Reviewerly/exHarmony huggingface dataset:
From Logic to Functional Logic Programs

Abstract Logic programming is a flexible programming paradigm due to the use of predicates without a fixed data flow. To extend logic languages with the compact notation of functional programming, there are various proposals to map evaluable functions into predicates in order to stay in the logic programming framework. Since amalgamated functional logic languages offer flexible as well as efficient evaluation strategies, we propose an opposite approach in this paper. By mapping logic programs into functional logic programs with a transformation based on inferring functional dependencies, we develop a fully automatic transformation which keeps the flexibility of logic programming but can improve computations by reducing infinite search spaces to finite ones.
BLOCKCHAIN TECHNOLOGY, AN ALTERNATIVE TO THE TRADITIONAL BANKING SYSTEM

With the advent of new technologies and the rising trend of digitalization, the financial sector is being reshaped, evolving, and adapting to improve its efficiency and keep pace with people’s needs. One of the most current technologies that has the potential to develop the financial sector is Blockchain technology. The main idea of this technology is that it is based on a decentralized public registry, which allows transactions to be performed in a secure, efficient way and has several advantages that are not found in the traditional banking system we know. Blockchain and cryptocurrencies could become one of the most important innovations in the financial sector, capable of creating a digital economy, based on decentralization, since they have features that optimize and simplify transactions, without the need for an intermediary, compared to the traditional banking system
Resolving the ambiguity in recognizing case-sensitive characters gesticulated in mid-air through post-decision support modules

Unlike real-world objects which remains the same irrespective of the changes in size on a fixed/varying scale, few English alphabets become identical to each other because of case ambiguity. Recognizing alphabets becomes further complex when different characters are gesticulated with the same pattern or become similar due to the gesticulation style. The generalization ability of deep convolutional neural networks (DCNN) results in misclassifying these characters. To overcome this, we propose a two-stage recognition model that comprises of DCNN and advisor unit (AU) followed by a post-decision support module (P-DSM). It differentiates these similar characters based on actual gesticulated size and extracts features from the 1D, 2D perspective and captures the demographics in the gesticulation. This model is able to discriminate these similar characters with an accuracy of ~92% for the NITS hand gesture database. Experimenting with this on popular handwritten EMNIST database suggests that pre-processing steps followed in it make the characters lose their size information.
The Impact of Combining Arabic Sarcasm Detection Datasets On The Performance Of BERT-based Model

Sarcasm Detection (SD) is the task of predicting sarcasm in text. It is crucial to the success of sentiment analysis since recognizing both the literal and figurative meaning of the text is essential to understanding users’ opinions on various topics on social media. However, to date, the limited availability of annotated data for Arabic Sarcasm Detection obstructs building robust models. One way to overcome this issue is by collecting more labeled data. However, data annotation is time and effort-consuming. One possibility is to explore combining several available training sets to obtain one more extensive training set without additional effort. This paper investigates the impact of combining the training data from different datasets on the performance of Bert-based SD models. We have chosen ArSarcasm and IDAT datasets as they are relatively recent and vary in size, class distribution, and the annotation approaches employed during data collection. ArSarcasm is a comparatively large corpus labeled manually and has a skewed class distribution, while IDAT is a smaller dataset annotated by distance supervision with a nearly even class distribution. We define different merging scenarios to examine the impact of combining variant training data subsets of ArSarcasm and IDAT and to measure the effect of merging them on the Bert-based SD model evaluated on the testing set of each of ArSarcasm and IDAT individually. The results show that merging datasets could enhance the sarcasm detection model’s performance if the merging process improves the class balance and reduces the discrepancy between the domains resulting from the divergent vocabulary and the inconsistent labeling.
Construction of Teaching Mode of Film and TV Art in Institution of Higher Learning Using Virtual Simulation Technology

The modernization of education necessitates the enhancement of instructional quality. This paper begins with a basic overview of the current state of film and television art education and then goes on to examine the current state of film and television art education and develop a virtual simulation-based teaching mode for film and television art. This paper develops an instructional quality evaluation model to assess the feasibility of this teaching mode. In this model, GA is used to optimize BPNN's initial weights and thresholds, reducing the time it takes BPNN to find the weights and thresholds that satisfy the training termination conditions. Improve BPNN's prediction accuracy and convergence speed in relation to instructional quality evaluation results. The evaluation accuracy of this algorithm can reach 94.31 percent, which is 6.35 percent higher than other methods. Furthermore, the use of virtual simulation technology in the teaching of film and television art in higher education institutions can have a significant impact and have far-reaching research implications. The findings of this study can be used to improve the teaching of film and television art in institutions of higher learning.
Piano Intelligent Teaching Evaluation with IoT and Multimedia Technology

