greatly on the author’s attitude and that the context plays crucial role. The second approach showed mostly the agreement between the expert’s and non-expert’s annotations in case of relations between the participants in sentiment frames, but the author’s attitudes were estimated differently in some cases.
Using Convolutional Neural Networks and Recurrent Neural Network for Human Gesture Recognition and Problem Solving

Bang, Sanghun Tijus, CharlesProblem solving, such as Tower of Hanoi, is a mental process that involves identifying problems, developing strategies and organizing knowledge. We have acquired a series of data from participants solving the Tower of Hanoi problem to understand human behavior and cognitive reasoning in the problem-solving process. These data have a lot of semantic information, including the objects around participants, actions and interactions with objects. Therefore, given a series of data that are all labeled with a multiple categories, we have predicted these categories for a novel set of test data with the help of convolutional neural networks (CNNs). In addition to category inference, we use the Long Short-Term Memory(LSTM) model to do behavioral reasoning from sequential data.
Biodigestor con variables monitoreadas

The project consists of the design and construction of a biodigester that allows the generation of methane gas for heating and cooking food in domestic stoves. The Biodigestor will have as byproduct Biol, from leached organic matter, this byproduct can be used as fertilizer in gardens or domestic crops. The monitored variables of the Biodigestor use a data acquisition card to obtain the level of Ph, biol, temperature, humidity and pressure during the methane generation process, as well as the quantity and quality of methane gas produced. This project has been developed in several stages, design, prototype scale tests, connection of sensors and tests that allow to define reference data and data during methane production. A design has been achieved that allows simultaneous production in three biodigestors, this proposal will allow through a specific protocol, the continuity in obtaining the gas can be maintained with the possibility of emptying, cleaning and replacing organic matter without stopping the methane production process
Central Bedfordshire Council takes over Test and Trace from national NHS team

We have become one of the first local authorities in the country to bring contact tracing in-house in a new pilot scheme for local residents.
Reactive Sensing and Multiplicative Frame Super-Resolution

The problem is to evaluate the behavior of an object when primary sources of information about the object become unavailable, so that any information must be obtained from the intelligent use of available secondary sources. This evaluative process is reactive sensing. Reactive sensing is initially viewed in terms of spatial super-resolution. The theory of reactive sensing is based on two equivalent ideas, one physical and one mathematical. The physical idea models volume, e.g., engine volume in the case of analyzing engine health, and the sensitivity of sensors to such volume. The mathematical idea of multiplicative frames provides the factorization theory to compare quantitatively such volume and sensitivity. This equivalence is the foundation for reactive sensing theory and its implementation.
Multi-Label Classification Using Problem Transformation Approach and Machine Learning on Text Mining for Multiple Event Detection

Social media is a source that stores a lot of valuable information. One of which can be used to know the event that occurs in urban areas that are shared by the urban society. Information shared such as congestion and floods can be used as decision-making materials in city management. One of the social media that is popularly used is Twitter. A tweet sometimes contains more than one type of event information at the same time. That means the tweets are associated with more than one label or called multi-label classification. The purpose of this research is to find the best models for event detection from user's tweets into multi-label classification using problem transformation approach and machine learning (ML) techniques. In this paper, events detected are the natural events that were related to traffic and natural disasters. The results of multi-label classification experiments show the accuracy of 87.0%, F-score 89.1%, and hamming loss 9.2%. In this paper, we also calculated out of vocabulary (OOV), where the number of OOV tokens not found in the training data reached 79.96%. Nevertheless, the difference in vocabulary and OOV values is very small, the accuracy of 4.5%, F-score 5.2%, and hamming loss 3.2%.
Machine Translation of LATEX Based Mathematical Equations to Spoken Mathematics

