Reviewerly/exHarmony huggingface dataset:
Automated COVID-19 Dialogue System Using a New Deep Learning Network

During the coronavirus disease 2019 (COVID-19) pandemic outbreak, it is necessary to apply social distancing measurements and search for an alternative to physical contact due to the spread of viral infection.The interest in task-oriented dialogue systems has grown remarkably in healthcare, using natural language in the dialogue between patients and doctors.However, the doctor's advice is implicit and unclear in most conversations, and the patient may also be nervous when describing symptoms or may have difficulty describing them.Therefore, the patient's description of symptoms is insufficient for a diagnosis by doctors.This study aims to provide suitable medical advice based on the patients' symptoms during the conversation between doctors and patients by proposing a new deep learning method for automated medical dialogue systems.The model is based on an encoder and two stages of learning to make reliable decisions.The encoder extracts important words using text normalization, resulting in two vectors: symptom vectors and doctor utterance vectors.The symptom vectors are represented as a weighted bag-of-words feature.The first stage is used to cluster the patients' utterances by applying Hopfield network while considering the semantic similarity, whereas the second stage extracts an implicit label as a template of advice using clustering.Additionally, the external evaluation model used the applied feedforward neural network classification algorithm using labels obtained in the second stage.The CovidDialog-English dataset is used to evaluate the model.The experimental results indicate the high performance of the feedforward neural network with an F1-score of 0.972 and presents a comparison of three clusters using the k-nearest neighbours and naïve Bayes-based models.
Information carrier and resource optimization of counterfactual quantum communication

Counterfactual quantum communication is unique in its own way that allows remote parties to transfer information without sending any message carrier in the channel. Although no message carrier travels in the channel at the time of successful information transmission, it is impossible to transmit information faster than the speed of light, thus without an information carrier. In this paper, we address an important question What carries the information in counterfactual quantum communication? and optimize the resource efficiency of the counterfactual quantum communication in terms of the number of channels used, time consumed to transmit 1-bit classical information between two remote parties, and the number of qubits required to accomplish the counterfactual quantum communication.
Two Identification Methods for a Nonlinear Membership Function

This paper proposes two parameter identification methods for a nonlinear membership function. An equation converted method is introduced to turn the nonlinear function into a concise model. Then a stochastic gradient algorithm and a gradient-based iterative algorithm are provided to estimate the unknown parameters of the nonlinear function. The numerical example shows that the proposed algorithms are effective.
Weight distributions and weight hierarchies of two classes of binary linear codes

Linear codes with a few weights can be applied to communication, consumer electronics and data storage system. In addition, the weight hierarchy of a linear code has many applications such as on the type II wire-tap channel, dealing with t-resilient functions and trellis or branch complexity of linear codes and so on. In this paper, we present a formula for computing the weight hierarchies of linear codes constructed by the generalized method of defining sets. Then, we construct two classes of binary linear codes with a few weights and determine their weight distributions and weight hierarchies completely. Some codes of them can be used in secret sharing schemes.
TurboIKOS: Improved Non-interactive Zero Knowledge and Post-quantum Signatures

In this work, we present a zero knowledge argument for general arithmetic circuits that is public-coin and constant rounds, so it can be made non-interactive and publicly verifiable with the Fiat-Shamir heuristic. The construction is based on the MPC-in-the-head paradigm, in which the prover jointly emulates all MPC protocol participants and can provide advice in the form of Beaver triples whose accuracy must be checked by the verifier. Our construction follows the Beaver triple sacrificing approach used by Baum and Nof [PKC 2020]. Our improvements reduce the communication per multiplication gate from 4 to 2 field elements, matching the performance of the cut-and-choose approach taken by Katz, Kolesnikov, and Wang [CCS 2018] and with lower additive overhead for some parameter settings. We implement our protocol and analyze its cost on Picnic-style post-quantum digital signatures based on the AES family of circuits.
Explaining a neural attention model for aspect-based sentiment classification using diagnostic classification

