Reviewerly/exHarmony huggingface dataset:
A compositional semantics for Repairable Fault Trees with general distributions

Fault Tree Analysis (FTA) is a prominent technique in industrial and scientific risk assessment. Repairable Fault Trees (RFT) enhance the classical Fault Tree (FT) model by introducing the possibility to describe complex dependent repairs of system components. Usual frameworks for analyzing FTs such as BDD, SBDD, and Markov chains fail to assess the desired properties over RFT complex models, either because these become too large, or due to cyclic behaviour introduced by dependent repairs. Simulation is another way to carry out this kind of analysis. In this paper we review the RFT model with Repair Boxes as introduced by Daniele Codetta-Raiteri. We present compositional semantics for this model in terms of Input/Output Stochastic Automata, which allows for the modelling of events occurring according to general continuous distribution. Moreover, we prove that the semantics generates (weakly) deterministic models, hence suitable for discrete event simulation, and prominently for rare event simulation using the FIG tool.
SK-MOEFS: A Library in Python for Designing Accurate and Explainable Fuzzy Models

Recently, the explainability of Artificial Intelligence (AI) models and algorithms is becoming an important requirement in real-world applications. Indeed, although AI allows us to address and solve very difficult and complicated problems, AI-based tools act as a black box and, usually, do not explain how/why/when a specific decision has been taken. Among AI models, Fuzzy Rule-Based Systems (FRBSs) are recognized world-wide as transparent and interpretable tools: they can provide explanations in terms of linguistic rules. Moreover, FRBSs may achieve accuracy comparable to those achieved by less transparent models, such as neural networks and statistical models. In this work, we introduce SK-MOEFS (acronym of SciKit-Multi Objective Evolutionary Fuzzy System), a new Python library that allows the user to easily and quickly design FRBSs, employing Multi-Objective Evolutionary Algorithms. Indeed, a set of FRBSs, characterized by different trade-offs between their accuracy and their explainability, can be generated by SK-MOEFS. The user, then, will be able to select the most suitable model for his/her specific application.
Learning Adversarially Robust Representations via Worst-Case Mutual  Information Maximization

Training machine learning models that are robust against adversarial inputs poses seemingly insurmountable challenges. To better understand adversarial robustness, we consider the underlying problem of learning robust representations. We develop a notion of representation vulnerability that captures the maximum change of mutual information between the input and output distributions, under the worst-case input perturbation. Then, we prove a theorem that establishes a lower bound on the minimum adversarial risk that can be achieved for any downstream classifier based on its representation vulnerability. We propose an unsupervised learning method for obtaining intrinsically robust representations by maximizing the worst-case mutual information between the input and output distributions. Experiments on downstream classification tasks support the robustness of the representations found using unsupervised learning with our training principle.
Implicit Kernel Attention

Attention computes the dependency between representations, and it encourages the model to focus on the important selective features. Attention-based models, such as Transformer and graph attention network (GAT), are widely utilized for sequential data and graph-structured data. This paper suggests a new interpretation and generalized structure of the attention in Transformer and GAT. For the attention in Transformer and GAT, we derive that the attention is a product of two parts: 1) the RBF kernel to measure the similarity of two instances and 2) the exponential of L2 norm to compute the importance of individual instances. From this decomposition, we generalize the attention in three ways. First, we propose implicit kernel attention with an implicit kernel function instead of manual kernel selection. Second, we generalize L2 norm as the Lp norm. Third, we extend our attention to structured multi-head attention. Our generalized attention shows better performance on classification, translation, and regression tasks.
Pattern Atlas

Pattern languages are well-established in the software architecture community. Many different aspects of creating a software architecture are addressed by such languages. Thus, several pattern languages have to be considered when building a particular architecture. But these pattern languages are isolated, i.e. it is hard to determine the relevant patterns to be applied from the different pattern languages. Moreover, the sum of patterns from different languages may be huge, i.e. restriction to relevant patterns is desirable. In this contribution we envision an encompassing tool, the pattern atlas, that supports building complex systems based on pattern languages. The analogy to cartography motivates the name of the tool.
Multiobjective Evolutionary Algorithms NSGA-II and NSGA-III for Software Product Lines Testing Optimization

Software Product line (SPL) engineering methodology utilizes reusable components to generate a new system for a specific domain. In fact, the product line establishes requirements, reusable components, architecture, and shared products to develop new products' functionalities. In order to maintain high quality, there is a need for a thorough testing process. Each product in SPL having a different number of features need to be tested. Hence, the testing process of SPL can utilize a multi-objective optimization algorithm to optimize the testing process. This research, reports on the performance of a multi-objective Evolutionary Algorithms Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and NSGA-III on Feature Models (FMs) to optimize SPL testing.
Disentangling User Interest and Popularity Bias for Recommendation with Causal Embedding.

