nils-herrmann/clean_scirepeval huggingface dataset:
Searching RDF Graphs with SPARQL and Keywords

The proliferation of knowledge-sharing communities like Wikipedia and the advances in automated information extraction from Web pages enable the construction of large knowledge bases with facts about entities and their relationships. The facts can be represented in the RDF data model, as so-called subject-property-object triples, and can thus be queried by structured query languages like SPARQL. In principle, this allows precise querying in the database spirit. However, RDF data may be highly diverse and queries may return way too many results, so that ranking by informativeness measures is crucial to avoid overwhelming users. Moreover, as facts are extracted from textual contexts or have community-provided annotations, it can be beneficial to consider also keywords for formulating search requests. This paper gives an overview of recent and ongoing work on ranked retrieval of RDF data with keyword-augmented structured queries. The ranking method is based on statistical language models, the state-of-the-art paradigm in information retrieval. The paper develops a novel form of language models for the structured, but schema-less setting of RDF triples and extended SPARQL queries. 1  Entity-Relationship graphs are receiving great attention for information management outside of mainstream database engines. In particular, the Semantic-Web data model RDF (Resource Description Format) is gaining popularity for applications on scientific data such as biological networks , social Web2.0 applications [4], large-scale knowledge bases such as DBpedia [2] or YAGO [13], and more generally, as a light-weight representation for the “Web of data” . An RDF data collection consists of a set of subject-property-object triples, SPO triples for short. In ER terminology, an SPO triple corresponds to a pair of entities connected by a named relationship or to an entity connected to the value of a named attribute. As the object of a triple can in turn be the subject of other triples, we can also view the RDF data as a graph of typed nodes and typed edges where nodes correspond to entities and edges to relationships (viewing attributes as relations as well). Some of the existing RDF collections contain more than a billion triples. As a simple example that we will use throughout the paper, consider a Web portal on movies. Table 1 shows a few sample triples. The example illustrates a number of specific requirements that RDF data poses for querying:  Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering
BOCK : Bayesian Optimization with Cylindrical Kernels

A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball geometry of the search space using a cylindrical transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary, while concentrating its efforts relatively more near the center of the search region, where we expect the solution to be located. We evaluate BOCK extensively, showing that it is not only more accurate and efficient, but it also scales successfully to problems with a dimensionality as high as 500. We show that the better accuracy and scalability of BOCK even allows optimizing modestly sized neural network layers, as well as neural network hyperparameters.
E-learning environment compared to traditional classroom

Throughout history, the educational process used different educational technologies which did not significantly alter the manner of learning in the classroom. By implementing e-learning technology to the educational process, new and completely different innovative learning scenarios are made possible, including more active student involvement outside the traditional classroom. The quality of the realization of the educational objective in any learning environment depends primarily on the teacher who creates the educational process, mentors and acts as a moderator in the communication within the educational process, but also relies on the student who acquires the educational content. The traditional classroom learning and e-learning environment enable different manners of adopting educational content, and this paper reveals their key characteristics with the purpose of better use of e-learning technology in the educational process.
Effects of Field of View and Visual Complexity on Virtual Reality Training Effectiveness for a Visual Scanning Task

Virtual reality training systems are commonly used in a variety of domains, and it is important to understand how the realism of a training simulation influences training effectiveness. We conducted a controlled experiment to test the effects of display and scenario properties on training effectiveness for a visual scanning task in a simulated urban environment. The experiment varied the levels of field of view and visual complexity during a training phase and then evaluated scanning performance with the simulator's highest levels of fidelity and scene complexity. To assess scanning performance, we measured target detection and adherence to a prescribed strategy. The results show that both field of view and visual complexity significantly affected target detection during training; higher field of view led to better performance and higher visual complexity worsened performance. Additionally, adherence to the prescribed visual scanning strategy during assessment was best when the level of visual complexity during training matched that of the assessment conditions, providing evidence that similar visual complexity was important for learning the technique. The results also demonstrate that task performance during training was not always a sufficient measure of mastery of an instructed technique. That is, if learning a prescribed strategy or skill is the goal of a training exercise, performance in a simulation may not be an appropriate indicator of effectiveness outside of training-evaluation in a more realistic setting may be necessary.
Beyond Trade-Off: Accelerate FCN-Based Face Detector with Higher Accuracy

Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels, which boiled down all the redundant calculation, and most recent state-of-the-art methods such as Faster-RCNN, SSD, YOLO and FPN use FCN as their backbone. So here comes one question: Can we find a universal strategy to further accelerate FCN with higher accuracy, so could accelerate all the recent FCN-based methods? To analyze this, we decompose the face searching space into two orthogonal directions, 'scale' and 'spatial'. Only a few coordinates in the space expanded by the two base vectors indicate foreground. So if FCN could ignore most of the other points, the searching space and false alarm should be significantly boiled down. Based on this philosophy, a novel method named scale estimation and spatial attention proposal (S2AP) is proposed to pay attention to some specific scales in image pyramid and valid locations in each scales layer. Furthermore, we adopt a masked-convolution operation based on the attention result to accelerate FCN calculation. Experiments show that FCN-based method RPN can be accelerated by about 4Ã— with the help of S2AP and masked-FCN and at the same time it can also achieve the state-of-the-art on FDDB, AFW and MALF face detection benchmarks as well.
Odyssey's end: lay conceptions of nostalgia reflect its original Homeric meaning.

