Reviewerly/exHarmony huggingface dataset:
How the Length and Characteristics of Routing Module Affect Ability Estimation in ca-MST?

In this study, how the length and characteristics of routing module in different panel designs affect measurement precision is examined.In the scope of the study, six different routing module length, nine different routing module characteristics, and two different panel design are handled.At the end of the study, the effects of conditions on measurement precision are examined by estimating correlation, RMSE and SEE for final theta estimation values.According to the results, it is seen that as the routing module length increases, lower RMSE and higher correlation are obtained.Nevertheless; good results were obtained in both panel designs even if the a and c distributions were outside the range suggested in the literature where routing module length is 15 and above.In addition, the three-stage panel design generally yielded better results in terms of measurement accuracy.
Digital Image Watermarking Using Sine Transform Technique

In this chapter, digital image watermarking using sine transformation has been introduced in the frequency domain. In the proposed technique, the secret image is first compressed using a sine transformation and then embedded into the host image. The resultant images obtained from the proposed algorithm are subjected to various security attacks and the results are compared with other existing algorithms. The results obtained are better compared to the existing techniques.
EFTSA: Evaluation Framework for Twitter Sentiment Analysis

Sentiment analysis is a characteristic task that aims to detect the sentiment of opinions in content.Twitter sentiment analysis (TSA) is a promising field that has gained attention in the last decade.Investigators in the TSA field have faced difficulties comparing existing TSA techniques, as there is no agreed systematic framework.This means that the evaluation of existing techniques relies on selecting different datasets without meaningful justification.Another issue that arises when comparing different TSA techniques is that there are no unified metrics.Some researchers select classification accuracy and others choose recall, precision, and F-measure metrics.In this paper, we propose a framework called Evaluation Framework for Twitter Sentiment Analysis (EFTSA) for TSA evaluation based on individual or multiple datasets.This would help researchers compare their Twitter sentiment approaches against others.
LocalPKI: An Interoperable and IoT Friendly PKI

A public-key infrastructure (PKI) binds public keys to identities of entities. Usually, this binding is established through a process of registration and issuance of certificates by a certificate authority (CA) where the validation of the registration is performed by a registration authority. In this paper, we propose an alternative scheme, called LocalPKI , where the binding is performed by a local authority and the issuance is left to the end user or to the local authority. The role of a third entity is then to register this binding and to provide up-to-date status information on this registration. The idea is that many more local actors could then take the role of a local authority, thus allowing for an easier spread of public-key certificates in the population. Moreover, LocalPKI represents also an appropriate solution to be deployed in the Internet of Things context. Our scheme’s security is formally proven with the help of Tamarin, an automatic verification tool for cryptographic protocols.
Digital rights of LGBTI communities: a roadmap for a dual human rights framework

This chapter aims to move beyond the existing narratives on digital technologies and LGBTI rights by sketching a preliminary roadmap for the development of a combined LGBTI and digital rights analytical framework and research programme. Such a framework is necessary for two reasons. First, it is vital to address the growing complexity of the implications of the Internet and digital technology for human rights that in reality go well beyond the so-called classical digital rights issues of censorship and surveillance, which have captured and even expropriated scholarly and activist attention at the expense of other pressing issues. Second, such a framework is instrumental for the LGBTI movement to expand its political spectrum beyond ‘traditional’ issues, such as marriage equality and gender identity, and confront other pressing civil rights concerns to assure the effective and full equality of LGBTI communities in the digital age. The modest goal of this chapter is to illuminate the problematic issues at the intersection of LGBTI communities and digital technologies and hopefully stimulate a research programme on these topics.
Use of ICT by Biology Teachers in the Secondary Schools: Bangladesh Perspective

Information and communication technologies (ICT) have changed the pedagogy in education offering more student-centered learning incidents.The purpose of this study was to evaluate the use of ICT by the biology teachers in teaching-learning process of secondary schools in Bangladesh.A total of 50 biology teachers from several secondary schools of Bangladesh participated in the study.It was found that teachers have better access to computer than the average populace of Bangladesh.They have working knowledge on Microsoft Office Word, Excel, Power Point, and different web browsers.A good number of the teachers use computer for preparing their lectures, use internet for accessing educational materials, takes classes with multimedia with the support of ICT.However, about 17% to 25% were found to be not using these ICT means.Many teachers opined that they have less number of computers in the school to prepare for class work.They also assessed that hardware troubleshooting, software malfunction and unexpected power failure as challenges for using ICT.This paper recommends to provide continuous in-servicing training on ICT, interest free loan to teachers for purchasing laptop computer and to ensure technical support to overcome software and hardware problems in the schools.
An Ethnic and Folk Art Space Course based on TPACK

