Reviewerly/exHarmony huggingface dataset:
INFORMATION SECURITY MANAGEMENT FRAMEWORK SUITABILITY ESTIMATION FOR SMALL AND MEDIUM ENTERPRISE

Information security is one of the key concerns of an enterprise or organization. To assure suitable management of information security a list of information security management frameworks has been developed by a number of institutions and authors. A condensed information in information security management framework is very important to a small and medium enterprise as this type of enterprise usually lacks resources for information security expertise and deep analysis. Despite the fact, the information security management process and its frameworks, on the other hand, are very complex and require a big number of different elements. At the moment the comparison it is very shallow, as all properties of the comparison are treated equally important. In real life, the importance of different criteria of information security management framework and their suitability for small and medium enterprise vary. Therefore we use the Analytic Hierarchy Process to construct a hierarchy of information security management frameworks quality and applicability in small and medium enterprise and define the weights for each of the criteria. Weighted criteria express the importance of the criteria and executed the final comparison of alternatives (five information security management frameworks) is more realistic (similar to experts opinion) comparing to existing comparisons.
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling

The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft ICECAPS) is an upcoming open-source natural language processing repository.ICECAPS wraps Tensor-Flow functionality in a modular componentbased architecture, presenting an intuitive and flexible paradigm for constructing sophisticated learning setups.Capabilities include multitask learning between models with shared parameters, upgraded language model decoding features, a range of built-in architectures, and a user-friendly data processing pipeline.The system is targeted toward conversational tasks, exploring diverse response generation, coherence, and knowledge grounding.ICECAPS also provides pretrained conversational models that can be either used directly or loaded for fine-tuning or bootstrapping other models; these models power an online demo of our framework.
Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) Model for Human Action Recognition

Human action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although significant results have been achieved in simple scenarios, HAR is still a challenging task due to issues associated with view independence, occlusion and inter-class variation observed in realistic scenarios. In previous research efforts, the classical bag of visual words approach along with its variations has been widely used. In this paper, we propose a Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition without compromising the strengths of the classical bag of visual words approach. Expressions are formed based on the density of a spatio-temporal cube of a visual word. To handle inter-class variation, we use class-specific visual word representation for visual expression generation. In contrast to the Bag of Expressions (BoE) model, the formation of visual expressions is based on the density of spatio-temporal cubes built around each visual word, as constructing neighborhoods with a fixed number of neighbors could include non-relevant information making a visual expression less discriminative in scenarios with occlusion and changing viewpoints. Thus, the proposed approach makes the model more robust to occlusion and changing viewpoint challenges present in realistic scenarios. Furthermore, we train a multi-class Support Vector Machine (SVM) for classifying bag of expressions into action classes. Comprehensive experiments on four publicly available datasets: KTH, UCF Sports, UCF11 and UCF50 show that the proposed model outperforms existing state-of-the-art human action recognition methods in term of accuracy to 99.21%, 98.60%, 96.94 and 94.10%, respectively.
Organization of Student Project Based Activities through Individual Learning Routes

The article deals with the organization of project training within the university education through the construction of individual educational routes. The authors believe that the value orientationsand the motivationsystem are not only interrelated, but also mutually influence each other, which confirms the need to study value orientations when building an individual route. Based on the Milton Rokich questionnaire, the authors identify three groups of students based on their value orientations. The stages of building individual educational routes are also presented.
Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning

To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segmentlevel phonotactic acquisition, we compare the performance of two recurrent neural network models of phonotactic learning: one that has access to distinctive features at the start of the learning process, and one that does not.Though the predictions of both models are significantly correlated with human judgments of non-words, the feature-naive model significantly outperforms the feature-aware one in terms of probability assigned to a held-out test set of English words, suggesting that distinctive features are not obligatory for learning phonotactic patterns at the segment level.
Security, Confidentiality and Privacy in Health of Healthcare Data

