Reviewerly/exHarmony huggingface dataset:
Documentology: The background and development

The article explains the objective reason for the emergence of documentology along with documentation and documentary studies, the relevance and practical significance for library science and bibliography. Attention is drawn to the outstanding role of Paul Otlet (Paul Marie Ghislain Otlet; 1868–1944) in the promotion and development of the theoretical foundations of the general theory of the document. The concept of three worlds, in particular, “the world of logical content of books, libraries, computer memory, and the like”, developed by Karl Popper (Karl Raimund Popper; 1902–1994) enabled to bring documentology to the level of a philosophical discipline. The stages of development of documentology are defined. The first stage embraces the late 19th – first half of the 20th centurys, the second covers the mid-20th century (1950–60s), when in the USSR it was known as “documentalistics”. Simultaneously, since the early 1960s, the document science has been developing with its specific methodology, individual acts and systems of documenting and creating documents, and on this foundation, their complexes and systems (the third stage). The documentation is contradictory: it claims to study both documents as proper objects, and those objects of workflow management, which have got the term “records” used in the Western countries. The fourth stage of development of the document general theory originates in the second half of the 1980s. In the early 1990s, Moscow State Institute of Culture proposed the term documentology to be used for the general theory of documents. The discipline core embraces the issues related to the conceptual apparatus, functional analysis of documents, study of their characteristics, parameters and properties, document classification, as well as study of the documents as a means of communication and an element of document collections.
Development of a sequence automaton for recognition of deviations indicators in diagnosis of natural systems

Abstract Much attention is now being paid to the development of automated devices for rapid disease diagnosis in veterinary medicine and medicine in general. One of the directions in medical instrumentation designing is the development of methods for automated disease diagnosis. This article is devoted to the automatization of pathology detection in histological analysis. The topology of pathologies of morphostructural changes in cells may have a tree-like structure, which makes the development of methods, software and hardware implementation of tools relevant for determination of the disease pathology. The three-level pathology system of structural changes in cell tissues obtained by histological analysis has been studied. It was suggested to use sequential logic, based on the Mealy machine, for the development of automated system for pathology indicators detection. Hartley binary measure for this topology of structural changes in tissues has been calculated and the indicator encoding has been given. The method of automated diagnosis based on the analysis of pathology indicators has been developed. An example of building an automatic recognition machine of pathology indicators on hard logics considered. It is relevant when using programmable logic device. The logical equations implemented in the combination scheme of the device are obtained. Simulation modeling to identify pathology indicators is performed. It is suggested to use the developed method of determining the disease indicators when designing the automated histological analyzer.
Analysis of Online Grocery Recommendation Systems

Recommender system has been recognized as the most effective method for information overload problem. But a generic approach with small changes is used to get results in most types of recommender systems. Grocery recommendations are unique to this generic approach because of the possibility of reordering items. This reordering criterion can very well be taken as a measure of preference compared to general rating system that is followed. This paper focuses on using the idea of reorders to make and compare different systems of online grocery recommendations. Two types of systems have been tested for grocery recommendation taking reorders into account and a comparison between them has been made at the end. The methods and metrics used for making these two recommender systems including truncated SVD, cosine similarity and k-nearest neighbours. The paper has also discussed the results of reordering criteria for grocery sales.
Using a Three-step Social Media Similarity (TSMS) Mapping Method to Analyze Controversial Speech Relating to COVID-19 in Twitter Collections

Addressing increasing calls to surface hidden and counter-narratives from within archival collections, this paper reports on a study that provides proof-of-concept of automatic methods that could be used on archived social media collections.Using a test collection of 3,457,434 unique tweets relating to COVID-19, China and Chinese people, it sought to identify instances of Hate Speech as well as hard-to-pinpoint trends in anti-Chinese racist sentiment.The study, part of a larger archival research effort investigating automatic methods for appraisal and description of very large digital archival collections, used a Three-step Social Media Similarity (TSMS) mapping method that aggregates hashtag mapping, TF-IDF Similarity Selection, and Emotion Similarity Calculation on the test collection.Compared to using a purely lexicon-based method to identify and analyze controversial speech, this method successfully expanded the amount of controversial contents detected from 21,050 tweets to 212,605, and the detection rate from 0.6% to 6.1%.We argue that the TSMS method could be similarly applied by archives in automatically identifying, analyzing, describing other controversial content on social media and in other rapidly evolving and complex contexts in order to increase public awareness and facilitate public policy responses.
Trustworthy AI

