Reviewerly/exHarmony huggingface dataset:
Master Students’ Perceptions of Online Teaching and Learning during the Pandemic

During the pandemic, the traditional education system shifted to online teaching and learning. In the time that has passed since the beginning of the pandemic, important changes were recorded in the way that education is thought and delivered, at any level of education. Starting with March 2020, when schools were physically closed, online learning became the only way to continue the educational process, although teachers, parents, students, institutions, and officials from the educational system, as well as decision-makers, were not prepared for such a big change. With advantages and limitations, this substitute for traditional education represented a major challenge and opened up multiple opportunities for transforming education, rethinking pedagogical paradigms in line with changes in education-related fields, and transforming the school ethos into a space for inclusion, promoting values, for access to quality education. This research aims to analyze the perceptions of master students as regard online teaching in two East-European countries: Lithuania and Romania. The focus is on the suitability of online teaching, feedback, and support. The provided feedback illustrates that the online teaching/learning process is strongly influenced by a series of variables related to the teachers’ competencies, their ability to introduce proper didactic methods and adapt the content, their capacity to motivate students, to raise the students’ attention, and to stimulate the students’ involvement. More, providing real support, offering fast feedback, and strongly mediating teacher-student communication represent important issues for creating a successful format for online education.
Riding the Waves Towards Generic Single-Cycle Masking in Hardware

Research on the design of masked cryptographic hardware circuits in the past has mostly focused on reducing area and randomness requirements. However, many embedded devices like smart cards and IoT nodes also need to meet certain performance criteria, which is why the latency of masked hardware circuits also represents an important metric for many practical applications.The root cause of latency in masked hardware circuits is the need for additional register stages that synchronize the propagation of shares. Otherwise, glitches would violate the basic assumptions of the used masking scheme. This issue can be addressed to some extent, e.g., by using lightweight cryptographic algorithms with low-degree Sboxes, however, many applications still require the usage of schemes with higher-degree S-boxes like AES. Several recent works have already proposed solutions that help reduce this latency yet they either come with noticeably increased area/randomness requirements, limitations on masking orders, or specific assumptions on the general architecture of the crypto core.In this work, we introduce a generic and efficient method for designing single-cycle glitch-resistant (higher-order) masked hardware of cryptographic S-boxes. We refer to this technique as (generic) Self-Synchronized Masking (“SESYM”). The main idea of our approach is to replace register stages with a partial dual-rail encoding of masked signals that ensures synchronization within the circuit. More concretely, we show that WDDL gates and Muller C-elements can be used in combination with standard masking schemes to design single-cycle S-box circuits that, especially in case of higher-degree S-boxes, have noticeably lower requirements in terms of area and online randomness. We apply our method to DOM-based S-boxes of Ascon and AES and compare the resulting circuits to existing latency optimized circuits based on TI, GLM, and LMDPL. The latency of all three designs is reduced to single-cycle operation and are dth-order secure. Compared to GLM-masked Ascon, our approach comes with a 6.4 times reduction in online randomness for all protection orders. Compared to 1st-order LMDPL-masked AES, our approach achieves comparable results, while it is more generic, amongst others, by also supporting higher-order designs. We also underline the practical protection of our constructions against power analysis attacks via empirical and formal verification approaches.
PROLEAD

Even today, Side-Channel Analysis attacks pose a serious threat to the security of cryptographic implementations fabricated with low-power and nanoscale feature technologies. Fortunately, the masking countermeasures offer reliable protection against such attacks based on simple security assumptions. However, the practical application of masking to a cryptographic algorithm is not trivial, and the designer may overlook possible security flaws, especially when masking a complex circuit. Moreover, abstract models like probing security allow formal verification tools to evaluate masked implementations. However, this is computationally too expensive when dealing with circuits that are not based on composable gadgets. Unfortunately, using composable gadgets comes at some area overhead. As a result, such tools can only evaluate subcircuits, not their compositions, which can become the Achilles’ heel of such masked implementations.In this work, we apply logic simulations to evaluate the security of masked implementations which are not necessarily based on composable gadgets. We developed PROLEAD, an automated tool analyzing the statistical independence of simulated intermediates probed by a robust probing adversary. Compared to the state of the art, our approach (1) does not require any power model as only the state of a gate-level netlist is simulated, (2) can handle masked full cipher implementations, and (3) can detect flaws related to the combined occurrence of glitches and transitions as well as higher-order multivariate leakages. With PROLEAD, we can evaluate masked mplementations that are too complex for existing formal verification tools while being in line with the robust probing model. Through PROLEAD, we have detected security flaws in several publicly-available masked implementations, which have been claimed to be robust probing secure.
Intelligent matching: Supply chain management and financial accounting technology

