Reviewerly/exHarmony huggingface dataset:
Combining Deep Neural Network with SVM to Identify Used in IOT

Along with the development of science and technology, especially one of internet of things (IOT), products related to IOT have improved human life these days. In IOT-related utility products, it is impossible not to mention devices for smarts city, self-driving cars and especially for smart home. Devices for smarts home are usually controlled by voice. Therefore, voice processing technology is also in need of improvement. Speech processing enhancement that ensures safety in the process helps smart home devices bring about an evolutionary change. In the article, we mainly focus on human voice processing independently od the text. Particularly, we will integrate Convolutional network(CNN) and Suport Vector Machine(SVM) to create a Feature Building Machine. SVMs are often used in speech and image classification, which accordingly is a critical and swift data sorter. The article analyzes the advantages of the combination Deep Neural Network (DNN) and SVMs in speech recognition and is the foundation to develop devices for smart home. The results of the experiment, which was used in the standard Voxcelb database, demonstrate the superiority in sound recognition compared to traditional i-vector methods or other CNN methods.
The syntactic parsing of ASD-STE100adverbials in ARTEMIS

&lt;p&gt;This paper seeks to offer an update of the syntactic representation of adverbials in the Lexical Constructional Model and the multipurpose lexico-conceptual knowledge base, FunGramKB, and to implement the conditions that are necessary for an effective parsing of such constituents within ARTEMIS, a NLP prototype within FunGramKB for the computational treatment of the syntax and semantics of sentences. As regards the status of adverbials in the Layered Structure of the Clause (LSC) proposed in Role and Reference Grammar, it is striking that they have been somewhat sidelined, despite the programmatic proposal in Van Valin (2005) for a distribution of adjuncts along the different layers in the LSC. Since then, almost no further contribution has been offered to fully expand this new scheme. Thus, our aim is to fully embody a layered approach for adverbials and to adapt our proposal to the conditions imposed by the Grammar Development Environment in ARTEMIS, by developing the rules that are necessary for the parsing of the catalogue of adverbials of a corpus written in the controlled natural language ASD-STE100.&lt;/p&gt;
Cut Elimination for the Weak Modal Grzegorczyk Logic via Non-well-Founded Proofs

We present a sequent calculus for the weak Grzegorczyk logic $$\mathsf {Go}$$ allowing non-well-founded proofs and obtain the cut-elimination theorem for it by constructing a continuous cut-elimination mapping acting on these proofs.
Neural Network Approach for Semantic Coding of Words

This article examines and analyzes the use of the word2vec method for solving semantic coding problems. The task of semantic coding has acquired particular importance with the development of search system. The relevance of such technologies is associated primarily with the ability to search in large-volume databases. Based on the obtained practical results, a prototype of a search system based on the use of selected semantic information for the implementation of relevant search in the database of documents has been developed. Proposed two main scenarios for the implementation of such a search. The training set has been prepared on the basis of documents in the English version of Wikipedia, which includes more than 100,000 original articles. The resulting set was used in the experimental part of the work to test the effectiveness of the developed prototype search system.
Cloud Computing and Academic Libraries in Nigeria

The advent of computers and other communication technologies led to major transformations in the way library services are rendered and the profession practiced. The libraries have been automated, networked, and are now moving towards paperless or virtual libraries. This chapter examines various facets of cloud computing, describes types and applications. It also outlines the benefits and challenges of cloud computing in academic libraries. Further, the chapter gives direction about the use of cloud computing services to the library professionals and academic libraries in Nigeria. This chapter may be helpful in generating cloud-based services for academic libraries. The chapter suggests that quality service assurance by cloud providers must be ensured, upgrading of internet bandwidth for fast access and quick information dissemination that would improve academic library services. It concludes that cloud computing is crucial and vital in modern information innovation, especially in academic libraries.
VRPSOFC

The focused crawler is the key technology of the search engine. It filters webpages based on relevant algorithms until certain conditions are met. The current focused crawler is prone to topic-drift and low precision in the process of crawling the webpages. Therefore, this paper proposes a focused crawler framework (VRPSOFC) based on mutation improving particle swarm optimization. First of all, for each topic, VRPSOFC gets 3 different types of seed pages that are easy to generate large-scale web page aggregation based on the page click rate of Google search, which are official website, wikipedia, forum or video page. Then VRPSOFC uses the mutation improved particle swarm optimization algorithm proposed in this paper to crawl webpages, where each seed page will be used as the initial page. Finally, experiment in the real web environment and analyze the results. Compared with traditional VSM and other methods, VRPSOFC can obtain more accurate URL priority and crawl high quality web pages. Therefore, the topic crawler framework proposed in this paper is effective and important.
In the Search of Web of Intelligence

