Reviewerly/exHarmony huggingface dataset:
Pengembangan Bahan Ajar Interaktif Materi Kehidupan Masyarakat Pada Masa Pra Aksara Di Indonesia

The purpose of this development is to make interactive teaching materials products of public life materials during pre-literacy in Indonesia that are suitable for use in learning for social science subjects in junior high school class VII. This interactive teaching material can be used as a complementary supplement in the learning process so that it can increase students' motivation and interest in learning. This product can be used independently or in groups. Interactive teaching materials are packaged with a powerpoint show display, using personal computers (PCs), laptops and the like. The detailed content of the material and the interface design are designed with a user friendly system so that it is easy for users to understand and use. This development procedure uses the model of Lee &amp; Owens, 2004. Responses from media experts and material experts to this interactive teaching material get a positive response. The trial of the use of interactive teaching materials was carried out on grade VII junior high school students. Overall, interactive teaching materials on people's life during pre-literacy in Indonesia are suitable for use in learning.
Heterodyne spectrometer sensitivity limit for quantum networking

Optical heterodyne detection-based spectrometers are attractive due to their relatively simple construction and ultrahigh resolution. Here we demonstrate a proof-of-principle single-mode optical-fiber-based heterodyne spectrometer that has picometer resolution and quantum-limited sensitivity around 1550 nm. Moreover, we report a generalized quantum limit of detecting broadband multispectral-temporal-mode light using heterodyne detection, which provides a sensitivity limit on a heterodyne detection-based optical spectrometer. We then compare this sensitivity limit to several spectrometer types and dim light sources of interest such as spontaneous parametric downconversion, Raman scattering, and spontaneous four-wave mixing. We calculate that the heterodyne spectrometer is significantly less sensitive than a single-photon detector and is unable to detect these dim light sources, except for the brightest and narrowest-bandwidth examples.
Breast Cancer Prediction using KNN, SVM, Logistic Regression and Decision Tree

Abstract: Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as astrong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), RandomForest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer WisconsinDiagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of thisresearch paperisto predict and diagnosis breast cancer, using machine- learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%). All the work is done in the Anaconda environment based on python programming language and Scikit-learn library. Keywords: Decision Tree, KNN, SVM, Malignant, Benign , Logistic Regression
An appraisal of ethical issues and the effect of artificial intelligence on the cryptocurrency market

The world has gradually embraced Cryptocurrency at various levels of capacity and it is not governed or regulated by any Control system. It has over time been used by participants as a means to invest despite the volatility of the market. Though Government does not have the means to outright stop the virtual marketing deals of Cryptocurrency, recently Nigerian government called for deposit money banks to close all corresponding accounts which are perceived to be involved in Cryptocurrency trading. The Crypto market has evolved as the digital and internet world exponentially evolved. Sadly, Cryptocurrency has been misused by illegal participants and exploited wrongly which has caused a lot of worrisome issues around the world like loss of funds, hacking of financial databases, terrorist financing, and identity fraud. This paper aims to identify the methodology surrounding the world of Cryptocurrency and how advancement in technology through artificial intelligence can help solve ethical issues related to cryptocurrency trading. Based on these appraisals, we highlighted procedures to provide a safer, secure, and relatable market for every interested individual.
S <scp>u</scp> G <scp>e</scp> R: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation

Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networks (GNNs) have been applied to this problem and achieved superior performance, existing methods underexplore the graph-level GNN methods, which exhibit great potential in traditional recommender system. Furthermore, they usually lack the transferability from one domain with sufficient supervision to another domain which might suffer from the label scarcity issue. In this work, we propose a subgraph-based Graph Neural Network model, SuGeR, for bundle recommendation to handle these limitations. SuGeR generates heterogeneous subgraphs around the user-bundle pairs and then maps those subgraphs to the users' preference predictions via neural relational graph propagation. Experimental results show that SUGER significantly outperforms the state-of-the-art baselines in the basic and the transfer bundle recommendation tasks by up to 77.17% by [email protected] The source code is available at: https://github.com/Zhang-Zhenning/SUGER.
Contact tracing solutions for COVID-19: applications, data privacy and security

