Reviewerly/exHarmony huggingface dataset:
CircConv: A Structured Convolution with Low Complexity

Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications. However, the large model sizes of DNNs yield high demands on computation resource and weight storage, thereby limiting the practical deployment of DNNs. To overcome these limitations, this paper proposes to impose the circulant structure to the construction of convolutional layers, and hence leads to circulant convolutional layers (CircConvs) and circulant CNNs. The circulant structure and models can be either trained from scratch or re-trained from a pre-trained non-circulant model, thereby making it very flexible for different training environments. Through extensive experiments, such strong structureimposing approach is proved to be able to substantially reduce the number of parameters of convolutional layers and enable significant saving of computational cost by using fast multiplication of the circulant tensor.
Generating Character Descriptions for Automatic Summarization of Fiction

Summaries of fictional stories allow readers to quickly decide whether or not a story catches their interest. A major challenge in automatic summarization of fiction is the lack of standardized evaluation methodology or high-quality datasets for experimentation. In this work, we take a bottomup approach to this problem by assuming that story authors are uniquely qualified to inform such decisions. We collect a dataset of one million fiction stories with accompanying author-written summaries from Wattpad, an online story sharing platform. We identify commonly occurring summary components, of which a description of the main characters is the most frequent, and elicit descriptions of main characters directly from the authors for a sample of the stories. We propose two approaches to generate character descriptions, one based on ranking attributes found in the story text, the other based on classifying into a list of pre-defined attributes. We find that the classification-based approach performs the best in predicting character descriptions.
Coherent and Squeezed States: Introductory Review of Basic Notions, Properties, and Generalizations

A short review of the main properties of coherent and squeezed states is given in the introductory form. The efforts are addressed to clarify concepts and notions, including some passages of the history of science, with the aim of facilitating the subject for nonspecialists. In this sense, the present work is intended to be complementary to other papers of the same nature and subject in current circulation.
Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting

We approach the time-series forecasting problem in the presence of concept drift by automatic learning rate tuning of stochastic gradient descent (SGD). The SGD-based approach is preferable to other concept drift algorithms in that it can be applied to any model and it can keep learning efficiently whilst predicting online. Among a number of SGD algorithms, the variance-based SGD (vSGD) can successfully handle concept drift by automatic learning rate tuning, which is reduced to an adaptive mean estimation problem. However, its performance is still limited because of its heuristic mean estimator. In this paper, we present a concept-drift-aware stochastic gradient descent (Cogra), equipped with more theoretically-sound mean estimator called sequential mean tracker (SMT). Our key contribution is that we define a goodness criterion for the mean estimators; SMT is designed to be optimal according to this criterion. As a result of comprehensive experiments, we find that (i) our SMT can estimate the mean better than vSGD’s estimator in the presence of concept drift, and (ii) in terms of predictive performance, Cogra reduces the predictive loss by 16–67% for real-world datasets, indicating that SMT improves the prediction accuracy significantly.
Secure and Efficient Multi-Authority Attribute-Based Encryption Scheme From Lattices

Attribute-based encryption (ABE) mechanism on lattices can against quantum attack and guarantees fine-grained access control of encrypted data, and it has been widely used in cloud computing and privacy protection.However, the high complexity of the trapdoor generation and delegation and preimage sampling are the main barriers for the development of ABE schemes in a post-quantum world.Thus, we proposed a security and efficient multi-authority ABE scheme from lattices in this paper.The scheme is based on the theory of MP12 and is proved to be secure under the standard model.In this scheme, we construct an optimized sampling algorithm to generate a Z q -invertible matrix with a lower runtime.Based on it, combining the trapdoor generation and delegation algorithm in MP12, we designed multiple attribute authorities which can manage different attribute sets and generate private keys for the user independently.In addition, the Shamir's secret sharing technique is introduced to support policies expressed in any monotone access structures.Compared with the existing related schemes, the proposed scheme can improve the functional agility and the flexibility of the access policy prominently, and it also can achieve a better performance with less lattice dimension, trapdoor storage occupation, and ciphertext expansion rate.The analysis shows that our scheme is feasible and superior in the large-scale distributed environment.
A hybrid and exploratory approach to knowledge discovery in metabolomic data