With deepening of the economic development process, the overall spiritual and cultural needs of the people have increased, and quality education has received more and more attention. Piano education is an important part of quality education. Nowadays, piano education has also been able to move forward continuously. As the king of musical instruments, the piano is widely used in music learning. Learning the piano can exercise willpower, develop intelligence, and cultivate sentiment. With development of computer software as well as hardware, Internet of Things technology and multimedia technology provide the material basis and preconditions for intelligent piano teaching. Therefore, how to evaluate the impact of the IoT and multimedia technology on intelligent teaching of piano has become a very important topic. Based on this, this work combines BP network with artificial fish swarm algorithm (AFS) and proposes network model (IAFS-BP) for evaluating the quality of piano intelligent teaching in the context of Internet of Things and multimedia. The specific contents of this work are as follows: (1) Aiming at the shortcomings of AFS, an IAFS with mutation operator, dynamic vision, and step size is proposed. (2) In view of shortcomings for BP network, IAFS is used in BP network. By using the IAFS algorithm in the training of the weights as well as thresholds, the training speed and training accuracy of the network are improved. (3) This work has carried out a comprehensive and systematic experiment to verify effectiveness and feasibility of IAFS-BP applied to quality assessment of piano intelligent teaching in the context of the IoT and multimedia.
Recommendation Ranking Method Combining Graph Convolutional Network and Factorization Machine

With the rapid development of graph neural network technology and the wide application of personalized recommender systems in the industry, how to better apply graph representation learning to recommender systems to continuously improve the recommendation effect and improve the user experience has become a hot field of the industry research. Based on hot issues such as sparse feature data, cold start, and multi-feature combination in massive data, this paper proposes a personalized recommendation ranking method that combines graph convolutional network and factorization machine. The method represents the network relationship between users and items based on the graph structure and generates the graph embeddings on the hyperparameter space through graph representation learning, uses the factorization machine to combine the learning of four categories of features of user attributes, item attributes, interaction, and context, and generates the vector representation separately, and finally predicts personalized recommendation score based on the ranking model. The comparison experiments of multiple groups of ranking methods show that the ranking learning method proposed in this paper is better than the baseline in six indicators of AUC, Logloss, UV_CTR, CVR, ATV and the exposure ratio of new items on the online e-commerce data set, which proves the effectiveness of the proposed method.
RLBWT Tricks

Until recently, most experts would probably have said we cannot backwards-step in constant time with a run-length compressed Burrows-Wheeler Transform (RLBWT), since doing so relies on rank queries on sparse bitvectors and those inherit lower bounds from predecessor queries. At ICALP&#x0027;21, however, Nishimoto and Tabei [1] described a new, simple and constant-time implementation.
A Methodology to Develop Extended Reality Applications for Exhibition Spaces in Museums

Web 2.0 brought the development of social networks and new elements of interaction that were incorporated into Web sites and devices that supported them. In the field of museums, the emergence of Web 2.0 and Museum 2.0 has made it possible to improve access to information regarding art collections. Due to the COVID-19 pandemic, museums have opted for the introduction of technology permitting virtual access to their collections. However, many applications were developed using traditional development process without considering a methodology dedicated to extended reality applications. This paper describes a methodology for the implementation of extended reality applications to exhibition spaces in museums. Using a quasi-experiment, we evaluate the data collection stage of the proposed methodology to develop an extended reality application to visit exhibition spaces in a museum. Results show that the proposed methodology helps to the software engineers/designers in the development process of extended reality applications.
An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars

With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not only the type or individual of the emitter but also its current state. Existing methods identify MFR states through hierarchical modeling, but most of them rely heavily on prior information. In the paper, we focus on the MFR state recognition with actual intercepted MFR signals and develop it by introducing recurrent neural networks (RNNs) of deep learning into the modeling of MFR signals. According to the layered MFR signal architecture, we propose a novel end-to-end state recognition approach with two RNNs' connections. This approach makes full use of RNNs' ability to directly tackle corrupted data and automatically learn the features from input data. So, it is practical and less dependent on prior information. In addition, the hierarchical modeling method applied to the end-to-end network effectively restricts the scale of the end-to-end model so that the model can be trained with a small amount of data. Simulation results on a real MFR show the excellent recognition performance of our end-to-end approach with little prior information.
A Novel Approach to Privacy Preservation on E-Healthcare Data in a Cloud Environment

