Reviewerly/exHarmony huggingface dataset:
SACBP: Belief space planning for continuous-time dynamical systems via stochastic sequential action control

We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends sequential action control to stochastic dynamics. The resulting algorithm, which we name SACBP, does not require discretization of spaces or time and synthesizes control signals in near real-time. SACBP is an anytime algorithm that can handle general parametric Bayesian filters under certain assumptions. We demonstrate the effectiveness of our approach in an active sensing scenario and a model-based Bayesian reinforcement learning problem. In these challenging problems, we show that the algorithm significantly outperforms other existing solution techniques including approximate dynamic programming and local trajectory optimization.
The Trouble with Sharing Your Privates: Pursuing Ethical Open Science and Collaborative Research across National Jurisdictions Using Sensitive Data

Open science and effective collaboration both require the sharing of data between researchers. This is especially true for computational methods, as the technical complexity and heterogeneous data sources often require collaboration between researchers in different institutions and jurisdictions. Many data sources, however, cannot be shared openly because of copyright law and contracts such as terms of service. These regulations can be complex, sometimes untested in case law, and vary between countries and over time. This paper details our experiences in conducting international comparative research on very large collections of news items from multiple countries. We set out the main problems we have encountered and some short- term approaches we have used to mitigate some of these problems. We end with listing some additional long-term actions that will advance our research community’s ability to collaborate on computational research using sensitive data.
Methodology of the geoecological functional zoning of territories subjected to anthropogenic disturbance due to waste disposal

In the present contribution, the geochemical relationships of territorial units (landscapes), namely sewage sludge disposal sites and historical municipal solid waste landfills, that can be considered as chemical elements migration models and as dynamic components of ecological-geological systems (landfill site adjacent area recreation area)., is described. Since the study of pollutants migration in multicomponent systems is extremely time-consuming, the development of theoretical methods, able to simulate these processes, is of particular relevance. Therefore, in the present study, a geochemical model for substance migration in anthropogenically modified geoecological bodies and their surrounding was conceived, making use of complex mathematical processes and data analysis methodologies (parallel use of factor, cluster, canonical and discriminant analysis tools), and considering both geochemical specialization/composition of parent rocks and land use (functional geoecological zoning).
THE USE OF INSTAGRAM TO DEVELOP STUDENTS’ WRITING ABILITY

The research consists of two variables, namely the independent variable (Instagram) and the dependent variable (writing ability). The aim of this research is to find out whether or not the use of Instagram is effective in teaching writing Descriptive Text. The population of this research was all grade ten students at SMAN 3 Bekasi, consisted of 370 students divided into 10 classes. The sample of this research was 74 students divided into 2 classes, taken from the population using Cluster Random Sampling Technique. This research used the Quasi Experimental Method, and the design was randomized post-test only control group design. To collect the data, the writers used instrument. The instrument consists of 10 question items in an essay. They were developed from the indicators of writing as specified in BluePrint. To get a good instrument, it was tried out to other classes outside the sample classes, and the instrument was analyzed in the item of its validity, reliability, difficulty index, and discrimination power by using Ms. Excel 2010. After it has been analyzed, it gained 5 items which were used to collect data for the research. The writers used a T-test to find out whether the alternative hypothesis (Ha) was accepted or rejected. The result of this research presented that there was a significant difference of Instagram on students’ writing ability at SMAN 3 Bekasi. The significance (Sig.) score of T-test 0.000 is lower than (&lt;) 0.05. Therefore, the research had a significant difference in Instagram on Grade Ten Students’ Writing Ability at SMAN 3 Bekasi. In addition, Instagram helped the students to develop their writing, and also, it can ease the teaching and learning process.
A Comparative Study of Machine Learning Methods for Automatic Classification of Academic and Vocational Guidance Questions

Academic and vocational guidance is a particularly important issue today, as it strongly determines the chances of successful integration into the labor market, which has become increasingly difficult. Families have understood this because they are interested, often with concern, in the orientation of their child. In this context, it is very important to consider the interests, trades, skills, and personality of each student to make the right decision and build a strong career path. This paper deals with the problematic of educational and vocational guidance by providing a comparative study of the results of four machine-learning algorithms. The algorithms we used are for the automatic classification of school orientation questions and four categories based on John L. Holland's Theory of RIASEC typology. The results of this study show that neural networks work better than the other three algorithms in terms of the automatic classification of these questions. In this sense, our model allows us to automatically generate questions in this domain. This model can serve practitioners and researchers in E-Orientation for further research because the algorithms give us good results.
End-to-end Named Entity Recognition from English Speech

