Reviewerly/exHarmony huggingface dataset:
Job Offers Classifier Using Neural Networks and Oversampling Methods

Both policy and research benefit from a better understanding of individuals’ jobs. However, as large-scale administrative records are increasingly employed to represent labor market activity, new automatic methods to classify jobs will become necessary. We developed an automatic job offers classifier using a dataset collected from the largest job bank of Mexico known as Bumeran. We applied machine learning algorithms such as Support Vector Machines, Naive-Bayes, Logistic Regression, Random Forest, and deep learning Long-Short Term Memory (LSTM). Using these algorithms, we trained multi-class models to classify job offers in one of the 23 classes (not uniformly distributed): Sales, Administration, Call Center, Technology, Trades, Human Resources, Logistics, Marketing, Health, Gastronomy, Financing, Secretary, Production, Engineering, Education, Design, Legal, Construction, Insurance, Communication, Management, Foreign Trade, and Mining. We used the SMOTE, Geometric-SMOTE, and ADASYN synthetic oversampling algorithms to handle imbalanced classes. The proposed convolutional neural network architecture achieved the best results when applied the Geometric-SMOTE algorithm.
Encrypted Network Traffic Classification with Higher Order Graph Neural Network

Encryption protects internet users’ data security and privacy but makes network traffic classification a much harder problem. Network traffic classification is essential for identifying and predicting user behaviour which is important for the overall task of network management. Deep learning methods used to tackle this problem have produced promising results. However, the conditions on which these experiments are carried out raise questions about their effectiveness when applied in the real world. We tackle this problem by modelling network traffic as graphs and applying deep learning for classification. We design a graph classifier based on higher order graph neural network with the aim of optimum generalisation. To demonstrate the robustness of our model, we cross validate it on the ISCXVPN and USTC-TFC datasets with varying input specifications. We use our model to demonstrate the impact of network data truncation on traffic classification and define benchmarks for input specifications. Our best results outperform the state-of-the-art in terms of generalisation strength. Our tool is available online ( https://github.com/zuluokonkwo/Encrypted-Network-Traffic-Classification-with-Higher-Order-Graph-Neural-Network ).
Legal Contracts Amending with 

Legal contracts can be amended during their lifetime through the agreement of the parties or in accordance with the doctrines of force majeure and hardship. When legal contracts are defined using a programming language, amendments are made through runtime adjustments to the contract’s behavior and must be expressed by means of appropriate language features. In this paper, we examine the extension of Stipula, a formal language for legal contracts, with higher-order functionality to enable the dynamic updating of contract codes. We discuss the semantics of the language when amendments either extend or override the contract’s functionality. Additionally, we study two techniques for constraining amendments, one using annotations within the contract and another that allows for runtime agreements between parties.
Malcolm: Multi-agent Learning for Cooperative Load Management at Rack Scale

We consider the problem of balancing the load among servers in dense racks for microsecond-scale workloads. To balance the load in such settings, tens of millions of scheduling decisions have to be made per second. Achieving this throughput while providing microsecond-scale latency is extremely challenging. To address this challenge, we design a fully decentralized load-balancing framework, which allows servers to collectively balance the load in the system. We model the interactions among servers as a cooperative stochastic game. To find the game's parametric Nash equilibrium, we design and implement a decentralized algorithm based on multi-agent-learning theory. We empirically show that our proposed algorithm is adaptive and scalable while outperforming state-of-the art alternatives. The full paper of this abstract can be found at https://doi.org/10.1145/3570611.
A Study on the Optimization Strategies of Oral English Teaching in Secondary Vocational Schools under the Second Language Acquisition Theory

With the deepening of economic globalization, the relationship between countries in the world has become increasingly close.English, as the common language of communication among countries in the world, plays a very important role in the world.At present, China is in a critical period of educational reform, and universities are constantly improving teaching methods and teaching quality.In English teaching, oral English teaching is an important link.Improving the quality of oral English teaching can comprehensively improve students' English learning ability.Second language acquisition theory, as a new teaching concept and teaching method, has an important impact on the educational reform of major universities in China.Therefore, colleges and universities can try to apply the second language acquisition theory to oral English teaching to further improve students' oral English ability.Based on the second language acquisition theory, this paper deeply analyzes the current situation and problems of oral English teaching in secondary vocational schools, and puts forward corresponding optimization strategies for these problems, aiming at providing reference for secondary vocational English teaching.
A VR User Behavior Classification Method Integrating Scene Information and Operation Information

