Reviewerly/exHarmony huggingface dataset:
Accessible Video Analytics: the Use Case of Basketball

The development of detailed and accurate analytics is nowadays considered among the essential elements that drive the preparation of a competition. In particular, video analytics contribute by providing both the athletes and the technical team an all-round perspective on the opponent through multiple recordings, in different environmental conditions and configurations, observing the technical and tactical performances across time. However, the chance of collecting video analytics is generally linked to the availability of suitable hardware and software, to collect, store, and analyze the video streams, so as to extract the relevant information. This is often considered as a privilege reserved to professional teams, which can also benefit from the presence of a dedicated personnel assigned to this task. In this paper we present a study that demonstrates that the recent developments in computer vision and machine learning have enabled the possibility of deploying accessible solutions, yet retrieving reasonable and interpretable analytics.
A new three-term conjugate gradient method for training neural networks with global convergence

Conjugate gradient methods (CG) constitute excellent neural network training methods that are simplicity, flexibility, numerical efficiency, and low memory requirements. In this paper, we introduce a new three-term conjugate gradient method, for solving optimization problems and it has been tested on artificial neural networks (ANN) for training a feed-forward neural network. The new method satisfied the descent condition and sufficient descent condition. Global convergence of the new (NTTCG) method has been tested. The results of numerical experiences on some wellknown test function shown that our new modified method is very effective, by relying on the number of functions evaluation and number of iterations, also included the numerical results for training feed-forward neural networks with other well-known method in this field.
A Study on College English Teaching under OBE Education Concept

With the deepening of college English teaching reform, college English teaching is paying more and more attention to application and goal orientation.OBE education concept emphasizes the output orientation of learning in the process of education and the practicality and application of knowledge in the practical workplace.Based on OBE education concept, this paper analyzes the advantages of OBE education concept, and studies the new college English teaching mode under OBE education concept.
The Advantages and Disadvantages of Introducing a New User-Generated Content Exception Into Copyright Legislation

User-generated content (UGC) has arose many arguments while thriving. It is important but difficult to keep a balance between encourage creation and tolerance of light infringement in the field of UGC. Introducing a new exception for UGC maybe a proper method to keep the balance which may lead uncertainty and lack of protection. On contrary, a future compulsory-licensing approach may play a better role to keep the balance. Compulsory-licensing poor provide right holder a platform to sharing their content and gain profits according to the policy of sharing economy. Besides, it also enables right holder to drop out from this sharing pool when the right holder aims to control the copyright as much as possible. This new licensing method can protect the copyright as well as encouraging the prosperity of UGC industry.
Clinical data sharing using Generative Adversarial Networks

Obtaining data is challenging for researchers, especially when it comes to medical data. Moreover, using medical data as there are concerns about privacy and confidentiality issues requires specific considerations. Generative models aim to learn data distribution via various statistical learning approaches. Among generative models, a machine learning-based approach named Generative Adversarial Networks (GANs) has proved their potential in the implicit density estimation of high dimensional data. Therefore, we suggest an approach that each healthcare organization, especially hospitals, could create and share their own GAN model, entitled Hospital-Based GANs (H-GANs), instead of sharing raw data of patients.
Coin Transfer Unlinkability Under the Counterparty Adversary Model

Unlinkability is a crucial property of cryptocurrencies that protects users from deanonymization attacks. However, currently, even anonymous cryptocurrencies do not necessarily attain unlinkability under specific conditions. For example, Mimblewimble, which is considered to attain coin unlinkability using its transaction kernel offset technique, is vulnerable under the assumption that privacy adversaries can send their coins to or receive coins from the challengers. This paper first illustrates the privacy issue in Mimblewimble that could allow two colluded adversaries to merge a person’s two independent chunks of personally identifiable information (PII) into a single PII. To analyze the privacy issue, we formulate unlinkability between two sets of objects and a privacy adversary model in cryptocurrencies called the counterparty adversary model. On these theoretical bases, we define an abstract model of blockchain-based cryptocurrency transaction protocols called the coin transfer system, and unlinkability over it called coin transfer unlinkability (CT-unlinkability). Furthermore, we introduce zero-knowledgeness for the coin transfer systems to propose a method to easily prove the CT-unlinkability of cryptocurrency transaction protocols. Finally, we prove that Zerocash is CT-unlinkable by using our proving method to demonstrate its effectiveness.
Spraying Agricultural Crops Based on Internet of Things (IoT)

