Reviewerly/exHarmony huggingface dataset:
Constructing Arabic Reading Comprehension Datasets: Arabic WikiReading and KaifLematha

Neural machine reading comprehension models have gained immense popularity over the last decade given the availability of large-scale English datasets. A key limiting factor for neural model development and investigations of the Arabic language is the limitation of the currently available datasets. Current available datasets are either too small to train deep neural models or created by the automatic translation of the available English datasets, where the exact answer may not be found in the corresponding text. In this paper, we propose two high quality and large-scale Arabic reading comprehension datasets: Arabic WikiReading and KaifLematha with around +100 K instances. We followed two different methodologies to construct our datasets. First, we employed crowdworkers to collect non-factoid questions from paragraphs on Wikipedia. Then, we constructed Arabic WikiReading following a distant supervision strategy, utilizing the Wikidata knowledge base as a ground truth. We carried out both quantitative and qualitative analyses to investigate the level of reasoning required to answer the questions in the proposed datasets. We evaluated competitive pre-trained language model that attained F1 scores of 81.77 and 68.61 for the Arabic WikiReading and KaifLematha datasets, respectively, but struggled to extract a precise answer for the KaifLematha dataset. Human performance reported an F1 score of 82.54 for the KaifLematha development set, which leaves ample room for improvement.
Local unitary classes of states invariant under permutation subgroups

The study of entanglement properties of multiqubit states that are invariant under permutations of qubits is motivated by potential applications in quantum computing, quantum communication, and quantum metrology. In this work we generalize the notions of symmetrization, Dicke states, and the Majorana representation to the alternating, cyclic, and dihedral subgroups of the full group of permutations. We use these tools to characterize states that are invariant under these subgroups and analyze their entanglement properties.
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center

This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods. Conventional heuristic solutions like Weighted-Cost Multi-Path (WCMP) and Local Shortest Queue (LSQ) are less flexible to the changing workload distributions and arrival rates, with a poor balance among multiple load balancers. The cooperative network load balancing task is formulated as a Dec-POMDP problem, which naturally induces the MARL methods. To bridge the reality gap for applying learning-based methods, all models are directly trained and evaluated on a real-world system from moderate- to large-scale setups. Experimental evaluations show that the independent and "selfish'' load balancing strategies are not necessarily the globally optimal ones, while the proposed MARL solution has a superior performance over different realistic settings. Additionally, the potential difficulties of the application and deployment of MARL methods for network load balancing are analysed, which helps draw the attention of the learning and network communities to such challenges.
On Codes and Learning with Errors over Function Fields

It is a long standing open problem to find search to decision reductions for structured versions of the decoding problem of linear codes. Such results in the lattice-based setting have been carried out using number fields: Polynomial–LWE, Ring– $$\textsf{LWE}$$ , Module– $$\textsf{LWE}$$ and so on. We propose a function field version of the $$\textsf{LWE}$$ problem. This new framework leads to another point of view on structured codes, e.g. quasi-cyclic codes, strengthening the connection between lattice-based and code-based cryptography. In particular, we obtain the first search to decision reduction for structured codes. Following the historical constructions in lattice–based cryptography, we instantiate our construction with function fields analogues of cyclotomic fields, namely Carlitz extensions, leading to search to decision reductions on various versions of Ring- $$\textsf{LPN}$$ , which have applications to secure multiparty computation and to an authentication protocol.
Span-based model for overlapping entity recognition and multi-relations classification in the food domain

Information extraction (IE) is an important part of the entire knowledge graph lifecycle. In the food domain, extracting information such as ingredient and cooking method from Chinese recipes is crucial to safety risk analysis and identification of ingredient. In comparison with English, due to the complex structure, the richness of information in word combination, and lack of tense, Chinese IE is much more challenging. This dilemma is particularly prominent in the food domain with high-density knowledge, imprecise syntactic structure. However, existing IE methods focus only on the features of entities in a sentence, such as context and position, and ignore features of the entity itself and the influence of self attributes on prediction of inter entity relationship. To solve the problems of overlapping entity recognition and multi-relations classification in the food domain, we propose a span-based model known as SpIE for IE. The SpIE uses the span representation for each possible candidate entity to capture span-level features, which transforms named entity recognition (NER) into a classification mission. Besides, SpIE feeds extra information about the entity into the relation classification (RC) model by considering the effect of entity's attributes (both the entity mention and entity type) on the relationship between entity pairs. We apply SpIE on two datasets and observe that SpIE significantly outperforms the previous neural approaches due to capture the feature of overlapping entity and entity attributes, and it remains very competitive in general IE.
Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images

Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convolution of the original image with the associated point spread function, along with additive noise. However, the blurred image often contains inadequate information to uniquely determine the plausible original image. Based on the availability of blurring information, image deblurring methods can be classified as blind and non-blind. In non-blind image deblurring, some prior information is known regarding the corresponding point spread function and the added noise. The objective of this study is to determine the effectiveness of non-blind image deblurring methods with respect to the identification and elimination of noise present in blurred images. In this study, three non-blind image deblurring methods, namely Wiener deconvolution, Lucy-Richardson deconvolution, and regularized deconvolution were comparatively analyzed for noisy images featuring salt-and-pepper noise. Two types of blurring effects were simulated, namely motion blurring and Gaussian blurring. The said three non-blind deblurring methods were applied under two scenarios: direct deblurring of noisy blurred images and deblurring of images after denoising through the application of the adaptive median filter. The obtained results were then compared for each scenario to determine the best approach for deblurring noisy images.
FreSCo: Mining Frequent Patterns in Simplicial Complexes

Simplicial complexes are a generalization of graphs that model higher-order relations. In this paper, we introduce simplicial patterns —that we call simplets— and generalize the task of frequent pattern mining from the realm of graphs to that of simplicial complexes. Our task is particularly challenging due to the enormous search space and the need for higher-order isomorphism. We show that finding the occurrences of simplets in a complex can be reduced to a bipartite graph isomorphism problem, in linear time and at most quadratic space. We then propose an anti-monotonic frequency measure that allows us to start the exploration from small simplets and stop expanding a simplet as soon as its frequency falls below the minimum frequency threshold. Equipped with these ideas and a clever data structure, we develop a memory-conscious algorithm that, by carefully exploiting the relationships among the simplices in the complex and among the simplets, achieves efficiency and scalability for our complex mining task. Our algorithm, FreSCo, comes in two flavors: it can compute the exact frequency of the simplets or, more quickly, it can determine whether a simplet is frequent, without having to compute the exact frequency. Experimental results prove the ability of FreSCo to mine frequent simplets in complexes of various size and dimension, and the significance of the simplets with respect to the traditional graph patterns.
“Merging Results Is No Easy Task”: An International Survey Study of Collaborative Data Analysis Practices Among UX Practitioners

Analysis is a key part of usability testing where UX practitioners seek to identify usability problems and generate redesign suggestions. Although previous research reported how analysis was conducted, the findings were typically focused on individual analysis or based on a small number of professionals in specific geographic regions. We conducted an online international survey of 279 UX practitioners on their practices and challenges while collaborating during data analysis. We found that UX practitioners were often under time pressure to conduct analysis and adopted three modes of collaboration: independently analyze different portions of the data and then collaborate, collaboratively analyze the session with little or no independent analysis, and independently analyze the same set of data and then collaborate. Moreover, most encountered challenges related to lack of resources, disagreements with colleagues regarding usability problems, and difficulty merging analysis from multiple practitioners. We discuss design implications to better support collaborative data analysis.
Deep Learning-Based Yoga Learning Application

Yoga has been into practice for thousands of years, and in today’s era, it is highly suggested for people to practice it to revitalize them in their physical and mental wellness. Although yoga is considered safe, there are several asanas that need to be practiced under the guidance and supervision of a trained instructor. Using human action recognition techniques for yoga pose estimation, we have tested and designed an automated yoga asana identification and correction system that performs well in the absence of a trained instructor. The system identifies yoga asana in real time with the help of deep learning techniques based on convolutional neural networks (CNN) and transfer learning for pose identification and corrects the user for right yoga posture using multi-person 2D pose estimation algorithm, OpenPose. The system is designed for 18 different asanas which the users can practice. The system can predict an asana with over 87.6% accuracy considering the fact that user can face the camera with multiple different views while practicing yoga, namely left-hand side view, right-hand side view or front view. The system corrects the user by showing the direction in which they should move the body part which is not in the correct position in real time.
Real-Time Digital Video Stabilization of Bioinspired Robotic Fish Using Estimation-and-Prediction Framework

