Reviewerly/exHarmony huggingface dataset:
Evaluating Agents without Rewards

Reinforcement learning has enabled agents to solve challenging tasks in unknown environments. However, manually crafting reward functions can be time consuming, expensive, and error prone to human error. Competing objectives have been proposed for agents to learn without external supervision, but it has been unclear how well they reflect task rewards or human behavior. To accelerate the development of intrinsic objectives, we retrospectively compute potential objectives on pre-collected datasets of agent behavior, rather than optimizing them online, and compare them by analyzing their correlations. We study input entropy, information gain, and empowerment across seven agents, three Atari games, and the 3D game Minecraft. We find that all three intrinsic objectives correlate more strongly with a human behavior similarity metric than with task reward. Moreover, input entropy and information gain correlate more strongly with human similarity than task reward does, suggesting the use of intrinsic objectives for designing agents that behave similarly to human players.
Research on visual communication design system based on embedded network

Visual communication Architects must have scholars' tools to develop complex visual development and adapt to the new medium and work as much as possible. The communication is based upon the cycle of inquiry with the development skill to create an exchange and idea for recognizing the process of innovation in the visual plan studio. In this regard, the Visual Correspondence Plan section makes a plan to investigate those who are regulated in the planning cycle at the foundation of the planning training. The communication based embedded system correspondence and configuration on a planned learning environment with a common cause of integration with innovation. The Detection Combination Algorithm investigation begins by presenting the course material plan and uncovers teaching and learning methods on the Essential Plan course's final work that dictates the establishment of a planned track in the educational program. Understood pieces are an example as far as visual animation, photography, and appearance are concerned. Text and self-reports are used as projections of the Understudies Thinking Measurement that practically memorize faculty opinions and comments for the course. Consequences of Investigation The configuration can provide instructors with experiences in shaping educational and learning approaches by working on a cycle of thought, investigation, and disclosure.
Super-Linear Time-Memory Trade-Offs for Symmetric Encryption

We build symmetric encryption schemes from a pseudorandom function/permutation with domain size N which have very high security – in terms of the amount of messages q they can securely encrypt – assuming the adversary has $$S < N$$ bits of memory. We aim to minimize the number of calls k we make to the underlying primitive to achieve a certain q, or equivalently, to maximize the achievable q for a given k. We target in particular $$q \gg N$$ , in contrast to recent works (Jaeger and Tessaro, EUROCRYPT ’19; Dinur, EUROCRYPT ’20) which aim to beat the birthday barrier with one call when $$S < \sqrt{N}$$ . Our first result gives new and explicit bounds for the Sample-then-Extract paradigm by Tessaro and Thiruvengadam (TCC ’18). We show instantiations for which $$q =\varOmega \left( (N/S)^{k} \right) $$ . If $$S < N^{1- \alpha }$$ , Thiruvengadam and Tessaro’s weaker bounds only guarantee $$q > N$$ when $$k = \varOmega (\log N)$$ . In contrast, here, we show this is true already for $$k = \varTheta (1/\alpha )$$ . We also consider a scheme by Bellare, Goldreich and Krawczyk (CRYPTO ’99) which evaluates the primitive on k independent random inputs, and masks the message with the XOR of the outputs. Here, we show $$q= \varOmega \left( (N/S)^{k/2} \right) $$ , using new combinatorial bounds on the list-decodability of XOR codes which are of independent interest. We also study best-possible attacks against this construction.
PsuedoProp at SemEval-2020 Task 11: Propaganda Span Detection Using BERT-CRF and Ensemble Sentence Level Classifier

This paper presents our solution for Span Identification (SI) Task under "Task 11: Detection of Propaganda Techniques in News Articles" of SemEval 2020.This task aims to identify if a given sentence, taken from a corpus of news articles, contains a propaganda span and hence aims to identify the character level offsets of the identified propaganda element.Our solution proposes a sequential approach in which the span identification is preceded by an ensemble sentence level classifier (SLC).We only perform span identification on those samples which are flagged as propaganda samples by the SLC Model.We perform token level classification by fine-tuning BERT and use CRF to perform sequence tagging.Additionally, we present our analysis of different voting ensembles for the SLC model.Our system ranks 14 th on the test set and 22 nd on the development set and with an F1 score of 0.41 and 0.39 respectively.
The Potential Of Online Writing Tools For Efl University Students During The Covid-19 Pandemic

