Reviewerly/exHarmony huggingface dataset:
MSVEC: A Multidomain Testing Dataset for Scientific Claim Verification

The increase of disinformation in scientific news across a variety of domains has generated an urgency for a robust and generalizable approach to automated scientific claim verification (SCV). Available methods of SCV are limited in either domain adaptability or scalability. To facilitate building and evaluating more robust models on SCV we propose MSVEC, a multidomain dataset containing 200 pairs of verified scientific news claims with evidence research papers. To understand the capability of large language models on the SCV task, we evaluated GPT-3.5 against MSVEC. While methods of fact-checking exist for specific domains (e.g., political and health), the use of large language models exhibits better generalizability across multiple domains and is potentially compared with state-of-the-art models based on word embeddings. The data and software used and developed for this project are available at https://github.com/lamps-lab/msvec.
THE RELATIONSHIP BREAKFAST HABITS AND NUTRITIONAL STATUS WITH STUDENTS OF SD NEGERI 28 SUNGAI LANGKA GEDONG TATAAN DISTRICT PESAWARAN REGENCY

The state of nutritional status and grade point average is a description of what elementary school children consume in the long term, which can be in the form of undernutrition or overnutrition. Nutrients such as protein carbohydrates, as well as other nutrients, especially iron, play a role in the body's metabolism in thinking processes or reasoning processes as well as concentration power and are very closely related to learning efficiency. With a good nutritional state is expected to have an impact on good learning achievement. The purpose of this study was to determine the relationship between nutritional status and breakfast habits with learning achievement in grades IV and V SD Negeri 28 Sungai Langka, Gedong Tataan District, Pesawaran Regency. The type of research used in this research is quantitative. The design in this study uses an analytical survey, using a cross sectional approach. The population in this study were 51 students in class IV and V of SD Negeri 28 Sungai Langka, Gedong Tataan District, Pesawaran Regency. The sample technique in this study uses Total Sampling. The results of the study determined that there was a relationship between nutritional status and learning achievement with a p-value of 0.028 and there was a relationship between breakfast habits and learning achievement in SD Negeri 28 Sungai Langka students, Gedong Tataan District, Pesawaran Regency in 2019 with a p-value of 0.036 and OR: 4.071. It is hoped that it will educate students about the importance of choosing healthy and nutritious food and provide input to parents of students to provide healthy and nutritious food.Keywords: Nutrition Status, Breakfast &amp; Learning Achievement
Penerapan Model Project Based Learning dengan Media Video untuk Meningkatkan Hasil Belajar Peserta Didik pada Mata Pelajaran Bahasa Indonesia Kelas 3 SDN 48 Cakranegara

This research aims to determine the improvement in participant learning outcomes class 3A students at SDN 48 Cakranegara using the Project Based Learning model with educational video media. This research includes collaborative PTK research with a descriptive qualitative approach. This research was carried out in stages in two cycles. The subjects of this research were all class 3A students at SDN 48 Cakranegara for the 2022/2023 academic year. Classroom action research (PTK) is carried out in 2 cycles consisting of planning, action implementation, observation, evaluation and reflection stages. Each cycle consists of one meeting. The results of this research show an increase from cycle to cycle. Then there was an increase until in cycle II, where there was a significant increase in learning outcomes. This shows that the application of a project-based learning model with video media can improve Indonesian language learning outcomes for class 3A students at SDN 48 Cakranegara.
A Social Recommendation Model Based on Basic Spatial Mapping and Bilateral Generative Adversarial Networks

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user–item interaction data. Therefore, how to effectively fuse interaction information and social information becomes a hot research topic in social recommendation, and how to mine and exploit the heterogeneous information in the interaction and social space becomes the key to improving recommendation performance. In this paper, we propose a social recommendation model based on basic spatial mapping and bilateral generative adversarial networks (MBSGAN). First, we propose to map the base space to the interaction and social space, respectively, in order to overcome the issue of heterogeneous information fusion in two spaces. Then, we construct bilateral generative adversarial networks in both interaction space and social space. Specifically, two generators are used to select candidate samples that are most similar to user feature vectors, and two discriminators are adopted to distinguish candidate samples from high-quality positive and negative examples obtained from popularity sampling, so as to learn complex information in the two spaces. Finally, the effectiveness of the proposed MBSGAN model is verified by comparing it with both eight social recommendation models and six models based on generative adversarial networks on four public datasets, Douban, FilmTrust, Ciao, and Epinions.
Research on Keywords Variations in Linguistics Based on TF-IDF and N-gram

