Reviewerly/exHarmony huggingface dataset:
The Harms of Demographic Bias in Deep Face Recognition Research

In this work we demonstrate the existence of demographic bias in the face representations of currently popular deep-learning-based face recognition models, exposing a bad research and development practice that may lead to a systematic discrimination of certain demographic groups in critical scenarios like automated border control. Furthermore, through the simulation of the template morphing attack, we reveal significant security risks that derive from demographic bias in current deep face models. This widely ignored problem poses important questions on fairness and accountability in face recognition.
Developing a Research Data Policy Framework for All Journals and Publishers

An output of the Data policy standardisation and implementation Interest Group (IG) of the Research Data Alliance (RDA) More journals and publishers – and funding agencies and institutions – are introducing research data policies. But as the prevalence of policies increases, there is potential to confuse researchers and support staff with numerous or conflicting policy requirements. We define and describe 14 features of journal research data policies and arrange these into a set of six standard policy types or tiers, which can be adopted by journals and publishers to promote data sharing in a way that encourages good practice and is appropriate for their audience's perceived needs. Policy features include coverage of topics such as data citation, data repositories, data availability statements, data standards and formats, and peer review of research data. These policy features and types have been created by reviewing the policies of multiple scholarly publishers, which collectively publish more than 10,000 journals, and through discussions and consensus building with multiple stakeholders in research data policy via the Data Policy Standardisation and Implementation Interest Group of the Research Data Alliance. Implementation guidelines for the standard research data policies for journals and publishers are also provided, along with template policy texts which can be implemented by journals in their Information for Authors and publishing workflows. We conclude with a call for collaboration across the scholarly publishing and wider research community to drive further implementation and adoption of consistent research data policies. <strong>Publisher's Note:</strong> a correction article relating to this publication has been published and can be found at <a href="http://doi.org/10.5334/dsj-2020-017">http://doi.org/10.5334/dsj-2020-017</a>
Factors affecting risky cybersecurity behaviors by U.S. workers: An exploratory study

Behavioral determinants of cybersecurity have gained greater attention among information technology experts in recent years. However, drivers of risky cybersecurity behavior have not been widely studied. This exploratory study examines the extent to which risky cybersecurity behavior is predicted by factors of cybersecurity-related avoidance behavior. Self-reported risky cybersecurity behavior was examined in light of technology threat avoidance factors in a sample of 184 working adults in the United States. Risky behaviors were measured using the instrument by Hadlington (2017), previously used by researchers to measure behavioral associations with non-technology threat avoidance-related items. Hierarchical regression noted significant predictive associations between several technology threat avoidance factors and self-reported risky cybersecurity behavior: perceived susceptibility (p = .027), perceived cost (p = .003), and self-efficacy (p = .043). Combined, these variables explained 9.4% of the adjusted variance in levels of risky cybersecurity behavior (p = .001). Effect size calculations revealed predictive impacts in the low-medium range. Age was also confirmed as a confounding covariate (p = .045). The impact findings uniquely distinguish this study from previous works. Findings also infer that training in protective behavior can mitigate a significant portion of risky cybersecurity behavior.
The MGB-5 Challenge: Recognition and Dialect Identification of Dialectal Arabic Speech

This paper describes the fifth edition of the Multi-Genre Broadcast Challenge (MGB-5), an evaluation focused on Arabic speech recognition and dialect identification. MGB-5 extends the previous MGB-3 challenge in two ways: first it focuses on Moroccan Arabic speech recognition; second the granularity of the Arabic dialect identification task is increased from 5 dialect classes to 17, by collecting data from 17 Arabic speaking countries. Both tasks use YouTube recordings to provide a multi-genre multi-dialectal challenge in the wild. Moroccan speech transcription used about 13 hours of transcribed speech data, split across training, development, and test sets, covering 7-genres: comedy, cooking, family/kids, fashion, drama, sports, and science (TEDx). The fine-grained Arabic dialect identification data was collected from known YouTube channels from 17 Arabic countries. 3,000 hours of this data was released for training, and 57 hours for development and testing. The dialect identification data was divided into three sub-categories based on the segment duration: short (under 5 s), medium (5-20 s), and long (>20 s). Overall, 25 teams registered for the challenge, and 9 teams submitted systems for the two tasks. We outline the approaches adopted in each system and summarize the evaluation results.
Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm

Abstract Massive unstructured geoscience data are buried in geological reports. Geological text classification provides opportunities to leverage this wealth of data for geology and mineralization research. Existing studies of massive geoscience documents/reports have not provided effective classification results for further knowledge discovery and data mining and often lack adequate domain‐specific knowledge. In this paper, we present a novel and unified framework (namely, Dic‐Att‐BiLSTM) that combines domain‐specific knowledge and bidirectional long short‐term memory (BiLSTM) for effective geological text classification. Dic‐Att‐BiLSTM benefits from a matching strategy by incorporating domain‐specific knowledge developed based on geoscience ontology to grasp the linguistic geoscience clues. Furthermore, Dic‐Att‐BiLSTM brings together the capacity of a geoscience dictionary matching approach and an attention mechanism to construct a dictionary attention layer. Finally, the network framework of Dic‐Att‐BiLSTM can utilize domain‐specific knowledge and classify geological text automatically. Experimental verifications are conducted on two constructed data sets, and the results clearly indicate that Dic‐Att‐BiLSTM outperforms other state‐of‐the‐art text classification models.
Graph Signal Processing in the Presence of Topology Uncertainties

The goal of this paper is to expand graph signal processing tools to deal with cases where the graph topology is not perfectly known. Assuming that the uncertainty affects only a limited number of edges, we make use of small perturbation analysis to derive closed form expressions instrumental to formulate signal processing algorithms that are resilient to imperfect knowledge of the graph topology. Then, we formulate a Bayesian approach to estimate the presence/absence of uncertain edges based only on the observed data and on the statistics of the data. Finally, we exploit our perturbation analysis to analyze clustering and semi-supervised learning algorithms. Numerical tests confirm the benefits of our perturbation-aware methods.
Dynamic Image-Based Output Feedback Control for Visual Servoing of Multirotors

This article proposes a novel adaptive image-based output feedback visual servoing approach to control a multirotor to the desired pose by using a minimum onboard sensor suite, which consists of an inertial measurement unit and a monocular camera. Different from “perspective moment,” a new type of image feature is designed as “rotated perspective moment,” whose dynamics is independent of roll, pitch, and yaw rates. On this basis, a nonlinear adaptive observer is designed to estimate the scaled linear velocity, which is more accurate, since the observer does not involve noisy angular velocity measurements. Then, a novel image-based output feedback controller is proposed with the designed image features and the observer, wherein the new saturated integral terms of linear and angular velocity errors are introduced into the controller design, respectively, to compensate system uncertainties. As a result, the steady-state error is decreased considerably. In addition, without the assumption of the separation principle between the observer and the controller, the small-angle approximation, or the time-scale separation assumption, the error signals of image features, attitude, velocity, and observer estimation can all converge to the origin asymptotically, which is proven by rigorous Lyapunov analysis. Comparative experiments are conducted to show the superior performance of the proposed approach in terms of more accurate velocity estimation, smaller steady-state errors, and stronger robustness.
Learning Adaptive Neighborhood Graph on Grassmann Manifolds for Video/Image-Set Subspace Clustering

The objective of self-expression based spectral clustering is to learn an affinity matrix which accurately reflects the similarity among data, and the Laplacian constraint is usually exploited to make the affinity matrix preserve the global structure of raw data. However, there exist two drawbacks: firstly, these methods are mostly designed for vectorial data in Euclidean spaces, which are not suitable for multidimensional data with nonlinear manifold structure, e.g., videos and image-sets. Secondly, the clustering performance heavily relies on the quality of a pre-learned Laplacian matrix in which the global structure may be mis-interpreted without considering manifold structures. In this paper, we firstly provide a unified framework about self-expression learning on Grassmann manifolds, which implements the clustering tasks for multidimensional data under subspace views. Then, to assign optimal neighbors to each data depending on the local distance, we adaptively learn the neighborhood relationship from the obtained self-expression coefficient matrix, referred to Learning Adaptive Neighborhood Graph on Grassmann manifolds (GMAN). In the optimization process, the neighborhood relationship can be adaptively learned and updated with the coefficient matrix. The experimental results on five public datasets show that the proposed method is obviously better than many related clustering methods based on Grassmann manifolds, proving the effectiveness of GMAN in multidimensional data clustering.
Data Anonymization Using Pseudonym System to Preserve Data Privacy