This paper describes the machine translation of LATEX encoded mathematical equations to spoken mathematical sentences. A LATEX- Spoken math parallel corpus (5,600 sentences) was developed. In this paper, the 10-fold cross-validation experiments were carried out by applying Phrase-based Statistical Machine Translation (PBSMT), Weighted Finite-State Transducers (WFST) and Ripple Down Rules (RDR) based tagging approaches. The BLEU, RIBES, F1 and WER evaluation scoring metrics are used for measuring translation performance. The experimental results show that the PBSMT approach achieved the highest translation performance for LATEX mathematical equations to spoken mathematical sentences translation. Moreover, we found that the translation performance of RDR approach is comparable with PBSMT.
Representing Built Environments with Digital Tools: Problems with Human Perception

The idea of sustainable development in architecture focuses on health care and environmental protection, but not only: Building a healthy human-environment relationship also plays an important role. Demands of sustainable development are fulfilled through ensuring a general sense of well-being, which comprises permanence, safety, and comfort of adapting the built environment to human perceptual abilities and needs. In the twentieth century, research on how the built environment is perceived had gained much attention. Recent studies have applied digital tools in the research process, and sooner than later virtual reality simulations will become an essential tool and an important reference in the science of architecture. Data extracted from virtual models combined with interactive feed-back will be used to learn viewers’ understanding of architecture and its surroundings. Until now, researchers have primarily focused on tracing observers’ visual dimension of perception and estimating measurable spatial qualities rather than on simulating perceptual experience in real-life contexts. Combined with another problem of human cognitive abilities—that their many different faces reach far beyond visual observations—such an approach to simulation wanders too far from the reality of the build environment. This paper reviews what we known about observing architecture in illusions created in digital environments.
An Overview of Recommendation System Based on Generative Adversarial Networks

With the growth of explosive information resources, information overload has become increasingly serious. Users are eager to find what they need in a large amount of information resources. The recommendation system is an effective solution for filtering information and alleviating information overload. Traditional recommendation algorithms have achieved great results in applications and are widely used, but they also face serious data sparse problems. The proposal of the generative adversarial network provides another idea for modeling implicit feedback information-adversarial learning. By reviewing the research of recommendation algorithms based on generative adversarial networks in recent years, we analyze the differences and advantages of generative adversarial networks and traditional recommendation algorithms, and summarize the practical applications of these algorithms. Finally, the future of recommendation algorithms is discussed based on generative adversarial networks prospects for development trends.
TSGAN: A Two-Stream Generative Adversarial Network for Bit-Depth Expansion

The rapid development of display devices and the continuous improvement of visual quality requirements put urgent demands on high bit-depth (HBD) image, but most legacy data are stored and transmitted at low bit-depth (LBD). Bit-depth expansion (BDE) methods are designed to handle this dilemma by reconstructing HBD contents from LBD. But the expansion process often introduces artifacts such as false contours and color distortion. In this paper, we propose a two-stream generative adversarial network to expand bit-depth while suppressing artifacts. The generator network decomposes input image into base stream (BS) and detail stream (DS), then processes two data streams with different sub-networks respectively, and finally fuse two feature streams to reconstruct HBD image. Quantitative and qualitative experiments show that the proposed method outperforms tested BDE methods and CNN variants.
Dual Link Image Coding Based on CCSDS-123

Predictive coding techniques are attractive for image codecs because they can yield high compression efficiency while spending few computational resources. In remote sensing, predictive techniques are employed in prominent standards to transmit images captured by Earth Observation (EO) satellites. Although EO satellites have full duplex capacity, compression standards for spatial data are devised to use the downlink only. Recently, we presented a dual-link image coding system that employs both the uplink and the downlink to accelerate the transmission of such images. The proposed system was introduced in the wavelet-based JPEG2000 standard, which is not well-suited for satellites due to its complexity. This letter approaches the dual-link scheme to a more suitable standard for spatial data based on predictive coding, more precisely, the Lossless Multispectral and Hyperspectral image compression standard CCSDS-123.0-B.2. The proposed method adapts the dual-link image coding scheme to CCSDS-123.0-B-2 by incorporating a quantizer, a lightweight arithmetic coder, and a rate control technique. Experimental results suggest that the resulting system achieves higher coding ratios than CCSDS-123.0-B-2 and JPEG2000 with dual link.
Usability testing of digital map application using hand gesture recognition as a historical learning media for elementary school