The rapid development of natural language processing (NLP) holds great promise for bridging the divide among languages. One of its main innovative applications is to use broad data to explore the historical trend of a subject. However, since Saussure pioneered modern linguistics, there is relatively inadequate research work done in the linguistic research on the field's variations to comprehensively reveal the linguistic trends. To trace the changes in linguistic research hotspots, we use a dataset of more than 30,000 linguistics-related literature with their titles from the Web of Science and apply NLP techniques to the data consisting of their keywords and publication years. It is found that the co-occurrence relationship between keywords, NGRAM, and their relationship with years can effectively present changes in linguistic research themes. This research is supposed to provide further insights and new methods that can be applied in the field of linguistics and related disciplines.
Analog photonic neural networks for large-scale AI at the quantum limit

I will overview our work on analog neural networks based on photonics and other controllable physical systems. I will show how backpropagation can efficiently train physical neural networks (PNNs), and how to design physical network architectures for physics-based machine learning. I will review our work showing how nonlinear photonic neural networks may enhance computational sensing and how photonic neural networks may be operated robustly deep into low-energy regimes where quantum noise would ordinarily be a limiting factor. Finally, I will show that PNNs offer fundamental advantages for scaling AI models such as Transformers.
PEMANFAATAN PROSEDUR STIMULASI BAGI GURU BAHASA INDONESIA

Stimulation procedures in Indonesian language learning need to be utilized by Indonesian language teachers, including at SMA N 9 Pontianak. This procedure is placed in apperception activities with the aim of being a solution for students to be more focused and have motivation for Indonesian language learning. The reason for utilizing stimulation procedures for teachers is because students have a lack of preparedness in receiving Indonesian language materials. Therefore, to dissect the flow of utilization of stimulation procedures for Indonesian language teachers, descriptive qualitative methods are used. Subjects and data sources are Indonesian language teachers with observation and interview data collection techniques. The data obtained is then analyzed by reducing data explanation, and conclusion. Based on review of the research scheme, it is revealed that Indonesian language teachers are ready to utilize stimulation procedures in the hope that Indonesian language learning can run effectively.
AIRE 2023: 10th International Workshop on Artificial Intelligence and Requirements Engineering

Requirements Engineering (RE) researchers have employed Artificial Intelligence (AI) techniques to tackle different notions of requirements quality, have applied the techniques to different case studies and domains, and have used different metrics to assess the performance of their techniques. Given the pervasiveness of AI-based systems in our daily life, recent years have also seen an increasing need for RE techniques to support sound and structured development of AI system, with particular interest in explainability of system behaviour. The primary purpose of the AIRE workshop is to explore synergies between AI and RE in order to identify complex RE problems that could benefit from the application of AI techniques and the other way round, thus addressing RE for AI challenges. The 2023 edition of the workshop received 14 submissions, which were independently reviewed by at least three program committee members. In the end, 9 papers were accepted. All the conflicts of interest were treated seriously and independently. The workshop takes place on September 5, 2023. We hope that you enjoy the AIRE'23 workshop and its proceedings. We believe that in the days when AI is gaining prominence in our daily lives, the RE community cannot neglect the benefits that AI techniques can deliver to the practice of requirements engineering. The workshop will feature a keynote by Dr. Alessio Ferrari from CNR-ISTI (Italy) on Artificial Intelligence in Engineering and Society: Blue Skies, Black Holes, and the Job of Requirements Engineers. We look forward to seeing you all at this workshop and the future editions. We are very grateful to the Program Committee members and authors of the submissions for their hard work and dedication in putting together this program. We would like to thank you all for your participation in AIRE'23.
NFRNet-LT:Improving Accuracy in Extracting Long-tailed Non-functional Requirements

Automatic extraction of non-functional requirements plays a crucial role in improving efficiency of requirements elicitation, change management, and validation testing. In recent years, machine learning methods have been widely applied in the field of requirements engineering. Although these methods have achieved some promising results, many of them have been evaluated only on small-scale and relatively balanced datasets of non-functional requirements, which may not reflect their typical characteristics in real-world applications. Therefore, in this paper, we propose a novel deep neural network model called NFRNet-LT to address the challenges of extracting non-functional requirements from various types of documents, by considering the higher granularity of non-functional requirement categories and the imbalanced long-tail distribution of data.
A Design of File Sharing System

Sharing files with others is one of the most common actions in our daily life. There are many ways to share files with others, for example, Dropbox, OneDrive or Google Drive. But the shortcomings are also easy to find. We cannot allow others to upload files to our cloud storage, making the file sharing more complex. Thus, we design a file sharing system called Cloud Folder to allow users to do that. This paper aims to provide a solution for file sharing systems. This paper builds an overall architecture after analyzing the present problems and some special demands. Then, this paper gives a detailed design of the basic features of the file sharing system and introduces their implementations. Finally, through a specific experiment, this paper shows the time consumed by all the data storage and processing features of this system, which indicates high efficiency of this system.
Mathematical Analysis of the Tensile Behavior of DP 980 Steel Using Digital Image Correlation (DIC)

