Reviewerly/exHarmony huggingface dataset:
A Deep Neural Information Fusion Architecture for Textual Network Embeddings

Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations.Traditionally, the structural and textual embeddings were learned by models that rarely take the mutual influences between them into account.In this paper, a deep neural architecture is proposed to effectively fuse the two kinds of informations into one representation.The novelties of the proposed architecture are manifested in the aspects of a newly defined objective function, the complementary information fusion method for structural and textual features, and the mutual gate mechanism for textual feature extraction.Experimental results show that the proposed model outperforms the comparing methods on all three datasets.
NRC Parallel Corpus Filtering System for WMT 2019

We describe the National Research Council Canada team’s submissions to the parallel corpus filtering task at the Fourth Conference on Machine Translation.
Efficient Image Set Compression

The explosion of digital photos has posed a challenge for storage and transmission bandwidth. The thesis briefly discusses my work on efficient image set compression. The lossless compression of near-duplicate image collection is carried out using multiple steps, where each step is computationally demanding. My task is to make them work faster without compromising on compression efficiency. In this pursuit, we propose approaches for efficient clustering, fast direction oriented motion estimation algorithm, and an image reordering scheme with minimum predictive costs for better compression. The preliminary results for the proposed approach are promising. We also aim to extend our approach to hyperspectral and medical image set compression.
Generalization of Spectrum Differential based Direct Waveform Modification for Voice Conversion

We present a modification to the spectrum differential based direct waveform modification for voice conversion (DIFFVC) so that it can be directly applied as a waveform generation module to voice conversion models. The recently proposed DIFFVC avoids the use of a vocoder, meanwhile preserves rich spectral details hence capable of generating high quality converted voice. To apply the DIFFVC framework, a model that can estimate the spectral differential from the F0 transformed input speech needs to be trained beforehand. This requirement imposes several constraints, including a limitation on the estimation model to parallel training and the need of extra training on each conversion pair, which make DIFFVC inflexible. Based on the above motivations, we propose a new DIFFVC framework based on an F0 transformation in the residual domain. By performing inverse filtering on the input signal followed by synthesis filtering on the F0 transformed residual signal using the converted spectral features directly, the spectral conversion model does not need to be retrained or capable of predicting the spectral differential. We describe several details that need to be taken care of under this modification, and by applying our proposed method to a non-parallel, variational autoencoder (VAE)-based spectral conversion model, we demonstrate that this framework can be generalized to any spectral conversion model, and experimental evaluations show that it can outperform a baseline framework whose waveform generation process is carried out by a vocoder.
A Wide Scale Survey on Handwritten Character Recognition using Machine Learning

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Algorithm Based on Population With a Flexible Search Mechanism

In this paper, we propose a new population-based algorithm (PBA) that adjusts its search mechanism to the problem which is to be optimized. It uses a set of search operators used in commonly-used PBAs and this selection can be made freely. The proposed algorithm selects two operators from this set, i.e. one for exploration and the other for exploitation of the search space of the solution. This approach can be an alternative for finding new inspirations for the development of PBAs and for modifying the existing PBAs. The effectiveness of the proposed algorithm has been tested using typical benchmarks for testing PBAs. An interesting aspect of the performed simulations is the analysis of how operators are used during the optimization process.
The many ways to hack 2FA

As the days of pervasive password authentication are starting to wane, administrators and end users are increasingly turning to stronger types of authentication such as two-factor authentication (2FA) and multi-factor authentication (MFA). All other considerations considered equal, MFA solutions (which include 2FA) are usually stronger than single-factor authentication (1FA) solutions such as the ubiquitous duo of username and password. This is generally agreed upon by all computer security professionals. People are increasingly turning to stronger types of authentication such as two-factor authentication (2FA) and multi-factor authentication (MFA). But somewhere along the way, many people have come to associate MFA solutions with being far less hackable or even unhackable. Nothing could be further from the truth. In fact, it's possible to use nothing more than a simple phishing email to easily bypass many beloved MFA solutions, says Roger Grimes of KnowBe4.
Data-driven decision making in graduate students’ research topic selection

