Reviewerly/exHarmony huggingface dataset:
Fully Homomorphic Encryption based Privacy-Preserving Data Acquisition and Computation for Contact Tracing

For public health surveillance systems, privacy is a major issue in storing and sharing of personal medical data. Often, patients and organizations are unwilling to divulge personal medical data for fear of compromising their privacy because although the data may be encrypted, the encrypted values typically need to be first decrypted to perform any computation on the data. Unfortunately, such a barrier in easy sharing of data can severely hamper the ability to respond in a timely and effective manner to a crisis scenario, as evident in the case of the ongoing COVID-19 pandemic. To overcome this critical obstacle, we propose in this paper a novel privacy-preserving encryption mechanism for storage and computation of sensitive healthcare data. Our scheme is based on the use of a secure fully homomorphic encryption scheme, so that the required computations can be performed directly on the encrypted data values without the need for any decryption. The ability to execute queries or computation directly on encrypted data, without the need for decryption, is not present in any existing public-health surveillance system. We propose a novel computational model and also develop an algorithm for contact tracing with COVID-19 pandemic as a case study. We have simulated our proposed approach using the ElGamal encryption algorithm to check the correctness and effectiveness of our proposed approach. The results show that our proposed solution is effective in providing adequate security while supporting the computational needs for contact-tracing. Besides contact-tracing, our new data-encryption technique can have a much broader impact in the field of healthcare. By executing queries or computations directly on encrypted data, our innovative solution would make the sharing of data in healthcare-related research and industry significantly simpler and faster. The use of such a data encryption scheme to store and transmit sensitive healthcare data over a network can not only allay the fear of compromising sensitive information but also ensure HIPAA-compliance.
A game-theoretical model of firm security reactions responding to a strategic hacker in a competitive industry

The tendency of strategic hackers to attack specific industries brings new challenges for information security management. This paper examines the interaction between firms in a specific industry and a strategic hacker by considering industry-specific characteristics including the intrinsic vulnerability, intentions of the hacker, competition between firms, and similarity of security technologies. We find that firms in an overly dangerous industry should consider reforming their business mode to reduce the intrinsic vulnerability rather than investing heavily in security protection. Moreover, we distinguish the hacker as profit-seeking and fame-seeking and find that different intentions generate different hacker's behaviour. Furthermore, keep exerting effort is still a better strategy for the firms when the competition becomes more intense even the threat of the hacker reduces. Besides, the technical similarity enhances the hacker's incentive to exert attack effort while induces a free-riding problem for competitive firms. Accordingly, we introduce a social planner to regulate the security decisions of competitive firms, and identify that the supervision of a social planner could partly alleviate the free-riding behaviour, but will only be accepted by competitive firms when facing a less or highly competitive environment. Our results imply that introducing a social planner to enforce security protection may not be advisable for all industries. Finally, we extend our model to discuss two additional cases, including the case of sequential game and the case of asymmetric condition.
Visual attention prediction for Autism Spectrum Disorder with hierarchical semantic fusion

Visual attention for the diagnosis of Autism Spectrum Disorder (ASD) which is a kind of mental disorder has attracted the interests of increasing number of researchers. Although multiple visual attention prediction models have been proposed, this problem is still open. In this paper, considering the shift of visual attention, we propose that an image can be viewed as a pseudo sequence. Besides, we propose a novel visual attention prediction method for ASD with hierarchical semantic fusion (ASD-HSF). Specifically, the proposed model mainly contains a Spatial Feature Module (SFM) and a Pseudo Sequential Feature Module (PSFM). SFM is designed to extract spatial semantic features with a fully convolutional network, while PSFM implemented by two Convolutional Long Short-Term Memory networks (ConvLSTMs) is applied to learn pseudo sequential features. And the outputs of these two modules are fused to extract the final saliency map which simultaneously includes spatial semantic information and pseudo sequential information. Experimental results show that the proposed model not only outperforms ten state-of-the-art general saliency prediction counterparts, but also reaches the first and the second ranks under four metrics and the rest ones of ASD saliency prediction respectively.
Improving eye movement biometrics in low frame rate eye-tracking devices using periocular and eye blinking features