With the rapid growth of the virtual reality industry, the number of users participating in virtual reality has rapidly increased, and analyzing user behavior in virtual reality scenarios has become increasingly important.Compared with general time series classification tasks, the input sources in virtual reality are more complex and diverse, and traditional time series classification models are difficult to handle this complexity.This article designs a series of methods for classifying VR user behavior and conducts experiments.Apply video motion recognition methods to VR motion recognition based on VR scene data.Due to the ability to obtain a series of image data from both videos and scenes, this article uses the method of video action recognition to process VR scene data.On this basis, a series of fusion methods were designed to fuse the operational information of VR.Experiments have shown that these methods can effectively handle action recognition problems in VR scenes.
Research on the Construction of the Cultivation Model of Thinking Ability in Higher Vocational English Teaching

The cultivation of speculative ability is the most important task in higher vocational English education.When students can master mind analysis skills, they can better understand and apply what they have learned, and better adapt to future career needs.Although the topic of attaching importance to students' thinking ability has attracted widespread attention, there are still some problems in practical teaching.Therefore, we need to build a training model for thinking ability in higher vocational English teaching to improve students' thinking and analysis ability.This article aims to provide feasible strategies and programs to help higher vocational English education better cultivate students' thinking ability.
Reinforcement Learning With Non-Cumulative Objective

In reinforcement learning, the objective is almost always defined as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cumulative</i> function over the rewards along the process. However, there are many optimal control and reinforcement learning problems in various application fields, especially in communications and networking, where the objectives are not naturally expressed as summations of the rewards. In this paper, we recognize the prevalence of non-cumulative objectives in various problems, and propose a modification to existing algorithms for optimizing such objectives. Specifically, we dive into the fundamental building block for many optimal control and reinforcement learning algorithms: the Bellman optimality equation. To optimize a non-cumulative objective, we replace the original <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">summation operation</i> in the Bellman update rule with a generalized operation corresponding to the objective. Furthermore, we provide sufficient conditions on the form of the generalized operation as well as assumptions on the Markov decision process under which the globally optimal convergence of the generalized Bellman updates can be guaranteed. We demonstrate the idea experimentally with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bottleneck</i> objective, i.e., the objectives determined by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimum</i> reward along the process, on classical optimal control and reinforcement learning tasks, as well as on two network routing problems on maximizing the flow rates.
Infinite Mixture Prototypical Variational Autoencoder for Shilling Attack Detection in Recommender Systems

With the rapid development of the Internet, recommendation systems have become increasingly important components of various e-commerce and social media platforms.However, recommendation systems also face some security issues, including the problem of shilling attacks.Shilling attacks refer to malicious users who use multiple fake accounts or provide fake reviews to influence the recommendation results and evaluations of the recommendation system, in order to obtain their own interests.The existence of shilling attacks not only affects users' shopping experiences, but also disrupts the normal operation of the recommendation system, affecting the platform's economic benefits.At present, some researchers have attempted to use prototype network methods to solve the problem of underattack detection with low attack filling rate and small attack scale, and have achieved good detection results.However, when faced with more complex underattack data, the effectiveness will deteriorate.We propose an attack detection method based on an infinite hybrid prototype network, and validate our method on real datasets to achieve good results in the face of more complex attacks.
Streamlit Web Application for Finding Similar Face Using Deep Learning

Face recognition is a type of biometric technique which is used to detect faces. Face recognition has gained more attention in the last few years. A mostly convolutional neural network is used for face recognition, and the proposed system focuses on finding the similarity of faces using Multi-Task Cascaded Convolutional Neural Networks which are used for face detection and alignment. In this proposed system, Bollywood actor’s dataset is used with a few more additional images. The similarity is checked using various angles of faces like front face, side face, spectacle face, and mask face images, and illumination for the images is performed.
Memory effects displayed in the evolution of continuous variable system

