Reviewerly/exHarmony huggingface dataset:
AAJS: An Anti-Malicious Attack Graphic Similarity Judgment System in Cloud Computing Environments

With the rapid development of cloud computing and other modern technologies, collaborative computing between data is increasing, and privacy protection and secure multi-party computation are also attracting more attention. The emergence of cloud computing provides new options for data holders to perform complex computing problems and to store images; however, data privacy issues cannot be ignored. If a graphic is encrypted and stored in the cloud, the cloud server will perform confidential similar matching when the user searches. At present, most research on searchable encryption is focused on text search, with few schemes researched on how to finish the graphic search. To solve this problem, this paper proposes a secure search protocol based on graph shape under the semi-honest model. Using the cut-choose method and zero-knowledge proof, further designs of the anti-malicious attack graphic similarity judgment system (AAJS) based on the Paillier encryption algorithm, can achieve the secure search and matching of the graph while resisting malicious adversary attacks. The proposed protocol’s security is proved by the real/ideal model paradigm. This paper conducts performance analysis and experimental simulation on the existing scheme and the experiments demonstrate that the system achieves high execution efficiency.
Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model

Object detection is a classic image processing problem. For instance, in autonomous driving applications, targets such as cars and pedestrians are detected in the road scene video. Many image-based object detection methods utilizing hand-crafted features have been proposed. Recently, more research has adopted a deep learning approach. Object detectors rely on useful features, such as the object’s boundary, which are extracted via analyzing the image pixels. However, the images captured, for instance, in an outdoor environment, may be degraded due to bad weather such as haze and fog. One possible remedy is to recover the image radiance through the use of a pre-processing method such as image dehazing. We propose a dehazing model for image enhancement. The framework was based on the conditional generative adversarial network (cGAN). Our proposed model was improved with two modifications. Various image dehazing datasets were employed for comparative analysis. Our proposed model outperformed other hand-crafted and deep learning-based image dehazing methods by 2dB or more in PSNR. Moreover, we utilized the dehazed images for target detection using the object detector YOLO. In the experimentations, images were degraded by two weather conditions—rain and fog. We demonstrated that the objects detected in images enhanced by our proposed dehazing model were significantly improved over those detected in the degraded images.
Budget Manager

The Internet is nowadays an inseparable part of our lives and web development is one of the growing industries with the expansion of the internet. With the growing use of the internet and internet being the most preferred choice for improved reach, the demand for web developers is increasing. Under this project, We have developed a Budget manager that will help in tracking the expense of any group of people for any purpose including tours, etc. It will help to understand the basic client-server model and use the database to work with permanent data. To be able to use this basic website, the user only needs an active internet connection and a web browser. In the client-server model, the client sends the request to the server. Server is a remote PC which can be accessed using the internet. The frontend of the application is developed through Reactjs. Reactjs is a javascript library that enhances the performance of the application which makes the development of dynamic applications easier. For the backend we will be using Express.js and Nodejs that is also a javascript based server side framework and for the database we will be using MongoDb. For adding authentication to the application we will be using Firebase. A payment gateway is an interface between a website and payment transaction. In this project, Razorpay payment gateway is used to provide the feature of in-app payment
The Process of Identifying Automobile Joint Failures during the Operation Phase: Data Analytics Based on Association Rules

The increasing complexity of vehicle design, the use of new engine types and fuels, and the increasing intelligence of automobiles are making it increasingly difficult to ensure trouble-free operation. Finding faulty parts quickly and accurately is becoming increasingly difficult, as the diagnostic process requires analyzing a great amount of information. Therefore, we propose an approach based on association rules, a machine learning technique, to simplify the defect detection process. To facilitate its use in a real repair company environment, we have developed a web service that allows a repairman to simultaneously identify nodes with a high probability of failure. We have described the structure and working principles of the developed web service, as well as the procedure for its application, which resulted in the discovery of several useful non-trivial rules. We have presented several rules resulting from the use of this interactive tool, which allow repairers to detect possible defects in the relevant components, during the diagnostic process, quickly and easily. These rules are also well supported and can be used by procurement departments to make tactical decisions when selecting the most promising suppliers and manufacturers. The methodology developed allows the evaluation of the effectiveness of changes in the design and technology for the manufacture and operation of individual vehicle components, analyzing the change in the composition of parts combinations over time.
TS fuzzy controller with s- and z- type MFs to control a coupled-tank system