Named entity recognition (NER) from text has been a widely studied problem and usually extracts semantic information from text. Until now, NER from speech is mostly studied in a two-step pipeline process that includes first applying an automatic speech recognition (ASR) system on an audio sample and then passing the predicted transcript to a NER tagger. In such cases, the error does not propagate from one step to another as both the tasks are not optimized in an end-to-end (E2E) fashion. Recent studies confirm that integrated approaches (e.g., E2E ASR) outperform sequential ones (e.g., phoneme based ASR). In this paper, we introduce a first publicly available NER annotated dataset for English speech and present an E2E approach, which jointly optimizes the ASR and NER tagger components. Experimental results show that the proposed E2E approach outperforms the classical two-step approach. We also discuss how NER from speech can be used to handle out of vocabulary (OOV) words in an ASR system.
Adapting a health video library for use in Afghanistan: provider-level acceptability and lessons for strengthening operational feasibility

Abstract Background Community health workers (CHWs) in Afghanistan are a critical care extender for primary health services, including reproductive, maternal, neonatal, and child health (RMNCH) care. However, volunteer CHWs face challenges including an ever-expanding number of tasks and insufficient time to conduct them. We piloted a health video library (HVL) intervention, a tablet-based tool to improve health promotion and counseling by CHWs. We qualitatively assessed provider-level acceptability and operational feasibility. Methods CHWs implemented the HVL pilot in three rural districts of Balkh, Herat, and Kandahar provinces. We employed qualitative methods, conducting 47 in-depth interviews (IDIs) with male and female CHWs and six IDIs with community health supervisors. We used semi-structured interview guides to explore provider perceptions of program implementation processes and solicit feedback on how to improve the HVL intervention to inform scale-up. We conducted a thematic analysis. Results CHWs reported that the HVL increased time efficiencies, reduced work burden, and enhanced professional credibility within their communities. CHWs felt video content and format were accessible for low literacy clients, but also identified challenges to operational feasibility. Although tablets were considered easy-to-use, certain technical issues required continued support from supervisors and family. Charging tablets was difficult due to inconsistent electricity access. Although some CHWs reported reaching most households in their catchment area for visits with the HVL, others were unable to visit all households due to sizeable populations and gender-related barriers, including women’s limited mobility. Conclusions The HVL was acceptable and feasible for integration into existing CHW duties, indicating it may improve RMNCH counseling, contributing to increased care-seeking behaviors in Afghanistan. Short-term challenges with technology and hardware can be addressed through continued training and provision of solar chargers. Longer-term challenges, including tablet costs, community coverage, and gender issues, require further consideration with an emphasis on equitable distribution.
Data-driven child behavior prediction system based on posture database for fall accident prevention in a daily living space

Abstract Ten thousand children are admitted to emergency rooms due to accidents every year in Tokyo. The most frequent accident is a fall accident. Fall accidents may occur when climbing to a high place in a daily living space. Since injury prevention by human supervision does not work well, the World Health Organization recommends an environmental modification approach as an effective preventive countermeasure to this problem. Predicting children’s behavior is necessary in order to improve the environment. However, even for advanced human modeling technology, predicting where children can climb in everyday life situations remains difficult. In the present study, the authors developed a new method for predicting places that children can climb in a data-driven manner by integrating cameras, a behavior recognition system (OpenPose), and a climbing motion planning algorithm based on a rapidly exploring random tree. Thirty five children participated in an experiment to collect climbing posture data. A simulation is performed based on the posture database and allows us to visually understand how children climb up in daily living space. This makes it possible to improve to achieve a safe environment for children without the need for specialized knowledge, which is useful for parents, nursery teachers, nurses, etc. The present paper describes fundamental functions of the developed system and presents an evaluation of the feasibility of the prediction function.
Concurrent Separation Logic Meets Template Games