Many high performance machine learning models for Aspect-Based Sentiment Classification (ABSC) produce black box models, and therefore barely explain how they classify a certain sentiment value towards an aspect. In this paper, we propose explanation models, that inspect the internal dynamics of a state-of-the-art neural attention model, the LCR-Rot-hop, by using a technique called Diagnostic Classification. Our diagnostic classifier is a simple neural network, which evaluates whether the internal layers of the LCR-Rot-hop model encode useful word information for classification, i.e., the part of speech, the sentiment value, the presence of aspect relation, and the aspect-related sentiment value of words. We conclude that the lower layers in the LCR-Rot-hop model encode the part of speech and the sentiment value, whereas the higher layers represent the presence of a relation with the aspect and the aspect-related sentiment value of words.
Efficient chaos-based image encryption approach for secure communication

Recently, data hiding and encryption methods have been very important for transmitting information to the relevant people correctly and reliably. Although each newly developed computer-aided data hiding and encryption method is stronger than previous methods, there may be security gaps or speed factors in future use. Therefore, faster and more secure encryption methods are needed. Chaotic systems can be used for high-performance random number generators because their frequency spectrum is wide. Moreover, they show extreme sensitivity to the initial conditions and eliminate the need for large sized keying. This means that a faster and more secure encryption can be made with random numbers generated from a chaotic system. All of these features make chaotic systems stand out in data hiding and encryption applications. Chaos-based image encryption and decryption applications were performed differently than in traditional encryption algorithms in the literature. For the application, a three-dimensional chaotic system was discretized by the Runge–Kutta-4 method, and random numbers were generated. These random numbers were tested by the statistical NIST-800-22 tests, the international top random standard, and their suitability was determined. With the random numbers that passed the tests successfully, the image was successfully encrypted and sent via e-mail; then the encrypted image was decrypted back to the original image by the receiver. Finally, an electronic circuit realization of the chaotic encryption approach was depicted using LabVIEW to confirm the operability of the application.
Using Factor Decomposition Machine Learning Method to Music Recommendation

The user data mining was introduced into the model construction process, and the user behavior was decomposed by analyzing various influencing factors through the factorization machine (FM) learning method. In the recommendation screening stage, the collaborative filtering recommendation is combined to screen the recommendation candidate set. The idea of user-based collaborative filtering (CF) is used for reference to obtain music works favored by similar users. On the other hand, we learn from item-based CF, which ensures that the candidate set covers user preference. Firstly, the user’s interest value is predicted by using dynamic interest model. Then, the common problems such as cold start and hot items processing are fully considered. The frequent pattern growth algorithm is compared with the association rule algorithm based on the collaborative filtering recommendation algorithm and the content-based recommendation algorithm, which proves the superiority of the algorithm and its role in solving the recommendation problem after applying the recommendation. The music data in the database data conversion effectively improve the efficiency and accuracy of mining. According to the implementation of the algorithm described in this article, the accuracy of the music recommendation results used to recommend user satisfaction is proved. And the recommended music is indeed feasible and practical.
Perceptions of teachers on teaching and learning with mobile devices in higher education classrooms in Oman: A pilot study

Mobile learning is a new educational paradigm.The use of mobile technology is popular on a global scale among students of higher learning.Mobile handheld devices have emerged as a learning tool for teachers and students in higher education classrooms.Nevertheless, the impact of mobile technology on learners has been an area of concern to teachers and administrators.The purpose of this pilot study was to explore the perceptions of teachers on the use of mobile devices such as smart phones, iPads, notes and tablets in higher education classrooms.In this pilot study, in-depth one-to-one face-to-face interviews were conducted using semi-structured questionnaires.Limitations included the relatively small number of participants and restriction to only one of the many higher education institutions in Oman.The findings of this pilot study indicated that these technological tools can be potentially beneficial to the educational sector in the future and that more research should be conducted in the future in which a greater number of interviewees are sampled from more education institutions in Oman.
Real-time monitoring system for weather and air pollutant measurement with HTML-based UI application

This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.
A New Semantic and Statistical Distance-Based Anomaly Detection in Crowd Video Surveillance

