Reviewerly/exHarmony huggingface dataset:
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration

Confidence calibration is of great importance to the reliability of decisions made by machine learning systems. However, discriminative classifiers based on deep neural networks are often criticized for producing overconfident predictions that fail to reflect the true correctness likelihood of classification accuracy. We argue that such an inability to model uncertainty is mainly caused by the closed-world nature in softmax: a model trained by the cross-entropy loss will be forced to classify input into one of K pre-defined categories with high probability. To address this problem, we for the first time propose a novel K+1-way softmax formulation, which incorporates the modeling of open-world uncertainty as the extra dimension. To unify the learning of the original K-way classification task and the extra dimension that models uncertainty, we 1) propose a novel energy-based objective function, and moreover, 2) theoretically prove that optimizing such an objective essentially forces the extra dimension to capture the marginal data distribution. Extensive experiments show that our approach, Energy-based Open-World Softmax (EOW-Softmax), is superior to existing state-of-the-art methods in improving confidence calibration.
Deep neural network approach for a serendipity-oriented recommendation system

Most of the available recommender systems focus on the accuracy of recommendations. As a result, their recommendations are often popular and very close to user preferences, which make them repetitious and predictable, hence adversely affecting user satisfaction. Recent studies on recommender systems, however, aim for factors beyond accuracy as accuracy alone cannot ensure the satisfaction of all users. One of the most important criteria beyond the accuracy is serendipity, which includes relevant, unexpected, and novel recommendations that cannot be easily discovered by users themselves. In this paper, a Convolutional Neural Network (CNN) is integrated with the Particle Swarm Optimization (PSO) algorithm to generate serendipitous recommendations. The proposed method is based on the focus shift points, consisting of unexpectedness and relevance parameters. In this approach, these points are considered as the factors showing whether recommendations are serendipitous. The CNN is employed to predict the focus shift points for each user. Then, the PSO is utilized to search for recommendations close to the predicted focus shift points and generate the list of candidate recommendations. After that, the Serendipitous Personalized Ranking (SPR) method is employed to re-rank the candidate recommendations and generate the final list. According to the evaluation results, the proposed approach outperforms other state-of-the-art methods in SRDP, Hit Ratio, and NDCG factors.
Learning graph attention-aware knowledge graph embedding

The knowledge graph, which utilizes graph structure to represent multi-relational data, has been widely used in the reasoning and prediction tasks, attracting considerable research efforts recently. However, most existing works still concentrate on learning knowledge graph embeddings straightforwardly and intuitively without subtly considering the context of knowledge. Specifically, recent models deal with each single triple independently or consider contexts indiscriminately, which is one-sided as each knowledge unit (i.e., triple) can be derived from its partial surrounding triples. In this paper, we propose a graph-attention-based model to encode entities, which formulates a knowledge graph as an irregular graph and explores a number of concrete and interpretable knowledge compositions by integrating the graph-structured information via multiple independent channels. To measure the correlation between entities from different angles (i.e., entity pair, relation, and structure), we respectively develop three attention metrics. By making use of our enhanced entity embeddings, we further introduce several improved factorization functions for updating relation embeddings and evaluating candidate triples. We conduct extensive experiments on downstream tasks including entity classification, entity typing, and link prediction to validate our methods. Empirical results validate the importance of our introduced attention metrics and demonstrate that our proposed method can improve the performance of factorization models on large-scale knowledge graphs.
A comprehensive survey on the biometric systems based on physiological and behavioural characteristics

With the fast increasing of the electronic crimes and their related issues, deploying a reliable user authentication system became a significant task for both of access control and securing user’s private data. Human biometric characteristics such as voice, finger, iris scanning, face, signature and other features provide a dependable security level for both of the personal and the public use. Many biometric authentication systems have been approached for long time. Due to the uniqueness of human biometrics witch played a master role in degrading imposters’ attacks. Such authentication models have overcome other traditional security methods like passwords and PIN. This paper aims to briefly address the psychological biometric authentication techniques and a brief summary to the advantages, disadvantages of each method. Main contribution it found that used EEG signals, as biometrics is the best technique compare to with five other techniques.
Towards Fair Federated Learning with Zero-Shot Data Augmentation

