Reviewerly/exHarmony huggingface dataset:
Development of Study Room Blog in Learning Reading Literacy in Elementary School

Life has been changing rapidly. Industrial Revolution 4.0 is developed for people to make a novelty. Education as one of the important parts of people’s life is needed to be prepared for every change to improve the teaching and learning system. This article discusses a study conducted by using developmental research with the development of the 4-D design. This study was a Study Room Blog that was conducted for students who could learn individually based on the previous research and only focused on web development or e-module. The advantage of this Study Room Blog was equipped with an evaluation of students’ scores that could be accessed by teachers. The study involved the students in Elementary School to see their reading literacy. Based on the validator assessment result, the average score from the validation of IT-Technology Experts was 80, which was categorized as good. And the average score from the validation of the Linguist was 81.17, which was categorized as very good. It means that the blog could be used in the teaching and learning activity. After obtaining the validation assessment result from validators, then, this study room blog was applied by using a limited try-out which was applied in two classes, the experimental and control classes. In the experimental class, the average score of pre-test result was 50.71 which increased to the average score of post-test result, 80.9. In the control class, meanwhile, the average score of pre-test result was 60.57 which increased to the average score of post-test result, 75.89. Thus, the use of the study room blog was increasingly effective in learning reading literacy. It means that the study gives a contribution to the teacher and students in increasing reading literacy through the Study Room Blog as an interactive media in teaching and learning activities.
Evaluation of hybrid algorithms (constraint base and bound and collapse) in building Bayesian networks structures

Bayesian Networks (BN) is a simple probabilistic graph model built from Bayes probability theory and graph theory. The probability theory is directly related to the data, while the graph theory is directly related to the form of representation to get. BN is a simple Probabilistic Graphical Model (PGM) built from Bayes probability and graph theory. BN can provide simple and solid information about opportunity information. Based on its components, BN consists of Bayesian structure (Bs) and Bayesian parameters (Bp). The constraint base (CB) algorithm is an algorithm that combines two approaches, namely the dependency analysis approach and the search and scoring approach. The purpose of this research is to build a BN structure for incomplete data and to obtain a structure search algorithm that is computationally easy to work and does not require node ordering. The algorithm consists of two phases, namely the first phase is obtained (as part of) the CB algorithm, the result is node ordering. The second phase is designed to study the BN structure from data that has missing values, which is the same as that applied by the Bound and Collapse (BC) algorithm. BN has two algorithms that can work on complete and incomplete databases, namely the hybrid algorithm. Hybrid Algorithm is an algorithm that combines two methods in building structural construction, namely dependency analysis and search and scoring methods. The Hybrid Algorithm can construct structures in the form of graphs and relationships between nodes and display variable probability values based on complete and incomplete database inputs.
Steganalysis of Compressed Speech Based on Global and Local Correlation Mining

Most of the existing steganalysis methods are designed for specific steganography methods in low-bit-rate compressed speech stream and lack of generalization ability.In practical applications, the steganography methods in compressed speech are various and cannot be predicted in advance.We can only employ numerous possible steganalysis method to detect, which is laborious and time-consuming, and cannot achieve real-time detection.Therefore, it is necessary to develop a general steganalysis method that can detect multiple steganography methods simultaneously for compressed speech.To this end, we propose a steganalysis method based on global and local correlation mining in this paper.Firstly, we introduce a codeword distributed embedding module to transform the compressed codewords into a compact feature representation.Then, we propose global-guided correlation mining module and local-guided correlation mining module to extract the correlation change before and after steganography in the view of global and local.Finally, the detection results can be obtained by the full connection layers.Experimental results show that the proposed method can reach a better detection performance than the existing steganalysis methods at different embedding rates and speech lengths.
Design Patterns for a Knowledge-Driven Analytical Platform

The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. In order to be viable a modern knowledge-based analytical platform should be able to integrate heterogeneous information, present it to users in an understandable way and to support tools for functionality extensibility. In this paper we introduce an ontological approach to information integration and propose design patterns for developing analytical platform core functionality such as ontology repository management, domain-specific languages (DSLs) generation and source code round-trip synchronization with DSL-models.
Optimizing Hyperparameters for Improvement in Software Vulnerability Prediction Models

