Reviewerly/exHarmony huggingface dataset:
Smartphone Application for Support Library Operations in the Internet of Things Era

The library can be referred to as a storage place for books and other references. The reference can be in the form of digital storage media. Libraries if not managed properly will cause chaos in the library organization. Many books were lost due to the entry and exit of books that were out of control. Currently, the library is not only a place to store books but can be maximized by managing and adding other digital devices. The use of Radio Frequency Identification (RFID) in libraries adds sophistication to the management of books and library items. In addition, currently many libraries have taken advantage of Internet of Things Technology, by adding various sensors and integrating with cloud-based storage devices. It provides a service that makes it easy for library members to find and track the current whereabouts of books. This research does not only create a library by providing hardware in the form of sensors to be installed in the library. This paper also proposes the use of smartphones as an alternative in replacing sensor hardware. This study uses a QR Code sensor to match the book you are looking for and simulates dancing a book in blocks and bookcases. with augmented reality. The purpose of this research is to make a smart library prototype to make it easier for library members to find books or other references. The results of the experiment to find books and DVDs that have been carried out achieve an accuracy of 83.33%.
Pet Care Information System at Darussalam Pet Shop Based on Android

Modern technology has changed people's lifestyles, including how to raise pets. Pet is an animal kept at home or in a cage and exclusively cared for by its owner. Recently, more people owning pets. This had led to questions related to the hygiene and health of pets. An unkempt pet can cause problems for the owner, so numerous pet stores have emerged to help owners provide their animals with the care they require. However, many pet shops still use a manual system, which is considered ineffective for service, and utilizing information technology will make jobs easier while improving accuracy and information quality. Based on the problems described above, an application is required to assist and facilitate the pet shop itself and customers in caring for their pets. The research goal is to design and build suitable applications. In this journal, the researcher utilized both the Quantitative research methods and the Waterfall method for application development. For testing, the researcher used the Likert Scale. The Likert Scale calculation yielded a total score of 80.3 (Satisfied). Therefore, it can be concluded that the application is operating as intended and makes it easier to obtain pet-related information for user and manage schedules and incoming orders for administrators.
TOGAF Framework For an AI-enabled Software House

The integration of artificial intelligence (AI) in software development has revolutionized the industry, leading to faster and more accurate results. However, the implementation of AI requires a robust framework to ensure effective planning, design, implementation, and maintenance of AI-enabled software systems. The Open Group Architecture Framework (TOGAF) provides such a framework, enabling organizations to develop a structured and integrated approach to AI-enabled software development. In this journal, we present a case study of how a software house utilized the TOGAF framework to integrate AI in their software development processes. We discuss the challenges faced by the organization in the integration process and how the TOGAF framework provided a structured approach to overcome these challenges. We also highlight the benefits that the organization realized through the implementation of AI-enabled software systems. The case study presented in this journal demonstrates the applicability of the TOGAF framework in AI-enabled software development, and its potential to enhance the capabilities and competitiveness of software houses. The TOGAF framework provides a structured approach to the integration of AI in software development, ensuring that organizations can effectively leverage the benefits of AI while minimizing the associated risks and challenges.
Space Theory

The author introduces a novel theoretical framework that suggests matter and energy are both converted from curved space. In Space Theory, gravitation is generated by the flow of space, instead of being transmitted by the graviton as in String Theory. This theory also suggests that Newton's gravitational constant, denoted as G, may not be truly constant but could vary over time. The equivalent equation of space is S=Ec2=mc4, and for the gravitation of hollow sphere space, the equation is S&amp;mu;&amp;nu;=4&amp;pi;Gm=(4/3)&amp;pi;((r+a)3-r3).
An Advanced Approach on Enhancing Accommodation Maintenance Via Text-Based Customer Review Analysis

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.
PERBANDINGAN MODEL KLASIFIKASI RANDOM FOREST DENGAN RESAMPLING DAN TANPA RESAMPLING PADA PASIEN PENDERITA GAGAL JANTUNG