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models, while having no access to the client data. Although it is recognized that statistical heterogeneity of the client local data yields slower global model convergence, it is less commonly recognized that it also yields a biased federated global model with a high variance of accuracy across clients. In this work, we aim to provide federated learning schemes with improved fairness. To tackle this challenge, we propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity, and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server). Empirical results on a suite of datasets demonstrate the effectiveness of our methods on simultaneously improving the test accuracy and fairness.
Examining wind energy deployment pathways in complex macro-economic and political settings using a fuzzy cognitive map-based method

Long-term sustainable wind energy deployment faces an array of challenges due to various complex interconnected impediment factors. These inherent endogenous and exogenous uncertainties preclude obtaining an accurate future trend, which in turn complicates the design of the good policy. This study employs Fuzzy Cognitive Maps (FCMs) to semi-quantitatively explore the scenarios of wind energy deployment in Iran. The FCM-based framework was built using participatory workshops and a subsequent questionnaire survey to identify 26 influential factors shaping the dynamics of the system. The developed scenarios originated from the latest narratives of real-world geopolitical variations. The findings demonstrated that the sector is governed by six major groupings of factors predominated by economic and political concepts with strong interconnections between them. Five key concepts, including two economic, one legal, and two political, were ascertained that contribute to the stability of the system. Of the four scenarios, only one optimistic trajectory expects an acceleration in the deployment. Another scenario projects that there will not be any considerable change between the future scheme and the current state of development. However, the other scenarios envisage a substantially slower growth than the current trend.
Learning with joint cross-document information via multi-task learning for named entity recognition

In information extraction, named entity recognition (NER) aims to locate named entities in unstructured text and classify them into predefined categories. Most existing methods for NER are sentence-level approaches, which only leverage the context information within a sentence and may cause inconsistent entity prediction. Several researchers have identified latent relationships between sentences in a document and therefore present numerous document-level frameworks that utilize the context information in a document. However, these frameworks cannot establish the correlation between sentences in different documents. To address this problem, we present a cross-document NER model that builds internal relationships for each token among all its multiple occurrences in different documents. A cross-document attention module is designed to calculate the cross-document representations. Furthermore, we add a multiclassification auxiliary task to utilize the coarse-grained entity information. The multi-objective optimization is employed through weighted summation and an autonomous approach utilizing the homoscedastic uncertainty of each task. Extensive experimental results on various data sets clearly demonstrate the effectiveness of our proposed approach, and our model achieves better results than the sentence-level and document-level NER models.
Multi-Relation Attention Network for Image Patch Matching

Deep convolutional neural networks attract increasing attention in image patch matching. However, most of them rely on a single similarity learning model, such as feature distance and the correlation of concatenated features. Their performances will degenerate due to the complex relation between matching patches caused by various imagery changes. To tackle this challenge, we propose a multi-relation attention learning network (MRAN) for image patch matching. Specifically, we propose to fuse multiple feature relations (MR) for matching, which can benefit from the complementary advantages between different feature relations and achieve significant improvements on matching tasks. Furthermore, we propose a relation attention learning module to learn the fused relation adaptively. With this module, meaningful feature relations are emphasized and the others are suppressed. Extensive experiments show that our MRAN achieves best matching performances, and has good generalization on multi-modal image patch matching, multi-modal remote sensing image patch matching and image retrieval tasks.
Toward Top-Down Just Noticeable Difference Estimation of Natural Images

Just noticeable difference (JND) of natural images refers to the maximum pixel intensity change magnitude that typical human visual system (HVS) cannot perceive. Existing efforts on JND estimation mainly dedicate to modeling the diverse masking effects in either/both spatial or/and frequency domains, and then fusing them into an overall JND estimate. In this work, we turn to a dramatically different way to address this problem with a top-down design philosophy. Instead of explicitly formulating and fusing different masking effects in a bottom-up way, the proposed JND estimation model dedicates to first predicting a critical perceptual lossless (CPL) counterpart of the original image and then calculating the difference map between the original image and the predicted CPL image as the JND map. We conduct subjective experiments to determine the critical points of 500 images and find that the distribution of cumulative normalized KLT coefficient energy values over all 500 images at these critical points can be well characterized by a Weibull distribution. Given a testing image, its corresponding critical point is determined by a simple weighted average scheme where the weights are determined by a fitted Weibull distribution function. The performance of the proposed JND model is evaluated explicitly with direct JND prediction and implicitly with three applications including JND-guided noise injection, JND-guided image compression, and distortion detection and discrimination. Experimental results have demonstrated that promising performance of the proposed JND model. The data and code of this work are available at https://github.com/Zhentao-Liu/KLT-JND.
Learning discriminative region representation for person retrieval

