Reviewerly/exHarmony huggingface dataset:
NS3: Neuro-Symbolic Semantic Code Search

Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional text, and multi-step reasoning. To overcome this limitation, we propose supplementing the query sentence with a layout of its semantic structure. The semantic layout is used to break down the final reasoning decision into a series of lower-level decisions. We use a Neural Module Network architecture to implement this idea. We compare our model - NS3 (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods, and evaluate on two datasets - CodeSearchNet and Code Search and Question Answering. We demonstrate that our approach results in more precise code retrieval, and we study the effectiveness of our modular design when handling compositional queries.
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities

Identifying related entities and events within and across documents is fundamental to natural language understanding. We present an approach to entity and event coreference resolution utilizing contrastive representation learning. Earlier state-of-the-art methods have formulated this problem as a binary classification problem and leveraged large transformers in a cross-encoder architecture to achieve their results. For large collections of documents and corresponding set of $n$ mentions, the necessity of performing $n^{2}$ transformer computations in these earlier approaches can be computationally intensive. We show that it is possible to reduce this burden by applying contrastive learning techniques that only require $n$ transformer computations at inference time. Our method achieves state-of-the-art results on a number of key metrics on the ECB+ corpus and is competitive on others.
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures

The Expectation-Maximization (EM) algorithm has been predominantly used to approximate the maximum likelihood estimation of the location-scale Gaussian mixtures. However, when the models are over-specified, namely, the chosen number of components to fit the data is larger than the unknown true number of components, EM needs a polynomial number of iterations in terms of the sample size to reach the final statistical radius; this is computationally expensive in practice. The slow convergence of EM is due to the missing of the locally strong convexity with respect to the location parameter on the negative population log-likelihood function, i.e., the limit of the negative sample log-likelihood function when the sample size goes to infinity. To efficiently explore the curvature of the negative log-likelihood functions, by specifically considering two-component location-scale Gaussian mixtures, we develop the Exponential Location Update (ELU) algorithm. The idea of the ELU algorithm is that we first obtain the exact optimal solution for the scale parameter and then perform an exponential step-size gradient descent for the location parameter. We demonstrate theoretically and empirically that the ELU iterates converge to the final statistical radius of the models after a logarithmic number of iterations. To the best of our knowledge, it resolves the long-standing open question in the literature about developing an optimization algorithm that has optimal statistical and computational complexities for solving parameter estimation even under some specific settings of the over-specified Gaussian mixture models.
Towards Secure Virtual Elections: Multiparty Computation of Order Based Voting Rules

Electronic voting systems are essential for holding virtual elections, and the need for such systems increases due to the COVID-19 pandemic and the social distancing that it mandates. One of the main challenges in e-voting systems is to secure the voting process: namely, to certify that the computed results are consistent with the cast ballots, and that the privacy of the voters is preserved. We propose herein a secure voting protocol for elections that are governed by order-based voting rules. Our protocol offers perfect ballot secrecy, in the sense that it issues only the required output, while no other information on the cast ballots is revealed. Such perfect secrecy, which is achieved by employing secure multiparty computation tools, may increase the voters' confidence and, consequently, encourage them to vote according to their true preferences. Evaluation of the protocol's computational costs establishes that it is lightweight and can be readily implemented in real-life electronic elections.
Transforming Points of Single Contact Data into Linked Data

In this paper, we present a method to map information regarding service activity provision residing in governmental portals across European Commission. In order to perform this, we used as a basis the enriched Greek e-GIF ontology, modeling concepts, and relations in one of the two data portals (i.e., Points of Single Contacts) examined, since relevant information on the second was not provided. Mapping consisted in transforming information appearing in governmental portals in RDF format (i.e., as Linked data), in order to be easily exchangeable. Mapping proved a tedious task, since description on how information is modeled in the second Point of Single Contact is not provided and must be extracted in a manual manner.
A Four-Valued Logical Framework for Reasoning About Fiction

In view of the limitations of classical, free, and modal logics to deal with fictional names, we develop in this paper a four-valued logical framework that we see as a promising strategy for modeling contexts of reasoning in which those names occur. Specifically, we propose to evaluate statements in terms of factual and fictional truth values in such a way that, say, declaring ‘Socrates is a man’ to be true does not come down to the same thing as declaring ‘Sherlock Holmes is a man’ to be so. As a result, our framework is capable of representing reasoning involving fictional characters that avoids evaluating statements according to the same semantic standards. The framework encompasses two logics that differ according to alternative ways one may interpret the relationships among the factual and fictional truth values.
Fine-Grained Counting with Crowd-Sourced Supervision

