Reviewerly/exHarmony huggingface dataset:
AttractionDetailsQA: An Attraction Details Focused on Chinese Question Answering Dataset

With the increase in the number of domestic tourists and the popularity of digital upgrades in attractions, it is crucial to develop a question-answering(QA) system about the details of the attractions. However, there is little work on attractions QA, and the main bottleneck is the lack of available datasets. While previous QA datasets usually focus on news domain like CNN/DAILYMAIL and NewsQA, we present the first large-scale dataset for QA over attraction details. To ensure that the data we collected are useful, we only gather the data from public travel information website. Unlike other QA datasets like SQuAD in which formed by manual, we formed the dataset by manual and question-answer pair generation(QAG) model annotated. Finally, we obtained a dataset covering 2,808 attractions with a total of 18,245 QA pairs, including seven types of attraction details: location, time, component, area, layout, rating, and character. The dataset is available at https://github.com/wyman130/AttractionDetailsQA. Considering that QAG has not been much studied in attraction details, we experimented some QAG models on this dataset and obtained the benchmark. This provides a basis for subsequent improvements to the dataset and research on QAG in attraction details.
Security Enhanced Symmetric Key Encryption Employing an Integer Code for the Erasure Channel

An instance of the framework for cryptographic security enhancement of symmetric-key encryption employing a dedicated error correction encoding is addressed. The main components of the proposal are: (i) a dedicated error correction coding and (ii) the use of a dedicated simulator of the noisy channel. The proposed error correction coding is designed for the binary erasure channel where at most one bit is erased in each codeword byte. The proposed encryption has been evaluated in the traditional scenario where we consider the advantage of an attacker to correctly decide to which of two known messages the given ciphertext corresponds. The evaluation shows that the proposed encryption provides a reduction of the considered attacker’s advantage in comparison with the initial encryption setting. The implementation complexity of the proposed encryption is considered, and it implies a suitable trade-off between increased security and increased implementation complexity.
SEARCH FOR A NEW PARADIGM IN THE EDUCATIONAL` SYSTEM IN THE ERA OF ARTIFICIAL INTELLIGENCE AND DIGITAL TECHNOLOGIES: CHALLENGES, OPPORTUNITIES

The article explores the problem of searching for a new paradigm in the education system in the era of artificial intelligence and digital technologies, the transition to the VI economic order, the formation of a list of new competencies of the 21st century, and the analysis of challenges to the education and training system. It is noted that the state and prospects for the development of the higher educational system are today among the most acutely discussed issues in every society. The requirements for knowledge and competencies are dynamically changing, and what was in demand at the beginning of the new 21st century may differ significantly from the current needs of society and the state. It becomes obvious: to be in demand, you need to study all your life. In this regard, it seems necessary to consider the basic conceptual approaches and paradigms used to analyze educational policy. The state has an objective need to predict and implement new competencies, as well as to modify existing ones. At the same time, questions arise: what educational paradigms can become the driving forces for the formation of a flexible and proactive system of lifelong learning for citizens of all ages? In conclusion, the author notes that the era of digital transformation dictates the transition to a new concept of education, there are completely new drivers and trends that form the model of education of the future, including in the context of large-scale educational experiments. The educational system is already behind the digitalization curve. It must do more to take advantage of artificial intelligence and new technologies, while addressing possible negative trends.
Application of Singular Value Decomposition technique for compressing images

Image processing is becoming increasingly important as imaging technology has advanced. A storage constraint might occur even when image quality is an influential factor. This means finding a way to reduce the volume of data while still retaining quality, since compactable systems and minimal space are more desirable in the current computing field. An image compression technique that is frequently used is singular value decomposition (SVD). SVD is a challenging and promising way to loosely compress images, given how many people use images now and how many different kinds of media there are. SVD can be employed to compress digital images by approximating the matrices that generate such images, thereby saving memory while quality is affected negligibly. The technique is a great tool for lowering image dimensions. However, SVD on a large dataset might be expensive and time-consuming. The current study focuses on its improvement and implements the proposed technique in a Python environment. We illustrate the concept of SVD, apply its technique to compress an image through the use of an improved SVD process, and further compare it with some existing techniques. The proposed technique was used to test and evaluate the compression of images under various r-terms, and the singular value characteristics were incorporated into image processing. By utilization of the proposed SVD technique, it was possible to compress a large image of dimension 4928 x 3264 pixels into a reduced 342 x 231 pixels with fair quality. The result has led to better image compression in terms of size, processing time, and errors.
A Survey of GIS and AR Integration: Applications

