Reviewerly/exHarmony huggingface dataset:
Deep Multi-Task Multi-Label CNN for Effective Facial Attribute Classification

Facial Attribute Classification (FAC) has attracted increasing attention in computer vision and pattern recognition. However, state-of-the-art FAC methods perform face detection/alignment and FAC independently. The inherent dependencies between these tasks are not fully exploited. In addition, most methods predict all facial attributes using the same CNN network architecture, which ignores the different learning complexities of facial attributes. To address the above problems, we propose a novel deep multi-task multi-label CNN, termed DMM-CNN, for effective FAC. Specifically, DMM-CNN jointly optimizes two closely-related tasks (i.e., facial landmark detection and FAC) to improve the performance of FAC by taking advantage of multi-task learning. To deal with the diverse learning complexities of facial attributes, we divide the attributes into two groups: objective attributes and subjective attributes. Two different network architectures are respectively designed to extract features for two groups of attributes, and a novel dynamic weighting scheme is proposed to automatically assign the loss weight to each facial attribute during training. Furthermore, an adaptive thresholding strategy is developed to effectively alleviate the problem of class imbalance for multi-label learning. Experimental results on the challenging CelebA and LFWA datasets show the superiority of the proposed DMM-CNN method compared with several state-of-the-art FAC methods.
Privacy-Preserving Multi-Authority Attribute-Based Data Sharing Framework for Smart Grid

In smart power grid systems, real-time electricity data sharing can bring many benefits. However the security and privacy challenges have been a concern. This is because shared data may contain some private and sensitive information of data owners or users. So it has been an important issue to balance data utilization, data privacy and users privacy. Privacy-preserving multi-authority attribute-based encryption (MA-ABE) is a promising tool to tackle this issue, but it cannot be directly applied to addressed scenario of smart power grid systems. In this paper, we propose a framework to achieve secure data sharing in smart grid. The scheme uses the technique of inner product encryption (IPE). Under this framework, the access policy and the attribute set are fuzzy by converting them into two vectors. Only when the two vectors are orthogonal, users can successfully obtain the sharing data. Besides, our scheme allows that the threshold access policy only involves attribute name indexes, and sensitive attribute values are not sent along with the shared data so the attribute privacy is preserved. In addition, in order to improve efficiency, our scheme adds a testing phase to avoid the unnecessary operation before recovering data. Finally, our performance analysis and experiments confirm the merits of the proposed framework.
University Teachers’ Ambivalence about the Digital Transformation of Higher Education

In contemporary higher education institutions, the digital transformation is obvious and necessary; new times call for new teaching approaches with the help from digital technology. This is a requirement not only from students but also from society at large, and it implies a partly changed teacher role, including digital competence alongside content, didactic and pedagogical competence. For many teachers this means an increased workload and stress, at the same time as they recognize the opportunities and possible benefits that digital tools offer. To provide a contribution to the understanding of the complexity that digital transformation in higher education means, we investigate how theoretical assumptions about technology integration in education relate to the teaching practice of university teachers. The article draws from two separate studies with university teachers, carried out in a small Swedish university; a survey with 254 respondents and a focus group interview with six participants. Results indicate that the teachers are hesitant yet positive towards using digital technology in their pedagogical practice and that there is ambivalence surrounding the issue at hand, as the integration of technology in teaching is connected to organizational and societal processes beyond their direct control. We argue that one-sided theoretical assumptions about technology integration may be an explanation of the situation where the practice and rhetoric of technology use in higher education diverge. https://doi.org/10.26803/ijlter.18.13. 7
Recognizing the HRRP by Combining CNN and BiRNN With Attention Mechanism

In this paper, we integrate the advantages of convolutional neural network (CNN) and bidirectional recurrent neural network (BiRNN) with attention mechanism, and propose a CNN-BiRNN based method to recognize the individual high resolution range profile (HRRP). In the proposed method, the CNN is utilized to explore the spatial correlation of raw HRRP data and extract expressive features followed by a BiRNN taking the full consideration of temporal dependence between range cells. Furthermore, in order to enhance the robustness to misalignment, an attentional mechanism is employed after BiRNN to allow the CNN-BiRNN model to focus on the discriminative target area. The combination of CNN and BiRNN with attention mechanism makes the extracted features are not only efficient, but also strongly resistant to the time-shift sensitivity. Experimental results on measured HRRP data demonstrate the effectiveness and the robustness to misalignment of the proposed method.
SAO2Vec: Development of an algorithm for embedding the subject–action–object (SAO) structure using Doc2Vec

