Reviewerly/exHarmony huggingface dataset:
The Spatiotemporal Pattern and Driving Factors of Cyber Fraud Crime in China

As a typical cybercrime, cyber fraud poses severe threats to civilians’ property safety and social stability. Traditional criminological theories such as routine activity theory focus mainly on the effects of individual characteristics on cybercrime victimization and ignore the impacts of macro-level environmental factors. This study aims at exploring the spatiotemporal pattern of cyber fraud crime in China and investigating the relationships between cyber fraud and environmental factors. The results showed that cyber fraud crimes were initially distributed in southeastern China and gradually spread towards the middle and northern regions; spatial autocorrelation analysis revealed that the spatial concentration trend of cyber fraud became more and more strong, and a strong distinction in cyber fraud clustering between the north and the south was identified. To further explain the formative causes of these spatial patterns, a generalized additive model (GAM) was constructed by incorporating natural and social environmental factors. The results suggested that the distribution of cyber fraud was notably affected by the regional economy and population structure. Also, the high incidence of cyber fraud crime was closely associated with a large nonagricultural population, a high proportion of tertiary industry in GDP, a large number of general college students, a longer cable length, and a large numbers of internet users.
Influence of TTS Systems Performance on Reaction Times in People with Aphasia

Text-to-speech (TTS) systems provide fundamental reading support for people with aphasia and reading difficulties. However, artificial voices are more difficult to process than natural voices. The current study is an extended analysis of the results of a clinical experiment investigating which, among three artificial voices and a digitised human voice, is more suitable for people with aphasia and reading impairments. Such results show that the voice synthesised with Ogmios TTS, a concatenative speech synthesis system, caused significantly slower reaction times than the other three voices used in the experiment. The present study explores whether and what voice quality metrics are linked to delayed reaction times. For this purpose, the voices were analysed using an automatic assessment of intelligibility, naturalness, and jitter and shimmer voice quality parameters. This analysis revealed that Ogmios TTS, in general, performed worse than the other voices in all parameters. These observations could explain the significantly delayed reaction times in people with aphasia and reading impairments when listening to Ogmios TTS and could open up consideration about which TTS to choose for compensative devices for these patients based on the voice analysis of these parameters.
PERAMALAN JUMLAH TITIK PANAS PROVINSI KALIMANTAN TIMUR MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK

Radial Basis Function Neural Network (RBFNN) is a neural that uses a radial base function in hidden layers for classification and forecasting purposes. Neural Network is developed into a radial function base with an information processing system that has characteristics similar to biological neural networks, consisting of input layers, hidden layers, and output layers. The data used in this study is data on the number of hotspots in East Kalimantan Province obtained from the official website of the National Aeronautics and Space Administration (NASA). The purpose of this research is to obtain the RBFNN model and the results of forecasting the number of hotspots for the period January 2020 to March 2020. The radial basis function used is the local Gaussian function and the linear activation function. In this study using the proportion of training data and testing data 70: 30; 80:20; and 90:10. The results showed that the input network using significant Partial Autocorrelation Function (PACF) at lag 1 and lag 2, so that the RBFNN model that was formed involved Xt-1 and Xt-2. The best Mean Absolute Percentage Error (MAPE) minimum obtained the 80:20 data proportion with 2 hidden networks. The RBFNN architecture that is formed is 2 input layers, 2 hidden layers and 1 output layer. Data from forecasting the number of hotspots in East Kalimantan Province shows that from January 2020 to February 2020 there was a decline and March 2020 an increase.
Web-sentiment Analysis Of Public Comments (Public Reviews) For Languages With Limited Resources Such As The Kazakh Language

In the pandemic period, the stay-at-home trend forced businesses to switch their activities to digital mode, for example, app-based payment methods, social distancing via social media platforms, and other digital means have become an integral part of our lives.Sentiment analysis of textual information in user comments is a topical task in emotion AI because user comments or reviews are not homogeneous, they contain sparse context behind, and are misleading both for human and computer.Barriers arise from the emotional language enriched with slang, peculiar spelling, transliteration, use of emoji and their symbolic counterparts, and codeswitching.
Content-Aware Optimization of Tiled 360° Video Streaming Over Cellular Network

