Reviewerly/exHarmony huggingface dataset:
Open Artificial Intelligence Analysis using ChatGPT Integrated with Telegram Bot

Chatbot technology uses natural language processing with artificial intelligence that can interact quickly in answering a question and producing relevant answer. ChatGPT is the latest chatbot platform developed by Open AI which allows users to interact with text-based engines. This platform uses the GPT-3 (Generative Pre-trained Transformer) algorithm to help understand the response humans want and generate relevant responses. Using the platform, users can find answers to their questions quickly and relevantly. The method used for OpenAI's research on ChatGPT integrated through Telegram chatbot is using a waterfall method which utilizes open API tokens from Telegram. In this research we develop OpenAI application connected with telegram bot. This application can help provide a wide range of information, especially information related to the Semarang State Polytechnic. By using Telegram chatbot in the program, users can find it easy to ask because it is integrated with OpenAI using the API. Telegram chatbot, which has a chat feature, allows easy communication between users and chatbots. Thus, it may reduce system errors on the bot.
The Influence of Digital Literacy on Learning Effectiveness Through Classroom Management

Digital Literacy is essential in the post-pandemic era. Teachers use Digital Literacy to provide learning materials, conduct the teaching process, and conduct evaluations online. However, Digital Literacy requires adequate information technology infrastructure to produce classroom management and learning effectiveness on an ongoing basis. The study used questionnaires to obtain data on Christian-based schools in Surabaya. The questionnaires were distributed to as many as 363 through a Google form, and 132 were filled out again. Data processing used partial least square. The results showed that digital literacy has an impact on classroom management. Classroom management influences learning effectiveness. Digital literacy affects learning effectiveness through classroom management. Digital Literacy has not influenced classroom management and learning effectiveness with information technology infrastructure as a moderating variable. This research provides a practical contribution for education managers to understand the adjustment of IT infrastructure to make digital literacy the basis for managing class management. In addition, teaching staff must be educated to keep abreast of technological developments to support the teaching process and improve class management. The study enriches the theory of the use of technology in education and interactional education in the classroom.
A Smart Strategy for Data Hiding using Cryptography and Steganography

Confidential data maintained by Security sources has always been a substantial aspect of hampering unintended access. Technology is enhancing day by day towards information, especially in terms of multimedia file transmission. Combining cryptography and steganography, a crossover approach serves information security. Cryptography is a strategy for changing over from plain content Cipher Text. Steganography is the specialty of concealing plain text, which refers to hiding data within a message or file. The proposed method provides a hybrid system in designing and utilizing cryptography and steganography techniques, making the communication system reliable in resisting attacks. In this paper, the play fair cipher method is used to encode the hidden textual content, which provides security in terms of effective level. Play fair cipher represents the plain text as a data unit and converts these units into unknown forms. Later, Discrete Cosine Transform (DCT) and Logical Operation Exclusive OR (XOR) techniques were combined with masking encrypted messages inside the picture. Noble security level results and histogram analysis are the achievements' values that indicate the offers designed by a system.
Reliability of IBM’s Public Quantum Computers

One of the challenges of the current ecosystem of quantum computers (QC) is the stabilization of the coherence associated with the entanglement of the states of their inner qubits.In this empirical study, we monitor the reliability of IBM's public-access QCs network on a daily basis.Each of these state-of-the-art machines has a totally different qubit association, and this entails that for a given (same) input program, they may output a different set of probabilities for the assembly of results (including both the right and the wrong ones).Although we focus on the computing structure provided by the "Big Blue" company, our survey can be easily transferred to other currently available quantum mainframes.In more detail, we probe these quantum processors with an ad hoc designed computationally demanding quaternary search algorithm.As stated, this quantum program is executed every 24 hours (for nearly 100 days) and its goal is to put to the limit the operational capacity of this novel and genuine type of equipment.Next, we perform a comparative analysis of the obtained results according to the singularities of each computer and over the total number of executions.In addition, we subsequently apply (for 50 days) an improvement filtering to perform noise mitigation on the results obtained proposed by IBM.The Yorktown 5-qubit computer reaches noise filtering of up to 33% in one day, that is, a 90% confidence level is reached in the expected results.From our continuous and long-term tests, we derive that room still exists regarding the improvement of quantum calculators in order to guarantee enough confidence in the returned outcomes.
Multi Agent Path Finding under Obstacle Uncertainty