The rhythmic movement of bioinspired robotic fish brings about undesirable visual jitter. Note that the unstable camera path of this kind of robot is characterized by obvious regularity and predictability, of which traditional stabilization methods have not made full advantage. This article proposes a novel estimation-and-prediction framework for real-time digital video stabilization of bioinspired robotic fish. First, based on the attitude information of an inertial measurement unit (IMU), a camera–IMU model is established, where the homography transformation with eight degrees of freedom (DOFs) is reduced to translation transformation with two DOFs. Second, traditional optical flow and gray projection methods as well as a novel translation estimation network are employed to estimate the translations between consecutive frames. Third, a lightweight long short-term memory (LSTM) network is constructed, allowing remarkable prediction and smoothing of the camera path. Finally, aquatic experiments under various scenarios are conducted on a manta-inspired robot, demonstrating the effectiveness of the proposed method. Specifically, compared with the state-of-the-art commercial offline stabilization software, our online stabilization algorithm achieves approximate visual stability and remarkably faster stabilization speed. The obtained results shed light on visual sensing and control applications of bioinspired underwater vehicles.
Using a GAN to Generate Adversarial Examples to Facial Image Recognition

Images posted online present a privacy concern in that they may be used as reference examples for a facial recognition system. Such abuse of images is in violation of privacy rights but is difficult to counter. It is well established that adversarial example images can be created for recognition systems which are based on deep neural networks. These adversarial examples can be used to disrupt the utility of the images as reference examples or training data. In this work we use a Generative Adversarial Network (GAN) to create adversarial examples to deceive facial recognition and we achieve an acceptable success rate in fooling the face recognition. Our results reduce the training time for the GAN by removing the discriminator component. Furthermore, our results show knowledge distillation can be employed to drastically reduce the size of the resulting model without impacting performance indicating that our contribution could run comfortably on a smartphone
Changing of the Guards: Certificate and Public Key Management on the Internet

Certificates are the foundation of secure communication over the internet. However, not all certificates are created and managed in a consistent manner and the certificate authorities (CAs) issuing certificates achieve different levels of trust. Furthermore, user trust in public keys, certificates, and CAs can quickly change. Combined with the expectation of 24/7 encrypted access to websites, this quickly evolving landscape has made careful certificate management both an important and challenging problem. In this paper, we first present a novel server-side characterization of the certificate replacement (CR) relationships in the wild, including the reuse of public keys. Our data-driven CR analysis captures management biases, highlights a lack of industry standards for replacement policies, and features successful example cases and trends. Based on the characterization results we then propose an efficient solution to an important revocation problem that currently leaves web users vulnerable long after a certificate has been revoked.
Dependable Navigation for Multiple Autonomous Robots with Petri Nets Based Congestion Control and Dynamic Obstacle Avoidance

Abstract In this paper, a novel path planning algorithm for multiple robots using congestion analysis and control is presented. The algorithm ensures a safe path planning solution by avoiding collisions among robots as well as among robots and humans. For each robot, alternative paths to the goal are realised. By analysing the travelling time of robots on different paths using Petri Nets, the optimal configuration of paths is selected. The prime objective is to avoid congestion when routing many robots into a narrow area. The movements of robots are controlled at every intersection by organising a one-by-one passing of the robots. Controls are available for the robots which are able to communicate and share information with each other. To avoid collision with humans and other moving objects (i.e. robots), a dipole field integrated with a dynamic window approach is developed. By considering the velocity and direction of the dynamic obstacles as sources of a virtual magnetic dipole moment, the dipole-dipole interaction between different moving objects will generate repulsive forces proportional to the velocity to prevent collisions. The whole system is presented on the widely used platform Robot Operating System (ROS) so that its implementation is extendable to real robots. Analysis and experiments are demonstrated with extensive simulations to evaluate the effectiveness of the proposed approach.
Guided Derivation of Conceptual Models from User Stories: A Controlled Experiment

