Reviewerly/exHarmony huggingface dataset:
Penerapan Metode Exponential Smoothing Winters Pada Prediksi Harga Beras

Forecasting is an attempt to predict future conditions through testing conditions in the past using previous data. According to the data obtained from Dinas Perdagangan, Koperasi, dan UMKM Sukoharjo in January 2016 - August 2019, the prices of rice in Sukoharjo Regency experienced instability. Based on this problem, a method is needed to predict the price of rice in Sukoharjo Regency to prevent price volatility and to provide an overview of the price of rice in Sukoharjo Regency in the coming period. The method used in this research is the Exponential Smoothing Winters method. The rice price prediction application is made using the vb.net programming language and the sql server database. in this application produces predictive calculations for rice and in it there are graphs and reports on the results of prediction calculations. This study resulted in an overall MAPE of 3.91% for the price of premium IR 64 rice, and 4.24% for the price of medium IR 64 rice.
Characterization and Properties of Bent‐Negabent Functions

A further characterization of the bent-negabent functions is presented. Based on the concept of complete mapping polynomial, we provide a necessary and sufficient condition for a class of quadratic Boolean functions to be bent-negabent. A new characterization of negabent functions can be described by using the parity of Hamming weight. We further generalize the classical convolution theorem and give the nega-Hadamard transform of the composition of a Boolean function and a vectorial Boolean function. The nega-Hadamard transform of a generalized indirect sum is calculated by this composition method.
Gratifications behind Sharing of Fake News on Social Media Regarding Covid-19

Due to their potential for causing serious damage, disinformation and fake news have greatly concerned the researchers of mass media globally during the vulnerable times of the COVID-19 pandemic.The study objectives are to identify the motivations behind sharing of fake news regarding COVID-19 on social media and to examine if the results of the current study are consistent with the previous study findings of Apuke & Omar (2021) relating to Nigeria.We have conducted a survey (N=300) by testing six hypothesis using the theoretical framework of uses and gratifications.The study results differ from that of the previous study and have indicated that entertainment and pass time are the major gratifications behind sharing news regarding COVID-19 on social media in Pakistan.The research recommends the government and policymakers in designing a strategy for controlling the spread of fake news related to health sector on social media in Pakistan.
Selective Medical Image Encryption Using Polynomial-Based Secret Image Sharing and Chaotic Map

The progressive development in telecommunication and networking technologies has led to the increased popularity of telemedicine usage, which involves the storage and transfer of medical images and related information. Recently, trust and privacy in the telemedicine system have attracted many researchers to investigate these topics. In medical image applications, selective image encryption plays an important role as it reduces computational cost and time. Therefore, a safe and efficient selective image encryption algorithm is designed in this work. In particular, the predetermined region of the original image data is encrypted to reduce the encryption/decryption time and the computational complexity of processing the huge image data. The image processing techniques are used to divide the image into a region of interest (ROI) and a region of non-interest (RONI), and then the more important component of the ROI is encrypted using a polynomial-based secret image sharing (SIS) and a chaotic map system. These techniques produce a test image cipher that has good confusion and diffusion properties. The experimental result shows that the Polynomial-based SIS and the chaotic image encryption are effectively performed for diffusion and confusion, which are crucial for concealment. According to the security research findings, sensitive encryption and decryption systems are extremely reliant on any improvement in the key. The encryption solution is sufficiently broad to withstand brute force attacks. Thus, protection may become an issue during the transmission of medical images via a network.
Study on border patrol task planning of heterogeneous UAVs group based on swarm intelligence

Heterogeneous UAVs performing patrol tasks are a new type of border patrol method with high flexibility, high patrol efficiency, and low operating cost In order to improve the ability of heterogeneous UAVs to perform border patrol task, by constructing a three-dimensional complex coordinated planning model, a multi-objective fitness function with the minimum patrol energy consumption and maximum patrol coverage of UAVs in a complex mountain environment is established. Design an improved shuffled frog leaping algorithm (ISFLA) based on spiral search mechanism to solve the problem of task planning in complex mountain environment. The proposed algorithm is verified by simulation experiments. The simulation results show that the ISFLA algorithm for solving the path problem in complex three-dimensional environment has significantly improved the solving efficiency, accuracy and global convergence compared with the particle swarm optimization (PSO), differential evolution (DE) and shuffled frog leaping algorithm (SFLA). The experiments show that the proposed algorithm also has excellent solving ability in solving complex planning problems.
CoCoFuzzing: Testing Neural <u>Co</u>de Models With <u>Co</u>verage-Guided <u>Fuzzing</u>

