Reviewerly/exHarmony huggingface dataset:
WassBERT: High-Performance BERT-based Persian Sentiment Analyzer and Comparison to Other State-of-the-art Approaches

Applications require the ability to perceive others' opinions as one of the most outstanding parts of knowledge. Finding the positive or negative feelings in sentences is called sentiment analysis (SA). Businesses use it to understand customer sentiment in comments on websites or social media. An optimized loss function and novel data augmentation methods are proposed for this study, based on Bidirectional Encoder Representations from Transformers (BERT). First, a crawled dataset from Persian movie comments on various sites has been prepared. Then, balancing and augmentation techniques are accomplished on the dataset. Next, some deep models and the proposed BERT are applied to the dataset. We focus on customizing the loss function, which achieves an overall accuracy of 94.06 for multi-label (positive, negative, neutral) sentences. And the comparative experiments are conducted on the dataset, where the results reveal the performance of the proposed model is significantly superior compared with other models.
A Transformer based Multi-Task Learning Approach Leveraging Translated and Transliterated Data to Hate Speech Detection in Hindi

The increase in usage of the internet has also led to an increase in unsocial activities, hate speech is one of them. The increase in Hate speech over a few years has been one of the biggest problems and automated techniques need to be developed to detect it. This paper aims to use the eight publicly available Hindi datasets and explore different deep neural network techniques to detect aggression, hate, abuse, etc. We experimented on multilingual-bidirectional encoder representations from the transformer (M-BERT) and multilingual representations for Indian languages (MuRIL) in four settings (i) Single task learning (STL) framework. (ii) Transfering the encoder knowledge to the recurrent neural network (RNN). (iii) Multi-task learning (MTL) where eight Hindi datasets were jointly trained and (iv) pre-training the encoder with translated English tweets to Devanagari script and the same Devanagari scripts transliterated to romanized Hindi tweets and then fine-tuning it in MTL fashion. Experimental evaluation shows that cross-lingual information in MTL helps in improving the performance of all the datasets by a significant margin, hence outperforming the state-of-the-art approaches in terms of weightedF1 score. Qualitative and quantitative error analysis is also done to show the effects of the proposed approach.
BUILDING A PERSONAL CAREER ROUTE AS A TECHNOLOGY OF PROFESSIONAL DEVELOPMENT

The article presents an analysis of the approaches of domestic and foreign authors to project-based learning as a technology of professional development at the stage of students' education in higher educational institutions. Project-based training is considered as a leading technology, as it is in demand by both students and employers. Using the example of the preparation and implementation of projects by bachelors in the HR field, the advantages of project training technology are revealed as a process that allows forming the competencies of project development and implementation, teamwork and leadership, effective communication and social interaction, support for group decision-making, critical and systemic thinking. It has been established that projects carried out by order and under the guidance of an employer on the current topic of professional activity form motivation for professional development among students from the first year of study. The authors describe in detail the technology of project training, the results and the product of the project "Building a personal career route" in the context of digitalization of education. The advantages of using digital tools for project management in the format of distance education are highlighted. Based on the identified advantages of project-based training, recommendations were formed for the administration of universities and students on the use of professional development technologies at the stage of preparation at the university.
ANALISIS LAJU INFILTRASI TERHADAP MUKA AIRTANAH,KECAMATAN CIPAYUNG, KOTA JAKARTA TIMUR

To be able to determine the water catchment area, the value of the infiltration rate and groundwater level are quite important in a parameter for determining the water catchment area, in Jakarta the lack of water catchment areas and the high rate of population growth cause frequent flooding in the capital city, so it is necessary to there is a long-term plan for spatial planning of water catchment areas in Jakarta as one of the steps to overcome flood disasters and become groundwater reserves in the capital city, the higher the groundwater level value in an area, the area has the potential for good catchment areas because it can still accommodate rainwater, infiltration rate data collection was carried out directly in the field using a Double Ring Infiltrometer and for groundwater level data direct measurements were made on dug wells in the study area
Adaptive wavelet transform based on artificial fish swarm optimization and fuzzy C-means method for noisy image segmentation

