Reviewerly/exHarmony huggingface dataset:
The Impact of Customer’s Reported Cybersecurity Breaches on Key Supplier’s Relationship-Specific Investments and Relationship Duration

We investigate the impact of customers’ cybersecurity breaches (CSBs) on its supplier’s relationship-specific investment decisions and the duration of supplier-customer relationship. We document a significant negative association between customer reported CSBs and suppliers’ relationship-specific investments in the subsequent year. In addition, we find customers’ reported CSBs increase the likelihood of supplier-customer relationship termination in the subsequent year. Suppliers reduce their relationship-specific investment by 6% of total sales in the subsequent year following customers’ reported CSBs. More importantly, supplier-customer relationships are two times more likely to be terminated the year after a customer reports a CSB incident. Our findings demonstrate that CSBs are an economically significant event to the breached firm in terms of indirect costs and provide further insight into how a CSB can influence the perception of the firm’s influential stakeholders, especially its key suppliers.
Efficient tensor decomposition approach for estimation of the number of endmembers in a hyperspectral image

Proper estimation of the number of endmembers is imperative for proper and accurate spectral unmixing of hyperspectral image. The presence of noise and perturbation poses a challenge in estimation and leads to underestimation or overestimation of the number of endmembers. We propose a noise invariant tensor-based rank estimation approach, which avoids reshaping of the data. We perform a noise-robust PARAFAC tensor decomposition using a generalized low-rank factorization in the initial stage and record the core consistency and reconstruction error for a different number of components. In the next stage, we identify the lower and upper bounds of the number of components. Next, we propose a cost function containing both core consistency value and reconstruction error and identify the number of components from the minima of the cost function. The proposed estimation approach is effective for high, mid, and low signal to the noise level. We analyze the performance of the proposed estimation approach on several synthetic as well as real image experiments and verify the proficiency of our proposed estimation even at higher levels of noise.
Teaching Using YouTube Tutorial Video to Improve Students’ Speaking Skills

Abstract : This study aimed at researching how YouTube Tutorial Videos can improve students’ speaking skill in performing procedure text. It employs Collaborative Action Research adapting Kemmis and Taggart design, which covers 1) planning, 2) implementing, 3) observing, 4) reflecting. Result of the study showed that the students’ speaking skill was improving in three aspects 1) accuracy, 2) fluency, 3) performance. However, it was revealed that native English speaker (NES) videos are not helpful for the students, especially the low-level student. Non-native English speaker (NNES) videos were more helpful for the students because they tend to speak slower with clearer articulation. Key Words : youtube tutorial videos, speaking, procedure text Abstrak : Penelitian bertujuan mengetahui bagaimana video tutorial YouTube dapat meningkatkan kemampuan berbicara siswa dalam menampilkan teks prosedur. Penelitian ini mengadaptasi desain penelitian tindakan kelas dari Kemmis dan Taggart, yang meliputi 1) perencanaan, 2) pelaksanaan, 3) observasi, 4) refleksi. Hasil penelitian menunjukkan bahwa kemampuan berbicara siswa meningkat dalam 3 aspek, yaitu 1) ketepatan, 2) kelancaran, dan 3) penampilan. Ditemukan juga bahwa video dengan penutur bahasa Inggris asli kurang bermanfaat bagi siswa terutama siswa yang berkemampuan rendah. Sebaliknya, yang bukan penutur asli lebih bermanfaat bagi siswa. Kata kunci : video tutorial youtube, berbicara, teks prosedur
Crypto-Wills: Transferring Digital Assets by Maintaining Wills on the Blockchain

Traditional methods of transferring assets post demise are tedious and problem-ridden. These methods do not account for the increasing variety of digital assets (including crypto-assets) present today. Hence, when a person dies, his/her tangible assets can be distributed according to his/her Will, or the prevailing legal framework. However, his/her digital assets are left inaccessible (due to their inherent security) and are, in turn, lost forever. In order to provide a reliable and secure way of transferring digital assets, we propose a system to execute Wills after the demise using blockchain. The proposed solution maps the assets using ERC-20 and ERC-721 tokens and enables the owner of the Will to deploy a smart contract mentioning his wishes. The contract is executed based on the defined consensus mechanism, and the assets are transferred to the beneficiaries.
Towards a Vocabulary to Implement Culturally Relevant Relationships Between Digital Collections in Heritage Institutions

