Reviewerly/exHarmony huggingface dataset:
Social and Cultural Consequences of Digitalization in Higher Humanitarian Education

Digitalization covers all spheres of life in modern society, this is our objective reality. In our opinion, in terms of importance and influence on the development of society, digitalization can be compared with the scientific and technological revolution of the twentieth century. In our article, we summarize the results of studies on the impact of digital technologies on the process and the results of training students who major in humanities, conducted on the basis of SSUSSE from 2012 to 2019.
Identity Management of Clustered Satellites with a Generalized Labeled Multi-Bernoulli Filter

Tracking clustered satellites in the presence of noisy measurements creates a scenario where satellites are likely to be cross-tagged. This work focuses on the identity management of a closely spaced set of CubeSats immediately after a deployment. The main contribution is an identity management process that augments the generalized labeled multi-Bernoulli filter to probabilistically handle identity assignments. The effectiveness of the method is demonstrated on simulated tracking data from the 88 Planet Labs Flock 3 CubeSats deployed on PSLV-C37. Simulation results indicate that the proposed identity management process can effectively establish identities within 3 days after deployment.
KNN-Q Learning Algorithm of Bitrate Adaptation for Video Streaming over HTTP

The increasing demands for seamless switching of high definition video streams have become the main driving force for the research of bitrate adaptive algorithms. Existing Q-learning based adaptive bitrate algorithm can obtain higher quality of experience (QoE) through dynamically learning the optimal behavior corresponding to the current network environment. However, ordinary Q-learning algorithm has the problem that it is difficult to encode continuous state values and the algorithm converges slowly in large state spaces. In this article, we combine the nearest neighbor (KNN) with Q-learning algorithms to proposes a KNN-Q learning algorithm that can deal with continuous state. Our experimental results show that in the bitrate adaptive scenario, the KNN-Q learning can achieve higher QoE and faster convergence speed than Q-learning algorithm.
Emotions and Interests of Evolving Twitter Bots

Bot accounts are computer algorithms populating our online ecosystem. They often feature goals that are far from being benign: distribution of spam, inflated sponsorship of public characters, biased influence on the audience's judgements. The endless game of cat and mouse between Academia and bot developers proves that bots evolve over time, imitating human online behaviour and making themselves undetectable even by state-of-art detection tools. This peculiarity inspires the current work: the adoption of text mining tools to evaluate if it is possible to infer typology and purpose of bots from the feelings and interests expressed in their posts. The experiments, carried out on a large corpus of Twitter data, show a connection between the two considered dimensions (feeling and interest) and the characteristics of the evolutionary waves of accounts.
Self-deliberation and the Strategy of the Pseudo-dialogue

The New Rhetoric identifies the self-deliberator as one of three main types of audience. But such a turn toward the self is at odds with studies of contemporary argumentation, particularly social argumentation. Argumentation takes place “out there”, modifying the environments in which audiences operate. Equally interesting is the use of self-deliberation as a rhetorical strategy. Arguing with oneself, especially when that self is distanced in some way from the individual involved, employs self-deliberation beyond the ends that Perelman assigned to it. In this paper, my goal is to explore the nature of the self-deliberator as an audience and self-deliberation as a rhetorical strategy employed in argumentation.
A QoE Evaluation of an Immersive Virtual Reality Autonomous Driving Experience

The driver/passenger role in autonomous driving experiences is an important subject that is concerned with how these systems will interact and communicate with the human stakeholder. Even though self-driving technologies have made rapid progress in recent years, their success will depend on ethics, safe capabilities and user acceptance. The overall satisfaction resulting from the interaction between humans, these technologies and their evaluation in simulators can help industry to improve their design. Such evaluations can be based on user's perceived Quality of Experience (QoE). The impact of the quality features that compound VR environments is an important factor to understand. Sense of depth, texture quality and resolution are some examples that influence the perceived quality of the simulation and immersion levels. This demonstration promotes a VR autonomous driving experience in one street of Athlone, Ireland. The application will be presented in two different formats: photogrammetry, a technique that uses photos to provide realistic 3D content, and secondly non-photorealistic simulated environment. Further investigations will be carried out to understand factors that enable the highest immersive experience in autonomous vehicles considering feedback modalities between the car and its users.
Deep Learning With PyTorch