Indonesia's Agriculture is a strategic sector in increasing economic growth. One of the efforts to increase agricultural production in Indonesia is through technology. Smart farming models can be developed to improve the efficiency and effectiveness of farmland management. The monitoring and watering regulation system using IOT has many benefits, including time efficiency, work effectiveness, remote operation, simplicity, and better irrigation accuracy. In this study, the Smart Farming Device was placed in a coastal area with a hot climate in the chili garden. The experiment was conducted 9 hours from 08.00 – 17.00 WIB (West Indonesian Time). The Smart Farming Device works in a modular manner controlled remotely by the internet network, which works by energy converted from the sunlight. The use of types of sensors and the sensors placed on the farmland will determine the accuracy of the information received by farmers. From the sensors parameter, we found that spraying water to the farmland does not raise the soil moisture. A wind speed sensor needs to be added to the device to determine the effect of wind blow on the environmental parameters of the farmland. The irrigation method looks more promising for improving soil moisture conditions than the method of spraying water through the air.
Probing Linguistic Knowledge in Italian Neural Language Models across Language Varieties

In this paper, we present an in-depth investigation of the linguistic knowledge encoded by the transformer models currently available for the Italian language.In particular, we investigate how the complexity of two different architectures of probing models affects the performance of the Transformers in encoding a wide spectrum of linguistic features.Moreover, we explore how this implicit knowledge varies according to different textual genres and language varieties.
Self-Mimic Mutual-Distillation for Cross-Modality Person Re-Identification

Cross-modality person re-identification is a newly rising and challenging problem, as there is a significant gap between the visible and infrared images. Though recent methods rapidly narrow the gap, the intra-modality variance is often ignored before inter-modality alignment. In this paper, we study this problem in the knowledge distillation perspective and design a self-mimic mutual-distillation method to reduce the discrepancy of each person from intra-modality feature alignment to cross-modality feature alignment. For intra-modality feature alignment, the self-mimic mechanism is implemented to simultaneously learn globally viewed, stable, and distinguish prototypes for each ID and minimize the intra-modality discrepancy. For inter-modality feature alignment, the mutual distillation is conducted to minimize the cross-modality distribution discrepancy of each person. Extensive experimental results on SYSU-MM01 and RegDB demonstrate that the proposed method achieves the best performance, outperforming state-of-the-art methods by a large margin without adding extra network parameters to the baseline. Especially, on the SYSU-MM01 dataset, our method achieves 64.8% Rank-1 and 60.2% mAP with significant gains over the latest related method.
Effects of Loneliness on Subjective Health and Moderating Effect of Having Companion Animals of Older People

The purpose of this study is to examine the effects of loneliness on subjective health and moderating effect of having companion animals of older people.For this purpose, Seoul survey data of year 2021 were utilized and 5,476 older people over age 65 living in Seoul were analyzed.Analyses methods include descriptive statistics including frequency distributions, correlation analyses, and multiple regression analyses.Results of the analyses showed that loneliness of older people had significant effects on subjective health.And having companion animals had moderating effect in the relationship between loneliness and subjective health.These results show that having companion animals can buffer the negative effects of loneliness on subjective health of older people.Results of this study indicate that interventions aimed at preventing decline of health from loneliness of older people should consider companion animals.
Construction of a semi‐dense point cloud model for a tube‐to‐tubesheet welding robot

Tube-to-tubesheet welding is widely applied in industrial fields. However, the current tubesheet welding robot still mainly relies on manual tubesheet models. Aiming to solve this problem, this paper proposed an improved direct method to automatically establish a tubesheet semi-dense point cloud model based on a selected monocular camera and a one-dimension (1D) laser rangefinder. Firstly, a laser filtering method was designed to acquire the distance between the camera and tubesheet through the 1D laser rangefinder. Then, from combing the 1D laser rangefinder data with keyframe data, the scale factor was obtained and proceeded to be processed by the Kalman filter to reduce the error. Then, the computed scale factor and all the keyframes were calculated to construct the tubesheet point cloud model through the graph optimisation algorithm. The experimental results showed that the semi-dense point cloud model of the tubesheet could be efficiently established by the proposed algorithm with row error and column error both less than 1 mm, satisfying the welding requirements.
Secure Data Sharing Based on Blockchain Technology

This report touches upon protecting information in the 5G network efficiently and securely.It further discusses upon a scheme based on blockchain technology to resolve the privacy issues in 5G networks.Illustration on how mutual trust is gained between content provider and user community by merging blockchain in data sharing is also explained.
Exploration of Digital Literacy Among Muslim Household Women in West Bengal, India

