Reviewerly/exHarmony huggingface dataset:
A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets

Tabular format is a common format in open data. However, the meaning of columns is not always explicit which makes if difficult for non-domain experts to reuse the data. While most efforts in making data FAIR are limited to semantic metadata describing the overall features of datasets, such a description is not enough to ensure data interoperability and reusability. This paper proposes to reduce this weakness thanks to a (FAIR) core semantic model that is able to represent different kinds of metadata, including the data schema and the internal structure of a dataset. This model can then be linked to domain-specific definitions to provide domain understanding to data consumers .
Evaluating the Interpretability of Threshold Operators

Weighted Threshold Operators are n-ary operators that compute a weighted sum of their arguments and verify whether it reaches a certain threshold. They have been extensively studied in the area of circuit complexity theory, as well as in the neural network community under the name of perceptrons. In Knowledge Representation, they have been introduced in the context of standard Description Logics (DL) languages by adding a new concept constructor, the Tooth operator ( $$\nabla \!\!\!\nabla $$ ). Tooth expressions can provide a powerful yet natural tool to represent local explanations of black box classifiers in the context of Explainable AI. In this paper, we present the result of a user study in which we evaluated the interpretability of tooth expressions, and we compared them with Disjunctive Normal Forms (DNF). We evaluated interpretability through accuracy, response time, confidence, and perceived understandability by human users. We expected tooth expressions to be generally more interpretable than DNFs. In line with our hypothesis, the study revealed that tooth expressions are generally faster to use, and that they are perceived as more understandable by users who are less familiar with logic. Our study also showed that the type of task, the type of DNF, and the background of the respondents affect the interpretability of the formalism used to represent explanations .
Population Economic Transfer Function Model Based on Genetic Tabu Search Algorithm

Aiming at the problem that the evaluation effect of the traditional analysis model of population economic transfer is not good, a population economic transfer function model based on the genetic tabu search algorithm is proposed. Firstly, the defects of the genetic algorithm are analyzed, and the tabu search algorithm is integrated into the genetic tabu search algorithm to obtain the genetic tabu search algorithm, which avoids the problems of premature convergence and low search efficiency. Secondly, the population migration algorithm is designed. On this basis, the design process of the population economic transfer function model is determined. Finally, the effectiveness of the model is verified by comparative experiments. The experimental results show that when the population area is 50 km2 and 500 km2, the evaluation accuracy of the proposed method is 99% and 97%, respectively, which can avoid falling into a local maximum advantage to a certain extent.
MAGNETIC MINERALOGICAL ANALYSIS OF SOILS AS A PART OF THE INTEGRATED GEOLOGICAL AND GEOPHYSICAL MODEL FOR THE HYDROCARBON PROSPECTING

Magnetic minerals form or alter in the presence of hydrocarbons, making them a potential magnetic proxy for identifying hydrocarbon migration pathways. We concentrate in the precision magnetic mineralogical analyzes to understand what magnetic minerals in soil from the hydrocarbon fields are responsible for the magnetic signal. To illustrate the results of our studying, we consider the study area of the hydrocarbon deposits near the village Balabanivka, Bogodukhiv district, Kharkiv region. A collection of soil samples is represented by deep medium-humus chernozems. The thermomagnetic analyzes, hysteresis parameters, and isothermal remanent magnetization curves (IRM) were studied for the samples collected near the well (point PR 28) and at the area located out of the hydrocarbon influence zone (point PR 0403). To study the thermomagnetic parameters, different types of the magnetization and magnetic susceptibility (MS, χ), and hysteresis loops we used the Variable Field Translation Balance (VFTB) instrument. The results are confirmed by the highest values of the gas geochemical parameters. The MS variations at temperatures ranging from 200 to 400 ˚C may reflect the presence of either iron sulphides or maghemite. The magnetite occurs for all soils. The wasp-waisted hysteresis loops identified either a mixture of two magnetic minerals (magnetite and hematite) and the admixture of the superparamagnetic and larger grains of the same minerals.
Designing an Inclusive Mobile App for People with Disabilities to Independently Use Autonomous Vehicles

Autonomous vehicles (AVs) could potentially provide independent mobility to people with physical and sensory disabilities. It is critical to understand the needs of people with disabilities and design interfaces specifically for accessible interaction before, during, and while ending an AV trip. Using a research-through-design approach, we identify needs and explore design concepts for a smartphone application to allow people with disabilities to use AVs independently. Through 20 individual interviews, 12 monthly meetings with a group of people with disabilities and transportation advocates, and design prototyping, we develop design concepts for accessible smartphone control of an AV trip. Our work contributes a set of interaction needs for accessible AV interaction, design concepts centered on confidence and control developed in partnership with people with disabilities, and a discussion of an accessible co-design process that other designers can learn from to create accessible automotive user interfaces.
Gesture and Voice Commands to Interact With AR Windshield Display in Automated Vehicle: A Remote Elicitation Study