In this paper, we propose a hybrid and exploratory knowledge discovery approach for analyzing metabolomic complex data based on a combination of supervised classifiers, pattern mining and Formal Concept Analysis (FCA). The approach is based on three main operations, preprocessing, classification, and postprocessing. Classifiers are applied to datasets of the form individuals × features and produce sets of ranked features which are further analyzed. Pattern mining and FCA are used to provide a complementary analysis and support for visualization. A practical application of this framework is presented in the context of metabolomic data, where two interrelated problems are considered, discrimination and prediction of class membership. The dataset is characterized by a small set of individuals and a large set of features, in which predictive biomarkers of clinical outcomes should be identified. The problems of combining numerical and symbolic data mining methods, as well as discrimination and prediction, are detailed and discussed. Moreover, it appears that visualization based on FCA can be used both for guiding knowledge discovery and for interpretation by domain analysts.
Refined security proof of the round-robin differential-phase-shift quantum key distribution and its improved performance in the finite-sized case

Among many quantum key distribution protocols, the round-robin differential-phase-shift (RRDPS) protocol is unique in that it can upper-bound the amount of information leakage without monitoring signal disturbance. To expedite implementation of the protocol, however, the number of pulses forming a single block should be kept small, which significantly decreases the key rates in the original security proof. In the present paper, we refine the security proof of the RRDPS protocol in the finite-sized regime and achieve a tighter estimation for the information leakage with neither monitoring signal disturbance nor changing the original experimental setups. As a consequence, we obtain better key rates in both asymptotic and finite-sized cases while keeping the preferable features of the protocol, such as omission of phase randomization.
A Sparse Corruption Non-Negative Matrix Factorization method and application in face image processing &amp; recognition

Non-negative Matrix Factorization (NMF) has attracted widely attentions in the areas of data analysis, image processing & measurement and noise separation. NMF can obtain the non-negative low-dimensional representation of the data and low-rank matrix is of great importance to classification and recognition. Classic NMF assumes that noise is subject to Gaussian noise or Poisson noise, while it is inapplicable to some other noise. In addition, corrupted data in the test set cannot be restored by NMF. Many scholars have studied NMF and proposed a variety of improved algorithms so far, such as Robust Non-Negative Matrix Factorization (RNMF), which is designed to deal with sparse corruption. Our paper presents Sparse Corruption Non-Negative Matrix Factorization (SCNMF). SCNMF separates a sparse noise matrix out of the corrupted input matrix, and the rest of the input matrix is represented as the product of two low-dimensional matrices. The product of two low-dimensional matrices approximates the non-corrupted input matrix. The base matrix calculated by the proposed method is tolerant to noise. It can reconstruct new data well regardless of whether the new data is corrupted and non-corrupted. Experiments on specific face databases verifies the validity of SCNMF. Reconstructed faces by the proposed method are clearer and more recognizable and the recognition rate is three percentage points higher by comparison.
Hierarchical Attention and Knowledge Matching Networks With Information Enhancement for End-to-End Task-Oriented Dialog Systems

Nowadays, most end-to-end task-oriented dialog systems are based on sequence-to-sequence (Seq2seq), which is an encoder-decoder framework.These systems sometimes produce grammatically correct, but logically incorrect responses.This phenomenon is usually due to information mismatching.To solve this problem, we introduce hierarchical attention and knowledge matching networks with information enhancement (HAMI) for task-oriented dialog systems.It contains a hierarchical attention dialog encoder (HADE) that models dialogs at the word and sentence level separately.HADE can focus on important words in a dialog history and generate context-aware representations.HAMI also contains a unique knowledge matching module to detect entities and interact with a knowledge base (KB).Then, dialog history and KB result representations serve as guidance for system response generation.Finally, HAMI's loss function is designed with an information regularization term to emphasize the importance of entities.The experimental results show that HAMI improves 9.8% in entity F1 compared with vanilla Seq2seq.HAMI also outperforms state-of-the-art models in both the entity F1 and dialog accuracy metrics.
Core Factors for Software Projects Success

The successful software project can be defined as the project that meets the planned quality, cost, schedule, and effort. It has been suggested that there are many reasons which might lead to software project success.Â Several success and failure factors for particular projects were discussed in the literature. This paper aims to identify the most common success factors through reviewing a set of software project reports and case studies. In order to fully understand reasons behind the project success, the reasons behind project failure and the definition of failure itself are discussed. Furthermore, case studies of successful and failed software projects are described. This paper also investigates techniques that have been developed to increase software project success rate and decrease probable failures.
Improving sentiment analysis performance on morphologically rich languages: Language and domain independent approach