Abstract This paper presents an analysis tool and preliminary, high-resolution results of material behavior and surface features during the tensile test of DP 980 steel specimen. The ultimate goal is a method for detection of localized through-thickness necking in stamping and welding samples based on measurement of the sample geometry and analyzing the evolution of surface features. It is thought that the onset of necking can be detected more precisely through characterization of the changes in the geometry of surface coordinate measurements than strain-based metrics. In this work, a MATLAB-based analysis tool was created to extract information from the ample data generated by DIC in tension testing. The results shown highlight the level of detail and granularity possible from DIC data. Several analysis results, such as the evolution of strains in x, y, and z directions, development of stress-strain cures, temporal surface profile, and curvature evolution, were presented for 201 surface points along the sample. The nature of the stress-strain behavior was plotted for every 0.25 mm along the sample, showing the increase in stress and strain at varying degrees in the diffuse neck. Surface profile and curvature evolution were shown at each 0.5-second time step. Comparing the temporal results of all points revealed the location of the diffuse neck on the sample.
A Novel Method for Spam Call Detection Using Graph Convolutional Networks

Unwanted calls from advertisers or phishers are a major nuisance for millions of mobile phone users. Manual blacklists are ineffective against this growing problem. A sophisticated approach is needed on the telephone operator side to detect spam calls based on malicious patterns. In this paper, we present a framework that uses network-relationship between mobile users for spam detection. Our framework has two main contributions: i) We propose an efficient and cost-effective method to construct a large, labeled spam detection graph by leveraging end-user collaboration; ii) We propose a graph neural network (GNN)-based model for the spam detection task. Our experiments show that our model outperforms strong baseline models in this research field.
A retrieval model of surface geochemistry composition based on remotely sensed data

Abstract The geochemical sampling work in the difficult and dangerous areas is very hard; hence, it can be greatly improved by combining with the remotely sensed data. Thus, a retrieval model is proposed by Kernel Principal Component Analysis and Artificial Bee Colony (ABC) optimized Support Vector Machine (SVM) models based on Landsat 8 remotely sensed data and the geochemical data in the study area. The analysis results show that the geochemical data delineate the areas with relatively enriched elements, but indicate the low-abnormal ore (chemical) points, and the anomalies delineated by the inversion data are better for this purpose, for better indication. At the same time, the distribution and intensity of the corresponding abnormal areas found that the abnormal areas delineated by the inversion data basically contain the abnormal areas delineated by the original data, and the anomalies located at the ore spots are obviously enhanced; it shows that the SVM model of ABC Optimization can establish the relation between geochemistry data and remote sensing data, can supply the original data effectively, and can also provide the direction for the next prospecting work.
Poster: Unraveling Reward Functions for Head-to-Head Autonomous Racing in AWS DeepRacer

AWS DeepRacer is a fully autonomous 1/18th scale race car designed to help developers learn and practice reinforcement learning through cloud-based simulations and real-world racing. What drives the reinforcement learning model is the reward function, a way to provide positive or negative feedback to an agent, guiding its learning process in reinforcement learning by assigning numerical values. In the AWS training environment, there are multiple modes in which you can run training simulations and evaluations in. These modes include Time Trial, Object Avoidance, and a relatively new mode: Head to Bot. The research done in this project was primarily focused on testing reward functions in the Head to Bot mode as well as developing a research function that would be suited for training in this new Head to Bot mode. The developed algorithm outperformed the default Centerline reward function, as well as the Object Avoidance reward function.
Application and Improvement of MFCC in Gesture Recognition with Surface Electromyography

As a physiological signal reflecting the state of muscle activation, surface electromyography (sEMG) plays a vital role in the assessment of neuromuscular health, human–computer interaction, and gait analysis. Inspired by the audio signal analysis outcome that features extracted with Mel Frequency Cepstral Coefficient (MFCC) empower better representation, this paper proposes a comparative study of a gesture recognition method by using and improving with the MFCC features of sEMG. Comparing and combining with the conventional time-domain and frequency-domain features, different learning-based techniques are deployed to evaluate the performance of the proposed approach on the NinaPro datasets. The proposed approach was evaluated on the NinaPro-DB1 and NinaPro-DB2 datasets, achieving the improvements of 3.42% and 3.67%, respectively, in terms of the highest accuracy using the standard MFCC method. Correspondingly, when combined with the improved MFCC, the accuracy was further increased, reaching the maximum values of 89.82% and 87.82%, respectively, on the two datasets. The impact on the performance reveals the effectiveness of MFCC, and the results show that the proposed method has the potential to realize high-precision gesture recognition.
Automated Deep Learning Model with Optimization Mechanism for Segmenting Leukemia from Blood Smear Images

