Reviewerly/exHarmony huggingface dataset:
Evolutionary Learning of Existential Rules

Declarative rules such as Prolog and Datalog are common formalisms to express expert knowledge and are used in a number of systems. Since developing such rules is time-consuming and requires scarce expert knowledge, it is essential to develop algorithms for learning such rules. We address the problem of learning existential rules, a richer class of rules which found applications in many use-cases such as Semantic Web and Web Data Extraction. In particular, we concentrate on developing evolutionary learning algorithms for rule learning.
Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields

Statistical relational models such as Markov logic networks (MLNs) and hinge-loss Markov random fields (HL-MRFs) are specified using templated weighted first-order logic clauses, leading to the creation of complex, yet easy to encode models that effectively combine uncertainty and logic. Learning the structure of these models from data reduces the human effort of identifying the right structures. In this work, we present an asynchronous deep reinforcement learning algorithm to automatically learn HL-MRF clause structures. Our algorithm possesses the ability to learn semantically meaningful structures that appeal to human intuition and understanding, while simultaneously being able to learn structures from data, thus learning structures that have both the desirable qualities of interpretability and good prediction performance. The asynchronous nature of our algorithm further provides the ability to learn diverse structures via exploration, while remaining scalable. We demonstrate the ability of the models to learn semantically meaningful structures that also achieve better prediction performance when compared with a greedy search algorithm, a path-based algorithm, and manually defined clauses on two computational social science applications: i) modeling recovery in alcohol use disorder, and ii) detecting bullying.
Relaxed BDDs: An Admissible Heuristic for Delete-Free Planning Based on a Discrete Relaxation

We investigate the use of relaxed binary decision diagrams (BDDs) as an alternative to linear programming (LP) for computing an admissible heuristic for the cost-optimal delete-free planning (DFP) problem. Our main contributions are the introduction of a novel BDD encoding, a construction algorithm for the sequential relaxation of a DFP task and a study of the effectiveness of relaxed BDD heuristics, both from a theoretical and practical perspective. We further show that relaxed BDDs can be used beyond heuristic computation to extract delete-free plans, find action landmarks, and identify redundant actions. Our empirical analysis shows that while BDD-based heuristics trail the state of the art, even small relaxed BDDs are competitive with the LP heuristic for the DFP task.
Considerations for Designing Accessible Educational Scenario-Based Assessments for Multiple Populations: A Focus on Linguistic Complexity

As the diversity of the test-taker population increases so should assessment development practices evolve to consider the various needs of the multiple populations taking the assessments. One need is the ability to understand the language used in test items and tasks so they do not present unnecessary challenges for the test-takers, which may be misconstrued as a lack of knowledge of the content assessed. This investigation is important because linguistic complexity may constitute a source of construct-irrelevant variance, which may render an assessments’ passages and questions less accessible and present unnecessary challenges to the multiple test-taker populations potentially leading to score misinterpretation, disengagement with the task, and increased cognitive load. To develop more linguistically accessible assessments for multiple populations, less accessible construct-irrelevant text may require modification and less accessible construct-relevant text may need scaffolding. In this paper, I discuss considerations for designing accessible assessments for multiple populations with a focus on linguistic complexity. To illustrate these considerations, I refer to digitally delivered scenario-based tasks of English Language Arts framed in a science context.
GraspSnooker: Automatic Chinese Commentary Generation for Snooker Videos

We demonstrate a web-based software system, GraspSnooker, which is able to automatically generate Chinese text commentaries for snooker game videos. It consists of a video analyzer, a strategy predictor and a commentary generator. As far as we know, it is the first attempt on snooker commentary generation, which might be helpful for snooker learners to understand the game.
VAStream

Processing high-volume, high-velocity data streams is an important big data problem in many sciences, engineering, and technology domains. There are many open-source distributed stream processing and cloud platforms that offer low-latency stream processing at scale, but the visualization and user-interaction components of these systems are limited to visualizing the outcome of stream processing results. Visual analysis represents a new form of analysis where the user has more control and interactive capabilities either to dynamically change the visualization, analytics or data management processes. VAStream provides an environment for big data stream processing along with interactive visualization capabilities. The system environment consists of hardware and software modules to optimize streaming data workflow (that includes data ingest, pre-processing, analytics, visualization, and collaboration components). The system environment is evaluated for two real-time streaming applications. The real-time event detection using social media streams uses text data arriving from sources such as Twitter to detect emerging events of interest. The real-time river sensor network analysis project uses unsupervised classification methods to classify sensor network streams arriving from the US river network to detect water quality problems. We discuss implementation details and provide performance comparison results of various individual stream processing operations for both stream processing applications.
Toward Pedagogy Driven Virtual Reality Learning Space Design