Nostalgia fulfills pivotal functions for individuals, but lacks an empirically derived and comprehensive definition. We examined lay conceptions of nostalgia using a prototype approach. In Study 1, participants generated open-ended features of nostalgia, which were coded into categories. In Study 2, participants rated the centrality of these categories, which were subsequently classified as central (e.g., memories, relationships, happiness) or peripheral (e.g., daydreaming, regret, loneliness). Central (as compared with peripheral) features were more often recalled and falsely recognized (Study 3), were classified more quickly (Study 4), were judged to reflect more nostalgia in a vignette (Study 5), better characterized participants' own nostalgic (vs. ordinary) experiences (Study 6), and prompted higher levels of actual nostalgia and its intrapersonal benefits when used to trigger a personal memory, regardless of age (Study 7). These findings highlight that lay people view nostalgia as a self-relevant and social blended emotional and cognitive state, featuring a mixture of happiness and loss. The findings also aid understanding of nostalgia's functions and identify new methods for future research.
A Ten Step Process for Forensic Readiness

A forensic investigation of digital evidence is commonly employed as a post-event response to a serious information security incident. In fact, there are many circumstances where an organisation may benefit from an ability to gather and preserve digital evidence before an incident occurs. Forensic readiness is defined as the ability of an organisation to maximise its potential to use digital evidence whilst minimising the costs of an investigation. The costs and benefits of such an approach are outlined. Preparation to use digital evidence may involve enhanced system and staff monitoring, technical, physical and procedural means to secure data to evidential standards of admissibility, processes and procedures to ensure that staff recognise the importance and legal sensitivities of evidence, and appropriate legal advice and interfacing with law enforcement. This paper proposes a ten step process for an organisation to implement forensic readiness.
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach. Accordingly, the choice of representation – the mapping of observation space to goal space – is crucial. To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation. We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice. Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods.12
A survey on indexing techniques for big data: taxonomy and performance evaluation

The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or ‘big data.’ Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques’ performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.
Implementing Knowledge Management Systems in Public Sector Organisations: A Case Study of Critical Success Factors

In 2006, the Government Department participating in this study deployed its Knowledge Management System (KMS): this case study describes the lessons learned by this public sector organisation in its journey towards becoming a knowledge organisation. Extant research on the implementation of KMS in organisations operating in the private sector posits a range of factors that are correlated with successful KMS implementation. This paper first outlines these factors and then employs them as an integrative analytic framework to evaluate the critical success factors (CSFs) for KMS implementation in this government organisation. The study supports the empirical fidelity of the factors presented in the framework. Yet despite the successful attainment of departmental specific CSFs, regulative influences from the institutional environment, which lay outside the locus of control of the department, led to sub-optimal outcomes in the use of the KMS in the short-term. Hence, government organisations need to understand and take into account such influences if they are to successfully implement KMS.
Noise Mitigation for Neural Entity Typing and Relation Extraction

In this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing and relation extraction. For the first noise type, we introduce multi-instance multi-label learning algorithms using neural network models, and apply them to fine-grained entity typing for the first time. Our model outperforms the state-of-the-art supervised approach which uses global embeddings of entities. For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction. Our experiments show that probabilistic predictions are more robust than discrete predictions and that joint training of the two tasks performs best.
Optimistic replication

Data replication is a key technology in distributed systems that enables higher availability and performance. This article surveys optimistic replication algorithms. They allow replica contents to diverge in the short term to support concurrent work practices and tolerate failures in low-quality communication links. The importance of such techniques is increasing as collaboration through wide-area and mobile networks becomes popular.Optimistic replication deploys algorithms not seen in traditional “pessimistic” systems. Instead of synchronous replica coordination, an optimistic algorithm propagates changes in the background, discovers conflicts after they happen, and reaches agreement on the final contents incrementally.We explore the solution space for optimistic replication algorithms. This article identifies key challenges facing optimistic replication systems---ordering operations, detecting and resolving conflicts, propagating changes efficiently, and bounding replica divergence---and provides a comprehensive survey of techniques developed for addressing these challenges.
Characteristics and Principles of Scaled Agile

The Agile Manifesto and Agile Principles are typically referred to as the definitions of "agile" and "agility". There is research on agile values and agile practises, but how should “Scaled Agility” be defined, and what might be the characteristics and principles of Scaled Agile? This paper examines the characteristics of scaled agile, and the principles that are used to build up such agility. It also gives suggestions as principles upon which Scaled Agility can be built.
Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System