We analyze non-Markovian memory effects displayed by the quantum Brownian motion modelled as a quantum harmonic oscillator coupled to a bath consisting of harmonic oscillators. In particular, the evolution of fidelity, relative entropy and quantum entanglement (measured by negativity) is considered for a family of Gaussian states (one and two mode squeezed vacuum and three mode basset-hound states). It is shown how to enhance detection of memory effects by adding an additional (ancillary) mode such that only a single mode (a system) is subjected to the evolution. It is observed that even a small amount of squeezing allows detection of non-Markovian memory effects.
Fake it till you break it: evaluating the performance of synthetically-optimized adversarial patches against real-world imagery

Deep neural networks (DNNs), enabled by massive open datasets like ImageNet, have produced impressive results in a wide range of fields and applications. ImageNet, a database of over 15 million high-resolution images categorized into 22,000 categories, has revolutionized the field of computer vision with state-of-the-art models achieving 98% accuracy. However, this performance comes at a cost. Recent advances in adversarial machine learning have revealed inherent vulnerabilities in DNN-based models. Adversarial patches have been successfully used to disrupt the performance of artificial intelligence (AI) systems that leverage DNN-based computer vision models, but the trade space of these attacks is not fully understood; adversarial attack generation and validation methods are still nascent. In this paper we explore the generation and performance of synthetically-trained attacks against models trained on real data like MSCOCO, VIRAT and VisDrone. Using a synthetic environment tool built on the Unreal Engine, we generate a synthetic dataset consisting of pedestrians and vehicles, train synthetic object detection models, and optimize adversarial patch attacks on the synthetic feature space of those models. We then apply our synthetic attacks to real image data and examine the efficacy of synthetic patch attacks against models trained on real-word image data. The implications of synthetically optimized attacks are broad: a much larger attack surface for DNN-based computer vision models, development of simulation-based validation pipelines, more effective attacks, and stronger defenses against adversarial examples.
Supplemental Material: Establishing an ethic of sampling for future generations of geoscientists

&lt;p&gt;Includes bar graphs showing responses to the GSA Survey on Ethics of Sampling and small group GSAT forum discussions for the Culture and Ethics of Geologic Sampling. &lt;/p&gt;
New approaches to classification and regression for very high dimensional data

Feature classification and regression tasks for high dimensional data have been handled by many well-known algorithms like feed-forward multi-level perceptron, decision tree, support vector machine, and many others. Recently, a new approach called subspace learning machine (SLM) has been found which finds a balance between simplicity and effectiveness by partitioning an input feature space into multiple discriminant subspaces in a hierarchical manner. The technique has been extended in many directions to handle high dimensional data. We will emphasize the significance of these developments and present experimental results.
Driving Mechanism of Entrepreneurship and Innovation Projects in Universities

Innovation is the soul of a nation's progress and an inexhaustible driving force for a country's prosperity.On the basis of building an innovative country, "mass entrepreneurship and multitude innovation" was put forward as a slogan.In order to respond to the national appeal, but also to provide students with innovative and entrepreneurial concepts, the course of the innovation and entrepreneurship system was established.The construction of innovation and entrepreneurship projects under the new development perspective of University of Shanghai for Science and Technology mainly includes two parts: innovation and entrepreneurship, and is more in line with the current national and industrial development situation through year-by-year revision and optimization.It is particularly important to investigate the project-driven innovation and entrepreneurship education system under the new development concept, especially to find an innovation and entrepreneurship project system and evaluation model that is more in accordance with the development of the times and students.Exploring and practicing the ways, methods and means of innovation and entrepreneurship education, establishing a sound education model driven by innovation and entrepreneurship projects in colleges and universities, and improving the comprehensive quality of modern college students are the keys discussed in this paper.
Application of Information Network Technology in Distance Training

With the continuous growth of people's demand for education, the traditional education model can no longer meet the needs of students.Therefore, scholars have begun to explore how to provide more flexible and convenient distance training through information network technology.The application of information network technology in distance training has become a hot topic in the field of education in recent years.With the rapid development of various Internet technologies, the application of distance education has become an indispensable part of modern education.This article aims to explore the application of information network technology in distance training, analyze the application of information network technology in distance training, and explore how to optimize the effectiveness of distance education and improve students' learning experience and learning outcomes.Through in-depth research on the application of information network technology in distance training, it will help promote the development of distance education and improve the utilization efficiency of educational resources.
Ontology-based indexing technologies in information retrieval: Building a topic map (ISO 13250) for a mathematics education database