This paper argues for a need to develop Virtual Reality (VR) learning spaces based on pedagogy rather than implementing pedagogy in a general purpose VR application. In doing so, the paper summarizes the challenges in using an immersive virtual reality application to unite together primary school children across national, regional, and political boundaries in the same learning space in order to promote natural learning. Data was collected through observations during the activity sessions among children from a Finnish and a Bangladeshi primary school. Furthermore, we collected data using semi-structured interviews and focus groups with teachers, heads of schools, and children from these two schools. The data analysis revealed three types of challenges: design, technological, and pedagogical. These findings guide us in designing and developing pedagogy centered learning spaces using virtual reality.
Towards Better Accuracy and Robustness with Localized Adversarial Training

As technology and society grow increasingly dependent on computer vision, it becomes important to make sure that these technologies are secure. However, even today’s stateof-the-art classifiers are easily fooled by carefully manipulated images. The only solutions that have increased robustness against these manipulated images have come at the expense of accuracy on natural inputs. In this work, we propose a new training technique, localized adversarial training, that results in more accurate classification of both both natural and adversarial images by as much as 6.5% and 99.7%, respectively.
Advantage Amplification in Slowly Evolving Latent-State Environments

Latent-state environments with long horizons, such as those faced by recommender systems, pose significant challenges for reinforcement learning (RL). In this work, we identify and analyze several key hurdles for RL in such environments, including belief state error and small action advantage. We develop a general principle called advantage amplification that an overcome these hurdles through the use of temporal abstraction. We propose several aggregation methods and prove they induce amplification in certain settings. We also bound the loss in optimality incurred by our methods in environments where latent state evolves slowly and demonstrate their performance empirically in a stylized user-modeling task.
A Multi-Task Learning Approach for Answer Selection: A Study and a Chinese Law Dataset

In this paper, we propose a Multi-Task learning approach for Answer Selection (MTAS), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains (tasks). Specifically, MTAS consists of two key components: (i) A category classification model that learns rich category-aware document representation; (ii) An answer selection model that provides the matching scores of question-answer pairs. These two tasks work on a shared document encoding layer, and they cooperate to learn a high-quality answer selection system. In addition, a multi-head attention mechanism is proposed to learn important information from different representation subspaces at different positions. We manually annotate the first Chinese question answering dataset in law domain (denoted as LawQA) to evaluate the effectiveness of our model. The experimental results show that our model MTAS consistently outperforms the compared methods.1
Challenges of Gri Sustainability Disclosure Standards Adoption by Africa’s Two Largest Economies

Global Reporting Initiative (GRI) could today be considered the world’s most popular sustainability information disclosure standards. Yet it is seen by many as a symbolic gesture to pacify the existing masses than an instrument of sustainability disclosure. It is therefore the aim of this study to examine the effectiveness of the application of GRI by organizations in Africa’s two biggest economies in relation to the global application. The paper is purely conceptual basing its analysis on past literatures. In the main, an attempt was made to review literatures from other countries though priority was given to Africa’s two largest and most developed economies (Nigeria and South Africa). The major discovery was that GRI application to some extent proves more successful in South Africa than is some developed economies. However, challenges like Africa’s unique business environment for the effective and efficient implementation of GRI standard and guidelines still remains in the continent.
Revisiting Suboptimal Search

Suboptimal search algorithms can often solve much larger problems than optimal search algorithms, and thus have broad practical use. This paper returns to early algorithms like WA*, A*_e and Optimistic search. It studies the commonalities between these approaches in order to build a new bounded-suboptimal algorithm. Combined with recent research on avoiding node re-expansions in bounded-optimal search, a new solution quality bound is developed, which often provides proof of the solution bound much earlier during the search. Put together, these ideas provide a new state-of-the-art in bounded-optimal search.
Using Blockchain Technology to Manage Membership and Legal Contracts in a Distributed Data Market

The Data Market Austria (DMA) is an ecosystem of federated data and service infrastructures. It aims at establishing a market platform where data assets can be made accessible and offered for purchase. To support this, the DMA offers a central portal with catalogue and search services as well as a set of microservices for metadata mapping or enrichment, data quality assessment, data set submission, storage, management, and dissemination. The DMA relies on blockchain technology to allow a network of DMA members sharing information about the provenance and trading of datasets and services. This paper describes the blockchain application scenarios and the implementation of the blockchain-based distributed setup of the DMA.
Probabilistic-Logic Bots for Efficient Evaluation of Business Rules Using Conversational Interfaces