An old dream of concurrency theory and programming language semantics has been to uncover the fundamental synchronization mechanisms which regulate situations as different as game semantics for higher-order programs, and Hoare logic for concurrent programs with shared memory and locks. In this paper, we establish a deep and unexpected connection between two recent lines of work on concurrent separation logic (CSL) and on template game semantics for differential linear logic (DiLL). Thanks to this connection, we reformulate in the purely conceptual style of template games for DiLL the asynchronous and interactive interpretation of CSL designed by Melli\`es and Stefanesco. We believe that the analysis reveals something important about the secret anatomy of CSL, and more specifically about the subtle interplay, of a categorical nature, between sequential composition, parallel product, errors and locks.
E-accessibility Analysis in User Experience for People with Disabilities

The purpose of writing this article is to know e-accessibility for users with disabilities. This study uses a descriptive research methodology with a qualitative approach. The subjects involved in this study were people with disabilities. Data obtained by conducting a literature study. The results of this study are the principle of e-accessibility in designing the User Interface (UI) must be considered because it will affect the User Experience (UX), so that internet users, especially users with disabilities have good accessibility so that it can be more easily to obtain information.
Enhancing advance encryption standard security based on dual dynamic XOR table and MixColumns transformation

An efficient approach to secure information is critically needed at present. Cryptography remains the best approach to achieve security. On this basis, the National Institute of Standards and Technology (NIST) selected Rijndael, which is a symmetric block cipher, as the advanced encryption standard (AES). The MixColumns transformation of this cipher is the most important function within the linear unit and the major source of diffusion. Dynamic MixColumns transformation can be used to enhance the AES security. In this study, a method to enhance the AES security is developed on the basis of two methods. The first method is an extension of a previous study entitled “A novel Approach for Enhancing Security of Advance Encryption Standard using Private XOR Table and 3D chaotic regarding to Software quality Factor.” In the current study, the fixed XOR operation in AES rounds is replaced with a dual dynamic XOR table by using a 3D chaotic map. The dual dynamic XOR table is based on 4 bits; one is used for even rounds, and the other is used for odd rounds. The second method is dynamic MixColumns transformation, where the maximum distance separable (MDS) matrix of the MixColumns transformation, which is fixed and public in every round, is changed with a dynamic MDS matrix, which is private, by using a 3D chaotic map. A 3D chaotic map is used to generate secret keys. These replacements enhance the AES security, particularly the resistance against attacks. Diehard and NIST tests, entropy, correlation coefficient, and histogram are used for security analysis of the proposed method. C++ is used to implement the proposed and original algorithms. MATLAB and LINX are used for the security analysis. Results show that the proposed method is better than the original AES.
Historical Graph Management in Dynamic Environments

Since dynamic graph data continuously change over time, it is necessary to manage historical data for accessing a snapshot graph at a specific time. In this paper, we propose a new historical graph management scheme that consists of an intersection snapshot and a delta snapshot to enhance storage utilization and historical graph accessibility. The proposed scheme constantly detects graph changes and calculates a common subgraph ratio between historical graphs over time. If the common subgraph ratio is lower than a threshold value, the intersection snapshot stores the common subgraphs within a time interval. A delta snapshot stores the subgraphs that are not contained in the intersection snapshot. Several delta snapshots are connected to the intersection snapshot to maintain the modified subgraph over time. The efficiency of storage space is improved by managing common subgraphs stored in the intersection snapshot. Furthermore, the intersection and delta snapshots can be connected to search a graph at a specific time. We show the superiority of the proposed scheme through various performance evaluations.
Mobile technologies in library work: foreign experience

The current foreign trends of library informatization based on mobile technologies, radical information personalization and diversification of user auditorium are examined. The study is novel in that the author investigates into the current trends in foreign librarianship like advance of mobile sites, services based on augmented reality interfaces and RFID method. The author emphasizes that optimization based on mobile technologies is being accomplished in scientific and public libraries – leaders of the digital revolution but also in the major libraries of the developing states. The modern library practice abroad is analyzed: the mobile versions of www-sites and catalogs and applications; resources for mobile devices are being designed and implemented; new services with mobile interfaces are offered. Mobile-based service features, like trans-border and instant access, time saving, absence of spatial constraints are discussed. The role of mobile technologies in transforming library services in the developing states as the chance to overcome the increasing digital gap is emphasized.
A Feature Space Focus in Machine Teaching