Cardiovascular disease that causes heart failure is one of the diseases with the highest mortality rate in the world. Therefore, there is a need for an accurate model to classify heart failure based on clinical information and the lifestyle of patients with the disease, as an alternative solution in administering appropriate drugs. This study compared the classification model of living and deceased heart failure patients based on clinical information and patient lifestyle using the random forest method when using resampling techniques and not using resampling techniques. The results obtained from this study are that the Random Forest model with a combination of the SMOTE and Edited Nearest Neighbors methods is the best model for classifying someone with heart failure as alive or dead. The Random Forest model with a combination of the SMOTE and Edited Nearest Neighbors methods has a high level of classification accuracy in the evaluation model that focuses on recall, namely rf_model_smoteenn can classify 82.96% of patients with living status and 90% of patients with death status.
Evaluation and Selection of Products Based on the Quality Dimensions by Word of Mouth (Case Study: Brands of the Mobile Phone)

Today, mobile phones are at the center of many consumers&amp;#039; lives and a part of people&amp;#039;s lives. Although this tool was considered a luxury initially, it has gradually become a necessary tool with the reduction of the price and the increase of its functions. Consumers choose specific models of mobile phones according to their needs. Mobile phones have quality features that are expressed in the form of quality dimensions. These dimensions are mostly expressed by word of mouth (WOM). In this research, first; we collected the opinions of 350 students about 4 brands of mobile phones based on Garvin&amp;#039;s quality of dimensions, finally; the students’ opinions about mobile phones were evaluated with integrated TOPSIS with interval-valued intuitionistic fuzzy numbers technique, and the priority of 4 phone brands was determined based on students&amp;#039; opinions.
Prospectivity Mapping of Tungsten Mineralization in Southern Jiangxi Province Using Few-shot Learning

Mineral prospectivity mapping (MPM), aiming to outline and prioritize mineral exploration targets, has been spurred by data-driven machine learning algorithms. Supervised data-driven MPM is a typical few-shot task, suffering from the scarcity of labeled data, over-fitting of models and uncertainty of predictions. The main objective of this contribution is to propose a robust framework of few-shot learning (FSL) combining data augmentation and transfer learning, which enables generation of prospectivity models with excellent predictive efficiency and low uncertainty. The mineral systems approach was used to transfer a conceptual mineral system into mappable exploration criteria. Synthetic minority over-sampling technique (SMOTE) was employed to augment and balance the labeled dataset, allowing for model pre-training with a large synthetic training dataset of source domain. The knowledge derived from pre-trained models was then transferred to the target domain by fine-tuning, and the prospectivity model was generated in light of over-fitting and uncertainty assessment. The proposed FSL framework was applied to tungsten prospectivity mapping in southern Jiangxi Province. The results indicate that the SMOTE-ed balanced dataset boosts the classification accuracy in the training process. The FSL models yield an arch-shaped prediction point pattern favorable for focusing potential targets with high probability and low uncertainty. The FSL models achieve a high predictive performance (test AUC=0.9172) and the lowest quantitative over-fitting value, compared to the models derived from the benchmark algorithms of random forest and support vector machine. Four levels of potential targeting zones, considering both predictive efficiency and uncertainty, are extracted from the resulting FSL prospectivity map. The final high-potential and low-risk exploration targets only cover 4.27% of the area, but capture 41.53% known tungsten deposits, achieving superior predictive performance. This study highlights the capability of FSL framework for controlling over-fitting and generating high-confidence exploration targets with low uncertainty.
Semi-Supervised Segmentation of Functional Tissue Units at the Cellular Level

We present a new method for functional tissue unit segmentation at the cellular level, which utilizes the latest deep learning semantic segmentation approaches together with domain adaptation and semi-supervised learning techniques. This approach allows for minimizing the domain gap, class imbalance, and captures settings influence between HPA and HubMAP datasets. The presented approach achieves comparable with state-of-the-art-result in functional tissue unit segmentation at the cellular level. The source code is available at https://github.com/VSydorskyy/hubmap_2022_htt_solution
Machine Learning and Structure Formation in Modified Gravity