&#x0D; &#x0D; &#x0D; Since the beginning of 2020, COVID-19 has had a strong impact on the health of the world population. The mostly used approach to stop the epidemic is the application of controls of a classic epidemic such as case isolation, contact monitoring, and quarantine, as well as physical distancing and hygienic measures. Tracing the contacts of infected people is one of the main strategies for controlling the pandemic. Manual contact tracing is a slow, error-prone (by omission or forgotten) process, and vulnerable in terms of security and privacy. Furthermore, it needs to be carried out by specially trained personnel and it is not effective in identifying contacts with strangers (for example in public transport, supermarkets, etc). Given the high rates of contagion, which makes difficult an effective manual contact tracing, multiple initiatives arose for developing digital proximity tracing technologies. In this paper, we discuss in depth the security and personal data protection requirements that these technologies must satisfy, and we present an exhaustive and detailed list of the various applications that have been deployed globally, as well as the underlying infrastructure models and technologies they used. In particular, we identify potential threats that could undermine the satisfaction of the analyzed requirements, violating hegemonic personal data protection regulations.&#x0D; &#x0D; &#x0D;
Contrastive Learning for Multi-Modal Automatic Code Review

Automatic code review (ACR), aiming to relieve manual inspection costs, is an indispensable and essential task in software engineering.The existing works only use the source code fragments to predict the results, missing the exploitation of developer's comments.Thus, we present a Multi-Modal Apache Automatic Code Review dataset (MACR) for the Multi-Modal ACR task.The release of this dataset would push forward the research in this field.Based on it, we propose a Contrastive Learning based Multi-Modal Network (CLMN) to deal with the Multi-Modal ACR task.Concretely, our model consists of a code encoding module and a text encoding module.For each module, we use the dropout operation as minimal data augmentation.Then, the contrastive learning method is adopted to pre-train the module parameters.Finally, we combine the two encoders to fine-tune the CLMN to decide the results of Multi-Modal ACR.Experimental results on the MACR dataset illustrate that our proposed model outperforms the state-of-the-art methods.
Investigating the reliability impacts of neutron-induced soft errors in aerial image classification CNNs implemented in a softcore SRAM-based FPGA GPU

This work investigates the impacts of neutron-induced soft errors on the reliability of aerial image classification neural networks running on a softcore GPU implemented in an SRAM-based FPGA. We designed and trained fixed-point and floating-point all-convolutional neural networks to classify four-channel aerial images from the SAT-6 dataset, extracted from the U.S. National Agriculture Imagery Program, and implemented on FGPU, a configurable open-source GPU-like processor with floating-point arithmetic hardware. Results from fast neutron and thermal neutron irradiation experiments coupled with configuration bitstream fault injection campaigns show that the impact of soft errors in the aerial image classification must be taken care of with hardening techniques.
Detection and Control of Epileptiform Regime in the Hodgkin–Huxley Artificial Neural Networks via Quantum Algorithms

The problem of detection and the following suppression of epileptiform dynamics in artificial neural networks (ANN) still is a hot topic in modern theoretical and applied neuroscience. For the purpose of such modeling, the Hodgkin–Huxley (HH) elements are important due to the variety of their behavior such as resting, singular spikes, and spike trains and bursts. This dynamical spectrum of individual HH neurons can cause an epileptiform regime originated in the hyper-synchronization of the cell outcomes. Our model covers the detection and suppression of ictal behavior in a small ANN consisting of HH cells. The model follows our approach [Borisenok et al., 2018] for the HH neurons as a classical dynamical system driving the collective neural bursting, but here we use a quantum paradigm-based algorithm emulated with the pair of HH neurons. Such emulation becomes possible due to the complexity of the individual 4d HH dynamics. The linear chain of two HH neurons is connected to the rest of ANN and works autonomously. The first neuron plays a role of the detecting element for the hyper-synchronization in the ANN and the quantum algorithm emulator; while the second one works as a measuring element (emulation of the quantum measurement converting the signals into the classical domain) and the trigger for the feedback suppressing the epileptiform regime. We use here the speed gradient algorithm for controling the emulating neuron and discuss its pros and cons to compare with our classical model of epileptiform suppression.
IMPACT OF CORPORATE SOCIAL RESPONSIBILITY PRACTICES ON FINANCIAL PERFORMANCE: EVIDENCE FROM SELECTED MENA REGION COMMERCIAL BANKS