Software vulnerability provides the gateway for attackers to breach the confidentiality, integrity, and availability of information systems. Therefore, the prediction of software vulnerability is an important concern for developing secure software. Software vulnerability prediction (SVP) models predict whether a software component is vulnerable or not. Studies have shown that the model’s efficiency is dependent on its hyperparameter settings. In order to build an effective model, their hyperparameters need to be optimized. In our study, we intend to find the impact of hyperparameter optimization on the performance of SVP models. We performed the experiments using python hyperparameter optimization framework ‘Optuna’ to find the best hyperparameters for eight machine learning algorithms on three public datasets Drupal, Moodle and PHPMyAdmin. We found the p-values to be less than 0.05 in 19 out of 24 cases. Hence, hyperparameter optimization is 79.17% effective in increasing the efficacy of SVP models in our study.
Impact of the Implementation of the E-Accounting System on the Internal Control System

This research was conducted to analyze the impact of implementing the system e-accounting on the internal control system at PT Colamas Indah Sejati.This research is descriptive research with a qualitative approach.Data collection procedures in this study were observation and interviews with managers, accounting, and finance company admins.The results of this study are that this company has used accounting for the company's operational activities, but there are still cases of fraud committed by employees that harm the company.Currently, the audit control process at PT Colamas Indah Sejati to avoid data loss every day, a closing process is carried out then the data is backed up to own cloud which is software for managing web-based data storage and sharing.Each employee is given their user which aims for privacy and to avoid fraud.So if the inspection will be easier.Each user is also given restrictions, meaning that if the accounts receivable part is settled, the employee cannot access the purchasing department.Currently, the company is preparing to use ERP (Enterprise Resource Planning) software.to be able to manage and integrate various operational activities within the company.
Sign Language Recognition using Deep Learning

Abstract: Sign language is a way of communicating using hand gestures and movements, body language and facial expressions, instead of spoken words. It can also be defined as any of various formal languages employing a system of hand gestures and their placement relative to the upper body, facial expressions, body postures, and finger spelling especially for communication by and with deaf people. The project that is being built is to recognize the action performed by the person/user in sign language using Deep learning. Ordinary people are not well versed in sign language. The project tries to solve this problem using deep learning that is precisely using TensorFlow. In the project a LSTM (long-short term memory) model in deep Learning is built using TensorFlow to categories the action the user is doing. This will help the user with special needs to communicate with other people using the application we built. By this we can bridge the gap between the especially abled people and ordinary people.
Controlling Remote Robots Based on Zidan’s Quantum Computing Model

In this paper, we propose a novel algorithm based on Zidan's quantum computing model for remotely controlling the direction of a quantumcontrolled mobile robot equipped with n-movements.The proposed algorithm is based on the measurement of concurrence value for the different movements of the robot.Consider a faraway robot that moves in the deep space (e.g., moves toward a galaxy), and it is required to control the direction of this robot from a ground station by some person Alice.She sends an unknown qubit α |0 + β |1 via the teleportation protocol to the robot.Then, the proposed algorithm decodes the received unknown qubit into an angle θ, that determines the motion direction of the robot, based on the concurrence value.The proposed algorithm has been tested for four and eight movements.Two simulators have been tested; IBM Quantum composer and IBM's system, The two simulators achieved the same result approximately.The motion of any part of the robot is considered, if it has a pre-existing sensor system and a locomotive system,.We can use this technique in many places like in space robots (16 directions).The results show that the proposed technique can be easily used for a huge number of movements.However, increasing the number of movements of the robot will increase the number of qubits.
Solar Energy Meteorology in Agriculture – an X-ray of Solar Irradiance

