Reviewerly/exHarmony huggingface dataset:
Bimodal-based Object Detection and Instance Segmentation Models for Substation Equipments

Detection and segmentation of the substation equipments is the important first step towards establishing an AI-based thermal fault detection of substation equipments. The traditional detection and segmentation methods have been built up based on the single mode of thermal or visible light image. In this paper, we propose the framework of bimodal fusion: the visible-light images and the temperature map, to establish the deep neural network models for object detection and instance segmentation of the substation equipments, based on the Mask R-CNN. In our private fused dataset, we realize and compare diverse fusion methods, including the pixel-based fusion, feature-based fusion and decision-level fusion methods for the detection and segmentation task of substation equipments. The comparison experiments shown that the FPN feature layer fusion model in the feature-based fusion can achieve better detection and segmentation effects than the others and the models of the single mode. We also demonstrate that the fused method can slightly improve the performance in the night scene by simulation. However, the improvement of performance measured by the mAP and AR of these method are all slight.
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing

Collecting training data for semantic parsing is a time-consuming and expensive task. As a result, there is growing interest in industry to reduce the number of annotations required to train a semantic parser, both to cut down on costs and to limit customer data handled by annotators. In this paper, we propose uncertainty and traffic-aware active learning, a novel active learning method that uses model confidence and utterance frequencies from customer traffic to select utterances for annotation. We show that our method significantly outperforms baselines on an internal customer dataset and the Facebook Task Oriented Parsing (TOP) dataset. On our internal dataset, our method achieves the same accuracy as random sampling with 2,000 fewer annotations.
Sequential measurements on qubits by multiple observers: Joint Best Guess strategy

We study sequential state discrimination measurements performed on the same qubit by subsequent observers. Specifically, we focus on the case when the observers perform a kind of a minimum-error type state discriminating measurement where the goal of the observers is to maximize their joint probability of successfully guessing the state that the qubit was initially prepared in. We call this the Joint Best Guess strategy. In this scheme, Alice prepares a qubit in one of two possible states. The qubit is first sent to Bob, who measures it, and then on to Charlie, and so on to altogether N consecutive receivers who all perform measurements on it. The goal for all observers is to determine which state Alice sent. In the Joint Best Guess strategy, every time a system is received the observer is required to make a guess, aided by the measurement, about its state. The price to pay for this requirement is that errors must be permitted, the guess can be correct or in error. There is a nonzero probability for all the receivers to successfully identify the initially prepared state, and we maximize this joint probability of success. This work is a step toward developing a theory of nondestructive sequential quantum measurements and could be useful in multiparty quantum communication schemes based on communicating with single qubits, particularly in schemes employing continuous variable states. It also represents a case where subsequent observers can probabilistically and optimally get around both the collapse postulate and the no-broadcasting theorem.
Pauli Channel Online Estimation Protocol for Quantum Turbo Codes

In this paper, we tackle the channel estimation problem for Pauli channels. Online estimation methods for the depolarizing channel have been proposed in previous literature. However, realistic quantum devices often exhibit an asymmetric behaviour not captured by the symmetric depolarizing model, implying that the estimation method used by Quantum Turbo Codes (QTC) should exploit such asymmetry for the error correcting operations to be successful. Consequently, we propose an online iterative method that aids in successfully estimating each of the individual error probabilities associated with the Pauli channel, ultimately increasing the probability of correct decoding. The benefits this method provides come at the expense of an increase in the decoding complexity.
Irregularity, paradigmatic layers, and the complexity of inflection class systems

The complexity of an inflection class system is the average extent to which elements in the system inhibit motivated inferences about the realization of lexemes’ paradigm cells. Systems tend to exhibit relatively low complexity in this sense. However, representations of inflectional systems tend to include only affixal and regular patterns, leaving questions about how irregular patterns and non-affixal ‘layers’ of inflectional exponence affect the complexity of systems. We address these questions by exploring four layers of inflectional exponence of Russian nouns, including irregular patterns within each layer. We find that Russian nouns exhibit relatively low complexity even when irregular and non-affixal exponence are included. Implicative structure and the uneven distribution of lexemes across classes mitigate the uncertainty associated with irregular and non-affixal exponence. Low systemic inflectional complexity is, thus, an emergent property that may extend beyond affixal and/or regular inflectional patterns.
Interaction Management Strategies in English Literature Lectures in Indonesian University Setting