In the past, the apprenticeship system was used to inherit folk art. However, the traditional and rigid teaching methods in the classroom led to the poor effect of learning ethnic and folk art and even the loss of many folk arts. Nowadays, related technologies such as the Internet, cloud computing, and big data are constantly being updated. Under this background of rapid technological development, it is of strong practical significance to integrate ethnic and folk art with technical means. Based on the Technological Pedagogical Content Knowledge (TPACK) framework, the ethnic and folk art space course was constructed in this study by combining the “technology + teaching method + subject knowledge” integration model. Scientific technology was integrated with the education of ethnic and folk art to propose the solution idea and system architecture. Meanwhile, the TPACK-based ethnic and folk art space course was put into practical application to verify its learning effect. The final results show that compared with the traditional teaching, the TPACK-based ethnic and folk art space course can improve the teaching efficiency and promote students’ mastery of various skills of ethnic and folk art.
A Comparative Study of different Oject Tracking Methods in a Video

Visual Object Tracking (VOT) is the most salient and an ongoing exploration field amongst the several disciplines of computer vision.The importance of this technology is due to the extensive range of applications such as robot navigation, human computer interaction, video surveillance, etc.The process of object tracking involves segmenting areas of a video scene and tracking its position, motion and occlusion.However, problems can appear during tracking on account of multiple issues including camera motion, object-to-object and object-to-scene occlusions, nonrigid structures, object and scene changes in patterns and appearance and abrupt object movement.The aim of this paper is to examine, analyze and provide a shortlist of the most ubiquitous object tracking techniques.This accomplish by providing a comprehensive review of the tracking process which involve object detection methods, object representation and features selection and object tracking over multiple frames.Object tracking methods are compared whilst elaborating upon the advantages and limitations.
Some classes of LCD codes and self-orthogonal codes over finite fields

Due to their important applications in theory and practice, linear complementary dual (LCD) codes and self-orthogonal codes have received much attention in the last decade. The objective of this paper is to extend a recent construction of binary LCD codes and self-orthogonal codes to the general $ p $-ary case, where $ p $ is an odd prime. Based on the extended construction, several classes of $ p $-ary linear codes are obtained. The characterizations of these linear codes to be LCD or self-orthogonal are derived. The duals of these linear codes are also studied. It turns out that the proposed linear codes are optimal in many cases in the sense that their parameters meet certain bounds on linear codes. The weight distributions of these linear codes are settled.
Computer Forensics Lab Requirements

With the increased number of cybercrime attacks that hit both the public and the private sector, the need for computer forensics lab to capture and analyze digital evidence with high accuracy increases. You may think that computer forensics labs are limited to law enforcement agencies. However, this is not true: many corporations in the United States maintain digital forensics labs with advanced investigation capabilities that exceed those of many police labs.
Effect of varying task attributes on augmented reality aided point layout

Augmented Reality (AR) has been shown to enable several construction tasks and supplement the use of Building Information Modeling (BIM) on construction sites. However, little effort has been made to evaluate the effect of specific task attributes and model-related factors on the accuracy and performance of current generation AR devices. To address this knowledge gap, the authors identified a commonly used AR device and conducted an experiment related to electrical construction point layout tasks. Furthermore, the effects of several task attributes and content variations were explored. The results suggest that the AR device can display content with a locational accuracy of five centimeters from intended design, equally distributed along the X-axis and Y-axis on the design plane. The location of the content is more accurate the closer it is to the paper marker used to locate content in space. Additionally, increased amounts of content shown and variations in types of content displayed significantly affect the accuracy of the points along the Y-axis (elevation). This paper provides an empirical understanding of certain capabilities and limitations of current AR devices using industry practitioners. The research enables practitioners to better plan for the use of AR in different construction and engineering tasks, and guide future research to develop use cases around the strengths of the technology.
Correction to: BREXIT perspectives: financial market dynamics, welfare aspects and problems from slower growth

The Article had been published accidentally in an uncorrected and unapproved state and therefore had to be updated.
Automatic Structured Abstract for Research Papers Supported by Tabular Format using NLP