From the very early days, and due to its unprecedented opportunities and advantageous facilities, the Web has been revolutionizing almost every aspects of human life, technology, etc. As a dynamic phenomenon, the Web itself has experienced several major evolution stages. Such fundamental changes never thought to abolish predecessor technologies and concepts but taking them to a new level of functionality. In fact, these paradigm shifts are necessary and inevitable in order to take advantages of state of the art technologies and approaches. In this regard, social Web, semantic Web and similar nomenclatures aimed to emphasize the key idea that best represents the latest achievements and possibly future directions. Thanks to many years of research and development, the current state (version) of the Web puts forward some outstanding opportunities both to make the most of available resources/capabilities and establish the foundation of the future Web. Specifically, convergence of human intelligence (of Web users), machine intelligence (of Web agents) and intelligence of things (through Web of Things) toward shaping Web of Intelligence (WoI) can be regarded as a breakthrough in the field. The major contribution of this work is proposing a Web-based conceptual model for intelligence harvesting (i.e. Web of Intelligence) through which humans, machines and things could collaborate to solve large scale intelligence-intensive problems in a more efficient and effective way. The rationale behind this idea, its building blocks and related considerations are delineated in this paper. Moreover, a novel application based on the proposed concept, entitled comprehensive intelligent search, is introduced.
SocialTree

News story understanding entails having an effective summary of a related group of articles that may span different time ranges, involve different topics and entities, and have connections to other stories. In this work, we present an approach to efficiently extract structured summaries of news stories by augmenting news media with the structure of social discourse as reflected in social media in the form of social tags. Existing event detection, topic-modeling, clustering and summarization methods yield news story summaries based only on noun phrases and named entities. These representations are sensitive to the article wording and the keyword extraction algorithm. Moreover, keyword-based representations are rarely helpful for highlighting the inter-story connections or for reflecting the inner structure of the news story because of high word ambiguity and clutter from the large variety of keywords describing news stories. Our method combines the news and social media domains to create structured summaries of news stories in the form of hierarchies of keywords and social tags, named SocialTree. We show that the properties of social tags can be exploited to augment the construction of hierarchical summaries of news stories and to alleviate the weaknesses of existing keyword-based representations. In our quantitative and qualitative evaluation the proposed method strongly outperforms the state-of-the-art with regard to both coverage and informativeness of the summaries.
Checks and Mass Balances for In Situ Quantification of Mineral Composition using Proximal Soil Sensors

Soil mineral composition affects soil behavior, but field estimation of mineral composition has proved difficult. To investigate the potential of predicting soil mineral composition in situ, 15 soils representing diverse mineral composition from New South Wales, Australia, were scanned with visible near‐infrared (VisNIR) and portable X‐ray fluorescence (pXRF) spectrometers to a depth of 1 m at 2.5‐cm scanning increments. The presence of phyllosilicate and Fe oxides was assessed using a pattern‐matching algorithm with VisNIR spectra from mineral end‐member libraries. Rule‐based iterative partitioning was then applied on pXRF elemental compositions based on mineral stoichiometry to determine the abundance of kaolinite, smectite, illite, hematite, goethite, calcite, gypsum, and quartz. This fusion model correctly identified the most abundant mineral in a sample with 72% accuracy. Mineral predictions were stable under variable moisture and surface conditions, as experienced when scanning samples in situ and under air‐dry and ground conditions in the laboratory. Relative changes in mineral composition within a profile and across horizon boundaries were accurately expressed. The fusion model accurately quantified the abundance of quartz (Cohen's kappa coefficient, 0.67), and CaCO 3 (Cohen's kappa coefficient, 0.76; Lin's concordance correlation coefficient, 0.96; RMSE, 20.9 g kg −1 ). The dominant phyllosilicate mineral was identified correctly with 86% accuracy, although accurate quantification of phyllosilicates and Fe oxides was not achieved. Conjoint use of VisNIR and pXRF spectroscopy as part of a fusion model approach showed great potential to provide comprehensive estimation of soil mineral composition in situ. Core Ideas VisNIR and pXRF were used to quantify soil mineral composition in situ. The fusion model approach leveraged the complementary strengths of VisNIR and pXRF. The most abundant mineral of a sample was predicted with 78% accuracy. Quantitative predictions were achieved for quartz, CaCO 3 , and gypsum. This is the most comprehensive investigation of its kind to date.
Success-History Based Position Adaptation in Co-operation of Biology Related Algorithms

