Reviewerly/exHarmony huggingface dataset:
ExAIS

Neural networks can be regarded as a new programming paradigm, i.e., instead of building ever-more complex programs through (often informal) logical reasoning in the programmers' mind, complex 'AI' systems are built by optimising generic neural network models with big data. In this new paradigm, AI frameworks such as TensorFlow and PyTorch play a key role, which is as essential as the compiler for traditional programs. It is known that the lack of a proper semantics for programming languages (such as C), i.e., a correctness specification for compilers, has contributed to many problematic program behaviours and security issues. While it is in general hard to have a correctness specification for compilers due to the high complexity of programming languages and their rapid evolution, we have a unique opportunity to do it right this time for neural networks (which have a limited set of functions, and most of them have stable semantics). In this work, we report our effort on providing a correctness specification of neural network frameworks such as TensorFlow. We specify the semantics of almost all TensorFlow layers in the logical programming language Prolog. We demonstrate the usefulness of the semantics through two applications. One is a fuzzing engine for TensorFlow, which features a strong oracle and a systematic way of generating valid neural networks. The other is a model validation approach which enables consistent bug reporting for TensorFlow models.
Higher Education Institutions

10 OUTCOMES: FROM STANDARDIZED KNOWLEDGE ACQUISITION TO PERSONALIZED KNOWLEDGE, SKILLS AND ATTRIBUTES was published in Envisioning the Future of Learning for Creativity, Innovation and Entrepreneurship on page 80.
Detection of Public Sentiment Analysis Model on the Implementation of PPKM in Indonesia

Covid-19 pandemic which has been being serious problem in Indonesia indirectly force Indonesian government to issue policies in order to decrease the number of Covid-19 spread. One of the policies is the Implementation of Restrictions on Community Activities (PPKM) in Java-Bali region from January 11-25, 2021. Due to its continued implementation, this policy raises pros and cons in the community. This research’s goal is to determine the best classification model and determine the effect of adding feature engineering in analyzing public sentiment on PPKM with scrapping data from Twitter so that with the best model, it is possible to classify public responses to PPKM automatically. The twitter scrapping dataset is preprocessed first, which includes case folding, tokenizing, filtering, stemming, and term weighting to clean the data. After preprocessing and through the analysis steps, it concludes that using feature engineering can increase the accuracy of the best selected four models. The logistic regression method with feature engineering with accuracy rate of 87.50% become the best method. In conclusion, the best suggested model to analyze public sentiment using Twitter scrappimg towards PPKM is by using the logistic regression.
Knowledge Management System in Official Statistics: An Empirical Investigation on Indonesia Population Census

National statistical offices around the world show a strong interest in producing reliable, objective, and accurate information in compliance with a high level of professional and scientific standards. Such a set of information provided by government agencies is known as the official statistics. To support the potential of knowledge-based business processes and deliver high-quality public services, knowledge management systems (KMS) are undoubtedly required. In this work, we study the impact of embracing KMS in one of the most massive scale statistical census in South East Asia, the 2020 Indonesia Population Census (IPC2020). The regression analysis is utilized in this study where the perceived usefulness is the dependent variable and the perceived ease of use become the independent variable. Our findings reveal that KMS utilization gains a positive influence on the perceived ease of use and usefulness among the stakeholders and organizing personnel. This provides an incentive to enlarge the range of implementation and improve the system and infrastructure capability to better support the knowledge-driven collaboration among stakeholders of the statistical office.
Implementation of the C45 Algorithm in Classifying Classes

Mts Swasta YPII Kotarih is one of the educational institutions which in its implementation classifies several classes for students, one of which is the superior class. However, in practice, each person is still selected based on the rank in each class, making the classification process less accurate and efficient, a computerized classification technique is needed in its implementation, the classification technique used by Data Mining is the c4.5 algorithm that works with the Decision tree to find the highest gain value from each criterion existing criteria, the criteria used in this study are the subjects of Q. Hadith, Akidah, Fiqh, SKI, Arabic, Tahfizh, General research results show that from There are 3 criteria that are most influential in the classification process, namely General, Arabic, Fiqh, implemented in the form of a computerized system. Based on the research results, the highest gain value is the highest gain is General with a value of 0.3629, so that the general attribute becomes node 1 or the root of the decision tree. second highest gain B.Arabic attribute, with a gain value of 1.7189, and the third highest gain is Fiqh with a value of 0.3486.
HRER: A New Bottom-Up Rule Learning for Knowledge Graph Completion