In the last decade, deep learning has seen exponential growth due to rise in computational power as a result of graphics processing units (GPUs) and a large amount of data due to the democratization of the internet and smartphones. This chapter aims to throw light on both the theoretical aspects of deep learning and its practical aspects using PyTorch. The chapter primarily discusses new technologies using deep learning and PyTorch in detail. The chapter discusses the advantages of using PyTorch compared to other deep learning libraries. The chapter discusses some of the practical applications like image classification and machine translation. The chapter also discusses the various frameworks built with the help of PyTorch. PyTorch consists of various models that increases its flexibility and accessibility to a greater extent. As a result, many frameworks built on top of PyTorch are discussed in this chapter. The authors believe that this chapter will help readers in getting a better understanding of deep learning making neural networks using PyTorch.
Kemampuan Literasi Membaca Siswa Indonesia (Berdasarkan hasil UN dan PISA)

National Literacy Movement (GLN) in Permendikbud No. 23/2015 requires Indonesian students to master at least six basic literacies. One of them is literacy. This literacy is the initial literacy that must be mastered because it includes functional literacy that is useful in everyday life. This literacy is not only how a people is free from illiteracy, but also has the life skills to be able to compete globally. This year is the fifth year since the program was launched. Ideally, a change in literacy ability can be seen in students. National Examinations, especially Indonesian Language test, can be used as a benchmark for these achievements. This test requires students to be able to answer problems related to reading and writing (literature and non-literature). Remarkably, for three consecutive years, the results of the Indonesian Language National Examination, especially at the Junior High School level, were ranked highest compared to other subjects. In general, students' literacy reading ability is in the ability to access and retrieve information in simple texts (150 to 200 words). They are able to determine the main sentence / main idea of a paragraph, determine the main character, and determine the background evidence of place / time / situation in the text. This is comparable to the 2018 PISA study, that is, the literacy ability of Indonesian students reading is at the level of detecting and handling conflict and representing information literally in the form of uncomplicated questions. Unfortunately, there are some abilities that students have not mastered based on the results of the two tests. In this paper these matters are discussed more deeply with the aim of becoming an enrichment material for teachers in schools to further enhance students' literacy reading skills.
Biteye: A System for Tracking Bitcoin Transactions

Blockchain is a public distributed ledger, which has the characteristics of decentralization and anonymization, which leads to the frequent occurrence of money laundering and theft. Taking Bitcoin as an example, traders can have multiple addresses, and these addresses have nothing to do with their identities in real life, their identities are difficult to identify, and it is difficult to track the flow of transaction funds on the blockchain. This paper proposes a transaction tracking system that can effectively and accurately track the source and destination of a certain amount of funds on the blockchain, which is superior to existing Bitcoin transaction tracking methods and has a substantial reference value.
Text sentiment in the Age of Enlightenment: an analysis of spectator periodicals

Abstract Spectator periodicals contributed to spreading the ideas of the Age of Enlightenment, a turning point in human history and the foundation of our modern societies. In this work, we study the spirit and atmosphere captured in the spectator periodicals about important social issues from the 18 th century by analyzing text sentiment of those periodicals. Specifically, based on a manually annotated corpus of over 3 700 issues published in five different languages and over a period of more than one hundred years, we conduct a three-fold sentiment analysis: First, we analyze the development of sentiment over time as well as the influence of topics and narrative forms on sentiment. Second, we construct sentiment networks to assess the polarity of perceptions between different entities, including periodicals, places and people. Third, we construct and analyze sentiment word networks to determine topological differences between words with positive and negative polarity allowing us to make conclusions on how sentiment was expressed in spectator periodicals.Our results depict a mildly positive tone in spectator periodicals underlining the positive attitude towards important topics of the Age of Enlightenment, but also signaling stylistic devices to disguise critique in order to avoid censorship. We also observe strong regional variation in sentiment, indicating cultural and historic differences between countries. For example, while Italy perceived other European countries as positive role models, French periodicals were frequently more critical towards other European countries. Finally, our topological analysis depicts a weak overrepresentation of positive sentiment words corroborating our findings about a general mildly positive tone in spectator periodicals.We believe that our work based on the combination of the sentiment analysis of spectator periodicals and the extensive knowledge available from literary studies sheds interesting new light on these publications. Furthermore, we demonstrate the inclusion of sentiment analysis as another useful method in the digital humanist’s distant reading toolbox.
Simulating Suboptimal Steganographic Embedding

