Reviewerly/exHarmony huggingface dataset:
Bibliotecas corporativas e a aplicação da competência em informação em empresas: uma revisão sistemática da literatura

Knowledge is highly valued by companies in the information age, so many organizations have used different strategies to value information and knowledge. One of these strategies is the development of information literacy in business context. Therefore, this study has the following research question: how can information literacy contribute to business development and innovation? This paper aims to demonstrate the contribution of the corporate library to construction and development of professionals’ information literacy. The methodological procedures are based on a systematic literature review about information literacy and corporate library.  The results demonstrated that there are not many studies about information literacy in business context in Brazil and internationally. Because of that, this paper introduces a conceptual model of corporate library in the organizational processes. The corporate library encourages the development of professionals based on information literacy.
Improving ADNet for Robust Tracking

By taking advantages of the multi-domain structure and efficient searching strategy, ADNet (Action Decision Network) outperforms other approaches. However, its precision is limited due to the lack of detail feature caused by the improper network structure and other issues. Through our improvements, the proposed approach is more accurate than ADNet. Experiments demonstrate the advantages of our method.
Pilihan Karir Lulusan Program Studi Akuntansi Keuangan di Politeknik Pos Indonesia

Challenge in the accounting profession has competitively increased since the implementation of the ASEAN Economic Community (AEC) by 2015. The Indonesian accounting graduates should be ready to face a very tight competition with other graduates from outside Indonesia. Therefore, the strategy needs to be prepared, to help graduates determine selections carrier they will take. The carrier selections for graduates are the very beginning stage of career establishment. Economics-Accounting Graduates will be facing different selections of accountant profession, such as government accountants, accountants educators, company accountants, and public accountant. Or even accounting graduates can also have a career outside the accounting field if they are talented and expertise enough in another field. There are many studies related to career selections of accounting graduates has been made, but this kind of research has not been done in Financial Accounting Studies Program at the Politeknik Pos Indonesia. This research was quantitative with a descriptive cross-sectional design. The study was conducted in October 2016 using a questionnaire with a sample as many as 51 graduates using a whole sampling technique. The results showed that the majority of graduate accounting courses at the Politeknik Pos Indonesia in 2016 were female (70.59%) and the most demanding professions are Public accountants (37.25%). The location is the most demanding jobs are in Indonesia (36.47%). Selection work sector showed no significant differences between the public sector (45.09%) and private (47.05%).
Distributed Ledger Technologies for the Real Estate Market

Distributed ledger technologies such blockchain, crypto-currencies, tokenization and smart contracts have recently received a lot of attention. Despite this increase in attention, industries and governments around the world are not using such technologies to their full potential. Real estate is one of such industries who could greatly benefit from adopting such distributed ledger technologies, mainly due to their automated confirmation and transaction transparency nature. In this paper, we explore some of the main distributed ledger technologies and evaluate their potential impact on the real estate market. Our aim is to show how they could improve current methods such as title deed transfer or property valuation as well as point out any issues which might arise from digitising real estate methods. We also review any reasons for why the technologies have not been adopted already and evaluate what impact they could have if they were to be implemented.
@soapdog will always remember @toolness and Erin ...

@soapdog will always remember @toolness and Erin Night freakin when the lawyers freaked when I made PETA a bacon lovers webaite and NRA pro rationale
Detection and Correction of Grammatical Errors in Hindi Language Using Hybrid Approach

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.
Handling an ISO/IEC 15504 SPICE Assessment Wave, An Unique Incident

This paper reports the initial background, concepts, progression and outcomes of a ISO/IEC 15504 SPICE assessment wave occured at Turkey, handled by Turkish Standards Institution (TSE). It explains the certification programme of TSE and the continuous management of certified companies. The general status and profiles of certified companies depending on their behavioral approach to ISO/IEC 15504 SPICE standard are also indicated. On the later parts of the paper, the most common mistakes of many companies in the common scope of OMM level-2 of ISO/IEC 15504-7 are reported. As the “Turkish SPICE Wave” initiative affected many organizations and reshaped a whole sector in a different way, the outcomes of the wave rendered the selection of ISO/IEC 15504-7 standard questionable in terms of the compatibility to VSE’s (Very Small Entity); as the results makes an observer wonder whether selecting ISO/IEC 29110 standard instead of ISO/IEC 15504 SPICE could be more beneficial and easy to use for small scale companies.
Superimposed Attention Mechanism-Based CNN Network for Reading Comprehension and Question Answering