Recommendation models are usually trained on observational data. However, observational data exhibits various bias, such as popularity bias. Biased data results in a gap between online environment and offline evaluation, which makes it difficult to measure the real effect of recommendation. To bridge this gap, most existing methods exploit causal inference to eliminate the bias in historical observational data, e.g., by re-weighting training samples or leveraging a small fraction of unbiased data. However, different causes of an interaction are bundled together as a unified representation in these algorithms. In this paper, we present DICE, a general framework that learns representations where user interest and item popularity are structurally disentangled. We leverage separate embeddings for different causes, and make each embedding capture only one cause by training with cause-specific samples and imposing direct disentanglement supervision. Our algorithm outperforms state-of-the-art baselines with remarkable improvements on two real-world datasets with various backbone models. We further demonstrate that the learned embeddings successfully capture the desired causes.
An Empirical Evaluation of Attention and Pointer Networks for Paraphrase Generation

In computer vision, one of the common practices to augment the image dataset is by creating new images using geometric transformation preserving similarity. This data augmentation was one of the most significant factors for winning the Image Net competition in 2012 with vast neural networks. Unlike in computer vision and speech data, there have not been many techniques explored to augment data in natural language processing (NLP). The only technique explored in the text data is lexical substitution, which only focuses on replacing words by synonyms. In this paper, we investigate the use of different pointer networks with the sequence-to-sequence models, which have shown excellent results in neural machine translation (NMT) and text simplification tasks, in generating similar sentences using a sequence-to-sequence model and the paraphrase dataset (PPDB). The evaluation of these paraphrases is carried out by augmenting the training dataset of IMDb movie review dataset and comparing its performance with the baseline model. To our best knowledge, this is the first study on generating paraphrases using these models with the help of PPDB dataset.
Racial Categorization Methods: A Survey

Face explicitly provides the direct and quick way to evaluate human soft biometric information such as race, age and gender.Race is a group of human beings who differ from human beings of other races with respect to physical or social attributes.Race identification plays a significant role in applications such as criminal judgment and forensic art, human computer interface, and psychology science-based applications as it provides crucial information about the person.However, categorizing a person into respective race category is a challenging task because human faces comprise of complex and uncertain facial features.Several racial categorization methods are available in literature to identify race groups of humans.In this paper, we present a comprehensive and comparative review of these racial categorization methods.Our review covers survey of the important concepts, comparative analysis of single model as well as multi model racial categorization methods, applications, and challenges in racial categorization.Our review provides state-of-the-art technical information concerning racial categorization and hence, will be useful to the research community for development of efficient and robust racial categorization methods.
Vending Vs Lending How Can Public Libraries Improve Access to eBooks within Their Collections?

This paper seeks to make a human rights argument for the accessibility of eBooks in public libraries. Improvements to technology open new opportunities for print-disabled readers among others, and eBooks have become an incredible opportunity for these populations to exercise their human right to information, education, and culture. This paper examines the relationship between public libraries and publishing companies concerning eBooks over the last decade and looks to show a comparison of how the current changes to lending policies made by various publishing companies further affect the ability of public libraries to provide eBooks to their patrons. The responses to these changes by the ALA, library groups, librarians and the public is also compiled as part of this important subject. The purpose of this paper is to give a clear and broad view of how limiting the accessibility of eBooks in public libraries is hindering the human rights of patrons who depend upon that access.
Multiple-Step Randomly Delayed Adaptive Robust Filter With Application to INS/VNS Integrated Navigation on Asteroid Missions