Crowd-sourcing is an increasingly popular tool for image analysis in animal ecology. Computer vision methods that can utilize crowd-sourced annotations can help scale up analysis further. In this work we study the potential to do so on the challenging task of fine-grained counting. As opposed to the standard crowd counting task, fine-grained counting also involves classifying attributes of individuals in dense crowds. We introduce a new dataset from animal ecology to enable this study that contains 1.7M crowd-sourced annotations of 8 fine-grained classes. It is the largest available dataset for fine-grained counting and the first to enable the study of the task with crowd-sourced annotations. We introduce methods for generating aggregate "ground truths" from the collected annotations, as well as a counting method that can utilize the aggregate information. Our method improves results by 8% over a comparable baseline, indicating the potential for algorithms to learn fine-grained counting using crowd-sourced supervision.
A Design to Predict and Analyze Crime

Abstract: Crime is one of the dominant and alarming aspect of our society. Over the past few years, the crime rate across globe has increased exponentially. So, preventing the crime from occurring is a vital task. In the recent time, it is seen that artificial intelligence has shown its importance in almost all the field and crime prediction is one of them. However, it is necessary to maintain a proper database of the crime that has occurred. The ability to predict the crime on the basis of time, location and so on which can occur in future can help the law enforcement agencies in preventing the crime before it occurs from a strategical perspective. However, predicting the crime accurately is a challenging task because crimes are increasing at an alarming rate. Thus, the crime prediction and analysis methods are very important to detect the future crimes and reduce them. In Recent time, many researchers have conducted experiments to predict the crimes using various machine learning methods and particular inputs. For crime prediction, KNN, K-means and Random Forest and some other algorithms are used. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. The main purpose is to highlight the worth and effectiveness of machine learning in predicting violent crimes occurring in a particular region in such a way that it can be used by police to reduce crime rates in the society.
Smart Sanitizing Robot with Medicine Transport System for Covid -19 Situation

As the covid ruins life of peoples in last 2 years, we need to contribute in this pandemic with our few efforts.In paper, concept of is proposed.A sanitizer robot can spray sanitizer on patients area and supply medicine to them.This is embedded based system to control the wireless sanitizer spraying robot with Smartphone.Apart from this, there will be a holding plate on the robot so that the Doctors can send the medicines to patients without physically entering into the patient ward.We are also going to charge the robot's battery with solar panels to merge renewable energy in our robot's power.All efforts are taken to make this robot most economic product in this pandemic which will be definitely beneficial for health industry.
Quantum Key Distribution in Kubernetes Clusters

Quantum Key Distribution (QKD) represents a reasonable countermeasure to the advent of Quantum Computing and its impact on current public-key cryptography. So far, considerable efforts have been devoted to investigate possible application scenarios for QKD in several domains such as Cloud Computing and NFV. This paper extends a previous work whose main objective was to propose a new software stack, the Quantum Software Stack (QSS), to integrate QKD into software-defined infrastructures. The contribution of this paper is twofold: enhancing the previous work adding functionalities to the first version of the QSS, and presenting a practical integration of the QSS in Kubernetes, which is the de-facto standard for container orchestration.
Naive bayes machine learning framework for auto detection of spam mails

Nowadays, Email spam is a major problem, with the increase usage of internet for product promotions, e-banking, e-loans, health related articles, movie news etc to users. Besides the people are practicing unwanted and unethical conduct as in phishing and mailing fraudulent information. Sending suspicious links through spam emails can harm our system and can also be sought after our system. Creating a fake user identity and email account is such easy for spammers. They pretend like a genuine entity in their spam emails and target those people who are not aware of these frauds. So, it is essential to identify those spam emails which are fraud. In this proposed work, we have applied machine learning algorithm to identify whether the incoming mail is spam or ham. This project will discuss how the Naive Bayes machine learning algorithm is applied on our data sets and how it has been selected for the email spam detection with the best precision and accuracy
Attributes Oriented Software Trustworthiness Measure Based on Axiomatic Approaches