The abstract is an extensive summary of a scientific paper that supports making a quick decision about reading it.The employment of a structured abstract is useful to represent the major components of the paper.This, in turn, enhances extracting information about the study.Regardless of the importance of the structured abstract, many computer science research papers do not apply it.This may lead to weak abstracts.This paper aims at implementing the natural language processing (NLP) techniques and machine learning on conventional abstracts to automatically generate structured abstracts that are formatted using the IMRaD (Introduction, Methods, Results, and Discussion) format which is considered as a predominant in medical, scientific writing.The effectiveness of such sentence classification, which is the capability of a method to produce an expected outcome of classifying unstructured abstracts in computer science research papers into IMRAD sections, depends on both feature selection and classification algorithm.This can be achieved via IMRaD Classifier by measuring the similarity of sentences between the structured and the unstructured abstracts of different research papers.After that, it can be classified the sentences into one of the IMRaD format tags based on the measured similarity value.Finally, the IMRaD Classifier is evaluated by applying Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers on the same dataset.To conduct this work, we use dataset contains 250 conventional Computer Science abstracts for periods 2015 to 2018.This dataset is collected from two main websites: DBLP and IOS Press content library.In this paper, 200 xml based files are used for training, and 50 xml based files are used for testing.Thus, the dataset is 4x250 files where each file contains a set of sentences that belong to different abstracts but belong to the same IMRaD sections.The experimental results show that Naïve Bayes (NB) can predict better outcomes for each class (Introduction, method, results, Discussion and Conclusion) than Support Vector Machine (SVM).Furthermore, the performance of the classifier depends on an appropriate number of the representative feature selected from the text.
CORRELATIONS BETWEEN THE PROCESS OF THE ORGANISATIONAL CHANGE AND THE CHARACTERISTICS OF A COMMUNICATING ORGANISATION

This paper deals with a phenomenon that has become global, consumerism. The concept of scattering, accumulating more than you need, consuming in excess is the fines that have been fined by all civilizations and placed dogmatically among the first values that children learn from their first years of life. This has been perpetuated since the time when goods or products were not as easy as today. Consumerism is the need to consume, whether or not I need it, to meet some psychological needs, not to mention physiological needs or basic needs. This whole pack of the growing consumerist society is extremely well thought out to fool the self-conscious parts of our brains, all the centers that do their job well in our brains, and then I know that even if I do not need to buy , there will surely be created needs.
Estimation of Non-Normalized Mixture Models

We develop a general method for estimat- ing a finite mixture of non-normalized mod- els. A non-normalized model is defined to be a parametric distribution with an in- tractable normalization constant. Existing methods for estimating non-normalized mod- els without computing the normalization con- stant are not applicable to mixture mod- els because they contain more than one in- tractable normalization constant. The pro- posed method is derived by extending noise contrastive estimation (NCE), which esti- mates non-normalized models by discrimi- nating between the observed data and some artificially generated noise. In particular, the proposed method provides a probabilistically principled clustering method that is able to utilize a deep representation. Applications to clustering of natural images and neuroimag- ing data give promising results.
Coarse-to-Fine Clothing Image Generation with Progressively Constructed Conditional GAN

Clothing image generation is a task of generating clothing product images from input fashion images of people dressed. Results of existing GAN based methods often contain visual artifact with the global consistency issue. To solve this issue, we split the difficult single image generation process into relatively easy multiple stages for image generation process. We thus propose a coarse-to-fine strategy for the image-conditional image generation model, with a multi-stage network training method, called rough-to-detail training. We incrementally add a decoder block for each stage that progressively configures an intermediate target image, to make the generator network appropriate for rough-to-detail training. With this coarse-to-fine process, our model can generate from small size images with rough structures to large size images with details. To validate our model, we perform various quantitative comparisons and human perception study on the LookBook dataset. Compared to other conditional GAN methods, our model can create visually pleasing 256 × 256 clothing images, while keeping the global structure and containing details of target images.
Towards Clustering High-dimensional Gaussian Mixture Clouds in Linear Running Time

