Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Data Sharing System with Enhanced Auditability

Cloud platforms provide a low-cost and convenient way for users to share data. One important issue of cloud-based data sharing systems is how to prevent the sensitive information contained in users’ data from being disclosed. Existing studies often utilize cryptographic primitives, such as attribute-based encryption and proxy re-encryption, to protect data privacy. These approaches generally rely on a centralized server which may cause a single point of failure problem. Blockchain is known for its ability to solve such a problem. Some blockchain-based approaches have been proposed to realize privacy-preserving data sharing. However, these approaches did not fully explore the auditability provided by the blockchain. The dishonest cloud server can share data with a requester without notifying the data owner or being logged by the blockchain. In this paper, we propose a blockchain-based privacy-preserving data sharing system with enhanced auditability. The proposed system follows the idea of hybrid encryption to protect data privacy. The data to be shared are encrypted with a symmetric key, and the symmetric key is encrypted with a joint public key which is the sum of multiple blockchain nodes’ public keys. Only if a data requester is authorized, the blockchain nodes will be triggered to execute a verifiable key switch protocol. By using the output of the protocol, the data requester can get the plaintext of the symmetric key. The blockchain nodes participate in both the authorization process and the key switch process, which means the behavior of the data requester is witnessed by multi-parties and is auditable. We implement the proposed system on Hyperledger Fabric. The simulation results show that the performance overhead is acceptable.
Degree adverbs in spoken Mandarin

Abstract Managing near-synonymous morphosyntactic constructions in language is often at the core of a speaker’s grammatical competence. This study investigates the usage patterns of four near-synonymous degree adverb constructions (i.e., hěn, tài, mán , and chāo ) in the Taiwan Mandarin Corpus in TalkBank. After retrieving the concordance lines of these four constructions from the corpus, we manually annotated their co-occurring linguistic patterns/structures at multiple linguistic levels. In particular, we utilized a corpus-based behavioral profile approach to determine the interrelationship of the four constructions based on their distributional patterns and identified their distinctive behavioral patterns, producing a comprehensive delineation of their functional differences. Our analysis suggests that these four constructions fall into two super-clusters, i.e., chāo - mán and tài - hěn. Chāo and mán differ mainly in the pragmatic sentiments of their associated predicates, their co-occurrences with the nominalization structure, and their productivity in lexicalization. Tài and hěn differ mainly in their co-occurrences with the final particle -le , the semantic and pragmatic functions of their associated predicates, and the semantics of their associated head nouns. We have also connected these corpus-based distributional patterns to previous research findings, demonstrating the effectiveness and applicability of the behavioral profile approach for the analysis of near-synonymous morphosyntactic alternations in language.
Temporal positional lexicon expansion for federated learning based on hyperpatism detection

Abstract Internet‐based information exchange has resulted in the propagation of false and misleading information, which is highly detrimental to individuals and humankind. Due to the speed and volume of social media news production, supervised artificial intelligence algorithms require many annotated data, which is difficult, costly, and time‐consuming. To address this issue, we offer a novel federated semi‐supervised framework based on self‐ensembling that utilizes the linguistic and stylometric information of annotated news articles and searches for hidden patterns in unlabeled data to denoise labels. Self‐ensembling predicts the labels of unlabeled data by using the outcomes of network‐in‐training from earlier epochs. These cumulative predictions should be a stronger predictor for unknown labels than the output of the most recent training epoch; hence, they may be utilized as a substitute for the labels of unlabeled data. The approach is distinctive in collecting all of the outputs from the neural network's past training periods. It utilizes them as an unsupervised target against which to assess the current output prediction of unlabeled articles. We intend to create a dataset centred on denoising to forward the study. The dataset is mapped using (1) the shifting focus time from published news articles and (2) the semi‐supervised method based on coincidence contexts for a neural contrast embedding model for learning low‐dimensional continuous vectors that generate a focus time‐based query in sequential news articles for temporal comprehension. The model achieved 0.83% F‐measure with lexicon expansion semi‐supervised learning.
Multi-Vehicle Tracking Based on Monocular Camera in Driver View