Augmented Reality (AR) is a technology that enhances a person’s sensory perception by overlaying virtual objects in the user’s immediate surroundings. Furthermore, with the development of technologies, devices such as smartphones and head-mounted displays are being launched and are expanding the AR technology application sectors from research labs to a wide range of domains. On the other hand, Geospatial Information System (GIS) is capable of dealing with geospatial information, so it can be beneficial in most AR systems mainly because those systems are connected to location and information related to locations. The ultimate integrated solution could be beneficial for Sustainable Development Goals (SDGs). This paper investigates the combination of AR and GIS. Specifically, it studies the advantages of integration to address the challenges available in systems employing merely one of the technologies. The presented findings would assist researchers in future studies on utilizing GIS and AR simultaneously by giving an overview of the current applications and challenges.
Social Media Used In Language Learning: Benefits And Challenges

The use of social media that is rampant today also impacts the increasing use of social media in language learning. Seeing this reality, it is necessary to research to find the benefits and challenges of using social media in language learning. The research method used in this article is in the form of library research. Sources of research data are articles from the Scopus database and Google Scholar. The study results show that using social media in language learning brings good and various benefits, such as Increasing reading, writing, and vocabulary mastery to increase students' motivation. The benefits of social media in language learning are fully discussed in the text. The challenges faced are: integrating learning with social media and giving assignments during learning. The results of this study indicate the increasing use of technology in learning. Therefore, this research provides an opportunity to develop the use of social media in language learning in the future.
A Study on Key Management Infrastructure in Cloud Computing Environmental Survey for Secured Communication

The term "cryptography," which literally translates to "secret writing," has emerged as the fundamental building block for supplying security for numerous communication applications.There is a need to conceal sensitive information, such as passwords, encryption keys, recipes, etc. in many applications, especially in group communication.Here, effective key management policies are needed to protect the group's confidential information.Because it can be exceedingly difficult to preserve a group's secret information, especially in two situations: when there are more group members and when they are dispersed across different locations with different defences in place.the cloud computing model, where application services are delivered online.A flexible, affordable, and tested platform for delivering commercial or consumer IT services over the internet is cloud computing.Along with more computer power, the cloud also offers network infrastructure that facilitates group communication scenarios.Several security methods are needed to communicate with the various cloud services and to store the data produced/processed by those services.This paper examines the fundamental issue of cloud computing key management in this context and provides support for communication between cloud cryptography clients and cloud key management servers.Cloud key management, including its development and subsequent use to lower infrastructure costs, hazards, and complexity associated with managing encryption keys, as well as to improve the functionality of both private and public storage clouds, is covered.This study explores an extensive assessment of current key management methods used to secure cloud computing.
An Analytical Perspective on Various Deep Learning Techniques forDeep Fake Detection

The advent of deep fake technology has become a crucial concern in this digital world.A serious threat to an individual's privacy, democracy, and national security can be caused by deep fake.Deep fake algorithms can develop forgery multimedia content that we cannot distinguish from genuine ones.In this era of the cyber age, it has become seemingly difficult to identify between real digital content and fake content which are published across the Internet.It is a widely used technology used by cybercriminals to deceive security systems.If we are not cautious, deep fake technology can bring about a serious threat to the future of identity verification.There are many open-source and free software available to create deep fake content which makes it easy for amateurs to create technically brilliant digital content which is fake.On the other hand, many structured and efficient technologies have been developed to identify deep fakes.A few of the techniques available are like comparing the background, analyzing the pattern in the image, considering the blinking of the Eye, considering facial attributes, considering the head position, etc.This paper gives an introduction to deep fake, and a brief on deep fake creation and detection techniques..
Second Life Lessons [Virtual Worlds]

This article discusses online virtual worlds and gaming platforms such as Second Life, metaverse platforms and Facebook. Topics include avatars, security, safety and anonymity issues, gaming abilities, VR headsets, game backgrounds, game graphic rendering, and user generated content.
Level of motion sickness based on heart rate variability when reading inside a fully automated vehicle