Knowledge graphs (KGs) are collections of structured facts, which have recently attracted growing attention. Although there are billions of triples in KGs, they are still incomplete. These incomplete knowledge bases will bring limitations to practical applications. Predicting new facts from the given knowledge graphs is an increasingly important area. We investigate the models based on logic rules in this paper. This paper proposes HRER, a new bottom-up rule learning for knowledge graph completion. First of all, inspired by the observation that the known information of KGs is incomplete and unbalanced, HRER modifies the indicators for screening based on the existing relation rule mining methods. The new metric HRR is more effective than traditional confidences in filtering Horn rules. Besides, motivated by the differences between the embedding-based methods and the methods based on logic rules, HRER proposes entity rules. The entity rules make up for the limited expression of Horn rules to some extent. HRER needs a few parameters to control the number of rules and can provide the explanation for prediction. Experiments show that HRER achieves the state-of-the-art across the standard link prediction datasets.
Improving Model-Based Deep Reinforcement Learning with Learning Degree Networks and Its Application in Robot Control

Deep reinforcement learning is the technology of artificial neural networks in the field of decision-making and control. The traditional model-free reinforcement learning algorithm requires a large amount of environment interactive data to iterate the algorithm. This model’s performance also suffers due to low utilization of training data, while the model-based reinforcement learning (MBRL) algorithm improves the efficiency of the data, MBRL locks into low prediction accuracy. Although MBRL can utilize the additional data generated by the dynamic model, a system dynamics model with low prediction accuracy will provide low-quality data and affect the algorithm’s final result. In this paper, based on the A3C (Asynchronous Advantage Actor-Critic) algorithm, an improved model-based deep reinforcement learning algorithm using a learning degree network (MBRL-LDN) is presented. By comparing the differences between the predicted states outputted by the proposed multidynamic model and the original predicted states, the learning degree of the system dynamics model is calculated. The learning degree represents the quality of the data generated by the dynamic model and is used to decide whether to continue to interact with the dynamic model during a particular episode. Thus, low-quality data will be discarded. The superiority of the proposed method is verified by conducting extensive contrast experiments.
Application of Speech Recognition Technology in Chinese English Simultaneous Interpretation of Law

Speech recognition is an important research field in natural language processing. In Chinese and English, which have rich data resources, the performance of end-to-end speech recognition model is close to that of Hidden Markov Model—Deep Neural Network (HMM-DNN) model. However, for the low resource speech recognition task of Chinese English hybrid, the end-to-end speech recognition system does not achieve good performance. In the case of limited mixed data between Chinese and English, the modeling method of end-to-end speech recognition is studied. This paper focuses on two end-to-end speech recognition models: connection timing distribution and attention based codec network. In order to improve the performance of Chinese English hybrid speech recognition, this paper studies how to improve the performance of the coder based on connection timing distribution model and attention mechanism, and tries to combine the two models to improve the performance of Chinese English hybrid speech recognition. In low resource Chinese English mixed data, the advantages of different models are used to improve the performance of end-to-end models, so as to improve the recognition accuracy of speech recognition technology in legal Chinese English simultaneous interpretation.
Features of evaluation during distance learning

In the context of the COVID-19 pandemic, distance learning has become the main way to get an education. This gave impetus to the development and involvement of innovative information technologies in the management of the educational process. It, like other forms, has both advantages and disadvantages. One of the main components of the educational process is the verification and evaluation of educational achievements, which should be carried out at a distance. Control over the assimilation of material in this way of acquiring knowledge has its difficulties. Analysis of the literature of foreign sources only confirms that the topic of distance learning assessment is becoming increasingly important and needs to be developed and improved. The article considers the problem of knowledge assessment during distance learning and searches for the optimal model of control and ways to increase the effectiveness of control of educational achievements with the involvement of new forms and innovative technologies. Obviously, assessment during distance learning requires systematization and standardization and has prospects for further development in the field of control of knowledge acquisition. This direction of distance education development is a priority today and requires close attention of modern pedagogy.
Perspective Taking and Avatar-Self Merging