Interaction management strategies are increasingly set to become a vital factor in fostering active participants in university lectures. Following Rido, Ibrahim &amp; Nambiar’s (2015) framework, this study investigated interaction management strategies used in English literature lectures in a university setting in Indonesia. This study employed a qualitative method and the data were gathered through video recordings. The findings showed that all literature lecturers employed three kinds of interaction management strategies such as code strategies, emotional strategies, and managing strategies. In code strategies, the lecturers used English during the entire class for clarity in opening, explanation, example, and closing. Meanwhile, in emotional strategies, the lecturers shared common strategies namely using non-verbal gestures and using humours or jokes in order to invite participation, to increase student’s motivation, and to create conducive learning environment. Then, in terms of managing strategies, the lecturers decided topic, controlled or switched the topic of discussion, decided who speaks, gave direction or commanded the students, gave extended wait time, and offered opportunity for student to speak in order to enable the students express their ideas and engage with the lectures. It is hoped that insights gleamed from this study will assist such lecturers to successfully manage their lectures and navigate the distribution of participation as well as content of the lesson.
Effects and Risks of Distance Learning in Higher Education

The forced work of Russian universities remotely in the context of the pandemic (COVID-19) has generated a lot of discussion about the benefits of the new form of education. The first results were summed up and reports were presented, the materials of which showed that the main goal of online education — the prevention of the spread of infection, - has been achieved. Against this background, proposals and publications have appeared substantiating the effectiveness of the massive introduction of distance learning in Russia, including in higher education. However, the assessment of such training by the population and students in publications and in social networks was predominantly negative and showed that the number of emerging problems exceeds the possible benefits of the new educational technology. Based on the analysis of the materials of publications and personal experience of teaching online, the potential benefits and problems of distance learning in higher education in Russia are considered. It is proposed to consider the effects separately for the suppliers of new technology (government, universities) and consumers (students, teachers, society). It is substantiated that the massive introduction of online education allows not only to reduce the negative consequences of epidemics, but also to reduce budgetary funding for universities, optimize the age composition of teachers, and reduce the cost of maintaining educational buildings. However, there will be a leveling / averaging of the quality of education, and responsibility for the quality of training will shift from the state/universities to students. The critical shortcomings of online education are the low degree of readiness of the digital infrastructure, the lack of a mechanism for identifying and monitoring the work of students, information security problems, and the lack of trust in such training of the population. The massive use of online education creates a number of risks for the country, the most critical of which is the destruction of the higher education system and a drop in the effectiveness of personnel training. The consequences of this risk realization are not compensated by any possible budget savings.
Propose Model for Consumers’ Perceptions and Acceptance of e-Health Systems and Services in Jordan

E-health can improve health-care quality and reduce health costs.However, few studies have addressed the antecedent factors that influence health-care consumers' acceptance of and intentions to use e-Health systems and services, which is called health web-based systems and services (HWBSS) in this study, especially in developing countries such as Jordan.Furthermore, it has been claimed that technology acceptance theories and models do not fit equally across cultures.Therefore, the main objective of this study is to empirically investigate the factors that influence patients' perceptions and acceptance of e-Health systems and services in Jordan.It does so by extending the technology acceptance model (TAM) to include three other variables (privacy, trust and perceived empowerment) and cultural variables as a moderator, forming the basis for a future in-depth and expanded study.The proposed research model was built on the literature review presented here.A reliability analysis through a pilot study was performed to check its internal consistency using Cronbach's alpha.The instrument was reviewed by a panel of experts in order to validate its content.The results of the pilot study verified the feasibility of conducting a full-scale investigation and can be used to help refine the instrument.The outcomes demonstrate that the model is highly reliable and has a positive impact on patients' attitudes to using e-Health systems and services.
Unsupervised Learning-Based Sentiment Analysis with Reviewer’s Emotion

Jigneshkumar Patel, Harsh Prakash Verma, Jai Patel, AtulThe sentiment analysis performed using the general methodologies, i.e., lexicon and neural networks based mainly on the content written by the user. They all are mainly content-centric methodologies. The aspect of the user’s mindset and sentiment for writing the reviews is never considered and the emotions of the writer. In this paper, we are proposing the consideration of these aspects and their impact. They are accommodated on the basis of the sentiment score of the review written by the user. The intensity of the words used to describe the product or an issue matters significantly in the classification of the product features. Unsupervised learning methods were used to calculate more precise sentence-level sentiments with the help of contextual dependencies. They are more suitable for the aspect-based sentiment analysis as they are found to be more adaptable to different contexts and domains with the change in information rather than changing the entire model structure. The clustering algorithms are used for segregating the different types of groups related to viral sharing of ads. Various factors can be analysed to decide whether the ad is shared by a user or not.
Learning Dynamic Bayesian Networks structure based on a new hybrid K2-Bat learning algorithm