Digital literacy is the ability of an individual to locate, organize, evaluate, analyze and use the required information using digital technologies. It includes a working knowledge of several technologies and understanding of how it can be used. The main objective of the study was to explore the digital literacy of the Muslim household women from West Bengal. The present study also determines the type of device used by them, determining the usage purpose and examines the digital literacy skill.The study is Descriptive survey nature where purposive sampling techniques has been adopted for selecting the respondents from the study area of West Bengal. The total number of respondents were 103. As a research instrument investigator used two different tools- firstly Self-made questionnaire on the usage purpose. Secondly, Digital skill questionnaire developed by “Clinically validated Integrated Support for Assistive Care and Lifestyle Improvement: the Human Link –Vinci”. The data were analyzed with the help of descriptive and inferential statistics using SPSS software.The findings of the study reveals that there is significant relationship between the usage purpose of internet and the digital literacy.The highly significant relationship is found between involving in social networking sites for communication i.e., .937, which indicates that due to rapid increase of internet use through digital gadgets for communicating with different social networking sites has gradually empowered their digital literacy.The cause and effect level is high and the null hypothesis is rejected. Here digital skill is influenced by the factors of competency level, communication level, content creation level, ability to adopt safety measures and communication level.Therefore now days household women while maintaining the home tradition and culture parallel they are also maintaining track with the digital world as because some of them may have pre- literacy digital skill before their marriage or they may have learned after their marriage.
Algorithmic Trading and Challenges on Retail Investors in Emerging Markets

Mixed views on automated trading in the extant literature lead to ongoing debates on algorithmic trading (AT) and high-frequency trading (HFT). This study elaborates on the rising ethical issues and regulatory challenges of algorithmic trading and high-frequency trading in emerging markets. While developed capital markets are dominated by institutional investors, emerging markets consist of a large proportion of retail investors who may suffer from aggravated liquidity asymmetry and stock price turbulence due to HFT and AT. Furthermore, we review current regulations of HFT in the U.S. and European markets and provide a framework of regulatory enforcements on AT and HFT for investor protection in emerging markets. This study cautions policymakers in emerging markets that legal and regulatory monitoring of AT and HFT activities is especially necessary.
Optimization of local scale seismic networks applied to geothermal fields. The case of the Acoculco caldera, Mexico

The exploitation of a geothermal field can be accompanied by both natural and induced seismicity . Hence the installation of a seismic network suitable for also locating low-magnitude earthquakes, is of great interest for geothermal development, especially for monitoring the activity related to the injection or production. Most of the aspects that affect the location of earthquakes are data quality and phase picking, the inverse method chosen to solve the problem, the velocity model, and the seismic network configuration. We focus on the optimization of network configuration and the reduction of the location error is one of the alternatives to finding optimal station positions. Here we propose an improvement of the D-OPTIMAL algorithm (Tramelli et al., 2013) that tries and find optimal station positions minimizing the amount of the error ellipsoid of the event location using the D-criterion. This algorithm is computationally efficient and easy to be implemented. In this version, we also introduced the possibility to account for several prior information that is generally available when implementing a monitoring site permanently or temporarily. These a priori parameters introduced are: i) three-dimensional seismic velocity models, ii) seismic noise levels, iii) topographic gradient, and iv) H/V ratio values. The last three parameters are introduced in the station position selection using a weighted system scheme. We applied this methodology to the Acoculco geothermal field (Mexico) where an injection test was planned and executed in 2021. The comparison between the network defined using the standard approach, which considers a 1D velocity model and the installation costs, and this improved version shows the importance of introducing a priori information during the selection of the network. Installation sites showed better distribution in the region, resulting in an overall increase in the sensitivity and a decreasing of the error location estimation in the target region. The methodology presented here is easy to apply to other study cases such as active volcanoes , anthropogenic activities, or other kinds of location studies at local scale. • Microseismicity detection needs a local seismic network optimization. • A priori information improves the design of a local seismic network. • 3D seismic velocity models improve the optimization networks planning. • Topography is an important parameter to plan the logistic of the installation.
Uranium migration and radioactive characteristics of the Sarıçiçek and Sarıhan Granodiorites