Deep learning (DL)-based code processing models have demonstrated good performance for tasks such as method name prediction, program summarization, and comment generation. However, despite the tremendous advancements, DL models are frequently susceptible to adversarial attacks, which pose a significant threat to the robustness and generalizability of these models by causing them to misclassify unexpected inputs. To address the issue above, numerous DL testing approaches have been proposed; however, these approaches primarily target testing DL applications in the domains of image, audio, and text analysis, etc., and cannot be “directly applied” to “neural models for code” due to the unique properties of programs. In this article, we propose a coverage-based fuzzing framework, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoCoFuzzing</monospace> , for testing DL-based code processing models. In particular, we first propose 10 mutation operators to automatically generate validly and semantically preserving source code examples as tests, followed by a neuron coverage (NC)-based approach for guiding the generation of tests. The performance of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoCoFuzzing</monospace> is evaluated using three state-of-the-art neural code models, i.e., NeuralCodeSum, CODE2SEQ, and CODE2VEC. Our experiment results indicate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoCoFuzzing</monospace> can generate validly and semantically preserving source code examples for testing the robustness and generalizability of these models and enhancing NC. Furthermore, these tests can be used for adversarial retraining to improve the performance of neural code models.
Unified Parametrization of Phonetic Features and Numerical Calculation of Phonetic Distances between Speech Sounds

A metric method to numerically measure phonetic and phonemic distances or contrasts, between speech sounds, is put forward. The feature values of the compared phones taken from the standard IPA charts are treated as independent parameters that give rise to corresponding Euclidean distances. As an illustration, the general phone set is mapped to Ukrainian phonemes. The proposed model agrees well with the historical linguistic facts and experimental phonetic data. The described approach may find its due applications in various fields of linguistics and speech technologies, including historical and typological linguistics, language acquisition, phonetic studies, computational phonology, machine translation, information retrieval, and text-to-speech conversion.
Learning based versus heuristic based: A comparative analysis of visual saliency prediction in immersive virtual reality

Abstract While visual saliency has been used for various purposes in virtual reality (VR), the efforts to properly understand the saliency mechanism in VR remain insufficient. In this paper, we present an extensive comparative analysis of learning‐based and heuristic‐based approaches to visual saliency prediction in immersive VR experienced using head‐mounted‐displays with a particular focus on the contribution of the depth cue. To this end, we use three learning‐based RGB‐D image saliency detection methods and two heuristic‐based RGB‐D image saliency detection methods on a VR dataset curated from three distinct virtual environments under two‐dimensional and three‐dimensional viewing conditions. Additionally, we extend the analysis by including a heuristic‐based RGB video saliency detection method and its depth‐infused version. The results acquired using these seven methods reveal the superiority of the learning‐based RGB‐D image saliency prediction methods in VR and validate the importance of the depth cue in the saliency prediction of virtual environments.
Enhancing the Performance of Software Authorship Attribution Using an Ensemble of Deep Autoencoders

Software authorship attribution, defined as the problem of software authentication and resolution of source code ownership, is of major relevance in the software engineering field. Authorship analysis of source code is more difficult than the classic task on literature, but it would be of great use in various software development activities such as software maintenance, software quality analysis or project management. This paper addresses the problem of code authorship attribution and introduces, as a proof of concept, a new supervised classification model AutoSoft for identifying the developer of a certain piece of code. The proposed model is composed of an ensemble of autoencoders that are trained to encode and recognize the programming style of software developers. An extension of the AutoSoft classifier, able to recognize an unknown developer (a developer that was not seen during the training), is also discussed and evaluated. Experiments conducted on software programs collected from the Google Code Jam data set highlight the performance of the proposed model in various test settings. A comparison to existing similar solutions for code authorship attribution indicates that AutoSoft outperforms most of them. Moreover, AutoSoft provides the advantage of adaptability, illustrated through a series of extensions such as the definition of class membership probabilities and the re-framing of the AutoSoft system to address one-class classification.
Web-ChatLine: An Innovative Chatting Platform