[Context and Motivation] User stories are a popular notation for representing requirements, especially in agile development. Although they represent a cornerstone for developing systems, limited research exists on how user stories are refined into conceptual design. [Question/Problem] We study the process of deriving conceptual models from user stories, which is at the basis of information systems development. We focus our attention on the derivation of a holistic view of structural and interaction aspects, represented via class diagrams and use case diagrams, respectively. In this paper, we examine whether providing guidelines has an effect on the ability of humans to derive complete and valid conceptual models. [Principal Ideas/Results] We design example-based guidelines for the derivation of class and use case diagrams from user stories. Then, we conduct a two-factor, two-treatment controlled experiment with 77 undergraduate students serving as subjects. The results indicate that the guidelines improve the completeness and validity of the conceptual models in cases of medium complexity, although the subjects were neutral on the usefulness of the guidelines. [Contribution] The guidelines may assist analysts in the refinement of user stories. Our initial evidence, besides showing how the guidelines can help junior analysts derive high-quality conceptual models, opens the doors for further studies on the refinement of user stories, and to the investigation of alternative guidelines.
Civil Rights Framework of the Internet (BCRFI; Marco Civil da Internet): Advance or Setback? Civil Liability for Damage Derived from Content Generated by Third Party

This article examines the controversies involving the clash between personality rights in cyberspace and freedom of expression, especially in relation to the new rules of the Brazilian Civil Rights Framework of the Internet (Law 12.695) and its consequences to the problem of civil liability of Internet providers in connection with harmful content generated by third parties. The regime is extremely restrictive, which represents an undeniable setback when compared to the path that was being trod by Brazilian case law on this matter. In this context, this article analyses the “specific court order” requirement mentioned by Article 19 of the Civil Rights Framework of the Internet to set the civil liability of Internet providers in contrast to the general discipline of civil liability. The compatibility of Article 19 with the Brazilian Constitution is also discussed, in light of the fundamental rights of the human being in cyberspace.
KGBoost: A classification-based knowledge base completion method with negative sampling

Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs). The new method, named KGBoost, adopts a modularized design and attempts to find hard negative samples so as to train a powerful classifier for missing link prediction. We conduct experiments on multiple benchmark datasets, and demonstrate that KGBoost outperforms state-of-the-art methods across most datasets. Furthermore, as compared with models trained by end-to-end optimization, KGBoost works well under the low-dimensional setting so as to allow a smaller model size.
Learning Explicit User Interest Boundary for Recommendation

The core objective of modelling recommender systems from implicit feedback is to maximize the positive sample score sp and minimize the negative sample score sn, which can usually be summarized into two paradigms: the pointwise and the pairwise. The pointwise approaches fit each sample with its label individually, which is flexible in weighting and sampling on instance-level but ignores the inherent ranking property. By qualitatively minimizing the relative score sn − sp, the pairwise approaches capture the ranking of samples naturally but suffer from training efficiency. Additionally, both approaches are hard to explicitly provide a personalized decision boundary to determine if users are interested in items unseen. To address those issues, we innovatively introduce an auxiliary score bu for each user to represent the User Interest Boundary(UIB) and individually penalize samples that cross the boundary with pairwise paradigms, i.e., the positive samples whose score is lower than bu and the negative samples whose score is higher than bu. In this way, our approach successfully achieves a hybrid loss of the pointwise and the pairwise to combine the advantages of both. Analytically, we show that our approach can provide a personalized decision boundary and significantly improve the training efficiency without any special sampling strategy. Extensive results show that our approach achieves significant improvements on not only the classical pointwise or pairwise models but also state-of-the-art models with complex loss function and complicated feature encoding.
Knowledge Rocks: Adding Knowledge Assistance to Visualization Systems

We present Knowledge Rocks, an implementation strategy and guideline for augmenting visualization systems to knowledge-assisted visualization systems, as defined by the KAVA model. Visualization systems become more and more sophisticated. Hence, it is increasingly important to support users with an integrated knowledge base in making constructive choices and drawing the right conclusions. We support the effective reactivation of visualization software resources by augmenting them with knowledge-assistance. To provide a general and yet supportive implementation strategy, we propose an implementation process that bases on an application-agnostic architecture. This architecture is derived from existing knowledge-assisted visualization systems and the KAVA model. Its centerpiece is an ontology that is able to automatically analyze and classify input data, linked to a database to store classified instances. We discuss design decisions and advantages of the KR framework and illustrate its broad area of application in diverse integration possibilities of this architecture into an existing visualization system. In addition, we provide a detailed case study by augmenting an it-security system with knowledge-assistance facilities.