This study aims to give an overview of potential online writing tools that can be used in teaching writing for EFL university students during the COVID-19 pandemic. This study was a literature review. It explicates online writing tools and reviews previous research exploring the use of these applications for teaching writing. The data was obtained from articles in online academic journals. There were three databases and sites used in finding the articles; Science Direct, Google Scholar, and Research gate. The result shows that there are three potential online writing tools applicable for EFL university students in learning writing from home namely Paragraph Punch, Pro-Writing Aid, and Edmodo. Finally, some suggestions are also offered for lecturers and students regarding the use of online writing tools in teaching writing.
AN OPERANT CONDITIONING APPROACH FOR LARGE SCALE SOCIAL OPTIMIZATION ALGORITHMS

The changes that positive or negative results cause in an individual's behavior are called Operant Conditioning. This paper introduces an operant conditioning approach (OCA) for large scale swarm optimization models. The proposed approach has been applied to social learning particle swarm optimization (SL-PSO), a variant of the PSO algorithm. In SL-PSO, the swarm particles are sorted according to the objective function and all particles are updated with learning from the others. In this study, each particle's learning rate is determined by the mathematical functions that are inspired by the operant conditioning. The proposed approach adjusts the learning rate for each particle. By using the learning rate, a particle close to the optimum solution is aimed to learn less. Thanks to the learning rate, a particle is prevented from being affected by particles close to the optimum point and particles far from the optimum point at the same rate. The proposed OCA-SL-PSO is compared with SL- PSO and pure PSO on CEC 13 functions. Also, the proposed OCA-SL-PSO is tested for large-scale optimization (100-D, 500-D, and 1000-D) benchmark functions. This paper has a novel contribution which is the usage of OCA on Social Optimization Algorithms. The results clearly indicate that the OCA is increasing the results of large-scale SL-PSO.
Artificial Intelligence in Information and Communication Technologies and Its Influence on Foreign Language Teaching at Higher School

The study aims to determine how artificial intelligence (AI) influences a student through the use of information and communication technologies (ICT) during foreign language classes at higher school. The article explains why some types of ICT can be considered as AI-systems, how their inclusion into educational process influences cognitive changes in a student, as well as what role a student’s degree of trust in such a system plays. Scientific novelty of the work lies in considering ICT influence on cognitive abilities of a student learning a foreign language through the lens of AI. As a result, it was found that the closer the capacities of AI in ICT to human activities while working with a foreign language are, the lower the activation of cognitive performance in a student is.
Prototyping Using a Mobile Robot Platform Equipped with Low-End In-Wheel Motors

In the near future, autonomous mobile robots are expected to operate effectively in various locations, such as in living spaces as well as industrial establishments. Against this background, a new autonomous mobile robot platform was designed and prototyped in this research. For simplicity of design and easy assembly of the drive units, a robot with two low-end in-wheel motors is considered. It is also effective in saving space, and can be used for high-power operations and travelability in various road surface conditions. This paper presents a concept for developing a new type of autonomous mobile robot platform, its control system for autonomous operation, actual prototyping using this platform, and sample applications of this platform.
FORMATION OF THE TUTOR ICT-COMPETENCE IN THE PROCESS OF FUTURE TEACHERS’ PROFESSIONAL TRAINING

The widespread proliferation of information and communication technologies and the task of meeting the sustainable development goals by overcoming the uneven access of the younger generation to information and communication technologies require the presence of the high level of the ICT competence and the ability to carry out tutoring activities in the information society for teachers-to-be. The paper offers the methodological approaches (contents, methods, tools, and technologies) of forming the tutor ICT competence in the process of future teachers’ professional training. The educational content, which aims at building the ICT competence of teachers as tutors is distributed over the entire period of students’ study at higher educational institutions. During the study of compulsory subjects in the Bachelor program and the "Organization of distance learning in the educational institution" elective discipline in the Master program, students are familiarized with theoretical and methodological aspects of the use of ICT in tutoring. The main method of forming the tutor ICT competence is the portfolio method, in particular, the use of e-portfolio. As the main means of the formation of the tutor ICT competence, computer training programs, e-resources, Internet resources, mobile applications, virtual and augmented reality are determined. E-mail, blogs, social networks, webinars, chats have been used as the technologies of network interaction for forming the tutor ICT competence. In order to identify effective methodological approaches to the formation of the ICT competence of future teachers as tutors, the study has been conducted based on the collection of information by means of questionnaires and statistical processing of the results. The analysis of the results of the pedagogical experiment and comparison of indicators of control and experimental groups before and after the study have shown the effectiveness of the proposed methodological approaches to the formation of the tutor ICT competence. Implementation of these methodological approaches has become possible due to the introduction of information and educational environment with distance learning technologies and information and communication technologies as its main components.
Analysis of Students' Internet Utilization to Improve Public Information Literacy in the Industrial Revolution Era 4.0

