Reviewerly/exHarmony huggingface dataset:
Applying an Improving Strategy that embeds Functional and Non-Functional Requirements Concepts

Organizations should set and reach business goals for varied purposes using the suitable strategies. Basically, a strategy specifies the activities, methods and another related resources that should be considered in order to achieve a given goal purpose. Goal purposes and their associated strategies can aim at evaluating, testing, developing, or maintaining some entity. Some concrete evaluation purposes such as to understand or monitor can be achieved by strategies embracing non-functional requirements definition, measurement, evaluation and analysis activities. Other specific evaluation purposes such as to improve or control also imply changing the target entity; therefore, strategies should embrace functional requirements definition activities as well. Moreover, specific development and maintenance purposes always involve functional requirements. In this work, we relate business and information need goals with functional and non- functional requirements concepts, which are paramount for well-defined strategies. Therefore, we specify vocabularies for them, and illustrate the applicability of an improving strategy –which embeds these concepts- in the context of a running example. Having well-structured vocabularies serving as common ground for diverse strategies may promote a more effective operationalization of projects dealing with evaluation, testing, development and maintenance goal purposes.
Long Short Term Memory Based Model for Abnormal Behavior Prediction in Elderly Persons

Smart home refers to the independency and comfort that are ensured by remote monitoring and assistive services. Assisting an elderly person requires identifying and accurately predicting his/her normal and abnormal behaviors. Abnormal behaviors observed during the completion of activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we propose a method, based on long short-term memory recurrent neural networks (LSTM), to automatically predicting an elderly person's abnormal behaviors. Our method allows to model the temporal information expressed in the long sequences collected over time. Our study aims to evaluate the performance of LSTM on identifying and predicting elderly persons abnormal behaviors in smart homes. We experimentally demonstrated, through extensive experiments using a dataset, the suitability and performance of the proposed method in predicting abnormal behaviors with high accuracy. We also demonstrated the superiority of the proposed method compared to the existing state-of-the-art methods.
Casting about in the dark: an empirical study of cast operations in Java programs

The main goal of a static type system is to prevent certain kinds of errors from happening at run time. A type system is formulated as a set of constraints that gives any expression or term in a program a well-defined type. Yet mainstream programming languages are endowed with type systems that provide the means to circumvent their constraints through casting. We want to understand how and when developers escape the static type system to use dynamic typing. We empirically study how casting is used by developers in more than seven thousand Java projects. We find that casts are widely used (8.7% of methods contain at least one cast) and that 50% of casts we inspected are not guarded locally to ensure against potential run-time errors. To help us better categorize use cases and thus understand how casts are used in practice, we identify 25 cast-usage patterns---recurrent programming idioms using casts to solve a specific issue. This knowledge can be: (a) a recommendation for current and future language designers to make informed decisions (b) a reference for tool builders, e.g., by providing more precise or new refactoring analyses, (c) a guide for researchers to test new language features, or to carry out controlled programming experiments, and (d) a guide for developers for better practices.
Derivative grammars: a symbolic approach to parsing with derivatives

We present a novel approach to context-free grammar parsing that is based on generating a sequence of grammars called derivative grammars from a given context-free grammar and input string. The generation of the derivative grammars is described by a few simple inference rules. We present an O ( n 2 ) space and O ( n 3 ) time recognition algorithm, which can be extended to generate parse trees in O ( n 3 ) time and O ( n 2 log n ) space. Derivative grammars can be viewed as a symbolic approach to implementing the notion of derivative languages , which was introduced by Brzozowski. Might and others have explored an operational approach to implementing derivative languages in which the context-free grammar is encoded as a collection of recursive algebraic data types in a functional language like Haskell. Functional language implementation features like knot-tying and lazy evaluation are exploited to ensure that parsing is done correctly and efficiently in spite of complications like left-recursion. In contrast, our symbolic approach using inference rules can be implemented easily in any programming language and we obtain better space bounds for parsing. Reifying derivative languages by encoding them symbolically as grammars also enables formal connections to be made for the first time between the derivatives approach and classical parsing methods like the Earley and LL/LR parsers. In particular, we show that the sets of Earley items maintained by the Earley parser implicitly encode derivative grammars and we give a procedure for producing derivative grammars from these sets. Conversely, we show that our derivative grammar recognizer can be transformed into the Earley recognizer by optimizing some of its bookkeeping. These results suggest that derivative grammars may provide a new foundation for context-free grammar recognition and parsing.
Improved Bounds for Separable Codes and $B_2$ Codes