Modern AI systems are reaping the advantage of novel learning methods. With their increasing usage, we are realizing the limitations and shortfalls of these systems. Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, high opacity in terms of revealing the lineage of the system, how they were trained and tested, and under which parameters and conditions they can reliably guarantee a certain level of performance, are some of the most prominent limitations. Ensuring the privacy and security of the data, assigning appropriate credits to data sources, and delivering decent outputs are also required features of an AI system. We propose the tutorial on “Trustworthy AI” to address six critical issues in enhancing user and public trust in AI systems, namely: (i) bias and fairness, (ii) explainability, (iii) robust mitigation of adversarial attacks, (iv) improved privacy and security in model building, (v) being decent, and (vi) model attribution, including the right level of credit assignment to the data sources, model architectures, and transparency in lineage.
Online Mathematics Learning during the Covid-19 Pandemic

Abstract This research is a descriptive quantitative research that focuses on evaluating learning using online media with the aim of (1) knowing the effectiveness of online mathematics learning that is expected by class XI students of SMA Negeri Kerjo as an effort to suppress the spread of covid-19 in the school environment; (2) knowing the obstacles in online learning; (3) knowing student suggestions to increase the effectiveness of online mathematics learning. The study population was all students of SMA Negeri Kerjo. The research sample was students of class XIA1 selected using simple random sampling by considering population homogeneity. Data were collected using an online questionnaire. The data were analyzed using descriptive statistical analysis. The results are(1) students rated online mathematics learning as ineffective (46%), very ineffective (27%), effective (17%), and very effective (10%); (2) the obstacles faced by students were the unstable internet network, limited internet quota, lack of time in discussion and assignment collection, and too many assignments; (3) to improve online mathematics learning during the Covid-19 pandemic, suggestions from students that need to be considered: learning through easy media, providing simple material, minimizing sending video to save quota, extending assignment time, and reducing assignments.
What do information centric networks, trusted execution environments, and digital watermarking have to do with privacy, the data economy, and their future?

What if instead of having to implement controversial user tracking techniques, Internet advertising &amp; marketing companies asked explicitly to be granted access to user data by name and category, such as Alice→Mobility→05-11-2020? The technology for implementing this already exists, and is none other than the Information Centric Networks (ICN), developed for over a decade in the framework of Next Generation Internet (NGI) initiatives. Beyond named access to personal data, ICN's in-network storage capability can be used as a substrate for retrieving aggregated, anonymized data, or even for executing complex analytics within the network, with no personal data leaking outside. In this opinion article we discuss how ICNs combined with trusted execution environments and digital watermarking, can be combined to build a personal data overlay inter-network in which users will be able to control who gets access to their personal data, know where each copy of said data is, negotiate payments in exchange for data, and even claim ownership, and establish accountability for data leakages due to malfunctions or malice. Of course, coming up with concrete designs about how to achieve all the above will require a huge effort from a dedicated community willing to change how personal data are handled on the Internet. Our hope is that this opinion article can plant some initial seeds towards this direction.
Color Image Enhancement based on Gamma Encoding and Histogram Equalization

Image Enhancement is used as a preprocessing step in many computer vision applications. It provides enhanced input for other computerized image processing methods. Many preprocessing techniques can be applied to images depending on the application domain. In this paper we are proposing an image enhancement technique for color images that can be used as preprocessing step in many computer vision applications. It can also be used as a data augmentation technique in object detection. Luminance component of images is sometimes not captured by cameras and displayed by monitors properly. To remove this drawback of devices we have used gamma encoding. Four different values of gamma are evaluated depending on the quality of images. Image is then converted into YUV Color space. Y component represents the luminance. U and V components represent color. After that Contrast Limited Adaptive Histogram Equalization is applied to the Y component to improve the contrast of the image. The results are compared with the state-of-the-art methods on the basis of Peak Signal to noise Ratio (PSNR) and Mean Square Error (MSE). Quantitative results show that proposed algorithm results in improved value of PSNR and decreased value of MSE as compared to existing methods. Qualitative comparison is also done and results show improvement over the existing techniques.
“Why attacking the Bureau of Industry and Commerce?”: news value flow to news comments on Chinese social media