Background: One of the most important facts that should be considered is confidentiality in order to maintain privacy turning out to be matters of security.Keeping-up confidentiality is a crucial factor in any field, as well as health realms.Professionals who have the ingress to approach the patients' communications must keep confidentiality in health.The priority for any human being is privacy to information especially related to health.Security enables us to live peacefully, without anxiety and in full insurance. Methods:The interpretive methodology was used in this research as it gives an impression of face to face interactions in healthcare bringing in social reality of what is happening in the health society. Results:In consultations on gathering these results for our research, we also realized that the most common threats of loss of data and theft come under certain types of disclosures mainly third parties, routine and inadvertent.Upon this realization, there must be notification to protect security, confidentiality and privacy when security breaches occur mainly to patients.As a result, patients must provide consent about their medical information in electronically form or in writing and the consent must be signed by the patient or family member or trusted entity.The patients must come clear on the nature of the information to be disclosed and where it should be disclosed and also when the consent should expire.At the same time, a health facility must take care of the institution's database and can only disclose to the management of the health institution whose obligation would also be to protect the data, as they might need the information for research purposes, where the researchers have approval from their institution's or to legal representatives. Conclusion:The advent of the hype of electronic information technology leads to major inconvenience in the main areas of human life.This manuscript explores issues in maintaining confidentiality and privacy in healthcare and other analysis of its value to individual and society as a whole."Right to privacy is really important.You pull that brick out and another and pretty soon the house falls." Tim Cook (2016)
Mechanically Regularized FE DIC for Heterogeneous Materials

In situ tensile tests in a scanning electron microscope (SEM) have been conducted on a 8-layer 5-harness satin carbon fibre and epoxy matrix composite to observe the first stages of damage at the scale of fibres and matrix. A speckle pattern based on a suspension of alumina particles was applied onto the surface of the specimen to facilitate the use of digital image correlation (DIC). Local and finite element (FE) DIC are compared on pictures acquired during the tensile tests, with and without a speckle pattern. FE DIC with mechanical regularization was found to be the only approach able to measure displacement fields at a fine enough resolution in both cases. This method, initially created for homogeneous materials, was then adapted to heterogeneous materials. First, a microstructure consistent mesh was created and used for correlation purposes. Second, the difference between the mechanical properties of the constituents is taken into account in the mechanical regularization. Last, the accuracy of the method is analysed. The adaptation presented herein was proved to be able to measure displacement fields in the matrix between fibres with an error of 10 nm (a fifth of a pixel) and to detect the initiation of the first damage mechanisms by means of the mechanical residuals.
Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks

Adversarial domain adaptation has been recently introduced as an effective technique for textual matching tasks, such as question deduplication (Shah et al., 2018).Here we investigate the use of gradient reversal on adversarial domain adaptation to explicitly learn both shared and unshared (domain specific) representations between two textual domains.In doing so, gradient reversal learns features that explicitly compensate for domain mismatch, while still distilling domain specific knowledge that can improve target domain accuracy.We evaluate reversing gradients for adversarial adaptation on multiple domains, and demonstrate that it significantly outperforms other methods on question deduplication as well as on recognizing textual entailment (RTE) tasks, achieving up to 7% absolute boost in base model accuracy on some datasets.
Domain-Informed Spline Interpolation

Standard interpolation techniques are implicitly based on the assumption that the signal lies on a single homogeneous domain. In contrast, many naturally occurring signals lie on an inhomogeneous domain, such as brain activity associated to different brain tissue. We propose an interpolation method that instead exploits prior information about domain inhomogeneity, characterized by different, potentially overlapping, subdomains. As proof of concept, the focus is put on extending conventional shift-invariant B-spline interpolation. Given a known inhomogeneous domain, B-spline interpolation of a given order is extended to a domain-informed, shift-variant interpolation. This is done by constructing a domain-informed generating basis that satisfies stability properties. We illustrate example constructions of domain-informed generating basis and show their property in increasing the coherence between the generating basis and the given inhomogeneous domain. By advantageously exploiting domain knowledge, we demonstrate the benefit of domain-informed interpolation over standard B-spline interpolation through Monte Carlo simulations across a range of B-spline orders. We also demonstrate the feasibility of domain-informed interpolation in a neuroimaging application where the domain information is available by a complementary image contrast. The results show the benefit of incorporating domain knowledge so that an interpolant consistent to the anatomy of the brain is obtained.
Epistemic Oughts in Stit Semantics