Previously, a meta-heuristic approach called Co-Operation of Biology Related Algorithms or COBRA based on a fuzzy logic controller for solving real-parameter optimization problems was introduced and described. COBRA’s basic idea consists in a cooperative work of well-known bio-inspired algorithms with similar schemes, while the fuzzy logic controller determines which bio-inspired algorithms should be included in the co-operative work at a given moment for solving optimization problems using the COBRA approach. COBRA’s performance has been evaluated on a set of test functions and its workability demonstrated. However, COBRA’s search efficiency depends significantly on its ability to keep the balance between exploration and exploitation when solving complex multimodal problems. In this study, a new technique for generating potential solutions in biology-inspired algorithms is proposed. This technique uses a historical memory of successful positions found by individuals to guide them in different directions and thus to improve the exploration and exploitation abilities. The proposed method was applied to the components of the COBRA approach. The modified meta-heuristic as well as its original variant and components (with and without the proposed modification), were evaluated on a set of various well-known test functions. The obtained experimental results are presented and compared. It was established that the fuzzy-controlled COBRA with success-history based position adaptation allows better solutions with the same computational effort to be found. Thus, the usefulness of the proposed position adaptation technique was demonstrated.
Exploring the Adoption of Virtual and Augmented Reality in Enhancing Interactive Learning in Tanzania

The challenge of inadequate teaching and learning facilities impedes the development of the education system in Tanzania. This instigates the need to combine education and technology for provision of education. This paper assessed the adoption of Virtual and Augmented Reality as a tool to improve interactive learning. The research assessed the awareness and perceived usefulness of virtual and augmented realities in education. The research utilized a quantitative methodology whereby an online survey was used to collect data. Results indicate 69.7% of the respondents were aware of virtual and augmented reality. Moreover, 87.4% of the respondents agreed that virtual and augmented reality can be adopted to improve interactive learning. Interestingly, Science, Engineering, and Medical fields were proposed for adoption. These technologies influence students' interest and motivation to learn through visualizations and interactivity. However, further research is recommended on cost-effective gadgets for virtual reality technology and suitable content for augmented reality technology.
ICT Support Environment in Developing Countries: The Multiple Cases of School Teachers in Rural South Africa

South African teachers are under pressure due to rising expectations of their content knowledge and digital skills without guaranteed infrastructural or institutional support in using ICTs. The purpose of this study is to find out to what extent ICTs are being used by teachers in rural South African schools and also what support systems, if any, these teachers have. The theoretical lens is that of social network theory to investigate the network teachers use to acquire ICT knowledge and assistance. The data capturing involved a baseline survey where data were collected from 197 rural school teachers in South Africa. The data analysis included descriptive statistics and social network analysis using Kumu as a tool. Our findings reveal that despite the infrastructural challenges and lack of institutional support, rural teachers use technology for teaching and learning. Furthermore, there is evidence that strong social ties with friends and family, faith groups, department of education and businesses that sell or fix ICTs, provide the essential support network. The research provides novel insights into the social networks that sustain the ICT use of rural teachers in South Africa and makes a theoretical contribution through empirical evidence that confirms the strength (degree) of weak ties in explaining sustainable ICT teacher support in rural schools.
A New Feature Selection Algorithm Based on Category Difference for Text Categorization

The feature selection is an important step which can reduce the dimensionality and improve the performance of the classifiers in text categorization. Many popular feature selection methods do not consider the difference in the distribution of different categories on a feature. In this paper, we propose a new filter based feature selection algorithm, namely fused distance feature selection (FDFS), which evaluates the significance of a feature by taking account of the difference in the distribution of different categories and selects more discriminative features with the minimal number. The proposed algorithm is investigated both inside and outside perspectives on four benchmark document datasets, 20-Newsgroups, WebKB, CSDMC2010 and Ohsumed, using Linear Support Vector Machine (LSVM) and Multinomial Naïve Bayes (MNB) classifiers. The experimental results indicate that our proposed method provides a competitive result, where its average ranking is 1.25 on LSVM and 1 on MNB.
A Deep Learning Approach for Extracting Polarity from Customers’ Reviews

Due to the expansion of social networks and media such as Twitter, Facebook, LinkedIn, and different weblogs, and the great increase in information sharing and comments, Which typically are in the form of text data, big enough to be recognized as big data., and with respect to the importance of these data for the analysis of customers' priorities, needs and their attitudes toward different products, finding and extracting data from their comments, are the primary goals of this research. To serve this purpose, this research has used deep learning approach, and multilayer neural network methods in order to extract the polarity of customers' opinions and comments in two domains of products/services ranging from restaurant to laptop. The findings of this study indicate that the proposed model using the potencies of the long short-term-memory networks, is able to determine the comments' polarity with 85 % and 84.62 % precision for restaurant and laptop domains respectively, in such a way that the results are relatively more accurate than the results of other methods.
Traversing Latent Space Using Decision Ferns

