Reviewerly/exHarmony huggingface dataset:
SoK: Privacy-enhancing Smart Home Hubs

Smart homes are IoT systems enabling the automation of household operation. The unrestricted collection and processing of data by smart home systems raises legitimate privacy concerns for their users. Over the past decade, there has been significant interest in privacy-enhancing technologies applied at the level of a local smart hub physically located in the home and acting as a gateway between sensors, applications, platform providers, and services in the cloud. The number and variety of projects and research proposals can, however, make their comparison a daunting and unnecessarily complex task. We systematize existing knowledge in this field through the analysis and categorization of 10 industrial and community-contributed systems and 37 research proposals from the literature of the past 11 years. Our results shed light on the diversity of system and trust models considered in the state-of-the-art and on the associated privacy-enhancing technologies. We further identify open research problems and promising approaches that would benefit the smart home hub model and the protection of smart home users’ privacy.
Applying Fuzzy Inference and Machine Learning Methods for Prediction with a Small Dataset: A Case Study for Predicting the Consequences of Oil Spills on a Ground Environment

Applying machine learning (ML) and fuzzy inference systems (FIS) requires large datasets to obtain more accurate predictions. However, in the cases of oil spills on ground environments, only small datasets are available. Therefore, this research aims to assess the suitability of ML techniques and FIS for the prediction of the consequences of oil spills on ground environments using small datasets. Consequently, we present a hybrid approach for assessing the suitability of ML (Linear Regression, Decision Trees, Support Vector Regression, Ensembles, and Gaussian Process Regression) and the adaptive neural fuzzy inference system (ANFIS) for predicting the consequences of oil spills with a small dataset. This paper proposes enlarging the initial small dataset of an oil spill on a ground environment by using the synthetic data generated by applying a mathematical model. ML techniques and ANFIS were tested with the same generated synthetic datasets to assess the proposed approach. The proposed ANFIS-based approach shows significant performance and sufficient efficiency for predicting the consequences of oil spills on ground environments with a smaller dataset than the applied ML techniques. The main finding of this paper indicates that FIS is suitable for prediction with a small dataset and provides sufficiently accurate prediction results.
Serial Decoders-Based Auto-Encoders for Image Reconstruction

Auto-encoders are composed of coding and decoding units; hence, they hold an inherent potential of being used for high-performance data compression and signal-compressed sensing. The main disadvantages of current auto-encoders comprise the following aspects: the research objective is not to achieve lossless data reconstruction but efficient feature representation; the evaluation of data recovery performance is neglected; it is difficult to achieve lossless data reconstruction using pure auto-encoders, even with pure deep learning. This paper aims at performing image reconstruction using auto-encoders, employs cascade decoders-based auto-encoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides a solid theoretical and applicational basis for auto-encoders-based image compression and compressed sensing. The proposed serial decoders-based auto-encoders include the architectures of multi-level decoders and their related progressive optimization sub-problems. The cascade decoders consist of general decoders, residual decoders, adversarial decoders, and their combinations. The effectiveness of residual cascade decoders for image reconstruction is proven in mathematics. Progressive training can efficiently enhance the quality, stability, and variation of image reconstruction. It has been shown by the experimental results that the proposed auto-encoders outperform classical auto-encoders in the performance of image reconstruction.
Analysis of Digital and Technological Competencies of University Students

This study aims to examine the digital and technological competencies of university students studying at different faculties. In this context, digital and technological competencies of university students were examined based on the comparative relational screening model according to gender, class and academic achievement variables. The participants of the study are 373 students studying at different faculties of Dokuz Eylül University, Düzce University, Kırklareli University and Necmettin Erbakan University. Data were collected through digital competence and technological competence scales. The findings showed that the digital competence and technological competence of university students were high in some dimensions and moderate in some dimensions. In addition, the digital competencies and technological competencies of university students differed in terms of grade level and achievement status. On the other hand, no significant difference was found in the digital competencies and technological competencies of the participants with regard to gender. Finally, a significant positive relationship was found between the digital competencies and technological competencies of the participating university students.
Re-Empowerment of Intangible Cultural Heritage under the Meta-Cosmos: The Case of Dunhuang Cave Art