We present an approach for designing conversational interfaces (chatbots) that users interact with to determine whether or not a business rule applies in a context possessing uncertainty (from the point of view of the chatbot) as to the value of input facts. Our approach relies on Bayesian network models that bring together a business rule’s logical, deterministic aspects with its probabilistic components in a common framework. Our probabilistic-logic bots (PL-bots) evaluate business rules by iteratively prompting users to provide the values of unknown facts. The order facts are solicited is dynamic, depends on known facts, and is chosen using mutual information as a heuristic so as to minimize the number of interactions with the user. We have created a web-based content creation and editing tool that quickly enables subject matter experts to create and validate PL-bots with minimal training and without requiring a deep understanding of logic or probability. To date, domain experts at a well-known insurance company have successfully created and deployed over 80 PLbots to help insurance agents determine customer eligibility for policy discounts and endorsements.
Mammographic, Ultrasonographic and Pathologic Correlations of Focal Asymmetric Breast Densities among a Sample of Iraqi Women

Objectives: is to evaluate the mammographic focal asymmetric breast densities FABD in order to highlight which FABD might need further workup through detailed ultrasonographic characterization and comparison with the pathological results in indicated cases.&#x0D; Methods: A cross sectional analytic study was performed in the Oncology Teaching Hospital/Medical City from March 2018 to November 2018. The study included 70 women who attended the Main Referral Center for Early Detection of Breast Tumors with focal asymmetric breast densities FABD were detected by mammography. The focal asymmetry was analyzed and the other associated findings were assessed and registered. While breast ultrasound was performed for all patients, fine needle aspiration (FNA) was carried out for cases with suspicious findings on ultrasound and any FNA suspicious or malignant lesion was subsequently biopsied and the results were recorded.&#x0D; Results: mammographic FABD was found in the right breast in 37 cases (52.9%) and in the left in 33 cases (47.1%). The upper outer quadrant (UOQ) was the most common location of FABD (43 cases). In 25 cases (34.2%) with FABD, there was no abnormality found on ultrasound, but normal looking breast parenchymal tissue. Ultrasound had shown benign findings in 30 cases (44.2%). On the other hand, suspicious and/or malignant features were observed in 15 cases (22.4%) which subsequently proved to be malignant by FNA and biopsy. Architectural distortion or grouped microcalcifications and a clinically palpable FABD were associated with malignant FABD; no benign FABD revealed to be associated with these findings.&#x0D; Conclusion: The finding of FABD is common on mammography and mostly represents a benign entity of normal breast parenchymal tissue. However; it may indicate an underlying hidden malignancy especially in the presence of superadded mammographic findings as ill-defined mass, architectural distortion or grouped microcalcification.
Twenty Years of Quantum State Teleportation at the Sapienza University in Rome

Quantum teleportation is one of the most striking consequence of quantum mechanics and is defined as the transmission and reconstruction of an unknown quantum state over arbitrary distances. This concept was introduced for the first time in 1993 by Charles Bennett and coworkers, it has then been experimentally demonstrated by several groups under different conditions of distance, amount of particles and even with feed forward. After 20 years from its first realization, this contribution reviews the experimental implementations realized at the Quantum Optics Group of the University of Rome La Sapienza.
Pelatihan Membuat Media Pembelajaran Digital

Muhammadiyah University of Surabaya (UMSurabaya) responded to the development of the 4.0 industrial revolution by developing e-learning starting in the odd semester of the 2018/2019 academic year. This matter gives a big impact on the lecture system for the academic community. The Teaching and Education Faculty (FKIP) UMSurabaya as a faculty that prints millennial era teacher candidates responded well by making training to make digital learning media intended for all lecturers. The training was followed by 60 lecturers from FKIP UMSurabaya with the help of ms Office Powerpoint, Fastone, and Filmora software. The method used is the presentation (lecture) and direct practice to produce digital learning media desired by each participant.
Eliminating Redundant Actions in Partially Ordered Plans — A Complexity Analysis

In this paper we study the computational complexity of postoptimizing partially ordered plans, i.e., we investigate the problem that is concerned with detecting and deleting unnecessary actions. For totally ordered plans it can easily be tested in polynomial time whether a single action can be removed without violating executability. Identifying an executable subplan, i.e., asking whether k plan steps can be removed, is known to be NP-complete. We investigate the same questions for partially ordered input plans, as they are created by many search algorithms or used by real-world applications – in particular time-critical ones that exploit parallelism of non-conflicting actions. More formally, we investigate the computational complexity of removing an action from a partially ordered solution plan in which every linearization is a solution in the classical sense while allowing ordering insertions afterwards to repair arising executability issues. It turns out that this problem is NP-complete – even if just a single action is removed – and thereby show that this reasoning task is harder than for totally ordered plans. Moreover, we identify the structural properties responsible for this hardness by providing a fixed-parameter tractability (FPT) result.
Synergistic Impacts of Entrepreneurial and Learning Orientations on Performance: A Meta-Analysis