Separable codes and B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> codes are combinatorial structures which could be applied to identify traitors in multimedia fingerprinting and to uniquely decode messages in multiple access communication respectively. In this letter we provide new lower and upper bounds for the largest code rates of q-ary separable codes and B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> codes, which improve the existing results when the alphabet size q is relatively small.
Application of Linked Open Data to the coding and dissemination of Spanish Civil War photographic archives

Purpose The purpose of this paper is to present a conceptual model for coding and dissemination of data associated with historical photographic archives. The model is based on Linked Open Data technology and seeks to exhaustively represent the most relevant characteristics for the tasks of contextualization of the documentary groupings and units, management, document retrieval, dissemination and sharing of data about the historical photographs. Design/methodology/approach An OWL ontology, called Ontophoto, was constructed following an adaptation of the methodology proposed by Uschold and Gruninger and Gruninger and Fox. The ontology was implemented using Protégé 5.5 software. Next a Graph DB® graph database application (Ontotext) was created to generate a query system based on the SPARQL language. To validate the consistency and effectiveness of the model and ontology, a competency questions methodology has been applied using a sample from the Skogler photographic archive. Findings The model facilitates the generation of systems for dissemination and retrieval of iconographic data for historical research, overcoming some of the limitations with respect to the design of methods of content and contextual information representation for heritage photographic archives. Research limitations/implications This study is based on a sample. Future work should consider the implementation of the model on the totality of a photographic collection. Originality/value This paper presents a comprehensive ontological model that allows the creation of distributed systems of knowledge representation, which can be queried through SPARQL language.
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach

Many tasks in modern machine learning can be formulated as finding equilibria in \emph{sequential} games. In particular, two-player zero-sum sequential games, also known as minimax optimization, have received growing interest. It is tempting to apply gradient descent to solve minimax optimization given its popularity and success in supervised learning. However, it has been noted that naive application of gradient descent fails to find some local minimax and can converge to non-local-minimax points. In this paper, we propose \emph{Follow-the-Ridge} (FR), a novel algorithm that provably converges to and only converges to local minimax. We show theoretically that the algorithm addresses the notorious rotational behaviour of gradient dynamics, and is compatible with preconditioning and \emph{positive} momentum. Empirically, FR solves toy minimax problems and improves the convergence of GAN training compared to the recent minimax optimization algorithms.
A Bitwise Design and Implementation for Privacy-Preserving Data Mining: From Atomic Operations to Advanced Algorithms

Homomorphic encryption (HE) is considered as one of the most powerful solutions to securely protect clients’ data from malicious users and even severs in the cloud computing. However, though it is known that HE can protect the data in theory, it has not been well utilized because many operations of HE are too slow, especially multiplication. In addition, existing data mining research studies using encrypted data focus on implementing only specific algorithms without addressing the fundamental problem of HE. In this paper, we propose a fundamental design and implementation of data mining algorithm through logical gates. In order to do this, we design various logic of atomic operations in encrypted domain and finally apply these logic to well-known data mining algorithms. We also analyze the execution time of atomic and advanced algorithms.
Experimental orthogonalization of highly overlapping quantum states with single photons

We experimentally realize a nonlinear quantum protocol for single-photon qubits with linear optical elements and appropriate measurements. Quantum nonlinearity is induced by postselecting the polarization qubit based on a measurement result obtained for the spatial degree of freedom of the single photon which plays the role of a second qubit. Initially, both qubits are prepared in the same quantum state and an appropriate two-qubit unitary transformation entangles them before the measurement of the spatial part. We analyze the result by quantum state tomography of the polarization degree of freedom. We then demonstrate the usefulness of the protocol for quantum state discrimination by iteratively applying it to either of two slightly different quantum states which rapidly converge to different orthogonal states by the iterative dynamics. Our work creates an opportunity to employ effective quantum nonlinear evolution in quantum information processing.
Spectral gap for measure-valued diffusion processes

The spectral gap is estimated for some measure-valued processes, which are induced by the intrinsic/extrinsic derivatives on the space of finite measures over a Riemannian manifold. These processes are symmetric with respect to the Dirichlet and Gamma distributions arising from population genetics. In addition to the evolution of allelic frequencies investigated in the literature, they also describe stochastic movements of individuals.
A decadal survey of zero-shot image classification