In natural-language processing, the subject-action-object (SAO) structure is used to convert unstructured textual data into structured textual data comprising subjects, actions, and objects. This structure is suitable for analyzing the key elements of technology, as well as the relationships between these elements. However, analysis using the existing SAO structure requires a substantial number of manual processes because this structure does not represent the context of the sentences. Thus, we introduce the concept of SAO2Vec, in which SAO is used to embed the vectors of sentences and documents, for use in text mining in the analysis of technical documents. First, the technical documents of interest are collected, and SAO structures are extracted from them. Then, sentence vectors are extracted through the Doc2Vec algorithm and are updated using word vectors in the SAO structure. Finally, SAO vectors are drawn using an updated sentence vector with the same SAO structure. In addition, document vectors are derived from the document's SAO vectors. The results of an experiment in the Internet of things field indicate that the SAO2Vec method produces 3.1% better accuracy than the Doc2Vec method and 115.0% better accuracy than SAO frequency alone. This proves that the proposed SAO2Vec algorithm can be used to improve grouping and similarity analysis by including both the meanings and the contexts of technical elements.
Characterizing the Occurrence of Dockerfile Smells in Open-Source Software: An Empirical Study

Dockerfile plays an important role in the Docker-based software development process, but many Dockerfile codes are infected with smells in practice.Understanding the occurrence of Dockerfile smells in open-source software can benefit the practice of Dockerfile and enhance project maintenance.In this paper, we perform an empirical study on a large dataset of 6,334 projects to help developers gain some insights into the occurrence of Dockerfile smells, including its coverage, distribution, co-occurrence, and correlation with project characteristics.Our results show that smells are very common in Dockerfile codes and there exists co-occurrence between different types of Dockerfile smells.Further, using linear regression analysis, when controlled for various variables, we statistically identify and quantify the relationships between Dockerfile smells occurrence and project characteristics.We also provide a rich resource of implications for software practitioners.
Arbitrary-View Human Action Recognition: A Varying-View RGB-D Action Dataset

Current researches of action recognition which focus on single-view and multi-view recognition can hardly satisfy the requirements of human-robot interaction (HRI) applications for recognizing human actions from arbitrary views. Arbitrary-view recognition is still a challenging issue due to view changes and visual occlusions. In addition, the lack of datasets also sets up barriers. To provide data for arbitrary-view action recognition, we collect a new large-scale RGB-D action dataset for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences. The dataset includes action samples captured in 8 fixed viewpoints and varying-view sequences which cover the entire 360° view angles. In total, 118 persons are invited to act 40 action categories. Our dataset involves more participants, more viewpoints and a large number of samples. More importantly, it is the first dataset containing the entire 360° varying-view sequences. The dataset provides sufficient data for multi-view, cross-view and arbitrary-view action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition. Experiment results show that the VS-CNN achieves superior performance, and our dataset provides valuable but challenging data for the evaluation of arbitrary-view recognition.
Restructuring Educational Institutions for Growth in the Fourth Industrial Revolution (4IR): A Systematic Review

Industrial Revolutions basically have transformed human lives. We have gone from hand production to mechanized production into computerization or automation of concepts into products (Fourth Industrial Revolution (4IR)). However, 4IR urges the process of transferring data from digital domains and offline reality via interconnected systems to improve lives. The technologies in 4IR enter into varying areas, such as the economy, medicine and education. Educational institutions have contributed greatly to reshaping future technologies by being the test laboratories for innovations. In the meanwhile, there is an immense need for looking beyond the traditional educational approach. This can be achieved by strategically employing the trending technologies to prepare students and educators with the right kind of knowledge and set of skills. It is imperative to ask questions about how the delivery of education will be undertaken and how educational institutions will be restructured by the 4IR to prepare students for the challenges ahead.
Pairs Trading in Cryptocurrency Markets