Cultural heritage institutions are publishing their digital collections over the web as LOD. This is is a new step in the patrimonialization and curatorial processes developed by such institutions. Many of these collections are thematically superimposed and complementary. Frequently, objects in these collections present culturally relevant relationships, such as a book about a painting, or a draft or sketch of a famous painting, etc. LOD technology enables such heritage records to be interlinked, achieving interoperability and adding value to digital collections, thus empowering heritage institutions. An aim of this research is characterizing such culturally relevant relationships and organizing them in a vocabulary. Use cases or examples of relationships between objects suggested by curators or mentioned in literature and in the conceptual models as FRBR/LRM, CIDOC CRM and RiC-CM, were collected and used as examples or inspiration of cultural relevant relationships. Relationships identified are collated and compared for identifying those with the same or similar meaning, synthesized and normalized. A set of thirty-three culturally relevant relationships are identified and formalized as a LOD property vocabulary to be used by digital curators to interlink digital collections. The results presented are provisional and a starting point to be discussed, tested, and enhanced.
An elderly assistive device substitutes for traditional online library catalogs

Purpose This paper aims to present an augmented reality (AR) application to aid the elderly in library reference search. The application supports the elderly in searching books and videos in a large library when librarians have insufficient time to meet their needs. The elderly searching in a large library could be difficult for them due to the vast area, complicated online library catalogs and their physical deterioration, such as failing eyesight, tendinitis and memory problems. Our system, SearchAR, fully supports the 3D images of object content based on books and videos in Ling Tung University (LTU) library catalogs. Using the latest AR technology, users can use mobile phone to scan the quick response (QR) code in front of the LTU library, and then the elderly can experience LTU library map labeled by different colors in real-time and understand the locations of the video areas and book shelves areas. Later, they can use SearchAR to scan the QR codes on bookshelves, books or videos to view the 3D graphics without reading complicated catalogs and content. The results show that among the images, the elderly recognition of the “3 D images” was the highest, and the experimental group using AR technology as a searching aid shows a higher searching effectiveness than the control group; 60 per cent of the elderly felt they saved more time for searching books and videos with the help of SearchAR, and it was easy to arouse their association; the elderly indicated that combined image of the library map and the labels with different colors are clear and simple; the content of books and videos are easy associated with the 3D images; the elderly mentioned that using SearchAR is a great experience without any reading complicated online catalogs. Finally, this study concludes that AR technology does enhance the elderly searching ability in LTU large library. Design/methodology/approach The SearchAR application was an integrating library catalog, which provides AR and 3D graphics. The elderly can also use SearchAR to see a library map and every library element via 3D graphics before they start searching in a wide area. Findings This makes it easier for elderly to navigate a library without using complicated online catalogs, and it reduces time spent on reading and searching. Originality/value AR has been used for a variety of guiding and educational purposes. However, AR has been rarely applied to supplement traditional library services for elderly.
Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset

Labelling of human behavior analysis data is a complex and time consuming task. In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed. Domain knowledge can be added to the data recording paradigm and later labels can be generated in an automatic manner using speech to text conversion. In order to remove the noise in STT due to different ethnicity, the speech frequency and energy are analysed. The resultant Driver Gaze in the Wild DGW dataset contains 586 recordings, captured during different times of the day including evening. The large scale dataset contains 338 subjects with an age range of 18-63 years. As the data is recorded in different lighting conditions, an illumination robust layer is proposed in the Convolutional Neural Network (CNN). The extensive experiments show the variance in the database resembling real-world conditions and the effectiveness of the proposed CNN pipeline. The proposed network is also fine-tuned for the eye gaze prediction task, which shows the discriminativeness of the representation learnt by our network on the proposed DGW dataset.
Art History, Heritage Games, and Virtual Reality

This chapter focuses on how the engaging and entertaining medium of virtual reality and serious games has the potential to connect traditional galleries, libraries, archives, and museum (GLAM) sect ...
Aggressiveness of emojis before the court: a sociosemiotic interpretation

Emojis, used evidentially as any language is used in communication, have posed new challenges for courts to adopt emojis as evidence beyond the traditional mode of speech and communication and to interpret them appropriately, especially their aggressive side of meaning. To meet these challenges, the present study, based on a comprehensive literature review, starts with case studies with aims to unravel the nature of emojis and develop a sociosemiotic approach to interpret emojis in the context of social media with a focus on the aggressiveness of emojis in social platforms and their consequences (social and legal ones). It is argued that the meaning of emojis could not simply depend on their grammar or general rules but lies in communication within discourse communities.
Effective multi-dialectal arabic POS tagging