This paper explores ways in which agentive, deontic, and epistemic concepts combine to yield ought statements, or "oughts," of different characters.I am especially interested in agentive ought statements whose violation invites criticism of the agent.I refer to these statements as "epistemic oughts," since an appeal to knowledge seems to play such an important role in their description.The investigation takes place in the setting of stit semantics, a modal framework for the analysis of agentive statements.I begin by supplementing stit semantics with an epistemic operator, and then exploring an initial account of epistemic oughts that results from combining this operator with agentive and deontic concepts in a straightforward way.After showing that this initial proposal is flawed, I then offer an account of epistemic oughts in which the role of knowledge is more complex, but which escapes the flaws of the initial proposal.Finally, I mention two directions for generalization: to relativistic oughts, and to conditional oughts. IntroductionThis paper explores some of the ways in which agentive, deontic, and epistemic concepts combine to yield ought statements-or simply, oughts-of different characters.Consider an example.Suppose I place a coin on the table, either heads up or tails up, though the coin is covered and you do not know which.And suppose you are then asked to bet whether the coin is heads up or tails up, with $10 to win if you bet correctly.If the coin is heads up but you bet tails, there is a sense in which we would naturally say that you ought to have made the other choice-at least, things would have turned out better for you if you had.But an ought statement like this does not involve any suggestion that you should be criticized for your actual choice.Nobody could blame you, in this situation, for betting incorrectly.By contrast, imagine that the coin is placed in such a way that you can see that it is heads up, but you bet tails anyway.Again we would say that you ought to have done otherwise, but this time it seems that you could legitimately be criticized for your choice.These two scenarios have much in common: the coin is placed heads up but you bet tails, so that, in both cases, we would naturally say that you ought to have
Learning Network-to-Network Model for Content-rich Network Embedding

Recently, network embedding (NE) has achieved great successes in learning low dimensional representations for network nodes and has been increasingly applied to various network analytic tasks. In this paper, we consider the representation learning problem for content-rich networks whose nodes are associated with rich content information. Content-rich network embedding is challenging in fusing the complex structural dependencies and the rich contents. To tackle the challenges, we propose a generative model, Network-to-Network Network Embedding (Net2Net-NE) model, which can effectively fuse the structure and content information into one continuous embedding vector for each node. Specifically, we regard the content-rich network as a pair of networks with different modalities, i.e., content network and node network. By exploiting the strong correlation between the focal node and the nodes to whom it is connected to, a multilayer recursively composable encoder is proposed to fuse the structure and content information of the entire ego network into the egocentric node embedding. Moreover, a cross-modal decoder is deployed to mapping the egocentric node embeddings into node identities in an interconnected network. By learning the identity of each node according to its content, the mapping from content network to node network is learned in a generative manner. Hence the latent encoding vectors learned by the Net2Net-NE can be used as effective node embeddings. Extensive experimental results on three real-world networks demonstrate the superiority of Net2Net-NE over state-of-the-art methods.
RobustiQ

GPU-based methods represent state-of-the-art in approximate nearest neighbor (ANN) search, as they are scalable (billion-scale), accurate (high recall) as well as efficient (sub-millisecond query speed). Faiss, the representative GPU-based ANN system, achieves considerably faster query speed than the representative CPU-based systems. The query accuracy of Faiss critically depends on the number of indexing regions, which in turn is dependent on the amount of available memory. At the same time, query speed deteriorates dramatically with the increase in the number of partition regions. Thus, it can be observed that Faiss suffers from a lack of robustness, that the fine-grained partitioning of datasets is achieved at the expense of search speed, and vice versa. In this paper, we introduce a new GPU-based ANN search method, Robust Quantization (RobustiQ), that addresses the robustness limitations of existing GPU-based methods in a holistic way. We design a novel hierarchical indexing structure using vector and bilayer line quantization. This indexing structure, together with our indexing and encoding methods, allows RobustiQ to avoid the need for maintaining a large lookup table, hence reduces not only memory consumption but also query complexity. Our extensive evaluation on two public billion-scale benchmark datasets, SIFT1B and DEEP1B, shows that RobustiQ consistently obtains 2-3 × speedup over Faiss while achieving better query accuracy for different codebook sizes. Compared to the best CPU-based ANN systems, RobustiQ achieves even more pronounced average speedups of 51.8 × and 11 × respectively.
Unraveling Antonym’s Word Vectors through a Siamese-like Network