Purpose With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks. Design/methodology/approach This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students. Findings The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation. Practical implications These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS. Originality/value This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.
Embedding OWL ontologies with OWL2Vec

In this paper, we present a preliminary study to compute embeddings for OWL 2 ontologies by projecting the ontology axioms into a graph and performing (random) walks over the ontology graph to create a corpus of sentences. This corpus is then given to a neural language model to create concept embeddings. The conducted preliminary evaluation shows promising results.
Research on multicloud access control policy integration framework

Multicloud access control is important for resource sharing and security interoperability across different clouds, and heterogeneity of access control policy is an important challenge for cloud mashups. XAC-ML is widely used in distributed environment as a declaratively fine-grained, attribute-based access control policy language, but the policy integration of XACML lacks formal description and theory foundation. Multicloud Access Control Policy Integration Framework (MACPIF) is proposed in the paper, which consists of Attribute-based Policy Evaluation Model (ABPEM), Four-value Logic with Completeness (FLC) and Four-value Logic based Policy Integration Operators (FLPIOs). ABPEM evaluates access control policy and extends XACML decision to four-value. According to policy decision set and policy integration characteristics, we construct FLC and define FLPIOs including Intersection, Union, Difference, Implication and Equivalence. We prove that MACPIF can achieve policy monotonicity, functional completeness, canonical suitability and canonical completeness. Analysis results show that this framework can meet the requirements of policy integration in Multicloud.
A Hybrid Model for Topic Modeling Using Latent Dirichlet Allocation and Feature Selection Method

In this information age, Knowledge discovery and pattern matching plays a significant role. Topic Modeling, an area of Text mining is used detecting hidden patterns in a document collection. Topic Modeling and Document Clustering are two important key terms which are similar in concepts and functionality. In this paper, topic modeling is carried out using Latent Dirichlet Allocation-Brute Force Method (LDA-BF), Latent Dirichlet Allocation-Back Tracking (LDA-BT), Latent Semantic Indexing (LSI) method and Nonnegative Matrix Factorization (NMF) method. A hybrid model is proposed which uses Latent Dirichlet Allocation (LDA) for extracting feature terms and Feature Selection (FS) method for feature reduction. The efficiency of document clustering depends upon the selection of good features. Topic modeling is performed by enriching the good features obtained through feature selection method. The proposed hybrid model produces improved accuracy than K-Means clustering method.
Complete monitors for gradual types

In the context of gradual typing, type soundness guarantees the safety of typed code. When untyped code fails to respect types, a runtime check finds the discrepancy. As for untyped code, type soundness makes no promises; it does not protect untyped code from mistakes in type specifications and unwarranted blame. To address the asymmetry, this paper adapts complete monitoring from the contract world to gradual typing. Complete monitoring strengthens plain soundness into a guarantee that catches problems with faulty type specifications. Furthermore, a semantics that satisfies complete monitoring can easily pinpoint the conflict between a type specification and a value. For gradual typing systems that fail complete monitoring, the technical framework provides a source-of-truth to assess the quality of blame.
CONVERGENCE AS METHODOLOGICAL BASIS FOR DEVELOPMENT OF TRANS-PROFESSIONALISM OF ACTIVITY SUBJECTS

