Reviewerly/exHarmony huggingface dataset:
Serving the flow of requests when flying around ground objects by aircraft in the “air taxi” mode

Abstract The scientific work solves the problem of organizing a route group flight of aircraft, when pre-flight planning is carried out only for the beginning of the flight, and then operational planning is carried out by analogy with a taxi. The peculiarity of the problem being solved is to ensure the required planning efficiency during the flight of the aircraft, taking into account the safety of their flights without crossing routes. As a result of solving the problem, an approach was substantiated to solving the problem of target distribution of vacant aircraft between incoming orders, taking into account all the factors and constraints under consideration; the effectiveness of the chosen approach was assessed. A minimax scheduling criterion is proposed. The criterion at the same time provides: minimum time for completing the flight task; minimization of the total path length and non-intersection of the flight sections, which increases the efficiency and safety of flight in “peak” dispatch modes. The solution to the problem is proposed using the queuing theory, according to which the considered system belongs to the class of multichannel queuing systems with waiting.
Hashing for cleaner reverse engineered queries for the Entity Comparison Problem in RDF Graphs

There are many information retrieval tasks over the Web, which cannot be attended with a simple keyword-based lookup search. Such an important exploratory search problem is the comparison of two Web resources. To manually compare two data resources by looking for information from one Web page to another without any software support is inefficient and time-consuming. This paper discusses a solution to automatize the comparison of two data resources present in a RDF graph. In our work, we provide an improvement over the current state-of-the-art method, by reverse engineering SPARQL queries using a hashing based recursive procedure. We empirically verify how hashing could largely benefit in reducing the size of the returned query and hence making it practically comprehensible for users or agents to understand the similarity concepts returned.
Learning to Generate News Headlines with Media’s Stance

News headline generation can be viewed as a subtask of summarization. When it comes to news headlines or their source articles, positions and stances are usually the main concern for both the publisher and the readers. However, this aspect of headlines is rarely explored along with the developed headline generators. In this paper, we initiate a headline generation task considering the stance for a specific party. We extend the base model with different ways to include stances and investigate their pros and cons. We also propose approaches to evaluate the performance of handling stances. Finally, we come up with an ensemble model to cope with generation quality and stance consideration at the same time, whose results are comparable to human-written headlines.
Memory Efficient Edge Addition Designs for Large and Dynamic Social Networks

The availability of large volumes of social network data from a variety of social and socio-technical networks has greatly increased. These networks provide critical insights into understanding various domains including business, healthcare, and disaster management. The relationships and interactions between different entities represented in most of these data sources are constantly evolving. Graph processing and analysis methodologies that can effectively integrate data changes while minimizing recomputations are needed to handle these dynamic networks. In addition, the size of these information sources is constantly increasing, therefore we need designs that can perform analysis that are memory efficient in order to address resource constraints. In this paper, we show how our anytime anywhere framework can be used to construct memory-efficient closeness centrality algorithms. In particular, we will show how dynamic edge additions can be efficiently handled in the proposed scheme.
Helmet Detection and Number Plate Extraction using Machine Learning

The continual motorization of traffic has led to an increase in road accidents across the globe. The government is trying hard to make people follow driving rules to reduce accidents and fatal deaths, especially in India. In our country, the motorcycle is the most used vehicle. As a recommendation, a system for automatically detecting helmet wear is mandatory for road safety. Where a significant number of people resulted in the death of not wearing a helmet. This project's main aim is to automatically detect bike-riders without helmets using CCTV videos. As a result, a machine learning-based algorithm is used to generate a unique object detection model that can specify motorcycle riders. The first step is to use the background subtraction technique to identify the bike riders. Then it detects whether the rider is wearing a helmet or not. If the person who is riding the motorcycle without the helmet is detected then the number plate is extracted from the video frame. Then with the help of an Optical character recognizer, the number plate is recognized.
Incremental Learning Algorithm Based on Graph Regularized Non-negative Matrix Factorization with Sparseness Constraints

Focused on the issues that the sparseness of the data obtained after factorization is reduced and with the increasing of training samples, the computing scale increases rapidly, an incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints was proposed. It not only considers the geometric structure in the data representation, but also introduces sparseness constraint to coefficient matrix and combines them with incremental learning. Using the results of previous factorization involved in iterative computation with sparseness constraints and graph regularized, the cost of the computation is reduced and the sparseness of data after factorization is highly improved. Experiments on both ORL and PIE face recognition databases demonstrate the effectiveness of the proposed method.
Research on the Sensitive Data Protection Method Based on Game Theory Algorithm