Coupled-tank systems (CTS) are widely used in the chemical industry. Controlling the liquid level of CTS is quite challenging due to the nonlinear dynamics and the uncertainties posed by the environment. This paper deals with the TS fuzzy PI control of liquid level of CTS under uncertain environment. Novel TS fuzzy PI controller is designed using s- and z- type membership functions with reference to linear PI controller. Two TS rules and weighted-average defuzzification is used to compute the controller structure. This controller is then implemented to control the liquid level of CTS. Simulation results are presented to show the usefulness of the proposed controller.
Use of locative postposition-verb construction in Korean: analysis of L1-Korean corpora and L2-Korean textbooks

A usage-based account argues that the actual experience of language use greatly affects the course of language acquisition. On the basis of this premise, this study investigates how language textbooks, as a main input source for L2 learners, reflect the properties of a target language. We focus on a Korean locative postposition–verb construction, consisting of one of the three locative postpositions (– ey, – eyse and –( u) lo) and particular sets of verbs. For this purpose, we adopt representative L1 written/spoken corpora in Korean and two L2-Korean textbook corpora, and analysed three aspects of them: frequency of these postpositions and verbs, their association strength, and change in use of the construction’s components across the corpora. We find that ( i) verb types co-occurring with each postposition follow the Zipfian distribution in both datasets, ( ii) L1 corpora and L2 textbooks are inconsistent with their manifestation of postposition–verb associations within the construction, and ( iii) the two textbook types differ in verb use co-occurring with the postpositions. The implications of this study’s findings are discussed in terms of the acquisitional benefits of constructional frames and using corpora for L2 learning and teaching
RECOGNITION OF SPORTS EXERCISES USING INERTIAL SENSOR TECHNOLOGY

Supervised learning as a sub-discipline of machine learning enables the recognition of correlations between input variables (features) and associated outputs (classes) and the application of these to previously unknown data sets. In addition to typical areas of application such as speech and image recognition, fields of applications are also being developed in the sports and fitness sector. The purpose of this work was to implement a workflow for the automated recognition of sports exercises in the Matlab® programming environment and to carry out a comparison of different model structures. First, the acquisition of the sensor signals provided in the local network and their processing were implemented. The functionalities to be realised included the interpolation of lossy time series, the labelling of the activity intervals performed and, in part, the generation of sliding windows with statistical parameters. The preprocessed data were used for the training of classifiers and artificial neural networks (ANN). These were iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models were finally trained with an increased data set, validated and compared with regard to the achieved performance. In addition to the usual evaluation metrics such as F1 score and accuracy, the temporal behaviour of the assignments was also displayed graphically, which enabled statements to be made about potential causes for incorrect assignments. In this context, especially the transition areas between the classes were detected as erroneous assignments as well as exercises with insufficient or clearly deviating execution. The best overall accuracy achieved with ANN and the increased dataset was 93.7 %.
A LIGHTWEIGHT MULTI-PERSON POSE ESTIMATION SCHEME BASED ON JETSON NANO

As the basic technology of human action recognition, pose estimation is attracting more and more researchers' attention, while edge application scenarios pose a higher challenge. This paper proposes a lightweight multi-person pose estimation scheme to meet the needs of real-time human action recognition on the edge end. This scheme uses AlphaPose to extract human skeleton nodes, and adds ResNet and Dense Upsampling Revolution to improve its accuracy. Meanwhile, we use YOLO to enhance AlphaPose’s support for multi-person pose estimation, and optimize the proposed model with TensorRT. In addition, this paper sets Jetson Nano as the Edge AI deployment device of the proposed model and successfully realizes the model migration to the edge end. The experimental results show that the speed of the optimized object detection model can reach 20 FPS, and the optimized multi-person pose estimation model can reach 10 FPS. With the image resolution of 320×240, the model’s accuracy is 73.2%, which can meet the real-time requirements. In short, our scheme can provide a basis for lightweight multi-person action recognition scheme on the edge end.
A Survey on Performance Evaluation Mechanisms for Privacy-Aware Web Search Schemes