Comments on social media provide a suitable site to view text-reader relations from the perspective of news reading. This article interrogates readers’ evaluative responses to Weibo shared news in China. The study, drawing upon Discursive News Values Analysis and appraisal, first identifies the news values of Eliteness, Personalization, Negativity and Positivity in a news story about car quality sourced from the Weibo network of People’s Daily. Then the following 1027 comments, including Chinese characters and emojis, are investigated by using a mixed-methods approach. The corpus analysis shows that business Eliteness (the Mercedes dealership) and Personalization (the buyer) are convergently valued news actors, while readers evaluate authoritative Eliteness (the Bureau) in an unexpected way. Close examination of the appraisal devices in the comments uncovers a divergence between negative judgment toward Eliteness and positive affect/judgment for Personalization. Emojis play an important role in activating attitudes through the interplay with language. In commentary journalism, the readers’ response can influence news value decisions when there is a mismatch between the news values promoted by news organizations and the values that readers perceive as newsworthy.
Unpacking the Quantifying and Qualifying Potential of Semi-Open Job Satisfaction Questions through Computer-Aided Sentiment Analysis

Abstract Despite their suitability for mitigating survey biases and their potential for enhancing information richness, open and semi-open job satisfaction questions are rarely used in surveys. This is mostly due to the high costs associated with manual coding and difficulties that arise when validating text measures. Recently, advances in computer-aided text analysis have enabled researchers to rely less on manual coding to construct text measures. Yet, little is known about the validity of text measures generated by computer-aided text analysis software and only a handful of studies have attempted to demonstrate their added value. In light of this gap, drawing on a sample of 395 employees, we showed that the responses to a semi-open job satisfaction question can reliably and conveniently be converted into a text measure using two types of computer-aided sentiment analysis: SentimentR, and Linguistic Inquiry and Word Count (LIWC) 2015. Furthermore, the substantial convergence between the LIWC2015 and, in particular, SentimentR measure with a closed question measure of job satisfaction and logical associations with closed question measures of constructs that fall within and outside job satisfaction’s nomological network, suggest that a semi-open question has adequate convergent and discriminant validity. Finally, we illustrated that the responses to our semi-open question can be used to fine-tune the computer-aided sentiment analysis dictionaries and unravel antecedents of job satisfaction.
Triplet Network Template for Siamese Trackers

Siamese network based trackers describe object tracking as a similarity matching problem and these trackers achieve state-of-the-art performance on multiple benchmarks. However, due to the non-update of the appearance template and the change of the object appearance, the tracking drift problem often occurs, especially in the background clutter scene. Effective appearance template update methods can improve tracker performance, but most trackers use simple linear interpolation to update the template or do not update the initial template at all. Through experiments, we find that the channel response of the search region with the adjacent frame appearance template is often better than that with the initial frame appearance template. So we add the results of the previous frame prediction as a new template branch to the Siamese network to form a Triplet network. We applied the Triplet network to the SiamFC and SiamCAR, called TripFC and TripCAR. We tested on four challenging benchmarks (GOT-10K, OTB2013, OTB2015, UAV123). The experiments show that our method is powerful and effective, it can be easily embedded into the Siamese trackers. TripFC has a good effect on solving the problem of tracking drift. If necessary we can publish the code to facilitate research in this area.
Example-Based Facial Animation of Virtual Reality Avatars Using Auto-Regressive Neural Networks

This article presents a hybrid animation approach that combines example-based and neural animation methods to create a simple, yet powerful animation regime for human faces. Example-based methods usually employ a database of prerecorded sequences that are concatenated or looped in order to synthesize novel animations. In contrast to this traditional example-based approach, we introduce a light-weight auto-regressive network to transform our animation-database into a parametric model. During training, our network learns the dynamics of facial expressions, which enables the replay of annotated sequences from our animation database as well as their seamless concatenation in new order. This representation is especially useful for the synthesis of visual speech, where coarticulation creates interdependencies between adjacent visemes, which affects their appearance. Instead of creating an exhaustive database that contains all viseme variants, we use our animation-network to predict the correct appearance. This allows realistic synthesis of novel facial animation sequences like visual-speech but also general facial expressions in an example-based manner.
Queer considerations: Exploring the use of social media for research recruitment within LGBTQ communities