Recently, attention toward autonomous surveillance has been intensified and anomaly detection in crowded scenes is one of those significant surveillance tasks. Traditional approaches include the extraction of handcrafted features that need the subsequent task of model learning. They are mostly used to extract low-level spatiotemporal features of videos, neglecting the effect of semantic information. Recently, deep learning (DL) methods have been emerged in various domains, especially CNN for visual problems, with the ability to extract high-level information at higher layers of their architectures. On the other side, topic modeling-based approaches like NMF can extract more semantic representations. Here, we investigate a new hybrid visual embedding method based on deep features and a topic model for anomaly detection. Features per frame are computed hierarchically through a pretrained deep model, and in parallel, topic distributions are learned through multilayer nonnegative matrix factorization entangling information from extracted deep features. Training is accomplished through normal samples. Thereafter, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means is applied to find typical normal clusters. At test time, after achieving feature representation through deep model and topic distribution for test frames, a statistical earth mover distance (EMD) metric is evaluated to measure the difference between normal cluster centroids and test topic distributions. High difference versus a threshold is detected as an anomaly. Experimental results on the benchmark Ped1 and Ped2 UCSD datasets demonstrate the effectiveness of our proposed method in anomaly detection.
Preliminary Experimental and Numerical Study of Metal Element with Notches Reinforced by Composite Materials

The presented study is related to the application of the composite overlays used in order to decrease the effect of the stress concentrations around the cut-outs in structural metal elements. The proposed approach with the application of the digital image correlation extends the recently presented studies. Such structural elements with openings of various shapes have been accommodated for a wide range of industrial applications. These structures exhibit certain stress concentrations which decrease their durability and strength. To restore their strength, various reinforcing overlays can be used. In the present paper, the flat panel structure without and with the composite overlays made of HEXCEL TVR 380 M12/26%/R-glass/epoxy is under the experimental and the numerical study. Particular attention is paid to the investigation of the samples with the rectangular holes, which for smooth rounded corners offer a higher durability than the samples with the circular hole of the same size. The experimental results are obtained for the bare element and are reinforced with composite overlay samples. The experimental results are obtained with the use of the Digital Image Correlation method, while the numerical results are the product of the Finite Element Analysis. In the numerical analysis, the study of the shape, size and fiber orientation in applied overlays is done. The reduction of the stress concentration observed in opening notches has confirmed the effectiveness of the overlay application. In the investigated example, the application of the square composite overlay increased the structure strength even by 25%.
Multi-Scale, Class-Generic, Privacy-Preserving Video

In recent years, high-performance video recording devices have become ubiquitous, posing an unprecedented challenge to preserving personal privacy. As a result, privacy-preserving video systems have been receiving increased attention. In this paper, we present a novel privacy-preserving video algorithm that uses semantic segmentation to identify regions of interest, which are then anonymized with an adaptive blurring algorithm. This algorithm addresses two of the most important shortcomings of existing solutions: it is multi-scale, meaning it can identify and uniformly anonymize objects of different scales in the same image, and it is class-generic, so it can be used to anonymize any class of objects of interest. We show experimentally that our algorithm achieves excellent anonymity while preserving meaning in the visual data processed.
Investigating Factors Influencing the Adoption of IT Cloud Computing Platforms in Higher Education

Cloud computing has become an emerging IT platform for productive services and efficient use of innovation in higher education. The purpose of this research study is to investigate the factors that influence the adoption of cloud computing in Sub-Saharan Africa under Ethiopian higher education. These factors professed to influence the adoption of cloud computing built on the integrated framework of technology-organization-environment (TOE) framework, diffusion of innovation (DoI) theory, and sociocultural theory. In-depth interviews have been taken with the University of Gondar key professionals in Ethiopia to accomplish this research study. The results show that the organisational factors are more influential factors than the technological, environmental, and sociocultural factors to the adoption of cloud computing (CC) in the Ethiopian higher education (EHE) sector. This qualitative study aids researchers and practitioners in the field of IT technology cloud to employ these key factors suggestive to cloud computing adoption in the higher educational institutions.
Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things