Discriminating antonyms and synonyms is an important NLP task that has the difficulty that both, antonyms and synonyms, contains similar distributional information.Consequently, pairs of antonyms and synonyms may have similar word vectors.We present an approach to unravel antonymy and synonymy from word vectors based on a siamese network inspired approach.The model consists of a two-phase training of the same base network: a pre-training phase according to a siamese model supervised by synonyms and a training phase on antonyms through a siamese-like model that supports the antitransitivity present in antonymy.The approach makes use of the claim that the antonyms in common of a word tend to be synonyms.We show that our approach outperforms distributional and patternbased approaches, relaying on a simple feed forward network as base network of the training phases.
Automatic Calibration of a Six-Degrees-of-Freedom Pose Estimation System

Systems for estimating the six-degrees-of-freedom human body pose have been improving for over two decades. Technologies such as motion capture cameras, advanced gaming peripherals and more recently both deep learning techniques and virtual reality systems have shown impressive results. However, most systems that provide high accuracy and high precision are expensive and not easy to operate. Recently, research has been carried out to estimate the human body pose using the HTC Vive virtual reality system. This system shows accurate results while keeping the cost under a 1000 USD. This system uses an optical approach. Two transmitter devices emit infrared pulses and laser planes are tracked by use of photo diodes on receiver hardware. A system using these transmitter devices combined with low-cost custom-made receiver hardware was developed previously but requires manual measurement of the position and orientation of the transmitter devices. These manual measurements can be time consuming, prone to error and not possible in particular setups. We propose an algorithm to automatically calibrate the poses of the transmitter devices in any chosen environment with custom receiver/calibration hardware. Results show that the calibration works in a variety of setups while being more accurate than what manual measurements would allow. Furthermore, the calibration movement and speed has no noticeable influence on the precision of the results.
You Only Need Attention to Traverse Trees

In recent NLP research, a topic of interest is universal sentence encoding, sentence representations that can be used in any supervised task.At the word sequence level, fully attention-based models suffer from two problems: a quadratic increase in memory consumption with respect to the sentence length and an inability to capture and use syntactic information.Recursive neural nets can extract very good syntactic information by traversing a tree structure.To this end, we propose Tree Transformer, a model that captures phrase level syntax for constituency trees as well as word-level dependencies for dependency trees by doing recursive traversal only with attention.Evaluation of this model on four tasks gets noteworthy results compared to the standard transformer and LSTM-based models as well as tree-structured LSTMs.Ablation studies to find whether positional information is inherently encoded in the trees and which type of attention is suitable for doing the recursive traversal are provided.
Culture, Environment and E-learning as Factor in Student Performance (Case Studies in Management Accounting Study Programs)

The aim of the study is to solve the problem of decreasing student academic performance which is influenced by satisfaction in using e-learning, the quality of e-learning systems, environment, and organizational culture.The research method used is verification by using structural equational modeling test with partial least square approach, primary data obtained from questionnaires designed by researchers.Respondents are students who are in management accounting courses with the number of samples using the power analysis.The results of the study answer the problems that occur that there is no evidence of environmental uncertainty, organizational culture towards e-learning.But, e-learning affects user satisfaction, and user satisfaction influences student academic performance.The results of the study are expected to provide input for managers to consider the application of e-learning system learning in management accounting courses.The uniqueness of this research is the development of a success model of information systems from DeLone & McLane, shown by the addition of exogenous variables, namely the environment and organizational culture that were not previously present in the model.
Hierarchical quality-guided phase unwrapping algorithm

A hierarchical quality-guided phase unwrapping algorithm in a shared memory environment is proposed. First, the wrapped phase is divided into regular blocks, and local wrap counts of every block are obtained by a quality-guided strategy. Then, the gradient of the block wrap counts of adjacent blocks and the quality of every block are defined by the boundary local wrap counts of every block. Each block&#x2019;s data can be regarded as an abstract phase point, and the quality-guided strategy can be used again to solve the block wrap counts of each block. Finally, the absolute wrap counts of each phase point are obtained by adding local wrap counts and corresponding block wrap counts, and then the final unwrapped phase is obtained. The performance of the proposed algorithm is verified through an unwrapping experiment performed on simulated data and the real interferometric synthetic aperture sonar wrapped phase in a shared memory environment. The results show that the proposed method greatly improves phase unwrapping efficiency while maintaining the correctness of unwrapped results.