The main objective of this paper is to investigate the effect of accounting Fintech, Financial Technology Matching (FTM) and Supply Chain Management (SCM) in Jordanian manufacturing corporations. A questionnaire was created in order to meet the goal of this article to collect the data related to the financial technology and supply chain management and 850 questionnaires out of 1300 distributed questionnaires were collected from respondents, working in Jordanian manufacturing corporations. Linkways hypothesis testing indicates that financial technology and company size had a positive effect on the management of supply chain towards adoption of fintech. In addition, the results also found that integrated supply chain management strategy and environmental uncertainty are significantly affecting the adoption of fintech for supply chain management. However, supply chain optimization uses technologies and resources such as blockchain, artificial intelligence, and Internet of things in the best possible way to improve the efficiency and performance of a supply network.
Avoiding uncertainty by measuring the impact of perceived risk on the intention to use financial artificial intelligence services

The moderating role of influencer endorsement and perceived monetary benefits on the relationship between perceived risk and financial artificial intelligence services was explored in this study. Data were obtained through questionnaires distributed to 200 respondents who were selected using a purposive sampling method. The respondents were customers receiving financial artificial intelligence services in Jordan. Analysis was performed using a structural equation modeling approach run by Smart-partial least squares (PLS) 3.2.9 involving data from 138 returned questionnaires. The results show a negative impact of perceived risk on financial artificial intelligence services, and a moderation effect of influencer endorsement and perceived monetary benefits on the relationship between perceived risk and financial artificial intelligence services. The findings can assist companies in their strategies of decreasing perceived risks that individuals could be encouraged to utilize business intelligence applications, for instance, financial technology services.
High-resolution trace element chemistry across the Cretaceous-Tertiary boundary in Denmark

Closely spaced samples on millimeter scale across the Cretaceous-Tertiary boundary clay in Denmark were analyzed by instrumental neutron activation analysis. The results show a strong correlation between Cr and Ir while Co, Zn, As, Sb and Au are correlated with Fe. The elemental peaks for Ir-Cr and Fe are separated by 6 mm. The Fe peak is found in burrow-fillings at the lowermost part of the boundary clay. Chromite grains extracted from boundry clay in Denmark and New Zealand show a compositional common group. The composition points to an ultrabasic (kimberlitic) origin of the chromites. Tandem accelerator mass spectroscopy demonstrated, that there are no Ir carriers in addition to elemental carbon in the boundary clay from two localities in Denmark.
English and Arabic Chatbots: A Systematic Literature Review

In recent years, the availability of chatbot applications has increased substantially with the advancement of artificial intelligence techniques, and research efforts have been active in the English language, which presents state-of-the-art solutions.However, despite the popularity of the Arabic language, its research community is still in an immature stage.Therefore, the main objective of this systematic literature review is studying state-of-the-art researchfor both the English and Arabic languagesto answer the proposed research questions regarding the development approaches, application domains, evaluation metrics, and development challenges of chatbot applications.The findings show that researchers have devoted more attention to the education domain using retrieval-based approaches while the generation-based approach has grown in popularity recently for providing new responses tasks.Whereas the hybrid approach for ranking multi-possible responses of combining both previous approaches shows a performance improvement.Besides, most metrics used to evaluate chatbot performance are human-based, followed by bilingual evaluation understudy and accuracy metrics.However, defining a common framework for evaluating chatbots remains a challenge.Finally, the open problems and future directions are highlighted to help in developing chatbots with minimal human interference to simulate natural conversations.
Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance

Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation of such tools is the difficulty of efficiently repre-senting obstacle avoidance conditions for non-trivial shapes. For example, it is possible to define collision avoidance constraints suitable for nonlinear programming solvers in the canonical set-ting of a circular robot navigating around M convex polytopes over N time steps. However, it requires introducing (2+L)MN additional constraints and LMN additional variables, with L being the number of halfplanes per polytope, leading to larger nonlinear programs with slower and less reliable solving time. In this paper, we overcome this issue by building closed-form representations of the collision avoidance conditions by outer-approximating the Minkowski sum conditions for collision. Our solution requires only MN constraints (and no additional variables), leading to a smaller nonlinear program. On motion planning problems for an autonomous car and quadcopter in cluttered environments, we achieve speedups of 4.8x and 8.7x respectively with significantly less variance in solve times and negligible impact on performance arising from the use of outer approximations.
A quantum segmentation algorithm based on local adaptive threshold for NEQR image