Contemporary Machine Learning (ML) often focuses on large existing and labeled datasets and metrics around accuracy and performance. In pervasive online systems, conditions change constantly and there is a need for systems that can adapt. In Machine Teaching (MT) a human domain expert is responsible for the knowledge transfer and can thus address this. In my work, I focus on domain experts and the importance of, for the ML system, available features and the space they span. This space confines the, to the ML systems, observable fragment of the physical world. My investigation of the feature space is grounded in a conducted study and related theories. The result of this work is applicable when designing systems where domain experts have a key role as teachers.
A Cooperative Coevolutionary Approach to Discretization-Based Feature Selection for High-Dimensional Data

Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.
Explicit effect subtyping

As popularity of algebraic effects and handlers increases, so does a demand for their efficient execution. Eff, an ML-like language with native support for handlers, has a subtyping-based effect system on which an effect-aware optimizing compiler could be built. Unfortunately, in our experience, implementing optimizations for Eff is overly error-prone because its core language is implicitly-typed, making code transformations very fragile. To remedy this, we present an explicitly-typed polymorphic core calculus for algebraic effect handlers with a subtyping-based type-and-effect system. It reifies appeals to subtyping in explicit casts with coercions that witness the subtyping proof, quickly exposing typing bugs in program transformations. Our typing-directed elaboration comes with a constraint-based inference algorithm that turns an implicitly-typed Eff-like language into our calculus. Moreover, all coercions and effect information can be erased in a straightforward way, demonstrating that coercions have no computational content. Additionally, we present a monadic translation from our calculus into a pure language without algebraic effects or handlers, using the effect information to introduce monadic constructs only where necessary.
Morfessor EM+Prune: Improved Subword Segmentation with Expectation Maximization and Pruning

Data-driven segmentation of words into subword units has been used in various natural language processing applications such as automatic speech recognition and statistical machine translation for almost 20 years. Recently it has became more widely adopted, as models based on deep neural networks often benefit from subword units even for morphologically simpler languages. In this paper, we discuss and compare training algorithms for a unigram subword model, based on the Expectation Maximization algorithm and lexicon pruning. Using English, Finnish, North Sami, and Turkish data sets, we show that this approach is able to find better solutions to the optimization problem defined by the Morfessor Baseline model than its original recursive training algorithm. The improved optimization also leads to higher morphological segmentation accuracy when compared to a linguistic gold standard. We publish implementations of the new algorithms in the widely-used Morfessor software package.
Joint Representations of Knowledge Graphs and Textual Information via Reference Sentences

The joint representations of knowledge graph have become an important approach to improve the quality of knowledge graph, which is beneficial to machine learning, data mining, and artificial intelligence applications. However, the previous work suffers severely from the noise in text when modeling the text information. To overcome this problem, this paper mines the high-quality reference sentences of the entities in the knowledge graph, to enhance the representation ability of the entities. A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, which embeds the entity, the relations, and the words into a unified vector space. The proposed framework consists of knowledge graph representation learning module, textual relation representation learning module, and textual entity representation learning module. Experiments on entity prediction, relation prediction, and triple classification tasks are conducted, results show that the proposed framework can significantly improve the performance of mining and fusing the text information. Especially, compared with the state-of-the-art method[15], the proposed framework improves the metric of H@10 by 5.08% and 3.93% in entity prediction task and relation prediction task, respectively, and improves the metric of accuracy by 5.08% in triple classification task.
FastSV: A Distributed-Memory Connected Component Algorithm with Fast Convergence.

This paper presents a new distributed-memory algorithm called FastSV for finding connected components in an undirected graph. Our algorithm simplifies the classic Shiloach-Vishkin algorithm and employs several novel and efficient hooking strategies for faster convergence. We map different steps of FastSV to linear algebraic operations and implement them with the help of scalable graph libraries. FastSV uses sparse operations to avoid redundant work and optimized MPI communication to avoid bottlenecks. The resultant algorithm shows high-performance and scalability as it can find the connected components of a hyperlink graph with over 134B edges in 30 seconds using 262K cores on a Cray XC40 supercomputer. FastSV outperforms the state-of-the-art algorithm by an average speedup of 2.21x (max 4.27x) on a variety of real-world graphs.