The practice of transforming raw data to a feature space so that inference can be performed in that space has been popular for many years. Recently, rapid progress in deep neural networks has given both researchers and practitioners enhanced methods that increase the richness of feature representations, be it from images, text or speech. In this work we show how a constructed latent space can be explored in a controlled manner and argue that this complements well founded inference methods. For constructing the latent space a Variational Autoencoder is used. We present a novel controller module that allows for smooth traversal in the latent space and construct an end-to-end trainable framework. We explore the applicability of our method for performing spatial transformations as well as kinematics for predicting future latent vectors of a video sequence.
Challenges in Parallel Operation of Quantum Key Distribution and Data Transmission

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text T. A. Eriksson, T. Hirano, G. Rademacher, B. J. Puttnam, R. S. Luis, M. Fujiwara, R. Namiki, Y. Awaji, M. Takeoka, N. Wada, and M. Sasaki, "Challenges in Parallel Operation of Quantum Key Distribution and Data Transmission," in OSA Advanced Photonics Congress (AP) 2019 (IPR, Networks, NOMA, SPPCom, PVLED), OSA Technical Digest (Optica Publishing Group, 2019), paper QtW3E.2. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
Impact of Feature Extraction Techniques on a CBIR System

Feature extraction is a key step and plays a deciding role for the performance of an image retrieval system. Success of a Content Based Image Retrieval System depends on the used features of the image. This paper includes a wide-range of survey on the various feature extraction process and their impact on the working behavior of an image retrieval system. This impact is calculated on the basis of retrieval accuracy, retrieval time, space complexity and feature extraction time. Comprehensive survey on the recent trends and challenges to the retrieval system has also been discussed. Furthermore, directions and suggestions, based on the real world applications are also suggested for encouraging the researchers in the area of image processing for adopting the optimized feature extraction process. This survey also tries to fill the gap between the traditional approaches and recent trends of feature extraction. More importantly, this paper also surveyed the issues with the feature extraction techniques in spatial as well as spectral domain.
Document Clustering Using Different Unsupervised Learning Approaches: A Survey

One of the fastest growing research fields in recent times is Document Clustering. It gained its importance in text mining due to the tremendous increase of documents on internet. Textual data management is the need of every organization and clustering the documents is one of the fastest and widely used techniques. Document clustering is an unsupervised technique that organizes similar documents into classes in order to improve information retrieval. The overall evaluation of the research work is performed by comparing the working procedure and merits of each method with other in terms of some performance metrics. This research work concluded with the better research method which can be applied to cluster the similar documents on the basis of text. The comparative analysis shows the accuracy of documents clustered using K-Means are high compare to other approaches of unsupervised learning in terms of F-Measure, Precision, recall and time complexity.
Three attacks of certificateless aggregate signature scheme

After in-depth research, this paper finds that there are three types of attacks in the existing certificateless aggregate signature scheme. For these three types of attacks, this paper gives detailed attack steps for specific schemes, analyzes the reasons for the existing attacks and proposes improvement measures. This paper provides ideas for the next step to design a more secure and efficient certificateless aggregate signature scheme.
Simulations and Subjective Rating of Acoustic Conditions in a Symphony Orchestra—A Case Study

Acoustic conditions in a symphony orchestra on a concert hall stage are very different from those on an empty stage. Since inter-orchestral sound transmission and other acoustic conditions with the orchestra present is easier to simulate than to measure, a method for simulations in Odeon models of orchestras in different rooms was developed by this author. This method was applied in the Grieghallen Renewal Project, which involved changes in concert hall, orchestra pit, and rehearsal hall. The resident orchestra members gave their overall rating of playing conditions in the home venues in addition to a number of international venues. Acoustical conditions in the rated venues were simulated and compared with ratings. Several metrics were investigated, and their correlation with subjective ratings varied between r2 = 0.09 and r2 = 0.85. It turned out the orchestra clearly preferred to play in conditions where the direct component and the reverberant component of the inter-orchestral sound-transmission on average were equally strong; |D-R| = 0. Any deviation from equality was associated with reduced preference, with correlation coefficient r = −0.92. Several interesting implications and interpretations of the result are discussed in the paper.