Multi-vehicle tracking is used in advanced driver assistance systems to track obstacles, which is fundamental for high-level tasks. It requires real-time performance while dealing with object illumination variations and deformations. To this end, we propose a novel multi-vehicle tracking algorithm based on a monocular camera in driver view. It follows the tracking-by-detection paradigm and integrates detection and appearance descriptors into a single network. The one-stage detection approach consists of a backbone, a modified BiFPN as a neck layer, and three prediction heads. The data association consists of a two-step matching strategy together with a Kalman filter. Experimental results demonstrate that the proposed approach outperforms state-of-the-art algorithms. It is also able to solve the tracking problem in driving scenarios while maintaining 16 FPS on the test dataset.
A Novel Localization Method of Wireless Covert Communication Entity for Post-Steganalysis

Recently, some criminals have begun to use multimedia steganography to conduct covert communication, such as transmitting stolen trade secrets. After using steganalysis to find covert communication entities, obtaining their locations can effectively help criminal forensics. This paper proposes a novel localization method of wireless covert communication entity for post-steganalysis. The method is based on hybrid particle swarm optimization and gray wolf optimization to improve localization precision (ILP-PSOGWO). In this method, firstly, the relationship model between received signal strength (RSS) and distance is constructed for the indoor environment where the target node exists. Secondly, dichotomy is used to narrow the region where the target node is located. Then, the weighted distance strategy is used to select the reference point locations with strong and stable RSS. Finally, the intersection region of the reference points is taken as the region where the target node is located, and the hybrid PSOGWO is used to locate and optimize the target node location. Experimental results demonstrate that ILP-PSOGWO can maintain high stability, and 90% of the localization errors are lower than 0.9012 m. In addition, compared with the existing methods of PSO, GWO and extended weighted centroid localization (EWCL), the average localization error of ILP-PSOGWO is also reduced by 28.2–49.0%.
Document-Level Relation Extraction with Path Reasoning

Document-level relation extraction (DocRE) aims to extract relations among entities across multiple sentences within a document by using reasoning skills (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the reasoning paths between two entities. However, most of the advanced DocRE models only attend to the feature representations of two entities to determine their relation, and do not consider one complete reasoning path from one entity to another entity, which may hinder the accuracy of relation extraction. To address this issue, this article proposes a novel method to capture this reasoning path from one entity to another entity, thereby better simulating reasoning skills to classify relation between two entities. Furthermore, we introduce an additional attention layer to summarize multiple reasoning paths for further enhancing the performance of the DocRE model. Experimental results on a large-scale document-level dataset show that the proposed approach achieved a significant performance improvement on a strong heterogeneous graph-based baseline.
A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream

With the rapid development of data stream, multi-label algorithms for mining dynamic data become more and more important. At the same time, when data distribution changes, concept drift will occur, which will make the existing classification models lose effectiveness. Ensemble methods have been used for multi-label classification, but few methods consider both the accuracy and diversity of base classifiers. To address the above-mentioned problem, a Weighted Ensemble classification algorithm based on Nearest Neighbors for Multi-Label data stream (WENNML) is proposed. WENNML uses data blocks to train Active candidate Ensemble Classifiers (AEC) and Passive candidate Ensemble Classifiers (PEC). The base classifiers of AEC and PEC are dynamically updated using geometric and diversity weighting methods. When the difference value between the number of current instances and the number of warning instances reaches the passive warning value, the algorithm selects the optimal base classifiers from AEC and PEC according to the subset accuracy and hamming score and puts them into the predictive ensemble classifiers. Experiments are carried out on 12 kinds of datasets with 9 comparison algorithms. The results show that WENNML achieves the best average rankings among the four evaluation metrics.
TSN-A: An Efficient Deep Learning Model for Automatic Modulation Classification Based on Intra-Class Confusion Reduction of Modulation Families

Automatic modulation classification (AMC) is an impressive technology, which is widely used in military and civilian fields. Recently, deep learning-based AMC (DL-AMC) methods have shown outstanding performance. However, most DL-AMC methods employ a single-stage network framework, which is effective for reducing only inter-class confusion of modulation families, while the intra-class confusion of modulation families attracts less attention and remains to be addressed. This letter proposes a two-stage network with attention (TSN-A), which consists of a main network and two sub-networks. In the first stage, the main network can effectively tackle the problem of inter-class confusion. In the second stage, the sub-networks can efficiently reduce the intra-class confusion of AM-SSB and AM-DSB families. TSN-A achieves the top results on the public challenging RML 2018.01a dataset and is more competitive in low SNR environments. Meanwhile, our main network exhibits stronger and more robust classification ability on different datasets than the benchmark models. Moreover, our sub-networks are very applicable to be migrated to other DL-AMC models for helping them improve the classification accuracy by reducing the intra-class confusion.
HCI Research Experiences During the Pandemic: Lessons Learned for the Road Ahead