Region-level representation learning plays a key role in providing discriminative information for person retrieval. Current methods rely on heuristically coarse-grained region strips or directly borrow pixel-level annotations from pretrained human parsing models for region representation learning. How to learn a discriminative region representation within fine-grained segments while avoiding expensive pixel-level annotations is rarely discussed. To that end, we introduce a novel identity-guided human region segmentation (HRS) method for person retrieval. Via learning a set of distinct region bases that are consistent across a given dataset, HRS can predict informative region segments by grouping intermediate feature vectors based on their similarity to these bases. The predicted segments are iteratively refined for discriminative region representation learning. HRS enjoys two advantages: (1) HRS learns region segmentation using only identity labels, making it a much more practical solution to person retrieval. (2) By jointly learning global appearance and local granularity cues, HRS enables a comprehensive feature representation learning. We verify the effectiveness of the proposed HRS on four challenging benchmark datasets of Market1501, DukeMTMC-reID, CUHK03, and Occluded-DukeMTMC. Extensive experiments demonstrate superior performance over the state-of-the-art region-based methods. For instance, on the CUHK03-labeled dataset, the performance increases from 74.1% mAP and 76.5% rank-1 accuracy to 81.5% (+7.4%) mAP and 83.2% (+6.7%) rank-1 accuracy.
Blockchain as a mean to secure Internet of Things ecosystems – a systematic literature review

Purpose Blockchain is evolving to become a platform for securing Internet of things (IoT) ecosystems. Still, challenges remain. The purpose of this literature review is to highlight the applicability of blockchain as a medium to secure IoT ecosystems. A two-dimensional framework anchored on (1) IoT layers and (2) security goals is used to organize the existent IoT security threats and their corresponding countermeasures identified in the reviewed literature. The framework helped in mapping the IoT security threats with the inherent features of blockchain and accentuate their prominence to IoT security. Design/methodology/approach An approach integrating computerized natural language processing (NLP) with a systematic literature review methodology was adopted. A large corpus of 2,303 titles and abstracts of blockchain articles was programmatically analyzed in order to identify the relevant literature. The identified literature was subjected to a systematic review guided by a well-established method in IS research. Findings The literature evidently highlights the prominence of blockchain as a mean to IoT security due to the distinctive features it encompasses. The authors’ investigation revealed that numerous existent threats are better addressed with blockchain than conventional mechanisms. Nevertheless, blockchain consumes resources such as electricity, time, bandwidth and disk space at a rate that is not yet easily accessible to common IoT ecosystems. Research limitations/implications Results suggest that a configurational approach that aligns IoT security requirements with the resource requirements of different blockchain features is necessary in order to realize the proper balance between security, efficiency and feasibility. Practical implications Practitioners can make use of the classified lists of convention security mechanisms and the IoT threats they address. The framework can help underline the countermeasures that best achieve their security goals. Practitioners can also use the framework to identify the most important features to seek for in a blockchain technology that can help them achieve their security goals. Originality/value This study proposes a novel framework that can help classify IoT threats based on the IoT layer impacted and the security goal at risk. Moreover, it applies a combined man-machine approach to systematically analyze the literature.
A Novel Robust Kalman Filtering Framework Based on Normal-Skew Mixture Distribution

In this article, a novel normal-skew mixture (NSM) distribution is presented to model the normal and/or heavy-tailed and/or skew nonstationary distributed noises. The NSM distribution can be formulated as a hierarchically Gaussian presentation by leveraging a Bernoulli distributed random variable. Based on this, a novel robust Kalman filtering framework can be developed utilizing the variational Bayesian method, where the one-step prediction and measurement-likelihood densities are modeled as NSM distributions. For implementation, several exemplary robust Kalman filters (KFs) are derived based on some specific cases of NSM distribution. The relationships between some existing robust KFs and the presented framework are also revealed. The superiority of the proposed robust Kalman filtering framework is validated by a target tracking simulation example.
The Political Economy of Attention