The classical image segmentation algorithm based on local adaptive threshold can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem gradually emerges. In this paper, a quantum segmentation algorithm based on local adaptive threshold for NEQR image is proposed, which can use quantum mechanism to simultaneously compute local thresholds for all pixels in a gray-scale image and quickly segment the image into a binary image. In addition, several quantum circuit units, including median calculation, quantum binarization, etc. are designed in detail, and then a complete quantum circuit is designed to segment NEQR images by using fewer qubits and quantum gates. For a [Formula: see text] image with [Formula: see text] gray-scale levels, the complexity of our algorithm can be reduced to O[Formula: see text], which is an exponential speedup compared to the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
The Performance of Artificial Neural Network Using Heterogeneous Transfer Functions

Neural networks have been very important models across computer vision, natural language processing, speech and image recognition, aircraft safety and many more. It uses a variety of architectures that centres on the Multi-Layer Perceptron (MLP) which is the most commonly used type of Artificial Neural Network. MLP has been found to be good in terms of model precision in the usage of Homogenous Transfer/activation Functions (HTFs), especially with large data set. Based on the preliminary investigations of ranking of transfer functions by error variance (Udomboso, 2014), three HTFs are considered to perform better than other HTFs in prediction. These HTFs are the Hyperbolic Tangent Transfer functions (TANH), Hyperbolic Tangent Sigmoid Transfer function (TANSIG) and the Symmetric Saturating Linear Transfer Function (SSLTF). In this work, the performance of two Heterogeneous Transfer Functions (HETFs), which came as a result of the convolution of the three best HTFs, were compared with the performance of the three above listed HTFs. The hidden neurons used are 2, 5 and 10, while the sample sizes include 50, 100, 200, 500 and 1000. The data were divided into training sets of 90, 80 and 70 respectively.&#x0D; The results showed that the HETFs performed better in terms of the forecast using Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error as the forecast prediction criteria.
Communication challenges and transformations in the Digital Era: emoji language and emoji translation

Abstract The Digital Age has significantly changed how people communicate. Thanks to technology, people have a large variety of communication tools to choose from, such as emails or instant messaging applications, that allows communication to be easier, quicker and, to a certain extent, more efficient. In this technological scenario, emojis play a fundamental role as a kind of universal form of communication. Furthermore, thanks to translation, which acts as a bridge for people from different linguistic and cultural backgrounds, communication can cross national barriers and spread worldwide. Communication and translation share a common goal that is ‘mutual understanding’. What happens, therefore, when translation meets both technology and emojis in the Digital Age? A new form of communication through translation has emerged, that is emoji translation. The aim of this paper is to show, through a series of examples, how emojis have been recently employed in communication and translation in an attempt to establish whether their use, besides creativity, can be universally accepted and understood worldwide.
End-to-End Data Quality Assessment Using Trust for Data Shared IoT Deployments

Continued development of communication technologies has led to widespread Internet-of-Things (IoT) integration into various domains, including health, manufacturing, automotive, and precision agriculture. This has further led to the increased sharing of data among such domains to foster innovation. Most of these IoT deployments, however, are based on heterogeneous, pervasive sensors, which can lead to quality issues in the recorded data. This can lead to sharing of inaccurate or inconsistent data. There is a significant need to assess the quality of the collected data, should it be shared with multiple application domains, as inconsistencies in the data could have financial or health ramifications. This article builds on the recent research on trust metrics and presents a framework to integrate such metrics into the IoT data cycle for real-time data quality assessment. Critically, this article adopts a mechanism to facilitate end-user parameterization of a trust metric tailoring its use in the framework. Trust is a well-established metric that has been used to determine the validity of a piece or source of data in crowd-sourced or other unreliable data collection techniques such as that in IoT. The article further discusses how the trust-based framework eliminates the requirement for a gold standard and provides visibility into data quality assessment throughout the big data model. To qualify the use of trust as a measure of quality, an experiment is conducted using data collected from an IoT deployment of sensors to measure air quality in which low-cost sensors were colocated with a gold standard reference sensor. The calculated trust metric is compared with two well-understood metrics for data quality, root mean square error (RMSE), and mean absolute error (MAE). A strong correlation between the trust metric and the comparison metrics shows that trust may be used as an indicative quality metric for data quality. The metric incorporates the additional benefit of its ability for use in low context scenarios, as opposed to RMSE and MAE, which require a reference for comparison.
AFR-BERT: Attention-based mechanism feature relevance fusion multimodal sentiment analysis model