Cloud environment enables healthcare professionals to work together. Despite its many benefits, it faces several difficulties: technical, legal, and managerial. Cloud computing can enhance healthcare facilities by utilizing its skills to assist information transfer within the health monitoring system, and without any geographical limitations, users such as patients, physicians, pharmacists, and health insurance agents can obtain health-associated information at any moment. With the widespread use of healthcare information and communication technology (ICT), creating a stable and sustainable data sharing scenario has attracted increasing interest in both academic research and the healthcare sector. This paper evaluates and compare the present situation with the security criteria for cloud-based medical e-health record. In addition, this paper outlines proposed cloud model and comparison of our proposed cloud model with different frameworks listed in different research papers are discussed through which how we can safeguard patient e-healthcare record over cloud environment.
EFL STUDENTS’ PERCEPTIONS TOWARD QUIZIZZ AS AN ASSESSMENT TOOL DURING ONLINE LEARNING

Assessment is an ongoing process on students’ learning achievement. It is not about giving students the value of numbers or letters; an assessment tool is used to measure and monitor students’ understanding of the material taught. Quizizz is a beneficial assessment tool with interactive features that can help students and teachers do the assessment during online learning. However, some students find difficulties in accessing Quizizz. Therefore, this research aims to investigate EFL students’ perceptions toward Quizizz as an assessment tool during online learning. This study involved 100 twelfth graders of one junior high school students in Malang, Indonesia. These students have experienced using Quizizz to do the assessment for four weeks. In this research, quantitative method with a survey research design was chosen. A close-ended questionnaire was used to collect the data on students’ perceptions about the efficacy, the benefit, and the challenges of using Quizizz, and the data were analyzed through descriptive statistics. The result of the study revealed that although there was an obstacle to fully implementing Quizizz, most of the students gave a positive attitude to the use of Quizizz as an assessment tool.
Mind your words: Affective experience during reading mediates the effect of textual valence on comprehension

Abstract In today's society, we are constantly absorbing information via text (e.g., news, social media), much of which may be affectively charged. However, to date, little is known about how the affective framing of the text itself may give rise to various affective experiences during reading. We examined how subtle changes to wording (negatively or positively valenced framing of the same content) influenced affective experiences during reading and subsequent comprehension. Results show that (a) affective framing in text elicited congruent subjective affective states throughout reading, (b) subjective affective valence throughout reading impacted comprehension at various levels, and (c) subjective affective valence mediated the relationship between affective framing and how participants integrated information from the text in their summaries, as well as if they included affect in their summaries. This indicates that even subtle affectively valenced content in text can indirectly influence some aspects of comprehension through eliciting subjective affective experiences in the reader.
Research on Modulation Mode Identification of PSK Signal Based on M-th Power Spectrum Features

With the variety and complexity of communication signal modulation, it is more important and urgent to solve the problem of modulation identification. The modulation identification of PSK signal is studied based on the features of M-th power spectrum. The M-th power spectrum of BPSK signal, QPSK signal, OQPSK signal, DQPSK signal and π/4-DQPSK signal are simulated by Matlab. The features of M -th power spectrum of these five signals are summarized through the simulation results. Based on the results, a set of identification rules and identification process are designed, which can be used for intra-class identification of PSK signals.
Simple C2-finite Sequences

A sequence is called C-finite, if it satisfies a linear recurrence with constant coefficients and holonomic, if it satisfies a linear recurrence with polynomial coefficients. The class of C2-finite sequences is a natural generalization of holonomic sequences and consists of sequences satisfying a linear recurrence with C-finite coefficients whose leading coefficient has no zero terms. Recently, we investigated computational properties of $C^2$-finite sequences: we showed that these sequences form a difference ring and provided methods to compute in this ring.
Deep-learning-based short-term photovoltaic power generation forecasting using improved self-organization map neural network

As a vital function of an energy management system for distributed energy resources, optimal operation in distribution systems, and mitigating potentially adverse effects of photovoltaic (PV) systems, accurate forecasting of PV power generation is required. This article presents an alternative technique to improve the accuracy of deep-learning-based short-term PV power-generation forecasting models by clustering the input data using a self-organization map (SOM). To validate the proposed model, long short-term memory (LSTM), feedforward neural network (FNN), FNN with the proposed SOM clustering method (FNN-SOM), and LSTM with the proposed SOM clustering method (LSTM-SOM) were tested and compared with one-year hourly datasets (8760 samples). Root mean square error, mean absolute error, and mean absolute percentage error were used as validation factors in this work. The results show that the proposed method provides a more accurate solar power generation forecast than other methods. Moreover, the proposed method can work effectively even with a few inputs system.
Speeding up compact planar graphs by using shallower trees