Due to the exponential growth of information on the internet, web search engines (WSEs) have become indispensable for effectively retrieving information. Web search engines store the users' profiles to provide the most relevant results. However, the user profiles may contain sensitive information, including the user's age, gender, health condition, personal interests, religious or political affiliation, and others. However, this raises serious concerns for the user's privacy since a user's identity may get exposed and misused by third parties. Researchers have proposed several techniques to address the issue of privacy infringement while using WSE, such as anonymizing networks, profile obfuscation, and private information retrieval (PIR) protocols. In this chapter, the authors give a brief survey of the privacy attacks and evaluation models used to evaluate the performance of private web search techniques.
Beyond Cryptocurrencies

Blockchain technology is the leading and revolutionary technology in this modern era of computing. Many countries around the world are diverting towards digital currency which is the initial popular service provided by blockchain technology e.g., Bitcoin, Litecoin, etc. The main feature of blockchain is to omit the central authority by introducing distributed ledger structures. The consensus protocols play a vital role in the performance and efficiency of blockchain-based frameworks. This study introduces the solution of different e-services and associated problems that are faced in developing countries for making the system transparent, smart, and secure. These features make Web 3.0 applications, which is the ultimate goal of blockchain-based technology. This study also explains the numerous aspects of blockchain-based e-services infrastructure, implementation issues, advantages, disadvantages, and challenges. This study may help practitioners for making smart, intelligent, highly secure, and robust applications even in developing countries.
M. I. Akilina – Scientist and Person

The Biography of the well-known Russian librarian and scientist Maria Ivanovna Akilina is presented. It is prepared on the basis of her autobiography and other documents of the Human Resources Department of the Russian State Library. The main scientific works of M. I. Akilina are characterized. The subjects of scientific works of the scientist are revealed: scientific and methodological activity of libraries, theoretical problems of library science, national libraries of the republics of the Russian Federation and central libraries of the constituent entities of the Russian Federation, mass, rural libraries. The periodicals in which M. I. Akilina published, her awards are named. The personal qualities of Maria Ivanovna are shown.
Design, Development, and Testing of Web Applications

Today, with the changes and developments in software technologies, web applications have gained an important place by being actively used in many sectors. Due to the fact that web applications do not require installation costs and are easily accessible and operable, the increased usage rate in recent years makes these systems the target of cyber hackers. As a result of cyber attacks, services are blocked, and material and moral damages and data privacy violations are experienced. Within the scope of this study, web applications are explained, vulnerabilities that threaten software security and the measures that can be taken against these vulnerabilities are included. Particularly, security threats to web applications, security principles, secure software development lifecycles, test tools, and hardware and software products used for security are examined. In addition, SAMM and BSIMM models, which are maturity models used in secure software development, are discussed.
State of the Art in Distributed Privacy-Preserving Protocols in Private Web Search

Web search engine (WSE) is an inevitable software system used by people worldwide to retrieve data from the web by using keywords called queries. WSE stores search queries to build the user's profile and provide personalized results. User search queries often hold identifiable information that could compromise the user's privacy. Preserving privacy in web searches is the primary concern of users from various backgrounds. Many techniques have been proposed to preserve a person's web search privacy with time. Some techniques preserve an individual's privacy by obfuscating a user's profile by sending fictitious queries with the original ones. Others hide their identity and preserve privacy through unlinkability. However, a distributed technique preserves privacy by providing unlinkability and obfuscation. In distributed protocols, a group of users collaborate to forward each other queries to WSE, providing unlinkability and obfuscation. This work presents a survey of distributed privacy-preserving protocols. The benefits, limitations, and evaluation parameters are detailed in this work.
On the Current State of Privacy Risks in Software Requirement Elicitation Techniques

Requirement engineering has gained tremendous popularity among the research and developers communities and comprises five activities, i.e., feasibility study, elicitation, analysis, specification, and validation. In the elicitation phase, the data is collected from different stakeholders. Hence, the overall system, process, and even data may be compromised and sometimes lead to privacy threats if the data is shared with a third party. To this end, this work proposes a comparative analysis of the eminent techniques, including privacy violation factors. The authors targeted 32 techniques for analysis from different project scenarios. This study may help the analysts with adequate knowledge and help project management teams to select the best and most secure elicitation techniques concerning the nature of projects.
Mathematics Learning With Augmented Reality in Early Childhood Education