This study investigates the level of experienced motion sickness when performing reading while being driven in fully automated driving under three different conditions. One condition was without any intervention while the other two conditions were with the visual (VPIS) and haptic (HPIS) peripheral information system. Both systems provided the upcoming navigational information in the lateral direction three seconds before the turning/cornering was done. It was hypothesized that with the peripheral information systems, the experienced motion sickness would be reduced compared to the condition where a peripheral information system was not present. Eighteen participants with severe motion sickness susceptibility were carefully chosen to undergo the conditions using an instrumented vehicle with the Wizard-of-Oz approach. The participants were required to read from a tablet during the whole 15-minutes of automated driving. Results from the heart rate variability (beats per minute, root means square of successive differences, and high-frequency component) indicated no statistically significant changes (p &lt; 0.05) in motion sickness found with the presence of HPIS and VPIS when performing reading when being driven in automated mode. However, results from this study were mixed and inconclusive, but overall findings indicated mild motion sickness was found in both VPIS and HPIS conditions.
AN AUTOMATED GRADING FRAMEWORK FOR THE MOBILE DEVELOPMENT PROGRAMMING LANGUAGE KOTLIN

With the recent rise of the Kotlin programming language as the main contender for Android mobile development, very few courses in Higher Education Institutions exist which incorporate Kotlin as one of the main languages.In addition, various online course platforms which offer learning Kotlin are still very low in number, and the ones that do exist are expensive.In this paper, an e-learning framework for the Kotlin programming language is presented, supporting automatic grading of given assessments.This framework is aimed at students who already have basic knowledge of Java (or similar) programming languages, and want to switch to mobile development.The solution focuses on the development of an interactive course in Kotlin.Furthermore, to compare our solution with commercially available ones, we point out the disadvantages of currently available Kotlin courses, such as the level of previous knowledge needed, or a need for a specific development environment.
Prototype for Position and Trajectory Control of a Ball on Plate using Image Sensor

The goal of this paper is the design and implementation of a control prototype using a camera for experiments of control algorithms and strategies. The physical implementation relies on a prototype built for a ball on a planar system. The prototype uses additive manufacturing techniques for printing special parts. The system actuation is made with servomotors and the sensing system is an intelligent position and velocity sensor made from a standard USB camera. Using a standard PID controller the system has able send the ball to a desired location and make the ball follow a circular trajectory. Results were obtained by simulation and validated experimentally.
Automatic Speech Recognition in German: A Detailed Error Analysis

The amount of freely available systems for automatic speech recognition (ASR) based on neural networks is growing steadily, with equally increasingly reliable predictions. However, the evaluation of trained models is typically exclusively based on statistical metrics such as WER or CER, which do not provide any insight into the nature or impact of the errors produced when predicting transcripts from speech input. This work presents a selection of ASR model architectures that are pretrained on the German language and evaluates them on a benchmark of diverse test datasets. It identifies cross-architectural prediction errors, classifies those into categories and traces the sources of errors per category back into training data as well as other sources. Finally, it discusses solutions in order to create qualitatively better training datasets and more robust ASR systems.
QoC-Driven MEC Transfer System Framework in Wireless Networks

In this paper, we propose a Heterogeneous MEC System Framework based on Transfer Learning (HMECSF-TL), which uses convolutional neural network (CNN) to process few training samples. In view of the time-varying network environment and the limited end devices resources, the HMECSF-TL framework uses transfer learning (TL) technology to optimize the CNN model and jointly optimizes the allocation of computing resources and communication resources, which is beneficial to achieve the dual goals of extending the use time of end devices and improving the speed and the accuracy of image classification. We first introduce the Quality of Content (QoC)-driven MEC transfer system architecture of cloud-edge-end. The cloud server uses the existing image dataset to train the general neural network model in advance and transfer the general model to the edge servers, and then the edge servers deploy the local models to the end devices to form the personalized models. Then, considering the time-varying situation of the network environment, in order to get the updated model faster and better, we present the process of collaborative optimization of model between the edge sever and multiple end devices, using an edge server as an example. Considering the limited resources of the end devices, we propose a joint optimization of energy and latency with the goal of minimizing offloading cost, in order to rapidly improve the speed and the accuracy of image classification with few training samples under the premise of rational resource allocation and verify the performance of the framework experimentally. Simulation results show that the proposed HMECSF-TL framework outperforms the benchmark strategy without TL in terms of reducing the model training time and improving the image classification accuracy, as well as reducing the offloading cost.
Leveraging Clinical Digitized Data to Understand Temporal Characteristics and Outcomes of Acute Myocardial Infarctions at a Tertiary Care Medical Centre in Pakistan from 1988–2018 – Methods and Results