Due to the gradual application of 5G, Internet of things, the smart transportation and telemedicine, etc., the explosive growth of data volume brings challenges as well as opportunities. While the big data brings more economic benefits, the security issues have become more severe. In response to this problem, this paper proposes a privacy protection method for the big data oriented to user location. It firstly designs a big data system model for user location. Then a game theory based privacy protection algorithm for user location is proposed. The core idea of the algorithm is to allow users who generate virtual paths to obtain some certain benefits through the game method. Furthermore, this paper gives the detailed steps of the algorithm. Finally, the validity of the method is established via simulation and analysis.
Algorithm of spatio-temporal multidimensional signal processing of triangulation optical sensor for restore shape object

The article describes an important task for restore object shape, which is measured by triangulation optical sensor. The restored object shape (object contour) makes by spatio-temporal multidimensional signal processing. The data of object contour is received at close moments in time. The core of spatio-temporal multidimensional signal processing bases on the hypothesis that object shape changes slightly at close moments in time. The right restore object shape is provided by the superposition of contour. The superposition algorithm is based on the iteration procedure. The processing contours have matched closer to each other with each new iteration. The first contour is the etalon multidimensional signal. The second contour is a measured signal at close moments in time. Superposition makes by feature points. The feature point of the etalon signal is the contour point, the feature point of the measured signal is the intersection of a normal line (it is calculated by etalon signal) and measured contour. The computer simulation and verification test show improvement result measurement by the developed algorithm.
Adapting Online Messaging Based on Emotional State

Online messaging is an increasingly popular form of communication, yet it has difficulty facilitating an accurate interpretation of users’ emotions. In the absence of body language and vocal characteristics, emojis, gifs, and stickers help users communicate their emotions. A challenging but effective approach to improving these communication types requires interpreting emotions accurately and adapting the conversation accordingly. In this research, we focus on modeling emotional state in an online messaging session. Using Affect Control Theory (ACT), machines can predict emotional change during the interaction. For example, a chatbot that understands the emotional states can adapt its messages and offers. This framework helps the chatbot to communicate naturally and efficiently. Similarly, a messaging platform uses this framework to recommend more appropriate stickers and gifs to a user.
Face Recognition for a Student Violation Point System in Schools Using the Fisherface Algorithm

Abstract The violation point system is one of the policies to increase discipline in schools. If a student commits a violation, then the student will be processed by the teacher and record the violation into the violation note held by each teacher. If a student’s violation record is contained in the notebooks of several teachers, it will be difficult to recapitulate the violations committed by students. This study makes a system approach and development that can centrally record student violations that run on the Android platform. To facilitate student recognition, the system is made using face recognition. Face recognition is implemented into this research because data retrieval can be done in real time with an Android smartphone camera so there is no need for additional devices. Face recognition uses the Fisherface method, which is a facial recognition method that uses Linear Discriminant Analytics for its feature extraction process. The accuracy obtained from the research is 90%.
Visual Object Categorization Using Combination Rules For Multiple Classifiers

This paper discusses the ensemble learning algorithms of Support Vector Machines (SVMs) by combining multiple edge and orientation descriptors based on several basic primitive filters. The methodology divided into two parts. the first section provides a low-level description for describing images. In this part, the proposed method starts with constructing several feature maps of the input image. The maps are constructed by convolving the image data using a set of M different filters namely Robinson filters resulting M different feature maps. After that, the edge and orientation descriptors namely edge histogram, histograms of oriented gradients and Scale Invariant Feature Transform (SIFT) on fixed grid are used to describe the maps for indexing. The second part provides the image classification task. In this part, the support vector machine algorithm is used to train models of different filter based descriptors. After that, all models are used to estimate the correct output class by using the class probabilities of all trained support vector models. In this case, single or individual classifier, naive classifier and ensemble learning classifiers namely product rule, mean rule and majority voting rule are used to measure the recognition performance. Experimental results on 20 classes of the Caltech-101 object dataset prove that the ensemble approaches superior single and naive approaches at about 71.23% and 60.67% of accuracy rates subsequently.
Hand disambiguation using attention neural networks in the egocentric perspective