Augmented reality (AR) windshield display (WSD) offers promising ways to engage in non-driving tasks in automated vehicles. Previous studies explored different ways WSD can be used to present driving and other tasks-related information and how that can affect driving performance, user experience, and performance in secondary tasks. Our goal for this study was to examine how drivers expect to use gesture and voice commands for interacting with WSD for performing complex, multi-step personal and work-related tasks in an automated vehicle. In this remote unmoderated online elicitation study, 31 participants proposed 373 gestures and 373 voice commands for performing 24 tasks. We analyzed the elicited interactions, their preferred modality of interaction, and the reasons behind this preference. Lastly, we discuss our results and their implications for designing AR WSD in automated vehicles.
An Emergent Design Framework for Accessible and Inclusive Future Mobility

Future mobility will be highly automated, multimodal, and ubiquitous and thus have the potential to address a broader range of users. Yet non-average users with special needs are often underrepresented or simply not thought of in design processes of vehicles and mobility services, leading to exclusion from standard transportation. In consequence, it is crucial for designers of such vehicles and services to consider the needs of non-average users from the begin on. In this paper, we present a design framework that helps designers taking the perspective and thinking of the needs of non-average users. We present a set of exemplary applications from the literature and interviews and show how they fit into the framework, indicating room for further developments. We further demonstrate how the framework supports in designing a mobility service in a fictional design process. Overall, our work contributes to universal design of future mobility.
A Battle of Voices: A Study of the Relationship between Driving Experience, Driving Style, and In-Vehicle Voice Assistant Character

This study extends efforts to understand the interplay of contextual factors in the personalization of in-vehicle voice interfaces. In particular, an online study found that neither aggressiveness nor gender of voice assistants (VAs) would influence users’ attitudes like trust, perceived usefulness and overall positive emotions, towards in-vehicle VAs. Our results contradict the similarity-attraction effect as the VAs’ perceived aggressiveness, and drivers’ preferences for aggressive driving styles did not correlate. In addition, the results showed that prior experiences might affect users’ reliance and trust in VAs. This study also discovered relationships between users’ attitudes to in-vehicle VAs and their age and driving experiences.
Parallel Big Bang-Big Crunch-LSTM Approach for Developing a Marathi Speech Recognition System

The Voice User Interface (VUI) for human-computer interaction has received wide acceptance, due to which the systems for speech recognition in regional languages are now being developed, taking into account all of the dialects. Because of the limited availability of the speech corpus (SC) of regional languages for doing research, designing a speech recognition system is challenging. This contribution provides a Parallel Big Bang-Big Crunch (PB3C)-based mechanism to automatically evolve the optimal architecture of LSTM (Long Short-Term Memory). To decide the optimal architecture, we evolved a number of neurons and hidden layers of LSTM model. We validated the proposed approach on Marathi speech recognition system. In this research work, the performance comparisons of the proposed method are done with BBBC based LSTM and manually configured LSTM. The results indicate that the proposed approach is better than two other approaches.
Super-Resolution-Empowered Adaptive Medical Video Streaming in Telemedicine Systems

Due to influence of COVID-19, telemedicine is becoming more and more important. High-quality medical videos can provide a physician with a better visual experience and increase the accuracy of disease diagnosis, but this requires a dramatic increase in bandwidth compared to that required by regular videos. Existing adaptive video-streaming approaches cannot successfully provide high-resolution video-streaming services under poor or fluctuating network conditions with limited bandwidth. In this paper, we propose a super-resolution-empowered adaptive medical video streaming in telemedicine system (named SR-Telemedicine) to provide high quality of experience (QoE) videos for the physician while saving the network bandwidth. In SR-Telemedicine, very low-resolution video chunks are first transmitted from the patient to an edge computing node near the physician. Then, a video super-resolution (VSR) model is employed at the edge to reconstruct the low-resolution video chunks into high-resolution ones with an appropriate high-resolution level (such as 720p or 1080p). Furthermore, the neural network of VSR model is designed to be scalable and can be determined dynamically. Based on the time-varying computational capability of the edge computing node and the network condition, a double deep Q-Network (DDQN)-based algorithm is proposed to jointly select the optimal reconstructed high-resolution level and the scale of the VSR model. Finally, extensive experiments based on real-world traces are carried out, and the experimental results illustrate that the proposed SR-Telemedicine system can improve the QoE of medical videos by 17–79% compared to three baseline algorithms.
Air-CSL: Chinese Sign Language Recognition Based on the Commercial WiFi Devices