Today, avatars often represent users in digital worlds such as in video games or workplace applications. Avatars embody the user and perform their actions in these artificial environments. As a result, users sometimes develop the feeling that their self merges with their avatar. The user realizes that they are the avatar, but the avatar is also the user-meaning that avatar's appearance, character, and actions also affect their self. In the present paper, we first introduce the event-coding approach of the self and then argue based on the reviewed literature on human-avatar interaction that a self-controlled avatar can lead to avatar-self merging: the user sets their own goals in the virtual environment, plans and executes the avatar's actions, and compares the predicted with the actual motion outcomes of the avatar. This makes the user feel body ownership and agency over the avatar's action. Following the event-coding account, avatar-self merging should not be seen as an all-or-nothing process, but rather as a continuous process to which various factors contribute, including successfully taking the perspective of the avatar. Against this background, we discuss affective, cognitive, and visuo-spatial perspective taking of the avatar. As evidence for avatar-self merging, we present findings showing that when users take the avatar's perspective, they can show spontaneous behavioral tendencies that run counter to their own.
Authentication of rotogravure print-outs using a regular test pattern

The medicine falsification is an important problem nowadays which represents a real danger for human lives. Therefore, it is important to find a cheap and efficient solution that can be used for all kinds of medicines. In this paper, we explore the domain of pharmaceutical packaging printed on blister foils using rotogravure process. It was shown that the chemical etching has a stochastic nature that can be spotted by correlation or classical machine learning methods. We propose an authentication system that uses a novel regular test pattern for authentication of blister foils. Thanks to this regular test pattern we can identify the cylinder used for printing and the position of the regular test pattern engraved on the authentic cylinder, as well as easily reject the fake patterns printed using counterfeiter cylinder and rotogravure press. The proposed identification/authentication system cannot be easily attacked as it is not possible to imitate the signature of chemical etching process. Additionally, we will shortly discussed the possibility to enlarge the proposed system to another types of engraving processes and formulate some future paths for this work
Empirical Relationships Between Global and Diffuse Radiation and Sunshine Duration in Chad: Polynomial Regression Approach

Chad, in regards to its geographical position is considered as one of the countries in the world that receives an important annual solar radiation over its entire territory. The average sunshine duration is about 10 hours in the North, 8 hours in the Center and 7 hours in the South. This can be used to quantify the solar radiation over the whole territory and evaluate the solar potential. In this paper, some new correlations between sunshine duration and solar radiation have proposed in four cities (Abeche, Bitkine, Mongo and Ndjamena) located in the Sahelian climate in order to have tools for quantifying all components of solar radiation. The polynomial regression model approach is used and their parameters are estimated by the ordinary least squares method. A statistical evaluation was carried out to compare the different correlations obtained. The results show that the third order polynomial approach is the most appropriate for an empirical relationship between global, diffuse and relative sunshine duration in each of the cities. Considering the long dry seasons characterized by high temperatures in this region, the consideration of the temperature variable in the functional form could improve the quality and accuracy of the models in their estimation.
Multi-strategy improved salp swarm algorithm and its application in reliability optimization

To improve the convergence speed and solution precision of the standard Salp Swarm Algorithm (SSA), a hybrid Salp Swarm Algorithm based on Dimension-by-dimension Centroid Opposition-based learning strategy, Random factor and Particle Swarm Optimization's social learning strategy (DCORSSA-PSO) is proposed. Firstly, a dimension-by-dimension centroid opposition-based learning strategy is added in the food source update stage of SSA to increase the population diversity and reduce the inter-dimensional interference. Secondly, in the followers' position update equation of SSA, constant 1 is replaced by a random number between 0 and 1 to increase the randomness of the search and the ability to jump out of local optima. Finally, the social learning strategy of PSO is also added to the followers' position update equation to accelerate the population convergence. The statistical results on ten classical benchmark functions by the Wilcoxon test and Friedman test show that compared with SSA and other well-known optimization algorithms, the proposed DCORSSA-PSO has significantly improved the precision of the solution and the convergence speed, as well as its robustness. The DCORSSA-PSO is applied to system reliability optimization design based on the T-S fault tree. The simulation results show that the failure probability of the designed system under the cost constraint is less than other algorithms, which illustrates that the application of DCORSSA-PSO can effectively improve the design level of reliability optimization.
The Classification of Phishing Websites using Naive Bayes Classifier Algorithm

The background of this research is how to find out the selected websites which are classified through the naive Bayes classifier algorithm. With this algorithm, it can be seen how far the classification of Phishing is. The method used in this study is to use experimental methods or research on the data obtained, these tests will include new data that can be accounted for and can determine whether the data is suitable for use. The problem raised in this study is how to find out which principles have been clarified with the dizziness method using the naive Bayes classifier algorithm, with the algorithm, which websites can be classified properly. The purpose of this study is how to find out the data tested through training data will produce new data, especially well for items and produce from values Naive Bayes algorithm obtained an average accuracy value of 92.98% with a TP Rate of 0.930%, FP Rate of 0.076%, Precision of 0.930%, Recall of 0.930% and Fmeasure of 0.930%.&#x0D;
Health Datasets as Assets: Blockchain-Based Valuation and Transaction Methods