The proliferation of 360° video content and easy availability of head-mounted devices propelled the popularity of 360° video streaming on various platforms, including smart-phones. However, the high bandwidth requirement of 360° video streaming impedes its further growth, specifically in bandwidth constraint cellular networks. Some viewport adaptive 360° video streaming methods have been proposed recently to reduce the bandwidth requirement by spatially reducing the transmitted data. However, these methods require accurate prediction of the future viewport, and a prediction error can significantly impair the user’s Quality of Experience (QoE). In this work, we took a different approach and propose a content-aware solution for tiled 360° video streaming that does not require viewport predictions. In this work, we exploit the fact that the background scene does not contain prominent details and can be transmitted at a lower bit rate without affecting the user’s QoE. The proposed approach uses the saliency map to identify prevalent tiles of the 360° video through machine learning-based classification using the Kernel Density Estimation (KDE). First, the tiles are classified based on their saliency information, and then bitrates are assigned to the tile clusters. The results show that the proposed approach reduces the bandwidth requirement by 50% while delivering the equivalent user’s QoE.
Quantum and Classical Registers.

We present a generic theory of in imperative programs and instantiate it in the classical and quantum setting. Roughly speaking, a register is some mutable part of the program state, e.g., mutable classical variables and quantum registers and wires in quantum circuits are examples of this. However, registers in our setting can also refer to subparts of other registers, or combinations of parts from different registers, or quantum registers seen in a different basis, etc. Our formalization is intended to be well suited for formalization in theorem provers and as a foundation for modeling quantum/classical variables in imperative programs. We implemented most results (including a minimal quantum Hoare logic and an analysis of quantum teleportation) in the Isabelle/HOL theorem prover.
Blockchain-based intelligent medical IoT healthcare system

It has seen an unavoidable interest in medical services difficulties, as well as faster and more secure patient help. Using new pattern improvements in the healthcare field could provide additional options for dealing with patients' health records while also improving health quality. Researchers are seeking for permanent and simple solutions to remotely monitor patients' records using a patient monitor system. One of these ways is the use of the Internet of Things (IoT), which allows for remote patient monitoring by healthcare providers. However, as the number of IoT devices grows, privacy and security concerns have developed. Another privacy concern is the disclosure of patient information. According to several research, blockchain technology is a trustworthy network that ensures the privacy and security of patient data sent through IoT devices. Following that, this section examines IoT advancement in the healthcare sector as a current exploration and advantageous trend. This research aims to present a new framework that works with the restoration and transfer of medical information utilising blockchain via Django by combining health records with patient monitoring systems that communicate data with various peers via smart contracts.
UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery

As unmanned aerial vehicles (UAVs) become more accessible with a growing range of applications, the potential risk of UAV disruption increases. Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single camera. However, the coverage of a single camera is limited, necessitating the need for multicamera configurations to match UAVs across cameras - a problem known as re-identification (reID). While there has been extensive research on person and vehicle reID to match objects across time and viewpoints, to the best of our knowledge, there has been no research in UAV reID. UAVs are challenging to re-identify: they are much smaller than pedestrians and vehicles and they are often detected in the air so appear at a greater range of angles. Because no UAV data sets currently use multiple cameras, we propose the first new UAV re-identification data set, UAV-reID, that facilitates the development of machine learning solutions in this emerging area. UAV-reID has two settings: Temporally-Near to evaluate performance across views to assist tracking frameworks, and Big-to-Small to evaluate reID performance across scale and to allow early reID when UAVs are detected from a long distance. We conduct a benchmark study by extensively evaluating different reID backbones and loss functions. We demonstrate that with the right setup, deep networks are powerful enough to learn good representations for UAVs, achieving 81.9% mAP on the Temporally-Near setting and 46.5% on the challenging Big-to-Small setting. Furthermore, we find that vision transformers are the most robust to extreme variance of scale.
Proportional inverse Gaussian distribution: A new tool for analysing continuous proportional data