In this paper, the biometric potential of eye movement patterns extracted from low frame rate eye-tracking devices is evaluated. Also, possible improvement in recognition rates is investigated using other static and dynamic features extracted from the eyes including eye blinking patterns and periocular shape features. These modalities can be applicable for specific biometric applications like continuous driver authentication for law enforcement. For this purpose, two databases are collected with two low frame rate eye-tracking systems that capture the eye movements. Data were recorded from 55 participants while watching real driving sessions. For eye gaze, features from fixations and saccades are extracted separately including duration, amplitude, and statistical features. For eye blinking, features from the blinking pattern, its speed, acceleration, and power per unit mass profiles are extracted. Periocular features include the eye-opening height, width, axial ratio, etc. Each modality is evaluated first, then, these modalities are combined in a multi-modal setup for performance improvement. While each trait achieved a moderate performance in a single-modality setup, the fusion of the static and the dynamic features from the eye provides a great performance improvement up to 98.5% recognition rate and 0% error rate in both modes of authentication. Although the single-modality setup might not be secure enough, the fusion of these traits achieves high levels of identification making these traits effective for continuous driver authentication application.
Use of a domain-specific ontology to support automated document categorization at the concept level: Method development and evaluation

Voluminous, conveniently accessible textual documents, created and disseminated by modern information technology, makes automated document organization increasingly important for both individuals and organizations. Many existing techniques rely on document content analysis that classifies new, unlabeled documents by examining the similarity based on the overlap between their important features and the representative features of each document category. However, the performance of feature-based techniques can be significantly hindered by word mismatch and ambiguity problems. As a remedy, this study takes a concept-based approach and propose a text categorization method that incorporates a domain-specific ontology to support automated document categorization more effectively. The proposed method classifies documents according to their respective range of relevant concepts. We empirically evaluate our method versus several prevalent benchmarks that include feature-based k-nearest neighbors (kNN) and semantic-based techniques. The results show the proposed method more effective than the benchmark techniques; it achieves better performances when using a complete concept hierarchy without considering the hierarchical relationships among concepts. The proposed method illustrates how to incorporate a domain-specific ontology to improve document classification. Our method is computationally efficient because it produces a concept space of relatively few dimensionalities and does not require semantic space reconstruction as new documents arrive. Moreover, the relationships and patterns for classifying documents, generated by our method, are explicit and comprehensible.
Learn-Prune-Share for Lifelong Learning

In lifelong learning, we wish to maintain and update a model (e.g., a neural network classifier) in the presence of new classification tasks that arrive sequentially. In this paper, we propose a learn-prune-share (LPS) algorithm which addresses the challenges of catastrophic forgetting, parsimony, and knowledge reuse simultaneously. LPS splits the network into task-specific partitions via an ADMM-based pruning strategy. This leads to no forgetting, while maintaining parsimony. Moreover, LPS integrates a novel selective knowledge sharing scheme into this ADMM optimization framework. This enables adaptive knowledge sharing in an end-to-end fashion. Comprehensive experimental results on two lifelong learning benchmark datasets and a challenging real world radio frequency fingerprinting dataset are provided to demonstrate the effectiveness of our approach. Our experiments show that LPS consistently outperforms multiple state-of-the-art competitors.
An enhanced cosine-based visual technique for the robust tweets data clustering

Purpose The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents, which is useful for achieving the robust tweets data clustering results. Design/methodology/approach Let “ N ” be the number of tweets documents for the topics extraction. Unwanted texts, punctuations and other symbols are removed, tokenization and stemming operations are performed in the initial tweets pre-processing step. Bag-of-features are determined for the tweets; later tweets are modelled with the obtained bag-of-features during the process of topics extraction. Approximation of topics features are extracted for every tweet document. These set of topics features of N documents are treated as multi-viewpoints. The key idea of the proposed work is to use multi-viewpoints in the similarity features computation. The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents (here N = 5) and corresponding documents are defined in projected space with five viewpoints, say, v 1 , v 2 , v 3 , v 4 , and v 5 . For example, similarity features between two documents (viewpoints v 1 , and v 2 ) are computed concerning the other three multi-viewpoints ( v 3 , v 4 , and v 5 ), unlike a single viewpoint in traditional cosine metric. Findings Healthcare problems with tweets data. Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding term frequency and inverse document frequency (TF–IDF) for unlabelled tweets. Originality/value Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding TF-IDF for unlabelled tweets.
A Subject-Centric Credential Management Method based on the Verifiable Credentials