The detection of emotion is becoming an increasingly important field for humancomputer interaction as the advantages emotion recognition offer become more apparent and realisable. Emotion recognition can be achieved by a number of methods, one of which is through the use of bio-sensors. Bio-sensors possess a number of advantages against other emotion recognition methods as they can be made both inobtrusive and robust against a number of environmental conditions which other forms of emotion recognition have difficulty to overcome. In this paper, we describe a procedure to train computers to recognise emotions using multiple signals from many different bio-sensors. In particular, we describe the procedure we adopted to elicit emotions and to train our system to recognise them. We also present a set of preliminary results which indicate that our neural net classifier is able to obtain accuracy rates of 96.6% and 89.9% for recognition of emotion arousal and valence respectively.
A Blockchain Framework for Insurance Processes

We design a distributed platform with blockchain as a system service for supporting transaction execution in insurance processes. The insurance industry is heavily dependent on multiple processes between transacting parties for initiating, maintaining and closing diverse kind of policies. Transaction processing time, payment settlement time and security protection of the process execution are major concerns. Blockchain technology, originally conceived as an immutable distributed ledger for detecting double spending of cryptocurrencies, is now increasingly used in different FinTech systems to address productivity and security requirements. The application of blockchain in FinTech processing requires a deep understanding of the underlying business processes. It supports automated interactions between the blockchain and existing transaction systems through the notion of smart contracts. In this paper, we focus on the design of an efficient approach for processing insurance related transactions based on a blockchain-enabled platform. An experimental prototype is developed on Hyperledger fabric, an open source permissioned blockchain design framework. We discuss the main design requirements, corresponding design propositions, and encode various insurance processes as smart contracts. Extensive experiments were conducted to analyze performance of our framework and security of the proposed design.
Modeling and nonlinear PD regulation for ball and plate system

This work discusses the regulation of the ball and plate system, the problemis to design a control laws which generates a voltage u for the servomotors to move the ball from the actual position to a desired one. The controllers are constructed by introducing nonlinear compensation terms into the traditional PD controller. In this paper, a complete physical system and controller design is explored from conception to modeling to testing and implementation. The stability of the control is presented. Experiment results are obtained via our prototype of the ball and plate system.
Perceptions about Eclampsia, Birth Preparedness, and Complications Readiness among Antenatal Clients Attending a Specialist Hospital in Kano, Nigeria

Eclampsia is a reliable indicator of poor birth preparedness and complications readiness. We determined perceptions about eclampsia, birth preparedness, and complications readiness among antenatal clients in Kano, Nigeria.  A cross-sectional design was used to study 250 randomly selected antenatal clients. Data was analyzed using SPSS 16.0.  The mean age of the respondents was 26.1 ± 6.4 years. The majority perceived that eclampsia is preventable through good ANC (76.4%) and hospital delivery (70.8%). Overall, 66.8% had good perception about eclampsia. Having at least secondary school education and multigravidity were associated with good perception about eclampsia on multivariate analysis. About a third (39.6%) of the mothers was less prepared. On binary logistic regression, good perception about eclampsia and multigravidity were associated with being very prepared for birth. Up to 37.6% were not ready for complications. Half (50.4%) knew at least three danger signs of pregnancy, and 30.0% donated blood or identified suitable blood donor. On multivariate analysis, having at least secondary school education, being very prepared for birth, and multigravidity emerged as the only predictors of the respondents' readiness for complications.  Health workers should emphasize the practicability of birth preparedness and complications readiness during ANC and in the communities, routinely review plans, and support clients meet-up challenging areas. The importance of girl-child education to at least secondary school should be buttressed.
Perceived supervisor support: contributions to perceived organizational support and employee retention.

Three studies investigated the relationships among employees' perception of supervisor support (PSS), perceived organizational support (POS), and employee turnover. Study 1 found, with 314 employees drawn from a variety of organizations, that PSS was positively related to temporal change in POS, suggesting that PSS leads to POS. Study 2 established, with 300 retail sales employees, that the PSS-POS relationship increased with perceived supervisor status in the organization. Study 3 found, with 493 retail sales employees, evidence consistent with the view that POS completely mediated a negative relationship between PSS and employee turnover. These studies suggest that supervisors, to the extent that they are identified with the organization, contribute to POS and, ultimately, to job retention.
Machine Learning Classification over Encrypted Data

Machine learning classification is used for numerous tasks nowadays, such as medical or genomics predictions, spam detection, face recognition, and financial predictions. Due to privacy concerns, in some of these applications, it is important that the data and the classifier remain confidential. In this work, we construct three major classification protocols that satisfy this privacy constraint: hyperplane decision, Naïve Bayes, and decision trees. We also enable these protocols to be combined with AdaBoost. At the basis of these constructions is a new library of building blocks, which enables constructing a wide range of privacy-preserving classifiers; we demonstrate how this library can be used to construct other classifiers than the three mentioned above, such as a multiplexer and a face detection classifier. We implemented and evaluated our library and our classifiers. Our protocols are efficient, taking milliseconds to a few seconds to perform a classification when running on real medical datasets.