This research aims to determine the level of information literacy, internet utilization as a source of information literacy, and internet utilization to increase student reading interest at Institut Keguruan dan Ilmu Pengetahuan Siliwangi (IKIP Siliwangi). This research uses qualitatif descriptive method. Data were collected using questionnaires, interviews, and documentation studies. Selection of research subjects using the snowball sampling technique. This research's subjects consisted of 23 students related to literacy, 16 students related to information literacy, 18 students related to the results of internet utilization in increasing students' reading interest, and 1 lecturer of Community Education at IKIP Siliwangi. The results showed that students 'understanding of information seeking and information selection was still low compared to students' understanding of information dissemination. Students are still not optimal in using information literacy. Only a few students understand information literacy, how to use information literacy, and select information. Students understand the use of the internet and use it only for social media activities, but low in utilizing the internet as a learning source. Also, the results of the study indicated that students' reading interest was low. Students choose to use the internet instead of reading books. Students do not feel the need to come to the library to find books that match what they need to learn. Therefore, students have to understand the concept of information literacy and take advantage of various applications that support learning.
Vietnamese Text Classification Algorithm using Long Short Term Memory and Word2Vec

In the context of the ongoing forth industrial revolution and fast computer science development the amount of textual information becomes huge. So, prior to applying the seemingly appropriate methodologies and techniques to the above data processing their nature and characteristics should be thoroughly analyzed and understood. At that, automatic text processing incorporated in the existing systems may facilitate many procedures. So far, text classiﬁcation is one of the basic applications to natural language processing accounting for such factors as emotions’ analysis, subject labeling etc. In particular, the existing advancements in deep learning networks demonstrate that the proposed methods may fit the documents’ classifying, since they possess certain extra efficiency; for instance, they appeared to be eﬀective for classifying texts in English. The thorough study revealed that practically no research effort was put into an expertise of the documents in Vietnamese language. In the scope of our study, there is not much research for documents in Vietnamese. The development of deep learning models for document classiﬁcation has demonstrated certain improvements for texts in Vietnamese. Therefore, the use of long short term memory network with Word2vec is proposed to classify text that improves both performance and accuracy. The here developed approach when compared with other traditional methods demonstrated somewhat better results at classifying texts in Vietnamese language. The evaluation made over datasets in Vietnamese shows an accuracy of over 90%; also the proposed approach looks quite promising for real applications.
The effect of acceleration parameter on thermal entanglement and teleportation of a two-qubit Heisenberg XXX model with Dzyaloshinski-Moriya interaction in non-inertial frames

Thermal entanglement and teleportation properties in non-inertial frames are investigated when a two-qubit Heisenberg XXX model with Dzyaloshinski-Moriya (DM) interaction is used as the quantum channel. We find that thermal entanglement and teleportation fidelity decrease with the acceleration parameter or temperature, in other words, the entanglement teleportation of a teleported state will degenerate in non-inertial frames. Furthermore, our results also show that the average fidelity may be promoted by DM interaction for the ferromagnetic case, which is opposite to the antiferromagnetic case in non-inertial frames, although diminished by acceleration parameter, and their influence can be counteracted.
How “BERTology" Changed the State-of-the-Art also for Italian NLP

The use of contextualised word embeddings allowed for a relevant performance increase for almost all Natural Language Processing (NLP) applications. Recently some new models especially developed for Italian became available to scholars. This work aims at evaluating the impact of these models in enhancing application performance for Italian establishing the new state-of-the-art for some fundamental NLP tasks.
Assessing the Impact of Expert Labelling of Training Data on the Quality of Automatic Classification of Lithological Groups Using Artificial Neural Networks

Abstract Machine learning (ML) methods are nowadays widely used to automate geophysical study. Some of ML algorithms are used to solve lithological classification problems during uranium mining process. One of the key aspects of using classical ML methods is causing data features and estimating their influence on the classification. This paper presents a quantitative assessment of the impact of expert opinions on the classification process. In other words, we have prepared the data, identified the experts and performed a series of experiments with and without taking into account the fact that the expert identifier is supplied to the input of the automatic classifier during training and testing. Feedforward artificial neural network (ANN) has been used as a classifier. The results of the experiments show that the “knowledge” of the ANN of which expert interpreted the data improves the quality of the automatic classification in terms of accuracy (by 5 %) and recall (by 20 %). However, due to the fact that the input parameters of the model may depend on each other, the SHapley Additive exPlanations (SHAP) method has been used to further assess the impact of expert identifier. SHAP has allowed assessing the degree of parameter influence. It has revealed that the expert ID is at least two times more influential than any of the other input parameters of the neural network. This circumstance imposes significant restrictions on the application of ANNs to solve the task of lithological classification at the uranium deposits.
Teaching and User Satisfaction in an Academic Chat Reference Consortium