Nowadays, corporations are being held accountable for their actions that affect their surroundings. Thus, corporate social responsibility (CSR) has been integrated in their business models, which can impact their financial performances (FP). Previous researches regarding the relationship between CSR and FP have yielded mixed results. Thus, this research aims at identifying the impact of CSR practices in the environment (ENV), human resources (HR), products and consumers (PC) and community involvement (CI) on FP of 81 commercial banks operating in selected MENA countries for the year 2018. Data were gathered from CSR report and annual report for each bank. The multiple regression analysis reveals that there is a positive significant relationship between CSR practices in HR and PC and FP of MENA banks. However, there is a non-significant relationship between CSR practices in the ENV and CI and FP of these banks. Thus, MENA banks are encouraged to engage in CSR practices revolving around HR and PC in order to enhance their FP.
Penerapan Algoritma Genetika untuk Penjadwalan Mata Pelajaran

Scheduling of subjects in schools often has problems with the course of teaching and learning activities. There are various variations in this problem, due to differences in different policies in scheduling, at Lazuardi Global Compassionate School, Depok City, the schedule is made by making a rough description of the schedule and then transferring it to the Google Sheet application, scheduling in this way is still often mismatched. scheduling is made and also takes quite a long time. This causes the optimal scheduling difficult to obtain. This study discusses the application of genetic algorithms for scheduling subjects at Blue Blue Compassionate School Elementary School. Genetic algorithm is an algorithm that can solve problems in lesson scheduling and provide solutions to get optimal scheduling results. Based on the results of the tests carried out, the parameters used are the number of populations, the number of generations, crosses and mutations that the genetic algorithm can work well and solve problems by producing optimal scheduling and shorten the time of making the schedule so that the making of the schedule becomes effective and efficient.
COVID-19 Outbreak, State of a Questionable Dilemma, or a Learning Escape: Second Language Acquisition Within Virtual Learning and Social Contact

Digital learning, social contact and COVID-19 outbreak are three different phrases have been often heard, read or spoken about since 2020. Undoubtedly, these phrases have brought fear of uncertainty, but also hope to find a new perspective to keep life matters on-going including learning in general, and language acquisition in particular. In order to find the impact of virtual learning and social contact on second language acquisition, a total of 389 second language university students have either online surveyed or been interviewed to validate the results and avoid any misleading. After analyzing the data, results revealed that virtual learning has given the learner a chance to develop understanding of different aspects of learning a second language. Flexibility, free-learning, and richness of knowledge source are positive aspects of virtual learning. Likewise, social contact has an essential role in deepen the understanding of contextual use, semantics and lexis, and other aspects of second language acquisition. Interestingly, both social and virtual aspects of learning motivate the learner to reach language mastery. Therefore, the findings urge the use of virtual learning regarding social contact in developing linguistic skills in second language acquisition. Further insights are recommended to enrich the future research in this topic.
Emotional Education: A New Paradigm

The purpose of this article is to make an approach from the advances of neuroscience and neuroeducation to the importance of the implementation of emotional education in the Colombian educational system. First, some constructs related to emotional intelligence, emotional education and socioemotional competencies are examined, and the theories and concepts that support these terms are approached from the perspectives of different authors. Secondly, a general analysis of the Colombian educational system is made, describing the predominant role played by educators in emotional education, and the current regulations related to this type of education. Finally, a reflection is made on the reality of the Colombian educational system and how important it would be for the country to include emotional education in the educational plan of students.
Implementasi Bee Colony Optimization Pada Pemilihan Centroid (Klaster Pusat) Dalam Algoritma K-Means

Clustering is a method that is used to divide the data into several groups of parts. K-means (KM) is an algorithm that is often used in clustering, only just the result of KM often times get stuck in local optima i.e. the optimal solution (both maximum or minimal) on the candidate solution in the nearest neighbor only, not the whole of all existing solutions or what is commonly called the global optima. In this study aims to do improve the cluster determination process on the Kmeans algorithm using the Bee Colony Optimization (BCO) algorithm. BCO is an algorithm that works based on the way the bees search for food , BCO is famous for being able to escape from the local optima trap by recognizing which results are best from a series of optimal results . Combining BCO with KM begins with selecting a source of food early in random and using KM to resolve all the problems of clustering at every step BCO next and keep sources of food best in each iteration. The result of this research is that the BCOKM method has been proven to be able to solve the problem of data sharing, where the BCOKM method is able to form a good cluster, as shown by the resulting fitness value (the lowest value is 1221.53 and the highest value is 1233.28) all of which are better than the fitness value using K-means (1251.42). Likewise in terms of accuracy, where the use of BCOKM all showed better results (83.16%-83.30%) than the use of only K-means (83.09%)
On the Bahadur Representation of Quantiles for a Sample from ρ*-Mixing Structure Population