Sentiment analysis has become a phenomenon with the proliferation of social media and the popularity of opinion-rich resources such as online reviews and blogs. Even though significant advances have been achieved in this field, there are still some major challenges to be addressed – i.e. sentiment analysis in multiple languages or thematic domains. Only a few studies have focused on minor or morphologically rich languages. Moreover, it is a question of whether the results of sentiment analysis could be further improved by incorporating the surrounding context (local or chronological) of the analyzed document. This paper presents a language- and domain-independent sentiment analysis model based on character n-grams which improves the classifiers performance by utilizing surrounding context. Four experiments on various datasets were conducted in order to validate the model. The datasets included a reference corpus containing movie reviews in English, movie reviews in the Czech language, the bestselling Amazon book of 2012 Fifty Shades of Grey novel reviews dataset from three Amazon language mutations (English, German, and French), another reference corpus containing Amazon reviews in multiple languages (German, French, and Japanese), and a multi-domain dataset (movies, books, and product categories ranging from electronics and home appliances to sports gear and supplies for hobbies and pets). The experiments confirmed the approach of incorporating the surrounding context in order to be effective for datasets from various languages and domains, suggesting a strong performance of a character n-gram based model for multi-domain and language datasets as well. A simple all-in-one classifier, which uses a mixture of labeled data from multiple languages (or domains) to train a sentiment classification model, may rival more sophisticated domain/language adaptation techniques. Such an approach reflects the needs of companies – with the interconnectedness of today's world, most companies operate across multiple markets and would find it difficult to obtain a specific sentiment analysis solution for each market they serve.
A neural classification method for supporting the creation of BioVerbNet

VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data. Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet. This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine.
A novel 3-pass identification scheme and signature scheme based on multivariate quadratic polynomials

Identification schemes are used to verify identities of parties and signatures. Recently, systems based on multivariate polynomials have been preferred in identification schemes due to their resistance against quantum attacks. In this paper, we propose a quantum secure $3$-pass identification scheme based on multivariate quadratic polynomials. We compare the proposed scheme with the previous ones in view of memory requirements, communication length, and computation time. We define an efficiency metric by using impersonation probability and computation time. According to the comparison results, the proposed one has the same computation time as that of Monteiro et al. and reduces impersonation probability compared to the work of Sakumoto et al. We also propose a new signature scheme constructed from the proposed identification scheme. In addition, we compare the signature scheme with the previous schemes in view of signature and key sizes. We improve the signature size compared to that given in previous work by Chen et al.
A secure hash function based on feedback iterative structure

Rapid growth of internet data has created enormous security challenges on authenticity, availability and integrity protection of these outsourced data. Hash function is one of the main solutions to face these challenges. This paper proposes a feedback iterative structure of hash function which utilises the variable feedback to resist attacks. Furthermore, to accelerate message diffusion, two novel modules are designed, one for iteration and the other for truncation. Experimental results show that the proposed hash function can effectively resist existing attacks. Moreover, comparing with other existing hash functions, it displays better on statistical performance, collision resistance and avalanche.
I-vector features and deep neural network modeling for language recognition

We combine Total Variability algorithm with Deep Learning theory to complete the language recognition task. The Total Variability algorithm can compensate for the influence of differences in channels and speakers among various languages, while deep learning methods have a stronger ability of nonlinear modeling compared with traditional statistical models. In this paper, I-vector feature is extracted using Total Variability algorithm, and model training is established using fully connected neural network. Meanwhile, the dropout strategy is also used to suppress overfitting. The experimental results show that the new system outperforms the baseline system on the NIST LRE 2007 corpus.
Creative Innovative Higher Education of Researchers with Flexible Skills and Synergy of Cooperation