&lt;p&gt;Software trustworthiness measurement can provide evidence for increasing the trustworthiness of the design and implementation of software. We once studied the application of axiomatic approaches in software trustworthiness measurement, presented seven desirable properties of software trustworthiness measure based on attributes, and established three measures. However, none of the three measures can fully satisfy these seven properties. In this paper, we propose an improved software trustworthiness measure based on a partition which satisfies all of the seven properties. Meanwhile, we conduct an empirical validation of this measure by a real case. Comparative study shows that this measure is better than the three measures we once built.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Discussing the Prudence Application of “Essential Facilities Doctrine” in Anti-Data Monopoly Strategies in the Digital Economy Era

As one of the most fundamental production elements of the digital economy, data has become a critical factor in determining enterprises’ competitiveness. Consequently, it is highly problematic that data monopolies frequently form in the market. Recognizing the issue, principle fourteen in “Anti-trust Guidelines on Platform Economy (Draft for Comments)” establishes a methodology for regulating data access restrictions. In addition, it reflects on the viral and debatable data monopoly problem by clearly stating that data can be perceived as an “essential facility”. In the study, it is argued that the “Essential Facilities Doctrine” has a high practical significance for regulating vertical monopoly in the traditional economy; however, it should be applied with caution under certain restrictions when dealing with data monopoly.
Constructive Fuzzy Cognitive Map for Depression Severity Estimation

Depression is a common and serious medical disorder that negatively affects the mood and the emotions of people, especially adolescents. In this paper, a novel framework for automatically creating Fuzzy Cognitive Maps (FCMs) is proposed. It is applied for the estimation of the severity of depression among adolescents, based on their electroencephalogram (EEG). The introduced Constructive FCM (CFCM) utilizes features extracted by a Constructive Fuzzy Representation Model (CFRM), which conduces to detect in a more intuitive way the cause-and-effect relationships between the brain activity and depression. CFCM contributes to limiting the participation of experts, and the manual interventions in the traditional construction of FCMs, it provides an embedded mechanism for dimensionality reduction, and it constitutes an inherently interpretable approach to decision making, while being uncertainty-aware and simple to implement. The results of the experiments, using a recent publicly available dataset, demonstrate the effectiveness of the proposed framework and highlight its advantages.
APPLICATION OF MIND OR MENTAL MAP IN TEACHING STUDENTS OF LAW UNIVERSITY FOREIGN LANGUAGE IN PROFESSIONAL FIELDS OF ACTIVITY

In the literature we studied, we found evidence of the effectiveness of using mind maps in various types of educational activities: to study vocabulary, various concepts, mastering and structuring large amounts of educational information, brain assault, project activities, mastery of oratory, writing creative essays and other written works, presentations to the research organization activities, Undoubtedly, the method will be useful and effective in all of the listed types.Activities and not only, provided that the type and rules for creating a product of cognitive visualizations will clearly match the goals.Therefore, it seems to us it is advisable to proceed from the goals and objectives when choosing the most appropriate method cognitive visualization.And for the correct selection it is necessary to distinguish between terminologically different mind maps, identify areas of their effectiveness and own technique for their construction.
Impact of the Text Simplification on Understanding

The reduction of the linguistic complexity of medical texts to make them more understandable to a larger population is an important task. The simplification of texts involves several steps, among which our study focuses on the definition of complex constructions and on study of the impact of the simplification. For this study, we selected 20 texts from the medical domain on different topics, namely drugs, diseases, substances, and medical institutions. We identified complex linguistic constructions and carried out their manual simplification at syntactic, lexical and semantic levels. We then designed a questionnaire to test comprehension of the texts and conducted a study with 26 participants. The results of this study shows that simplified texts obtained higher number of correct answers than technical texts. This difference is statistically significant. The self-evaluation questionnaire, done at the beginning of the test, indicates that the participants tend to overestimate their understanding of medical information. Besides, there is no correlation between the time taken to complete the interview and the correct answers provided.
A comprehensive survey on semantic facial attribute editing using generative adversarial networks

Generating random photo-realistic images has experienced tremendous growth during the past few years due to the advances of the deep convolutional neural networks and generative models. Among different domains, face photos have received a great deal of attention and a large number of face generation and manipulation models have been proposed. Semantic facial attribute editing is the process of varying the values of one or more attributes of a face image while the other attributes of the image are not affected. The requested modifications are provided as an attribute vector or in the form of driving face image and the whole process is performed by the corresponding models. In this paper, we survey the recent works and advances in semantic facial attribute editing. We cover all related aspects of these models including the related definitions and concepts, architectures, loss functions, datasets, evaluation metrics, and applications. Based on their architectures, the state-of-the-art models are categorized and studied as encoder-decoder, image-to-image, and photo-guided models. The challenges and restrictions of the current state-of-the-art methods are discussed as well.
Privacy-Preserving Algorithm for Medical Data