Artificial intelligence and Internet of Things (IoT) devices are experiencing explosive growth. Currently, the commonly used gesture recognition methods are difficult to deploy and expensive, so this paper uses the Channel State Information (CSI) for Chinese sign language recognition. Aiming at the problems of current gesture recognition methods, such as strong personnel dependence, high computational resource consumption, and low robustness, we proposed a Chinese sign language gesture recognition method named Air-CSL. In this method, the Local Outlier Factor (LOF) removal algorithm and the Discrete Wavelet Transform (DWT) are used to reduce the noise in the data, and the subcarriers that best represent the gesture data are selected by principal component analysis. After denoising, mathematical statistics were extracted from the gesture waveform as the eigenvalues, and the features were fused by the Deep Restricted Boltzmann Machine (DBM). Finally, the result of gesture classification and recognition is obtained by the Gated Recurrent Unit (GRU). In this way, the prediction model realizes as well as the classification of sign language gestures. The results show that the proposed method can effectively recognize Chinese sign language gestures of different people in different environments and has good robustness.
Discrete Logarithmic Factorial Problem and Einstein Crystal Model Based Public-Key Cryptosystem for Digital Content Confidentiality

Public-key encryption is extensively used to provide digital data confidentiality and deliver the security features, such as nonrepudiation (digital signature) and secure key exchange. Conventional public-key schemes are based on mathematical problems with inflexible constraints, and the security of digital contents relies on computational complexity. In the era of emerging technologies, most public-key image encryption schemes are susceptible to various threats. We propose a novel public-key encryption in this article with near-ring criteria and provide confidentiality to private data with the microstates of the Einstein crystal model. The virtual oscillator generated by microstates of initial oscillators for the common secrets with the public-key scheme produces unique states to encrypt digital data. The privacy-preserved structure, that mimics the data stream of digital content with the behavior of the improved Einstein crystal model, describes a system in terms of microstates to generate diffusion in the plain data with unique states of a virtual oscillator. The performance and digital forensic evaluations, such as randomness, histogram uniformity, pixels’ correlation, pixels’ similarity, visual strength, pixels’ incongruity, key sensitivity, linear and differential attacks, noise, and occlusion attacks analyses, certify the resistivity of the proposed algorithm against potential threats and provide superior capacity in comparison to existing methodologies to hostile certain attacks.
Unscented Kalman Filter With General Complex-Valued Signals

For the estimation of real-valued Gaussian signals, the unscented Kalman filter (UKF) can provide a state estimate with second-order accuracy. However, when a general complex-valued system is considered, a direct extension of UKF from the real domain to the complex domain is inadequate, since the complementary covariance information associated with general improper complex-valued signals has been systematically ignored. To this end, in this work, we propose a general complex-valued unscented Kalman filter (GCUKF) algorithm which can be applied for both proper and improper signals. This is achieved by first proposing a novel sigma points selection scheme for the general complex-valued case, followed by a modified state update method to fully utilize both the innovation and its conjugate. A rigorous MSE analysis illustrates the superiority of the proposed state update method, and simulations support the analysis.
Optical Flow Computation for Video Under the Dynamic Illumination

Optical flow computation for video under the dynamic illumination is a challenging issue in video multimedia applications. In this paper, we solve this issue by introducing an illumination-invariant framework for variational optical flow estimation. It consists of an illumination-invariance model that handles complex illumination changes and a data enhancement model that guarantees highly accurate optical flow estimation. In this framework, we design a log-correlation descriptor for the data term, which handles complex illumination changes by eliminating the common parameters shared by the neighboring pixels in the corresponding illumination change model while improving the accuracy of optical flow estimation by enhancing the discriminability of the data term matching. We also introduce a novel optical flow model with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{0}$</tex-math></inline-formula> norm regularization, which reconstructs optical flow field by a sparse flow gradient counting scheme. Different from other edge-preserving regularizers, it does not depend on local motion features, but locates important flow edges globally. Therefore, it will not cause edge blurriness due to avoiding local filtering or average operation. It is particularly effective for enhancing major flow edges while eliminating a manageable degree of low-amplitude motion structures to control smoothing and reduce oversegmentation artifacts. Even small-scale motion structures with high contrast can be preserved remarkably well. The experimental results show our method significantly outperforms previous illumination-robust optical flow methods in handling complex illumination changes, and achieves competitive evaluation results on the challenging MPI-Sintel and Kitti datasets.
DataShift: A Cross-Modal Data Augmentation Method for Speech Recognition and Machine Translation

