Reviewerly/exHarmony huggingface dataset:
ON THE POSSIBILITY OF A RATIONAL CONCEPT OF ABSOLUTE NON-BEING

The subject of research is the problem of non-existence, or rather absolute nothingness. This is one of the central ontological problems as it is, in essence, one of the formulations of the first principle problem.This paper directly addresses the problem of nothingness from the perspective of the approach that identi-fies itself as a «philosophy of nothingness». According to this approach, substance is claimed to be abso-lute nothingness, which gives rise to existence. Currently, there can be distinguished three varieties in the «philosophy of nothingness»: rationalistic (dialectical), irrational (metaphysical), and radical (absolute, total). The immediate objective of this study is to analyze the possibility of identifying absolute nothing-ness by rational means. This possibility is declared by a rationalist version of the «philosophy of nothing-ness». The problem is considered in the paper from the point of view of dialectical methodology, which is also used in rationalist «philosophy of nothingness». The study shows the difficulty of substantializationof nothingness. The author provides arguments for the impossibility of a consistent rational concept of absolute nothingness. It is concluded that the problem of the relationship between existence and non-existence can be rationally solved only through consistent dialectical methodology. This methodology denies substantialist obscurity and implies a substantialist dialectical relationship between being and noth-ingness as the essential characteristics of the first principle. A positive value of the «philosophy of noth-ingness» is that draws attention to the ontological study of the first principle, makes it possible to look beyond the traditional understanding of this problem. The study results can be helpful in the search for a universally accepted ontological definition of matter.
A Semantic Search System for the Supremo Tribunal de Justiça

Many information retrieval systems use lexical approaches to retrieve information. Such approaches have multiple limitations, and these constraints are exacerbated when tied to specific domains, such as the legal one. Large language models, such as BERT, deeply understand a language and may overcome the limitations of older methodologies, such as BM25. This work investigated and developed a prototype of a Semantic Search System to assist the Supremo Tribunal de Justiça (Portuguese Supreme Court of Justice) in its decision-making process. We built a Semantic Search System that uses specially trained BERT models (Legal-BERTimbau variants) and a Hybrid Search System that incorporates both lexical and semantic techniques by combining the capabilities of BM25 and the potential of Legal-BERTimbau. In this context, we obtained a $$335\%$$ increase on the discovery metric when compared to BM25 for the first query result. This work also provides information on the most relevant techniques for training a Large Language Model adapted to Portuguese jurisprudence and introduces a new technique of Metadata Knowledge Distillation.
Sketching Data Distribution by Rotation

Kernel density estimation is a useful method for estimating the probability distribution of data. It is a challenge to achieve efficient kernel density estimation, especially for large-scale and high-dimension stream data. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rotation kernel</i> , a novel kernel function for density estimation. The rotation kernel density can be fast estimated by a data structure named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rotation Kernel Density Sketch</i> (RKDS). RKDS is a time- and memory-efficient method for kernel density estimation, even over data streams and distributed systems. RKDS is applicable for estimating density at specific points and also for representing data distribution. We provide theoretical analysis for rotation kernel and RKDS. Furthermore, we apply RKDS to outlier detection, concept drift detection, and personalized federated learning. Experiments show that our method improves time efficiency by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3\times 10^{3}$</tex-math></inline-formula> times compared with baselines. RKDS also provides comparable detecting precision and better delay on outlier detection and concept drift detection tasks.
TCNAEC: Advancing Sentence-level Revision Evaluation through Diverse Non-native Academic English Insights

In the domain of Natural Language Processing (NLP), the English Writing Fluency Improvement for non-native speakers, particularly in academic contexts, poses significant challenges. While Sentence-level Revision (SentRev) endeavors to address this concern, the existing evaluation corpus, SMITH, falls short in offering a robust and comprehensive assessment of the task. To bridge this gap, our research offers a novel evaluation corpus generation scheme, leading to the creation of Ten-Country Non-native Academic English Corpus (TCNAEC). A meticulous analysis revealed the superior characteristics of TCNAEC over SMITH in various dimensions. Our evaluation also uncovered intriguing linguistic phenomena, offering valuable insights for fellow researchers. In contrast, the Grammatical Error Correction (GEC) task, which shares similarities with SentRev, has been more extensively explored, resulting in a richer set of training and evaluation corpora. However, the distinctive attributes of SentRev present a heightened challenge in NLP implementation. The TCNAEC, representing ten countries, captures the unique English expression styles of non-native speakers worldwide, offering a more holistic view compared to the Japan-centric SMITH. Furthermore, while SMITH primarily revolves around computational linguistics, TCNAEC spans multiple disciplines, accentuating its comprehensiveness. The construction strategy of TCNAEC, ensuring semantic consistency between Draft and Reference, emphasizes meaningful structural variations, reflecting the stylistic disparities between non-academic and academic texts.
Possibilities of using PSS®SINCAL, ETAP, PowerFactory software for modeling electric power networks