In this paper, we establish the strong consistency and the Bahadur representation of sample quantiles for ρ*-mixing random variables.Additionally, the asymptotic normality and the Berry-Esseen bound of sample quantiles for ρ*-mixing random variabl es are presented.Moreover, numerical simulation is presented to ilustrate obtained results.
An Adaptive Temporal-Concept Drift Model for Sequential Recommendation

Recently, owing to the great advances in Web 2.0 and mobile devices, various online commercial services have emerged. Recommendation systems play an important role in dealing with abundant product information from massive numbers of online e-commerce transactions. Providing an accurate recommendation at the correct time to customers can contribute to a surge in business success. In this paper, an adaptive temporal-concept drift learning-based recommendation system, ATCRec, is developed for precisely tackling the sequential recommendation problem. We embed sequences of items into the latent spaces and learn both general preferences and sequential patterns concurrently via a recurrent neural network. Specifically, ATCRec captures dynamic changes in the temporal and concept drift contexts by modifying the gate units in a traditional recurrent neural network. The proposed model provides a unified and flexible network structure to learn and reveal the opaque variation of user preferences over time. We evaluate the robustness and performance of ATCRec on two real-world datasets, and the experimental results demonstrate that ATCRec consistently outperforms existing sequential recommendation approaches on various metrics. This indicates that integrating users' temporal information and concept drift variation through time are indispensable in improving the performance of recommendation systems.
Quantum phase measurement of two-qubit states in an open waveguide

We present a method for quantum state tomography within a single-excitation subspace of two-qubit states in an open waveguide. The system under investigation consists of three qubits in an open waveguide, separated by a distance comparable to the wavelength of the electromagnetic field. We show that the modulation of the frequency of the central ancillary qubit allows us to obtain unambiguous information about the initial phase difference ${\ensuremath{\varphi}}_{1}\ensuremath{-}{\ensuremath{\varphi}}_{3}$ of the edge qubits via the measurement of the evolution of their probability amplitudes.
Tilt angle optimization for maximization of solar irradiance for a Photo- Voltaic panel for Chandil, Jharkhand

Being positioned over subtropical belt, India has ample amount of sunlight throughout the year. Thus, energy from day light is one of the most promising sources to fill the demand-supply gap of power and this can be done effortlessly by PV technology. In this work we have tried to explore the possibilities to achieve maximum benefit of day light energy by adjusting the PV system in accordance with the sun’s position. This can be done by placing the panel over inclined axis as compared to horizontal. The most effective inclination has been computed for Chandil, located in the state of Jharkhand, on monthly basis throughout the year. The orientation was changed from 0°-90° with lap of 0.05° in order to compute maximum radiation. It has been concluded that the average annual tilt in the axis of panel should be 22.454° which increases the energy by utmost 13%.
Penerapan Decision Tree Algoritma C4.5 Dalam Penentuan Izin Pembongkaran Muatan Kapal

Along with the increasing number of bulk cargoes that are dismantled every year at belawan port and for the creation of services in accordance with expectations, it is necessary to develop services in support of indonesia's logistics improvement readiness, especially in terms of demolition. Utilization of machine learning using the C4.5 algorithm can make it easier to conduct selection and classification of the feasibility of ships that get permission for demolition activities. The use of the C4.5 algorithm will produce a decision tree that can equalize the results of data mining, so that the information obtained from the data will be easier to identify in testing methods using the Orange Data Mining tool. The results obtained by the C4.5 algorithm in the form of a decision tree with an accuracy value of 84%, 90% precision and 84% recall.
Target Tracking Algorithm in Football Match Video Based on Deep Learning

After the introduction of online learning mechanism, the traditional target tracking algorithm in football game video based on TLD has good tracking ability, but it will lose the target when the target is seriously obscured. Therefore, a soccer game video target tracking algorithm based on deep learning is proposed. The target detection algorithm of GoogLeNet-LSTM is used for faltung to obtain the feature mapping array. After processing, a high reliability candidate box for training and matching is obtained, and the feature maps of the detection results are collected to obtain the depth features required for tracking. Scale space discriminant tracking algorithm and Markov Monte Carlo algorithm are used to track single target or multi-target, respectively. Experimental results show that the average frame rate of the algorithm is maintained above 35 Hz, and the tracking time is about 12.5 s. The average center position deviation index is 39, the average coverage index is 40, and the resource utilization is low. The algorithm can track the target in the football game video well.