Attention has become an issue of intense political, economic, and moral concern over recent years: from the commodification of attention by digital platforms to the alleged loss of the attentional capacities of screen-addicted children (and their parents). While attention has rarely been an explicit focus of anthropological inquiry, it has still played an important if mostly tacit part in many anthropological debates and subfields. Focusing on anthropological scholarship on digital worlds and ritual forms, we review resources for colleagues interested in this burgeoning topic of research and identify potential avenues for an incipient anthropology of attention, which studies how attentional technologies and techniques mold human minds and bodies in more or less intentional ways.
Sign Language Recognition Based on Computer Vision

The study of sign language involves the intersection of many fields and disciplines. At present, the two mainstream research directions of sign language recognition are data gloves <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> and visual sign language recognition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> . The former uses the data collected by the sensor for sign language recognition and translation, while the latter uses the camera to capture the user's hand characteristics for sign language recognition and translation. In this paper, an improved convolutional neural network (CNN) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</sup> and long short-term memory(LSTM) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</sup> neural network combined sign language recognition system, which is different from the current only for sign language recognition and translation, but also for sign language generation function is designed. For the first time, this system uses a PyQt designed GUI interface. Once in the system, users can select sign language recognition and translation capabilities, capture images via OpenCV, and then use the trained CNN neural network for special processing. The model can then identify American sign language through LSTM decisions. The user can also click the voice button, the system will be based on the user's voice to convert the corresponding gesture image into the same pixels, and write to the video file. Experimental results show that sign language recognition rate is 95.52% compared with similar algorithms [5], and sign language <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</sup> (American sign language and Arabic numerals) is 90.3%.
Integrating virtual realities and psychotherapy: SWOT analysis on VR and MR based treatments of anxiety and stress-related disorders

The use of virtual reality (VR) and mixed reality (MR) technology in clinical psychology is growing. Efficacious VR-based treatments for a variety of disorders have been developed. However, the field of technology-assisted psychotherapy is constantly changing with the advancement in technology. Factors such as interdisciplinary collaboration, consumer familiarity and adoption of VR products, and progress in clinical science all need to be taken into consideration when integrating virtual technologies into psychotherapies. We aim to present an overview of current expert opinions on the use of virtual technologies in the treatment of anxiety and stress-related disorders. An anonymous survey was distributed to a select group of researchers and clinicians, using an analytic framework known as Strengths, Weaknesses, Opportunities, and Threats (SWOT). Overall, the respondents had an optimistic outlook regarding the current use as well as future development and implementation of technology-assisted interventions. VR and MR psychotherapies offer distinct advantages that can overcome shortcomings associated with traditional therapy. The respondents acknowledged and discussed current limitations of VR and MR psychotherapies. They recommended consolidation of existing knowledge and encouraged standardisation in both theory and practice. Continued research is needed to leverage the strengths of VR and MR to develop better treatments.Abbreviations: AR: Augmented Reality; MR: Mixed Reality; RCT: Randomised Controlled Trial; SWOT: Strengths, Weaknesses, Opportunities, and Threats; VR: Virtual Reality; VR-EBT: Virtual Reality Exposure-Based Therapy
Integrating Citizen Experiences in Cultural Heritage Archives: Requirements, State of the Art, and Challenges

Digital archives of memory institutions are typically concerned with the cataloguing of artefacts of artistic, historical, and cultural value. Recently, new forms of citizen participation in cultural heritage have emerged, producing a wealth of material spanning from visitors’ experiential feedback on exhibitions and cultural artefacts to digitally mediated interactions like the ones happening on social media platforms. Citizen curation is proposed in the context of the European project SPICE (Social Participation, Cohesion, and Inclusion through Cultural Engagement) as a methodology for producing, collecting, interpreting, and archiving people’s responses to cultural objects, with the aim of favouring the emergence of multiple, sometimes conflicting, viewpoints and motivating users and memory institutions to reflect upon them. We argue that citizen curation urges to rethink the nature of computational infrastructures supporting data management of memory institutions, bringing novel challenges that include issues of distribution, authoritativeness, interdependence, privacy, and rights management. To approach these issues, we survey relevant literature toward a distributed, Linked Data infrastructure, with a focus on identifying the roles and requirements involved in such an infrastructure. We show how existing research can contribute significantly in facing the challenges raised by citizen curation and discuss challenges and opportunities from the socio-technical standpoint.