The article is devoted to modeling of electrical networks. The article discusses the positive and negative features of simulation programs. Special attention is paid to the principles of the program. The article discusses such programs as PSS®SINCAL from Siemens; ETAP from Operation Technology and PowerFactory from DIgSILENT GmbH. In the work, we considered the programs and decided that the most successful solution for working with the modeling of the electrical network and the impact of compensating devices on the electrical network would be the use of the PSS®SINCAL software from Siemens. For more than 20 years, the PSS®SINCAL platform has enabled engineers to solve various challenges of variable distribution, transmission and industrial power systems, including maintaining high reliability of supply and efficient integration of distributed energy resources. With PSS®SINCAL's modular platform, power system planning and operation engineers are supported throughout the entire workflow, from raw data import and network modeling (taking into account past, current and future conditions) through basic and advanced calculations to large-scale simulations and analysis protection, as well as other methods in the time and frequency domains. PSS®SINCAL is used in more than 100 countries by transmission and distribution planning engineers, protection engineers, consultants, power plant and industrial grid operators, operations planning engineers, IT specialists, researchers, and more. Thanks to its modular design, PSS®SINCAL is highly flexible and customizable. It offers a wide range of analysis functions for the planning, design and operation of power systems, allowing you to model and study: power quality, frequency stability, distributed generation interconnection, protection coordination, power restoration, economic design solutions and much more.
IPFS-Blockchain Based Technology for Lease Agreement and Rental System in Real Estate

Rental and lease scams are becoming more common in India by the use of a rental property as a lure to obtain money from the landlord or prospective tenants. Unfortunately, these scams seek to extort money from prospective renters or the tenants by fake real estate agents and fake listings or by breaching the lease agreement, breach in rent payment and hiding the utility bill charges. These scams can occur with both, the tenant or the renter as the victim. The government also faces difficulty in monitoring and keeping track of the property tax. This paper proposes the adoption of blockchain technology to address this issue by creating a housing lease alliance chain through Smart Contracts which enables landlords and tenants to sign lease agreements, to incorporate utility bills while signing the contract and to automatically pay and collect rent on a regular basis. We also propose the use of an IPFS system (Interplanetary File System) for listing of properties and for the authenticity of property and personal identity information through government identification, making leasing safe and secure.
SepMM: A General Matrix Multiplication Optimization Approach for Privacy-Preserving Machine Learning

Privacy-preserving machine learning (PPML) has gained significant attention in recent years due to the increasing need to protect sensitive data while obtaining accurate predictions. This is attributed to secure encryption schemes such as homomorphic encryption and secure multi-party computation, which allow some parties to jointly compute the results without knowing others’ data. However, practical implementations of these methods require secure computation under encryption, which significantly increase communication and computation costs compared to plaintext computation. In view that matrix multiplication is a key operation in machine learning applications, and typically serves as a bottleneck under secure multi party computation framework due to the massive communications it requires between parties, we propose SepMM as a novel secure matrix multiplication optimization approach for 2-party computation. SepMM ensures that, assuming uniform distribution prior, the chances for the adverary to reveal any entry in the matrix decreases exponentially with the increase in bitlength. SepMM can be integrated with secure matrix multiplication methods that are bitwise equivalent to plaintext execution. Experimental results show that, by integrating SepMM with state-of-the-art PPML framework SIRNN, the communication cost and inference time are reduced by 4.67x - 13.29x and 3.64x - 9.44x, respectively, for widely adopted neural networks SqueezeNet, ResNet50, and DenseNet121.
Enhancing Cognitive Skills in Children through CogniHome: A Comprehensive Home-Based Training Solution

