Reviewerly/exHarmony huggingface dataset:
Generative-Discriminative Feature Representations for Open-Set Recognition

We address the problem of open-set recognition, where the goal is to determine if a given sample belongs to one of the classes used for training a model (known classes). The main challenge in open-set recognition is to disentangle open-set samples that produce high class activations from known-set samples. We propose two techniques to force class activations of open-set samples to be low. First, we train a generative model for all known classes and then augment the input with the representation obtained from the generative model to learn a classifier. This network learns to associate high classification probabilities both when image content is from the correct class as well as when the input and the reconstructed image are consistent with each other. Second, we use self-supervision to force the network to learn more informative featues when assigning class scores to improve separation of classes from each other and from open-set samples. We evaluate the performance of the proposed method with recent open-set recognition works across three datasets, where we obtain state-of-the-art results.
Practical and Provably Secure Three-Factor Authentication Protocol Based on Extended Chaotic-Maps for Mobile Lightweight Devices

Due to the limitations of symmetric-key techniques, authentication and key agreement (AKA) protocols based on public-key techniques have attracted much attention, providing secure access and communication mechanism for various application environments. Among these public-key techniques used for AKA protocols, chaotic-map is more effective than scalar multiplication and modular exponentiation, and it offers a list of desirable cryptographic properties such as un-predictability, un-repeatability, un-certainty, and higher efficiency than scalar multiplication and modular exponentiation. Furthermore, it is usually believed that three-factor AKA protocols can achieve a higher security level than single- and two-factor protocols. However, none of existing three-factor AKA protocols can meet all security requirements. One of the most prevalent problems is how to balance security and usability, and particularly how to achieve truly three-factor security while providing password change friendliness. To deal with this problem, in this article we put forward a provably secure three-factor AKA protocol based on extended chaotic-maps for mobile lightweight devices, by adopting the techniques of “Fuzzy-Verifiers” and “Honeywords”. We prove the security of the proposed protocol in the random oracle model, assuming the intractability of extended chaotic-maps Computational Diffie-Hellman problem. We also simulate the protocol by using the AVISPA tool. The security analysis and simulation results show that our protocol can meet all 13 evaluation criteria regarding security. We also assess the performance of our protocol by comparing with seven other related protocols. The evaluation results demonstrate that our protocol offers better balance between security and usability over state-of-the-art ones.
Hotel managers’ perceptions towards the use of robots: a mixed-methods approach

Adopting a supply-side perspective, the paper analyses Bulgarian hotel managers' perceptions of service robots using a convergent mixed methods design. Structured quantitative data were collected from 79 managers using a questionnaire, while interviews were used for the collection of qualitative data from 20 managers. The findings indicate respondents feel that repetitive, dirty, dull, and dangerous tasks in hotels would be more appropriate for robots, while hotel managers would rather use employees for tasks that require social skills and emotional intelligence. The individual characteristics of respondents and the organisational characteristics of the hotels they currently worked in played little role in their perceptions of service robots. The managers considered that robots would decrease the quality of the service and were generally not ready to use robots. Additionally, the interviewees indicated that skilled and well-trained employees were more valuable and more adequate than robots for the hospitality and tourism industry. Theoretical and managerial implications are provided as well.
Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network

Connected and Automated Vehicles (CAVs), owing to their characteristics such as seamless and real-time transfer of data, are imperative infrastructural advancements to realize the emerging smart world. The sensor-generated data are, however, vulnerable to anomalies caused due to faults, errors, and/or cyberattacks, which may cause accidents resulting in fatal casualties. To help in avoiding such situations by timely detecting anomalies, this study proposes an anomaly detection method that incorporates a combination of a multi-stage attention mechanism with a Long Short-Term Memory (LSTM)-based Convolutional Neural Network (CNN), namely, MSALSTM-CNN. The data streams, in the proposed method, are converted into vectors and then processed for anomaly detection. We also designed a method, namely, weight-adjusted fine-tuned ensemble: WAVED, which works on the principle of average predicted probability of multiple classifiers to detect anomalies in CAVs and benchmark the performance of the MSALSTM-CNN method. The MSALSTM-CNN method effectively enhances the anomaly detection rate in both low and high magnitude cases of anomalous instances in the dataset with the gain of up to 2.54% in F-score for detecting different single anomaly types. The method achieves the gain of up to 3.24% in F-score in the case of detecting mixed anomaly types. The experiment results show that the MSALSTM-CNN method achieves promising performance gain for both single and mixed multi-source anomaly types as compared to the state-of-the-art and benchmark methods.
Integrating Machine Learning with Human Knowledge

Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. This paper gives an overview of the knowledge and its representations that can be integrated into machine learning and the methodology. We cover the fundamentals, current status, and recent progress of the methods, with a focus on popular and new topics. The perspectives on future directions are also discussed.
Digital Literacy and Higher Education during COVID-19 Lockdown: Spain, Italy, and Ecuador