This study investigated 299 chat reference interactions from an academic library consortium for instances of teaching and compared these against other characteristics of the chat, such as question content, staff type, user status, user satisfaction, institutional affiliation, length, and shift busyness.Statistical analysis revealed that teaching was more likely to occur when the chat was a researchrelated question or when the operator was a graduate student worker employed by the consortium but less likely when the operator was a paraprofessional.Chats with teaching were longer but were also associated with higher user satisfaction scores.Teaching rates did not differ by institutional affiliation, shift busyness, or user type.These results indicate that busy consortial services can offer comparable teaching service to single-institution services.The researchers recommend updating operator training to better emphasize teaching to increase user satisfaction.
Dynamically Generated Hierarchical Neural Networks Designed With the Aid of Multiple Support Vector Regressors and PNN Architecture With Probabilistic Selection

The two issues on dynamically generated hierarchical neural networks such as the sort of basic neurons and how to compose a layer are considered in this article. On the first issue, a variant version of the least-square support vector regression (SVR) is chosen as a basic neuron. Support vector machine (SVM) is a representative classifier which usually shows good classification performance. Along with the SVMs, SVR was introduced to deal with the regression problem. Especially, least-square SVR has the advantages of high learning speed due to the substitution of the inequality constraints by the equality constraint in the formulation of the optimization problem. Based on the least-square SVR, the multiple least-square (MLS) SVR, which is a type of a linear combination of least-square SVRs with fuzzy clustering, is proposed to improve the modeling performance. In addition, a hierarchical neural network, where the MLS SVR is utilized as the generic node instead of the conventional polynomial, is developed. The key issues of hierarchical neural networks, which are generated dynamically layer by layer, are discussed on how to retain the diversity of the nodes located at the same layer according to the increase of the layer. In order to maintain the diversity of the nodes, various selection methods such as truncation selection and roulette wheel selection (RWS) to choose the nodes among candidate nodes are proposed. In addition, in order to reduce the computational overhead to determine all candidates which exhibit all compositions of the input variables, a new implementation method is proposed. From the viewpoint of the diversity of the selected nodes and the computational aspects, it is shown that the proposed method is preferred over the conventional design methodology.
CONSENSUS ALGORITHMS BASED BLOCKCHAIN OF THINGS FOR DISTRIBUTED HEALTHCARE

Internet of Things (IoT) consists of smart Things with evolution of ubiquitous computing. Fog Computing (FC) processes and analyzes data of these sensors near to users. However, the ever-increasing in number of Things and the consequent explosion in data traffic have led to fail traditional solutions of centralized storage. Blockchain is a new technology developed as a shared ledger build around peer-to-peer network to produce unchangeable blocks that contain multiple data. These blocks are linked to previous ones in sequence called chain through hash functions. Participants in blockchain selects a leader through one of consensus algorithms who adds new blocks in the chain to prevent dishonest nodes from creating invalid blocks. In this paper, we propose IoT based blockchain architecture named blockchain of Things to store medical records in a distributed manner. The architecture is emulated on Fog server Linux-based using Node.js and Postman. Three consensus protocols, namely: Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (PBFT) are emulated and communicated using WebSocket. The consensus algorithms are evaluated in terms of CPU utilization and memory usage. The results show that PoS compared to PoW and PBFT is more lightweight and beneficial to IoT application.
Pengaruh Pengetahuan dan Sikap Terhadap Kesiapsiagaan Kepala Keluarga dalam Menghadapi Bencana Banjir

Floods are natural disasters that need attention because they threaten people's lives and economies. Samarinda is the capital city of East Kalimantan, one of the cities that is always plagued by flood problems. The purpose of this study was to determine how much influence the knowledge and attitudes about the risk of floods have on the preparedness of the head of the family in facing flood disasters. The population in this study was 735 households in the Sempaja Timur village, North Samarinda district, Samarinda city, and the sample was 199 households. This study uses a mixed-method that combines qualitative and quantitative approaches. The data instrument was taken using a questionnaire and analyzed with linear regression statistical analysis, and F-test statistical test. The results showed that there was a significant influence of knowledge and attitudes on the level of family head preparedness in facing floods in East Sempaja Village. This is proven in the multiple linear regression equation: Y = 10.282 + 0.243 X1 + 0.034 X2, and the results of the F-test with a significance level of α = 0.05 obtained a calculated F value of 8.031 and a P value of 0.000 &lt;0.05, therefore knowledge and attitudes variable have simultaneous significant influences on the preparedness variable. The combined index of four parameters, namely knowledge and attitude, emergency response plans, resource mobilization and flood warning systems was 73.26. In the preparedness index classification, this number is in the ready category. This research is expected to give insight to the head of the family who is part of the community to increase awareness of the importance of knowledge and preparedness regarding flood disasters.