Reviewerly/exHarmony huggingface dataset:
Market Basket Analysis using A-Priori Algorithm and FP-Tree Algorithm

Market Basket Analysis is used for many applications like online marketing, recommendation engines, information security, etc. Over the past few years, it has been one of the hot topics among research groups as its widely used e-commerce site to recommend related products or arrangements of layouts on the basis of frequently purchased items in supermarkets and fixing consumer index price as per consumer’s demands. In this paper, we have focused on two widely used market basket analysis algorithms i.e. Apriori algorithm and FP-growth algorithm. This paper mainly compares these two algorithms and compares the efficiency on the basis of database sizes, time complexity and space complexity. As a finding of comparison of these two algorithms we discovered that the Apriori algorithm required more time complexity while Fp-growth required more space complexity. Apriori algorithm can be used when there are no time constraints but low space available whereas FP-growth Algorithm used for low time constraint as it uses tree repeatedly to add new types of transactions to reduce time complexity.
Survey on Using Blockchain Technologies in Electronic Voting Systems

Due to the large population movement, many countries and institutions need to modernize their voting systems to support a moving society that is becoming more oriented towards Internet technologies. Electronic voting is a service that is not only used by governments but can be used by numerous institutions to benefit in time, cost, and distance access. The use of new technologies should enable the promotion of confidence and credibility of the voting system by allowing auditing of control mechanisms that deal with the privacy of vote, ballot security, and integrity. One of the recent technologies that have attracted a lot of interest in many different areas is Blockchain technology, which represents a collection of linked (chained) data blocks through cryptographic algorithms and stored in a public distributed database. In other words, Blockchain is a distributed data ledger governed by well-known and approved cryptographic principles that are managed by a group of computers that are not owned by a single person or authority. This paper analyses the current trends of usage of Blockchain technologies in the electronic voting system, highlights the challenges and issues raised so far in the community regarding the security and privacy of the vote and the voter, but also other legal aspects.
NECESSARY COMPETENCES OF STUDENTS AND UNIVERSITY GRADUATES FOR SUCCESSFUL EMPLOYMENT

Since 2000, the focus of employers’ attention in recruiting personnel for a company has been gradually shifting from hard skills towards soft skills. The results of a study carried out by Harvard University and Stanford Research Institute show that the contribution of hard skills to the professional success of an employee accounts for only 15 %, while soft skills add up to the remaining 85 %. Today, it is not enough for a graduate to acquire special knowledge and expertise – they are required to have soft skills to succeed in a job search. The paper discusses the issue of transformation of the qualifying requirements to the job seekers claimed by employers when hiring personnel. The objective of the work is to identify the structure of the key skills and competencies of university students and graduates essential for their successful employment. The analysis of the requirements of employers to graduates of one of the universities was carried out in 2017–2021. The empirical base was a sociological survey of the heads of Russian companies on the issues of the important soft and hard skills of young specialists. The results of the analysis prove that the rapidly changing economic conditions affect the pool of soft skills that are significant in the labor market. The paper presents the structure of soft skills, describes the pool of important skills and competencies that encourage an employer’s decision to hire an inexperienced university graduate. Thus, the results of the study confirm the importance of the development of soft and hard skills of university students and graduates.
The relationship of personal data protection towards internet addiction: Cyber crimes, pornography and reduced physical activity

Internet as a platform has provided negative implications in the form of addiction, where certain users can present harm and damage to the other individual or community due to its availability and accessibility of personal data. Consequently, cybercrime, pornography, and reduced physical activity are several examples of user misdirection to achieve objective based on their desire toward prestige, curiosity, independence, pleasure, wealth, and so on, which drive user behavior in the process. Since the essence of communication and interaction is to process different types of information about individuals in utilizing the service or product, it is important to be clear about how this data is processed and protected. This study explores the proper framework based on previous research in certain fields by aligning the expectancy and satisfaction to meet the requirement in applying the technology devices. It will define four layers of protection in channels, factors, measures, and outcomes by understanding the variance and the relationship strength to predict the utilization. It showed that Q2 of respective variables are greater than zero while privacy concerns have the largest T-statistics of 10.003 compared to the others.
Deep Graph Convolutional Quantization Networks for Image Retrieval