ProblemKeyword-based search (e.g., Google) is the most popular tool for web searching and information retrieval; however, keyword-based searching also produces many irrelevant results because keywords can have multiple meanings or they often inadequately express users' intent (Michael & Rajesh, 2011).In order to find accurate information, users waste time browsing long lists of often-irrelevant results.In addition, keyword-based search does not provide adequate support to users but strictly returns the documents whose vocabulary matches the keyword terms (Wilson et al., 2009); therefore, it fails to recognize relevant documents that do not match the query (Mann, 2008). Ontology and Topic MapsSome previous research tries to improve information searching by optimizing information organization.Ontology-based indexing technologies area promising approach (Patkar, 2011).By establishing an ontology on the basis of semantic relationships between concepts, the improved information organization may improve users' searching performance (Jimeno-Yepes et al., 2010;Yi, 2008).Building ontologies can be done using Topic Maps, which is an international standard (ISO13250) for knowledge representation and exchange.According to Pepper (2000), Topic Maps represent information concepts and their relationships using the following elements:• Topics: represent any concept, from people, countries, and organizations to software modules, individual files, and events; •Associations: representing relationships between topics; •Occurrences: representing information resources relevant to a particular topic.This paper describes a project that has created a Topic Map search tool for a mathematics educational database containing articles from the journal For the Learning of Mathematics.The resulting website enables users to retrieve research articles based on a variety of topics such as mathematics classification, research methods, educational objectives, in addition to traditional bibliographic information.
Corpus construction and mining for citation context analysis

Context Analysis (CCA) is a typical data-driven research field based on full-text information, which breaks the limitations of traditional citation analysis using only bibliographic data, and benefits further studies on various citation behaviors and other core issues behind them, such as citation motivation, citation function and citation sentiment.Corpus for CCA is the most important guarantee and support for these issues.This paper attempts to discuss the corpus construction and mining for CCA in order to comprehensively review the research significance, research status and existing deficiencies in this area.Two main sections in our paper are:1) corpus construction for CCA, its three building tasks, such as citation sentence extraction, citation-reference mapping and citation context extraction, are discussed; 2)corpus mining and utilization for CCA, following related topics or situations are explored, including classification of citation motivation (or behavior) and citation sentiment, indexing and retrieval based on citation, citation recommendation and evaluation, citation-based abstracting and review generation automatically, and domains knowledge metrics.Finally, some suggestions and future research directions are briefly listed.
Certification of Bottleneck Task Assignment With Shortest Path Criteria

Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment with obstacles is NP-hard however. In this letter, we investigate when polynomial-time approximations of the shortest path search are sufficient to determine the optimal assignment of robots to goals. In particular, we propose an algorithm in which the accuracy of the path planning is iteratively increased. The approach provides a certificate when the uncertainties on estimates of the shortest paths become small enough to guarantee the optimality of the goal assignment. To this end, we apply results from assignment sensitivity assuming upper and lower bounds on the length of the shortest paths. We then provide polynomial-time methods to find such bounds by applying sampling-based path planning. The upper bounds are given by feasible paths, the lower bounds are obtained by expanding the sample set and leveraging the knowledge of the sample dispersion. We demonstrate the application of the proposed method with a multi-robot path-planning case study.
TELECONSULTATION IN ORAL MEDICINE DURING THE COVID-19 PANDEMIC: A BRAZILIAN CENTER'S EXPERIENCE

such as malignant tumors.In 7 cases clinically diagnosed with MRONJ, bone alterations were viewed through Cone-Beam Computed Tomography (CBCT), analyzing the quadrants with bone necrosis and the other quadrants with no clinical diagnosis.Therefore, the following characteristics were consistently observed in both the affected quadrant and those considered to be healthy: periodontal ligament thickening, osteosclerosis, thickening of the basal mandibular cortical, osteolytic areas, thickening of the dental lamina dura, and persistent alveolus.Two or more imaging bone changes identified in the CBCT images occurred in at least in one contralateral quadrant, leading to a suggestion that the bone alterations might occur equally in the entire maxillo-mandibular complex.