Zero-shot image classification refers to learning a visual classifier for categories with zero training examples. This method can effectively solve problems in which the labeled data for some classes are absent and has therefore gained a considerable attention recently. It has been approximately a decade since this technology was first developed. This paper systematically summarizes the research progress over the past decade in this field. First, we introduce the significance and practical application value of zero-shot image classification. Next, the research processes and typical approaches are summarized in detail. Further, we comprehensively review existing datasets and evaluation metrics, together with the relation between zero-shot image classification and other related techniques. Finally, we analyze the hot spots and existing challenges that need to be further studied and emphasize the future trends in this research area.
Style Mixer: Semantic‐aware Multi‐Style Transfer Network

Recent neural style transfer frameworks have obtained astonishing visual quality and flexibility in Single-style Transfer (SST), but little attention has been paid to Multi-style Transfer (MST) which refers to simultaneously transferring multiple styles to the same image. Compared to SST, MST has the potential to create more diverse and visually pleasing stylization results. In this paper, we propose the first MST framework to automatically incorporate multiple styles into one result based on regional semantics. We first improve the existing SST backbone network by introducing a novel multi-level feature fusion module and a patch attention module to achieve better semantic correspondences and preserve richer style details. For MST, we designed a conceptually simple yet effective region-based style fusion module to insert into the backbone. It assigns corresponding styles to content regions based on semantic matching, and then seamlessly combines multiple styles together. Comprehensive evaluations demonstrate that our framework outperforms existing works of SST and MST.
Pengembangan Media Pembelajaran Matematika Interaktif Berbasis Android Pada Materi Dimensi Tiga Kelas X SMA

Abstract: This study aims to develop and produce android-based mathematics learning media on the subject of three dimensions of point, line, and field positions in space, for class X high school students and find out the validity, effectiveness, and practicality of interactive multimedia-based learning media. This type of research is a research development in the form of a three dimensional interactive learning media. Media development refers to the ADDIE development model which includes 5 stages, namely Analysis, Design, Development, Implementation, Evaluation. The subjects of this study were students of class X IPA and students of class X IPS SMA Negeri 1 Maronge. The instruments used in this study were questionnaire validation of material experts and media experts, student questionnaire responses, and student evaluation tests. The results of the development research are: (1) The results of the validation of the material and the media obtain an average score of 36 with the category "quite valid"; (2) The results of the effectiveness of the media in small group trials have achieved 100% mastery learning in the "very effective" category; (3) The results of the practicality of the media in small group trials obtained an average score of 57 in the "very practical" category; (4) The results of the effectiveness of the media in large group trials gained mastery learning reaching 80% with the category of "effective"; and (5) The practicality of the media in the large group test obtained an average of 54,485 in the "practical" category.
X-RDMA: Effective RDMA Middleware in Large-scale Production Environments

X-RDMA is a communication middleware deployed and heavily used in Alibaba's large-scale cluster hosting cloud storage and database systems. Unlike recent research projects which purely focus on squeezing out the raw hardware performance, it puts emphasis on robustness, scalability and maintainability of large-scale production clusters. X-RDMA integrates necessary features, not available in current RDMA ecosystem, to release the developers from complex and imperfect details. X-RDMA simplifies the programming model, extends RDMA protocols for application awareness, and proposes mechanisms for resource management with thousands of connections per machine. It also reduces the work for administration and performance tuning with built-in tracing, tuning and monitoring tools. X-RDMA has been deployed in several large-scale clusters with over 4000 servers in Alibaba cloud since 2016. It can save at least 70% development and maintenance time over RDMA, effectively improve performance and reduce network jitter especially when production servers are under pressure. It also helped locate over 30 issues in different layers of productions with over 5000 connections for each server on average.
Almost ordinary abelian varieties over finite fields

We provide a characterization of almost ordinary abelian varieties over finite fields, and use this characterization to provide lower bounds for the sizes of some almost ordinary isogeny classes.
Automatic Facet Generation and Selection over Knowledge Graphs