To achieve real-time online search, most image retrieval methods aim to learn compact feature representation while keeping their semantic information or intra-class relevance. In this paper, we propose a new compact feature learning method to embed the underlying manifold information from database. It integrates deep convolutional neural network (CNN) and graph convolutional neural networks (GCN) into a unified end-to-end learning framework. In the proposed method, the deep feature extracted by CNN is automatically embedded with the information from its neighbors by GCN, which possesses the ability of exploring the semantic relevance on the database manifold. Since constructing a graph over the whole database costs unaffordable memory, we build a landmark graph as database sketch. The landmark graph contains two kinds of nodes, including codewords and memory bank samples. Given an image, the deep architecture outputs the discriminative feature and its similarity with all the graph nodes. We directly use the indices of the most similar codeword nodes as the compact feature representation. To make the proposed method scalable to large datasets, a multi-graph strategy is adopted to generate compact features with adaptable code length. The experiments on two benchmark datasets demonstrate the effectiveness of the proposed method.
Cross-Domain Slot Filling as Machine Reading Comprehension: A New Perspective

With intelligent dialogue systems becoming more and more important in our daily lives, slot filling, one of the most important components of an intelligent dialogue system, has gotten a lot of attention from academia and industry. Despite many advancements in the single-domain learning paradigm for slot filling, leveraging resources from different domains to boost learning for a target domain remains a challenge. In contrast to prior methods that supplemented a sequence labeling model with slot meta-information, we address cross-domain slot filling as a machine reading comprehension (MRC) problem for the first time, where the extraction of slot values is viewed as a question answering process. In the framework above, we present both static and dynamic question generating mechanisms, which have complimentary effects in diverse cross-domain contexts. Furthermore, we devise a dynamic question generation approach that can generate numerous values for a slot at the same time. Finally, we construct a pre-training and fine-tuning training approach that enables us to improve learning by utilizing MRC’s resources. We conducted extensive experiments on four datasets to evaluate our approach, and the experimental results clearly justified the advantages of our approach in various cross-domain settings.
Using L2 interactional-pragmatic resources in CMC: A case of Japanese orthography and emoji

Increasingly prevalent use of technologies such as instant messaging and online chat has transformed our traditional ways of learning and teaching pragmatics. This study presents an example of such transformation by demonstrating how computer-mediated communication (CMC) may be employed as a tool to provide second language (L2) learners opportunities to use interactional resources specific to the context of CMC, namely unique orthography and emoji. For the study, we introduced four university-level learners of Japanese to a language exchange messaging application and recorded their online text-based chat interactions with native Japanese speakers over a period of 12 weeks. We followed up the chat data with weekly stimulated verbal recalls (SVR) to investigate L2 learners’ intentions and perceptions surrounding their own and others’ use of unique orthography and emoji (48 SVR sessions total; average 30 minutes per session). Coding and thematic analysis of the chat data revealed learners’ agentive use of orthography and emoji as resources for communication. In addition, the SVR data revealed a variety of personal and interpersonal reasons behind their use, including learners’ concerns over self-presentation, interpersonal relationships, identity, and discourse management. The findings not only demonstrate how CMC can afford learners a unique environment for experimenting with a range of context-appropriate interactional resources to convey pragmatic meaning, but also shed light on the various, sometimes competing, considerations and complex processes underlying learners’ pragmatic choices in CMC.
Phase measuring deflectometry based on calibration of the entrance pupil center of the camera lens

A camera calibration method for phase measuring deflectometry (PMD) based on the entrance pupil center (EPC) of the camera lens is proposed. In our method, the position of the entrance pupil of the camera lens is first measured; next the absolute coordinates of the EPC are calibrated by using a reference flat and an external stop that is mounted in front of the camera lens; then the EPC as the camera coordinates is used for PMD. The feasibility of the proposed method is verified by simulation. The surface shapes of a planar optical element and a planar window glass are separately measured in our experiments, and a subwavelength accuracy level is achieved. Meanwhile, the effects of the camera lens with different aperture settings on captured images are investigated (including exposure time, image contrast, and measurement accuracy). The experimental results show that the exposure time required declines with the decrease in the f-number, and the measurement accuracy is higher than others when the f-numbers are changed from f/5.6 to f/11.
Zonotopic Filtering for Uncertain Nonlinear Systems: Fundamentals, Implementation Aspects, and Extensions [Applications of Control]