The concepts "creative activity", "creative abilities", "creative education" are already widely used both in western, and in the Russian scientific thought today and have innovative contents. Innovations have creative force: high novelty and usefulness and also provide competitive advantages and successful corporate activity in the market in the long-term period. Creativity has various forms: scientific (opening); technical (inventions); economic (business); social (relations with people); political (public administration); educational (cultivation of innovators). For training orientation to creative innovative approach of the higher education which ideally has to reveal, support and develop the creative potential of each personality is productive, growing up multi and between disciplinary researchers on fundamental subject knowledge, especially mathematicians – the queen of sciences. Within multi disciplinary approach communications of the studied subject domains come to light. As a result there are cross-disciplinary researches. As a result of cross-disciplinary researches new disciplines appear. To accelerate process of innovative cultivation the field and between disciplinary researchers and also to increase synergy of their cooperation, the advanced digital technologies will help.
Nutshell—Simulation Toolkit for Modeling Data Center Networks and Cloud Computing

Cloud computing provides flexibility, reliability, and scalability to its consumers.Applications that run on Cloud are now more resource hungry than ever, and the need is constantly growing.The number of Cloud applications with diverse nature is growing, expecting greater performance in low cost.Fulfilling the quality of service (QoS) demands of such diverse applications is a challenging task and requires efficient network architectures, robust scheduling schemes, powerful and adaptable routing algorithms, reliable resource management, effective data exchange policies, and QoS improvement policies.Research community endeavors to provide solutions to various challenges faced by Cloud computing.These solutions must be tested thoroughly before their implementation in a real Cloud.Simulation seems a viable choice for testing a solution in different scenarios.There are a number of simulators available, focusing on certain aspects of Cloud while neglecting others.Their generalized implementation of the Cloud ignores various critical factors, such as networking characteristics, consequently affecting the tests and results of a solution.To tackle these issues, simplify simulation process, and provide the detailed implementation of the Cloud, we present Nutshell: a novel Cloud simulator that makes it easy to model, simulate, and experiment new Cloud solutions.Salient features offered by Nutshell include 1) provision of a platform for modeling and simulating Cloud infrastructure, 2) built-in essential components and helpers to create new architectures, 3) pre-built data center architectures, 4) plug-in-based architecture, 5) communication protocols, 6) prebuilt virtual machine scheduler, 7) addressing schemes, 8) user applications, 9) virtual machines, 10) Job splitting, and 11) simulation data collection and exporting.
AxoPy: A Python Library for Implementing Human-Computer Interface Experiments

AxoPy is a system for creating human-computer interface experiments involving the use of electrophysiological signals, such as electromyography (EMG) or electroencephalography (EEG).It is intended to provide an infrastructure for rapidly developing common kinds of experiments while allowing for more complex, customized designs.
Prevalence, pattern and predictors of premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) among college girls

Objective -To determine the prevalence, pattern and predictors of PMS and PMDD among college girls of South India.To identify the knowledge and attitude of college girls with PMS and PMDD.Methodology -A cross sectional questionnaire study based on Premenstrual Syndrome Screening Tool was conducted among the college student of Madurai, Tamilnadu.Results -The prevalence of moderate to severe PMS was 14.3% and PMDD was 3.7%.The commonest premenstrual symptom among college girls was fatigue/lack of energy, in the moderate to severe PMS category was anger and in the PMDD category was anxiety.Impairment of college efficiency or productivity was seen in 82.66% and 100% of students with moderate to severe PMS and PMDD respectively.The prevalence of moderate to severe PMS and PMDD correlated significantly with the mean age, education, heavy menstrual flow, dysmenorrhoea and family history of PMS and 85.6% college students with moderate to severe PMS and PMDD didn't perceived their symptoms as abnormal and only 16.4% had a physician consultation.Conclusion -PMS and PMDD are prevalent among substantial proportion of college girls with a significant negative influence on academic performance, emotional well being and behaviour.Strategies should be adopted in the college health program for timely recognition and management of PMS and PMDD in college girls.
How to observe and quantify quantum-discord states via correlations

Quantum correlations between parts of a composite system most clearly reveal themselves through entanglement. Designing, maintaining, and controlling entangled systems is very demanding, which raises the stakes for understanding the efficacy of entanglement-free, yet quantum, correlations, exemplified by quantum discord. Discord is defined via conditional mutual entropies of parts of a composite system, and its direct measurement is hardly possible even via full tomographic characterization of the system state. Here we design a simple protocol to detect and quantify quantum discord in an unentangled bipartite system. Our protocol relies on a characteristic of discord that can be extracted from repeated direct measurements of certain correlations between subsystems of the bipartite system. The proposed protocol opens a way of extending experimental studies of discord to electronic systems but can also be implemented in quantum-optical systems.