The advancement of digital microscopic scanning has made the study of image processing as well as categorization an exciting field of diagnostic studies. The literature describes a number of methods for detecting acute lymphocytic leukemia (ALL) using blood smear pictures. The goal of this research is to create an efficient approach for segmenting and detecting leukemia. This research has created a leukemia diagnosis module predicated on deep learning (DL) using blood smear pictures. Pre-processing, segmentation, extraction of features, and classification are performed here by the identification scheme. The presented hybrid model of African Buffalo and African Vulture Optimization (AB-AVO) performs the segmentation process, in which cytoplasm and nucleus regions are segmented. The Local Directional Pattern (LDP) and color histogram characteristics have been then retrieved from the segmented pictures and given into the presented Recurrent Neural Network (RNN) for categorization. The ALL-IDB1 and ALL-IDB2 databases’ blood smear pictures are taken into account for the investigation and assessed using metrics including F1-score, sensitivity, dice coefficient, precision, specificity, recall, and accuracy. The presented AB-AVO-RNN approach exhibits 100% accuracy, according to simulation data. Modern methodologies are used to compare the effectiveness of the suggested AB-AVO-RNN methodology. The investigation demonstrates that the suggested classifier performs comparably better and can identify leukemia from blood smear pictures.
GENESIS AND MINERAL-GEOCHEMICAL ZONING OF THE QUARTZ-VEIN GOLD DEPOSIT CENTRAL (KUZNETSK ALATAU)

Link for citation: Voroshilov V.G., Molukpaeva D.K., Timkin T.V., Savinova O.V. Genesis and mineral-geochemical zoning of the quartz-vein gold deposit Central (Kuznetsk Alatau). Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 9, рр. 7-17. In Rus. Relevance. The peculiarity of the Central ore field consists in the fact that gold mineralization is entirely localized within the initially homogeneous granitoid massif, therefore, the identified mineral-geochemical zoning is almost entirely caused by superimposed post-magmatic processes. The ore-metasomatic zoning is extremely contrasting and is fixed by various methods, both traditional and the most modern. This creates a unique opportunity to refine and improve the model of formation of gold mineralization, which is widely manifested in the Altai-Sayan folded region, associated with the formation of granitoid massifs of «variegated composition». The purpose of the research is to study postmagmatic hydrothermal-metasomatic processes and the zoning of their location in the Central gold field (Kuznetsk Alatau); substantiate the genetic model of the formation of gold mineralization in the ore field. Objects: granitoids, metasomatites and ores of the Central ore field (Kuznetsk Alatau). Methods: detailed mineralogical and geochemical mapping, optical and electron microscopy, microprobe analysis, study of the typomorphic properties of minerals. Results. The products of postmagmatic hydrothermal activity mapped by the authors are combined into four formations that replace each other in time and space: 1) albite-amphibole metasomatites; 2) quartz-K-feldspar-biotite metasomatites; 3) epidote-chlorite propylites; 4) beresites with conjugated quartz-gold-sulfide veins. The earliest albite-amphibole metasomatites are confined to the bottom part of the granitoid massif and higher along the section (and from south to north) are replaced first by quartz-K-feldspar-biotite metasomatites, and then by epidote-chlorite propylites. Near-vein beresites are superimposed on all previous metasomatites. At the same time, in the zone of development of albite-amphibole metasomatites, beresites have a quartz-muscovite composition, and ores are represented by low-sulfide quartz veins. From south to north (and from bottom to top), quartz-muscovite beresites are replaced by quartz-carbonate-sericite ones, and the sulfide content of the veins consistently increases up to 50–80 %. A model is proposed for the formation of multi-tiered gold mineralization with the determining ore-controlling role of the Kuznetsk-Altai deep fault.
Challenges and Countermeasures for Improving the Quality of Master's Thesis in Universities in Guangxi Based on Mobile Learning

In this study, eight universities in Guangxi were selected for investigation, and through questionnaire, it was found that mobile learning was positively related to the quality of master's degree thesis in these universities. The study concluded that the awareness of mobile learning is weak and the material support for mobile learning is lacking among masters in universities in Guangxi. Therefore, suggestions are made to improve the quality of master's thesis in Guangxi universities through mobile learning.