Advent of digital sensors and machines led to a significant acceleration in industrial evolution. The desire to automate industrial processes with minimum human intervention paved the way for the onset of a new era of technological nomenclature called the industrial Internet of things (IIoT). A remarkable feature of IIoT is its underlying architecture which allows the managers/engineers/supervisors to remotely operate and access the performance of their machines. Industries ranging from healthcare, finance, logistics, and power have witnessed a major performance increment and quality stabilization by transforming themselves into an IIoT empowered smart environment. However, this transformation has brought with itself a whole new set of challenges with cybersecurity being the paramount. The vulnerabilities like bugs and broken processes can lead to a serious compromise or even collapse of security mechanisms of IIoT networks. Such a situation will have a devastating impact on the financial health, reputation, and credibility of companies. After an extensive review of existing technologies, we believe that blockchain, artificial intelligence (AI), and machine learning (ML) can complement each other in building a revolutionary deterrent to negate malicious activities that in any form intend to harm the system. While, blockchain offers public/private/consortium relationships, ML and AI, on the other hand, follow the principle of supervised/ unsupervised/reinforcement learning and reactive/memory approaches, respectively. Based on the distributed ledger system, blockchain mechanisms can be aided with self-learning algorithms which will update and strengthen the database by learning each time the system suffers new forms of network attacks and intrusions. This process of learning will help build a robust system which can learn to optimize its deterrence procedures against different forms of attacks. It is due to these overwhelming benefits, blockchain, AI, and ML find applications in smart logistics, predictive maintenance, autonomous vehicles, intelligent manufacturing, and smart grid maintenance.
Assessment of Acquisition Methods for a Juvenile Literature Collection at a Research University

This study assesses the content and usage of juvenile materials acquired via three acquisition methods by a research-intensive university library. The primary method of acquiring juvenile materials is an awards-based approval plan. Subject librarian selections and purchase suggestions from faculty, staff, and students are also used to acquire juvenile materials. Using acquisition reports, catalog records, and circulation data from four fiscal years (2017-2020), we examined how these three acquisition methods compared to each other in terms of title characteristics and patron usage. We found all three acquisition methods are needed to develop a collection that meets the needs of our users.
What triggers consumer adoption of Central Bank Digital Currency?

Central banks around the world are examining the possibility of introducing Central Bank Digital Currency (CBDC). The public’s preferences concerning the usage of CBDC for paying and saving are important determinants of the success of CBDC. Using data from a representative panel of Dutch consumers we find that roughly half of the public would open a CBDC current account. The same holds for a CDBC savings account. Thus, we find clear potential for CBDC in the Netherlands. This suggests that consumers perceive CBDC as distinct from current and savings accounts offered by traditional banks. Intended adoption is positively related to respondents’ knowledge of CBDC and trust in banks and in the central bank. Price incentives matter as well. The amount respondents want to deposit in the CBDC savings account depends on the interest rate offered. Furthermore, intended usage of the CBDC current account is highest among people who find privacy and security important and among consumers with low trust in banks in general. These results suggest that central banks can steer consumers’ adoption of CBDC via the interest rate, by a design of CBDC that takes into account the public’s need for security and privacy, and by clear communication about what CBDC entails.
DFBDehazeNet: an end-to-end dense feedback network for single image dehazing

The feedback mechanism method of simulating the biological vision system has not been widely used in deep learning dehazing algorithms. To alleviate the difficulty of feature interaction, we combine the feedback mechanism with dense skip connections to fuse features of different levels in a dehazing network. Inspired by the feedback network in which previous network layers can have access to rich information processed by the following network layers, we propose an end-to-end dense feedback network (DFBDehazeNet) for single image dehazing that implements the feedback mechanism using hidden states of constrained RNN. The low-level hazy feature information can be continuously corrected by the high-level feature information obtained from the dense feedback block via the recurrent feedback connection. The top-down feedback mechanism is adopted in DFBDehazeNet to refine the low-level hazy feature information, thereby achieving a powerful image restoration effect. The ablation experiment proves that the iterative structure of DFBDehazeNet and the projection unit play an important role in removing haze from images. The experimental results show that the results of image haze removal are superior to the great majority of existing methods both qualitatively and quantitatively.
Improved spatial-shifting two-wavelength algorithm for 3D shape measurement with a look-up table

Conventional two-wavelength algorithms have been broadly used for three-dimensional shape measurement. However, the maximum unambiguous range of phase unwrapping depends on the least-common multiple of two wavelengths, and thus coprime wavelengths are commonly selected. The recently proposed spatial-shifting two-wavelength (SSTW) algorithm can achieve the maximum unambiguous range with two non-coprime wavelengths, but this algorithm tends to fail for some wavelength selections. To address this problem, this paper presents a general look-up-table-based SSTW (LUT-SSTW) algorithm with arbitrary wavelength selection. The paper also analyzes the phase unwrapping robustness in terms of phase errors and provides guidance for wavelength selection. In addition, an improved LUT-SSTW algorithm is developed to enhance the phase unwrapping robustness, and further relax wavelength selection. Some experiments have been conducted, and their results verify the efficiency of the proposed method.