Reviewerly/exHarmony huggingface dataset:
kMVX

Kernel information leak vulnerabilities are a major security threat to production systems. Attackers can exploit them to leak confidential information such as cryptographic keys or kernel pointers. Despite efforts by kernel developers and researchers, existing defenses for kernels such as Linux are limited in scope or incur a prohibitive performance overhead. In this paper, we present kMVX, a comprehensive defense against information leak vulnerabilities in the kernel by running multiple diversified kernel variants simultaneously on the same machine. By constructing these variants in a careful manner, we can ensure they only show divergences when an attacker tries to exploit bugs present in the kernel. By detecting these divergences we can prevent kernel information leaks. Our kMVX design is inspired by multi-variant execution (MVX). Traditional MVX designs cannot be applied to kernels because of their assumptions on the run-time environment. kMVX, on the other hand, can be applied even to commodity kernels. We show our Linux-based prototype provides powerful protection against information leaks at acceptable performance overhead (20--50% in the worst case for popular server applications).
Multi-time-scale TFe prediction for iron ore sintering process with complex time delay

Iron ore sintering is a critical process in steel-making industry. It produces sinter with qualified iron grade (TFe) for the blast furnace process. The process variables display a multi-time-scale feature that is derived from the complex time delays involved in a sintering process. To resolve the contradiction between a single-time-scale data acquisition of TFe and a multi-time-scale feature that the process displays. A multi-scale prediction approach in offline and online modes was presented for the prediction of TFe. The approach not only solves the contradiction, but also meets the requirements of online and offline optimizations of a sintering process. First, a discrete wavelet transform method was combined with process knowledge to decompose the online and offline TFe component with different time scales. Then, an improved back-propagation neural network (IBPNN) with its input neurons not only connected to the hidden neurons but also to the output neurons was built for the offline TFe prediction under large-time scale. Last, a just-in-time-learning-based online model was built under the mixture time scales of medium and small. The simulation results of actual run data show that an IBPNN has a good overall performance compared with an extreme learning machine (ELM), an improved ELM, and a back-propagation neural network for the offline TFe prediction. The results also show the superiority of the multi-time-scale prediction model compared with an offline prediction and a one-time-scale prediction models.
Query Formulation Assistance for Kids

Children use popular web search tools, which are generally designed for adult users. Because children have different developmental needs than adults, these tools may not always adequately support their search for information. Moreover, even though search tools offer support to help in query formulation, these too are aimed at adults and may hinder children rather than help them. This calls for the examination of existing technologies in this area, to better understand what remains to be done when it comes to facilitating query-formulation tasks for young users. In this paper, we investigate interaction elements of query formulation--including query suggestion algorithms--for children. The primary goals of our research efforts are to: (i) examine existing plug-ins and interfaces that explicitly aid children's query formulation; (ii) investigate children's interactions with suggestions offered by a general-purpose query suggestion strategy vs. a counterpart designed with children in mind; and (iii) identify, via participatory design sessions, their preferences when it comes to tools / strategies that can help children find information and guide them through the query formulation process. Our analysis shows that existing tools do not meet children's needs and expectations; the outcomes of our work can guide researchers and developers as they implement query formulation strategies for children.
The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers

Classical non-neural dependency parsers put considerable effort on the design of feature functions.Especially, they benefit from information coming from structural features, such as features drawn from neighboring tokens in the dependency tree.In contrast, their BiLSTM-based successors achieve state-ofthe-art performance without explicit information about the structural context.In this paper we aim to answer the question: How much structural context are the BiLSTM representations able to capture implicitly?We show that features drawn from partial subtrees become redundant when the BiLSTMs are used.We provide a deep insight into information flow in transition-and graph-based neural architectures to demonstrate where the implicit information comes from when the parsers make their decisions.Finally, with model ablations we demonstrate that the structural context is not only present in the models, but it significantly influences their performance.
Q-Learning for Autonomous Mobile Robot Obstacle Avoidance

