Reviewerly/exHarmony huggingface dataset:
Research on deep reinforcement learning basketball robot shooting skills improvement based on end to end architecture and multi-modal perception

Introduction In the realm of basketball, refining shooting skills and decision-making levels using intelligent agents has garnered significant interest. This study addresses the challenge by introducing an innovative framework that combines multi-modal perception and deep reinforcement learning. The goal is to create basketball robots capable of executing precise shots and informed choices by effectively integrating sensory inputs and learned strategies. Methods The proposed approach consists of three main components: multi-modal perception, deep reinforcement learning, and end-to-end architecture. Multi-modal perception leverages the multi-head attention mechanism (MATT) to merge visual, motion, and distance cues for a holistic perception of the basketball scenario. The deep reinforcement learning framework utilizes the Deep Q-Network (DQN) algorithm, enabling the robots to learn optimal shooting strategies over iterative interactions with the environment. The end-to-end architecture connects these components, allowing seamless integration of perception and decision-making processes. Results The experiments conducted demonstrate the effectiveness of the proposed approach. Basketball robots equipped with multi-modal perception and deep reinforcement learning exhibit improved shooting accuracy and enhanced decision-making abilities. The multi-head attention mechanism enhances the robots' perception of complex scenes, leading to more accurate shooting decisions. The application of the DQN algorithm results in gradual skill improvement and strategic optimization through interaction with the environment. Discussion The integration of multi-modal perception and deep reinforcement learning within an end-to-end architecture presents a promising avenue for advancing basketball robot training and performance. The ability to fuse diverse sensory inputs and learned strategies empowers robots to make informed decisions and execute accurate shots. The research not only contributes to the field of robotics but also has potential implications for human basketball training and coaching methodologies.
Deep Learning Approach for the Classification of Caprine Parasites

A parasitic infection is, by far and away, the most common element in animal infectious diseases whose origins may be traced back to most places worldwide. The term "parasite" refers to any organism that lives within another living thing. Because of the near closeness of the people and the animals, there is a danger that the sanitary conditions of the animals will not be adequately treated, which has the potential to have severe ramifications for the health of the people. The surrounding environment has become contaminated due to the spread of parasites and bacteria that dwell in the digestive systems of a wide variety of different animals into the surrounding environment. This has resulted in the contamination of the ecosystem. The main objective of this paper is to employ numerous methods of deep learning to identify the types of parasites found in faeces samples taken from caprine animals. The caprine animals served as the source for the later analyzed samples. Images of parasites such as Amphistome, Ascaris, B-Coli, Moniezia, Schistosoma spindale, Strongyle, and Trichuris are included in the database. These images train and evaluate several models using deep learning techniques such as YOLOv4, YOLOv5, SSD and CNN. The classification results from the study indicate that the YOLOv4 technique can diagnose infections caused by caprine parasites more promptly. This was determined by looking at the results of the performed classification. The fact that the approach produced categorization results provides support for this hypothesis.
Cultural-historical and socio-aesthetic connotations of the vocal art of the Middle Kingdom

This article is devoted to the study of cultural, historical, and socio-aesthetic connotations of the vocal art of the Middle Kingdom. It is said that the process of development of vocal music in Chinese culture can be divided into three stages. The conclusion is made about the prospects for the inclusion of Chinese vocal and performing culture in the international global musical space.
Two-Dimensional Walsh Spectral Transform in Problems of Automated Analysis of Ultrasound Images

The scanning window with the calculation of the local two-dimensional Walsh transform in it is proposed to be used for automated systems of processing ultrasound images. Preliminary studies of the computational capacity of such a process have been carried out and the high speed of this approach has been shown. Frequencies are selected on the spectral two-dimensional plane. The analysis of these frequencies makes it possible to determine the degree of echogenicity in the scan area, as well as to detect the edges of ultrasound image objects. The architecture of the ultrasonic image pixel state detector is proposed based on a two-dimensional scanning windowed Walsh transform, a significant frequency selector, and a neural network tuned to classify the pixel state. The detector was tested both on simulated images and on ultrasound images of phantoms and real medical ultrasound examinations of the human abdominal cavity.
Robust Perceptual Image Hashing for Screen-Shooting Attack