In multi-agent path finding (MAPF), several agents must move from their current positions to their target positions without colliding. Prior work on MAPF commonly assumed perfect knowledge of the environment. We consider a MAPF setting where this is not the case, and the planner does not know a-priori whether some positions are blocked or not. To sense whether such a position is traversable, an agent must move close to it and adapt its behavior accordingly. In this work we focus on solving this type of MAPF problem, for cases where planning is centralized but cannot be done during execution. In this setting, a solution can be formulated as a plan tree for each agent, branching on the observations. We propose algorithms for finding such plans trees for two modes of executions: centralized, where the agents share information concerning observed obstacles during execution, a decentralized, where such communication is not allowed. The proposed algorithms are complete and can be configured to optimize solution cost, measured for either the best case or the worst case. We implemented these algorithms and provide experimental results demonstrating how our approach scales with respect to the number of agents and the number of positions we are uncertain about. The results show that our algorithms can solve non-trivial problems, but also highlight that this type of MAPF problems is significantly harder than classical MAPF.
Task Phasing: Automated Curriculum Learning from Demonstrations

Applying reinforcement learning (RL) to sparse reward domains is notoriously challenging due to insufficient guiding signals. Common RL techniques for addressing such domains include (1) learning from demonstrations and (2) curriculum learning. While these two approaches have been studied in detail, they have rarely been considered together. This paper aims to do so by introducing a principled task-phasing approach that uses demonstrations to automatically generate a curriculum sequence. Using inverse RL from (suboptimal) demonstrations we define a simple initial task. Our task phasing approach then provides a framework to gradually increase the complexity of the task all the way to the target task, while retuning the RL agent in each phasing iteration. Two approaches for phasing are considered: (1) gradually increasing the proportion of time steps an RL agent is in control, and (2) phasing out a guiding informative reward function. We present conditions that guarantee the convergence of these approaches to an optimal policy. Experimental results on 3 sparse reward domains demonstrate that our task-phasing approaches outperform state-of-the-art approaches with respect to asymptotic performance.
Combining Clinical and Spatial Constraints into Temporal Planning to Personalize Physical Rehabilitation

This work investigate temporal planning to synthesize personalized physical rehabilitation programs. The first contribution of the work concerns the representation of (heterogeneous) clinical and spatial constraints into a planning framework. The second contribution is the integration of numerical and symbolic reasoning to synthesize technically valid and coherent plans with respect to different clinical objectives. The experimental section discusses the developed planner from a technical view, assessing solving and personalization capabilities, and from a clinical view, assessing the efficacy of plans on the involved patients.
Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+

This paper studies how a domain-independent planner and combinatorial search can be employed to play AngryBirds, a well established AI challenge problem. To model the game, we use PDDL+, a planning language for mixed discrete/continuous domains that supports durative processes and exogenous events. The paper describes the PDDL+ model and identifies key design decisions that reduce the problem complexity. In addition, we propose several domain-specific enhancements including heuristics and a search technique similar to preferred operators. Together, they alleviate the complexity of combinatorial search. We evaluate our approach by comparing its performance with dedicated domain-specific solvers on a range of Angry Birds levels. The results show that our performance is on par with these domain-specific approaches in most levels, even without using our domain-specific search enhancements.
GePA*SE: Generalized Edge-Based Parallel A* for Slow Evaluations

Parallel search algorithms have been shown to improve planning speed by harnessing the multithreading capability of modern processors. One such algorithm PA*SE achieves this by parallelizing state expansions, whereas another algorithm ePA*SE achieves this by effectively parallelizing edge evaluations. ePA*SE targets domains in which the action space comprises actions with expensive but similar evaluation times. However, in a number of robotics domains, the action space is heterogenous in the computational effort required to evaluate the cost of an action and its outcome. Motivated by this, we introduce GePA*SE: Generalized Edge-based Parallel A* for Slow Evaluations, which generalizes the key ideas of PA*SE and ePA*SE, i.e., parallelization of state expansions and edge evaluations, respectively. This extends its applicability to domains that have actions requiring varying computational effort to evaluate them. The open-source code for GePA*SE, along with the baselines, is available here: https://github.com/shohinm/parallel_search
Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study