Intangible cultural heritage in Dunhuang cave art is now suffering from a lack of interest feedback, insufficient social awareness, and insufficient media utilization leading to difficulties in transmission and non-transmission; this article argues whether the concept of metaverse can be combined with new media forms to re-empower Dunhuang cave art, exploring its possibilities and expecting methods of implementation. The article is based on a desktop research methodological argument to explore the empowerment of Dunhuang cave art in the context of the metaverse, which explores and builds a worldview of NFT heritage in the metaverse world and obtains offline experiences such as cultural and creative products, offline cave experiences, and NFT cultural exchanges by combining the virtual world of NFT collections, virtual tours, and virtual learning of NFT techniques in exchange for tokens. We are willing to break the barrier between reality and virtual, link the stagnation and empowerment of NFT culture. The article concludes that it is feasible and reasonable to realize the empowerment of intangible cultural heritage under the Meta-Cosmos.
Practical Use of Secret Sharing for Enhancing Privacy in Clouds

The topic of this contribution is the practical use of secret sharing schemes in securing private data. In the first part, we focus on the security model and the overview of the available solutions. In the second part, we describe our solution for storing sensitive data in commercial cloud storage systems, an application called Datachest. The application uploads the data in encrypted form. Cryptographic keys are divided into shares. Each cloud receives one share. This solution improves the security of users’ sensitive data in the cloud. In the final part, we explore the ramifications of secret sharing on the security and management of IoT devices, especially home appliances.
Generic Structure Extraction with Bi-Level Optimization for Graph Structure Learning

Currently, most Graph Structure Learning (GSL) methods, as a means of learning graph structure, improve the robustness of GNN merely from a local view by considering the local information related to each edge and indiscriminately applying the mechanism across edges, which may suffer from the local structure heterogeneity of the graph (i.e., the uneven distribution of inter-class connections over nodes). To overcome the drawbacks, we extract the graph structure as a learnable parameter and jointly learn the structure and common parameters of GNN from the global view. Excitingly, the common parameters contain the global information for nodes features mapping, which is also crucial for structure optimization (i.e., optimizing the structure relies on global mapping information). Mathematically, we apply a generic structure extractor to abstract the graph structure and transform GNNs in the form of learning structure and common parameters. Then, we model the learning process as a novel bi-level optimization, i.e., Generic Structure Extraction with Bi-level Optimization for Graph Structure Learning (GSEBO), which optimizes GNN parameters in the upper level to obtain the global mapping information and graph structure is optimized in the lower level with the global information learned from the upper level. We instantiate the proposed GSEBO on classical GNNs and compare it with the state-of-the-art GSL methods. Extensive experiments validate the effectiveness of the proposed GSEBO on four real-world datasets.
Development of a mobile expert system for the diagnosis on motorcycle damage using forward chaining algorithm

Indonesia is an &lt;span&gt;ASEAN country with the most motorcycle users, where one-third of its population own motorcycles. The automatic motorcycle is the most widely used type due to its agility and fuel-efficient abilities. Sudden motorcycle damage could hamper the users' activities. However, most of these users do not know the reason for the damage. This paper presents the development of expert system for diagnosing the damage of motorcycle using forward chaining method. This system was implemented in mobile application. Through a mobile application, a solution for these users can be obtained. This application immediately discovers the damaged location and repair process. Furthermore, it acts as the first solution before motorcycle repair is carried out in a shop. In this study, the forward chaining method was implemented. It is based on a pattern-matching algorithm whose primary objective is to match facts (input data) with appropriate rules from the rule base. Various test results showed that the diagnostic application used for automatic motorcycle damages 100% worked.&lt;/span&gt;
Offline Handwritten Signature Recognition Using Deep Convolution Neural Network

In the modern age, technological advancement reached a new limit where authentication plays a vital role in security management. Biometric-based authentication is the most referenced procedure for authentication where signature verification is a significant part of it for authentication of a person. To prevent the falsification of signatures on important documents &amp; legal transactions it is necessary to recognize a person's signature accurately. This paper focused on recognizing offline handwritten original &amp; forged signatures using a deep convolution neural network. We use a completely new dataset &amp; also downloaded datasets to train the system &amp; verify a random signature as genuine or forgery. All testing samples are collected from several individuals after several steps of preprocessing the model is fed with the resultant image to our system, the experimental results give us an accuracy of 95.5% from the dataset.
Video Quality Analysis: Steps towards Unifying Full and No Reference Cases

