Reviewerly/exHarmony huggingface dataset:
Levy Tunicate Swarm Algorithm for Solving Numerical and Real-World Optimization Problems

The proposed Levy Tunicate Swarm Algorithm (LTSA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic algorithm called Tunicate Swarm Algorithm (TSA) to solve numerical and real-world optimization problems. TSA has been newly designed to mimic the propulsion of jets and swarm behaviour of tunicates during navigation and feed processes. However, in solving a variety of optimization problems, TSA like metaheuristics is often trapped in local optima. Therefore, we used the Levy distribution rather than the conventional uniform distribution in the candidate selection procedure to solve the TSA algorithm local optima problem. We took advantage of Levy flight, which solved the local optima problem and improved traditional TSA efficiency. The proposed LTSA algorithm performance was evaluated using 23 well-known benchmark test functions, namely unimodal benchmark functions, multimodal benchmark functions, and fixed-dimension multimodal benchmark functions, as well as compared with the traditional TSA. The effectiveness is tested by identifying one real-world engineering application known as the twin-rotor system. The performance is evaluated based on the mean, best, worst and Std. value and the convergence curve. Experimental findings have shown that the proposed LTSA algorithm delivers better performance with 23 benchmark test functions and successfully modelled the twin-rotor system.
Facial Emotion Recognition for Sentiment Analysis of Social Media Data

Despite the diversity of work done in the area of image sentiment analysis, it is still a challenging task. Several factors contribute to the difficulty, like socio-cultural issues, the difficulty in finding reliable and properly labeled data to be used, as well as problems faced during classification (e.g. the presence of irony) that affect the accuracy of the developed models. In order to overcome these problems, a multitasking model was developed, which considers the entire image information, information from the salient areas in the images, and the facial expressions of faces contained in the images, together with textual information, so that each component complements the others during classification. The experiments showed that the use of the proposed model can improve the image sentiment classification, surpassing the results of several recent social media emotion recognition methods.
Mean hitting time formula for positive maps

In the classical theory of Markov chains, one may study the mean time to reach some chosen state, and it is well-known that in the irreducible, finite case, such quantity can be calculated in terms of the fundamental matrix of the walk, as stated by the mean hitting time formula. In this work, we present an analogous construction for the setting of irreducible, positive, trace preserving maps. The reasoning on positive maps generalizes recent results given for quantum Markov chains, a class of completely positive maps acting on graphs, presented by S. Gudder. The tools employed in this work are based on a proper choice of block matrices of operators, inspired in part by recent work on Schur functions for closed operators on Banach spaces, due to F. A. Grünbaum and one of the authors. The problem at hand is motivated by questions on quantum information theory, most particularly the study of quantum walks, and provides a basic context on which statistical aspects of quantum evolutions on finite graphs can be expressed in terms of the fundamental matrix, which turns out to be an useful generalized inverse associated with the dynamics. As a consequence of the wide generality of the mean hitting time formula found in this paper, we have obtained extensions of the classical version, either by assuming only the knowledge of the probabilistic distribution for the initial state, or by enlarging the arrival state to a subset of states.
ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction

Currently available grammatical error correction (GEC) datasets are compiled using essays or other long-form text written by language learners, limiting the applicability of these datasets to other domains such as informal writing and conversational dialog.In this paper, we present a novel GEC dataset consisting of parallel original and corrected utterances drawn from open-domain chatbot conversations; this dataset is, to our knowledge, the first GEC dataset targeted to a human-machine conversational setting.We also present a detailed annotation scheme which ranks errors by perceived impact on comprehension, making our dataset more representative of realworld language learning applications.To demonstrate the utility of the dataset, we use our annotated data to fine-tune a state-of-theart GEC model.Experimental results show the effectiveness of our data in improving GEC model performance in a conversational scenario.
Report on the 3rd workshop of knowledge-aware and conversational recommender systems (KARS/ComplexRec) at RecSys 2021