With the turn of events and improvement in the web, an ever-increasing number of individuals have been choosing online chat platforms for correspondence. Their applications include being used for correspondence over significant distances. Along these lines, the application must both be continuous and multi-stage, and utilized by numerous clients. The online ongoing talking application need not bother with any extra outsider customer program, and visual correspondence could be set up advantageously. The programming instrument utilized to build this application is React.js, Node.js, which has an express structure and an in-memory data set. The text correspondence is moved to and from servers. Furthermore, the information transmission is achieved through highlight point association between servers. Because of the use of the response structure, a virtual space idea is carried out, which improves the presentation over existing applications created utilizing PHP by many times.
SDNet: mutil-branch for single image deraining using swin

Rain streaks degrade the image quality and seriously affect the performance of subsequent computer vision tasks, such as autonomous driving, social security, etc. Therefore, removing rain streaks from a given rainy images is of great significance. Convolutional neural networks(CNN) have been widely used in image deraining tasks, however, the local computational characteristics of convolutional operations limit the development of image deraining tasks. Recently, the popular transformer has global computational features that can further facilitate the development of image deraining tasks. In this paper, we introduce Swin-transformer into the field of image deraining for the first time to study the performance and potential of Swin-transformer in the field of image deraining. Specifically, we improve the basic module of Swin-transformer and design a three-branch model to implement single-image rain removal. The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features. In addition, we employ a jump connection to fuse deep features and shallow features. In terms of experiments, the existing public dataset suffers from image duplication and relatively homogeneous background. So we propose a new dataset Rain3000 to validate our model. Therefore, we propose a new dataset Rain3000 for validating our model. Experimental results on the publicly available datasets Rain100L, Rain100H and our dataset Rain3000 show that our proposed method has performance and inference speed advantages over the current mainstream single-image rain streaks removal models.The source code will be available at https://github.com/H-tfx/SDNet.
Improve Query Focused Abstractive Summarization by Incorporating Answer  Relevance

Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query. Most previous work on QFS only considers the query relevance criterion when producing the summary. However, studying the effect of answer relevance in the summary generating process is also important. In this paper, we propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model, to generate coherent and answer-related summaries. Furthermore, our model can take advantage of large pre-trained models which improve the summarization performance significantly. Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.
A Proposal for Improving the Essence Standard by Using Terminology Unification

Context: SEMAT (Software Engineering Method and Theory) is promoting a software engineering theory with adequate terminology to improve the transference of methods and practices between teams. Terminologies should be uniform in order to eliminate ambiguity, improve communication among teams, and support new concepts. Method: The process of reaching uniformity is called terminology unification. In this paper we propose a method for improving the Essence standard based on terminology unification. This method comprises four stages: selection of base models and definitions for structuring terms, identification of terminology problems by comparing the base models and definitions, unification of terms among the base models and definitions, and measurement of the gap between the current standard terms and the proposed changes. Results: We propose a set of modifications to the Essence standard in constructs like: alpha state cards, relationships among alphas, and names of activity spaces. Conclusions: By solving such conflicts, we can define a common, unambiguous terminology for software engineering teams.
<i>All My Time</i>: Experimental Subversions of Livestreamed Performance During the Covid-19 Pandemic

