Reviewerly/exHarmony huggingface dataset:
Laughing Matters: Humor, Free Speech and Hate Speech at the European Court of Human Rights

Abstract This article conducts an interdisciplinary analysis of a corpus consisting of ten cases from the European Court of Human Rights. The selected cases range from 2004 to 2022 and engage with the nexus between hate speech and humorous expression, spanning across various forms of humor (from political satire to disaster jokes) and different media (from verbal jokes to cartoons). Building on insights from linguistics, semiotics and literary theory, we discuss how the ECtHR deals with the interpretive challenges posed by humor, with particular attention to the following key aspects: (1) The rhetorical/semiotic mechanisms underlying the contested verbal, visual or multimodal texts; (2) The dialogue between the contested expression and previous texts by means of allusion, commentary or parody; (3) The role played by a broad range of contextual factors, including the conventions of a given humorous genre as well as the specific socio-political circumstances in which humor is produced and circulated; (4) The possible outcomes of the interpretive process, namely the different manners in which humor and disparagement may relate to each other in a given disputed expression; (5) The ways in which courts can reconstruct the actual or presumed reception of an ambiguous joke or cartoon within a given audience. In addition to highlighting a number of recurring issues and occasional inconsistencies in the corpus, this contribution aims to demonstrate how humanities-based humor research can set the basis for a more consistent and methodical approach to humor in court, with special but not exclusive regard to hate speech jurisprudence.
Spatial Rank-Based Augmentation for Nonparametric Online Monitoring and Adaptive Sampling of Big Data Streams

The age of Internet of Things (IoT) has witnessed the rapid development of modern data acquisition devices and communicating-actuating networks, which enables the generation of big data streams shared across platforms for remote and efficient decision making of many critical systems. The monitoring of big data streams remains a challenging task in various practical applications mainly due to their complexity in interrelationships, large volume, and high velocity, which places prohibitive demands on monitoring methodologies and resources. To tackle the challenges of monitoring unexchangeable and correlated big data streams with only partial observations available under resource constraints, we propose a method by incorporating spatial rank-based statistics with effective data augmentation techniques for the online unobservable data streams that can analytically inform the monitoring and sampling decisions based only on partially observed data streams. By exploiting historical data, the proposed method preserves strong descriptive power of general big data streams under partial observations and can explicitly utilize the correlation among data streams, and thus allows effective monitoring and equitable sampling over general heterogeneous and correlated big data streams, which is free of simplified assumptions (e.g., exchangeability) compared to existing methods. Theoretical investigations are carried out to evaluate the effectiveness of the augmentation statistics as well as the sampling strategy, which guarantee the superiority of the sampling performance over existing methods. Simulations under various scenarios and two real case studies are also conducted to evaluate and validate the performance of the proposed method.
Understanding Interviewees’ Perceptions and Behaviour towards Verbally and Non-verbally Expressive Virtual Interviewing Agents

Recent technological advancements have boosted the usage of virtual interviewing platforms where the candidates interact with a virtual interviewing agent or an avatar that has human-like behavior instead of face-to-face interviews. As a result, it is essential to understand how candidates perceive these virtual interviewing avatars and whether adding features to boost the system’s interaction makes a difference. In this work, we present the results of two studies in which a virtual interviewing avatar with verbal and non-verbal interaction capabilities was used to conduct employment interviews. We add two interactive capabilities to the avatar, namely the non-verbal gestures and the verbal follow-up questioning and compare it with a simple interviewing avatar. We analyze the differences in perception with self-rated measures and behaviour with automatically extracted audiovisual behavioural cues. The results show that the candidates speak for a longer time, feel less stressed and have a better chance to perform with verbally and non-verbally expressive virtual interviewing agents.
Multi-aperture optical flow estimation for artificial compound eye using non-subsampled contourlet transform and guided filter

Abstract A multi-scale optical flow estimation for the image captured by artificial compound eye (ACE) is investigated in this article. The optical flow estimation of ACE must be adapted by designing algorithms according to its unique multi-aperture characteristics. A more general filter for the regularization term, rather than a single iterative solution in the traditional variational model, is devised using the non-subsampled contourlet transform to enforce band decomposition and estimate the optical flow field. To circumvent the spillover and error of the single-aperture fringe flow field, a flow gradient weight is introduced to suppress it and enhance motion details. Furthermore, low-pass subbands adopt the Bayes threshold with the advantage of efficiently eliminating outliers. More high-pass subbands adopt guided filter with the benefit of separating important details from outliers. The prominent feature of the proposed method is that the accuracy of optical flow estimation is improved effectively by eliminating outliers. Finally, experimental results demonstrate the superiority of the examined optical flow estimation.
Story visualization using image-text matching architecture for digital storytelling