This paper develops a novel nonlinear adaptive robust filter called the multiple-step randomly delayed variational Bayesian adaptive high-degree cubature Huber-based filter (MRD-VBAHCHF) for a class of nonlinear stochastic systems whose measurements are randomly delayed by multiple sampling times and corrupted by contaminated Gaussian noise with unknown covariance. First, a system with randomly delayed measurement is modeled in terms of multiple Bernoulli random variables. Then, the multiple-step randomly delayed high-degree cubature Kalman filter (MRD-HCKF) is derived by employing the fifth-degree cubature rule to compute the mean and covariance of the nonlinear equations in the system model. Next, the MRD-HCKF is modified to the MRD-VBAHCHF by incorporating the variational Bayesian theory and Huber technique for estimating the measurement noise covariance online and suppressing the influence of non-Gaussian noise. Consequently, the proposed filter is not only adaptive to unknown measurement noise statistics but also robust to random measurement delays and non-Gaussian noise. Finally, the MRD-VBAHCHF is verified for use in inertial navigation system/visual navigation system (INS/VNS) integrated navigation on asteroid missions, and the results of Monte Carlo simulations demonstrate that the MRD-VBAHCHF outperforms the high-degree cubature Kalman filter (HCKF), the MRD-HCKF and the variational Bayesian adaptive high-degree cubature Huber-based filter (VBAHCHF), thus showing the superiority of the proposed filter.
Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles. It is important that a visual perception system for navigation purposes identifies obstacles, and it is also important that this system can extract essential information that may influence the vehicle's behavior, whether it will be generating an alert for a human driver or guide an autonomous vehicle in order to be able to make its driving decisions. In this paper we present an approach for the identification of obstacles and extraction of class, position, depth and motion information from these objects that employs data gained exclusively from passive vision. We performed our experiments on two different data-sets and the results obtained shown a good efficacy from the use of depth and motion patterns to assess the obstacles' potential threat status.
Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract)

One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents unique challenges, requiring scalable and transparent models that can account for learner knowledge and content novelty simultaneously, while also retaining accurate learners representations for long periods of time. We attempt to build a novel educational recommender, that relies on an integrative approach combining multiple drivers of learners engagement. Our first step towards this goal is TrueLearn, which models content novelty and background knowledge of learners and achieves promising performance while retaining a human interpretable learner model.
New Results on the SymSum Distinguisher on Round-Reduced SHA3

In ToSC 2017 Saha et al. demonstrated an interesting property of SHA3 based on higher-order vectorial derivatives which led to self-symmetry based distinguishers referred to as SymSum and bettered the complexity w.r.t the well-studied ZeroSum distinguisher by a factor of 4. This work attempts to take a fresh look at this distinguisher in the light of the linearization technique developed by Guo et al. in Asiacrypt 2016. It is observed that the efficiency of SymSum against ZeroSum drops from 4 to 2 for any number of rounds linearized. This is supported by theoretical proofs. SymSum augmented with linearization can penetrate up to two more rounds as against the classical version. In addition to that, one more round is extended by inversion technique on the final hash values. The combined approach leads to distinguishers up to 9 rounds of SHA3 variants with a complexity of only $$2^{64}$$ which is better than the equivalent ZeroSum distinguisher by the factor of 2. To the best of our knowledge this is the best distinguisher available on this many rounds of SHA3.
A Hybrid Face Identification System using a Compressed CNN in a Big Data Environment for Embedded Devices

Convolutional Neural Networks have proved an excellent efficiency in several modern applications, for example, systems of face identification, like VGGFace and DeepFace, have achieved an excellent performance.However, these models still require huge memory for computation that involves an expensive computation cost especially for applications that use huge databases and that are running in embedded devices.To deal with this problem, we propose in this paper a hybrid identification system that is based on the compression of the VGGFace model for the feature extraction step, and on the indexation and the parallelization for the identification task.The proposed system has been evaluated in term of the Rank-1 prediction, the time of identification and the speed-up using two public face databases.Our experimental results illustrate the ability of the proposed system to preserve the performance while keeping a reasonable time of identification compared to two face identification systems based, respectively, on the original VGGFace model and on the Inception V3 model.
Application of Multi-objective Evolutionary Algorithm in E-Commerce Recommendation System

Traditional recommendation methods often have incomplete understandings of recommendation evaluation indicators. They are limited to the accuracy of recommendation, but often ignore the diversity, novelty, coverage and other indicators. Therefore, the recommendation system should use more evaluation indicators to meet personalized demands of users so as to obtain satisfactory results. This is closely related to the problem of multi-objective optimization. This paper describes the mathematical expression of the multi-objective optimization problem and uses NSGA-II algorithm to test its accuracy and diversity indexes of Netflix dataset. The experimental results show that NSGA-II algorithm can meet multi-objective requirements of the recommendation system and the experimental results are satisfactory.
DomainScouter: Analyzing the Risks of Deceptive Internationalized Domain Names