In General Relativity approximations based on the spherical collapse model such as Press--Schechter theory and its extensions are able to predict the number of objects of a certain mass in a given volume. In this paper we use a machine learning algorithm to test whether such approximations hold in screened modified gravity theories. To this end, we train random forest classifiers on data from N-body simulations to study the formation of structures in $\Lambda$CDM as well as screened modified gravity theories, in particular $f(R)$ and nDGP gravity. The models are taught to distinguish structure membership in the final conditions from spherical aggregations of density field behaviour in the initial conditions. We examine the differences between machine learning models that have learned structure formation from each gravity, as well as the model that has learned from $\Lambda$CDM. We also test the generalisability of the $\Lambda$CDM model on data from $f(R)$ and nDGP gravities of varying strengths, and therefore the generalisability of Extended-Press-Schechter spherical collapse to these types of modified gravity.
The offline digital currency puzzle solved by a local blockchain

A major drawback in deploying central bank digital currencies (CDBC) is the offline puzzle, which requires that a CBDC must keep the provision given by cash, and, simultaneously, avoid double-spending, counterfeiting, and other issues. The puzzle is solved by minting the coins in serials, which are stored on a local blockchain (e.g. smartphone). The local blockchain is secured by keys embedded in the hardware and can be continuously mined by the wallet to enhance security. The coins can be either minted as hot coins, which can be retrieved in case of loss, or minted as cold coins, like physical cash.
Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies, investigates, and offers solutions to language-related real-life problems. The new coronavirus disease, otherwise known as Coronavirus disease (COVID-19), has severely affected the everyday life of people all over the world. Specifically, since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection, the country has initiated the appropriate preventive measures (like lockdown, physical separation, and masking) for combating this extremely transmittable disease. So, individuals spent more time on online social media platforms (i.e., Twitter, Facebook, Instagram, LinkedIn, and Reddit) and expressed their thoughts and feelings about coronavirus infection. Twitter has become one of the popular social media platforms and allows anyone to post tweets. This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based sentiment analysis (SCOBGRU-SA) on COVID-19 tweets. The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic. The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this. Moreover, the BGRU model is utilized to recognise and classify sentiments present in the tweets. Furthermore, the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter, which helps attain improved classification performance. The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset, and the results signify its promising performance compared to other DL models.
Correcting of Unexpected Localization Measurement for Indoor Automatic Mobile Robot Transportation Based on neural network

Abstract The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory automation. However, when mobile robots move in laboratory environments, mechanical errors, environmental disturbances, and signal interruptions are inevitable. This can compromise the accuracy of the robot's localization, which is crucial for the safety of staff, robots, and the laboratory. A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory environments. The proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile robots. The experimental results demonstrate the effectiveness of this proposed method.
Blockchain-Enabled Communication Framework for Secure and Trustworthy Internet of Vehicles

The Internet of Vehicles (IoV) is an emerging field that has attracted significant attention due to its potential to transform transportation and mobility. IoV enables the development of new services and applications that can improve transportation systems&amp;#039; efficiency, safety, and sustainability. However, ensuring secure and trustworthy communication among the different components of an IoV system is a critical challenge that needs to be addressed. This paper proposes a blockchain-enabled communication framework for secure and trustworthy IoV applications. The proposed framework leverages the security and decentralization features of blockchain technology to provide secure communication channels for different components of the IoV system, including vehicles, infrastructure, and service providers. The proposed framework provides an identity management system to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To evaluate the effectiveness of the proposed framework, we conducted experiments using a real-world IoV scenario, and the results demonstrate that the proposed framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework offers a promising approach to address the security and trust challenges in IoV communication systems.
Beyond Neural-on-Neural Approaches to Speaker Gender Protection

Recent research has proposed approaches that modify speech to defend against gender inference attacks. The goal of these protection algorithms is to control the availability of information about a speaker's gender, a privacy-sensitive attribute. Currently, the common practice for developing and testing gender protection algorithms is "neural-on-neural", i.e., perturbations are generated and tested with a neural network. In this paper, we propose to go beyond this practice to strengthen the study of gender protection. First, we demonstrate the importance of testing gender inference attacks that are based on speech features historically developed by speech scientists, alongside the conventionally used neural classifiers. Next, we argue that researchers should use speech features to gain insight into how protective modifications change the speech signal. Finally, we point out that gender-protection algorithms should be compared with novel "vocal adversaries", human-executed voice adaptations, in order to improve interpretability and enable before-the-mic protection.
Multi-Speaker End-to-End Multi-Modal Speaker Diarization System for the MISP 2022 Challenge