An approach to the problem of autonomous mobile robot obstacle avoidance using Reinforcement Learning, more precisely Q-Learning, is presented in this paper. Reinforcement Learning in Robotics has been a challenging topic for the past few years. The ability to equip a robot with a powerful enough tool to allow an autonomous discovery of an optimal behavior through trial-and-error interactions with its environment has been a reason for numerous deep research projects. In this paper, two different Q-Learning approaches are presented as well as an extensive hyperparameter study. These algorithms were developed for a simplistically simulated Bot'n Roll ONE A (Fig. 1). The simulated robot communicates with the control script via ROS. The robot must surpass three levels of iterative complexity mazes similar to the ones presented on RoboParty [1] educational event challenge. For both algorithms, an extensive hyperparameter search was taken into account by testing hundreds of simulations with different parameters. Both Q-Learning solutions develop different strategies trying to solve the three labyrinths, enhancing its learning ability as well as discovering different approaches to certain situations, and finishing the task in complex environments.
Reversible Data Hiding Scheme Using Adaptive Block Truncation Coding Based on an Edge-Based Quantization Approach

In this paper, we provide a novel reversible data hiding method using adaptive block truncation coding based on an edge-based quantization (ABTC-EQ) approach. We exploit the characteristic not being used in ABTC-EQ. To accomplish this, we first utilized a Canny edge detector to obtain an edge image and classify each block in a cover image into two versions, edge-block and non-edge-block. Subsequently, k-means clustering was used to obtain three quantization levels and derive the corresponding bit map while the current processing block was the case of an edge-block. Then Zero-Point Fixed Histogram Shifting (ZPF-HS) was applied to embed the secret information into compressed code. The experimental results show that our method provides a high embedding capacity for each test image and performance is better than other methods.
Static Identification of Injection Attacks in Java

The most dangerous security-related software errors, according to the OWASP Top Ten 2017 list, affect web applications. They are potential injection attacks that exploit user-provided data to execute undesired operations: database access and updates ( SQL injection ); generation of malicious web pages ( cross-site scripting injection ); redirection to user-specified web pages ( redirect injection ); execution of OS commands and arbitrary scripts ( command injection ); loading of user-specified, possibly heavy or dangerous classes at run time ( reflection injection ); access to arbitrary files on the file system ( path-traversal ); and storing user-provided data into heap regions normally assumed to be shielded from the outside world ( trust boundary violation ). All these attacks exploit the same weakness: unconstrained propagation of data from sources that the user of a web application controls into sinks whose activation might trigger dangerous operations. Although web applications are written in a variety of languages, Java remains a frequent choice, in particular for banking applications, where security has tangible relevance. This article defines a unified, sound protection mechanism against such attacks, based on the identification of all possible explicit flows of tainted data in Java code. Such flows can be arbitrarily complex, passing through dynamically allocated data structures in the heap. The analysis is based on abstract interpretation and is interprocedural, flow-sensitive, and context-sensitive. Its notion of taint applies to reference (non-primitive) types dynamically allocated in the heap and is object-sensitive and field-sensitive. The analysis works by translating the program into Boolean formulas that model all possible data flows. Its implementation, within the Julia analyzer for Java and Android, found injection security vulnerabilities in the Internet banking service and in the customer relationship management of large Italian banks, as well as in a set of open-source third-party applications. It found the command injection, which is at the origin of the 2017 Equifax data breach, one of the worst data breaches ever. For objective, repeatable results, this article also evaluates the implementation on two open-source security benchmarks: the Juliet Suite and the OWASP Benchmark for the automatic comparison of static analyzers for cybersecurity. We compared this technique against more than 10 other static analyzers, both free and commercial. The result of these experiments is that ours is the only analysis for injection that is sound (up to well-stated limitations such as multithreading and native code) and works on industrial code, and it is also much more precise than other tools.
Sentiment Analysis on Microblogging with K-Means Clustering and Artificial Bee Colony

Microblogging is a type of blog used by people to express their opinions, attitudes, and feelings toward entities with a short message and this message is easily shared through the network of connected people. Knowing their sentiments would be beneficial for decision-making, planning, visualization, and so on. Grouping similar microblogging messages can convey some meaningful sentiments toward an entity. This task can be accomplished by using a simple and fast clustering algorithm, [Formula: see text]-means. As the microblogging messages are short and noisy they cause high sparseness and high-dimensional dataset. To overcome this problem, term frequency–inverse document frequency (tf–idf) technique is employed for selecting the relevant features, and singular value decomposition (SVD) technique is employed for reducing the high-dimensional dataset while still retaining the most relevant features. These two techniques adjust dataset to improve the [Formula: see text]-means efficiently. Another problem comes from [Formula: see text]-means itself. [Formula: see text]-means result relies on the initial state of centroids, the random initial state of centroids usually causes convergence to a local optimum. To find a global optimum, artificial bee colony (ABC), a novel swarm intelligence algorithm, is employed to find the best initial state of centroids. Silhouette analysis technique is also used to find optimal [Formula: see text]. After clustering into [Formula: see text] groups, each group will be scored by SentiWordNet and we analyzed the sentiment polarities of each group. Our approach shows that combining various techniques (i.e., tf–idf, SVD, and ABC) can significantly improve [Formula: see text]-means result (41% from normal [Formula: see text]-means).
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff

Lossy compression algorithms are typically designed and analyzed through the lens of Shannon's rate-distortion theory, where the goal is to achieve the lowest possible (e.g., MSE or SSIM) at any given bit rate. However, in recent years, it has become increasingly accepted that low distortion is not a synonym for high perceptual quality, and in fact optimization of one often comes at the expense of the other. In light of this understanding, it is natural to seek for a generalization of rate-distortion theory which takes perceptual quality into account. In this paper, we adopt the mathematical definition of perceptual quality recently proposed by Blau & Michaeli (2018), and use it to study the three-way tradeoff between rate, distortion, and perception. We show that restricting the perceptual quality to be high, generally leads to an elevation of the rate-distortion curve, thus necessitating a sacrifice in either rate or distortion. We prove several fundamental properties of this triple-tradeoff, calculate it in closed form for a Bernoulli source, and illustrate it visually on a toy MNIST example.
Optimal and Bounded-Suboptimal Multi-Agent Motion Planning

Multi-Agent Motion Planning (MAMP) is the task of finding conflict-free kinodynamically feasible plans for agents from start to goal states. While MAMP is of significant practical importance, existing solvers are either incomplete, inefficient or rely on simplifying assumptions. For example, Multi-Agent Path Finding (MAPF) solvers conventionally assume discrete timesteps and rectilinear movement of agents between neighboring vertices of a graph. In this paper, we develop MAMP solvers that obviate these simplifying assumptions and yet generalize the core ideas of state-of-the-art MAPF solvers. Specifically, since different motions may take arbitrarily different durations, MAMP solvers need to efficiently reason with continuous time and arbitrary wait durations. To do so, we adapt (Enhanced) Conflict-Based Search to continuous time and develop a novel bounded-suboptimal extension of Safe Interval Path Planning, called Soft Conflict Interval Path Planning. On the theoretical side, we justify the completeness, optimality and bounded-suboptimality of our MAMP solvers. On the experimental side, we show that our MAMP solvers scale well with increasing suboptimality bounds.
Discourse Analysis and Its Applications

Discourse processing is a suite of Natural Language Processing (NLP) tasks to uncover linguistic structures from texts at several levels, which can support many downstream applications.This involves identifying the topic structure, the coherence structure, the coreference structure, and the conversation structure for conversational discourse.Taken together, these structures can inform text summarization, machine translation, essay scoring, sentiment analysis, information extraction, question answering, and thread recovery.The tutorial starts with an overview of basic concepts in discourse analysismonologue vs. conversation, synchronous vs. asynchronous conversation, and key linguistic structures in discourse analysis.We also give an overview of linguistic structures and corresponding discourse analysis tasks that discourse researchers are generally interested in, as well as key applications on which these discourse structures have an impact.
Promoting Identification and Use of Aid Resources by Caregivers of Seniors: Co-Design of an Electronic Health Tool

Background The importance of supporting caregivers is recognized in home care for older persons, and facilitating their help-seeking process is a way to meet that need. The use of electronic health (eHealth) is a potentially promising solution to facilitate caregivers’ help-seeking process. Objective The aim of this research was to develop, in partnership with community organizations, health and social service professionals and caregivers, an eHealth tool promoting the earlier identification of needs of older persons and an optimal use of available resources. Methods To design the tool, 8 co-design sessions (CoDs) were conducted and 3 advisory committees were created (in 11 regions) in Quebec between May 2017 and May 2018. A variety of methods were used, including the sorting method, the use of personas, eHealth tool analysis, brainstorming, sketching, prototyping, and pretesting. Results A total of 74 co-designers (women n=64 and men n=10) were recruited to participate in the CoDs or the advisory committees. This number allowed for the identification of needs to which the tool must respond and for the identification of its requirements (functionalities and content), as well as for the development of the information architecture. Throughout the study, adjustments were made to the planning of CoD, notably because certain steps required more sessions than expected. Among others, this was true for the identification of functionalities. Conclusions This study led to the development of an eHealth tool for caregivers of functionally dependent older persons to help them identify their needs and the resources available to meet them. International Registered Report Identifier (IRRID) RR2-10.2196/11634
AGE: authentication in gadget-free healthcare environments