Abstract The need for digital learning media is currently starting to increase, the use of conventional learning media has become less attractive to students of the current generation. Especially now, the need for digital learning media is starting to increase because the current generation of elementary school students is a generation that easily adapts to technology. Researchers have designed a digital map with a hand gesture recognition as a navigation control instead of a keyboard and mouse. After the application is made, the next step is validating user experience (UX) in the form of usability testing to see user responses. The questionnaire was distributed consisting of 13 questions which were grouped into 4 usability variables. This study involved 50 respondents of grade 4 elementary school students in 5 schools in the Cibiru hilir Bandung and using a linkert scale and get 4 usability variables with a percentage overall feasibility trial from elementary student are 79,25 % and 72,5 % from college student UPI Cibiru. Usability testing media consisting of learnability 80.5% with very good category, system performance is 75, 2% with good category, memorability is 80, 5%, with very good category, satisfaction is 83.3% with very good category.
Sampling Approach Matters: Active Learning for Robotic Language Acquisition

Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classification model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples.
Hybrid Shoulder Surfing Attack Proof Approach for User Authentication

With the advancement in technology, people register themselves for many websites or applications. Therefore, secure authentication is today’s prime need. Textual passwords are the most commonly used mechanism. But with the invent of software like keyloggers, using only strong textual passwords are not enough. So graphical password comes into the picture. In this paper, we focus on building animation-based hybrid authentication approach which is secure from attacks like the Brute-force attack and Shoulder-surfing attack. HAA is designed by using colors, animation of objects, and alpha-numeric characters and is intended to provide more secure authentication session.
An adaptive weight based particle swarm optimization scheduling algorithm on cloud computing system

The resource scheduling problem in cloud computing is one of the most concerned issues of researchers in related fields in recent years. With the aim of solving defects in particle swarm optimization algorithm (PSO) that it is easy to fall into local optimal solution and "premature", we propose a particle swarm optimization scheduling algorithm based on adaptive weights. By improving the inertia weight factor and using the number of iterations to control the size of the inertia weight, the algorithm convergence speed can be accelerated. Meanwhile, by updating the particle speed to update the weights of the cognitive and social coefficients in the number of iterations, the fitness function of the particles is improved to strengthen the adaptive ability of the particles and made the algorithm more in line with objective laws. A large number of simulation comparison experiments has been conducted on the Cloudsim platform. The experimental results show that the improved particle swarm optimization algorithm has better performance in adaptability, convergence speed, and capabilities of finding global optimums.
Data-Driven Template Discovery Using Graph Convolutional Neural Networks

Modeling adversarial activities is a critical component of developing high-confidence indicators of efforts to acquire, fabricate, proliferate, and/or deploy weapons of mass terror (WMTs). Current approaches to generating representative patterns of interest (a.k.a templates) from the real-world domains involve a Subject Matter Expert (SME)-guided manual process. The goal of Data-Driven Template Discovery (DDTD) is to use a (potentially small) set of SME generated templates to discover other previously unknown and interesting templates in an attributed graph. A template is an activity pattern describing a set of interactions among a group of nodes in the graph. The motivation behind DDTD is to expand the original set of templates, without having SMEs craft all the templates by hand. DDTD also provides seed templates to SMEs, to help them construct larger, high-fidelity, and scenario-oriented templates. In these cases, obtaining a larger set of templates that are related (contain similar signals) to the original set is of great value. In this work, we propose to use Graph Convolutional Neural Networks (GCNs) to discover new templates that are heavily related to the original set. GCNs are a family of Neural Network (NN) architectures especially designed to work directly on graphs. In contrast to the traditional NNs, that require considerable amounts of labeled data, GCNs do not require a big labeled training set because they can directly leverage the graph structure instead. This property makes GCNs the perfect tool for creating activity templates.
Watermarkable Signature with Computational Function Preserving