Multimodal sentiment analysis is an essential task in natural language processing which refers to the fact that machines can analyze and recognize emotions through logical reasoning and mathematical operations after learning multimodal emotional features. For the problem of how to consider the effective fusion of multimodal data and the relevance of multimodal data in multimodal sentiment analysis, we propose an attention-based mechanism feature relevance fusion multimodal sentiment analysis model (AFR-BERT). In the data pre-processing stage, text features are extracted using the pre-trained language model BERT (Bi-directional Encoder Representation from Transformers), and the BiLSTM (Bi-directional Long Short-Term Memory) is used to obtain the internal information of the audio. In the data fusion phase, the multimodal data fusion network effectively fuses multimodal features through the interaction of text and audio information. During the data analysis phase, the multimodal data association network analyzes the data by exploring the correlation of fused information between text and audio. In the data output phase, the model outputs the results of multimodal sentiment analysis. We conducted extensive comparative experiments on the publicly available sentiment analysis datasets CMU-MOSI and CMU-MOSEI. The experimental results show that AFR-BERT improves on the classical multimodal sentiment analysis model in terms of relevant performance metrics. In addition, ablation experiments and example analysis show that the multimodal data analysis network in AFR-BERT can effectively capture and analyze the sentiment features in text and audio.
Breaking HALFLOOP-24

HALFLOOP-24 is a tweakable block cipher that is used to protect automatic link establishment messages in high frequency radio, a technology commonly used by government agencies and industries that need highly robust long-distance communications. We present the first public cryptanalysis of HALFLOOP-24 and show that HALFLOOP-24, despite its key size of 128 bits, is far from providing 128 bit security. More precisely, we give attacks for ciphertext-only, known-plaintext, chosen-plaintext and chosen-ciphertext scenarios. In terms of their complexities, most of them can be considered practical. However, in the real world, the amount of available data is too low for our attacks to work. Our strongest attack, a boomerang key-recovery, finds the first round key with less than 210 encryption and decryption queries. In conclusion, we strongly advise against using HALFLOOP-24.
The Impact of Digital Contact Tracing Apps Overuse on Prevention of COVID-19: A Normative Activation Model Perspective

During the COVID-19 pandemic, many countries have used digital contact tracing apps (DCTAs) to implement contact tracing. Although the use of DCTAs has contributed to the prevention and control of COVID-19, there are doubts in academia about their actual effectiveness. In this study, the role of DCTAs in the prevention of COVID-19 was analyzed in terms of both the responsibility and inconvenience to life in a large-scale DCTA overuse environment, based on the normative activation model. The findings suggest that the overuse of a DCTA activates people's personal norms by triggering awareness of the consequences and ascription of responsibility, leading people to consistently cooperate with the government to prevent COVID-19. However, the inconvenience of living with DCTA overuse weakens the effect of the awareness of consequences and ascription of responsibility and the role of the ascription of responsibility in influencing personal norms. These effects may bear on people's willingness to consistently cooperate with the government to prevent COVID-19. The results of this study confirm the effectiveness of DCTA in counteracting pandemics from a social responsibility perspective in a large-scale environment where DCTA is used, enriching the literature on DCTA research in the COVID-19 pandemic. The results of this study can also help governments develop and improve policies to prevent COVID-19, as well as improve the DCTAs' operating patterns.
A Thermal Infrared Pedestrian-Detection Method for Edge Computing Devices

The thermal imaging pedestrian-detection system has excellent performance in different lighting scenarios, but there are problems regarding weak texture, object occlusion, and small objects. Meanwhile, large high-performance models have higher latency on edge devices with limited computing power. To solve the above problems, in this paper, we propose a real-time thermal imaging pedestrian-detection method for edge computing devices. Firstly, we utilize multi-scale mosaic data augmentation to enhance the diversity and texture of objects, which alleviates the impact of complex environments. Then, the parameter-free attention mechanism is introduced into the network to enhance features, which barely increases the computing cost of the network. Finally, we accelerate multi-channel video detection through quantization and multi-threading techniques on edge computing devices. Additionally, we create a high-quality thermal infrared dataset to facilitate the research. The comparative experiments on the self-built dataset, YDTIP, and three public datasets, with other methods show that our method also has certain advantages.