Software defect prediction is crucial used for detecting possible defects in software before they manifest. While machine learning models have become more prevalent in software defect prediction, their effectiveness may vary based on the dataset and hyperparameters of the model. Difficulties arise in determining the most suitable hyperparameters for the model, as well as identifying the prominent features that serve as input to the classifier. This research aims to evaluate various traditional machine learning models that are optimized for software defect prediction on NASA MDP (Metrics Data Program) datasets. The datasets were classified using k-nearest neighbors (k-NN), decision trees, logistic regression, linear discriminant analysis (LDA), single hidden layer multilayer perceptron (SHL-MLP), and Support Vector Machine (SVM). The hyperparameters of the models were fine-tuned using random search, and the feature dimensionality was decreased by utilizing principal component analysis (PCA). The synthetic minority oversampling technique (SMOTE) was implemented to oversample the minority class in order to correct the class imbalance. k-NN was found to be the most suitable for software defect prediction on several datasets, while SHL-MLP and SVM were also effective on certain datasets. It is noteworthy that logistic regression and LDA did not perform as well as the other models. Moreover, the optimized models outperform the baseline models in terms of classification accuracy. The choice of model for software defect prediction should be based on the specific characteristics of the dataset. Furthermore, hyperparameter tuning can improve the accuracy of machine learning models in predicting software defects.
Machine Learning Techniques in the Dosage of Biopolymers and Coagulants for Leachate Treatment

The leachate discharges generated in sanitary landfills contain many pollutants that are harmful to the environment; treatments are scarce and should be carried out better. The use of coagulation-flocculation processes has been one of the most widely used, but that; Due to the complexity of the characterization of the leachate, the dosing strategy of coagulants and biopolymers needs to be clarified. Therefore, the present study is carried out to determine the doses of coagulants and biopolymers suitable for coagulation-flocculation processes in the treatment of leachates by using computational models of machine learning techniques such as Artificial Neural Networks (ANN); these allow to decrease the operations of the tests of jars in the laboratory; optimizing resources. Through laboratory experimentation, there are real results of the effectiveness of applying biopolymers in leachate treatments at different concentration levels. The laboratory results were taken as input variables for the algorithms used; after the validation and calibration process, we proceeded to estimate predicted data with the computational model, obtaining predictions of optimal doses for treatment with high statistical adjustment indicators. It is verified that the applied coagulation-flocculation treatments reduce the turbidity values in the leachate and contaminants associated with suspended solids. In this way, the jar tests are optimized so that the operational costs decrease without affecting the results of adequate dosing.
Feature Extraction from Several Angular Faces Using a Deep Learning Based Fusion Technique for Face Recognition

Due to its non-interfering nature, face recognition has been the most suitable technology for designing biometric systems in recent years. This technology is used in various industries, such as health care, education, security, and surveillance. Facial recognition technology works best when a person is looking straight into the camera. On the contrary, the performance of facial recognition degrades when encountered with an angled facial image, because they are generally trained using images of a full face. The purpose of this paper is to estimate the feature vector of a full face image when there are several angular facial images of the same person, one example being angular faces in a video. This method extracts the basic features of a facial image using the non-negative matrix factorization (NMF) method. Then, the feature vectors are fused using a generative adversarial network (GAN) to estimate the feature vector associated with the frontal image. The experimental results on the angular images of the FERET dataset show that the proposed method can significantly improve the accuracy of facial recognition technology methods.
Using of Transformers Models for Text Classification to Mobile Educational Applications

In Q2 2022, educational apps were the second most popular category on the Google Play Store, accounting for 10.47% of the apps available worldwide. This work explores the application of five BERT-based pre-trained models with the Transformers architecture to classify mobile educational applications. These five models are according to the knowledge field: bert-base-cased, bert-base-uncased, roberta-base, albert-base-v2 and distilbert-base-uncased. This study uses a dataset with educational apps of Google Play, this dataset was enriched with description and category because it lacked this information. In all models, a tokenizer and fine-tuning works were applied for training in the classification task. After training the data, the testing phase was performed in which the models had to go through four training epochs to obtain better results: roberta-base with 81% accuracy, bert-base-uncased with 79% accuracy, bert-base-cased obtained 80% accuracy, albert-base-v2 obtained 78% accuracy and distilbert-base-uncased obtained 76% accuracy.
Safe and Quasi-Optimal Autonomous Navigation in Sphere Worlds

We propose a continuous feedback control strategy that steers a point-mass vehicle safely to a desired destination, in a quasi-optimal manner, from almost all initial conditions in an n-dimensional Euclidean space cluttered with spherical obstacles. The main idea consists in avoiding each obstacle via the shortest path within the cone enclosing the obstacle, and moving straight towards the target when the vehicle has a clear line of sight to the target location. The proposed control strategy ensures safe navigation with almost global asymptotic stability of the equilibrium point at the target location. Simulation results, illustrating the effectiveness of the proposed approach, are presented.
COOPERATIVE LEARNING METHOD IN SENIOR HIGH SCHOOL ENGLISH CLASSES: STUDENTS’ AND TEACHERS’ PERCEPTIONS