In this paper, a subject-centric structure is proposed that improves the holder-centric structural problems of verifiable credentials developed for self-sovereign identities. Holder-Centric structured verifiable credentials represent a structure in which a holder can control the credentials even if it is not a subject. This structure allows the holder to attempt authentication or transfer credentials without the subject's permission. The subject may lose some control over the credential, thus losing the meaning of self-sovereign identity. We propose a subject-centric structure that allows the subject to control over the transferred verifiable credentials.
Cone-KG: A Semantic Knowledge Graph with News Content and Social Context for Studying Covid-19 News Articles on Social Media

Semantic knowledge graphs provide very significant benefits for structuring and analysing huge amounts of aggregated data across diverse heterogeneous sources.Beyond quick and efficient data query and analysis, they facilitate inference from data and generation of insights for several purposes.With the multi-faceted global challenges posed by the COVID-19 pandemic, this research focused on the use of a semantic knowledge graph to model, structure and store COVID-related news articles centrally and semantically towards knowledge discovery, knowledge acquisition and advanced data analytics for understanding varying metrics relating to the virus towards curbing its spread.The semantic knowledge graph provides a platform for researchers, data analysts and data scientists across societal sectors to investigate and recommend strategies towards addressing the challenges it poses to the global society.
EduRecomSys: An Educational Resource Recommender System Based on Collaborative Filtering and Emotion Detection

Abstract Due to the large amount of data that is available on the Web, it has become increasingly difficult to locate educational resources that satisfy specific learning needs. Furthermore, the searching process can become increasingly frustrating, time-consuming and little accurate when users do not know how to perform a search. Recommender systems aim at reducing this burden by predicting and recommending users relevant elements of interest. In the educational domain, recommender systems can take advantage of user cognitive states and emotions to generate more personalized recommendations. This work proposes EduRecomSys, an educational recommender system that combines collaborative filtering with emotion detection techniques to suggest users educational resources based on the preferences/interests of other users and the user’s emotion previously detected through face recognition technologies. Likewise, EduRecomSys allows users to retrieve educational resources from multiple sources, including social networks, linked data and learning object repositories. EduRecomSys was evaluated in qualitative and quantitative terms. The qualitative evaluation relied on the participation of three domain experts: a teacher, a pedagogue and a software engineer. The quantitative evaluation was conducted with the help of 20 graduate students. The evaluation results seem encouraging and suggest that EduRecomSys has the potential to provide effective support to the teaching-learning process.
A Novel Diminish Smooth L1 Loss Model with Generative Adversarial Network

The training process of GAN can be regarded as a process in which the generation network and the identification network play against each other and finally reach a state where it cannot be further improved if the opponent does not change. At the same time, the start of the gradient descent method will choose a direction to reduce the defined loss. The loss function plays a key role in the performance of the model. Choosing the right loss function can help your model learn how to focus on the correct set of features in the data to achieve optimal and faster convergence. In this work, we propose a novel loss function scheme, namely, Diminish Smooth L1 loss. We improve a robust L1 loss called Smooth L1 loss by lowering the threshold so that the network can converge to a lower minimum. From our experimental results on several benchmark data, we found that our algorithm often outperforms the previous approaches.
A Hybrid Approach for 6DoF Pose Estimation

We propose a method for 6DoF pose estimation of rigid objects that uses a state-of-the-art deep learning based instance detector to segment object instances in an RGB image, followed by a point-pair based voting method to recover the object's pose. We additionally use an automatic method selection that chooses the instance detector and the training set as that with the highest performance on the validation set. This hybrid approach leverages the best of learning and classic approaches, using CNNs to filter highly unstructured data and cut through the clutter, and a local geometric approach with proven convergence for robust pose estimation. The method is evaluated on the BOP core datasets where it significantly exceeds the baseline method and is the best fast method in the BOP 2020 Challenge.
A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