Currently, the techniques for generating images from text used to visualize stories have serious limitations in terms of image quality, which prevents quantifying their impact in real life scenarios. An example of this occurs in the field of education, where digital storytelling is used as a tool to incite teaching. For this reason, we propose to design a web interface that allows primary school children to write a short story and obtain, as a result, a sequence of coherent and representative images of said content, emulating a conventional process of educational digital storytelling. We describe the use of an Image-text matching architecture based on NLP and Image Retrieval for the story visualization task focused on digital storytelling. To evaluate the performance of the architecture, the quantitative metrics: WuPalmer and cosine similarity were used, in addition to qualitative metrics.
Next-generation of sUAS 360 surround vision cameras designed for automated navigation in low-light conditions

The next generation of sUAS (small Unmanned Aircraft Systems) for automated navigation will have to perform in challenging conditions, bad weather, high and low temperature and from dusk-to-dawn. The paper presents experimental results from a new wide-angle vision camera module specially optimized for low-light. We present the optical characteristics of this system as well as experimental results obtained for different sense and avoid functionalities. We also show preliminary results using our camera module images on neural networks for different scene understanding tasks.
Research on identity authentication system of Internet of Things based on blockchain technology

With the development of the Internet of Things (IoT) and its applications, a large amount of data is generated regularly. If this information is used by malicious attackers, it will be a great disaster for the relevant users. In this regard, this article focuses on the user’s identity privacy issues involved in the IoT. By protecting the user’s identity privacy, the attacker cannot associate the obtained data with the user’s real identity, and so achieve the purpose of protecting the user. This article uses the features of blockchain that cannot be tampered with nor forged to strengthen the reliability of the system. The proposed scheme saves the transaction information of user information through the Hyperledger and uses the ring signature method to obscure the real identity. A key generator is used to generate system public parameters and ring membership information required for signature. Users can use this information to hide their identity in a ring group of n users so that other users can only guess the true identity of the user with a probability of 1/n. Additionally, the method of aggregated signature is used to shorten the time and space required for k signature verification to 1/k, which greatly improves the efficiency. Finally, this article also uses an accountability mechanism to punish some attackers who attempt to waste system resources by revealing the real identity of the attacker and refusing to serve him. In this paper, GO language is used to write chain code to realize the proposed algorithm, and a prototype system is built through HyperLeger Fabric blockchain network, and the prototype system is verified by experiment. The correctness and efficiency of the above scheme are also proved through theoretical analysis and experiments.
Modulator vulnerability in continuous-variable quantum key distribution

Encoding of key bits in the quadratures of the electromagnetic light field is an essential part of any continuousvariable quantum key distribution system. However, flaws of practical implementation can make such systems susceptible to leakage of secret information. We verify a side channel presence in an optical in-phase and quadrature modulator which is caused by limited suppression of a quantum information-carrying sideband. We investigate various strategies an unauthorized third party can exploit the vulnerability in a proof-of-concept experiment and theoretically assess the modulation leakage effect on a security of the Gaussian coherent-state continuous-variable quantum key distribution protocol and show that the leakage reduces the range of conditions which support secure key generation. Without the control of sideband modulation in practical in-phase and quadrature modulator-based systems the security can be compromised.
A supervised topic embedding model and its application

We propose rTopicVec, a supervised topic embedding model that predicts response variables associated with documents by analyzing the text data. Topic modeling leverages document-level word co-occurrence patterns to learn latent topics of each document. While word embedding is a promising text analysis technique in which words are mapped into a low-dimensional continuous semantic space by exploiting the local word co-occurrence patterns within a small context window. Recently developed topic embedding benefits from combining those two approaches by modeling latent topics in a word embedding space. Our proposed rTopicVec and its regularized variant incorporate regression into the topic embedding model to model each document and a numerical label paired with the document jointly. In addition, our models yield topics predictive of the response variables as well as predict response variables for unlabeled documents. We evaluated the effectiveness of our models through experiments on two regression tasks: predicting stock return rates using news articles provided by Thomson Reuters and predicting movie ratings using movie reviews. Results showed that the prediction performance of our models was more accurate in comparison to three baselines with a statistically significant difference.
Optimal alphabet for single text compression