This tutorial provides a comprehensive perspective on the study of zonotopic filtering for nonlinear uncertain dynamical systems. Unlike stochastic methods that characterize the states by means of random vectors, set-based observers estimate deterministic sets that are guaranteed to contain the system states. Among many classes of sets, the focus here is on zonotopes due to their appealing properties that pursue a good tradeoff between precision and computational cost. We start by reviewing the fundamentals of zonotopic filtering together with illustrative examples. As nonlinear systems bring up many challenges for this topic, we choose one of the first nonlinear zonotopic observers proposed in the literature to guide a series of discussions. After presenting this algorithm and understanding its limitations, we extend it by incorporating state inequality constraints. In addition, we motivate the use of linear programs when solving minimum-volume intersections. Additionally, we give a practical discussion on the implementation of discrete-time zonotopic algorithms for sampled-data nonlinear dynamical systems. To better introduce the zonotopic approaches, we briefly review unscented Kalman filtering to trace parallels between the stochastic and set-theoretic frameworks. Then the presented algorithms are experimented and compared in a case study involving a quadrotor unmanned aerial vehicle.
Mental models, computational explanation and Bayesian cognitive science: commentary on Knauff and Gazzo Castañeda (2022)

Knauff and Gazzo Castañeda (2022) object to using the term “new paradigm” to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term “paradigm” may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved.
Anomaly Detection in Time Series

Data mining has become an important task for researchers in the past few years, including detecting anomalies that may represent events of interest. The problem of anomaly detection refers to finding samples that do not conform to expected behavior. This paper analyzes recent studies on the detection of anomalies in time series. The goal is to provide an introduction to anomaly detection and a survey of recent research and challenges. The article is divided into three main parts. First, the main concepts are presented. Then, the anomaly detection task is defined. Afterward, the main approaches and strategies to solve the problem are presented.
Verbatim++: verified, optimized, and semantically rich lexing with derivatives

Lexers and parsers are attractive targets for attackers because they often sit at the boundary between a software system's internals and the outside world. Formally verified lexers can reduce the attack surface of these systems, thus making them more secure.
RealityCheck: A Tool to Evaluate Spatial Inconsistency in Augmented Reality

In augmented reality (AR), virtual objects can drift away from their original intended locations, significantly impairing a user’s experience. Traditionally, a virtual object’s drift is approximated by the device localization drift, which is measured using specialized hardware such as 3D scanners or laser-based positioning systems. However, with AR rapidly becoming more popular, there is a need for a lightweight, software-based approach to evaluate the drift of virtual objects. This software should be easy for researchers and developers to use, without requiring specialized hardware or extensive environment setup.Towards this, this paper presents RealityCheck, an opensource AR evaluation tool that reports the drift of AR virtual objects in the world coordinate system, requiring only paper printouts and minimal modifications to the AR app. RealityCheck is designed to measure the drift of a virtual object across time of a single user, as well as the positioning differences of the same virtual objects as seen by multiple users. Our prototype is implemented on an Android smartphone running the ARCore platform, and evaluated in indoor and outdoor scenarios under a variety of user mobility patterns with traces of different lengths. We compared the results of RealityCheck with the ground truth position of the virtual object, and showed that RealityCheck matches the ground truth within 1.5 cm on average.
cgSpan: Closed Graph-Based Substructure Pattern Mining

gSpan is a popular algorithm for mining frequent subgraphs. cgSpan (closed graph-based substructure pattern mining) is a gSpan extension that only mines closed subgraphs. A subgraph g is closed in the graphs database if there is no proper frequent supergraph of g that has equivalent occurrence with g. cgSpan adds the Early Termination pruning method to the gSpan pruning methods, while leaving the original gSpan steps unchanged.cgSpan also detects and handles cases in which Early Termination should not be applied.To the best of our knowledge, cgSpan is the first publicly available implementation for closed graphs mining.
Syntactic complexity in Finnish-background EFL learners’ writing at CEFR levels A1–B2

Abstract The increasing importance of the Common European Framework of Reference (CEFR) has led to research on the linguistic characteristics of its levels, as this would help the application of the CEFR in the design of teaching materials, courses, and assessments. This study investigated whether CEFR levels can be distinguished with reference to syntactic complexity (SC). 14- and 17-year-old Finnish learners of English (N=397) wrote three writing tasks which were rated against the CEFR levels. The ratings were analysed with multi-facet Rasch analysis and the texts were analysed with automated tools. Findings suggest that the clearest separators at lower CEFR levels (A1–A2) were the mean sentence and T-unit length, variation in sentence length, infinitive density, clauses per sentence or T-unit, and verb phrases per T-unit. For higher levels (B1–B2) they were modifiers per noun phrase, mean clause length, complex nominals per clause, and left embeddedness. The results support previous findings that the length of and variation in the longer production units (sentences, T-units) are the SC indices that most clearly separate the lower CEFR levels, whereas the higher levels are best distinguished in terms of complexity at the clausal and phrasal levels.
Survey on Improving Health Care System by Implementing an Air Ambulance System with the Support of Drones