There is increasing recognition that health-oriented datasets could be regarded as intangible assets: distinct assets with future economic benefits but without physical properties. While health-oriented datasets—particularly health records—are ascribed monetary value on the black market, there are few established methods for assessing value for legitimate research and business purposes. The emergence of blockchain has created new commerce opportunities for transferring assets without intermediaries. Therefore, blockchain is proposed as a medium by which research datasets could be transacted to provide future value. Blockchain methodologies also offer security, auditability, and transparency to authorized individuals for verifying transactions. The authors will share data valuation methodologies consistent with accounting principles and include discussions of black market valuation of health data. Further, this article describes blockchain-based methods of managing real-time payment/micropayment strategies.
Contrastive Graph Learning for Social Recommendation

Owing to the strength in learning representation of the high-order connectivity of graph neural networks (GNN), GNN-based collaborative filtering has been widely adopted in recommender systems. Furthermore, to overcome the data sparsity problem, some recent GNN-based models attempt to incorporate social information and to design contrastive learning as an auxiliary task to assist the primary recommendation task. Existing GNN and contrastive-learning-based recommendation models learn user and item representations in a symmetrical way and utilize social information and contrastive learning in a complex manner. The above two strategies lead to these models being either ineffective for datasets with a serious imbalance between users and items or inefficient for datasets with too many users and items. In this work, we propose a contrastive graph learning (CGL) model, which combines social information and contrastive learning in a simple and powerful way. CGL consists of three modules: diffusion, readout, and prediction. The diffusion module recursively aggregates and integrates social information and interest information to learn representations of users and items. The readout module takes the average value of user embeddings from all diffusion layers and item embeddings at the last diffusion layer as readouts of users and items, respectively. The prediction module calculates prediction rating scores with an interest graph to emphasize interest information. Three different losses are designed to ensure the function of each module. Extensive experiments on three benchmark datasets are implemented to validate the effectiveness of our model.
CPNCoverageAnalysis: An R package for parameter estimation in conceptual properties norming studies

In conceptual properties norming studies (CPNs), participants list properties that describe a set of concepts. From CPNs, many different parameters are calculated, such as semantic richness. A generally overlooked issue is that those values are only point estimates of the true unknown population parameters. In the present work, we present an R package that allows us to treat those values as population parameter estimates. Relatedly, a general practice in CPNs is using an equal number of participants who list properties for each concept (i.e., standardizing sample size). As we illustrate through examples, this procedure has negative effects on data's statistical analyses. Here, we argue that a better method is to standardize coverage (i.e., the proportion of sampled properties to the total number of properties that describe a concept), such that a similar coverage is achieved across concepts. When standardizing coverage rather than sample size, it is more likely that the set of concepts in a CPN all exhibit a similar representativeness. Moreover, by computing coverage the researcher can decide whether the CPN reached a sufficiently high coverage, so that its results might be generalizable to other studies. The R package we make available in the current work allows one to compute coverage and to estimate the necessary number of participants to reach a target coverage. We show this sampling procedure by using the R package on real and simulated CPN data.
Object Extraction of Tennis Video Based on Deep Learning

Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquires video image signals through visible light or infrared sensors, performs digital image processing on the video images, detects moving targets, and then extracts moving targets for target recognition. Then, the moving target is predicted and tracked according to the image features and spatiotemporal features, and the contour shape, position, and motion trajectory of the target are obtained, which provides data support for subsequent tasks. This paper uses the convolutional neural network model HyperNet as the technical support to study the deep learning (DL) tennis video target extraction. The final experimental results show that the loss value of the loss function in the training process of this method is stably maintained between 1.5% and 2.5%, and the speed performance is also greatly improved. The number of boxes for extracting candidate regions is significantly reduced, the calculation time for each frame will not exceed 1.8 s, the orientation accuracy of target extraction is 96.32%, and the size accuracy is 91.05%.
Noise-reducing encoding strategies for spin chains

We present an encoding technique that reduces the effects of noise on quantum spin systems whose operation is driven by Hamiltonian evolution. This technique is widely applicable, being most relevant to the scenarios where there are insufficient qubits to permit full-scale error correction. Instead, our technique can be implemented over small numbers of qubits and still leads to noticeable improvements in the fidelity of operations. The encoding scheme is easy to implement, flexible with respect to choice of Hamiltonian, and close to optimal.