Reviewerly/exHarmony huggingface dataset:
Arabic Machine Translation: A survey of the latest trends and challenges

Given that Arabic is one of the most widely used languages in the world, the task of Arabic Machine Translation (MT) has recently received a great deal of attention from the research community. Indeed, the amount of research that has been devoted to this task has led to some important achievements and improvements. However, the current state of Arabic MT systems has not reached the quality achieved for some other languages. Thus, much research work is still needed to improve it. This survey paper introduces the Arabic language, its characteristics, and the challenges involved in its translation. It provides the reader with a full summary of the important research studies that have been accomplished with regard to Arabic MT along with the most important tools and resources that are available for building and testing new Arabic MT systems. Furthermore, the survey paper discusses the current state of Arabic MT and provides some insights into possible future research directions.
Narrative Physicalization: Supporting Interactive Engagement With Personal Data

Physical engagement with data necessarily influences the reflective process. However, the role of interactivity and narration are often overlooked when designing and analyzing personal data physicalizations. We introduce Narrative Physicalizations, everyday objects modified to support nuanced self-reflection through embodied engagement with personal data. Narrative physicalizations borrow from narrative visualizations, storytelling with graphs, and engagement with mundane artifacts from data-objects. Our research uses a participatory approach to research-through-design and includes two interdependent studies. In the first, personalized data physicalizations are developed for three individuals. In the second, we conduct a parallel autobiographical exploration of what constitutes personal data when using a Fitbit. Our work expands the landscape of data physicalization by introducing narrative physicalizations. It suggests an experience-centric view on data physicalization where people engage physically with their data in playful ways, making their body an active agent during the reflective process.
HeadJoystick: Improving Flying in VR Using a Novel Leaning-Based Interface

Flying in virtual reality (VR) using standard handheld controllers can be cumbersome and contribute to unwanted side effects such as motion sickness and disorientation. This article investigates a novel hands-free flying interface-HeadJoystick, where the user moves their head similar to a joystick handle toward the target direction to control virtual translation velocity. The user sits on a regular office swivel chair and rotates it physically to control virtual rotation using 1:1 mapping. We evaluated short-term (Study 1) and extended usage effects through repeated usage (Study 2) of the HeadJoystick versus handheld interfaces in two within-subject studies, where participants flew through a sequence of increasingly difficult tunnels in the sky. Using the HeadJoystick instead of handheld interfaces improved both user experience and performance, in terms of accuracy, precision, ease of learning, ease of use, usability, long-term use, presence, immersion, sensation of self-motion, workload, and enjoyment in both studies. These findings demonstrate the benefits of using leaning-based interfaces for VR flying and potentially similar telepresence applications such as remote flight with quadcopter drones. From a theoretical perspective, we also show how leaning-based motion cueing interacts with full physical rotation to improve user experience and performance compared to the gamepad.
OSIEshort: A small stimulus set can reliably estimate individual differences in semantic salience

Recent findings revealed consistent individual differences in fixation tendencies among observers free-viewing complex scenes. The present study aimed at (1) replicating these differences, and (2) testing whether they can be estimated using a shorter test. In total, 103 participants completed two eye-tracking sessions. The first session was a direct replication of the original study, but the second session used a smaller subset of images, optimized to capture individual differences efficiently. The first session replicated the large and consistent individual differences along five semantic dimensions observed in the original study. The second session showed that these differences can be estimated using about 40 to 100 images (depending on the tested dimension). Additional analyses revealed that only the first 2 seconds of viewing duration seem to be informative regarding these differences. Taken together, our findings suggest that reliable individual differences in semantic salience can be estimated with a test totaling less than 2 minutes of viewing duration.
A Reference Vector-Based Simplified Covariance Matrix Adaptation Evolution Strategy for Constrained Global Optimization