Aiming at the problem that traditional fuzzy C-means (FCM) clustering algorithm is susceptible to noise in processing noisy images, a noisy image segmentation method based on FCM wavelet domain feature enhancement is proposed. Firstly, the noise image is decomposed by two-dimensional wavelet. Secondly, the edge enhancement of the approximate coefficient is carried out, and the artificial fish swarm (AFS) optimization algorithm is used to process the threshold value of the detail coefficient, and the processed coefficient is reconstructed by wavelet transform. Finally, the reconstructed image is segmented by FCM algorithm. Five typical gray-scale images are selected by adding Gaussian noise and Salt&amp; pepper noise, respectively, and segmented by various methods. The peak signal-to-noise ratio (PSNR) and error rate (MR) of segmented images are used as performance indexes. Experimental results show that compared with traditional FCM clustering algorithm segmentation method, particle swarm optimization (PSO) segmentation method and other methods, the indexes of image segmentation by the proposed method is greatly improved. It can be seen that the proposed segmentation method retains the texture information of image edge well, and its anti-noise performance and segmentation performance are improved.
Considering the Role of Guidelines in Visualization Design Practice

Guidelines are commonly viewed as an important form of design knowledge that can support the principled application of research findings to visualization design. Researchers regularly propose design guidelines as outputs from studies with the aim of prescribing design activity. However, inquiry into the actual use of design guidelines by practitioners is often unaddressed or left for future work. We propose there is a need for visualization researchers to better understand the realities of design practice, especially its epistemological foundations, and how different forms of design knowledge are actually employed rather than how researchers imagine them to be employed.
ANALISIS BIBLIOMETRIK PUBLISH OR PERISH (POP) KEPADA GURU MAN 3 KULONPROGO YOGYAKARTA

This Community Service Activity was designed to improve teachers’ scientific writing ability as their compulsory professional skills which has been stated in the Minister of Administrative Reform and Bureaucratic Reform Act number 16 of 2009 concerning Teacher Function and Credit Scores. Publishing scientific article becomes one of the required elements and sub-elements of teacher activities to be assessed as credit scores. Hence, the teachers needed the training to write scientific papers as a requirement for their promotion. The training applied a Participatory Learning approach that engaged lecture, practice, and Evaluation. The participants of the training were 30 teachers of MAN 3 Kulonprogo. They were given a lesson to collect articles as references using the bibliometric application Publish or Perish (POP). The evaluation of this Community Service Activity showed that the knowledge value after the training was 2.221 points higher than before training. The paired T-Test sample test worth 0.048 implied that this PKM activity has influenced MAN 3 Kulonprogo Yogyakarta teachers’ skills, especially in improving the teachers’ ability in finding recommended articles.
Neutrosophic Adaptive Clustering Optimization in Genetic Algorithm and Its Application in Cubic Assignment Problem

In optimization theory, the adaptive control of the optimization process is an important goal that people pursue.To solve this problem, this study introduces the idea of neutrosophic decision-making into classical heuristic algorithm, and proposes a novel neutrosophic adaptive clustering optimization thought, which is applied in a novel neutrosophic genetic algorithm (NGA), for example.The main feature of NGA is that the NGA treats the crossover effect as a neutrosophic fuzzy set, the variation ratio as a structural parameter, the crossover effect as a benefit parameter and the variation effect as a cost parameter, and then a neutrosophic fitness function value is created.Finally, a high order assignment problem in warehouse management is taken to illustrate the effectiveness of NGA.
On the benefits of self-taught learning for brain decoding

Abstract Context We study the benefits of using a large public neuroimaging database composed of functional magnetic resonance imaging (fMRI) statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. Results We show that such a self-taught learning process always improves the performance of the classifiers, but the magnitude of the benefits strongly depends on the number of samples available both for pretraining and fine-tuning the models and on the complexity of the targeted downstream task. Conclusion The pretrained model improves the classification performance and displays more generalizable features, less sensitive to individual differences.
Stability and Generalization for Markov Chain Stochastic Gradient Methods

Recently there is a large amount of work devoted to the study of Markov chain stochastic gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving minimization problems. In this paper, we provide a comprehensive generalization analysis of MC-SGMs for both minimization and minimax problems through the lens of algorithmic stability in the framework of statistical learning theory. For empirical risk minimization (ERM) problems, we establish the optimal excess population risk bounds for both smooth and non-smooth cases by introducing on-average argument stability. For minimax problems, we develop a quantitative connection between on-average argument stability and generalization error which extends the existing results for uniform stability \cite{lei2021stability}. We further develop the first nearly optimal convergence rates for convex-concave problems both in expectation and with high probability, which, combined with our stability results, show that the optimal generalization bounds can be attained for both smooth and non-smooth cases. To the best of our knowledge, this is the first generalization analysis of SGMs when the gradients are sampled from a Markov process.
Variance minimisation on a quantum computer for nuclear structure