Researchers who wish to benchmark the detectability of steganographic distortion functions typically simulate stego objects. However, the difference (coding loss) between simulated stego objects, and real stego objects is significant, and dependent on multiple factors. In this paper, we first identify some factors affecting the coding loss, then propose a method to estimate and correct for coding loss by sampling a few covers and messages. This allows us to simulate suboptimally-coded stego objects which are more accurate representations of real stego objects. We test our results against real embeddings, and naive PLS simulation, showing our simulated stego objects are closer to real embeddings in terms of both distortion and detectability. This is the case even when only a single image and message as used to estimate the loss.
Inductively Versus Deductively Structured Product Descriptions: Effects on Chinese and Western Readers

This study examines the effects of inductively versus deductively organized product descriptions on Chinese and Western readers. It uses a 2 × 3 experimental design with text structure (inductive versus deductive) and cultural background (Chinese living in China, Chinese living in the Netherlands, and Westerners) as independent variables and recall, reading time, and readers’ opinions as dependent variables. Participants read a product description that explained two refrigerator types and then recommended which one to purchase. The results showed that Chinese readers rated readability and persuasiveness higher when the text was structured inductively whereas Western readers rated these aspects equally high for the inductively and deductively structured text. The results suggest that culturally preferred organizing principles do not affect readers’ ability to read and understand texts but that these principles might affect their opinions about the texts.
Affective computing study of attention recognition for the 3D guide system

The eye-tracking has been widely used in multiple discipline studies in recent years. However, most of the studies focused on the analysis for static images or text but less for highly interactive operation application. In addition, the affective computing rose and development in recent years have changed completely the design of thinking pattern for the human–computer interaction. Therefore, this study hopes to integrate the affective computing into the 3D guide system which was developed for the real campus through the eye-tracking technology. The analysing user's gaze position and recognising attention emotion are according to the interest region which is stetted into the environment, and shows the feedback content corresponds to the area and the emotion. Through the most intuitive gaze analysis, the operation burden can be reduced and the user's interactive experience can be improved to achieve intuitive and user-friendly experience. The results can also apply for medical therapy on human attention training.
Development and Evaluation of a Test Setup to Investigate Distance Differences in Immersive Virtual Environments

Nowadays, with recent advances in virtual reality technology, it is easily possible to integrate real objects into virtual environments by creating an exact virtual replication and enabling interaction with them by mapping the obtained tracking data of the real to the virtual objects. The primary goal of our study is to develop a system to investigate distance differences for near-field interaction in immersive virtual environments. In this context, the term distance difference refers to the shift between a real object and the respective replication of the real object in the virtual environment of the same size. This could occur for a number of reasons e.g. due to errors in motion tracking or mistakes in designing the virtual environment. Our virtual environment is developed using the Unity3D game engine, while the immersive contents were displayed on an HTC Vive Pro head-mounted display. The virtual room shown to the user includes a replication of the real testing lab environment, while one of the two real objects is tracked and mirrored to the virtual world using an HTC Vive Tracker. Both objects are present in the real as well as in the virtual world. To find perceivable distance differences in the near-field, the actual task in the subjective test was to pick up one object and place it into another object. The position of the static object in the virtual world is shifted by values between 0 and 4 cm, while the position of the real object is kept constant. The system is evaluated by conducting a subjective proof-of-concept test with 18 test subjects. The distance difference is evaluated by the subjects through estimating perceived confusion on a modified 5-point absolute category rating scale. The study provides quantitative insights into allowable real-world vs. virtual-world mismatch boundaries for near-field interactions, with a threshold value of around 1 cm.
On the Confidentiality of Amounts in Grin

Pedersen commitments have been adopted by several cryptocurrencies for hiding transaction amounts. While Pedersen commitments are perfectly hiding in isolation, the cryptocurrency transaction rules can reveal relationships between the amounts hidden in the commitments involved in the transaction. Such relationships can be combined with the public coin creation schedule to provide upper bounds on the number of coins in a commitment. In this paper, we consider the Grin cryptocurrency and derive upper bounds on the number of coins which can be present in regular transaction outputs. In a March 2020 snapshot of the Grin blockchain, we find that out of the 110,149 unspent regular transaction outputs 983 of them have less than 1800 grin (number of coins typically minted in half an hour) stored in them. On the other hand, 95% of the unspent regular transaction outputs in the snapshot have an upper bound which is at least 90% of the total Grin supply at their respective block heights. We conclude that while our method does not violate the confidentiality of the amounts in most of the outputs on the Grin blockchain, the amounts in some outputs can be estimated to be in a narrow range.
Challenges Classification in Search-Based Refactoring