Abstract This work introduces robust multi-dialectal part of speech tagging trained on an annotated data set of Arabic tweets in four major dialect groups: Egyptian, Levantine, Gulf, and Maghrebi. We implement two different sequence tagging approaches. The first uses conditional random fields (CRFs), while the second combines word- and character-based representations in a deep neural network with stacked layers of convolutional and recurrent networks with a CRF output layer. We successfully exploit a variety of features that help generalize our models, such as Brown clusters and stem templates. Also, we develop robust joint models that tag multi-dialectal tweets and outperform uni-dialectal taggers. We achieve a combined accuracy of 92.4% across all dialects, with per dialect results ranging between 90.2% and 95.4%. We obtained the results using a train/dev/test split of 70/10/20 for a data set of 350 tweets per dialect.
Algebraic structure of additive conjucyclic codes over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:math>

In this paper, we are interested in finding an algebraic structure of conjucyclic codes of length n over the finite field F4. We show that conjucyclic codes of length n over F4 are related to binary cyclic codes of length 2n and show that there is a canonical bijective correspondence between the two sets. We illustrate how the factorization of the polynomial x2n+1 plays a critical role in each setting. Moreover, we construct the generator and parity check matrices of conjucyclic codes of length n over F4.
Efficient wavelet based scale invariant feature transform for partial face recognition

Even though, innumerable approaches have been proposed for holistic face recognition, problems caused by occlusions received less attention in the literature. However, partial faces frequently appear in many real time situations. Facial occlusions (by sunglasses, hat/cap, scarf, and beard) can significantly deteriorate the performances of face recognition systems under unconstrained scenarios. In such situations, algorithms developed under holistic face, results in catastrophic performance. In this paper, we have proposed a scale and rotation invariant wavelet feature transform for partial face recognition. Partial faces at different orientations are considered here for experimentation. Biorthogonal wavelet basis (4.4) is employed for obtaining the Discrete Wavelet Transform of the images. The scale invariant feature transform (SIFT) is then applied on low-low (LL) and high-high (HH) subbands of the images. Results obtained with wavelet SIFT method is compared with SIFT and appearance based face recognition technique (PCA) over (Milborrow / University of Cape Town) MUCT database. Experimental studies with 100 subjects show that the proposed method improves recognition accuracy and reduces false acceptance rate (FAR) and false rejection rate (FRR).
Optical effects on HDR calibration via a multiple exposure noise-based workflow

Abstract High dynamic range (HDR) technology allows more of the lighting in a specific scene to be captured at a set point in time, and thus is capable of delivering an overall view of the scene that more closely correlates with our visual experience in the real world, compared to standard, or low dynamic range (LDR) technology. Although HDR capabilities of single exposure capture systems are improving, the traditional method for creating HDR images still includes combing a number of different exposures, captured with an LDR system, into a single HDR image. Several use cases requiring absolute calibration of the resulting HDR luminance map have been undertaken, but none of these have provided a detailed analysis of the optical effects of glare on the results. We develop a calibrated HDR radiance map, including methodical linearization of captured image data, while characterizing the limitations due to the effects of optical glare. A purposely designed controlled test scene is used to challenge the calibrated reconstruction efforts, including low luminance levels, spatial inclusion of lens vignette over the full imaged area, and optical glare. Results demonstrate that even with careful processing and recombination of the LDR data, radiometric accuracy is limited as a result of glare. The proposed approach performs better than calibration methods in commercially available HDR recombination software.
Ontology of Properties and its Methods of Use: Properties and Unit extraction from texts

Methods of automatic extraction and identification of properties, quantities and units of measurement from texts are considered. The developed ontology of properties and units of measurement includes both fundamental connections of properties and units of measurement and connections that show the formation of concepts. The technology of property identification is based on fact that semantically significant text elements extracted within the boundaries of one or several sentences forming the semantic neighborhood of the property are correlated with the corresponding components of the ontology, which allows to restore the missing semantic fragments or identify discrepancies in the designations. The results of experimental studies of the effectiveness of the developed tools are presented.
Robust partly strong tracking consider SDRE filter for direct INS/GNSS integration with biases

To degrade the adverse effects of biases in the direct inertial navigation system/global navigation satellite system integration, a novel robust partly strong tracking state-dependent Riccati equation filter (PSTCSDREF) algorithm is proposed. A nonlinear consider approach is utilized to incorporate statistics of biases into state estimation error covariance of the state-dependent Riccati equation filter (SDREF), and a new SDREF(CSDREF) is proposed. Then, the prediction covariance of the state is partly multiplied by a strong tracking factor, which does not include the bias covariance, to mitigate the adverse effects navigation performance of model uncertainties. Numerical simulation demonstrates that the proposed PSTCSDREF has a better navigation accuracy comparing with extended Kalman filter, SDREF, and CSDREF.
Probability Distribution of Dependency Distance Based on a Treebank of Japanese EFL Learners’ Interlanguage