This paper presents the design and implementation of our system for Track 1 of the Multi-modal Information based Speech Processing (MISP) 2022 Challenge. We design an end-to-end transformer-based multi-talker system. The transformer backbone is well-suited to capture long-term features, which is crucial for multi-modal speaker diarization in cases where temporal modalities are missing. Besides, we employ several loss functions and image data augmentation techniques to prevent over-fitting during training. Moreover, to further improve the system’s performance, we incorporate Interchannel Phase Difference (IPD) to model the location features and pre-train an ECAPA-TDNN-based model to extract speaker embedding features. Our system achieved a diarization error rate (DER) of 10.82% on the evaluation set, which earned us second place in the audio-visual speaker diarization task of the MISP 2022 challenge.
Once-for-All Sequence Compression for Self-Supervised Speech Models

The sequence length along the time axis is often the dominant factor of the computation in speech processing. Works have been proposed to reduce the sequence length for lowering the computational cost in self-supervised speech models. However, different downstream tasks have different tolerance of sequence compressing, so a model that produces a fixed compressing rate may not fit all tasks. In this work, we introduce a once-for-all (OFA) sequence compression framework for self-supervised speech models that supports a continuous range of operating compressing rates. The framework is evaluated on various tasks, showing marginal degradation compared to the fixed compressing rate variants with a smooth performance-efficiency trade-off. We further explore adaptive compressing rate learning, demonstrating the ability to select task-specific preferred frame periods without needing a grid search.
Few-shot In-context Learning for Knowledge Base Question Answering

Question answering over knowledge bases is considered a difficult problem due to the challenge of generalizing to a wide variety of possible natural language questions. Additionally, the heterogeneity of knowledge base schema items between different knowledge bases often necessitates specialized training for different knowledge base question-answering (KBQA) datasets. To handle questions over diverse KBQA datasets with a unified training-free framework, we propose KB-BINDER, which for the first time enables few-shot in-context learning over KBQA tasks. Firstly, KB-BINDER leverages large language models like Codex to generate logical forms as the draft for a specific question by imitating a few demonstrations. Secondly, KB-BINDER grounds on the knowledge base to bind the generated draft to an executable one with BM25 score matching. The experimental results on four public heterogeneous KBQA datasets show that KB-BINDER can achieve a strong performance with only a few in-context demonstrations. Especially on GraphQA and 3-hop MetaQA, KB-BINDER can even outperform the state-of-the-art trained models. On GrailQA and WebQSP, our model is also on par with other fully-trained models. We believe KB-BINDER can serve as an important baseline for future research. Our code is available at https://github.com/ltl3A87/KB-BINDER.
Formation of subject competence students using competence-oriented tasks in biology

Introduction. Periodic updating of the content of education in accordance with the achievements in the study area and with the specifics of the discipline taught is one of the important conditions for improving the quality of education in secondary school. The integrated use of the competency-based approach is considered an important didactic condition for improving both the content and methods of teaching biology in secondary schools, which requires an analysis of the level of subject competencies and ways of their formation in the learning process. Materials and methods. The study involved 420 students of educational institutions of the Kyrgyz Republic (220 in the experimental group and 200 in the control group). Research methods: questioning, analysis of literary sources on the subject of the study. Methods of mathematical statistics: Pearson's chi-squared test. Research results. The levels of formation of subject competencies of students through the solution of competency-oriented biological tasks are revealed and experimentally tested. It has been established that students more effectively perform various options for laboratory and practical work in biology, can independently use conventional signs, diagrams and models of biological processes and objects, and are active in performing independent research work of varying degrees of complexity. On the basis of a comparative analysis of empirical data, statistical analysis, positive dynamics and a significant increase in the values of level indicators of subject competencies (χ2 = 15.825; p&lt;0.01) were established. Conclusions. The introduction of competence-oriented tasks in the educational process in the subject of biology will help increase students' cognitive activity, interest in obtaining biological knowledge and applying the formed subject competencies in practical activities. Keywords: updating, content of biological education, activity approach, competence-based approach, subject standard in biology, subject activity, learning activity, learning task