Data augmentation has been successful in the tasks of different modalities such as speech and text. In this paper, we present a cross-modal data augmentation method, DataShift, to improve the performance of automatic speech recognition (ASR) and machine translation (MT) by randomly shifting values of the feature sequence along the time or frequency dimensions respectively. Experimental results show that our data augmentation method can improve the performance by 4% of word error rate (WER) and 0.36 BLEU score on average on the ASR and MT datasets separately.
Named Entity Recognition Based on BERT-MBiGRU-CRF and Multi-head Self-attention Mechanism

When processing Chinese named entity recognition, the traditional algorithm model have been having the ambiguity of expressive words and the singleness of the word vector, and the training consequence of the algorithm model was not well. To solve this problem, a BERT-MBiGRU-CRE model was proposed to increase the accuracy of Named Entity Recognition (NER). This model used a Multilayer Bidirectional Gated Recurrent Unit (MBiGRU) network to replace the Bidirectional Long Short-Term Memory (BiLSTM) network in the BERT-BiLSTM-CRE model, which can extract global context semantic features more effectively. Next, a BERT-MBiGRU-MS-CRF model was suggested based on the model. It added a layer of Multi-Head Selfttention (MS) mechanism behind the MBiGRU layer, which can efficiently extract multiple semantic features and overcome the deficiencies of MBiGRU to get local features. Finally, the experimental results on the MSRA dataset showed that the training time of the two models was significantly lower than that of the BERT-BiLSTM-CRE model, but the accuracy, recall and F1 value of them were significantly higher than those of the BERT-BiLSTM-CRE model, reaching 97.26%, 97.53% and 97.39% respectively.
Construction of Ideological and Political Virtual Museum Based on VR Unity3D from the Perspective of Digital Humanities

In the digital age, the integration of new changes and directions of the media and the development of ideological and political teachings in colleges and universities has brought opportunities and challenges for teachers and students to study and think deeply about theories. Based on Virtual Reality (VR) technology, the virtual museum is developed by using Unity3D as the main development tool, C # programming language, and Virtual Reality toolkit. It has interactive functions such as exhibition viewing, text introduction, and video appreciation. It is an effective medium to promote the reform, innovation, and development of the teachings of the ideological and political theory course. It also leverages the strength of Chinese profound history and culture and explains profound theories in simple language. Therefore, the construction of the ideological and political virtual museum is one of the important ways to promote the innovation of ideological and political courses in colleges and universities.
Dynamic Knowledge Integration for Natural Language Inference

Natural language inference (NLI) aims to determine the entailment relationship between the premise and the hypothesis. It is a fundamental but difficult problem, since there may exists serious semantic and logistic gap between the premise and the hypothesis. Despite using strong pre-trained language model (PLM), previous work performs poorly on complicated reasoning for knowledge-sensitive cases ignoring the integration of external knowledge. We propose a dynamic knowledge integration strategy for NLI, where knowledge from multiple knowledge graphs (KGs) can be dynamically integrated. For each KG, it transforms input tokens into a graph according to the connectivity of the related entities. All the graphs are encoded by a group of parallel graph neural networks (GNNs), and after each layer the intermediate results are integrated dynamically by being conditioned on the input text. This strategy also facilitates the incorporation of PLM, simply by treating the input tokens as a fully connected graph and adapting the PLM outputs as the node embeddings. Experiments on SNLI, MNLI and SciTail show that, the dynamic integration of knowledge from WordNet and ConceptNet achieves significant improvements over the strongest baseline built upon RoBERTa.
Improved quantum attack on several generalized unbalanced Feistel structures

By studying the attack technology based on Simon, Grover and other quantum algorithms for the security of block cipher structure, this paper proposes new quantum attack on 6 generalized unbalanced Feistel structures. We improve the attack results of n-cell structure, New Structure I/III/IV structure and FBC-like structure, and give a quantum attack on Skipjack structure for the first time. Based on Simon algorithm, this paper constructs a n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -round, a 12/12/6-round and a 4-round quantum distinguisher about n-cell structure, New structure I/III/IV and FBC-like structure, improving n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -n-1 rounds, 6 rounds/3 rounds/1 round and 1 round separately, comparing to the previous results. In addition, this paper also constructs a 9-round quantum distinguisher about Skipjack structure. Moreover, this paper proposes a quantum key recovery attack on the above six structures in combination with Grover algorithm, and the time complexities of attacks are analyzed.