Purpose of the study: The need for knowledge, skills, expanding the established functionality of a professional is increasing more and more. That is why young professionals-trans-professionals, who are able to realize themselves not only in one sphere but who are ready to delve into problems of other, absolutely "foreign" for the scientific and technical fields and cooperate effectively with specialists of these areas in one team, will be responsible for creation of new technologies determining the wellness of our country. The article is aimed at the analysis and generalization of foundations of the convergence and identifying its sense-creating content in the development of trans-professionalism as an integral characteristic of the activity subject in a rapidly changing social-professional environment.&#x0D; Methodology: The leading methods in the study of this problem are the theoretical and methodological analysis of the subject and the problem of the research based on the study and logical generalization of scientific literature. Description of the phenomenon of convergence and trans-professionalism was carried out based on multidimensional, transdisciplinary, project and process approaches.&#x0D; Results: The results of the study are content components of trans-professionalism of the activity subject, justification of the convergence theory as a fundamental basis of integration of educational content and new technologies as well as technology foresight of the formation of trans-professionalism of subjects on the basis of convergence.&#x0D; Applications of this study: Materials of the article may be interesting for methodologists, Methodists, and teachers of the continuous professional education system.&#x0D; Novelty/Originality of this study: The components that allow combining the different parameters of the activity subject, showing its qualitative change in the problem field of trans-professionalism, are stated to be the content components of trans-professionalism for activity subjects. The methodology of convergence provides a synergistic effect of the interaction of social-humanitarian and natural sciences. Improving the process of development of trans-professional competencies of activity subjects is associated with the development of new technologies - tools and methods.
Requirements Engineering: The Quest for Meaningful Metrics: Time for a Change?

For many years, I quoted a misstatement of this quotation from statistician and quality guru W. Edwards Deming. From my graduate school days, "If you can't measure it, you can't manage it" has been drilled into my head as a mantra. While I gritted my teeth through many a metrics-focused activity, I consoled myself with the belief that the effort to get to some sort of objective measure-in requirements engineering (RE), defect density, the number of defects in an artifact or specification-would inevitably lead to better results. Measuring defects would lead to manageable requirements. Better requirements-requirements with fewer objective defects-would lead to better products.
A Developed Task Allotments Policy for Apache Hadoop Executing in the Public Clouds

Presently, data-intensive obstacles signify too common from infinite organizations in diverse trades have to challenge them in their business performance and computations. It is usually critical for many enterprises to have the strength of smoothly analyzing vast scale and volumes of datasets in an adequate and modern manner. apache Map Reduce(MR) dramatically clarified the improvement of parallel computing demands for everyday users, and the union of Hadoop and cloud-oriented computing did extensive -scale parallel and distribute data absolute Figure much stronger accessible to all inherent users than eternally. Although open free origin Hadoop has matched the new advanced and popular world digital data management software framework for parallel and distributive data-intensive computing as the efficient manner in the clouds, but the Hadoop default schedules is not an accurate equivalent for the cloud background. In this research survey, we address the significant issues among the Hadoop task allotment scheme and address a developed scheme for diverse computing environments, such as common clouds. The suggested scheme regarding on the optimal least makespan algorithm and calculates and examines the finishing times of each task slots’ and next data block, and explicitly try to overcome the finishing time of the MR phase jobs. We administered a comprehensive simulation to ascertain the entire performance through recommended scheme examined with the Hadoop scheme in two sorts of different computing situations which are common on the popular cloud policy. The current outcomes from simulation showed that the recommended scheme could notably decrease the map phase completion time, and it could decrease the quantity of remote processing employed on larger important extent which creates the world data processing in limited vulnerable to both network jam and disk contention.
Computer Science Replugged

By following the idea of not using computers at all, Computer Science Unplugged has set the course for many interactive, social, and hands-on activities dealing with concepts and problems of Computer Science Education. Through recent developments in immersive media, technologies like Virtual and Augmented Reality could enhance such activities or even enable new ones. When providing immersive educational media that induce a sense of presence in the virtual environment, the illusion of unmediated learning experience can be delivered. Hence, the concept of"Computer Science Replugged" can benefit from the affordances that Computer Science Unplugged thrives on while facilitating or enabling activities that might be impossible, dangerous, or expensive to carry out in reality. In order to foster research and activities associated with the Computer Science Replugged approach, this paper concludes with research questions for using immersive media in Computer Science Education.
Efficient Information-Flow Verification Under Speculative Execution