The temporal dimension makes it difficult and complex to learn the Dynamic Bayesian Networks structure for huge search space. We propose a new hybrid K2-Bat algorithm to learn the structure of Dynamic Bayesian Networks. This work contains two optimal strategies: an ordering-based algorithm INOK2 to learn initial network structure and an adaptive binary bat algorithm to learn transition network structure. Based on the requirement of K2 algorithm for prior knowledge, a fitness function is built to quantitatively score node order in INOK2. The initial population is generated by the node block sequence constructed by directional support tree. A dynamic learning factor, inverted mutation s`node sequence to improve the global searching ability and the convergence speed. Then, the optimal initial network structure can be obtained. In addition, an improved binary bat algorithm is proposed to improve the development behavior of bat algorithm by using dynamic selection strategy in transitional network learning. Finally, experiments on four well-known benchmark problems are performed. The results show that the proposed algorithm can successfully learn the structure of Dynamic Bayesian Networks without prior knowledge, and balance solutions quality and computational effort.
A NOVEL APPROACH TO HEART ATTACK PREDICTION IMPROVEMENT VIA EXTREME LEARNING MACHINES CLASSIFIER INTEGRATED WITH DATA RESAMPLING STRATEGY

The heart attack is a disorder that is frequently seen in low-income countries and causes the death of many people. Cardiologists benefit from electrocardiography (ECG) tests to determine this condition. Supervised classification algorithms are frequently used and provide very successful results in computer-aided diagnostic systems. In this study, a new approach to predict a heart attack is proposed for classification via extreme learning machines (ELM) integrated with the resampling strategy. This study aims to reveal a new diagnostic system that will increase the success of current studies. The study has three basic steps. In order to determine the features that will ensure the system’s optimized operation, firstly, the ReliefF feature selection method was applied to the data set, and then, the system was modeled by different classifiers via resampling. Besides, the as-proposed approach was applied to the breast cancer data to test the accuracy of the current system. The as-obtained results from both Statlog (heart disease) and the breast cancer data were seemed to be more successful than the studies in the literature. Thus, the as-proposed system reveals a successful and effective approach that can be applied in clinical data sets.
Multimodal Data Processing Framework for Smart City: A Positional-Attention Based Deep Learning Approach

In the past few years, edge computing has brought tremendous convenience to the development of smart cities, releasing computation pressure to edge compute nodes. However, a series of problems, such as the explosive growth of smart devices and limited spectrum resources, still greatly limit the application of edge computing. Different types of end devices generate and collect multimodal information, and substantial data is transmitted to upper nodes. Multimodal machine learning methods process data at edge nodes, and only high-level features are uploaded to the cloud in order to save bandwidth. In this article, we propose a novel multimodal data processing framework based on multiple attention mechanisms. Two distinct attention mechanisms are used to capture inter and intra-modality dependencies and align different modalities together. We conduct experiments on image captioning, a core research hotspot in multimodal machine learning. A unified hierarchical structure extracts features from images and natural language. Matching attention aligns visual and textual information. Besides, we propose a new attention mechanism, positional attention, which finds the relationship of each element within one sensory modality. The hierarchical structure realizes parallel computation in the training phase and speeds up the training of the model. Experiments and analysis demonstrate significant improvements over baselines, proving the effectiveness of our method.
Decision support system model using FIS Mamdani for determining tire pressure

Tire air pressure is very important in driving, providing comfort, safety, and efficiency in fuel consumption. This study aims to create a model that can determine the measurement of tire air pressure. The model was developed based on the Mamdani FIS with five input parameters: load weight (load capacity), weather, mileage, rim diameter, and tire thickness. Mamdani inference generates front and rear tire air pressure. The calculation of tire pressure using the system was compared with a manual that only considers the vehicle load. This comparison shows the difference in the mean size of 1.24% for the front tire pressure and 2.17% for the rear tire. The system can provide recommendations for tire air pressure by considering several parameters in addition to vehicle load.
Owicki-Gries Reasoning for C11 RAR (Artifact)

The paper Reasoning for C11 introduces a new proof calculus for the C11 RAR memory model that allows Owicki-Gries proof rules for compound statements, including non-interference, to remain unchanged. The proof method features novel assertions specifying thread-specific views on the state of programs. This is combined with a set of Hoare logic rules that describe how these assertions are affected by atomic program steps. The artifact includes the Isabelle formalisation of the proof method introduced in the paper. It also contains the formalisation and proof of all case studies presented in the paper. All of the theorems are accompanied with their respective proofs.
Efficient Mining of Non-Redundant Periodic Frequent Patterns