Drones have become one of the most popular electronic toys in recent years. Drones are mostly used for certain tasks such as photography and filming. Drones may be utilized in a variety of areas, including agriculture, medicine distribution, and cargo transportation. For medical purposes, a drone is used as an airbus. This drone-based ambulance service assists patients in arriving at the hospital on time. Ambulance services such as 108 and certain private ambulance services are in operation these days. Despite having all of these facilities, the victims location as well as the hospital cannot be reached in a timely manner owing to traffic, which may result in a high fatality rate, particularly in hill stations and rural regions. These problems can be solved by deploying an air bus ambulance to save more lives. Despite the fact that industrialized countries have a small population, the number of drones that are effectively adopted and performed in the health-care system is significant, making the sy stem smarter. If these types of air ambulance services can be adopted in developing countries with large populations, such as India, it would encourage people to use this air ambulance drones by which the people will be satisfied with the services provided by the air ambulance service than by the service provided by the roadway ambulances also makes the health-care systems more advanced. GPS and sensors are used to track the location of the drone like where the drone is and where it's travelling to. Three sensors are used in these drones: IM Us (inertial measurement units) to measure the drone's velocity and altimeter, GPS/GNSS to provide more accurate location information for maneuvering and navigating to difficult locations, and LiDAR light detection and ranging to navigate and reach the destination with collision avoidance as well as mapping and imaging techniques.
Blockchain: A novel paradigm for secured data transmission in telemedicine

Blockchain has three main features—decentralization, immutability, and encryption—that can cover multiple fields of use in the healthcare industry. Telemedicine, a relatively new field, stems from telecommunications' contribution to healthcare services' remote delivery. This area has observed many benefits of Blockchain technology. However, innovative techniques for secured and authenticated data transfers still need to be put forward in this new digitization era. Healthcare professions use the opportunities provided by the Blockchain technology (BCT) in accessing the patient's information in a decentralized format. Though the decentralization aspect improves the overall robustness of current healthcare systems, trust and traceability are the key action points that need to be focused on. BCT paired with smart contracts automates operations and services of telehealth and telemedicine in an efficient and trustful way. Several case studies and models have been discussed and proposed, demonstrating the practicality of secured data transfers using BCT in the telehealth and telemedicine domain. BCT has hopefully assisted in the safe sharing of data information, from cryptographic record keeping of a person's information to easy access and access everywhere. Telemedicine has had significant security issues, but Blockchain's ability to develop and maintain a secure network when exchanging data has allowed easy information flow. This chapter presents various models and frameworks proposed in the state-of-the-art and discusses their implications for patient engagement and empowerment. These models are discussed in terms of their performance and cost in providing secured and private data sharing. Cost, lack of awareness on how to implement it, and lack of standardization are obstacles preventing Blockchain's adoption in telemedicine. The COVID-19 pandemic has boosted telehealth and telemedicine technology uptake, where BCT could be a prevailing solution. The interest in providing hospital care in the patient's home is also growing, an approach where multiple investors are pouring money into companies working on remote monitoring of different health and telemedicine parameters. There is currently limited research on Blockchain applications for telemedicine, but more research is available every day. Blockchain is now one of the most active fields of software science, and by restoring authority over medical records and health data to the patient, it will shift the hierarchy of healthcare.
A Self-paced Learning based Transfer Model for Hypergraph Matching

Determination of correspondences between vertexes of two graphs is one of essential tasks in the computer vision fields. Despite the graph matching problem is NP-hard, hypergraph matching is well used in many matching methods from the perspective of higher order geometric information. However, it is still a challenge to learn graph models from observed samples of graph matching. In this paper, we present an effective scheme to parameterize a graph model through self-paced learning algorithm. Consequently, each iteration heuristically selects smaller loss samples in a data-driven manner, the learning and matching problems are aligned to learn a new transfer hypergraph model for constructing its high-order structural attributes for visual object matching. For the final matching task between two graphs, we develop the transfer matching method through the assignment matrix decomposition to achieve it. Several experiments on Willow-Object datasets and some other datasets indicate the good performance of our method.