Ouyang and Jiang (2018) measured the second language proficiency of English as a foreign language (EFL) learners based on the probability distribution of dependency distance. However, the typological features of the native language (Chinese) and the target language (English) they adopted are generally considered similar in word order and dependency direction. In addition, their method of classifying the learners' proficiency levels is based on the learners' grades, which might weaken the validity of the results. These results are strengthened and verified further in the current research by analysing a treebank of Japanese EFL learners' interlanguage since their native language and the target language are typologically distinctive. Moreover, the TOEIC score was used as a benchmark to classify the second language proficiency levels of the learners. We found that (1) the mean dependency distance can measure the syntactic complexity of Japanese EFL learners' interlanguage; (2) constrained by human working memory, the probability distribution of dependency distance based on Japanese EFL learners' interlanguage follows certain distribution patterns as unveiled in other natural human languages; (3) the parameters of the right truncated modified Zipf-Alekseev distribution can well reflect the changes of the Japanese EFL learners' second language proficiency, indicating the development of interlanguage.
Performance Analysis of Data Storage in a Hyperconverged Infrastructure Using Docker and GlusterFS

Hyperconverged infrastructures use distributed file systems to store and replicate data between multiple servers while using the processing capabilities of those same servers to host virtual machines and containers. In this work, the distributed file system GlusterFS and the VMware ESXi hypervisor are used to build a hyperconverged system to host Docker containers in order to evaluate the performance of this system compared to a traditional approach in which data is stored directly on local disks of the servers. The performance of container persistent data storage is assessed using FIO (Flexible I/O Tester) storage benchmark tool with different workloads from Microsoft data centers and with multiple disk configurations in the hyperconverged system. The experimental results show that with a greater amount of writing operations and that handle large files, the performance in the hyperconverged architecture is better than in local storage, which presents an economically viable option in the implementation of storage for datacenters.
A Corner Detection Method for Conventional Light Field Camera by Jointly Using Line-Features

Micro-lens array (MLA) based light field cameras have been commercialized and had a wide range of applications in various fields in recent years. To promote these applications, especially for 3D reconstruction, it is crucial to calibrate light field cameras. Among the calibration steps, it is of great importance to detect corners in checkerboard images and establish the point-to-point correspondence between the checkerboard and its images. However, almost all existing algorithms detect corners in the sub-aperture images instead of raw images for conventional light field cameras. In this paper, a corner detection algorithm that can recognize corners in the raw images of conventional light field cameras was proposed. Firstly, template matching was used to detect the 3D lines in the image space of the main lens. Then, the locations of the corners were obtained by calculating the intersection of 3D line segments and reprojecting it to the raw image again. Experimental results have demonstrated that the algorithm can still achieve good performance in actual datasets with blurred and low resolution micro lens image.
A novel chaotic dragonfly algorithm based on sine-cosine mechanism for optimization design

Dragonfly algorithm is a novel meta-heuristic algorithm, which solves the problem by simulating the biological behavior of dragonfly's predation and migration. There are also some common problems of meta-heuristic algorithms, such as slow convergence speed and entrapment in local optima. To improve the performance of dragonfly algorithm, this study combines singer chaos and sine-cosine mechanism to reinforce the original dragonfly algorithm. Singer chaos is used to optimize the population initialization of the algorithm, and the sine-cosine mechanism is applied to modify the iterative updating formula of the position. The performance of the proposed approach is examined by eight numerical benchmark functions. From the optimal solution and standard deviation of the experimental data, it can be seen that the convergence accuracy and stability of the proposed dragonfly algorithm are significantly strengthened. Besides, SC-DA is applied to optimize the design of cantilever beam problem with good performance.
Mississippi’s 2011 Concealed Carry Law:

This study examined differences in reported campus criminality on selected community college campuses in the years between 2005 and 2016 representing the years preceding and succeeding the implementation of Mississippi’s 2011 concealed carry legislation. Each campus included in this study was a public-funded community college in Mississippi. Using a significance level of 0.05 and an analysis of variance approach, the hypothesis testing showed five statistically significant findings reflecting the cases of Copiah-Lincoln, East Central, Jones County, Mississippi Delta, and Northeast Mississippi Community Colleges. Regarding these outcomes, an analysis of the means showed overall lowered crime reports.