A text written using symbols from a given alphabet can be compressed using the Huffman code, which minimizes the length of the encoded text. It is necessary, however, to employ a text-specific codebook, i.e. the symbol-codeword dictionary, to decode the original text. Thus, the compression performance should be evaluated by the full code length, i.e. the length of the encoded text plus the length of the codebook. We studied several alphabets for compressing texts – letters, n-grams of letters, syllables, words, and phrases. If only sufficiently short texts are retained, an alphabet of letters or two-grams of letters is optimal. For the majority of Project Gutenberg texts, the best alphabet (the one that minimizes the full code length) is given by syllables or words, depending on the representation of the codebook. Letter 3 and 4-grams, having on average comparable length to syllables/words, perform noticeably worse than syllables or words. Word 2-grams also are never the best alphabet, on the account of having a very large codebook. We also show that the codebook representation is important – switching from a naive representation to a compact one significantly improves the matters for alphabets with large number of symbols, most notably the words. Thus, meaning-expressing elements of the language (syllables or words) provide the best compression alphabet.
A decidable theory involving addition of differentiable real functions

This paper enriches a pre-existing decision algorithm, which in turn augmented a fragment of Tarski's elementary algebra with one-argument real functions endowed with a continuous first derivative. In its present (still quantifier-free) version, our decidable language embodies the addition of functions and multiplication of functions by scalars; the issue we address is the one of satisfiability. As regards real numbers, individual variables and constructs designating the basic arithmetic operations are available, along with comparison relators. As regards functions, we have variables of another sort, out of which compound terms are formed by means of constructs designating addition and differentiation. An array of predicates designates various relationships between functions, as well as function properties, that may hold over intervals of the real line; those are: function comparisons, strict and non-strict monotonicity / convexity / concavity, comparisons between the derivative of a function and a real-valued term. Our decision method consists in preprocessing the given formula into an equi-satisfiable quantifier-free formula of the elementary algebra of real numbers, whose satisfiability can then be checked by means of Tarski's decision method. No direct reference to functions will appear in the target formula, each function variable having been superseded by a collection of stub real variables; hence, in order to prove that the proposed translation is satisfiability-preserving, we must figure out a flexible-enough family of interpolating C1 functions that can accommodate a model for the source formula whenever the target formula turns out to be satisfiable. With respect to the results announced in earlier papers of the same stream, a significant effort went into designing the family of interpolating functions so that it could meet the new constraints stemming from the presence of function addition (along with differentiation) among the constructs of our fragment of mathematical analysis.
Country Life in the Digital Era: Comparison of Technology Use and Cybercrime Victimization between Residents of Rural and Urban Environments in Slovenia

Cybercrime is one of the most significant security challenges of the 21st century. However, official statistics do not provide insights into its prevalence and nature. Representative cross-sectional field studies may help fill this gap, focusing on differences between urban and rural technology users. We (a) investigated the association between the purpose of computers and other electronic device usage and perceived vulnerability, (b) compared the differences in the purpose of computers or other electronic device use and perceived vulnerability, and (c) compared the perceived cyber victimization between residents of rural and urban areas. We conducted a field study that resulted in a representative sample of the Republic of Slovenia in Europe. We found several significant differences in the purpose of technology use and perceived cyber victimization. Furthermore, the results indicate that the purpose of technology use is somehow associated with perceived vulnerability in cyberspace; however, such associations are different in cyberspace than in the material world.
Security Risk Assessments: Modeling and Risk Level Propagation

Security risk assessment is an important task in systems engineering. It is used to derive security requirements for a secure system design and to evaluate design alternatives as well as vulnerabilities. Security risk assessment is also a complex and interdisciplinary task, where experts from the application domain and the security domain have to collaborate and understand each other. Automated and tool-supported approaches are desired to help manage the complexity. However, the models used for system engineering usually focus on functional behavior and lack security-related aspects. Therefore, we present our modeling approach that alleviates communication between the involved experts and features steps of computer-aided modeling to achieve consistency and avoid omission errors. We demonstrate our approach with an example. We also describe how to model impact rating and attack feasibility estimation in a modular fashion, along with the propagation and aggregation of these estimations through the model. As a result, experts can make local decisions or changes in the model, which in turn provides the impact of these decisions or changes on the overall risk profile. Finally, we discuss the advantages of our model-based method.
Sandstorm Image Enhancement Using Image-Adaptive Eigenvalue and Brightness-Adaptive Dark Channel Network

