Reviewerly/exHarmony huggingface dataset:
Enabling easy Web of Things compatible device generation using a Model-Driven Engineering approach

One of the main ongoing standardization efforts of the Internet of Things (IoT) at the application layer is the Web of Things (WoT), which aims to enable interoperability using already existing standards. However, keeping up the design and implementation of IoT applications with the exponentially increasing number of devices being interconnected is costly in workforce resources. Model-Driven Engineering (MDE) approaches increase the level of abstraction using models, and allowing to reuse design and code. This lowers the use of resources for implementing solutions seamlessly. This is why in this work we implement a MDE approach based on the WoT, allowing easy WoT-based device generation. Besides, automated code generation is applied to reduce manual tasks even further. Using the Eclipse Modelling Framework (EMF) and its associated plugins, we provide a way of describing models graphically and generate the code automatically, reducing development and testing time.
A new framework for online content moderation

I wish to provide a description of context and practical changes in the institutions, places and tools of speech moderation before and after the internet. This description revolves around legally and institutionally relevant aspects of how excesses in speech were identified and countered in order to provide support for a normative claim about what online speech moderation should look like today and in the future. The article starts with a list of elements of content moderation up until three decades ago and the follow with a list of elements of content moderation today. The primary goal is to contrast the two scenarios in order to highlight the inconvenience of certain assumptions lawmakers, lawyers and judges make on how communication works in a networked society. I do not intend to provide alternative descriptions to the characteristics of this phenomenon in order to dispute prevailing descriptions. My point is merely to uncover certain aspect that usually remain unnoticed or underestimated in the legal debate about content moderation. The third part of the article will then propose the outline of a new procedural legal framework for moderation of online speech without dwelling too deep on considerations of substantive legal standards for balancing speech.
Visual Font Pairing

This paper introduces the problem of automatic font pairing. Font pairing is an important design task that is difficult for novices. Given a font selection for one part of a document (e.g., header), our goal is to recommend a font to be used in another part (e.g., body) such that the two fonts used together look visually pleasing. There are three main challenges in font pairing. First, this is a fine-grained problem, in which the subtle distinctions between fonts may be important. Second, rules and conventions of font pairing given by human experts are difficult to formalize. Third, font pairing is an asymmetric problem in that the roles played by header and body fonts are not interchangeable. To address these challenges, we propose automatic font pairing through learning visual relationships from large-scale human-generated font pairs. We introduce a new database for font pairing constructed from millions of PDF documents available on the Internet. We propose two font pairing algorithms: dual-space k-NN and asymmetric similarity metric learning (ASML). These two methods automatically learn fine-grained relationships from large-scale data. We also investigate several baseline methods based on the rules from professional designers. Experiments and user studies demonstrate the effectiveness of our proposed dataset and methods.
Realizability of three-valued semantics for abstract dialectical frameworks

We investigate fundamental properties of three-valued semantics for abstract dialectical frameworks (ADFs). In particular, we deal with realizability, i.e., the question whether there exists an ADF that has a given set of interpretations as its semantics. We provide necessary and sufficient conditions that hold for a set of three-valued interpretations whenever there is an ADF realizing it under admissible, complete, grounded, or preferred semantics. Moreover, we show how to construct such an ADF in case of realizability. Our results lay the ground for studying the expressiveness of ADFs under three-valued semantics. Since ADFs generalize many existing approaches, our results have immediate impact on many argumentation formalisms including abstract argumentation frameworks. As first applications, we study implications of our results on the existence of certain join operators on ADFs. Furthermore, we exploit our (exact) characterizations to obtain realizability results also for a more relaxed setting, where realizing ADFs may contain hidden statements.
No lab, no problem

Abstract We present new opportunities for psycholinguistic research that are made available by presenting experiments online over the web. We focus on PsychoPy3, which is a new version of a system for the development and delivery of behavioural experiments. Crucially, it allows for both these functions to be performed online. We note that experiments delivered over the web have significant efficiency advantages. They also open up new opportunities to increase the ecological validity of experiments and to facilitate the participation of members of populations that have thus far been less studied in the psycholinguistic literature. We discuss the crucial matter of millisecond timing in online experiments. The technical details of implementation of a behavioural psycholinguistic experiment are presented, along with listings of additional technical resources and support. Our overall evaluation is that although online experimentation still has technical challenges and improvements are ongoing, it may well represent the future of behavioural psycholinguistic research.
A rapid measurement method for structured surface in white light interferometry