Abstract: The future mental health of person is Depend on their childhood. To develop their cognitive skills in their childhood we are developing web application based on different technologies. CogniHome is also helpful to develop and increase their Intelligence Quotient (IQ). The abstract deals with the significance of cognitive skills. The IQ test in website is helpful for user to check their mental stage. User able to increase their cognitive skills using different Games, Therapies and Puzzles. Therapies include meditation, yoga, etc. which help user to stable mind. Puzzles and Quizzes are helpful to increase their IQ. The main purpose of CogniHome is to engage child in enjoyable and productive home learning experiences. Ultimately, the CogniHome is to enhance cognitive skills of child for their betterment future
On super quantum discord for high-dimensional bipartite state

By quantifying the difference between quantum mutual information through weak measurement performed on a subsystem one is led to the notion of super quantum discord. The super version is also known to be difficult to compute as the quantum discord which was captured by the projective (strong) measurements. In this paper, we give effective bounds of the super quantum discord with or without phase damping channels for higher-dimensional bipartite quantum states, and found that the super version is always larger than the usual quantum discord as in the 2-dimensional case.
Evolution of Computer Bots: From Simple Programs to Artificial Intelligence

This article explores the evolution of computer bots and their impact on the modern world. It examines the initial steps in the development of bots, their evolution from simple programs to complex artificial intelligence systems. In addition, the article examines the role of artificial intelligence in the development of bots and its impact on various aspects of our lives. A separate section is devoted to the ethical and legal challenges associated with the growing role of computer bots. The article also covers issues of confidentiality and privacy, ethical use, liability, workplace impacts, bias and discrimination, and controls and security. The findings highlight the importance of developing ethical standards, working with stakeholders, and consciously using computer bots to make progress and ensure a fair and secure future. This article provides readers with an overview of the evolution of computer bots and stimulates discussion of ethical and legal issues related to their use in our society.
Integrating Artificial Intelligence and Deep Learning for the Purpose of Detecting Arms in Defense

Abstract: There has been an increase in the relevance of security as a primary issue across all sectors as a result of the prominence of criminal activities that take place at public events or in settings that seem to be distant. It is important to note that computer vision plays a vital role in the fields of anomaly detection and monitoring. It provides a broad range of applications that may be used to handle a variety of issues. The technology of computer vision is used in several applications. An essential component of the intelligence monitoring process is the use of video surveillance systems that are equipped with the capacity to identify and assess the surrounding environment, in addition to identifying and analysing unexpected events. The explanation for this occurrence is that there is a rising need to protect personal assets, and there is also an increased focus on ensuring that there is an increased degree of safety and security. This study makes use of a Yolo algorithm in order to effectively accomplish the goal of automating the identification of guns and other types of weapons. The method that is being presented involves the use of two distinct types of datasets. The first dataset is made up of images that have already been classified, whereas the second dataset is made up of photographs that have been manually labelled. The use of these datasets is available to the public. Despite the fact that the laborious tabulation of data is carried out with accuracy, the actual use of these findings in real-world circumstances may be contingent on the delicate balance that is established between speed and precision.
Recognition of Sign Language by Using the Implementation of Convolutional Neural Networks

Abstract: The discipline of Sign Language Recognition (SLR) is primarily concerned with the development of techniques that may translate sign language into written or spoken language. The primary purpose of this field is to make it easier for people who are deaf-mute to communicate with the general public. Despite the inherent difficulties that are brought about by the activity's intricacy and the enormous repertory of manual movements that it demands, this specific activity has a significant influence on the larger social setting. The approaches that are now in use for sign language recognition (SLR) are dependent on features that are developed manually in order to portray the motion of sign language. These features are then used in the process of developing classification models. On the other hand, the issue of building dependable features that are capable of properly responding to the wide variety of hand gestures is a considerable difficulty. In this investigation, we suggest a unique convolutional neural network (CNN) architecture as a possible solution to the issue that was described before. One of the capabilities of the Convolutional Neural Network (CNN) is its capacity to independently extract discrete spatial-temporal properties from unprocessed video streams. This eliminates the need for previous information and eliminates the demand for feature building. CNN utilizes many video feeds that include color information, depth cues, and bodily joint locations. These streams are included into the network. A successful integration of color, depth, and trajectory information is achieved by the use of these streams as input. The effectiveness of the Convolutional Neural Network (CNN) is going to be improved as a result of this activity. Through the application of the proposed model to a real dataset received from Microsoft Kinect, the validity of the model is shown. The results of our investigation indicate that its performance is better to that of conventional techniques that are dependent on attributes that are manually developed.
Peningkatan Literasi Siswa Disleksia SDN 26 Ampenan dengan Mengadakan Kelas Literasi