Few data exist on trends in acute myocardial infarction (AMI) patterns spanning recent epidemiological shifts in low middle-income countries (LMICs). To understand temporal disease patterns of AMI characteristics and outcomes between 1988-2018, we used digitized legacy clinical data at a large tertiary care centre in Pakistan.We reviewed digital health information capture systems maintained across the Aga Khan University Hospital and obtained structured elements to create a master dataset. We included index admissions of patients >18 years that were discharged between January 1, 1988, and December 31, 2018, with a primary discharge diagnosis of AMI (using ICD-9 diagnoses). The outcome evaluated was in-hospital mortality.Clinical characteristics derived from the electronic database were validated against chart review in a random sample of cases (k 0.53-1.00).The final population consisted of 14,601 patients of which 30.6% (n = 4,470) were female, 52.4% (n = 7,651) had ST elevation MI and 47.6% (n = 6,950) had non-ST elevation MI. The median (IQR) age at presentation was 61 (52-70) years. Overall unadjusted in-hospital mortality was 10.3%. Across the time period, increasing trends were noted for the following characteristics: age, proportion of women, prevalence of hypertension, diabetes, proportion with NSTEMI (all ptrend < 0.001). In-hospital mortality rates declined significantly between 1988-1997 and 2008-2018 (13.8% to 9.2%, p < 0.001).The patterns of AMI have changed over the last three decades with a concomitant decline in in-hospital mortality at a tertiary care centre in Pakistan. Clinical digitized data presents a unique opportunity for gaining insights into disease patterns in LMICs.
BBARHS: Blockchain-Based Anonymous Ride-Hailing Scheme for Autonomous Taxi Network

In the past few years, ride-hailing platforms such as Uber, Waymo, and Baidu have built their own autonomous taxi system. Unlike public transit services, ride-hailing platforms raise severe privacy issues. To provide excellent autonomous taxi service, some significant security and privacy problems must be addressed. In this study, we present the security and privacy threats and first proposed blockchain-based anonymous ride-hailing scheme (BBARHS) for autonomous taxi network. We give the formal system model and security model of BBARHS. Then, we outline the concrete BBARHS scheme by making use of Monero and some efficient crypto tools. Through security analysis and performance analysis, the designed scheme is provably secure and efficient. The analysis results also show the designed BBARHS scheme is practical for autonomous taxi network.
End-to-End Retinex-Based Illumination Attention Low-Light Enhancement Network for Autonomous Driving at Night

Low-light image enhancement is a preprocessing work for many recognition and tracking tasks for autonomous driving at night. It needs to handle various factors simultaneously including uneven lighting, low contrast, and artifacts. We propose a novel end-to-end Retinex-based illumination attention low-light enhancement network. Specifically, our proposed method adopts multibranch architecture to extract rich features for different depth levels. Meanwhile, we consider the features from different scales in built-in illumination attention module. We encode reflectance features and illumination features into latent space based on Retinex in each submodule, which could cater for highly ill-posed image decomposition tasks. It aims to enhance the desired illumination features under different receptive fields. Subsequently, we propose a memory gate mechanism to learn adaptively long-term and short-term memory. Their weight could control how many high-level and low-level features should be reserved. This method could improve the image quality from both different feature scales and feature levels. Comprehensive experiments on BDD10K and cityscapes datasets demonstrate that our proposed method outperforms various types of methods in terms of visual quality and quantitative metrics. We also show that our proposed method has certain antinoise capability and generalizes well without fine-tuning when dealing with unseen images. Meanwhile, our restoration performance is comparable to that of advanced computationally intensive models.1
Discrete Dynamic Modeling Analysis Based on English Learning Motivation

With the popularization of the Internet, various online learning platforms have developed rapidly, providing users with abundant learning resources, and realizing personalized resource recommendation has become the development trend of online learning platforms. In this paper, a personalized learning recommendation model based on improved collaborative filtering is proposed. Firstly, a multilayer interest model of learners is established to accurately describe learners’ interest in knowledge topics, courses, and knowledge areas; then, in view of the sparse scoring matrix and cold-start problems of traditional collaborative filtering recommendation algorithms, an improved collaborative filtering-based personality is proposed. The personalized learning recommendation model is used to improve the similarity calculation of users by introducing user initialization tags and solve the cold-start problem of new users. Finally, the effectiveness of the algorithm is proved by experimental comparison, and the improved algorithm improves the recommendation effect of personalized learning.