In this paper, we propose a Swin Transformer-based perceptual image hashing scheme to resist the impact of screen-shooting distortion on content authentication. The system applies a Swin Transformer-based U-shaped architecture to form a feature extractor, cooperates through encoder-decoder for feature extraction and compression, and employs a hash generator consisting of several fully connected layers to generate the final hash sequence. In addition, the scheme adds a distortion simulator to the training process, which uses image processing operations to simulate distortion on the original image to assist the feature extractor and hash generator in learning to resist screen-shooting distortion. Our experiments show that the proposed hashing is robust to single and hybrid content-preserving operations in screen-shooting attack. In addition, we also demonstrate that our hashing scheme outperforms some state-of-the-art schemes in terms of robustness and discrimination.
Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{SED}$</tex-math></inline-formula> , and its variant - UCL-EC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{SED}$</tex-math></inline-formula> , for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012_100, Events2018_100, and CrisisLexT_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.
Key Technology of Cloud Service for the Aggregation of Satellite, Aerial and Terrestrial Multi-source Spatiotemporal Information

In this paper, a spatiotemporal data model and efficient indexing method suitable for cloud service management is proposed, a unified and high-precision registering technology system for spatiotemporal reference of satellite, aerial and terrestrial multi-source sensing information is constructed and a multi-source spatiotemporal sensing information interaction interface standards and distribution mechanism scheme are developed to improve the accuracy and reliability of multi-source sensing data of power IOT, promote the sharing of power IOT data effectively, and provide the necessary conditions for the integrated application of multi-source spatiotemporal data in the core business scenarios of power grid.
Comparative Study of Available Solar Potential for Six Stations of Sahel

Sahel is an African area with high solar potential. However, this potential is not uniform across the region. This paper examines the spatial distribution of the available solar potential by using six stations across the Sahel area. This comparative study was based on the analysis of in situ measurements in Dakar in Senegal, Niamey in Niger, Ouagadougou, Gaoua, Dori in Burkina Faso and N’Djamena in Chad. The results showed the presence of a good global solar potential with an average value of about 5.43 kWh/m2/day. The maxima of global potential are noted in the northern part in Niamey with a value of 6.24 kWh/m2/day while the minima are recorded in the south-eastern part in N’Djamena with an irradiation close to 4.71 kWh/m2/day. Then, the monthly evolution of this potential shows similar trends for all stations. Indeed, two maximums are observed during the year in Spring (March) and Autumn (October). However, for most of these stations, the minima of global potential are recorded in Winter (November, February) and during the rainy season (July, October). Moreover, the direct normal potential also shows seasonal trends for the two stations (Dakar, Niamey) where it was measured. The maxima of direct normal irradiation (DNI) are observed between February and May with a value of 5.5 kWh/m2/day in Dakar and in Niamey with a value around 5.32 kWh/m2/day between February and November.
Teleport to the Augmented Real World

New Zealand is well-known for its beautiful nature, which has contributed to it being a prime destination for many movies and commercials. A strong media and technology ecosystem has been built to support this industry. Computer Graphics (CG) and visual effects (VFX) enables seamless blending between computer generated imagery and recorded real footage. Recent advancements of real-time technologies actuate the transition from off-line post-production to real-time. Immersive media technologies transform the end-user experience from observing, to a high sense of presence within the story. Furthermore, high-speed networking changes the media distribution from a pre-recorded medium to live streaming. This talk will explain the recent convergence of immersive, interactive, and intelligent technologies, introduce a concept of augmented telepresence, the frame work and platform that allow user’s illusion to virtually teleport to the captured real-space to interact with the objects in the media and the state-of-the-art technologies that enhance users’ telepresence. The further extension of the platform with the high-speed network will be able to allow remote interaction and immersive collaboration with people in distance. Recent case studies with public end-users will be introduced including a live-streaming concert to online viewers (controlled by a producer in real-time with augmented live VFX), virtual tourisms, and remote education. Potential applications and future extensions is further discussed in the session.
The Impact of Financial Technology on Customer Behavior in the Jordanian Commercial Banks