Quantum computing opens up new possibilities for the simulation of many-body nuclear systems. As the number of particles in a many-body system increases, the size of the space if the associated Hamiltonian increases exponentially. This presents a challenge when performing calculations on large systems when using classical computing methods. By using a quantum computer, one may be able to overcome this difficulty thanks to the exponential way the Hilbert space of a quantum computer grows with the number of quantum bits (qubits). Our aim is to develop quantum computing algorithms which can reproduce and predict nuclear structure such as level schemes and level densities. As a sample Hamiltonian, we use the Lipkin-Meshkov-Glick model. We use an efficient encoding of the Hamiltonian onto many-qubit systems, and have developed an algorithm allowing the full excitation spectrum of a nucleus to be determined with a variational algorithm capable of implementation on today's quantum computers with a limited number of qubits. Our algorithm uses the variance of the Hamiltonian, $\langle H^2\rangle - \langle H\rangle ^2$, as a cost function for the widely-used variational quantum eigensolver (VQE). In this work we present a variance based method of finding the excited state spectrum of a small nuclear system using a quantum computer, using a reduced-qubit encoding method.
NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning

Our goal is a modern approach to answering questions via systematic reasoning where answers are supported by human interpretable proof trees grounded in an NL corpus of authoritative facts. Such a system would help alleviate the challenges of interpretability and hallucination with modern LMs, and the lack of grounding of current explanation methods (e.g., Chain-of-Thought). This paper proposes a new take on Prolog-based inference engines, where we replace handcrafted rules with a combination of neural language modeling, guided generation, and semiparametric dense retrieval. Our implementation, NELLIE, is the first system to demonstrate fully interpretable, end-to-end grounded QA as entailment tree proof search, going beyond earlier work explaining known-to-be-true facts from text. In experiments, NELLIE outperforms a similar-sized state-of-the-art reasoner [Tafjord et al., 2022] while producing knowledge-grounded explanations. We also find NELLIE can exploit both semi-structured and NL text corpora to guide reasoning. Together these suggest a new way to jointly reap the benefits of both modern neural methods and traditional symbolic reasoning.
Efficiency evaluation of background removal algorithms

The necessity to delete background on an image occurs quite often. Technologies are closely connected to our everyday life so with its change they have to be adapted to the new realities. The increasing amount of video conferences during the COVID19 epidemic brought the concept of virtual backgrounds to the fore. Any videoconference services either Google Meet, Zoom or MS Teams and others have this function. Virtual background could be really useful for confidential protection or hiding one's chaotic surroundings. The technology of automated background removal must be developed, because first of all, people want to leave their personal life private, that is why they hide the place where they are. Secondly, the usage of this technology is applied for visual tracing and image segmentation. But more often background removal is used when making new content, it is possible to add any kind of background to an extracted fragment thereby getting a completely new image. Currently, there are a lot of programs with a help of which a background removal could be done, but manual correction still remains the most trusted method, yet it is a labourintensive process with requires a lot of time. Frequently people need a quick solution for image editing and the services that provide such features are either paid services or they are not able to cope with the task, presenting the necessary level of quality. The article explores different modules of background removal and evaluates the efficiency of their work. The actuality of this problem is caused with the need of confidentiality protection with the increasing usage of services for videoconferences and also for making content, visual detection and operative problem solution. In this material we take a look at the methods used in the models, evaluate the work of the most popular models for background removal with the help of proposed new metrics for output results. All of this helps to define pluses and minuses of studied algorithms.
Should We Afford Affordances? Injecting ConceptNet Knowledge into BERT-Based Models to Improve Commonsense Reasoning Ability

Abstract Recent years have shown that deep learning models pre-trained on large text corpora using the language model objective can help solve various tasks requiring natural language understanding. However, many commonsense concepts are underrepresented in online resources because they are too obvious for most humans. To solve this problem, we propose the use of affordances – common-sense knowledge that can be injected into models to increase their ability to understand our world. We show that injecting ConceptNet knowledge into BERT-based models leads to an increase in evaluation scores measured on the PIQA dataset.
Detection of Face Mask Wearing Conditions with Lightweight CNN Models on Raspberry Pi 4 and Jetson Nano