With the continuous growth of the Linked Data Cloud, adequate methods to efficiently explore semantic data are increasingly required. Faceted browsing is an established technique for exploratory search. Users are given an overview of a collection's attributes that can be used to progressively refine their filter criteria and delve into the data. However, manual facet predefinition is often inappropriate for at least three reasons: Firstly, heterogeneous and large scale knowledge graphs offer a huge number of possible facets. Choosing among them may be virtually impossible without algorithmic support. Secondly, knowledge graphs are often constantly changing, hence, predefinitions need to be redone or adapted. Finally, facets are generally applied to only a subset of resources (e.g., search query results). Thus, they have to match this subset and not the knowledge graph as a whole. Precomputing facets for each possible subset is impractical except for very small graphs. We present our approach for automatic facet generation and selection over knowledge graphs. We propose methods for (1) candidate facet generation and (2) facet ranking, based on metrics that both judge a facet in isolation as well as in relation to others. We integrate those methods in an overall system workflow that also explores indirect facets, before we present the results of an initial evaluation.
Research on the University Innovation and Entrepreneurship Education Comprehensive Evaluation Based on AHP Method

Nowadays, innovation and entrepreneurship education in colleges and universities has been greatly valued and has obtained large-scale development in China.Meanwhile, "double innovation" education also has some worrying phenomena such as low quality and slow development.Systematic and scientific evaluation system is an important mean to ensure the effectiveness of entrepreneurship education, but it is still quite scarce.Therefore, this paper proposed an effective and comprehensive evaluation system to carry out the evaluation of entrepreneurship education in universities, so as to monitor the implementation process of entrepreneurship education and provide valuable feedback to promote the integrity of the "double innovation" education system.Afterwards, we used AHP and Delphi method to analyze the importance of the components of the evaluation system.Then, we conducted an empirical analysis, using the previous evaluation system, Delphi method and questionnaire survey to evaluate innovation and entrepreneurship education in Shenzhen University, and gave the results analysis.Finally, according to the survey results, this paper gave a series of analysis and suggestions, hoping to accelerate the development of "double innovation" education.
Federated and Differentially Private Learning for Electronic Health Records

The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitates communication of model weights or updates between collaborating entities, but it is unclear to what extent patient privacy is compromised as a result. To gain insight into this question, we study the efficacy of centralized versus federated learning in both private and non-private settings. The clinical prediction tasks we consider are the prediction of prolonged length of stay and in-hospital mortality across thirty one hospitals in the eICU Collaborative Research Database. We find that while it is straightforward to apply differentially private stochastic gradient descent to achieve strong privacy bounds when training in a centralized setting, it is considerably more difficult to do so in the federated setting.
Two‐phase Hair Image Synthesis by Self‐Enhancing Generative Model

Abstract Generating plausible hair image given limited guidance, such as sparse sketches or low‐resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs). Traditional image‐to‐image translation networks can generate recognizable results, but finer textures are usually lost and blur artifacts commonly exist. In this paper, we propose a two‐phase generative model for high‐quality hair image synthesis. The two‐phase pipeline first generates a coarse image by an existing image translation model, then applies a re‐generating network with self‐enhancing capability to the coarse image. The self‐enhancing capability is achieved by a proposed differentiable layer, which extracts the structural texture and orientation maps from a hair image. Extensive experiments on two tasks, Sketch2Hair and Hair Super‐Resolution, demonstrate that our approach is able to synthesize plausible hair image with finer details, and reaches the state‐of‐the‐art.
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning

The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve similar questions that result in the same answer as the original question.Such a system can be used to understand and answer rare and noisy reformulations of common questions by mapping them to a set of canonical forms.This task has large-scale applications for community Question Answering (cQA) and opendomain spoken language question-answering systems.In this paper, we describe a new QPR system implemented as a Neural Information Retrieval (NIR) system consisting of a neural network sentence encoder and an approximate k-Nearest Neighbour index for efficient vector retrieval.We also describe our mechanism to generate an annotated dataset for question paraphrase retrieval experiments automatically from question-answer logs via distant supervision.We show that the standard loss function in NIR, triplet loss, does not perform well with noisy labels.We propose the smoothed deep metric loss (SDML), and with our experiments on two QPR datasets we show that it significantly outperforms triplet loss in the noisy label setting.
Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference

This paper describes our submission to the shared task 1 on "Multi-hop Inference Explanation Regeneration" in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019).Our system identifies chains of facts relevant to explain an answer to an elementary science examination question.To counter the problem of 'spurious chains' leading to 'semantic drifts', we train a ranker that uses contextualized representation of facts to score its relevance for explaining an answer to a question.Our system 2 was ranked first w.r.t the mean average precision (MAP) metric outperforming the second best system by 14.95 points.