Abstract The social interaction restrictions imposed by COVID-19 required academic researchers to rethink the design of their research projects, especially those that involved human interaction. Considering this scenario, we report here the experience of conducting four studies in Human–Computer Interaction research projects that were adapted to be executed remotely: a focus group study, a communicability evaluation study, a speculative design study and a user observation study. Considering how the studies were planned, executed and analysed, we reflect on the adaptations needed, and lessons related to our experiences. From these lessons, we believe that, even after all the pandemic restrictions are lifted, we may continue conducting at least part of our research studies remotely to increase our geographical reach, reduce costs and facilitate data capture.
The Contribution of Digital Technology to the Forecasting of Supply Chain Development, in IT Products, Modeling and Simulation of the Problem

Aiming for the forecasting and predictability of their future development, corporations have developed appropriate strategies as a result of the necessity to optimize the distribution networks of new IT products over time. The necessity of diversifying manufacturing was brought on by the fierce competition between businesses and the sophisticated consumer demand trends for personalized items. For businesses looking to create more effective distribution networks for their products, mass adaptability may be advantageous. Fuzzy cognitive mapping (FCM), associations developed from web analytics data, and simulation results based on dynamic and agent-based simulation models work together to practically aid digital marketing experts, decision-makers and analysts in offering answers to their corresponding problems. In order to apply the measures in agent-based modeling, the current work is based on the gathering of web analysis data over a predetermined time period, as well as on identifying the influence correlations between measurements.
Giant panda age recognition based on a facial image deep learning system

The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.
DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation

Cardiac computed tomography angiography (CTA) is an emerging imaging modality for assessing coronary artery as well as various cardiovascular structures. Recently, deep learning (DL) methods have been successfully applied to many applications of medical image analysis including cardiac CTA structure segmentation. However, DL requires a large amounts of data and high-quality labels for training which can be burdensome to obtain due to its labor-intensive nature. In this study, we aim to develop a fully automatic artificial intelligence (AI) system, named DeepHeartCT, for accurate and rapid cardiac CTA segmentation based on DL. The proposed system was trained using a large clinical dataset with computer-generated labels to segment various cardiovascular structures including left and right ventricles (LV, RV), left and right atria (LA, RA), and LV myocardium (LVM). This new system was trained directly using high-quality computer labels generated from our previously developed multi-atlas based AI system. In addition, a reverse ranking strategy was proposed to assess the segmentation quality in the absence of manual reference labels. This strategy allowed the new framework to assemble optimal computer-generated labels from a large dataset for effective training of a deep convolutional neural network (CNN). A large clinical cardiac CTA studies (n = 1,064) were used to train and validate our framework. The trained model was then tested on another independent dataset with manual labels (n = 60). The Dice score, Hausdorff distance and mean surface distance were used to quantify the segmentation accuracy. The proposed DeepHeartCT framework yields a high median Dice score of 0.90 [interquartile range (IQR), 0.90-0.91], a low median Hausdorff distance of 7 mm (IQR, 4-15 mm) and a low mean surface distance of 0.80 mm (IQR, 0.57-1.29 mm) across all segmented structures. An additional experiment was conducted to evaluate the proposed DL-based AI framework trained with a small vs. large dataset. The results show our framework also performed well when trained on a small optimal training dataset (n = 110) with a significantly reduced training time. These results demonstrated that the proposed DeepHeartCT framework provides accurate and rapid cardiac CTA segmentation that can be readily generalized for handling large-scale medical imaging applications.
Robust Speech Recognition Using Teacher-Student Learning Domain Adaptation