A common technique to design compact representations for planar graphs is to decompose the graph into spanning trees, which are later represented compactly. In some representations of planar graphs, such as Turan&#x0027;s representation, the topology of such spanning trees is not fixed. In this work, we show that the topology of the spanning trees used in the representation impacts the performance of typical operations of compact planar graphs. Hence, by computing suitable spanning trees and improving their compact representation, we provide compact representations of planar graphs that are both smaller and faster than the state of the art.
Neural Networks Classify through the Class-Wise Means of Their Representations

In this paper, based on an asymptotic analysis of the Softmax layer, we show that when training neural networks for classification tasks, the weight vectors corre sponding to each class of the Softmax layer tend to converge to the class-wise means computed at the representation layer (for specific choices of the representation activation). We further show some consequences of our findings to the context of transfer learning, essentially by proposing a simple yet effective initialization procedure that significantly accelerates the learning of the Softmax layer weights as the target domain gets closer to the source one. Experiments are notably performed on the datasets: MNIST, Fashion MNIST, Cifar10, and Cifar100 and using a standard CNN architecture.
Lossy Compression of Gaussian Source Using Low Density Generator Matrix Codes

We present a tandem scheme for Gaussian source compression, where a dead-zone quantizer is concatenated with a ternary low density generator matrix (LDGM) code. Both theoretical analysis and simulation results show that the LDGM codes can be universally optimal for near-lossless compression of ternary sources. Consequently, the distortion with the tandem scheme is mainly caused by the quantization, which can be negligible for high-rate quantizer. The most distinguished feature of the proposed scheme is its flexibility. The dead-zone quantizer can choose a suitable quantization level according to the distortion allowed, while the LDGM codes can adapt the code rate to approach the entropy of the quantized sequence. This is helpful to trade off between bandwith and distortion. In the meanwhile, the proposed scheme is robust when combined with the channel codes for transmission over noisy channels because of the fixed-length feature.
Analyzing RMFCC Feature for Dialect Identification in Ao, an Under-Resourced Language

Ao is a language spoken in Nagaland in the North-East of India. It is a low-resource tone language under the Tibeto-Burman language family. It consists of three tones, namely, high, mid and low. It has three distinct dialects of the language viz. Chungli, Mongsen and Changki. This paper presents an automatic dialect identification in Ao using the excitation source feature. The objective of a dialect identification system is to identify a speech variety within a language. The goal of this study is to determine if the excitation source features such as Residual Mel Frequency Cepstral Coefficient (RMFCC) can be exploited to discriminate the three dialects in Ao automatically. In addition, vocal tract system features, namely Mel Frequency Cepstral Coefficients (MFCC) and Shifted Delta Cepstral (SDC) coefficients, are used as the baseline methods. The RMFCC features are obtained from the Linear Prediction (LP) residual signal, while MFCC features are derived from the smooth spectrum of the speech signal. SDC coefficients are explored to provide additional temporal information. This work is evaluated on trisyllabic words uttered by 36 speakers for the three dialects of Ao. A Gaussian Mixture Model (GMM) based classifier is used for classification. The performance of the system yields a better dialect identification accuracy rate when all three features are combined.
Fast Coding of Haar Wavelet Trees

Compression methods that focus on speed and computational efficiency are of particular interest when acquiring real time data. The Backwards Coding of Wavelet Trees (BCWT) algorithm by Guo et al. offers high computational efficiency and high peak signal-to-noise (PSNR) ratios at a high speed. In this paper, the BCWT algorithm is reworked using a Haar Wavelet Transform, rather than Debauchee 9&#x2013;7, to provide even faster and less complex code at the cost of lower PSNR. While in typical image compression applications this compromise is unfavorable, some real-time data acquisition systems require such improvements and are not significantly impacted by the reduced clarity.
CSTs for Terabyte-Sized Data

Generating pangenomic datasets is becoming increasingly common but there are still few tools able to handle them and even fewer accessible to non-specialists. Building compressed suffix trees (CSTs) for pangenomic datasets is still a major challenge but could be enor-mously beneficial to the community. In this paper, we present a method, which we refer to as Repfp-cst, for building CSTs in a manner that is scalable. To accomplish this, we show how to build a CST directly from VCF files without decompressing them, and to prune from the prefix-free parse (PFP) phrase boundaries whose removal reduces the total size of the dictionary and the parse. We show that these improvements reduce the time and space required for the construction of the CST, and the memory footprint of the finished CST, enabling us to build a CST for a terabyte of DNA for the first time in the literature.