Clustering mixtures of Gaussian distributions is a fundamental and challenging problem. State-of-the-art theoretical work on learning Gaussian mixture models has mostly focused on estimating the mixture parameters, where clustering is given as a byproduct. These methods have focused mostly on improving separation bounds for different mixture classes, and doing so in polynomial time and sample complexity. Less emphasis has been given to aligning these algorithms to the challenges of big data. In this paper, we focus on clustering $n$ samples from an arbitrary mixture of $c$-separated Gaussians in $\mathbb{R}^p$ in time that is linear in $p$ and $n$, and sample complexity that is independent of $p$. Our analysis suggests that for sufficiently separated Gaussians after $o(\log{p})$ random projections a good direction is found that yields a small clustering error.Specifically, for a user-specified error $e$, the expected number of such projections is small and bounded by $o(\ln p)$ when $\gamma\leq c\sqrt{\ln{\ln{p}}}$ and $\gamma=Q^{-1}(e)$ is the separation of the Gaussians with $Q$ as the tail distribution function of the normal distribution. Consequently, the expected overall running time of the algorithm is linear in $n$ and quasi-linear in $p$ at $o(\ln{p})O(np)$, and the sample complexity is independent of $p$. Unlike the methods that are based on $k$-means, our analysis is applicable to any mixture class (spherical or non-spherical). Finally, an extension to $k>2$ components is also provided.
Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator

We propose a class of optimal-rate primal-dual algorithms for minimization of the sum of three convex functions with a linear operator. We first establish the optimal convergence rates for solving the saddle-point reformulation of the problem only using first-order information under deterministic and stochastic settings, respectively. We then proceed to show that the proposed algorithm class achieves these rates. The studied algorithm class does not require matrix inversion and is simple to implement. To our knowledge, this is the first work to establish and attain the optimal rates for this class of problems with minimal assumptions. Numerical experiments show that our method outperforms state-of-the-art methods.
Vision-based Deformation Measurement for Pile-soil Testing

Image measurement technology has been widely used in monitoring the deformation of the soil field around the pile with its advantages of no damage, no contact, full-field measurement, no added quality and high sensitivity. But there are few researches on image-based bearing deformation measurement of the pile. Through an indoor pile-soil semi-model test, the rigid body displacement and load-bearing deformation of a new type of prefabricated steel tube pile foundation under horizontal load was measured based on image features. In this study, the concept of optical extensometer is first applied to the measurement of local average strain of a non-uniform deformed structure. Based on an improved feature point tracking algorithm SURF-BRISK, non-contact measurement of tiny strain of pile body is realized. In addition, based on DIC technology, this study also obtained the progressive development of displacement field of soil around pile. The above work fully reflects the non-contact convenience and full-field richness of the optical measurement method compared with the traditional measurement method.
Correction: Graphemic-phonetic diachronic linguistic invariance of the frequency and of the Index of Coincidence as cryptanalytic tools

[This corrects the article DOI: 10.1371/journal.pone.0213710.].
Portrait Extraction Algorithm Based on Face Detection and Image Segmentation

Aiming to solve a series of problems in photo collection over citizen&amp;rsquo;s license, this paper proposes Portrait Extraction Algorithm over our face based on facial detection technology and state-of-the-art image segmentation algorithm. Considering an input image where the foreground stands a man with unfixed size and its background is all sorts of complicated background, firstly we use Haar&amp;amp;Adaboost facial detection algorithm as a preprocessing method so as to divide the image into different sub-systems, and we get a fix-sized image of human face. Then we use GrabCut and closed-form algorithm to segment the preprocessed image and output an image which satisfies our requirements (i.e. the fixed size and fixed background). Up to now the GrabCut and closed-form algorithm has been realized, both of which have its own advantages and shortages.
DEVELOPMENT OF HIGH-PERFORMANCE AND LARGE-SCALE VIETNAMESE AUTOMATIC SPEECH RECOGNITION SYSTEMS

Automatic Speech Recognition (ASR) systems convert human speech into the corresponding transcription automatically. They have a wide range of applications such as controlling robots, call center analytics, voice chatbot. Recent studies on ASR for English have achieved the performance that surpasses human ability. The systems were trained on a large amount of training data and performed well under many environments. With regards to Vietnamese, there have been many studies on improving the performance of existing ASR systems, however, many of them are conducted on a small-scaled data, which does not reflect realistic scenarios. Although the corpora used to train the system were carefully design to maintain phonetic balance properties, efforts in collecting them at a large-scale are still limited. Specifically, only a certain accent of Vietnam was evaluated in existing works. In this paper, we first describe our efforts in collecting a large data set that covers all 3 major accents of Vietnam located in the Northern, Center, and Southern regions. Then, we detail our ASR system development procedure utilizing the collected data set and evaluating different model architectures to find the best structure for Vietnamese. In the VLSP 2018 challenge, our system achieved the best performance with 6.5% WER and on our internal test set with more than 10 hours of speech collected real environments, the system also performs well with 11% WER