In March 2020, the COVID-19 pandemic had spread internationally, resulting in governments shutting down concert halls, theatres, and other performance spaces. In this environment, the livestreamed performance flourished, with many musicians embracing the medium, performing to online audiences from their own homes via a variety of social media platforms. Whereas many major performing arts organisations created livestreams that attempted to emulate existing paradigms of concert films, many experimental musicians found ways of subverting these conventions, creating new works that could only have been created during these lockdowns. This article gives an overview of these experimental approaches, documenting the major types of ‘lockdown music’ observed across the international experimental music scene, and exploring their play with the relationships between sound and vision, and with the perception of online liveness. The article then examines a particular case study co-created by the author and composer Damian Barbeler. All My Time is an interdisciplinary work that draws on many of these games of diegesis and syncresis. The pianist duets with an unseen pianist in another country, household appliances become chamber partners with synthesisers, gin bottles are transformed into complex percussion instruments: image and sound seem incompatible, but the audience cannot tell which is live. By documenting the important body of work created during 2020–2021, the article explores how these online performances have expanded the possibilities of interdisciplinary music and the experience of liveness online, forming a basis for a continuing examination of the long-term legacy of ‘lockdown music’.
Multi-Perspective Representation to Part-Based Graph for Group Activity Recognition

Group activity recognition that infers the activity of a group of people is a challenging task and has received a great deal of interest in recent years. Different from individual action recognition, group activity recognition needs to model not only the visual cues of individuals but also the relationships between them. The existing approaches inferred relations based on the holistic features of the individual. However, parts of the human body, such as the head, hands, legs, and their relationships, are the critical cues in most group activities. In this paper, we establish the part-based graphs from different viewpoints. The intra-actor part graph is designed to model the spatial relations of different parts for an individual, and the inter-actor part graph is proposed to explore part-level relations among actors, in which visual relation and location relation are both considered. Furthermore, a two-branch framework is utilized to capture the static spatial and dynamic temporal representations simultaneously. On the Volleyball Dataset, our approach obtains a classification accuracy of 94.8%, achieving very competitive performance in comparison with the state of the art. As for the Collective Activity Dataset, our approach improves the accuracy by 0.3% compared with the state-of-the-art results.
Revisiting the Conditional Construal of Conditional Probability

We show how to extend any finite probability space into another finite one which satisfies the conditional construal of conditional probability for the original propositions, given some maximal allowed degree of nesting of the conditional. This mitigates the force of the well-known triviality results.
Long-Time Bit Storage and Retrieval without Cold Atom Technology

We report computer studies showing how the duration of memory for storage and retrieval of a classical bit can be increased to 100 times the decay time of an isolated atom, with no use of high-tech cold-atom preparations recently developed in the light-matter field. We suggest that our low-tech procedure can greatly enlarge the number of experimenters able to enter this field. The role of symmetry in this procedure arises in a careful interplay of incoherent and coherent excitations of a large collection of “two-level” atoms, the level separation being matched by the dominant frequency of the electromagnetic fields (short pulses and continuing field) applied to the system.
Computational Image Analysis Techniques, Programming Languages and Software Platforms Used in Cancer Research: A Scoping Review

Background: Cancer-related research, as indicated by the number of entries in Medline, the National Library of Medicine of the USA, has dominated the medical literature. An important component of this research is based on the use of computational techniques to analyse the data produced by the many acquisition modalities. This paper presents a review of the computational image analysis techniques that have been applied to cancer. The review was performed through automated mining of Medline/PubMed entries with a combination of keywords. In addition, the programming languages and software platforms through which these techniques are applied were also reviewed. Methods: Automatic mining of Medline/PubMed was performed with a series of specific keywords that identified different computational techniques. These keywords focused on traditional image processing and computer vision techniques, machine learning techniques, deep learning techniques, programming languages and software platforms. Results: The entries related to traditional image processing and computer vision techniques have decreased at the same time that machine learning and deep learning have increased significantly. Within deep learning, the keyword that returned the highest number of entries was convolutional neural network. Within the programming languages and software environments, Fiji and ImageJ were the most popular, followed by Matlab, R, and Python. Within the more specialised softwares, QuPath has had a sharp growth overtaking other platforms like ICY and CellProfiler. Conclusions: The techniques of artificial intelligence techniques and deep learning have grown to overtake most other image analysis techniques and the trend at which they grow is still rising. The most used technique has been convolutional neural networks, commonly used to analyse and classify images. All the code related to this work is available through GitHub: https://github.com/youssefarafat/Scoping-Review.