During the last two decades, the notion of multiobjective optimization (MOO) has been successfully adopted to solve the nonconvex constrained optimization problems (COPs) in their most general forms. However, such works mainly utilized the Pareto dominance-based MOO framework while the other successful MOO frameworks, such as the reference vector (RV) and the decomposition-based ones, have not drawn sufficient attention from the COP researchers. In this article, we utilize the concepts of the RV-based MOO to design a ranking strategy for the solutions of a COP. We first transform the COP into a biobjective optimization problem (BOP) and then solve it by using the covariance matrix adaptation evolution strategy (CMA-ES), which is arguably one of the most competitive evolutionary algorithms of current interest. We propose an RV-based ranking strategy to calculate the mean and update the covariance matrix in CMA-ES. Besides, the RV is explicitly tuned during the optimization process based on the characteristics of COPs in a RV-based MOO framework. We also propose a repair mechanism for the infeasible solutions and a restart strategy to facilitate the population to escape from the infeasible region. We test the proposal extensively on two well-known benchmark suites comprised of 36 and 112 test problems (at different scales) from the IEEE CEC (Congress on Evolutionary Computation) 2010 and 2017 competitions along with a real-world problem related to power flow. Our experimental results suggest that the proposed algorithm can meet or beat several other state-of-the-art constrained optimizers in terms of the performance on a wide variety of problems.
Bare-Bones Based Sine Cosine Algorithm for global optimization

The Meta-heuristic algorithm has become an effective solution to global optimization problems. Recently, a new meta-heuristic algorithm called sine-cosine algorithm (SCA) search algorithm is proposed, which uses the characteristics of sine-cosine trigonometric function in mathematical formulas to solve the optimal solution of the problem to be optimized. This paper presents a new variant of the SCA algorithm named Bare bones Sine Cosine Algorithm (BBSCA), which improves the exploitation ability of the solution, reduces the diversity spillover in the classical SCA search equation, and keeps the diversity of the solution very well. The proposed method uses Gaussian search equations and exponential decrement strategies to generate new candidate individuals, which use the valuable information hidden in the best individuals to guide the population to move in a better direction. At the same time, the greedy selection mechanism is adopted for the newly generated solution, which makes full use of the previously searched information to improve the individual's search ability. To evaluate the effectiveness in solving the global optimization problems, BBSCA has been tested on classic set of 23 well-known benchmark functions, standard IEEE CEC2014 and CEC2017 benchmark functions, and compared with several other state-of-the-art SCA algorithm variants. At the end of the paper, the performance of design algorithm BBSCA is also tested on classical engineering optimization problems. The numerical and simulation experimental results indicate that the proposed method can improve the performance of the algorithm and generate better statistical significance solutions in real-life global optimization problems.
Deep autoencoders as anomaly detectors: Method and case study in a distributed water treatment plant

Industrial Control Systems (ICS) are found in critical infrastructure, such as, water treatment plants and oil refineries. ICS are often the target of cyber-attacks leading to undesirable consequences. It is essential to detect process anomalies resulting from such attacks before appropriate defensive actions are considered. In this work, a deep autoencoder-based anomaly detector (DAE) is proposed. DAE is trained using data collected during normal operation of a plant. The detection effectiveness of three variants of DAE was experimentally evaluated on an operational Secure Water Treatment (SWaT) plant. Further, the amount of plant design knowledge needed to design DAE was compared with that needed to create design-centric approaches for anomaly detection. Experimental results indicate that the proposed DAE, constructed with minimal design knowledge, is effective in detecting process anomalies resulting due to single and multi-point coordinated attacks with high detection rate and few false alarms.
Computer Extracted Features from Initial H&amp;E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance

In this work, we assessed the ability of computerized features of nuclear morphology from diagnostic biopsy images to predict prostate cancer (CaP) progression in active surveillance (AS) patients. Improved risk characterization of AS patients could reduce over-testing of low-risk patients while directing high-risk patients to therapy. A total of 191 (125 progressors, 66 non-progressors) AS patients from a single site were identified using The Johns Hopkins University's (JHU) AS-eligibility criteria. Progression was determined by pathologists at JHU. 30 progressors and 30 non-progressors were randomly selected to create the training cohort D1 (n = 60). The remaining patients comprised the validation cohort D2 (n = 131). Digitized Hematoxylin & Eosin (H&E) biopsies were annotated by a pathologist for CaP regions. Nuclei within the cancer regions were segmented using a watershed method and 216 nuclear features describing position, shape, orientation, and clustering were extracted. Six features associated with disease progression were identified using D1 and then used to train a machine learning classifier. The classifier was validated on D2. The classifier was further compared on a subset of D2 (n = 47) against pro-PSA, an isoform of prostate specific antigen (PSA) more linked with CaP, in predicting progression. Performance was evaluated with area under the curve (AUC). A combination of nuclear spatial arrangement, shape, and disorder features were associated with progression. The classifier using these features yielded an AUC of 0.75 in D2. On the 47 patient subset with pro-PSA measurements, the classifier yielded an AUC of 0.79 compared to an AUC of 0.42 for pro-PSA. Nuclear morphometric features from digitized H&E biopsies predicted progression in AS patients. This may be useful for identifying AS-eligible patients who could benefit from immediate curative therapy. However, additional multi-site validation is needed.
Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link the tags with corresponding resources, therefore leading to a static user profile in the recommendation. In this article, we have proposed a new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation performance. Instead of simply including most relevant tags, the new model focuses on the completeness of a user profile through expanding tags by exploiting their relations and includes a sufficient set of tags to alleviate the tag sparsity problem. The novel STEM-based TRS contains three operations: (1) Tag cloud generation discovers potentially relevant tags in an application domain; (2) Tag expansion finds a sufficient set of tags upon original tags; and (3) User profile refactoring builds a dynamic user profile and determines the weights of the extended tags in the profile. We analysed the STEM property in terms of recommendation accuracy and demonstrated its performance through extensive experiments over multiple datasets. The analysis and experimental results showed that the new STEM technique was able to correctly find a sufficient set of tags and to improve the recommendation accuracy by solving the tag sparsity problem. At this point, this technique has consistently outperformed state-of-art tag-aware recommendation methods in these extensive experiments.
WES

We introduce the Web-Enabled Simulation (WES) research agenda, and describe FACEBOOK's WW system. We describe the application of WW to reliability, integrity and privacy at FACEBOOK1, where it is used to simulate social media interactions on an infrastructure consisting of hundreds of millions of lines of code. The WES agenda draws on research from many areas of study, including Search Based Software Engineering, Machine Learning, Programming Languages, Multi Agent Systems, Graph Theory, Game AI, and AI Assisted Game Play. We conclude with a set of open problems and research challenges to motivate wider investigation.
Global Semantic Consistency Network for Image Manipulation Detection

This letter focuses on image manipulation detection which aims to recognize the manipulated regions under the contextual semantic information. Existing approaches usually overlook the semantic discrepancy between different levels of feature maps, and directly fuse (e.g., addition, or concatenation) them for detection. In this letter, we argue that the semantic gap is the main reason for the low effectiveness of feature fusion in manipulation predictions. To address this problem, we propose a Global Semantic Consistency Network (GSCNet) for image manipulation detection, which is based on an encoder-decoder structure. Specifically, to make GSCNet include more global texture information which has been empirically confirmed to be beneficial to manipulation detection, gram block is first deployed on each level of feature maps in the encoding stage. Based on that, bi-directional convolutional LSTM is further implemented on the decoding stage, such that feature maps of the same level have semantic consistency. Experimental results on NIST16, and CASIA v1.0 declare that GSCNet can accurately locate the manipulated regions. Furthermore, compared to the existing models, GSCNet can achieve new state-of-the-art results.
Distributed Cooperative Deep Transfer Learning for Industrial Image Recognition

In this paper, a novel light-weight incremental class learning algorithm for live image recognition is presented. It features a dual memory architecture and is capable of learning formerly unknown classes as well as conducting its learning across multiple instances at multiple locations without storing any images. In addition to tests on the ImageNet dataset, a prototype based upon a Raspberry Pi and a webcam is used for further evaluation: The proposed algorithm successfully allows for the performant execution of image classification tasks while learning new classes at several sites simultaneously, thereby enabling its application to various industry use cases, e.g. predictive maintenance or self-optimization.
Algorithm 1011

In several papers of 2013–2016, Guglielmi and Protasov made a breakthrough in the problem of the joint spectral radius computation, developing the invariant polytope algorithm that for most matrix families finds the exact value of the joint spectral radius. This algorithm found many applications in problems of functional analysis, approximation theory, combinatorics, and so on. In this article, we propose a modification of the invariant polytope algorithm making it roughly 3 times faster (single threaded), suitable for higher dimensions, and parallelise it. The modified version works for most matrix families of dimensions up to 25, for non-negative matrices up to 3,000. In addition, we introduce a new, fast algorithm, called modified Gripenberg algorithm, for computing good lower bounds for the joint spectral radius. The corresponding examples and statistics of numerical results are provided. Several applications of our algorithms are presented. In particular, we find the exact values of the regularity exponents of Daubechies wavelets up to order 42 and the capacities of codes that avoid certain difference patterns.
Research data management policy and practice in Chinese university libraries