Abstract Mobile and sensor related technologies are significantly revolutionizing the medical and healthcare sectors. In current healthcare systems, gadgets are the prominent way of acquiring medical services. However, the recent technological advancements in smart and ambient environments are offering users new ways to access the healthcare services without using any explicit gadgets. One of the key challenges in such gadget-free environments is performing secure user authentication with the intelligent surroundings. For example, a secure, efficient and user-friendly authentication mechanism is essential for elderly/disabled people or patients in critical conditions requiring medical services. Hence, modern authentication systems should be sophisticated enough to identify such patients without requiring their physical efforts or placing gadgets on them. This paper proposes an anonymous and privacy-preserving biometrics based authentication scheme for such gadget-free healthcare environment. We performed formal security verification of our proposed scheme using CDVT /AD tool and our results indicate that the proposed scheme is secure for such smart and gadget-free environments. We verify that the proposed scheme can resist against various well-known security attacks. Moreover, the proposed system showed better performance as compared with existing biometrics based remote user authentication schemes.
TwitterBERT: Framework for Twitter Sentiment Analysis Based on Pre-trained Language Model Representations

Sentiment analysis has been a topic of discussion in the exploration domain of language understanding. Yet, the neural networks deployed in it are deficient to some extent. Currently, the majority of the studies proceeds on identifying the sentiments by focusing on vocabulary and syntax. Moreover, the task is recognised in Natural Language Processing (NLP) and, for calculating the noteworthy and exceptional results, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been employed. In this study, we propose a four-phase framework for Twitter Sentiment Analysis. This setup is based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model as an encoder for generating sentence depictions. For more effective utilisation of this model, we deploy various classification models. Additionally, we concatenate pre-trained representations of word embeddings with BERT representation method to enhance sentiment classification. Experimental results show better implementation when it is evaluated against the baseline framework on all datasets. For example, our best model attains an F1-score of 71.82% on the SemEval 2017 dataset. A comparative analysis on experimental results offers some recommendations on choosing pre-training steps to obtain improved results. The outcomes of the experiment confirm the effectiveness of our system.
Distributed Trust &amp; Reputation Models using Blockchain Technologies for Tourism Crowdsourcing Platforms

Crowdsourced repositories have become an increasingly important source of information for users and businesses in multiple domains. Everyday examples of tourism crowdsourcing platforms focusing on accommodation, food or travelling in general, influence consumer behaviour in modern societies. These repositories, due to their intrinsic openness, can strongly benefit from independent data quality modelling mechanisms. In this context, building trust & reputation models of contributors and storing crowdsourced data using distributed ledger technology allows not only to ascertain the quality of crowdsourced contributions, but also ensures the integrity of the built models. This paper presents a survey on distributed trust & reputation modelling using blockchain technology and, for the specific case of tourism crowdsourcing platforms, discusses the open research problems and identifies future lines of research.
Engineering Education: Transformation for Industry 4.0 (SYNERGY 2019 Conference Results Review)

The article summarizes the results of the plenary session of the international conference «Engineering education: transformation problems for industry 4.0 SYNERGY-2019», held at the Kazan National Research Technological University from 4 to 5 September 2019. Forum which brought together representatives of universities and industrial enterprises of Russia and abroad, was devoted to the training of engineers of the petrochemical industry. Among its delegates were representatives of international societies for engineering education, five national research universities and nine supporting universities of PC «Gazprom», state authorities and industrial enterprises of Tatarstan. It was possible to observe the work of the plenary session in real time on the Internet in all the supporting universities of Gazprom. The event was organized by the Ministry of Science and Higher Education of the Russian Federation, the International Society for Engineering Pedagogy IGIP, the National Fund of Training of Staff (NFTS), the Association of Engineering Education of Russia (AEER) as well as the Ministry of Industry and Trade of Tatarstan and the Kazan National Research Technological University. The General sponsor was the Public Joint Company «Gazprom». The conference gathered 155 participants from 15 universities of Russia, Kazakhstan and Estonia. Representatives of 11 industrial enterprises spoke, 55 reports made presentations.
Efficient Probabilistic Logic Reasoning with Graph Neural Networks

Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult. In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems. Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task. In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN. We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model. Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.