There are many methods in the English education world. One of them is a cooperative learning method. This paper aims to find the students’ and teachers’ perceptions of the cooperative learning method in Senior High School classes. This study used qualitative study. All data were analyzed qualitatively. The instruments used were questionnaires and semi-structured interviews. The participants were 45 students of one of the Senior High School English classes and 2 English teachers in Salatiga. Based on the findings, the cooperative learning method could potentially become an effective method for students. Based on the result, the students felt happy with this method. Moreover, this method could develop students’ teamwork skills. In teachers' perceptions, they taught through group work in this method could enhance students’ knowledge of the material. However, both students and teachers argued that this method could be less effective when there were uncooperative teammates in the group.
The copyright protection of AI-generated works under Chinese law

Who is the author of a work generated by AI? Can AI-generated works be protected by copyright law? This issue has attracted global attention. The vast majority of countries in the world have given a negative response to this question, but one Chinese court has given an affirmative answer, instead. Does this Chinese decision represent future thinking for the world in this area? It is necessary to investigate the reasons behind this decision, which are related to China's special interpretation of “human participation” and the criteria for judging originality. This judicial result was also related to China's current lack of a distinction between computer-assisted and AI-generated results. In the future, China may continue to uphold the existing determination; however, since China does not operate under case law, Chinese courts may still change their opinion. Moreover, China's choice may not have an impact on countries that are deeply influenced by natural law, but it may still impact some countries that are strongly influenced by utilitarianism.
The Digitisation of Writing in Higher Education: Exploring the Use of Wordtune as an AI Writing Assistant

Accelerated by the advent of AI-powered writing assistants, writing as a crucial aspect of higher education assessment and practice has undergone rapid digitisation in recent decades. Automatic written corrective feedback (AWCF) is one such technology that offers synchronous feedback on spelling, grammar, and style during the writing process. However, there is a paucity of empirical research on its use in the everyday practice of students and staff. This study explores the use of Wordtune, an AWCF tool, to determine its benefits and limits from a user perspective. The research was conducted through a large-scale survey of Wordtune users. Descriptive statistics were generated, Exploratory and Confirmatory Factor Analysis were performed, and open-ended questions were analysed using content analysis. Wordtune users are typically confident English speakers and use it alongside other tools such as Grammarly and Google translate. Wordtune is experienced by users as offering low-order benefits in terms of rephrasing and writing more grammatically but also has high-order benefits such as overcoming mental blocks and creating opportunities to learn. Users acknowledged very few drawbacks to using Wordtune. Our paper concludes with pedagogic suggestions for educators to support use of AI writing assistants, in the context of the accelerating wave of digitisation of writing.
Trust-DETM: Distributed Energy Trading Model Based on Trusted Execution Environment

The traditional centralized power trading model suffers from high maintenance costs, low processing efficiency and unsynchronized information, and it cannot adapt to the high-frequency and small-dollar distributed energy trading scenario. To address the above issues, we propose Trust-DETM, a model for the implementation of distributed energy trading based on a trusted execution environment. First, we introduce a reputation metric mechanism and propose a transaction matching algorithm based on the reputation metric to achieve the accurate matching of transaction objects. Secondly, as the distributed energy trading model lacks an effective trust mechanism, we propose a commitment scheme based on smart contracts and a trusted execution environment to solve the trust problem between producers and consumers. Finally, we conduct a comprehensive experimental evaluation of the efficiency of Trust-DETM. Through comparative experiments, we find that Trust-DETM achieves trade matching and trusted execution in a lower simultaneous running time than comparable distributed trading models.
Changes in modern university: challenges of today and development trends

Education is a process of interaction and communication among all participants, in which each of them has an impact on the outcome. Today, in the conditions of the modern world, cooperation between the teacher and the student acquires new forms and characteristics. The teacher is no longer just a source of knowledge but becomes a partner and helper for the student. The quality of communication between the student and the teacher depends on their readiness for dialogue and mutual understanding, absence of coercion and orders. A comprehensive approach to education and communication allows for a deep understanding of the processes and their improvement. Higher education institutions need to pay special attention to issues of strategic communication development, branding, and image. Clear and well-planned communication systems should be implemented at the level of structural units in order to achieve success. Providing adequate material and technical resources as well as information and communication technologies is crucial for effective use of various teaching methods and forms. However, communication between a teacher and a student should not only be about academic topics, but also about support, motivation, and personal development.