A common task in software maintenance is refactoring. The essential refactoring goal is improving software quality. The refactoring has extended for a wide range of software artifacts in different domains, such as web, database, and desktop applications. The difficulty of the manual artifacts inspecting is an impetus for automatic refactoring. Search-based software engineering (SBSE) has been used to solve many software engineering problems as optimization problems. Automating problem-solving is the goal of applying it to reduce human efforts. Many researchers have used the search-based approach in the field of refactoring that has known as search-based refactoring (SBR). There are many challenges in SBR. Identifying refactoring challenges help to understand refactoring problem and allow researchers to present new ideas for eliminating or reducing challenges. In this paper, we studied the researches in SBR and extracted researches challenges. We intend to categorize the challenges in this area from a new and more comprehensive perspective.
Formal Analysis of Unmanned Aerial Vehicles Using Higher-Order-Logic Theorem Proving

The continuous dynamics of unmanned aerial vehicles (UAVs) are generally modeled as a set of differential equations. Traditionally, these continuous dynamics of UAVs are analyzed using paper-and-pencil proof and computer-based testing or simulations to study the performance, stability, and various other control characteristics of the aircraft flying in the air. However, these techniques suffer from their inherent limitations such as human error proneness, sampling-based analysis, approximations of the mathematical results, and the usage of unverified algorithms. Thus, these methods cannot be trusted when considering the utility of UAVs in many safety-critical applications. To overcome the limitations of the aforementioned techniques, it is proposed to use higher-order-logic theorem proving for formally analyzing the continuous dynamics of UAVs. In particular, a formalization of complex-valued matrices in higher-order logic is provided using the HOL Light theorem prover, which is in turn used for the formalization of the navigation’s and aircraft’s body-fixed frames, as well as their associated transformations. Formal reasoning support is also provided for analyzing the multiple-input multiple-output systems, which are in turn used for formally analyzing the continuous dynamics of UAVs using HOL Light. For illustration, we use our proposed framework for the formal stability analysis of the CropCam UAV using HOL Light.
Customizable Inverse Sensor Model for Bayesian and Dempster-Shafer Occupancy Grid Frameworks

Occupancy grid mapping is an important component in a road scene understanding for autonomous driving. It can encapsulate data from heterogeneous sensor sources like radars, LiDARs, cameras and ultrasonics. At the core of occupancy grid (OG) generation, there is usually an inverse sensor model (ISM), which infers the occupancy representation from the sensor readings. Traditional ISMs are characterized by a very rigid structure, suited only for one sensor type, and specific occupancy grid representation. This paper proposes a novel ISM framework, which offers a separation between free and occupied space, supporting both Bayesian and Dempster-Shafer OG representations. The framework is especially useful when dealing with multiple different sensors where custom or preselected probability distribution can be applied. The presented ISM architecture is modular and flexible, which is described in an illustrative example of application customized for different detection sources.
Order-Constrained ROC Regression With Application to Facial Recognition

The receiver operating characteristic (ROC) curve is widely used to assess discriminative accuracy of two groups based on a continuous score. In a variety of applications, the distributions of such scores across the two groups exhibit a stochastic ordering. Specific examples include calibrated biomarkers in medical diagnostics or the output of matching algorithms in biometric recognition. Incorporating stochastic ordering as an additional constraint into estimation can improve statistical efficiency. In this article, we consider modeling of ROC curves using both the order constraint and covariates associated with each score given that the latter (e.g., demographic characteristics of the underlying subjects) often have a substantial impact on discriminative accuracy. The proposed method is based on the indirect ROC regression approach using a location-scale model, and quadratic optimization is used to implement the order constraint. The statistical properties of the proposed order-constrained least squares estimator are studied. Based on the theoretical results developed herein, we deduce that the proposed estimator can achieve substantial reductions in mean squared error relative to its unconstrained counterpart. Simulation studies corroborate the superior performance of the proposed approach. Its practical usefulness is demonstrated in an application to face recognition data from the "Good, Bad, and Ugly" face challenge, a domain in which accounting for covariates has hardly been studied.
DPLOOP: Detection and Prevention of Loopholes in Web Application Security

Web application loopholes are related to different components. Defeat correctly sanitized users’ given input is one of the prominent features that accompany to run illegal snippets in such type of programs. Due to the absence of proper input sanitization, common loopholes occur in web applications, such as SQL, Cross-site Scripting (XSS), XML, CSRF, and LDAP. Thus, research work presented in this paper deliberates possible methods to detect and mitigate vulnerabilities in order to prevent organizational websites against SQL and XSS loopholes. We have analyzed a dataset of URLs. SQL, XSS, and XML have the highest rate of detection and the least percentage of CSRF.