With the rapidly developing technology, the use of AR applications in the educational process is becoming widespread as a new field of study. When the literature was examined, there were no studies investigating AR applications and evaluating them as a mathematics education tool in early childhood. In this context, this study aims to determine the AR applications that can be used in the early childhood mathematics education process and to examine the content, mathematical skills, and functionality of these applications.
A Sequel About the Expansion of the Cinematic Universe

The narrators and the designers of the art of cinema build a universe from scratch according to the scenario. In the construction of this universe, cinematographic elements must reach a harmonious unity. The universe should not only contain content within itself, but also new and original content according to the previous design. Avatar, which was released in 2009, presented the planet Pandora in which the story takes place and its own universe with many elements to the audience. Avatar: The Way of Water, which was released in December 2022, also shows that this created universe has many more parts waiting to be discovered. The main purpose of this research is to reveal how Avatar: The Way of Water, which was shot as a sequel, uses methods in the expansion of the cinematic universe. This study, designed as an exploratory field research with descriptive analysis method, proves how the sequel structure of a universe created from scratch can be enriched in itself.
An Evolutionary Shaking‐Forecast‐Based Earthquake Early Warning Method

Abstract Here we propose a methodology for Earthquake Early Warning (EEW) able to issue the alert based on the real‐time estimation of the epicentral area where a peak ground Intensity measure is expected to exceed a user‐set ground shaking level. The method provides in output a P‐wave‐based, time‐evolutive “early” shake map. It combines the peak ground velocity predictions available from the observed P‐wave amplitudes and from the region‐specific ground motion prediction equation, using progressively updated estimates of earthquake location and magnitude. The P‐wave displacement, velocity and acceleration amplitudes are jointly measured on a progressively expanded P‐wave time window while the earthquake location and magnitude are evaluated using the first P‐arrival time and displacement amplitudes at near source stations. A retrospective analysis of the 2016, Mw 6.5 Central Italy earthquake records shows that depending on the network density and spatial source coverage, the method naturally accounts for effects related to the earthquake rupture directivity and spatial variability of strong ground motion related to crustal wave propagation and site amplification. Within 1.5 s from the first alert (5.15 s after the origin time), the simulated performance of the system in predicting the event ground shaking is very high: in the 40 km‐radius area that suffered an Intensity MCS VIII–IX, 41 over 42 strong‐motion instrumented sites would have been successfully alerted, with only one false alarm. Even considering the calculated blind‐zone of 15 km radius, a 15–55 km wide annular area would have received the alert 2–14.5 s before the occurrence of the strong ground shaking. The proposed EEW method evolves with time in a way that it minimizes the missed alarms while increasing successful alarms and to a lesser extent false alarms, so it is necessary for the end‐user to accept these possibilities and account for them in a probabilistic decision scheme depending on the specific safety actuation measure to be undertaken in real‐time.
Delayed-choice quantum erasure with nonlocal temporal double-slit interference

Abstract Wave–particle duality is a counterintuitive nature of quantum physics that challenges many common-sense assumptions, and Young’s double-slit interference is a prototypical example. While most quantum erasure experiments emphasized the choice of erasing or marking the which-path information of one quantum system, we use frequency entanglement to report a nonlocal temporal double-slit interferometer such that the which-time information determines the wave-like or particle-like behaviors. Since frequency-entangled photons are created simultaneously by using spontaneous parametric down conversion, the mark of temporal distinguishability is readily prepared by delaying one of the entangled photons, and its quantum eraser is implemented by using spectrally resolved detection with a tunable delayed choice. These results may provide an alternative aspect and insight into the role of the temporal degree in quantum-light complementarity and photon interference.
Construction Forecasting Using Recurrent Neural Networks

Despite all their advantages, univariate and multivariate time series models are linear statistical methods subject to significant limitations for characterizing nonlinear relationships. Machine learning models, such as neural networks, have established themselves as a serious alternative to classical statistical models for exploring nonlinear relationships. This chapter introduces recurrent neural networks (e.g., long short-term memory and gated recurrent units) for nonlinear time series forecasting and explains the life cycle of construction time series forecasting using such networks. These networks are designed and trained to forecast Highway Construction Spending (HCS) time series. Also, their forecasting performances are investigated and compared with those of seasonal ARIMA and VEC models. The comparison results show that recurrent neural networks (i.e., long short-term memory and gated recurrent unit networks) can provide higher accuracies in forecasting the long-term variations of HCS than statistical linear time series models based on typical error measures.