Abstract With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.
Development of some techniques for solving system of linear and nonlinear equations via hybrid algorithm

Abstract The objective of this article is to introduce several new methods or techniques for solving simultaneous linear and nonlinear system of equations with the help of a new hybrid algorithm based on advanced quantum behaved particle swarm optimization and the concept of binary tournamenting process. Depending on different options of binary tournamenting, six different variants of hybrid algorithms are proposed. To examine the effectiveness of the proposed hybrid algorithms five well known benchmark bound‐constrained optimization problems are solved. Among the six different variants of hybrid algorithms, the best algorithm is selected on the basis of their performances in these problems. This best algorithm is then applied in solving simultaneous linear and nonlinear system of equations transforming these equations into optimization problems. In case of linear system, just two systems are solved while in case of nonlinear system seven complicated problems are solved and finally a comparison of best found solutions are estimated with the same of existing algorithms provided in the literature.
Key-Size-Driven Wavelength Resource Sharing Scheme for QKD and the Time-Varying Data Services

Integrating quantum key distribution (QKD) into the optical network through wavelength division multiplex (WDM) is promising for establishing large-scale QKD networks. However, the existing wavelength assignment schemes for WDM-QKD network all require to reserve a dedicate waveband for quantum signals, which influences the provisioning of data services, especially when the resource is tight. In this article, considering the quantum key pool (QKP) technology in the current QKD network, we propose a flexible key-size-driven wavelength assignment (KSD-WA) scheme to recycle the wavelength fragment to transmit quantum signals. Additionally, considering the noise impacts and the limited reconfiguration operations, we propose a heuristic algorithm to optimize the fragment selection with the objective of maximizing the secure key rate in the dynamic scenario. We also design a deep reinforcement learning (DeepRL) based algorithm to automatically learn the optimization policy under different environments. The simulation results verify that comparing with the conventional fixed-band (FB) scheme, the KSD-WA scheme assisted by the proposed optimization algorithms can provide adequate keys for encryption without affecting the provisioning of coexisting data services, which improves the compatibility of QKD with the real-life optical networks.
Fast Privacy-Preserving Text Classification Based on Secure Multiparty Computation

We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification.In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier.At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing.Our solution is based on Secure Multiparty Computation (SMC).Our Rust implementation provides a fast and secure solution for the classification of unstructured text.Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340 ms in the case where the dictionary size of Bob's model includes all words (n = 5200) and Alice's SMS has at most m = 160 unigrams.In the case with n = 369 and m = 8 (the average of a spam SMS in the database), our solution takes only 21 ms.
Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification

Being able to learn discriminative features from low-quality images has raised much attention recently due to their wide applications ranging from autonomous driving to safety surveillance. However, this task is difficult due to high variations across images, such as scale, rotation, illumination, and viewpoint, and distortions in images, such as blur, low contrast, and noise. Image preprocessing could improve the quality of the images, but it often requires human intervention and domain knowledge. Genetic programming (GP) with a flexible representation can automatically perform image preprocessing and feature extraction without human intervention. Therefore, this study proposes a new evolutionary learning approach using GP (EFLGP) to learn discriminative features from images with blur, low contrast, and noise for classification. In the proposed approach, we develop a new program structure (individual representation), a new function set, and a new terminal set. With these new designs, EFLGP can detect small regions from a large input low-quality image, select image operators to process the regions or detect features from the small regions, and output a flexible number of discriminative features. A set of commonly used image preprocessing operators is employed as functions in EFLGP to allow it to search for solutions that can effectively handle low-quality image data. The performance of EFLGP is comprehensively investigated on eight datasets of varying difficulty under the original (clean), blur, low contrast, and noise scenarios, and compared with a large number of benchmark methods using handcrafted features and deep features. The experimental results show that EFLGP achieves significantly better or similar results in most comparisons. The results also reveal that EFLGP is more invariant than the benchmark methods to blur, low contrast, and noise.