In recent years, end-to-end models have been widely used in the fields of machine comprehension (MC) and question answering (QA). Recurrent neural network (RNN) or convolutional neural network (CNN) is combined with attention mechanism to construct models to improve their accuracy. However, a single attention mechanism does not fully express the meaning of the text. In this paper, recurrent neural network is replaced with the convolutional neural network to process the text, and a superimposed attention mechanism is proposed. The model was constructed by combining a convolutional neural network with a superimposed attention mechanism. It shows that good results are achieved on the Stanford question answering dataset (SQuAD).
CRFA-CRBM: a hybrid technique for anomaly recognition in regional geochemical exploration; case study: Dehsalm area, east of Iran

Identification of geochemical anomalies is a significant step during regional geochemical exploration. In this matter, new techniques have been developed based on deep learning networks. These simple-structure-networks act like our brains on processing the data by simulating deep layers of thinking. In this paper, a hybrid compositional-deep learning technique was applied to identify the anomalous zones in Dehsalm area which is located in 90 km of SW-Nehbandan, a town in South Khorasan province, Iran. The compositional robust factor analysis (CRFA) was applied as a tool to help select a meaningful subset as an input to Continuous Restricted Boltzmann Machine (CRBM). The dataset consists of 635 stream sediment geochemical samples analyzed for 21 elements. Using CRFA, the 3rd factor (i.e. Pb, Zn, Cu, Ag, Sb, Sr, Ba, Hg and W), indicating epithermal mineralization in the area, was considered as an input set to CRBM. The best-performed CRBM with 80 hidden units and stabilized parameters at 150 iterations was finalized and trained on all the geochemical samples of the study area. Average square contribution (ASC) and average square error (ASE) were determined as anomaly identifiers on the reconstructed error of the trained CRBM. A statistical threshold was applied on the values of the criteria (ASC & ASE) and the resulting outputs were mapped to delineate the anomalous samples. The maps indicated that ASC and ASE have the same performance in the multivariate geochemical anomaly recognition. The anomalies were spatially confirmed with the mineral indexes of Pb, Zn, Cu and Sb, as well as several active mines of Pb and Cu in the study area.
The Behavior of Using Simcard in Indonesia Border Area

Digital life exists in the border area. The cellphone is a part of daily life. Signals from many telecommunication service providers, both Indonesia and foreign, are captured. The problem is when foreign signals captured, then the roaming rate charged. Some people avoid it by switching their simcard to a local simcard. Some people in border area using local simcard (from Indonesian service providers) some use foreign simcard, and other use mixed of them (local and foreign). Knowing about border crosser behavior in using simcard is important not only for the telecommunication service provider but also for the government. The study aims to estimate the number of border crosser based on their behavior in using simcards when entering Indonesia. This study uses data from Survey of Foreign Tourist Based on Mobile Positioning Data conducted by Statistics Indonesia in 2017. Based on that survey, there were 28.305 of border crosser having cellphones. The method of analysis is multinomial logistic regression. The responsese variable is the behavior of using simcard with 3 categories; foreign, local, and mixed. The model fit the data well. The result show that 53,8 percent of border crosser are foreigners most of them entering Indonesia through Kalimantan. The use of simcard in the boundaries area is significantly influenced by sex, age, purpose to visit Indonesia, and region. There are 7,3 percent of foreigner use mixed cards and only 1,4 percent use local cards. For border crossers from Indonesia, there is 99,7 percent use local cards while the other 0,3 percent use mixed cards and no one uses foreign cards.&#x0D;
Reflections on the UK network week/ Eleanor Dare

Reflections on the UK network week/ Eleanor Dare, during the research week VR researchers from Japan and the UK explored and discussed a range of immersive experiences which are evaluated and analysed n this project blog post.
The Influence of Memory Matrix on Classroom Assessment Tcehnique (CATs) to Improve Students’ English Vocabulary at The Junior High School in Academic Year 2018/2019 (A Quasi Experimental Research at Second Grade of SMPN 1 Karang Tanjung)