Data collection and storage in a large size is done on a routine basis in any company or organization.To this end, wireless network infrastructure and cloud computing are two widely-used tools.With the use of such services, less time is needed to attain the required output, and also managing the jobs will be simpler for users.General services employ a unique identifier for the aim of storing data in a digital database.However, it may be associated with some limitations and challenges.There is a link between the unique identifier and the data holder, e.g., name, address, Identity card number, etc. Attackers can manipulate a unique identifier for stealing the whole data.To get the data needed, attackers may even eavesdrop or guess.It results in lack of data privacy protection.As a result, it is necessary to take into consideration the data privacy issues in any data digital data storage.With the use of current services, there is a high possibility of exposure and leak of data/information to an unauthorized party during their transfer process.In addition, attacks may take place against services; for instance spoofing attacks, forgery attacks, etc. in the course of information transaction.To address such risks, this paper suggests the use of a biometric authentication method by means of a palm vein during the authentication process.Furthermore, a pseudonym creation technique is adopted to make the database record anonymous, which can make sure the data is properly protected.This way, any unauthorized party cannot gain access to data/information.The proposed system can resolve the information leaked, the user true identity is never revealed to others.
A case-based reasoning system for supervised classification problems in the medical field

Case-Based Reasoning (CBR) system relies on reuse for solving new problems. The system uses the experiences it previously acquired and stored into its case base to address the newly faced problems. A static and non-evolutive case base hinders the system and limits the accuracy of the CBR in problem-solving. While a massive case base can affect the resolution time. Randomization represents a way to generate data without deteriorating the spatial image of the case base and by extension the search time as well. However, the cases generated by randomization are not necessarily valid and require a thorough validation process to access their validity. This paper presents a new amplification technique based on randomization for a CBR system incorporating a structured case-base that speeds up case retrieval while supporting case retention. The generated data by randomization is validated through a three-layer validation process: coherence verification, stochastic validation, and absolute validation. Furthermore, we propose a new way to segment the case base along with new similarity functions based on features’ weights to speed CBR retrieval. We carried out experiments on mammography mass and thyroid disease datasets to validate our approach, where the proposed approach is compared to several popular supervised machine-learning methods and other related works that utilize the same datasets. Experiments have shown that our approach can generate relevant data, which significantly improves the resolution accuracy and makes CBR a good competitor to classification methods.
Improved Method for Enhancing Dark Images based on CLAHE and Morphological Reconstruction

The image acquired are often flawed due to various defects from environmental scenarios such as indoor lighting, cloudy weather etc especially at night time. The dark image contains compressed dynamic range that can be enhanced for understanding the detailed information. In this investigative work, an improved method for enhancing extremely dark images is proposed utilizing the concept of illumination reflection model. This improved technique is based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and reconstruction done using morphological processing with Top-hat transformation. The image is perceived in HSV color system and V component is estimated. This intensity (V) component is normalized and the inverse of the intensity component is computed. Then CLAHE algorithm is applied on the negative image. Then multiscale image enhancement is applied to the resultant image. Gamma enhancement is used to adaptively adjust the brightness component of an image. After gamma enhancement, the results of two gamma enhanced images with different gamma values are obtained. Principal Component Analysis extracts the significant information from these images that can be used for image fusion. During the PCA based image fusion, the weight value is adaptively calculated by using morphological Top-hat transformation to improve the quality of the image and illuminate the inconsistent background pixels. The proposed method focuses detail enhancement of extremely dark images by preserving the edges and structures. Experimental validations and results show the better performance of the proposed method than the existing method in terms of qualitative and quantitative measures.
A Voice Interactive Multilingual Student Support System using IBM Watson

Systems powered by artificial intelligence are being developed to be more user-friendly by communicating with users in a progressively “human-like” conversational way. Chatbots, also known as dialogue systems, interactive conversational agents, or virtual agents are an example of such systems used in a wide variety of applications ranging from customer support in the business domain to companionship in the healthcare sector. It is becoming increasingly important to develop chatbots that can best respond to the personalized needs of their users, so that they can be as helpful to the user as possible in a real human way. This paper investigates and compares three popular existing chatbots API offerings and then propose and develop a voice interactive and multilingual chatbot that can effectively respond to users’ mood, tone, and language using IBM Watson Assistant, Tone Analyzer, and Language Translator. The chatbot was evaluated using a use case that was targeted at responding to users’ needs regarding exam stress based on university students survey data generated using Google Forms. The results of measuring the chatbot effectiveness at analyzing responses regarding exam stress indicate that the chatbot responding appropriately to the user queries regarding how they are feeling about exams 76.5%. The chatbot could also be adapted for use in other application areas such as student info-centers, government kiosks, and mental health support systems.
Transductive Zero-Shot Learning for 3D Point Cloud Classification

Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloud classification problem has not been meaningfully explored and introduces new challenges. This paper extends, for the first time, transductive ZeroShot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification. To this end, a novel triplet loss is developed that takes advantage of unlabeled test data. While designed for the task of 3D point cloud classification, the method is also shown to be applicable to the more common use-case of 2D image classification. An extensive set of experiments is carried out, establishing state-of-the-art for ZSL and GZSL in the 3D point cloud domain, as well as demonstrating the applicability of the approach to the image domain. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Multipartite entanglement measure and complete monogamy relation

Although many different entanglement measures have been proposed so far, much less is known in the multipartite case, which leads to the previous monogamy relations in the literature being not complete. We establish here a strict framework for defining the multipartite entanglement measure (MEM): apart from the postulates of the bipartite measure, i.e., vanishing on separable measures and nonincreasing under local operations and classical communication, a complete MEM should additionally satisfy the unification condition and the hierarchy condition. We then come up with a complete monogamy formula for the unified MEM (an MEM is called a unified MEM if it satisfies the unification condition) and a tightly complete monogamy relation for the complete MEM (an MEM is called a complete MEM if it satisfies both the unification condition and the hierarchy condition). Consequently, we propose MEMs which are multipartite extensions of entanglement of formation (EoF), concurrence, tangle, Tsallis $q$ entropy of entanglement, R\'enyi $\ensuremath{\alpha}$ entropy of entanglement, the convex-roof extension of negativity, and negativity. We show that (i) the extensions of EoF, concurrence, tangle, and Tsallis $q$ entropy of entanglement are complete MEMs; (ii) multipartite extensions of R\'enyi $\ensuremath{\alpha}$ entropy of entanglement, negativity, and the convex-roof extension of negativity are unified MEMs but not complete MEMs; and (iii) all these multipartite extensions are completely monogamous, and the ones which are defined by the convex-roof structure (except for the R\'enyi $\ensuremath{\alpha}$ entropy of entanglement and the convex-roof extension of negativity) are not only completely monogamous but also tightly completely monogamous. In addition, as a byproduct, we find a class of states that satisfy the additivity of EoF. We also find a class of tripartite states one part of which can be maximally entangled with the other two parts simultaneously according to the definition of mixed maximally entangled state (MMES) in Li et al. [Z. Li, M. Zhao, S. Fei, H. Fan, and W. Liu, Quantum Inf. Comput. 12, 0063 (2012)]. Consequently, we improve the definition of maximally entangled state (MES) and prove that the only MES is the pure MES.
Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem. In this paper, a novel class imbalance reduction (CIR) algorithm is proposed to create a symmetry between the defect and non-defect records in the imbalance datasets by considering distribution properties of the datasets and is compared with SMOTE (synthetic minority oversampling technique), a built-in package of many machine learning tools that is considered a benchmark in handling class imbalance problems, and with K-Means SMOTE. We conducted the experiment on forty open source software defect datasets from PRedict or Models in Software Engineering (PROMISE) repository using eight different classifiers and evaluated with six performance measures. The results show that the proposed CIR method shows improved performance over SMOTE and K-Means SMOTE.
BACombo—Bandwidth-Aware Decentralized Federated Learning

The emerging concern about data privacy and security has motivated the proposal of federated learning. Federated learning allows computing nodes to only synchronize the locally- trained models instead of their original data in distributed training. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized typologies and large nodes-to-server bandwidths. However, in real-world federated learning scenarios, the network capacities between nodes are highly uniformly distributed and smaller than that in data centers. As a result, how to efficiently utilize network capacities between computing nodes is crucial for conventional federated learning. In this paper, we propose Bandwidth Aware Combo (BACombo), a model segment level decentralized federated learning, to tackle this problem. In BACombo, we propose a segmented gossip aggregation mechanism that makes full use of node-to-node bandwidth for speeding up the communication time. Besides, a bandwidth-aware worker selection model further reduces the transmission delay by greedily choosing the bandwidth-sufficient worker. The convergence guarantees are provided for BACombo. The experimental results on various datasets demonstrate that the training time is reduced by up to 18 times that of baselines without accuracy degrade.