Periodic frequent patterns are frequent patterns which occur at periodic intervals in databases. They are useful in decision making where event occurrence intervals are vital. Traditional algorithms for discovering periodic frequent patterns, however, often report a large number of such patterns, most of which are often redundant as their periodic occurrences can be derived from other periodic frequent patterns. Using such redundant periodic frequent patterns in decision making would often be detrimental, if not trivial. This paper addresses the challenge of eliminating redundant periodic frequent patterns by employing the concept of deduction rules in mining and reporting only the set of non-redundant periodic frequent patterns. It subsequently proposes and develops a Non-redundant Periodic Frequent Pattern Miner (NPFPM) to achieve this purpose. Experimental analysis on benchmark datasets shows that NPFPM is efficient and can effectively prune the set of redundant periodic frequent patterns.
Hierarchical Transfer Learning for Text-to-Speech in Indonesian, Javanese, and Sundanese Languages

This research develops end-to-end deep learning-based text-to-speech (TTS) in Indonesian, Javanese, and Sundanese. While end-to-end neural TTS, such as Tacotron-2, has made remarkable progress recently, it still suffers from a data scarcity problem for low-resource languages such as Javanese and Sundanese. Our preliminary study shows that Tacotron-2-based TTS needs a large amount of training data; a minimum of 10 hours of training data is required for the model to be able to synthesize acceptable quality and intelligible speech. To solve this low-resource problem, our work proposes a hierarchical transfer learning to train TTS for Javanese and Sundanese, by taking advantage of a dissimilar high-resource language of English domain and a similar intermediate-resource language of Indonesian domain. We report that the evaluation of synthesized speech using the mean opinion score (MOS) reaches 4.27 for Indonesian, and 4.08 for Javanese, and 3.92 for Sundanese. The word accuracy (WAcc) evaluation on semantically unpredicted sentences (SUS) reaches 98.26% for Indonesian, 95.02% for Javanese, and 95.43% for Sundanese. The subjective evaluations of the synthetic speech quality demonstrate that our transfer learning scheme is successfully applied to TTS model for low-resource target domain. Using less than one hour of training data, 38 minutes for Indonesian, 16 minutes for Javanese, and 19 minutes for Sundanese, TTS models can learn fast and achieve adequate performance.
The Praxis of Literacy Movement in Indonesian Context

Indonesia faces challenges in improving its literacy rank. In 2011, the literacy level of Indonesian fourth graders were in the 45th rank (with score 428 under 500) from 48 countries in PIRLS. In 2015 PISA’s report the fifteen-year-old Indonesian Junior High School students, occupied the 64th rank of 72 countries with score 396 under 496. To overcome this problem, the Indonesian Ministry of Education and Culture issued its Ministerial Regulation no. 23/2015 pertaining the students’ character building. This constitutes the basis of school-based literacy movement (Gerakan Literasi Sekolah). This study explores the praxis of literacy movement at one school (has implemented GLS since 2016) located in the condensed slum area of North Jakarta. Stratified purposeful sampling was deployed to select the research participants, i.e. 290 students and 20 teachers. The data were obtained from questionnaires and interviews. The result showed that not all teachers read the literacy guidelines determined by the government and their reading habit had not met the ideal number. Both teachers and students have different perception regarding to the time and frequency of schoolbased literacy activity. The fifteen minutes reading activity and reading strategies taught by the teachers were not varied. The teachers merely supervised the students while conducting the literacy activity. The students read the books they brought from home. The school-based literacy program gained the students’ literacy competence in understanding level (understand the content of the book they read) and built students’ character such as tolerance, teamwork skills, perseverance, responsibility, confidence, independence, politeness, and composure.&#x0D; Keywords: literacy movement, reading literacy, literacy competence, 15-minutereading activity
Irony Detection in Bengali Tweets: A New Dataset, Experimentation and Results

Irony detection is a difficult task because the intended meaning of a sentence differs from the literal meaning or sentiment of that sentence. Most existing work on this subject has focused on irony detection in the English language. Since no public dataset is available for this task in the Bengali domain, we have created a Bengali irony detection dataset that contains a total of 1500 labeled Bengali tweets. This paper presents the description of the Bengali irony detection dataset developed by us and reports some results obtained on our Bengali irony dataset using several widely used machine learning algorithms such as Naïve Bayes, Support Vector Machine, K-Nearest Neighbor and Random Forest.