This study describes the role of solar energy meteorology in agriculture. The objectives of the study are to be familiar with solar meteorological parameters, evaluate the effects of solar meteorology on agricultural productivity and determine the solar irradiance of the Ado-Ekiti. The study was carried out using secondary data sourced for book, internet and from the Atmosphere 41 All-in-One automatic weather station of the Department of Agricultural and Bio-environmental Engineering of the Federal Polytechnic, Ado-Ekiti. Global solar radiation measured between 1995 and 2005 by Estonian Meteorological and Hydrological Institute was summarized. Also, sums and average solar irradiance in Ado-Ekiti as measured in the automatic weather station of the Department of Agricultural and Bio-Environmental Engineering, the Federal Polytechnic, Ado-Ekiti were determined and 1057 KW/m2 and 7.367 KW/m2 values were obtained for the month of August, 2020, respectively. The study concludes that these solar irradiance values are critical in the design of solar PV systems for crop drying, irrigation system and electrification of farms towards enhancing agricultural productivity in Ado-Ekiti. The study therefore recommends that the solar irradiance should be measured for a longer period of time.
Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

The complexity of the data type and distribution leads to the increase in uncertainty in the relationship between samples, which brings challenges to effectively mining the potential cluster structure of data. Ensemble clustering aims to obtain a unified cluster division by fusing multiple different base clustering results. This paper proposes a three-way ensemble clustering algorithm based on sample’s perturbation theory to solve the problem of inaccurate decision making caused by inaccurate information or insufficient data. The algorithm first combines the natural nearest neighbor algorithm to generate two sets of perturbed data sets, randomly extracts the feature subsets of the samples, and uses the traditional clustering algorithm to obtain different base clusters. The sample’s stability is obtained by using the co-association matrix and determinacy function, and then the samples can be divided into a stable region and unstable region according to a threshold for the sample’s stability. The stable region consists of high-stability samples and is divided into the core region of each cluster using the K-means algorithm. The unstable region consists of low-stability samples and is assigned to the fringe regions of each cluster. Therefore, a three-way clustering result is formed. The experimental results show that the proposed algorithm in this paper can obtain better clustering results compared with other clustering ensemble algorithms on the UCI Machine Learning Repository data set, and can effectively reveal the clustering structure.
Deep Neural Network based Anomaly Detection for Real Time Video Surveillance

One of the main concerns across all kinds of domains has always been security. With the crime rates increasing every year the need to control has become crucial. Among the various methods present to monitor crime or any anomalous behavior is through video surveillance. Nowadays security cameras capture incidents in almost all public and private place if desired. Even though we have abundance of data in the form of videos they need to be analyzed manually. This results in long hours of manual labour and even small human discrepancies may have huge consequences negatively. For this purpose, a Convolution Neural Network (CNN) based model is built to detect any form of abnormal activities or anomalies in the video footages. This model converts the input video into frames and detects the anomalous frames. To increase the efficiency of the model, the data is de-noised with Gaussian blur feature. The avenue dataset is used in this work to detect and predict various kinds of anomalies. The performance of the model is measured using classification accuracy and the results are reported.
Ontology Rules for Library Research Services Recommendation System

This research aims to present the design and construction of guidelines on the ontology rules for research support services of the Khon Kaen University Library. In this research, there was a usage of Protégé version 3.5 to build an ontology into a knowledge base in the form of Ontology Web Language (OWL). The results showed ten ontology rules: 1) Rules for plagiarism recommendations 2) Rules for plagiarism recommendations on journal quality audits for academic publications 3) Rules for displaying recommendations on information retrieval in Web OPAC 4) Rules for displaying recommendations for information literacy training programs 5) Rules for displaying recommendations on thesis database lists 6) Rules for displaying recommendations 7) Rules for displaying instructions on e-journals databases, 8) Rules for displaying instructions on citations in the next section, 9) Rules for displaying instructions on endnote citations, and 10) Rules for displaying advice on research aids. The measurement accuracy (Precision) was 92.73%, the completeness (Recall) was 90.74%, and the F-measure was 91.72%.
Where Does the Robustness Come from?