Pairs trading is a strategy based on exploiting mean reversion in prices of securities.Even though these strategies have been shown to perform well for equities, their performance is unknown for the field of cryptocurrencies, usually perceived as inefficient and predictable.We apply the distance and cointegration methods to a basket of 26 liquid cryptocurrencies traded on the Binance exchange, specifically at 5-minute, 1-hour and daily frequencies.In our backtests, the strategies underperform classical benchmarks.However, the results are quite sensitive to parameter settings and external factors such as transaction costs or execution windows.Higher-frequency trading delivers significantly better performance, and while the most common daily distance method returns -0.07%monthly, this increases to 11.61% monthly for 5-minute frequency.Additionally, we find evidence of simple mean-reverting behavior in intraday prices that is missing in daily data, and which provides further support for the inefficiency of cryptocurrency markets.
Efficient Chain Structure for High-Utility Sequential Pattern Mining

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases.Several works have been presented to reduce the computational cost by variants of pruning strategies.In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance.Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs.Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates.
Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.
A Quality of Experience assessment of haptic and augmented reality feedback modalities in a gait analysis system

Gait analysis is a technique that is used to understand movement patterns and, in some cases, to inform the development of rehabilitation protocols. Traditional rehabilitation approaches have relied on expert guided feedback in clinical settings. Such efforts require the presence of an expert to inform the re-training (to evaluate any improvement) and the patient to travel to the clinic. Nowadays, potential opportunities exist to employ the use of digitized "feedback" modalities to help a user to "understand" improved gait technique. This is important as clear and concise feedback can enhance the quality of rehabilitation and recovery. A critical requirement emerges to consider the quality of feedback from the user perspective i.e. how they process, understand and react to the feedback. In this context, this paper reports the results of a Quality of Experience (QoE) evaluation of two feedback modalities: Augmented Reality (AR) and Haptic, employed as part of an overall gait analysis system. The aim of the feedback is to reduce varus/valgus misalignments, which can cause serious orthopedics problems. The QoE analysis considers objective (improvement in knee alignment) and subjective (questionnaire responses) user metrics in 26 participants, as part of a within subject design. Participants answered 12 questions on QoE aspects such as utility, usability, interaction and immersion of the feedback modalities via post-test reporting. In addition, objective metrics of participant performance (angles and alignment) were also considered as indicators of the utility of each feedback modality. The findings show statistically significant higher QoE ratings for AR feedback. Also, the number of knee misalignments was reduced after users experienced AR feedback (35% improvement with AR feedback relative to baseline when compared to haptic). Gender analysis showed significant differences in performance for number of misalignments and time to correct valgus misalignment (for males when they experienced AR feedback). The female group self-reported higher utility and QoE ratings for AR when compared to male group.
SoK: Cyber Insurance – Technical Challenges and a System Security Roadmap

Cyber attacks have increased in number and complexity in recent years, and companies and organizations have accordingly raised their investments in more robust infrastructure to preserve their data, assets and reputation. However, the full protection against these countless and constantly evolving threats is unattainable by the sole use of preventive measures. Therefore, to handle residual risks and contain business losses in case of an incident, firms are increasingly adopting a cyber insurance as part of their corporate risk management strategy.As a result, the cyber insurance sector - which offers to transfer the financial risks related to network and computer incidents to a third party - is rapidly growing, with recent claims that already reached a $100M dollars. However, while other insurance sectors rely on consolidated methodologies to accurately predict risks, the many peculiarities of the cyber domain resulted in carriers to often resort to qualitative approaches based on experts opinions.This paper looks at past research conducted in the area of cyber insurance and classifies previous studies in four different areas, focused respectively on studying the economical aspects, the mathematical models, the risk management methodologies, and the predictions of cyber events. We then identify, for each insurance phase, a group of practical research problems where security experts can help develop new data-driven methodologies and automated tools to replace the existing qualitative approaches.
High-fidelity View Synthesis for Light Field Imaging With Extended Pseudo 4DCNN