This study investigated impact of financial technology on customer behavior in Jordanian commercial banks. The descriptive technique was employed based on the study of the research topic. The study population consisted of all administrative workers in Jordanian commercial banks. The data was collected through a random sample of administrative workers in Jordanian commercial banks, estimated at 300 male and female employees. The statistical package for the social sciences program was used to examine the data after obtaining it using the questionnaire (SPSS). The results showed that there is a statistically significant positive effect of financial technology (credit service, payment services, investment management services, financial market support services) on customer behavior in Jordanian banks, which indicates that (financial technology) explained an amount (84.1%) of the change in customer behavior in Jordanian banks. Banking will unquestionably concentrate on delivering financial services through data in the future, limiting the ability to react. By enhancing the promise of value, financial institutions that have been able to quickly adapt to financial innovations boost client loyalty and happiness. The study recommends that Jordanian banks, in light of global competitiveness and the global trend towards digital transformation, follow the financial technology approach and make the most of the opportunities and advantages offered by digitization to banks in order to preserve their customers and compete. The study also recommended researchers and those interested in banking studies expand their research on financial technology due to the relative lack of studies covering the subject.
PELATIHAN STATISTIK DENGAN MENGGUNAKAN SPSS (STATISTICAL PRODUCT AND SERVICE SOLUTION) UNTUK PENINGKATAN KETERAMPILAN DALAM MENYELESAIKAN TUGAS AKHIR MAHASISWA SEKOLAH TINGGI ILMU EKONOMI WIYATAMANDALA

The problem faced by final year students at the Wiyatamandala College of Economics is the lack of practice in producing scientific work. Students' lack of knowledge in making scientific work is due to a lack of understanding of research methodology and mastery of the SPSS application which can help process research data using statistical techniques, even though making research is very important to fulfill students' obligations as a graduation requirement in obtaining a bachelor's degree. The result of this service is that participants understand the concept of SPSS processing with simple research examples used as material for training in analyzing research data using the SPSS application so that students can read and analyze the output results from using the SPSS application
Security Challenges in Building Blockchains Bridges and Countermeasures

The implementation of distributed ledger technologies and Blockchains has recently become high in various domains, from finance to governance.There are multiple Blockchain frameworks and networks for different use cases but still seems to be a gap in how different frameworks, protocols, and ledgers interact with each other.With the increase in the adoption of Blockchains, there is an increasing need for suitable interoperable solutions so that more value can be provided to the end user.This research focuses on analyzing how current Blockchain bridges are built and evaluating common security risks and countermeasures within the scope of interoperability.A threat model is proposed to analyze blockchain interoperability's various components, vulnerabilities, risks, and corresponding mitigation techniques.Common security vulnerabilities like centralization of trust and vulnerable smart contracts and others were identified and classified based on the kind of bridge component along with possible mitigations.Each solution, like Relays, HTLCs, Notary Schemes, and Smart Contracts, is explored.Thus, the study will help developers understand the risks involved by providing insights and pointing out the need for standardization on Blockchain interoperable solutions.
Asymmetric Transfer Hydrogenation in Flow

Key words transfer hydrogenation - chiral phosphoric acid - enantioselectivity
A Proposal for an Open-Source Bitcoin Forensics Tool

Over recent decades, the global financial paradigm has experienced significant transformations, notably the emergence and adoption of cryptocurrencies. The escalating prominence of assets like Bitcoin has inadvertently catalysed a surge in illicit activities associated with the currency. Consequently, the forensic examination of transactions within blockchains becomes imperative for the detection and surveillance of malevolent undertakings. This research delineates a preliminary pipeline for a Bitcoin forensic analysis tool. Moving forward, the ambition is to conceptualize and empirically validate this tool utilizing data procured from blockchain and ancillary sources. The methodology will harness Open-Source Intelligence (OSINT), clustering of Bitcoin addresses, and an exhaustive financial analysis. Upon finalizing the pipeline, the implementation of an open-source instrument is envisioned, poised to confer substantial advantages to the broader cryptocurrency milieu.
Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers

Whole-Slide Imaging allows for the capturing and digitization of high-resolution images of histological specimen. An automated analysis of such images using deep learning models is therefore of high demand. The transformer architecture has been proposed as a possible candidate for effectively leveraging the high-resolution information. Here, the whole-slide image is partitioned into smaller image patches and feature tokens are extracted from these image patches. However, while the conventional transformer allows for a simultaneous processing of a large set of input tokens, the computational demand scales quadratically with the number of input tokens and thus quadratically with the number of image patches. To address this problem we propose a novel cascaded cross-attention network (CCAN) based on the cross-attention mechanism that scales linearly with the number of extracted patches. Our experiments demonstrate that this architecture is at least on-par with and even outperforms other attention-based state-of-the-art methods on two public datasets: On the use-case of lung cancer (TCGA NSCLC) our model reaches a mean area under the receiver operating characteristic (AUC) of 0.970 ± 0.008 and on renal cancer (TCGA RCC) reaches a mean AUC of 0.985 ± 0.004. Furthermore, we show that our proposed model is efficient in low-data regimes, making it a promising approach for analyzing whole-slide images in resource-limited settings. To foster research in this direction, we make our code publicly available on GitHub: https://github.com/FirasGit/cascaded_cross_attention .
USE OF ENGLISH LEARNING ACCOUNTS IN INSTAGRAM TO IMPROVE EFL STUDENTS VOCABULARY

Everyone may quickly access all information in this era of the Industrial Revolution 4.0. The technology used to facilitate human activities and give comfort is continually evolving. Digitization occurs in a variety of disciplines, including education. Along with the times, education or knowledge can not only be obtained through formal and non-formal education. Instagram as an application that is widely owned by all ages, provides various features and information that can help students learn English, especially vocabulary. This study aims to find out the use of English learning accounts on Instagram help students increase their vocabulary level and how it can happen. This study is qualitative research, which uses questionnaires as its data collection. The study indicates instagram contains interesting words and images and it is very hepful to improve students vocabualry.
OdegVul: An Approach for Statement-Level Defect Prediction

Defect prediction research has been conducted for more than 40 years, with the goal of estimating the defect-prone blocks of source code. Prior studies, however, had two major limitations: (1) coarse-grained defect prediction results and (2) weak long-term dependencies modeling. As a result, developers need to review the prediction results to figure out which function or even which line of code produced the issue. In this study, we present OdegVul, a novel statement-level defect prediction model, to address these concerns. To capture both semantic and structural relationships between statements, a statement representation framework combining deep learning and graph neural networks is designed. Then the long-term dependencies between statements are encoded as a partial differential equation of a graph neural network. Through the experiment of 32 releases of 9 open-source Java projects, we found that semantic and structural dependencies are crucial to statement-level defect prediction. OdegVul outperforms other state-of-the-art (SOTA) predictors and achieves reasonable performance in cross-project statement-level defect prediction scenarios. The finer granularity of predicting results reduces the developer’s workforce in reviewing the prediction results and increases the practicality of the defect prediction model. The source code of OdegVul is available at https://github.com/CoderYinDaqiang/OdegVul.
Perceptions on Drug Use and Behavioural Change Approach: Kanyanga in Rwanda

The newspapers and media as well as scientific papers reveal that alcohol and illicit drug abuse is of major concern in rural and urban areas of Rwanda. Especially in the areas nearing the borders of the country, the illicit gin known as Kanyanga is being consumed. The government of Rwanda has tried to take various measures to stop its use but seems to need more efforts because the local population has not stopped its use, and its smugglers and users increase day to day. The use of Kanyanga was found to bring some consequences that can even lead to death. With this background, the research was conducted to investigate people’s perceptions of its use and how a behavioural approach can be used to change them. To achieve the objective of the paper, a quantitative exploratory study was conducted where data were collected via an online survey, and respondents were citizens of Rwanda from six districts of the northern part where the gin is common. The data analysis was done through visualisation and explained using the prospect theory. The findings have shown that what people expect to get from the use of Kanyanga is the opposite of what they get from it because of bad consequences including death, poverty, and domestic conflicts. From these findings, the paper recommended counselling services along with nudging a behavioural perspective to deal sustainably with this illicit gin