Video quality assessment (VQA) is now a fast-growing field, maturing in the full reference (FR) case, yet challenging in the exploding no reference (NR) case. In this paper, we investigate some variants of the popular FR VMAF video quality assessment algorithm, using both support vector regression and feedforward neural networks. We also extend it to the NR case, using different features but similar learning, to develop a partially unified framework for VQA. When fully trained, FR algorithms such as VMAF perform very well on test datasets, reaching a 90%+ match in the popular correlation coefficients PCC and SRCC. However, for predicting performance in the wild, we train/test them individually for each dataset. With an 80/20 train/test split, we still achieve about 90% performance on average in both PCC and SRCC, with up to 7–9% gains over VMAF, using an improved motion feature and better regression. Moreover, we even obtain good performance (about 75%) if we ignore the reference, treating FR as NR, partly justifying our attempts at unification. In the true NR case, typically with amateur user-generated data, we avail of many more features, but still reduce complexity vs. recent algorithms VIDEVAL and RAPIQUE, while achieving performance within 3–5% of them. Moreover, we develop a method to analyze the saliency of features, and conclude that for both VIDEVAL and RAPIQUE, a small subset of their features provide the bulk of the performance. We also touch upon the current best NR methods: MDT-VSFA, and PVQ which reach above 80% performance. In short, we identify encouraging improvements in trainability in FR, while constraining training complexity against leading methods in NR, elucidating the saliency of features for feature selection.
Experimental Measurements Using Digital Image Correlation Methods: Brief Background and Perspective on Future Developments

Abstract Digital image correlation (DIC) methods initially were developed in the early 1980s to simplify basic in-plane laboratory experiments, modified in the 1990s to obtain full three-dimensional surface displacements and shape measurements on curved or planar specimens and then extended to interior measurements at the end of the last millennium. The enclosed article provides a brief description of the various digital image correlation methods, followed by a personal perspective regarding recent and future developments utilizing DIC measurements.
Analysis of Cryptocurrency, Bitcoin and the Future

Technology advances at a rapid rate, and the success of a given technology is almost solely dictated by the market upon which it seeks to improve. Cryptocurrencies may revolutionize digital trade markets by creating a free flowing trading system without fee. The rapid development and growth in the field of information and communication technologies has changed our day to day life. Not only day to day it has changed our traditional pattern of banking and financial transactions. This advancement of information communication technology has helps to use of paper work activities into digital operations. The upgraded technologies have also changed the pattern of financial transactions as well as created a new form of money. Cryptocurrencies are not likely to replace traditional fiat currency, they could change the way internet connected global markets interact with each other, clearing away barriers surrounding normative national currencies and exchange rates. Cryptocurrencies may revolutionize digital trade markets by creating a free flowing trading system without fees. Bitcoin is presented which illuminates some of the recent events and movements that could influence whether bitcoin contributes to a shift in economic paradigms.
Factors Contributing to the Gap Between Listening and Speaking Skills Performance Among University Students Post COVID-19

The present study explores the improvement in speaking skill as compared to the listening skill in EFL traditional classrooms post COVID-19. In this context, the grades of 168 female students at the intermediate level at the department of English language, faculty of languages and translation, King Khalid University were analyzed. A comparative analysis of the data of both listening and speaking exams was conducted to find out the gap between both skills. The data were gathered then analyzed using the SPSS statistical analysis and Pearson correlation coefficient to determine the relationship between the variables. The results indicated that students’ listening exam grades are better than their speaking grades. The investigations suggest that nonlinguistic factors (i.e. anxiety, stress because of speaking in front of the class, and other psychological fears) significantly affect the students’ speaking skill. The students were also requested to complete a survey about their experiences with traditional speaking sessions and face-to-face speaking exams. The study is both descriptive as well as prescriptive. It describes the current situation of the gap between students’ skills and the nonlinguistic factors resulting in students’ lower grades in speaking as compared to the listening skill. It ends up with suggesting some guidelines to help teachers improve their students’ speaking skill and overcome their fears of face-to-face speaking exams especially after a long period of virtual learning and examination environment. The study provides invaluable insights to make the best possible, data-based recommendations to optimize speaking skill in EFL traditional classrooms.
Multi-Parameter Support with NTTs for NTRU and NTRU Prime on Cortex-M4

We propose NTT implementations with each supporting at least one parameter of NTRU and one parameter of NTRU Prime. Our implementations are based on size-1440, size-1536, and size-1728 convolutions without algebraic assumptions on the target polynomial rings. We also propose several improvements for the NTT computation. Firstly, we introduce dedicated radix-(2, 3) butterflies combining Good–Thomas FFT and vector-radix FFT. In general, there are six dedicated radix-(2, 3) butterflies and they together support implicit permutations. Secondly, for odd prime radices, we show that the multiplications for one output can be replaced with additions/subtractions. We demonstrate the idea for radix-3 and show how to extend it to any odd prime. Our improvement also applies to radix-(2, 3) butterflies. Thirdly, we implement an incomplete version of Good–Thomas FFT for addressing potential code size issues. For NTRU, our polynomial multiplications outperform the state-of-the-art by 2.8%−10.3%. For NTRU Prime, our polynomial multiplications are slower than the state-of-the-art. However, the SotA exploits the specific structure of coefficient rings or polynomial moduli, while our NTT-based multiplications exploit neither and apply across different schemes. This reduces the engineering effort, including testing and verification.
Triplex: an Efficient and One-Pass Leakage-Resistant Mode of Operation