Summary Outcomes in the form of rates, fractions, proportions and percentages often appear in various fields. Existing beta and simplex distributions are frequently unable to exhibit satisfactory performances in fitting such continuous data. This paper aims to develop the normalised inverse Gaussian (N‐IG) distribution proposed by Lijoi, Mena &amp; Prünster (2005, Journal of the American Statistical Association, 100 , 1278–1291) as a new tool for analysing continuous proportional data in (0,1) and renames the N‐IG as proportional inverse Gaussian (PIG) distribution. Our main contributions include: (i) To overcome the difficulty of an integral in the PIG density function, we propose a novel minorisation–maximisation (MM) algorithm via the continuous version of Jensen's inequality to calculate the maximum likelihood estimates of the parameters in the PIG distribution; (ii) We also develop an MM algorithm aided by the gradient descent algorithm for the PIG regression model, which allows us to explore the relationship between a set of covariates with the mean parameter; (iii) Both the comparative studies and the real data analyses show that the PIG distribution is better when comparing with the beta and simplex distributions in terms of the AIC, the Cramér–von Mises and the Kolmogorov–Smirnov tests. In addition, bootstrap confidence intervals and testing hypothesis on the symmetry of the PIG density are also presented. Simulation studies are conducted and the hospital stay data of Barcelona in 1988 and 1990 are analysed to illustrate the proposed methods.
Information Density and the Extraposition of German Relative Clauses

This paper aims to find a correlation between Information Density (ID) and extraposition of Relative Clauses (RC) in Early New High German. Since surprisal is connected to perceiving difficulties, the impact on the working memory is lower for frequent combinations with low surprisal-values than it is for rare combinations with higher surprisal-values. To improve text comprehension, producers therefore distribute information as evenly as possible across a discourse. Extraposed RC are expected to have a higher surprisal-value than embedded RC. We intend to find evidence for this idea in RC taken from scientific texts from the 17th to 19th century. We built a corpus of tokenized, lemmatized and normalized papers about medicine from the 17th and 19th century, manually determined the RC-variants and calculated a skipgram-Language Model to compute the 2-Skip-bigram surprisal of every word of the relevant sentences. A logistic regression over the summed up surprisal values shows a significant result, which indicates a correlation between surprisal values and extraposition. So, for these periods it can be said that RC are more likely to be extraposed when they have a high total surprisal value. The influence of surprisal values also seems to be stable across time. The comparison of the analyzed language periods shows no significant change.
A PWM Nie-Tan Type-Reducer Circuit for a Low-Power Interval Type-2 Fuzzy Controller

A novel Type-Reduction/Defuzzification circuit architecture for an analog interval type-2 fuzzy inference system is proposed. Based on the Nie-Tan type-reduction method, the circuit operates with current-mode inputs, representing the firing intervals of the rules created by the inference engine, and generating a PWM output. It is demonstrated that by selecting an appropriate number of consequents it is possible to create the PWM output directly, without the need for analog multiplier/divider circuits. This feature makes the circuit very simple, aiding in the design process, while the PWM output makes it suitable for controlling DC-DC converters, maximum power point trackers (MPPT) for energy generators, or other switching applications. It is designed to achieve very low power consumption, allowing its use in power restrained environments, such as energy harvesting systems. The circuit was designed using TSMC 0.18μm technology, in CADENCE Virtuoso software, and simulated for different combinations of input values, demonstrating its capabilities. It was also simulated as part of a type-2 fuzzy inference system with two inputs, nine rules, and firing intervals represented by currents within 0 and 10μA. The circuit was prototyped, and the experimental average power consumption was only 53.8μW, validating its low power consumption characteristic.
Retraction Note: Discussion on ecological design and construction of river landscape based on fine-grained image classification

The fine-grained image classification method does not have a very good training effect when performing random weight training for neural networks. For the fine-grained classification under the condition of convolutional neural network, the main reason is that its upgrading method can better improve the stability of convolutional neural network. Fine-grained classification through bilinear convolutional neural networks is to effectively merge several convolutional neural networks, so as to obtain a high-resolution advantage to improve the method of fine-grained classification. With the development of society and the improvement of urban construction, the artificial reconstruction of rivers in the city will strengthen the connectivity and smoothness of the reconstructed river to a certain extent, but it will also affect the self-healing of the river. At present, the treatment of river pollution is a major aspect of the country’s treatment of environmental pollution. Obtain the transformation and restoration of the ecological environment around the river in the city through data exchange and other methods. Establishing a scientific and complete plant community with a naturalized revetment system can reduce the impact of river water on the riverbank. At the same time, these naturalized revetment plants can also play a role in flood prevention and discharge, and to a certain extent, it can affect the polluted water quality and give purification. While carrying out the ecological environment protection of the river, it also pays attention to the design and planning of the river landscape, so that the river can withstand the impact of flowing water while having a beautiful landscape design, so as to realize the ecological civilization construction of the river landscape.
USE OF ENHANCED FEEDBACK IN AN ELECTRONIC LEARNING COURSE “FUNDAMENTALS OF MOLECULAR PHYSICS AND THERMODYNAMICS”