Various mobile applications are emerging as a result of the rapid development of mobile internet and the growing popularity of smart terminals.Medical data has evolved into a valuable asset that is constantly assessed and applied, resulting in a significant improvement in the quality of medical care.However, publishing and using user data exposes the user to the possibility of an attack.Medical data carries not only the patient's medical state and medical knowledge, but also the individual's sensitive personal information of a huge number of patients, due to the unique character of the medical profession.Allowing users to fully benefit from social networks while maintaining security is a critical issue that must be addressed immediately in the age of big data.We begin by providing an overview of the privacy hazards of social network data and several sorts of assaults in this study.We propose a privacy protection algorithm based on privacy privacy to leak confidentiality to sensitive social networks.The system employs edge-based weight conversion, which drastically reduces the calculation value and allows for a quicker response from the user.Reduces user leakage of confidential user data while maintaining personal standards under data availability.This strategy, in comparison to more complex ways, protects users against thinking attacks and eliminates the distortion of standard findings produced by data misunderstanding, ensuring the correctness of the suggestions.Our system can ensure effective and long-term security of usersensitive data, according to real-world data sets
Pattern-Based Logical Definitions of Prenatal Disorders Grounded on Dispositions

Biomedical ontologies define concepts having biomedical significance and the semantic relations among them. Developing high-quality and reusable ontologies in the biomedical domain is a challenging task. Pattern-based ontology design is considered a promising approach to overcome the challenges. Ontology Design Patterns (ODPs) are reusable modeling solutions to facilitate ontology development. This study relies on ODPs to semantically enrich biomedical ontologies by assigning logical definitions to ontological entities. Specifically, pattern-based logical definitions grounded on dispositions are given to prenatal disorders. The proposed approach is performed under the supervision of fetal domain experts.
Implementation on Secure Storage for Cloud with Duplication Checking

Cloud computing provides computer resources to consumers as a utility on demand over the Internet, and it now plays a large role in the commercial domain.Cloud storage has become one of the most popular services provided by cloud computing.Customers profit the most from cloud storage since they may save money on storage equipment purchases and maintenance by just paying for the quantity of storage they use, which can be scaled up and down based on demand.With the growing quantity of data in cloud computing, a reduction in data volumes could help providers save money by cutting the expenses of running large storage systems and conserving energy.As a result, in order to improve storage efficiency, data DE duplication techniques have been employed in cloud storage.Data usage in the cloud fluctuates over time due to the dynamic nature of data in cloud storage.Some data pieces, for example, may be retrieved often one time but not the next.Some datasets are often seen or modified by multiple people at the same time, while others require a high level of redundancy to maintain stability.As a result, having this dynamic functionality in cloud storage is crucial.On the other hand, current solutions are primarily focused on a static scheme, limiting their full applicability in the dynamic nature of data in cloud storage.For cloud storage, we offer a dynamic DE duplication approach.in this research, with the goal of increasing storage economy while maintaining redundancy for fault tolerance.
LA EQUIDAD EN LA EDUCACIÓN Y EN UNA PEDAGOGÍA ACTUALIZANTE

Brazil is the fifth most popular destination for Brazilian migrants and refugees to live.From January 2017 to March 2022, Brazil received 325,763 Venezuelans who remained in the country.In first place is Colombia, with 1,842,390 Venezuelan refugees; followed by Peru, with 1,286,464.Ecuador (513,903) and Chile (448,138) occupy the third and fourth positions, respectively.The data comes from the R4V platform, which gathers information from the United Nations system and the Brazilian government.The numbers keep increasing.According to data from the Federal Police from April 2022, about 750 people cross the border from Santa Elena de Uairén to Pacaraima.This article aims to discuss how Brazil has developed its migration policy.For that, as a methodology, exploratory bibliographic research and qualitative and documentary analysis were used.An "in loco" study visit was also carried out in Boa Vista-RR, headquarters of Operação Acolhida.The results achieved with this work will serve as a subsidy and support in the process of critical and intellectual development of research related to the subject.