This paper aims to provide a thorough study on the effectiveness of the transformation-based ensemble defence for image classification and its reasons. It has been empirically shown that they can enhance the robustness against evasion attacks, while there is little analysis on the reasons. In particular, it is not clear whether the robustness improvement is a result of transformation or ensemble. In this paper, we design two adaptive attacks to better evaluate the transformation-based ensemble defence. We conduct experiments to show that 1) the transferability of adversarial examples exists among the models trained on data records after different reversible transformations; 2) the robustness gained through transformation-based ensemble is limited; 3) this limited robustness is mainly from the irreversible transformations rather than the ensemble of a number of models; and 4) blindly increasing the number of sub-models in a transformation-based ensemble does not bring extra robustness gain.
About Russian-Chinese Scientific and Educational Cooperation at the Present Stage

The article is dedicated to the analysis of the current state of international cooperation between Russia and China in the field of science and education. Scientific and educational cooperation between Russia and China is of great importance for both countries. In this regard, it is of interest to present the materials of the monitoring of scientific and educational cooperation with China, carried out through the Ministry of Education and Science of Russia, supplementing them with relevant information on measures to overcome the consequences of the «covid crisis» in the field of scientific and educational cooperation, implemented through the Ministry of Education and Science.
Artificial intelligence in the research of consciousness and in social life (in honor of 70-years anniversary of A. Turing’s paper “Computing Machinery and Intelligence” (papers of the “round table”)

The article contains the papers of the “round table” discussion that was held at the Depart­ment of Philosophy of the State Academic University for the Humanities on October 30th 2020. The “round table” was dedicated to the 70-years anniversary of the publication of Alan Turing’s famous paper “Computing Machinery and Intelligence” (1950). The speakers have discussed the role of the artificial intelligence research in understanding of human con­sciousness and natural intelligence, the possibilities and dangers of artificial intelligence in­troduction in different spheres of modern life – education, political activity, social communi­cations. The importance of the new philosophical analysis of the Turing’s paper considering new digital challenges and artificial intellectual systems development was demonstrated.
Convolutional Conditional Neural Processes

We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data. Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and images. The model embeds data sets into an infinite-dimensional function space as opposed to a finite-dimensional vector space. To formalize this notion, we extend the theory of neural representations of sets to include functional representations, and demonstrate that any translation-equivariant embedding can be represented using a convolutional deep set. We evaluate ConvCNPs in several settings, demonstrating that they achieve state-of-the-art performance compared to existing NPs. We demonstrate that building in translation equivariance enables zero-shot generalization to challenging, out-of-domain tasks.
Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data

Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.
Logical Performance of 9 Qubit Compass Codes in Ion Traps with Crosstalk Errors

We simulate four quantum error correcting codes under error models inspired by realistic noise sources in near-term ion trap quantum computers: $T_2$ dephasing, gate overrotation, and crosstalk. We use this data to find preferred codes for given error parameters along with logical error biases and a pseudothreshold which compares the physical and logical gate failure rates for a CNOT gate. Using these results we conclude that Bacon-Shor-13 is the most promising near term candidate as long as the impact of crosstalk can be mitigated through other means.
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability

Explaining AI systems is fundamental both to the development of high performing models and to the trust placed in them by their users. The Shapley framework for explainability has strength in its general applicability combined with its precise, rigorous foundation: it provides a common, model-agnostic language for AI explainability and uniquely satisfies a set of intuitive mathematical axioms. However, Shapley values are too restrictive in one significant regard: they ignore all causal structure in the data. We introduce a less restrictive framework, Asymmetric Shapley values (ASVs), which are rigorously founded on a set of axioms, applicable to any AI system, and flexible enough to incorporate any causal structure known to be respected by the data. We demonstrate that ASVs can (i) improve model explanations by incorporating causal information, (ii) provide an unambiguous test for unfair discrimination in model predictions, (iii) enable sequentially incremental explanations in time-series models, and (iv) support feature-selection studies without the need for model retraining.
Many-qutrit Mermin inequalities with three measurement settings

Mermin inequalities are derived for systems of three-state particles (qutrits) employing three local measurement settings. These establish perfect correlations which violate local realistic bounds more strongly than those previously reported with two bases. The quantum eigenvalue of the Mermin operator grows as the dimension of the Hilbert space, $3^N$, rather than $2^N$, as obtained with two measurement bases. The number of distinct GHZ contradictions also increases as $3^N$.