This paper introduces and analyzes Triplex, a leakage-resistant mode of operation based on Tweakable Block Ciphers (TBCs) with 2n-bit tweaks. Triplex enjoys beyond-birthday ciphertext integrity in the presence of encryption and decryption leakage in a liberal model where all intermediate computations are leaked in full and only two TBC calls operating a long-term secret are protected with implementationlevel countermeasures. It provides beyond-birthday confidentiality guarantees without leakage, and standard confidentiality guarantees with leakage for a single-pass mode embedding a re-keying process for the bulk of its computations (i.e., birthday confidentiality with encryption leakage under a bounded leakage assumption). Triplex improves leakage-resistant modes of operation relying on TBCs with n-bit tweaks when instantiated with large-tweak TBCs like Deoxys-TBC (a CAESAR competition laureate) or Skinny (used by the Romulus finalist of the NIST lightweight crypto competition). Its security guarantees are maintained in the multi-user setting.
Accurate Estimation of the CNN Inference Cost for TinyML Devices

Our society will be deeply impacted by neural network inference on embedded devices. Many of them are based on the use of microcontroller units (MCUs) which are extremely resource-scarce. The best modality to solve most of computer vision problems are artificial intelligence algorithms such as Convolutional Neural Networks (CNNs). Although, a CNN’s accuracy implies significant inference costs within the targeted hardware: an important energy consumption, a high latency, and a significant memory footprint. This is the Tiny Machine Learning (TinyML) paradigm that aims at bringing machine learning inference to ultra-low-power devices. Therefore, estimating this inference cost within a given microcontroller unit is a design key. To this end, we introduce an estimation method for the energy consumption, the latency, and the memory space required by a CNN within MCUs. The CNNs investigated are LeNet5 and ResNet8 within several TinyML devices. One inference cost for both neural networks is estimated in a range of 14 frequencies. The proposed method shows an average estimation error of 6.91% on energy consumption, 8.79% on latency, and 3.93% on the required memory space.
Self-Diagnosis in Healthcare Systems Using AI Chatbots

Self-diagnosis is the process of diagnosing, or identifying, medical conditions in oneself. It may be assisted by medical dictionaries, books, resources on the Internet, past personal experiences, or recognising symptoms or medical signs of a condition that a family member previously had. A chatbot is a computer program that simulates human conversation through voice commands or text chats or both. Chatbot, short for chatterbot, is an artificial intelligence (AI) feature that can be embedded and used through any major messaging application. Identify the right opportunity to develop an AI-driven chatbot. You can build a chatbot using established frameworks or development (non-coding) platforms depending on the features your start-up requires. Understand the goals of your business and your customer early on in the process. An AI chatbot makes a decision by leveraging pre-existing knowledge and one that it acquires continuously. To maximise the ability of AI chatbots to improve service, save money, and increase engagement, businesses and organisations need to understand how these programs work and what they can do. In this guide, you’ll get a crash course on talking bots, including the technology behind them, how they have transformed marketing and customer service, and how you can start putting them to work. Users can assuredly benefit from chatbots if they can diagnose several kinds of illness and render the required data. Text diagnosis bot enables sufferers to join in analyses of their medicinal matters and present a personalised analysis report with reference to the symptoms. This chapter represents the self-diagnosis in healthcare system using AI chatbots.
User-Centered Design as a Method for Engaging Users in the Development of Geovisualization: A Use Case of Temperature Visualization

Visualization of geographic data is part of many widely used solutions that aim to communicate the information to the end user. Effective visualizations are those that are tailored to a specific group of users and their tasks, as well as to specific properties of the data. Usability is a key concept for such solutions, and the most effective way to achieve high usability is to incorporate user-centered design (UCD) into the development process. In visualization, data are often coded by colors, so the choice of color schemes and color range is critical to enable the reverse mapping of colors to data. In this paper, we present a method for integrating the principles of UCD into the development process. In doing so, we use a case involving the selection of the most appropriate color scheme and associated parameters for representing temperature values on geographic maps. The method described is suitable for use in UCD-based research related to the visualization of various types of data and is useful for researchers and developers of geovisualizations.