In this report, we offer a brief overview of the contributions and takeaways from the Joint KaRS &amp; ComplexRec Workshop, co-located with the 15 th edition of the ACM RecSys in Amsterdam, The Netherlands. With this workshop, we aimed to merge the main objectives envisioned for the 3 rd Edition of the Workshop of Knowledge-aware and Conversational Recommender Systems and the 5 th Edition of the Workshop on Recommendation in Complex Environments. This joint workshop adopted a hybrid format aligned with the goal of this year's main conference congregating to continue to build community around recommender systems research and development. Date : 27 th September-1 st October, 2021. Website : https://kars-workshop.github.io/2021/program/.
Formalization of Security Requirements-A Case Study on a Web- Based Application

Security of web-based applications is one of the main concerns today. With the expanding cyberspace, utility and threats are increasing simultaneously. Like earlier, even today security is taken as an afterthought. Functional requirements override the security assurance. Therefore, different security issues such as threat, vulnerability, security breaches, etc. may arise after the deployment of web-based applications. The use of formal specifications for the security requirements while developing web-based applications is cost-effective, time-saving, and error-free. Most of the exiting models rarely deal with a formal approach. The objective of this research paper is to provide an idea about the formal specification and formal verification of web-based applications. In this paper, a novel and broad approach are proposed to specify the security requirement. The proposed approach has been verified through a case study of a web-based mobile banking application. This research paper mainly focused on the specification of security properties by considering some security requirements such as authentication, authorization, confidentiality, and integrity. These security properties are formally verified by the existing more formal tools which is an extension of the proposed work.
Inclusion at the University through technology: A case study in Italy

During the pandemic, it was important to recognize the need of involving students in the process of understanding and improving their academic experience. This study aimed to investigate whether or not the online learning environment provided by the university was accessible and which teaching strategies were most conducive to learning. Data were collected through a survey, of which 116 university students with disabilities and SpLDs took part in; 78 were female (67.2%) and 38 were male. Specifically, the study was meant to deepen how the voices of all students can be recorded even during an emergency period, such as the COVID-19 outbreak, and how students’ feedback can be the first step to start new cooperation in the implementation of educational paths, where dialogue and critical confrontation represent crucial elements to direct inclusive processes in the University environment. Keywords: Inclusion; Student Voice; Technological devices; University.
Attack Modeling and Cyber Deception Resources Deployment Using Multi-layer Graph

Cyber deception techniques are being used for the proactive defense against attacks. Several techniques were proposed in the literature to address the optimal and intelligent deployment of deception techniques but are unable to consider at the same time the wide sets of threats and defense data (attack tactics, techniques, exploited vulnerabilities, affected machines, generated traces), and the high uncertainty facing the selection of cyber deception resources and their locations. In this work, we provide a multi-layer graph that describes an attack with multi-views: a sequence of vulnerabilities, weaknesses, techniques and tactics. Based on this modeling, we provide algorithms for attack scenarios extraction, metrics for attack scenarios ranking and selection, and analytics for deception techniques assessment and comparison. Finally, a case study is presented to illustrate the efficiency of the proposed model.
Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology

Unmanned aerial vehicles (UAVs) also called as a drone comprises of a controller from the base station along with a communications system with the UAV. The UAV plane can be precisely controlled by a machine operator, similar to remotely directed aircraft, or with increasing grades of autonomy, as like autopilot assistance, up to completely self-directed aircraft that require no human input. Obstacle detection and avoidance is important for UAVs, particularly lightweight micro aerial vehicles, but it is a difficult problem to solve because pay load restrictions limit the number of sensors that can be mounted onto the vehicle. Lidar uses Laser for finding the distance between objects and vehicle. The speed and direction of the moving objects are detected and tracked with the help of radar. When many sensors are deployed, both thermal and electro-optro cameras have great clustering capabilities as well as accurate localization and ranging. The purpose of the proposed architecture is to create a fusion system that is cost-effective, lightweight, modular, and robust as well. Also, for tiny object detection, we recommend a novel Perceptual Generative Adversarial Network method that bridges the representation gap between small and large objects. It employs the Generative Adversarial Networks (GAN) algorithm, which iimproves object detection accuracy above benchmark models at the same time maintaining real-time efficiency in an embedded computer for UAVs. Its generator, in particular, learns to turn unsatisfactory tiny object representations into super-resolved items that are similar to large objects to deceive a rival discriminator. At the same time, its discriminator contests with the generator to classify the engendered representation, imposing a perceptual restriction on the generator: created representations of tiny objects must be helpful for detection. With three different obstacles, we were able to successfully identify and determine the magnitude of the barriers in the first trial. The accuracy of proposed models is 83.65% and recall is 81% which is higher than the existing models.
CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation

Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO. This gap exists largely because commonly used random crop augmentations obtain semantically inconsistent content in crowded scene images of diverse objects. In this work, we propose a framework which tackles this problem via joint learning of representations and segmentation. We leverage segmentation masks to train a model with a mask-dependent contrastive loss, and use the partially trained model to bootstrap better masks. By iterating between these two components, we ground the contrastive updates in segmentation information, and simultaneously improve segmentation throughout pretraining. Experiments show our representations transfer robustly to downstream tasks in classification, detection and segmentation. (Code and pretrained models available at https://github.com/renwang435/CYBORGS ).
Methods for Evaluating Network Characteristics on Blockchain-V2X System Nodes

The analysis of potential load on the blockchain node of the Internet of Vehicles and the evaluation of the possibility of application of blockchain technology is carried out in the work. The work discusses concepts and approaches for building the architecture of Internet of Vehicles with the integration of blockchain technology. Within the framework of the ongoing research, the main principles of constructing a testbench with integration of blockchain networks Ethereum and Waves, research methodology and analysis of the obtained results are reflected in the work. The paper detailed the main principles underlying the study and considered scenarios for the use of blockchain technology as a special case of distributed ledger technologies.
A Two-Phase Approach for Recognizing Tables with Complex Structures

Tables contain rich multi-dimensional information which can be an important source for many data analytics applications. However, table structure information is often unavailable in digitized documents such as PDF or image files, making it hard to perform automatic analysis over high-quality table data. Table structure recognition from digitized files is a non-trivial task, as table layouts often vary greatly in different files. Moreover, the existence of spanning cells further complicates the table structure and brings big challenges in table structure recognition. In this paper, we model the problem as a cell relation extraction task and propose T2, a novel two-phase approach that effectively recognizes table structures from digitized documents. T2 introduces a general concept termed prime relation, which captures the direct relations of cells with high confidence. It further constructs an alignment graph and employs message passing network to discover complex table structures. We validate our approach via extensive experiments over three benchmark datasets. The results demonstrate T2 is highly robust for recognizing complex table structures.
Digital citizenship in education and its implication

Nowadays, billions of people across the world communicate with one another using a variety of technological means. As a result of this connection, we now live in a digital world where people can learn, work, play, and socialize with ease. Likewise, educators of all skills levels may not understand how to use technology effectively. Hence, both students and teachers need to become a member of a digital citizenry. Findings revealed that in terms of the elements of digital citizenship, teachers are significantly higher than students. The results indicate that students are at risk in this digital environment along with the covid-19 pandemic. This critical aspect addresses the schools to provide initiative and integrate digital citizenship in the curriculum in order to provide a responsible digital citizen.&#x0D; Keywords: Digital citizenship, Basic Education, Digital education, Self-education, Responsible citizen
Modern Machine Learning: Applications and Methods

Machine Learning (ML) is now omnipresent in most fields of human knowledge. Despite this, it remains mysterious and, to some extent, a black box of statistical methods many have heard of, but few know in detail. Although arguably some minimal degree of experience in statistics is needed to master ML, it is not impossible at all to understand many important concepts with no mathematical formalism. In this chapter, we aim at providing a description as complete as possible of what ML is and which is the main methodology used nowadays, from the most basic methods, such as support vector machines, to the very popular neural networks, which appear in some of the trendiest applications of ML. The second part of the chapter will revolve around how ML is used in different areas of knowledge, from industry to basic science. This will be done, once again, with no mathematical formalism, but instead referring to the methods presented in the first part of the chapter. Our goal is that, once a reader has gone through the full chapter, they have a basic idea of what happens behind the scenes when a ML algorithm is run, which are the main types of algorithms that one can use, and they are familiar with the extremely wide range of applications of ML in the modern world.
Robust federated learning based on metrics learning and unsupervised clustering for malicious data detection