The use of social media platforms (such as Facebook) for research recruitment has continued to increase, especially during the global COVID-19 pandemic. Social media enables researchers to reach diverse communities that often do not have their voices heard in research. Social media research recruitment, however, can pose risks to both potential participants and the researchers. This topic paper presents ethical considerations related to social media recruitment, and offers an example of harassment and hate speech risks when social media is used for research recruitment. We explore the implications of hate speech risks for ethical research.
BISIMULATIONS FOR KNOWING HOW LOGICS

Abstract As a new type of epistemic logics, the logics of knowing how capture the high-level epistemic reasoning about the knowledge of various plans to achieve certain goals. Existing work on these logics focuses on axiomatizations; this paper makes the first study of their model theoretical properties. It does so by introducing suitable notions of bisimulation for a family of five knowing how logics based on different notions of plans. As an application, we study and compare the expressive power of these logics.
Simultaneous remote monitoring of transformers’ ambient parameters by using IoT

Transformers have an important role in the uninterrupted and reliable flow of electrical power in power systems. For this reason, it is necessary to monitor and control the proper and efficient operation of transformers regularly. The increase in the number of transformers, the differentiation of transformer operations, and the prolongation of the transformers’ operating time, accelerates the importance of remote monitoring. When remote monitoring structures are compared, even if conventional monitoring methods can perform many tasks, it has become suitable to establish lower cost, safer and simpler structures by using the Internet of Things (IoT). Apart from monitoring the electrical parameters of the transformer, it is possible to obtain information about the operation and fault status of the transformer by monitoring the physical and chemical changes in the environment. In this study, a measurement and remote monitoring system that detects temperature, humidity, light level and gas densities are employed for low and medium voltage transformers in the indoor environment. Tests are conducted to establish a connection between the obtained ambient data and transformer operating voltages. Different classification algorithms such as Bayesian Networks (BN), Multilayer Perceptron (MLP), and Random Forest (RF) are used to classify the operating voltages versus ambient data.
A comparative study of tense and aspect categories in Arabic and English

Based on the definition of “tense” and “aspect”, this review paper intends to compare and analyze the similarities and differences between Arabic and English tense and aspect categories in terms of their forms, functions and usage. Moreover, in the current review paper, I propose comparison and analysis that account for tense and aspect categories in Arabic and English. The comparison and analysis are based on the qualitative method which depends solely on the recent and previous research works. In addition, the comparison and analysis assume that there is a great deal of differences in tense and aspect categories in Arabic and English in terms of their forms, functions and usage. Furthermore, Arabic views temporality through aspectual distinctions of perfective category versus imperfective category. On the other hand, English expresses temporality through various verbal tense and aspect categories.
Automated Feature-Driven Flight Planning for Airborne Inspection of Large Linear Infrastructure Assets

Inspecting large-scale physical infrastructure assets such as powerlines from the air involves capturing data of thousands of features of an asset and its nearby surroundings, which makes inspection flight planning challenging. The objective of this article is to address the problems of automatically generating and optimizing flight paths and mission plans for airborne inspection of large linear infrastructure assets. This article proposes an automated feature-driven flight planning method using metaheuristic optimization with consideration of aircraft dynamic, sensor, terrain, and airfield location constraints. Results for flight planning the inspection of one of the world’s largest above-ground powerline networks are shown. Automated planning can reduce the human planning workload and enable the cost-saving benefits of automated aircraft to be realized through efficient deployment. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work extends previous research and development of an automated aircraft guidance system for infrastructure inspection, by addressing the problem of automated flight planning. Often, deploying manned or automated aircraft reliably and efficiently for large-scale inspection is a challenge. Designing efficient flight paths and missions by hand is complex and time-consuming. Traditional “top-down” planning approaches use region or waypoint-based planning, neglecting the spatial layout of the assets to be inspected, possibly leading to more missions than required, inefficient flight paths, or missed data capture. Since inspecting a large asset with few aircraft can take many days, a solution was sought that can plan across multiple time scales (from second to second path planning to daily mission planning). A solution was designed that can build flight plans automatically from the “bottom-up” while optimizing performance objectives and satisfying constraints. Flight plans can either be used to aid pilots conducting an aerial inspection, as input to an automated line-tracking system for aircraft or UAVs or for estimating the cost of large-scale inspection operations. The presented method and results are motivated by the specific problem of large-scale powerline inspection and the approach may be applied to the inspection of other above-ground infrastructures, such as roads and railways.