Internationalized domain names (IDNs) are abused to create domain names that are visually similar to those of legitimate/popular brands. In this work, we systematize such domain names, which we call deceptive IDNs, and analyze the risks associated with them. In particular, we propose a new system called DomainScouter to detect various deceptive IDNs and calculate a deceptive IDN score, a new metric indicating the number of users that are likely to be misled by a deceptive IDN. We perform a comprehensive measurement study on the identified deceptive IDNs using over 4.4 million registered IDNs under 570 top-level domains (TLDs). The measurement results demonstrate that there are many previously unexplored deceptive IDNs targeting non-English brands or combining other domain squatting methods. Furthermore, we conduct online surveys to examine and highlight vulnerabilities in user perceptions when encountering such IDNs. Finally, we discuss the practical countermeasures that stakeholders can take against deceptive IDNs.
Identification of exchange rate shocks with compositionaldata and written press

The evolution of exchange rates results from events that affect different countries differently, that is, asymmetric shocks. Being value ratios, exchange rates constitute what is known as compositional data. The compositional data analysis methodology transforms exchange rates in a way that ensures the validity of their subsequent statistical analysis. In this paper we submit the daily exchange rates of the US dollar, the yen, the pound sterling, the euro, the Brazilian real, and the yuan to transformation by centred log-ratios. We then use the residuals from a Box-Jenkins analysis to estimate shocks and represent them in statistical control charts for a simple visual identification of both significant revaluations and devaluations, and periods of greatest volatility, which we then relate to news stories in the written press.
Twelve dilemmas of reform in cyber security education

This final chapter contrasts two broad approaches to the challenges of education policy for cyber security: human capital and workforce development. The former is seated in a marriage between the idea of individual self-improvement through education and its economic effects on the individual, community, corporation or country. This concept has a very broad scope. In contrast, workforce development is a preoccupation largely of corporation or country. As a concept, it is much more narrowly focused than the idea of human capital. The purpose of making this contrast is to help situate the debates about cyber security workforce development. Against that background, the chapter presents twelve dilemmas that seem common to all countries grappling with choices about cyber security education.
Multimodal Coexistence Environment Design to Assist User Testing and Iterative Design of HiGame Emotional Interaction Design for Elderly

Usability testing is an important process in human-computer interaction and user experience design. It can help designers understand the difficult points of the user in the interaction process. Whether the interactive situation is correctly presented in the test process and whether the designer has correctly received the feedback from the subject will affect the accuracy of the test. With the rapid development of the IoT in recent years, more and more interactive designs are multimodal models. When performing traditional usability test, iterative design processes often take a lot of cost to fully present the interactive situation for the subject to experience. And because there are many kinds of adjustable elements, the subjects are also more complicated and time-consuming to communicate with the designer to describe the feelings. Therefore, this study takes the multi-object HiGame elderly interaction design as an example. Using MR technology combined with 3D printing in the coexistence environment to assist HiGame interactive design for user testing and iterative design. The virtual prototyping combined with some physical tactile methods gives the subject a more complete experience. With Hololens as the virtual body interactive medium, the subject can co-design with the designer in a dynamic feedback manner at the same time. To enable the elderly subject to achieve the highest efficiency, communicate with the designer in real time through dynamic feedback. At the same time, it helps designers reduce the time and cost required for the iterative design process of multi-object interactive testing.
Multi-Sensor Integration for Key-Frame Extraction From First-Person Videos

Key-frame extraction for first-person vision (FPV) videos is a core technology for selecting important scenes and memorizing impressive life experiences in our daily activities. The difficulty of selecting key frames is the scene instability caused by head-mounted cameras used for capturing FPV videos. Because head-mounted cameras tend to frequently shake, the frames in an FPV video are noisier than those in a third-person vision (TPV) video. However, most existing algorithms for key-frame extraction mainly focus on handling the stable scenes in TPV videos. The technical development of key-frame extraction techniques for noisy FPV videos is currently immature. Moreover, most key-frame extraction algorithms mainly use visual information from FPV videos, even though our visual experience in daily activities is associated with human motions. To incorporate the features of dynamically changing scenes in FPV videos into our methods, integrating motions with visual scenes is essential. In this paper, we propose a novel key-frame extraction method for FPV videos that uses multi-modal sensor signals to reduce noise and detect salient activities via projecting multi-modal sensor signals onto a common space by canonical correlation analysis (CCA). We show that the two proposed multi-sensor integration models for key-frame extraction (a sparse-based model and a graph-based model) work well on the common space. The experimental results obtained using various datasets suggest that the proposed key-frame extraction techniques improve the precision of extraction and the coverage of entire video sequences.