Abstract On April 2, 2018, the State Council of China formally released a national Research Data Management (RDM) policy “Measures for Managing Scientific Data”. In this context and given that university libraries have played an important role in supporting RDM at an institutional level in North America, Europe, and Australasia, the aim of this article is to explore the current status of RDM in Chinese universities, in particular how university libraries have been involved in taking the agenda forward. This article uses a mixed‐methods data collection approach and draws on a website analysis of university policies and services; a questionnaire for university librarians; and semi‐structured interviews. Findings indicate that Research Data Service at a local level in Chinese Universities are in their infancy. There is more evidence of activity in developing data repositories than support services. There is little development of local policy. Among the explanations of this may be the existence of a national‐level infrastructure for some subject disciplines, the lack of professionalization of librarianship, and the relatively weak resonance of openness as an idea in the Chinese context.
A survey of syntactic-semantic parsing based on constituent and dependency structures

Syntactic and semantic parsing has been investigated for decades, which is one primary topic in the natural language processing community. This article aims for a brief survey on this topic. The parsing community includes many tasks, which are difficult to be covered fully. Here we focus on two of the most popular formalizations of parsing: constituent parsing and dependency parsing. Constituent parsing is majorly targeted to syntactic analysis, and dependency parsing can handle both syntactic and semantic analysis. This article briefly reviews the representative models of constituent parsing and dependency parsing, and also dependency graph parsing with rich semantics. Besides, we also review the closely-related topics such as cross-domain, cross-lingual and joint parsing models, parser application as well as corpus development of parsing in the article.
User-defined semantics for the design of IoT systems enabling smart interactive experiences

Abstract Automation in computing systems has always been considered a valuable solution to unburden the user. Internet of Things (IoT) technology best suits automation in different domains, such as home automation, retail, industry, and transportation, to name but a few. While these domains are strongly characterized by implicit user interaction, more recently, automation has been adopted also for the provision of interactive and immersive experiences that actively involve the users. IoT technology thus becomes the key for Smart Interactive Experiences (SIEs), i.e., immersive automated experiences created by orchestrating different devices to enable smart environments to fluidly react to the final users’ behavior. There are domains, e.g., cultural heritage, where these systems and the SIEs can support and provide several benefits. However, experts of such domains, while intrigued by the opportunity to induce SIEs, are facing tough challenges in their everyday work activities when they are required to automate and orchestrate IoT devices without the necessary coding skills. This paper presents a design approach that tries to overcome these difficulties thanks to the adoption of ontologies for defining Event-Condition-Action rules. More specifically, the approach enables domain experts to identify and specify properties of IoT devices through a user-defined semantics that, being closer to the domain experts’ background, facilitates them in automating the IoT devices behavior. We also present a study comparing three different interaction paradigms conceived to support the specification of user-defined semantics through a “transparent” use of ontologies. Based on the results of this study, we work out some lessons learned on how the proposed paradigms help domain experts express their semantics, which in turn facilitates the creation of interactive applications enabling SIEs.
Decision support model for cybersecurity risk planning: A two-stage stochastic programming framework featuring firms, government, and attacker

We study the decision-making problem in cybersecurity risk planning concerning resource allocation strategies by government and firms. Aiming to minimize the social costs incurred due to cyberattacks, we consider not only the monetary investment costs but also the deprivation costs due to detection and containment delays. We also consider the effect of positive externalities of the overall cybersecurity investment on an individual firm's resource allocation attitude. The optimal decision guides the firms on the countermeasure portfolio mix (detection vs. prevention vs. containment) and government intelligence investments while accounting for actions of a strategic attacker and firm budgetary limitations. We accomplish this via a two-stage stochastic programming model. In the first stage, firms decide on prevention and detection investments aided by government intelligence investments that improve detection effectiveness. In the second stage, once the attacker's actions are realized, firms decide on containment investments after evaluating the cyberattacks. We demonstrate the applicability of our model via a case study. We find that externality can reduce the government's intelligence investment and that the firm's detection investment receives priority over containment. We also note that while prevention effectiveness has a decreasing impact on intelligence, it is beneficial to spend more on intelligence given its increasing returns to the reduction of social costs related to cybersecurity.