There are still instances where an individual fails to correctly use a face mask, giving them a false sense of security. In a pandemic, automated systems for monitoring public health safety are critical. This research aims to develop an embedded system that uses lightweight CNN models to detect the condition of people’s face masks. The method works by employing a camera to capture the faces. It will then determine if the recognized faces in the photos are wearing their face masks appropriately or incorrectly, displayed on an LCD monitor. The trained models performed admirably, with MobileNetV2 achieving 97 percent accuracy. The NasNetMobile model fared the lowest, with an overall accuracy of 92 percent. However, it is still higher than 90%.
Monitoring Provenance of Delegated Personal Data with Blockchain

Personal data are shared by their owners with service providers for different needs, and these data can be modified and further shared among other service providers. For transparency and accountability of such delegated data, a personal data provenance monitoring system recording how owners' data are propagated and modified is necessary. However, achieving this involves significant challenges, due to the decentralized relationship of different service providers. Therefore, we propose to use blockchain to track the provenance of owners' data. However, for privacy reasons, what service providers can upload to the blockchain is limited to some sharing records that do not reveal data contents, and blockchain peers must validate them without actual data contents. We propose a new extended vector commitment (EVC) scheme for monitoring personal data provenance in third-party services. Unlike existing vector commitment (VC) schemes, EVC has extended algorithms that keep data contents secret against verifiers. We use the EVC scheme to develop a system that allows (1) blockchain peers to check the consistency between insertion/deletion operations and sharing records and (2) data owners to monitor the provenance of delegated personal data. Our experiment with Hyperledger Fabric and real implementations shows that the extra overhead is negligible in most processes and well acceptable in others.
Blockchain Technology in Waste Management: Theoretical Evaluation for System Adoption

In recent years Blockchain technology has become increasingly important and accepted (i.e business sector, supply chain, and other aspects of sustainability). The purpose of this study was to determine the potential of Brewer spent yeast and determine the possibility of blockchain adoption in Brewer Spent Yeast (BSY) waste management. This study uses primary data and literature studies in the preparation of blockchain-based waste management. The data obtained were analyzed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to extract the relationship between criteria within the blockchain framework. From the results of this study, it is known in the blockchain management aspect, it was found that the availability of technology and understanding of technology use insights were the most dominant cause criteria (Ri–Ci)&gt;0.00. While the impact criteria with a value (Ri-Ci)&lt;0.00 such as the criteria can be used for many sources of waste, cheaper transactions, and aspects of a culture of transparency in the implementation of waste treatment. The cause criteria will influence the effect criteria in the process of adopting the blockchain model in Brewer Spent Yeast (BSY) waste management.
University Training of STEM-IT Professionals to Innovation Activity in 3D-Teams

The paper focuses on the training professionals in the field of natural sciences, engineering and technology, mathematics and IT with different levels of education and qualifications (bachelor, master, doctor) for joint innovation activity in interdisciplinary 3D teams. The paper substantiates the need for such training, which stems from digitalization and introduction of Industry 4.0 tools, as well as the desire to overcome the technological backlog of the country. Based on the analysis of the best world practices of innovation activity, the paper proposes to coordinate programs for training of STEM-IT professionals for effective collaboration in 3D-teams by planning the competencies of graduates, taking into account the priorities of scientists, engineers, technologists, mathematicians and IT specialists when working with the division of labor in a team at various stages of creating science-intensive innovative products, systems, processes, and services. The purpose of the research is to develop recommendations for program coordination.
The operational steps of the evaluation processes: a balance of the literature

Evaluation is a subcomponent of the control process, which, in turn, is the fourth step of the management process. Its essentiality lies in that it is through it that decisions are made about the effectiveness and efficiency of management processes. As science constantly advances in this field, it is necessary to periodically survey each management process step's state of the art of each subcomponent. In this sense, this study aimed to take stock of what evaluation is and what are its operationalization steps. The method used was the scientific-technological one, characterized by elaborating guiding research questions, data collection on scientific bases, data organization, and generating the sought answers. The results showed that a) evaluation is the decision process that allows comparing what was planned with what was executed, b) the evaluation process is composed of five basic steps: planning, execution, monitoring, analysis of results, and reporting analytical, and c) each step has its criteria to be executed. The conclusion shows that changing the order of these steps can decisively compromise the intended objectives of the entire evaluation process, which is knowledge about the difference between what was planned and what was executed.