We study the formal verification of information-flow properties in the presence of speculative execution and side-channels. First, we present a formal model of speculative execution semantics. This model can be parameterized by the depth of speculative execution and is amenable to a range of verification techniques. Second, we introduce a novel notion of information leakage under speculation, which is parameterized by the information that is available to an attacker through side-channels. Finally, we present one verification technique that uses our formalism and can be used to detect information leaks under speculation through cache side-channels, and can decide whether these are only possible under speculative execution. We implemented an instance of this verification technique that combines taint analysis and safety model checking. We evaluated this approach on a range of examples that have been proposed as benchmarks for mitigations of the Spectre vulnerability, and show that our approach correctly identifies all information leaks.
Uttered Kurdish digit recognition system

Speech recognition is a crucial subject in human computer interaction area. The ability of a machine to recognize words and phrases in spoken language is speech recognition and then convert them to a machine-readable format. Digit recognition is a part of the speech recognition system. In this paper, three spectral based features including Mel Frequency Cepstral Coefficient (MFCC), Linear predictive coding (LPC) and formant frequencies are proposed to classify ten Kurdish uttered digits (0-9). The features are extracted from entire speech signal, and feed a pairwise SVM classifier. Experiments including each individual feature and different forms of fusion are conducted and the results are shown. The fusion of the features significantly improves the result and shows that the different features carry complementary information. The proposed model is experimented on the dataset that have been collected in Kurdistan.&#x0D; Key words: Speech recognition, MFCC, LPC, Formant frequencies, uttered digits, SVM
Barriers to trainee teachers’ corpus use

The benefits of direct corpus exploration by language learners are widely attested for tertiary education contexts but less so for secondary school environments. The integration of data-driven learning (DDL) into a teacher education program is a promising method to spread the word, although there may still be barriers that hinder trainee teachers from viewing direct corpus exploration as a viable option for the secondary language learning classroom. Which sorts of barriers can be identified? To what extent are they linked to technical issues or to conceptual ones? This chapter attempts to answer these questions by exploring how a module on technology-enhanced course design was used to introduce selected features of Sketch Engine to trainee teachers of L2 German in a French secondary school context, training them in basic corpus exploration methods with a view to creating a corpus-based language learning task for use for younger learners. The results suggest both technical (“first order”) barriers (e.g. limited knowledge of corpus query syntax, complex corpus functions) and conceptual (“second order”) barriers (e.g. teacher beliefs about younger learners’ capabilities, preferences for other teaching tools) hinder both trainees’ DDL task construction and their eventual uptake of corpus use with younger learners. These issues could be resolved by providing the trainees with corpus query samples together with reusable and adaptable corpus-based activities, as well as user-friendly corpus tools and pedagogically relevant resources.
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots

Conventional chatbots focus on two-party response generation, which simplifies the real dialogue scene.In this paper, we strive toward a novel task of Response Generation on Multi-Party Chatbot (RGMPC), where the generated responses heavily rely on the interlocutors' roles (e.g., speaker and addressee) and their utterances.Unfortunately, complex interactions among the interlocutors' roles make it challenging to precisely capture conversational contexts and interlocutors' information.Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC.Specifically, we employ interactive representations to capture dialogue contexts for different interlocutors.Moreover, we leverage an addressee memory to enhance contextual interlocutor information for the target addressee.Finally, we construct a corpus for RGMPC based on an existing open-access dataset.Automatic and manual evaluations demonstrate that the ICRED remarkably outperforms strong baselines.
Learning Rate Dropout

Optimization algorithms are of great importance to efficiently and effectively train a deep neural network. However, the existing optimization algorithms show unsatisfactory convergence behavior, either slowly converging or not seeking to avoid bad local optima. Learning rate dropout (LRD) is a new gradient descent technique to motivate faster convergence and better generalization. LRD aids the optimizer to actively explore in the parameter space by randomly dropping some learning rates (to 0); at each iteration, only parameters whose learning rate is not 0 are updated. Since LRD reduces the number of parameters to be updated for each iteration, the convergence becomes easier. For parameters that are not updated, their gradients are accumulated (e.g., momentum) by the optimizer for the next update. Accumulating multiple gradients at fixed parameter positions gives the optimizer more energy to escape from the saddle point and bad local optima. Experiments show that LRD is surprisingly effective in accelerating training while preventing overfitting.