Multi-view properties of light field (LF) imaging enable exciting applications such as auto-refocusing, depth estimation and 3D reconstruction. However, limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards more practical vision applications. Existing view synthesis methods mainly break the task into two steps, i.e. depth estimating and view warping, which are usually inefficient and produce artifacts over depth ambiguities. We have proposed an end-to-end deep learning framework named Pseudo 4DCNN to solve these problems in a conference paper. Rethinking on the overall paradigm, we further extend pseudo 4DCNN and propose a novel loss function which is applicable for all tasks of light field reconstruction i.e. EPI Structure Preserving (ESP) loss function. This loss function is proposed to attenuate the blurry edges and artifacts caused by averaging effect of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm based loss function. Furthermore, the extended Pseudo 4DCNN is compared with recent state-of-the-art (SOTA) approaches on more publicly available light field databases, as well as self-captured light field biometrics and microscopy datasets. Experimental results demonstrate that the proposed framework can achieve better performances than vanilla Pseudo 4DCNN and other SOTA methods, especially in the terms of visual quality under occlusions. The source codes and self-collected datasets for reproducibility will be available online soon.
Multi-Level Deep Neural Network Adaptation for Speaker Verification Using MMD and Consistency Regularization

Adapting speaker verification (SV) systems to a new environment is a very challenging task. Current adaptation methods in SV mainly focus on the backend, i.e, adaptation is carried out after the speaker embeddings have been created. In this paper, we present a DNN-based adaptation method using maximum mean discrepancy (MMD). Our method exploits two important aspects neglected by previous research. First, instead of minimizing domain discrepancy at utterance-level alone, our method minimizes domain discrepancy at both frame-level and utterance-level, which we believe will make the adaptation more robust to the duration discrepancy between training data and test data. Second, we introduce a consistency regularization for unlabelled target-domain data. The consistency regularization encourages the target speaker embeddings robust to adverse perturbations. Experiments on NIST SRE 2016 and 2018 show that our DNN adaptation works significantly better than the previously proposed DNN adaptation methods. What's more, our method works well with backend adaptation. By combining the proposed method with backend adaptation, we achieve a 9% improvement over backend adaptation in SRE18.
A Novel Transfer Learning Method for Fault Diagnosis Using Maximum Classifier Discrepancy With Marginal Probability Distribution Adaptation

Effective fault diagnosis is essential to ensure the safe and reliable operation of equipment. In recent years, several transfer learning-based methods for diagnosing faults under variable working conditions have been developed. However, these models are designed to completely match the feature distributions between different domains, which is difficult to accomplish because each domain has unique characteristics. To solve this problem, we propose a framework based on the maximum classifier discrepancy with marginal probability distribution adaptation that focuses on task-specific decision boundaries. Specifically, this method captures ambiguous target samples through the predicted discrepancy between two classifiers for the target samples. Furthermore, marginal probability distribution adaptation facilitates the capture of target samples located far from the source domain, and these target samples are brought closer to the source domain through adversarial training. Experimental results indicate that the proposed method demonstrates higher performance and generalization ability than existing fault diagnosis methods.
A Cross-Modal Multi-granularity Attention Network for RGB-IR Person Re-identification

Cross-modal person re-identification(Re-id) under infrared light and visible light (RGB-IR) is of great significance for modern video surveillance, especially nighttime surveillance. The existing research results in the single-mode person re-identification field have reached a high level. Cross-model person re-identification, however, is rather challenging for the tremendous cross-modality and intra-modality difference in addition to common issues such as lighting conditions, human posture, camera angle, and etc..The Cross-modal Multi-granularity Attention Network (CMGN) proposed by this paper enables network to learn the common features of different modalities and map them to the same feature space. Major contributions made by this paper includes:1) A new ”butterfly” attention module that can be used for cross-modal tasks is designed to constrain the network attention to common areas of different modes. And in the RegDB and SYSU-MM01 dataset reached the effect of State-of-the-art(SOA). 2) An end-to-end multi-granularity feature fusion network dedicated to processing cross-modal problems is proposed.