With the development of wearable cameras a new environment has emerged, the egocentric perspective, and with it the computer vision task of detecting the hands and disambiguating them left from right. In order to address this challenge, we use an Attention Network with various egocentric hand properties to make the final classification. These hand features are inspired by the egocentric perspective and include the hand location in the image, the hand size, the fact there is at most only one object of each hand class and the probability of each hand to appear in the image. In addition, we use the YOLO object detector and its Tiny version to see their impact on the overall performance and speed, which is needed for wearable devices. Finally, we compare them with current object and hand detection approaches.
Virtual Traffic Stop

This paper aims to contribute to the literature by presenting a user-centered design research methodology for the design of police-community tools and by introducing the Virtual Traffic Stop mobile system. Though traffic stops may be an irregular occurrence to the everyday driver, when the driving population is looked at as a whole, they are, in fact, a frequent affair. Traffic stops make up the most common type of contact civilians have with the police. In recent years, media footage of traffic stops has shown how dangerous these encounters can be. Researchers conducted unstructured group interviews and participated in ride-along drives with local law enforcement officials. From this method, four user requirements are presented to the community at large: the system does not impair the safety of officers, the system allows for community engagement, the system should serve as a de-escalation tool, and the system should be cost-effective and easily adaptable for agencies and citizens alike.
Approximate Computation of Exact Association Rules

We adapt a polynomial-time approximation algorithm for computing the canonical basis of implications to approximately compute frequent implications, also known as exact association rules. To this end, we define a suitable notion of approximation that takes into account the frequency of attribute subsets and show that our algorithm achieves a desired approximation with high probability. We experimentally evaluate the proposed algorithm on several artificial and real-world data sets.
A Design Approach for Real-Time Data Level Multi-target Trajectory Simulator for Bi/Multistatic Radar

Modeling and simulation play a vital role in any radar design and development. It enhances the confidence level of any subsystem or software before it is actually integrated to the main system. Radar system performs multi-level data processing or funneling to abstract accurate position of the target by means of various software algorithms. It becomes important to test these algorithms before deployment into the system. This paper presents an approach for design and development of multi-target trajectory simulator for bi/multistatic radar system. The results shown are various trajectories that can be generated to test various radar parameters.
Cittadini sentinella : Percepire catastrofi tra Covid-19 e cambiamento climatico

In this contribution, we explore how the turn to sensing can enable a distinctive mode of engagement with ongoing socio-ecological disasters like the coronavirus pandemic and climate change. In line with Fleur Johns, sensing, in this context, refers to the work of eliciting, receiving, and processing impressions and information, both in the mode of intuitions or feelings, and in terms of data. Our essay starts by elaborating on the potential of sensing as a way to cope with unfolding events that qualify as ‘hyperobjects’. We see the turn to sensing as providing a productive means to engage differently with events that are massively distributed in time and space, and which manifestations call for a different configuration of existence. In doing so, we attempt to show how sensing can participate in Donna Haraway’s invitation to build more livable futures by 'staying with the trouble' that dwelling in a damaged Earth implies for human and more-than-human life forms. We then delve into situated examples of ‘citizen sensing’ initiatives and conclude by questioning how the insights drawn from such ‘sensing practices’ can be fruitful to cope and act upon risks associated with pandemics and climate change.
Tenet

As neural networks (NNs) are being widely deployed in many cloud-oriented systems for safety-critical tasks, the privacy and security of NNs become significant concerns to users in the cloud platform that shares the computation infrastructure such as memory resource. In this work, we observed that the memory timing channel in the shared memory of cloud multi-core architecture poses the risk of network model information leakage. Based on the observation, we propose a learning-based method to steal the model architecture of the NNs by exploiting the memory timing channel without any high-level privilege or physical access. We first trained an end-to-end measurement network offline to learn the relation between memory timing information and NNs model architecture. Then, we performed an online attack and reconstructed the target model using the prediction from the measurement network. We evaluated the proposed attack method on a multi-core architecture simulator. The experimental results show that our learning-based attack method can reconstruct the target model with high accuracy and improve the adversarial attack success rate by 42.4%.
Monte Carlo elites: quality-diversity selection as a multi-armed bandit problem