Background: Being an essential part of the educational process, blended learning still faces some problems concerning the interaction between teachers and students. They include a decrease in the level of knowledge and in the number of graduates as students experience a lack of live communication with the teacher, lack of sufficient experience of independent work, lack of interactive assessment. Aim: This study aimed to elaborate the model of enhanced feedback in an e-learning course, “Fundamentals of Molecular Physics and Thermodynamics,” to increase student educational achievements. Methods: The effectiveness of the model of enhanced feedback in the learning process was measured with the methodology for calculating statistical indicators of the quality of education: knowledge quality, level of student proficiency, progress, and average grade. To measure the emotional and evaluative attitude of students to educational activities and interaction with the teacher in an electronic course, a test-questionnaire satisfaction with learning activity was employed. Results and Discussion: the results of the initial and final control of the statistical indicators of the quality of education including knowledge quality, level of student proficiency, progress, and average grade in students showed a significant difference between the control and the experimental groups. At the end of the semester, the difference in knowledge quality was 23%, student proficiency level – 13%, progress – 3.5%, average grade – 0.6 scores. The analysis of student satisfaction with the learning process also confirms an increase in satisfaction with the learning process and with the interaction with the lecturer. Thus, the experimental methodology contributes to a significant improvement in the learning process results. Conclusions: The experiment demonstrated that replacing formative assessment with the model of enhanced feedback raises student educational achievements and compensates for lack of live communication with the teacher.
Neural Polysynthetic Language Modelling.

Many techniques in modern computational linguistics and natural language processing (NLP) make the assumption that approaches that work well on English and other widely used European (and sometimes Asian) languages are “language agnostic” – that is that they will also work across the typologically diverse languages of the world. In high-resource languages, especially those that are analytic rather than synthetic, a common approach is to treat morphologically-distinct variants of a common root (such as dog and dogs) as completely independent word types. Doing so relies on two main assumptions: that there exist a limited number of morphological inflections for any given root, and that most or all of those variants will appear in a large enough corpus (conditioned on assumptions about domain, etc.) so that the model can adequately learn statistics about each variant. Approaches like stemming, lemmatization, morphological analysis, subword segmentation, or other normalization techniques are frequently used when either of those assumptions are likely to be violated, particularly in the case of synthetic languages like Czech and Russian that have more inflectional morphology than English. Within the NLP literature, agglutinative languages like Finnish and Turkish are commonly held up as extreme examples of morphological complexity that challenge common modelling assumptions. Yet, when considering all of the world’s languages, Finnish and Turkish are closer to the average case in terms of synthesis. When we consider polysynthetic languages (those at the extreme of morphological complexity), even approaches like stemming, lemmatization, or subword modelling may not suffice. These languages have very high numbers of hapax legomena (words appearing only once in a corpus), underscoring the need for appropriate morphological handling of words, without which there is no hope for a model to capture enough statistical information about those words. Moreover, many of these languages have only very small text corpora, substantially magnifying these challenges. To this end, we examine the current state-of-the-art in language modelling, machine translation, and predictive text completion in the context of four polysynthetic languages: Guarani, St. Lawrence Island Yupik, Central Alaskan Yup’ik, and Inuktitut. We have a particular focus on Inuit-Yupik, a highly challenging family of endangered polysynthetic languages that ranges geographically from Greenland through northern Canada and Alaska to far eastern Russia. The languages in this family are extraordinarily challenging from a computational perspective, with pervasive use of derivational morphemes in addition to rich sets of inflectional suffixes and phonological challenges at morpheme boundaries. Finally, we propose a novel framework for language modelling that combines knowledge representations from finite-state morphological analyzers with Tensor Product Representations (Smolensky, 1990) in order to enable successful neural language models capable of handling the full linguistic variety of typologically variant languages.
Improving Students’ Speaking Skill through Students Vlog Project as PBL Output on Online Speaking Class

The aim of this research is to investigate the implementation and the result of project-based learning by using vlog as the output of learning process to increase students’ speaking skill. This research used mixed methods design (qualitative and quantitative research). The result of this research was the implementation of PBL successfully improved students’ speaking skill in five crucial aspects of speaking, pronunciation, grammar, fluency, vocabularies and comprehension in average of 27% based on the post-test given to them. Then, there were 89% of the students from observation and questionnaire results agreed that this PBL implementation was helping them in the speaking online learning during Covid-19 pandemic.