Muthia Edmatiara. 152301819.2019. The Influence of Memory Matrix on Classroom Assessment Tcehnique (CATs) to Improve Students’ English Vocabulary at The Junior High School in Academic Year 2018/2019 (A Quasi Experimental Research at Second Grade of SMPN 1 Karang Tanjung)This research investigates the influence of memory matrix to improving students vocabulary. The researcher one group pre-test and post-test design and quantitative research method. The instrument used in this reserach was tests (pre-test and posttest). This reserach involves 60 students as a sample.among second grade of SMPN 1 Karang Tanjung year 2019 as a population. The reserach finding of the last result showed that “t0” that the witer got from the calculation is higher than t-tabel both at significance level 5% and 1%. Significance 5% observation= 2,87 and t-tabel = 2,00 so significance 1% observation= 2,87 and t-tabel=2,66. In addition, the result of the observations showed that the students more motivated learning English using Memory Matrix. Based on the result above, it can be concluded that memory matrix can improve the students’ vocabulary mastery. The result of this research shows that the students’ vocabulary achieve better performance than those who do not use it.Key Words: Vocabulary, Classroom Assessment Technique (CATs), Memory Matrix.
Feature extraction of mammographic images using convolutional autoencoders

Breast cancer screening is usually done via digital mammography imaging. These screenings are creating large data sets that open up the possibility for further study in medical image analysis. Data sets of images may appear high dimensional when every pixel is considered as a dimension. However, for natural images or images belonging to a given category the signal can usually be represented with much fewer dimensions. In recent years, the advances in low dimensional representation of images can to a large extent be attributed to deep learning based methods. In this study, we apply two unsupervised methods for feature extraction on a data set of 4648 mammograms. First, we apply stochastic singular value decomposition (SSVD) to extract 200 features. Second, we train a convolutional autoencoder (CAE) with a bottleneck of size 200. With the two representations of the images, we do a feature-age correlation study. The CAE model features correlate significantly more with age than those of the SSVD model. Finally, we run the features through a correlation study with roughly 20,000 phenotypes. 190 breast related phenotypes were found to significantly correlate with features from the CAE model, while only 2 from the SSVD model. Our study suggests that CAE models are a good starting point for creating biologically meaningful phenotypes from mammograms.
Orchestrated Clusteres using Kubernetes on Cloud Web Services

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.
Gesture semantics reconstruction based on motion capturing and complex event processing

A fundamental problem in manual based gesture semantics reconstruction is the specification of preferred semantic concepts for gesture trajectories. This issue is complicated by problems human raters have annotating fast-paced three dimensional trajectories. Based on a detailed example of a gesticulated circular trajectory, we present a data-driven approach that covers parts of the semantic reconstruction by making use of motion capturing (mocap) technology. In our FA3ME framework we use a complex event processing approach to analyse and annotate multi-modal events. This framework provides grounds for a detailed description of how to get at the semantic concept of circularity observed in the data.
Self-attention StarGAN for Multi-domain Image-to-Image Translation

In this paper, we propose a Self-attention StarGAN by introducing the self-attention mechanism into StarGAN to deal with multi-domain image-to-image translation, aiming to generate images with high-quality details and obtain consistent backgrounds. The self-attention mechanism models the long-range dependencies among the feature maps at all positions, which is not limited to the local image regions. Simultaneously, we take the advantage of batch normalization to reduce reconstruction error and generate fine-grained texture details. We adopt spectral normalization in the network to stabilize the training of Self-attention StarGAN. Both quantitative and qualitative experiments on a public dataset have been conducted. The experimental results demonstrate that the proposed model achieves lower reconstruction error and generates images in higher quality compared to StarGAN. We exploit Amazon Mechanical Turk (AMT) for perceptual evaluation, and 68.1% of all 1,000 AMT Turkers agree that the backgrounds of the images generated by Self-attention StarGAN are more consistent with the original images.
Optimal Control for Complex Systems under Multi-granularity Based on Data Mining

This paper proposes a control scheme for a kind of complex systems whose dynamics follow statistical law. First, inspired by data mining technology, the samples are clustered into several classes reflecting the working pattern by modified ISODATA method. Second, based on the clustering result, the cell state space is constructed within global and local target cells respectively by Cartesian product, and the cell mapping further is created via the analysis of pattern time series. Third, by the reverse searching algorithm, global optimal paths to the target cell are found and the information provided by the searching paths constitute the optimal controller at a coarse-grained level so as to bring the system into the target cell quickly, once enter into the target cell, another controller which consists of the subcell mapping with the optimal cost increment in refined granularity level is applied in order to reduce steady-state error. Finally, a simulation in the dynamic trajectory of a sintering process is given to demonstrate the feasibility of the proposed approach.
Using an Ontology-based Approach to Build Open Assisting Tools in Foreign Language Writing