Federated Learning has emerged as a new paradigm for improving communication efficiency and data privacy in various machine learning tasks. It allows the distributed devices to train the model collaboratively using their local dataset only. However, correctly labeled training data is a precondition for generating a high-quality model, whereas the real-world scenario usually cannot promise this condition. Conventional countermeasures mainly detect the corrupt local update and preclude them from the global weights aggregation phase to mitigate the impact of malicious data. Instead of discarding the weights update of clients, we propose a novel robust federated learning method that utilizes Metrics Learning to encode the local data and leverages the unsupervised clustering method K-means to preclude malicious data during local training. Therefore, correctly labeled data still contribute to the global model weight update with that the global model tends to be more generic. We evaluate the proposed method on two public image classification datasets, Fashion-MNIST and CIFAR-10. The simulation results demonstrate that the proposed scheme is robust for performing federated learning in the presence of malicious data.
Current status analysis and countermeasures of talent training mode for environmental design specialty of vocational undergraduate under the background of big data

Under these circumstances of big data age, environmental design specialty of vocational undergraduate can make full use of the advantages of big data to realize the sharing of teaching resources, as well as provide students with more opportunities for autonomous learning and practical application. Combined with the characteristics of big data, this paper starts with analyzing the background and current situation of environmental design specialty of vocational undergraduate, recognizes the talent training objectives for environmental design specialty of vocational undergraduate under the background of big data. It puts forward the talent training strategies of environmental design specialty of vocational education at undergraduate level, as well as the relevant suggestions to improve the teaching level and teaching quality of environmental design major of vocational undergraduate from four aspects: professional subject construction, evaluation system, digital teaching environment construction and teaching objective system construction, hoping to provide reference for environmental art talents of vocational undergraduate.
Exploration of privacy preserving deep learning framework for computer vision tasks

Privacy-preserving visual recognition is an important area of research that is gaining momentum in the field of computer vision. In a production environment, it is critical to have neural network models learn continually from user data to provide personalized models. However, sharing raw user data with a server is less desirable from a regulatory, security and privacy perspective. Federated learning addresses the problem of privacy-preserving visual recognition. More specifically, we closely examine a framework known as Dual User Adaptation (DUA) presented by Lange et al. at CVPR 2020, due to its novel idea of bringing about user-adaptation on both the server-side and user device side while maintaining user privacy. Data in the server and user device is predefined into a series of tasks prior to training and testing. However, since user data is constantly evolving, it's important to see how DUA performs on unseen data or tasks. A few implementations are also executed to see if the performance of the DUA model can be improved on unseen data. Through this research we show that retraining the classifier layer of the merged model (combination of importance weights from user data with server trained models) with all data categories greatly improves the performance for real-world implementation of DUA on unseen data by 2-3 times.
Loan Prediction Using Machine Learning

In our banking system, banks have many products to sell but main source of income of any banks is on its credit line. So they can earn from interest of those loans which they credits. A bank's profit or a loss depends to a large extent on loans, whether the customers are paying back the loan or defaulting. By predicting the loan defaulters, the bank can reduce its Non- Performing Assets. This makes the study of this phenomenon very important. Previous research in this era has shown that there are so many methods to study the problem of controlling loan default. But as the right predictions are very important for the maximization of profits, it is essential to study the nature of the different methods and their comparison.
ItemSB: Itemsets with Statistically Distinctive Backgrounds Discovered by Evolutionary Method

In this paper, we propose a method for discovering combinations of attributes (i.e. itemsets) against a background of statistical characteristics without obtaining frequent itemsets. The method considers a database with numerous attributes and can directly find a combination of highly correlated attributes from small populations in two consecutive variables of interest even from an incomplete database. As the proposed method determines local patterns in large-scale data, it may be used as a basis for large-scale data analysis. Evolutionary computations characterized by a network structure and a strategy to pool solutions are used throughout generations. Moreover, association rules are used to generalize the analysis method as itemsets with statistically distinctive backgrounds (ItemSBs). The class-association rules used for classification constitute a discovery method of attribute combinations, which are characteristic when the ratio of class attributes is obtained. The proposed method is an extension to statistical bivariate analysis. In addition, we determine contrast ItemSBs that are statistically different between two subgroups of data while satisfying the same conditions. Experimental results show the characteristics and effectiveness of the proposed method.