The radionuclide concentrations of eU (ppm), eTh (ppm), K (%) and dose rate values were measured in Sarıçiçek (Gümüşhane) and Sarıhan (Bayburt) granodiorites for a duration of 5 minutes at each of 532 measurement points. The radioelement ratios (eU/eTh, eU/K, and eTh/K) indicating the origins of the rocks, the geochemical indicators (Ume, F parameter, and eU-(eTh/3,5) rate) showing the uranium mobility and the radioelement concentrations were calculated and mapped within the study areas. The average K, eU, and eTh concentrations were calculated as 2.98%, 3.15 ppm, and 12.45 ppm for Sarıçiçek granodiorite, and 1.83%, 2.73 ppm, and 13.6 ppm for Sarıhan granodiorite, respectively. Higher radioactivity values were observed in basaltic, sedimentary, and ultramafic rock combinations within the granodiorite masses. In the classification according to radioelement ratios, it was concluded that the rocks in the study areas formed as a mixture of upper mantle and crustal materials. In both study areas, there was uranium transport from the granodioritic masses into the surrounding rocks, and accordingly, the rocks in the surrounding formations were enriched in uranium. As a result, radioactivity levels, rock formation origins, and uranium transport of both granodioritic masses and rocks in the surrounding formations were determined by evaluation with radioelement concentration values and ratios and migration parameters. The study areas were characterized by associating them with geology in light of radioactive data.
Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning

With the rapid development of the Internet of Things (IoT), the curse of dimensionality becomes increasingly common. Feature selection (FS) is to eliminate irrelevant and redundant features in the datasets. Particle swarm optimization (PSO) is an efficient metaheuristic algorithm that has been successfully applied to obtain the optimal feature subset with essential information in an acceptable time. However, it is easy to fall into the local optima when dealing with high-dimensional datasets due to constant parameter values and insufficient population diversity. In the paper, an FS method is proposed by utilizing adaptive PSO with leadership learning (APSOLL). An adaptive updating strategy for parameters is used to replace the constant parameters, and the leadership learning strategy is utilized to provide valid population diversity. Experimental results on 10 UCI datasets show that APSOLL has better exploration and exploitation capabilities through comparison with PSO, grey wolf optimizer (GWO), Harris hawks optimization (HHO), flower pollination algorithm (FPA), salp swarm algorithm (SSA), linear PSO (LPSO), and hybrid PSO and differential evolution (HPSO-DE). Moreover, less than 8% of features in the original datasets are selected on average, and the feature subsets are more effective in most cases compared to those generated by 6 traditional FS methods (analysis of variance (ANOVA), Chi-Squared (CHI2), Pearson, Spearman, Kendall, and Mutual Information (MI)).
The effect of extensive reading on EFL learners writing performance

Extensive Reading (ER) is considered as a good learning technique to enhance every skill in language learning. It is considered to assist the improvement of other skills of language learners especially English as a Foreign Language (EFL) leaners. This study is designed to evaluate the effectiveness of ER on EFL learners’ writing performance and to identify the EFL learners’ attitude towards the effects of ER on writing performance. The study recruited a group of 59 non-English majors at a Bhinneka PGRI University. The participants were divided into two groups, i.e., experimental group and control group. The participants in the experimental group were assigned extensive reading materials as their homework to support their learning of writing meanwhile the participants in the control group were guided to write without the support of extensive reading materials. To check the differences of the participants’ writing ability before and after the intervention, pre-test and post-test were employed. A questionnaire was also delivered to the experimental group after the treatment. The results from the tests revealed that ER supported EFL learners in their writing performance. The experimental group had more significant enhancement. The results obtained from the questionnaire also indicated that the EFL learners had positive attitudes towards the use of extensive reading materials provided in their writing learning process.
An algorithm to factorize quantum walks into shift and coin operations

Abstract We provide an algorithm that factorizes one-dimensional quantum walks on an arbitrary but fixed cell structure into a protocol of two basic operations: a fixed conditional shift that transports particles between cells and suitable coin operators that act locally in each cell. This allows to tailor quantum walk protocols to any experimental set-up by rephrasing it on the cell structure determined by the experimental limitations. We give the example of a walk defined on a qutrit chain compiled to run on a qubit chain.
Detecting Object-Level Scene Changes in Images with Viewpoint Differences Using Graph Matching

We developed a robust object-level change detection method that could capture distinct scene changes in an image pair with viewpoint differences. To achieve this, we designed a network that could detect object-level changes in an image pair. In contrast to previous studies, we considered the change detection task as a graph matching problem for two object graphs that were extracted from each image. By virtue of this, the proposed network more robustly detected object-level changes with viewpoint differences than existing pixel-level approaches. In addition, the network did not require pixel-level change annotations, which have been required in previous studies. Specifically, the proposed network extracted the objects in each image using an object detection module and then constructed correspondences between the objects using an object matching module. Finally, the network detected objects that appeared or disappeared in a scene using the correspondences that were obtained between the objects. To verify the effectiveness of the proposed network, we created a synthetic dataset of images that contained object-level changes. In experiments on the created dataset, the proposed method improved the F1 score of conventional methods by more than 40%. Our synthetic dataset will be available publicly online.