In today’s globalised world where there is a growing need for international communication, non-native speakers (NNS) from a wide range of professional fields are increasingly called upon to write specialised texts in English. More often than not, however, the linguistic competence required to do so is well beyond that of the majority of NNS. While software applications can serve to assist NNS in their English writing tasks, most of the applications available are designed for users of English for general purposes as opposed to English for professional purposes. Therefore, these applications lack the specific vocabulary, style guidelines and common structures required in more specialised documents. Necessary modifications to meet the needs of English for professional purposes tend to be viewed as representing an overly complex and expensive task. To overcome these challenges, we present a software called O-WEAA (Ontology-Writing English Assistant Architecture) which makes use of an ontology that represents the knowledge which, according to our formalisation, is required to write most types of specialised professional documents in the English language. Our formalisation of the required knowledge is based on an exhaustive linguistic analysis of several written genres. The proposed software is composed of two parts: i) a web application named Acquisition Interface Module, which allows experts to populate the ontology with new data and ii) a user-friendly, general web interface named Writing Assistant Interface Module which guides the user throughout the writing process of the English document in the specific domain described in the ontology.
PyTest Testing Framework

There are several testing frameworks available for Python, although only one, unittest comes as part of the typical Python installation. Typical libraries include unittest (which is available within the Python distribution by default) and PyTest. This chapter introduces the PyTest testing framework.
Impact of Smartphone on Youngsters in Bangladesh: A Study on Comilla University

The progress of telecommunication has a deep influence on the academic issues of the students. Smartphone practice became prevalent to Youngsters on account of its educational and pleasurable choices by utilizing several uses. Amongst the youngsters, students are using Smartphones on a high scale. However, extreme Smartphone utilization frequently creates the students habituated to it and that influence on handler's academic performance, physical and mental health, daily events, withdrawal propensity, and societal relations. This present article wishes at recognizing the issues that impact the level of Smartphone obsession to the students and its influence on their complete academic performance. The key thought of this study is to examine the influences of Smartphone on Youngsters in Bangladesh. This study initiates studying the prevailing associated literature and progresses to create its methodological segment and arranges the different practices of data collection. It is obvious from this research that Smartphone holds a strong influence on Youngsters in Bangladesh. The data for this study has been collected from both primary and secondary bases of information. Bearing in mind the research objectives, the present study utilizes both quantitative and qualitative methods. Therefore, at one hand it provides a statistical analysis and alternatively it offers us with vital analysis as well by utilizing qualitative method.
Visual working memory for stimulus feature saturation

Stimuli in natural vision differ in various physical attributes such as size, location, and surface feature, posing a unique challenge for visual cognition. However, previous research in visual working memory (VWM) mostly used simple and well-controlled laboratory stimuli (colors with different hues but matched saturation), making it difficult to assess how diverse stimulus features affect VWM. To examine this issue, this study developed a two-dimensional face space with continuous variations in the circular dimension for face identities and in the radial dimension for feature saturation. The center of this face space represents the average (norm) face. The radial distance of a face represents its strength/intensity, similar to color saturation defined as the eccentricity of a color in standard color space. In Experiment 1, participants remembered either 1 or 2 faces randomly sampled from the face space across different radial and circular locations and recalled the face on both dimensions after a short delay. Modeling of recall errors on the circular axis showed that VWM precision was reduced by set size and increased by face saturation. However, the number of retained VWM items was only significantly influenced by set size but not by face saturation. Furthermore, participants’ recall on the radial axis showed more centripetal errors at a larger set size, suggesting a general bias toward the norm. To examine how face saturation affects neural activities underlying VWM maintenance, in Experiment 2, we recorded electrocorticography signals along the ventral processing pathway in epileptic patients during the face VWM task. Representational similarity analyses showed that face saturation strengthened the association between delay-period neural activities and retained VWM representations estimated by behavioral recall performance. Together, these preliminary findings reveal novel neurocognitive effects of feature saturation on VWM maintenance, suggesting that saturation may be a critical aspect of VWM representations beyond capacity and precision.