A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions. Quality-diversity search has explicitly walked this tightrope between a population's diversity and its quality. This paper extends a popular quality-diversity search algorithm, MAP-Elites, by treating the selection of parents as a multi-armed bandit problem. Using variations of the upper-confidence bound to select parents from under-explored but potentially rewarding areas of the search space can accelerate the discovery of new regions as well as improve its archive's total quality. The paper tests an indirect measure of quality for parent selection: the survival rate of a parent's offspring. Results show that maintaining a balance between exploration and exploitation leads to the most diverse and high-quality set of solutions in three different testbeds.
Absolutely entangled sets of pure states for bipartitions and multipartitions

A set of quantum states is said to be absolutely entangled when at least one state in the set remains entangled for any definition of subsystems, i.e., for any choice of the global reference frame. In this work we investigate the properties of absolutely entangled sets (AESs) of pure quantum states. For the case of a two-qubit system, we present a sufficient condition to detect an AES, and use it to construct families of $N$ states such that $N\ensuremath{-}3$ (the maximal possible number) remain entangled for any definition of subsystems. For a general bipartition $d={d}_{1}{d}_{2}$, we prove that sets of $N&gt;\ensuremath{\lfloor}({d}_{1}+1)({d}_{2}+1)/2\ensuremath{\rfloor}$ states are AESs with Haar measure 1. Then, we define AESs for multipartitions. We derive a general lower bound on the number of states in an AES for a given multipartition, and also construct explicit examples. In particular, we exhibit an AES with respect to any possible multipartitioning of the total system.
Collaborative Edge-Cloud Computing for Personalized Fall Detection

The use of smartwatches as devices for tracking one’s health and well-being is becoming a common practice. This paper demonstrates the feasibility of running a real-time personalized deep learning-based fall detection system on a smartwatch device using a collaborative edge-cloud framework. In particular, we demonstrate how we automate the fall detection pipeline, design an appropriate UI on the small screen of the watch, and implement strategies for the continuous data collection and automation of the personalization process with the limited computational and storage resources of a smartwatch.
Target-aware Aggregate Diversification in Recommendation

Businesses often deploy recommenders to help users fulfil their needs and to provide a better user experience with their platform. However, they may also need to guide users towards a certain content depending on time, situation, and business needs. For example, giving a fair exposure opportunity to less popular items provides a healthier business model in which more suppliers can survive and thrive. In this work, we explore target-aware diversification as an approach to mitigate exposure bias in a calibrated way. Provided with a target ratio for each category’s exposure, we balance this objective with the relevance of the recommended items through two main approaches: 1) diversification based only on the system’s predefined target and 2) diversification based on system’s target while taking into account the user’s tolerance for diversity. We explore the effectiveness of our proposed models on a publicly available dataset. Experimental results show that our approach can systematically diversify the recommendations towards a pre-defined target while maintaining the relevance of the recommendations to a good extent. We also conclude that the trade-off between achieving the target and maintaining relevance has a close connection with the feasibility of the defined target given the previous users’ consumption.
Risk-Resilient Supply Chain Using Blockchain Technology

Recently, blockchain technology has been recognized for other industries than finance, proving it's potential other than cryptocurrencies and bitcoin. Supply chain is one of the exponentially growing industries which needs to undergo through changes in order to survive in tomorrow's economy. There are many risks involved in current supply chains that can be potentially eliminated with the implementation of blockchain. This chapter analyses the various aspects of blockchain technology and how other technologies can be integrated with it to deliver exceptional solutions. Various risks present in the current system are discussed along with how those risks can be handled using blockchain, contributing towards building a risk resilient supply chain.
Performance Evaluation and Identification of Optimal Classifier for Credit Card Fraudulent Detection

With the increase in the usage of the credit card by the people, the transaction done by credit cards has increased dramatically in the world. With this drastic increase in the usage of credit cards, the number of fraudulent also increases enormously and it is very difficult to identify the difference between a fraudulent transaction and normal transaction. American Express-issued credit card to 53.7 Million users, however, recorded Rs. 73,380 fraud in a year on average. The credit card fraudulent causes serious losses to the individual and the organization. The credit card issuing companies offer credit card fraud detection applications to the users and individuals for their safety. This paper focuses on the different algorithms used for credit card fraud detection and to find the optimal algorithm for classification of credit card fraud detection. It uses Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Support Vector Machine, eXtreme Gradient Boosting, Random Forest and computes the accuracy, AUC-ROC values for all the classifiers.