Entrepreneurial orientation (EO) and learning orientation (LO) are two key strategic orientations that are thought to individually influence firm performance. However, there is little understanding regarding their mutual relationship – similarities and complementarities – and their combinative effect on performance. To address this, we meta-analyzed 60 samples from 59 studies based on 16762 firms using a random effects model, to find the relationships between EO, LO, and firm performance. We find that the correlation between EO and LO is fairly large (r = 0.44) and is moderated by the country’s entrepreneurship profile, represented by entrepreneurial intention and fear of failure. While EO and LO operate through similar processes, they have independent, additive and synergistic effects on performance. EO and LO explain 17% and 13% of performance respectively while synergistically explaining 21% when combined. Furthermore, the EO-LO intercorrelation moderates the EO-performance and LO-performance relationships, so under conditions of high association, their combined effect can reach 38% of the performance variance.
Hate Speech: The Phenomenon of Offensive Language

A characteristic inherent in a democratic State is a guarantee of freedom of opinion&#x0D; and expression by every citizen. However, the space for freedom cannot be misused&#x0D; to express various ideas or views so that it becomes a tool to attack human rights and&#x0D; the freedom of others manifested in the form of hate speech. Acts of hate speech&#x0D; are currently getting more and more attention from various circles, not only for law&#x0D; enforcers and practitioners, politicians, information and communication technology&#x0D; experts. But it is also a very serious concern for the Indonesian government to form&#x0D; and give birth to regulations concerning to handling of hate speech. Moreover, caring&#x0D; for diversity and harmonization in diversity in the era of globalization of information&#x0D; technology is the biggest challenge today. In this study, phenomenology is used as&#x0D; research design whereas purposive sampling from online media is used to collect&#x0D; the data. The aim is to maintain unity in the midst of a multicultural community life&#x0D; such as Batu Bara. On the other hand, the emergence of discussions about hate&#x0D; speech actually gave the object of a new study for linguistics. Based on the linguistic&#x0D; perspective, hate speech is a phenomenon of offensive language that can present&#x0D; linguistic data and can be analyzed linguistically. Therefore, this article conceptually&#x0D; describes the role of linguistics and linguists in understanding and explaining the&#x0D; subject of hate speech.
A Smart Eye Tracking System for Virtual Reality

Virtual reality (VR) technology provides specific three-dimensional (3D) scenes for users, which could be benefit for the applications in the field of medical diagnosis, psychological analysis, cognitive researches and entertainments. The current VR device provides the virtual 3D images, but cannot synchronously detect the user's eye movements which could be significant for deriving the users' gaze points and interest regions in varied applications. In this paper, we proposed a smart eye tracking system for VR device. Firstly, a smart eye movement detection kit is mounted inside the VR device to capture the human eye movement images with the rate of 30 Hz. Based on the proposed eye detection kit, a gaze detection algorithm is then established for VR devices. Next, a gaze estimation model is proposed for human gaze estimation. The proposed smart eye tracking system can detect the user's eye movements in real time under VR stimulation. Moreover, it also provide a new human-VR interaction mode.
Attributed Subspace Clustering

Existing methods on representation-based subspace clustering mainly treat all features of data as a whole to learn a single self-representation and get one clustering solution. Real data however are often complex and consist of multiple attributes or sub-features, such as a face image has expressions or genders. Each attribute is distinct and complementary on depicting the data. Failing to explore attributes and capture the complementary information among them may lead to an inaccurate representation. Moreover, a single clustering solution is rather limited to depict data, which can often be interpreted from different aspects and grouped into multiple clusters according to attributes. Therefore, we propose an innovative model called attributed subspace clustering (ASC). It simultaneously learns multiple self-representations on latent representations derived from original data. By utilizing Hilbert Schmidt Independence Criterion as a co-regularizing term, ASC enforces that each self-representation is independent and corresponds to a specific attribute. A more comprehensive self-representation is then established by adding these self-representations. Experiments on several benchmark image datasets have demonstrated the effectiveness of ASC not only in terms of clustering accuracy achieved by the integrated representation, but also the diverse interpretation of data, which is beyond what current approaches can offer.