Digital literacy constitutes the basis for citizenship in order to be effective and efficient in the 21st Century in professional and personal lives. The set of skills and competences integrating digital literacy are expected to be guaranteed in higher education. During the lockdown globally imposed for the COVID-19 pandemic, educational systems worldwide had to face many disruptive changes. The aim of this research is to present a comparative study of three countries’ higher education institutions (Spain, Italy, and Ecuador), analyzing how they have faced the global lockdown situation, focusing on the development of digital literacy. The methodological approach followed in this study was quantitative with an exploratory-correlational scope using a questionnaire designed ad hoc and applied in a sample of 376 students. Results point the necessity of enhancing the main aspects such as the teacher’s digital skills, sources for learning that may be adapted, communication between universities and students, and teaching methodologies that should be appropriate to the current context. Conclusions may suggest rethinking higher education learning and reinforcing main issues for this transformation, mainly: communication, teaching, and digital competences. Otherwise, digital literacy is not being guaranteed, which means higher education is not accomplishing one of its main objectives.
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature.
Improving Variational Quantum Optimization using CVaR

Hybrid quantum/classical variational algorithms can be implemented on noisy intermediate-scale quantum computers and can be used to find solutions for combinatorial optimization problems. Approaches discussed in the literature minimize the expectation of the problem Hamiltonian for a parameterized trial quantum state. The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically. This procedure is fully justified for quantum mechanical observables such as molecular energies. In the case of classical optimization problems, which yield diagonal Hamiltonians, we argue that aggregating the samples in a different way than the expected value is more natural. In this paper we propose the Conditional Value-at-Risk as an aggregation function. We empirically show -- using classical simulation as well as quantum hardware -- that this leads to faster convergence to better solutions for all combinatorial optimization problems tested in our study. We also provide analytical results to explain the observed difference in performance between different variational algorithms.
Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

Currently, a significant amount of interest is focused on research in the field of Human Activity Recognition (HAR) as a result of the wide variety of its practical uses in real-world applications, such as biometric user identification, health monitoring of the elderly, and surveillance by authorities. The widespread use of wearable sensor devices and the Internet of Things (IoT) has led the topic of HAR to become a significant subject in areas of mobile and ubiquitous computing. In recent years, the most widely-used inference and problem-solving approach in the HAR system has been deep learning. Nevertheless, major challenges exist with regard to the application of HAR for problems in biometric user identification in which various human behaviors can be regarded as types of biometric qualities and used for identifying people. In this research study, a novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented. In order to obtain advanced information regarding users during the performance of various activities, sensory data from tri-axial gyroscopes and tri-axial accelerometers of the wearable devices are applied. Additionally, a set of experiments were shown to validate this work, and the proposed framework’s effectiveness was demonstrated. The results for the two basic models, namely, the Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) deep learning, showed that the highest accuracy for all users was 91.77% and 92.43%, respectively. With regard to the biometric user identification, these are both acceptable levels.
Blockchain technology in the future smart grids: A comprehensive review and frameworks

Development of the smart grids (SGs) has led to many changes in the current power grid structure. Application of new devices, technologies, renewable energy resources, and electric vehicles (EVs) increases the need for decentralized energy management and the data transactions, i.e., the secure and economic transactions are realized through the decentralized networks. The blockchain technology can be used as a solution for these modifications, so a comparative literature review is presented to identify all possible applications of blockchain in SG. The renewable energy development and its interaction with the blockchain technology are investigated, so the blockchain technology is verified as a promising option to increase the renewable energy share. Then, the applications of blockchain technology in SG are categorized in different areas, including the smart contracts and demand response (DR), EVs, the Internet of Things (IoT), the decentralized energy management, the energy trading, the financial transactions, the cybersecurity, the testbeds, and the environmental issues. Therefore, all possible opportunities and challenges of blockchain’s applications in SG are identified. A comprehensive literature review is done to introduce the current improvements related to the blockchain technology’s applications in SG, while the future opportunities and current challenges of blockchain are discussed. This paper aims to present a structure of SG according to the blockchain application and discuss all benefits and drawbacks caused by blockchain in different areas of SG.
Phishing Attacks: A Recent Comprehensive Study and a New Anatomy

With the significant growth of internet usage, people increasingly share their personal information online. As a result, an enormous amount of personal information and financial transactions become vulnerable to cybercriminals. Phishing is an example of a highly effective form of cybercrime that enables criminals to deceive users and steal important data. Since the first reported phishing attack in 1990, it has been evolved into a more sophisticated attack vector. At present, phishing is considered one of the most frequent examples of fraud activity on the Internet. Phishing attacks can lead to severe losses for their victims including sensitive information, identity theft, companies, and government secrets. This article aims to evaluate these attacks by identifying the current state of phishing and reviewing existing phishing techniques. Studies have classified phishing attacks according to fundamental phishing mechanisms and countermeasures discarding the importance of the end-to-end lifecycle of phishing. This article proposes a new detailed anatomy of phishing which involves attack phases, attacker’s types, vulnerabilities, threats, targets, attack mediums, and attacking techniques. Moreover, the proposed anatomy will help readers understand the process lifecycle of a phishing attack which in turn will increase the awareness of these phishing attacks and the techniques being used; also, it helps in developing a holistic anti-phishing system. Furthermore, some precautionary countermeasures are investigated, and new strategies are suggested.
Human- versus Artificial Intelligence

AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence. In order to provide more agreement and to substantiate possible future research objectives, this paper presents three notions on the similarities and differences between human- and artificial intelligence: 1) the fundamental constraints of human (and artificial) intelligence, 2) human intelligence as one of many possible forms of general intelligence, and 3) the high potential impact of multiple (integrated) forms of narrow-hybrid AI applications. For the time being, AI systems will have fundamentally different cognitive qualities and abilities than biological systems. For this reason, a most prominent issue is how we can use (and “collaborate” with) these systems as effectively as possible? For what tasks and under what conditions, decisions are safe to leave to AI and when is human judgment required? How can we capitalize on the specific strengths of human- and artificial intelligence? How to deploy AI systems effectively to complement and compensate for the inherent constraints of human cognition (and vice versa)? Should we pursue the development of AI “partners” with human (-level) intelligence or should we focus more at supplementing human limitations? In order to answer these questions, humans working with AI systems in the workplace or in policy making have to develop an adequate mental model of the underlying ‘psychological’ mechanisms of AI. So, in order to obtain well-functioning human-AI systems, Intelligence Awareness in humans should be addressed more vigorously. For this purpose a first framework for educational content is proposed.
Weakly Supervised Object Localization and Detection: A Survey

As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received significant attention in the past decade. As methods have been proposed, a comprehensive survey of these topics is of great importance. In this work, we review (1) classic models, (2) approaches with feature representations from off-the-shelf deep networks, (3) approaches solely based on deep learning, and (4) publicly available datasets and standard evaluation metrics that are widely used in this field. We also discuss the key challenges in this field, development history of this field, advantages/disadvantages of the methods in each category, the relationships between methods in different categories, applications of the weakly supervised object localization and detection methods, and potential future directions to further promote the development of this research field.
Machine Unlearning

Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because any model trained with said data may have memorized it, putting users at risk of a successful privacy attack exposing their information. Yet, having models unlearn is notoriously difficult.We introduce SISA training, a framework that expedites the unlearning process by strategically limiting the influence of a data point in the training procedure. While our framework is applicable to any learning algorithm, it is designed to achieve the largest improvements for stateful algorithms like stochastic gradient descent for deep neural networks. SISA training reduces the computational overhead associated with unlearning, even in the worst-case setting where unlearning requests are made uniformly across the training set. In some cases, the service provider may have a prior on the distribution of unlearning requests that will be issued by users. We may take this prior into account to partition and order data accordingly, and further decrease overhead from unlearning.Our evaluation spans several datasets from different domains, with corresponding motivations for unlearning. Under no distributional assumptions, for simple learning tasks, we observe that SISA training improves time to unlearn points from the Purchase dataset by 4.63×, and 2.45× for the SVHN dataset, over retraining from scratch. SISA training also provides a speed-up of 1.36× in retraining for complex learning tasks such as ImageNet classification; aided by transfer learning, this results in a small degradation in accuracy. Our work contributes to practical data governance in machine unlearning.
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

This paper presents HITNet, a novel neural network architecture for real-time stereo matching. Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses. To achieve a high level of accuracy, our network not only geometrically reasons about disparities but also infers slanted plane hypotheses allowing to more accurately perform geometric warping and upsampling operations. Our architecture is inherently multi-resolution allowing the propagation of information across different levels. Multiple experiments prove the effectiveness of the proposed approach at a fraction of the computation required by state-of-the-art methods. At the time of writing, HITNet ranks 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> -3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> on all the metrics published on the ETH3D website for two view stereo, ranks 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on most of the metrics amongst all the end-to-end learning approaches on Middlebury-v3, ranks 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on the popular KITTI 2012 and 2015 benchmarks among the published methods faster than 100 ms.
Intra-Inter Camera Similarity for Unsupervised Person Re-Identification

Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by measuring the feature similarity without considering the distribution discrepancy among cameras, leading to degraded accuracy in label computation across cameras. This paper targets to address this challenge by studying a novel intra-inter camera similarity for pseudo-label generation. We decompose the sample similarity computation into two stage, i.e., the intra-camera and inter-camera computations, respectively. The intra-camera computation directly leverages the CNN features for similarity computation within each camera. Pseudo-labels generated on different cameras train the re-id model in a multi-branch network. The second stage considers the classification scores of each sample on different cameras as a new feature vector. This new feature effectively alleviates the distribution discrepancy among cameras and generates more reliable pseudo-labels. We hence train our re-id model in two stages with intra-camera and inter-camera pseudo-labels, respectively. This simple intra-inter camera similarity produces surprisingly good performance on multiple datasets, e.g., achieves rank-1 accuracy of 89.5% on the Market1501 dataset, outperforming the recent unsupervised works by 9+%, and is comparable with the latest transfer learning works that leverage extra annotations.