Dyslexia is a learning difficulty in children that causes difficulties in writing, reading, spelling and speaking, but there are also difficulties that generally appear between the ages of 7 and 12 years. Dyslexia factors are divided into three areas: educational, psychological, and biological. This service aims to describe dyslexia disorders in children, the factors that cause children to become dyslexic disorders, and educators' efforts to overcome the problem of dyslexia disorders and be able to understand the conditions experienced by people with dyslexia so that they can improve the literacy skills of dyslexic students. This service uses observation and interview methods and there are several stages of the implementation method. The service target consisted of 4 students from SDN 26 Ampenan. In this service, the writing team seeks to improve the literacy skills of dyslexic students. Students who experience dyslexia are affected by difficulty recognizing letters so they tend to experience delays during the learning process.
Optimal teleportation fidelity and its deviation in noisy scenarios

In this work, we study quantum teleportation, a fundamental protocol of quantum physics. In particular, we present a mathematical methodology to study the combined effects of noisy resource state and noisy classical communication on teleportation fidelity and its deviation. We describe a teleportation protocol where an arbitrary two-qubit state in canonical form is used as a resource. Thereafter, to teleport an unknown qubit, Alice measures her qubits in Bell basis and conveys the measurement outcome to Bob via noisy classical channel(s). We derive the exact formulae of optimal teleportation fidelity and corresponding fidelity deviation where the resource state and the classical communication, both can be noisy. To provide the proof of optimality, we provide a systematic method. We further find conditions for non-classical fidelity and dispersion-free teleportation for our teleportation protocol. In this way, we identify noisy environments where it is possible to achieve dispersion-free teleportation without compromising non-classical fidelity. We also demonstrate scenarios where the increase of entanglement in the resource state, may degrade the quality of teleportation — a counter-intuitive instance. Finally, we discuss about the minimum classical communication cost required to achieve non-classical fidelity for our protocol. Here we mainly focus on a possible way to optimize the classical communication cost.
The Implementation of Expectation-Confirmation Model (ECM) in Analyzing the Relationship Between Gamification and Continuance Use Intention in Online Travel Industry

Online Travel Agent or abbreviated as OTA is one form of technological development in the tourism industry. Competence in the OTA business is getting tougher, along with that each OTA provides different services. One of the trends to increase user loyalty is by implementing the concept of gamification into the system. This study aims to determine the gamification factors that have an influence on continuance use intention in OTA users. This research uses a quantitative approach with 300 respondents taken using purposive sampling. The data was analyzed using PLS-SEM with SmartPLS. The research model used is taken from the modified Expectation-Confirmation Model (ECM). The results of this study found that two out of twelve hypotheses were rejected. Based on the ten accepted hypotheses, it can be concluded that novelty, trendiness and intimacy of gamification have a positive influence on continuance use intention through hedonic value and utilitarian value variables.
Research on Backward Cloud Transformation Algorithm Based on Adding Cloud Drops

As a tool for studying uncertain information, cloud model is of great significance in uncertain artificial intelligence and data mining. Among them, the backward cloud transformation method is one of the important algorithms of cloud model, which can realize the conversion from quantitative data to qualitative concepts. In this paper, a dynamic incremental backward cloud transformation algorithm is proposed to solve the problem that the estimated value of hyper entropy is imaginary when the backward cloud transformation method is used to calculate hyper entropy. First, according to the formation characteristics of cloud drops, the generated samples and the original samples are fused as new samples by dynamically and randomly generating samples, and then the hyper entropy is estimated until the estimated value of hyper entropy is real. Secondly, the stability and convergence of the algorithm proposed in this paper are analyzed through simulation experiments. The experimental results show that the new dynamic incremental backward cloud transformation algorithm solves the problem that the hyper entropy estimation value is imaginary while the estimation error is small and the stability is good. Finally, the algorithm is applied to brain CT segmentation, and the results show that the algorithm is effective and practical.