Sandstorm images suffer from dust particles and the attenuation of light. Degraded sandstorm images have a reddish or yellowish color cast owing to dust particles, which have a certain color. Because of this phenomenon, distorted sandstorm images have imbalanced red and blue channels. Existing sandstorm enhancement methods improve the image by only focusing on dehazing, due to sandstorm images having similar features to hazy or dusty images. However, the enhanced images using previous methods have a color cast. Therefore, to enhance the sandstorm image naturally, a balancing procedure of color components is needed. This paper proposes a color-balancing method using image-adaptive eigenvalues. Because eigenvalues describe image characteristics, improved images have balanced color components. However, these enhanced images have dusty features. Therefore, to enhance balanced images a dehazing procedure is needed, something which this paper applies using a multiscale convolution neural network, thus generating a transmission map with brightness-adaptive features. Images enhanced using the proposed method compare well with state-of-the-art methods in subjective and objective measures of quality.
Investment opportunity of blockchain technology in the education sector of Saudi Arabia: A systematic literature review

The primary objective of this research is to explore the literature on blockchain technology and its investment opportunity in the education sector. Studies on the investment opportunities of blockchain technology in education have remained limited and little is known about the existing state of knowledge and practice of blockchain technology in the education sector of Saudi Arabia, especially for its sustainable development. In this study, the author tried to synthesise literature on blockchain technology to understand the difficulties and prospects of this technology in Saudi Arabia. A total of 15 empirical studies from 2017 to 2020 were reviewed. The descriptive and thematic analysis identified four types of challenges of blockchain technology in the education sector. They include leaking privacy and security, processing cost, setting the boundaries, and weakening school credentials. The review also revealed several opportunities for adopting blockchain technology such as certifying identity authentication, improving learning assessment, maintaining student records, enhancing trust, and reducing costs. Implications and recommendations related to education for sustainable development are provided accordingly.
Roadside Unit-based Unknown Object Detection in Adverse Weather Conditions for Smart Internet of Vehicles

For Internet of Vehicles applications, reliable autonomous driving systems usually perform the majority of their computations on the cloud due to the limited computing power of edge devices. The communication delay between cloud platforms and edge devices, however, can cause dangerous consequences, particularly for latency-sensitive object detection tasks. Object detection tasks are also vulnerable to significantly degraded model performance caused by unknown objects, which creates unsafe driving conditions. To address these problems, this study develops an orchestrated system that allows real-time object detection and incrementally learns unknown objects in a complex and dynamic environment. A you-only-look-once–based object detection model in edge computing mode uses thermal images to detect objects accurately in poor lighting conditions. In addition, an attention mechanism improves the system’s performance without significantly increasing model complexity. An unknown object detector automatically classifies and labels unknown objects without direct supervision on edge devices, while a roadside unit (RSU)-based mechanism is developed to update classes and ensure a secure driving experience for autonomous vehicles. Moreover, the interactions between edge devices, RSU servers, and the cloud are designed to allow efficient collaboration. The experimental results indicate that the proposed system learns uncategorized objects dynamically and detects instances accurately.
Customized Instance Random Undersampling to Increase Knowledge Management for Multiclass Imbalanced Data Classification

Imbalanced data constitutes a challenge for knowledge management. This problem is even more complex in the presence of hybrid (numeric and categorical data) having missing values and multiple decision classes. Unfortunately, health-related information is often multiclass, hybrid, and imbalanced. This paper introduces a novel undersampling procedure that deals with multiclass hybrid data. We explore its impact on the performance of the recently proposed customized naïve associative classifier (CNAC). The experiments made, and the statistical analysis, show that the proposed method surpasses existing classifiers, with the advantage of being able to deal with multiclass, hybrid, and incomplete data with a low computational cost. In addition, our experiments showed that the CNAC benefits from data sampling; therefore, we recommend using the proposed undersampling procedure to balance data for CNAC.