Software watermarking enables one to embed some information called “mark” into a program while preserving its functionality, and to read it from the program. As a definition of function preserving, Cohen et al. (STOC 2016) proposed statistical function preserving which requires that the input/output behavior of the marked circuit is identical almost everywhere to that of the original unmarked circuit. They showed how to construct watermarkable cryptographic primitives with statistical function preserving, including pseudorandom functions (PRFs) and public-key encryption from indistinguishability obfuscation. Recently, Goyal et al. (CRYPTO 2019) introduced more relaxed definition of function preserving for watermarkable signature. Watermarkable signature embeds a mark into a signing circuit of digital signature. The relaxed function preserving only requires that the marked signing circuit outputs valid signatures. They provide watermarkable signature with the relaxed function preserving only based on (standard) digital signature. In this work, we introduce an intermediate notion of function preserving for watermarkable signature, which is called computational function preserving. Then, we examine the relationship among our computational function preserving, relaxed function preserving by Goyal et al., and statistical function preserving by Cohen et al. Furthermore, we propose a generic construction of watermarkable signature scheme satisfying computational function preserving based on public key encryption and (standard) digital signature.
Emotion Classification and Textual Clustering Techniques for Gang Intervention Data

We study a recent dataset documenting the nature of gang involvement among 14-25 year-olds participating in the Los Angeles Mayor's Office of Gang Reduction and Youth Development (GRYD) Intervention Family Case Management Program. We use natural language processing techniques, including emotion classification and textual clustering, to perform quantitative analyses of free-form responses in the data. These analyses yield insights into the effectiveness of the program and provide a better understanding of its participants. We also compare several computational techniques and remark on their relative effectiveness in application to this dataset.
Hubungan antara penguasaan diksi dan struktur bahasa dengan keterampilan menulis cerita peserta didik kelas IV sekolah dasar

This study aims to obtain empirical data of determining the corellation between mastery of diction and language stucture, either individually or simultaneously with story writing skill. This research was conducted to determine the aspects related to writing skilss so as to improve the quality of learning outcomes in writing in schools. The method implemented in this research was corellational method. The population gathered in this research was all 4th grade student of public elementary school in Laweyan district, Surakarta. The samples were taken by using cluster random sampling. The instrument applied for data collection by using multiple choice test for mastery of diction (X1) and language structure (X2), while the performance test was for story writing skill (Y). The result showed that (1) there is a significant corellation between the mastery of diction and story writing skill 0,56)>(0,184); (2) there is a significant correlation between mastery of language structure and story writing skill 0,66)>(0,184); (3) there is a significant between mastery of diction and language structure with story writing skill   (0,66) )>(0,184).
Transliteration of Residential Addresses Having Hindi, Urdu and English

As India is a versatile diverse nation, there are people from various communities speaking various languages. There are people from Hindu, Muslim, Sikh and Christian religions living in India. The places of accommodation or the residential addresses mostly consist of the named entities from Hindi, Urdu and English languages. It is observed that there are many spelling mistakes in names and residential addresses on Aadhar card, PAN card, driving license, etc. which are our important identity proofs due to different context of making the spellings in Hindi, Urdu and English languages. This paper proposes to design and develop a system to give the correct spelling of the names and residential addresses which are mostly having named entities from different languages. Even though Hindi is the national language of India, English is the official language used all over the country, and thus, these identity proofs contain the address in both the languages. The major problem is identifying the context of named entity from language point of view and to spell it in the correct context. The mapping of words written in Devanagari in residential address should be as per the phonetics of Hindi language and similar to Urdu and English. This work aims to investigate the problem of transliterating names and residential addresses written in Devanagari script into Roman Script using phonology context of Hindi, Urdu and English. This system takes input in Devanagari lipi (i.e., script) and transliterates it into Latin script of English. The system works on the machine learning tools IRSTLM as a language model, GIZA++ for word alignment and MOSES for decoding. It shows that the performance is sufficiently good.
Armenia: Police impeded journalistic work during protest

Police prevented the work of journalists and attempted to detain the cameraman Hayk Sukiasyan of Yerkir Media TV during a protest in Yerevan, on ...