Recently, robust speech recognition for real-world applications has attracted much attention. This paper proposes a robust speech recognition method based on the teacher-student learning framework for domain adaptation. In particular, the student network will be trained based on a novel optimization criterion defined by the encoder outputs of both teacher and student networks rather than the final output posterior probabilities, which aims to make the noisy audio map to the same embedding space as clean audio, so that the student network is adaptive in the noise domain. Comparative experiments demonstrate that the proposed method obtained good robustness against noise.
Display Size and Targeting Performance: Small Hurts, Large May Help

Which display size helps gamers win? Recommendations from the research and PC gaming communities are contradictory. We find that as display size grows, targeting performance improves. When size increases from 13′′ to 26′′, targeting time drops by over 3%. Further size increases from 26′′ through 39′′, 52′′ and 65′′, bring more modest improvements, with targeting time dropping a further 1%. While such improvements may not be meaningful for novice gamers, they are extremely important to skilled and competitive players. To produce these results, 30 gamers participated in a targeting task as we varied display size by placing a display at varying distances. We held field of view constant by varying viewport size, and resolution constant by rendering to a fixed-size off-screen buffer. This paper offers further experimental detail, and examines likely explanations for the effects of display size.
Realistic Luminance in VR

As virtual reality (VR) headsets continue to achieve ever more immersive visuals along the axes of resolution, field of view, focal cues, distortion mitigation, and so on, the luminance and dynamic range of these devices falls far short of widely available consumer televisions. While work remains to be done on the display architecture side, power and weight limitations in head-mounted displays pose a challenge for designs aiming for high luminance. In this paper, we seek to gain a basic understanding of VR user preferences for display luminance values in relation to known, real-world luminances for immersive, natural scenes. To do so, we analyze the luminance characteristics of an existing high-dynamic-range (HDR) panoramic image dataset, build an HDR VR headset capable of reproducing over 20,000 nits peak luminance, and conduct a first-of-its-kind study on user brightness preferences in VR. We conclude that current commercial VR headsets do not meet user preferences for display luminance, even for indoor scenes.
Optimizing an adaptive fuzzy logic controller of a 3-DOF helicopter with a modified PSO algorithm

This paper investigates the controller optimization for a helicopter system with three degrees of freedom (3-DOF). The system is extensively nonlinear and highly sensitive to the controller's parameters, making it a real challenge to study these parameters' effects on the controller's performance. We combined fuzzy logic with adaptive control theory to control the system and used metaheuristic algorithms to determine these parameters. Then, we compare the results with the controller optimized through the standard PSO and PID controller. The results indicate the high ability of MPSO to perform the global search and to find a reasonable search space. The proposed method's effectiveness and robustness properties are shown through computer simulations, while the system is subject to uncertainties and disturbance. We also prove the efficiency of the MPSO algorithm by comparing it with the standard PSO and six other well-known metaheuristic algorithms and analyzing the results by statistical tests.
Detection and classification of road signs in raining condition with limited dataset

The road sign recognition (RSR) system is used to complete two tasks: localizing the traffic sign in an image and then classifying it according to the image features. Some of the applications of such system are incorporated into the Advanced Driver Assistance System (ADAS) and autonomous vehicles. However, the accuracy of the model decreases when changes in lighting or weather occurs, and the lack of training samples taken in rainy condition causes the model to be sub-optimal. The research was conducted with the focus on solving two problems, the accuracy of an object detection model decreases when changes in lighting or weather occurs, and the lack of training data, especially images taken under adverse condition. In this work, we compare and analyze three methods; automatic white balance (AWB), policy augmentations and Image-to-Image-translation (I2IT) technique on their performance to detect traffic signs in raining conditions. All methods were built upon the pre-trained SSD-MobileNetV2 model and using the TensorFlow2 framework. The images from the Malaysia Traffic Sign Dataset (MTSD) are used to train all models. Finally based on the result of the three model a final combination model is proposed that achieved the best performance in rainy condition. Experimental results showed that AWB was not that effective in detecting road sign in raining condition, while the other two techniques were highly effective. The final proposed model was implemented by combining policy augmentation and I2IT, obtained an mAP of 0.7967 in clear images and mAP of 0.7160 when rainy images were added to the testing dataset. These corresponds to mAP at 50% IoU of 0.8921mAP@0.5 during clear weather and 0.8340mAP@0.5 in raining images, which outperformed other models. Thus, a road sign detection and classification system that can perform well in rainy condition with limited training dataset has been successfully developed.