White light interferometry (WLI) is an effective and widely-used technique for structured surface measurement. However, it requires multiframe interferograms with vertical scanning to realise large-scale measurement, which is time consuming and computationally intensive. This paper proposes a rapid surface measurement method to realise surface recovery with a single interferogram by white light interferometry. First, the feasibility to solve the wrapped phase of a single white-light interferogram by Hilbert transform is certified. Then, unwrapped phases against zero optical path difference position (OPD) are achieved by a zero optical path difference detection algorithm applied to unwrapping process, which provides efficient surface recovery. To ensure the accuracy of phase solution in the proposed method, the necessary number and width of the interference fringes in the interferogram are analysed and determined based on Hilbert transform and sampling analysis. Finally, measurement results of a standard step sample and a standard reticle template are presented, which prove the accuracy and efficiency of the proposed method. LAY DESCRIPTION: As an effective and widely-used technique for structured surface measurement, white light interferometry (WLI) has the major advantage to measure noncontinuous surfaces using the short coherence length of a wide bandwidth source. However, frequently vertical scanning is required to get series of white light interferograms at different axial positions for surface recovery by recovered algorithms. The vertical scanning process is complicated and time consuming. This paper proposes a fast and efficient method to realise rapid surface measurement using only a single-frame interferogram based on WLI. First, the feasibility of using only one single white light interferogram to solve wrapped phases by Hilbert transform (HT) is discussed. Next, unwrapping process and zero optical path difference（OPD） detection algorithms are combined to unwrap phases against zero OPD position, which makes the structured surface recovery much easier. After that, the feasible number and width of interference fringes are determined based on sampling analysis and HT to guarantee the reliability and accuracy of phase solution in the proposed method. Finally, the accuracy and efficiency of this method is verified by measurement experiments of a standard step sample and a standard reticle template.
Text Recognition and Classification in Floor Plan Images

In this paper we present a method for text recognition in floor plan images. In particular, we are concerned about locating, reading, and categorizing text inside floor plan images to obtain information about the building. Furthermore, the aim of this paper is to compare traditional text detection methods, based on image processing techniques, with recent approaches relying on convolutional neural networks. To improve results we combined several methods outperforming the original ones. Text regions are also classified in four semantic classes according to their purpose. Two datasets with different features, including quality and size, were considered in the experiments performed.
Applications of machine learning to brain disorders

In this chapter, we explore the potential applications of machine learning to brain disorders. Specifically, we illustrate why the use of machine learning in brain disorders is attracting so much interest among researchers and clinicians by highlighting three key applications: prediction of illness onset, assistance with diagnosis, and prediction of longitudinal outcomes. After illustrating these applications, we discuss the challenges that need to be overcome for a successful translational implementation of machine learning in everyday psychiatric and neurologic care. In particular, we identify three main pitfalls in the absence of biomarkers, the unreliability of clinical diagnosis, and the heterogeneity of the patients. In the final part of the chapter, we consider the requirements a machine learning algorithm needs to fulfill to be eligible for clinical use and discuss potential future directions.
Leveraging Past References for Robust Language Grounding

Grounding referring expressions to objects in an environment has traditionally been considered a one-off, ahistorical task.However, in realistic applications of grounding, multiple users will repeatedly refer to the same set of objects.As a result, past referring expressions for objects can provide strong signals for grounding subsequent referring expressions.We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object.The network combines information from vision and past referring expressions to resolve which object is being referred to.Our experiments show that detecting referring expression coreference is an effective way to ground objects described by subtle visual properties, which standard visual grounding models have difficulty capturing.We also show the ability to detect object coreference allows the grounding model to perform well even when it encounters object categories not seen in the training data.
VIStory

Many visual analytics have been developed for examining scientific publications comprising wealthy data such as authors and citations. The studies provide unprecedented insights on a variety of applications, e.g., literature review and collaboration analysis. However, visual information (i.e., figures) that are widely employed for storytelling and methods description are often neglected. We present VIStory, an interactive storyboard for exploring visual information in scientific publications. We harvest the data using an automatic figure extraction method, resulting in a large corpora of figures. Each figure contains various attributes such as dominant color and width/height ratio, together with faceted metadata of the publication including venues, authors, and keywords. To depict these information, we develop an intuitive interface consisting of three components: 1) Faceted View enables efficient query by publication metadata, benefiting from a nested table structure, 2) Storyboard View arranges paper rings -- a well-designed glyph for depicting figure attributes, in a themeriver layout to reveal temporal trends, and 3) Endgame View presents a highlighted figure together with the publication metadata. The system is especially useful for scientific publications containing substantial visual information, such as the visualization publications. We demonstrate the effectiveness of our approach using two case studies conducted on past ten-year IEEE VIS publications in 2009 - 2018.
How to Use Blockchain Effectively in Auditing and Assurance Services

The last decade has been quite innovative and revolutionary for both digital tools and financial reporting. Until now, auditing and assurance services have dealt with sorting mountains of data. With firms upgrading their business methods and internal control systems through the use of information technologies, it has become mandatory for those same information technologies to be used in auditing and assurance services. Accordingly, the quality of auditing has increased, taking out what used to be a drudgery for auditors. However, at the same time new areas of auditing and assurance services have opened up.There has been speculation about whether the accounting information system will replace what accountants do, that is, whether blockchain systems will replace auditors. It is undeniable that when used in the audit process, blockchain systems increase the quality of the audit without increasing the time spent on the audited data. In some articles blockchain is referred to as one open, unmodifiable ledger. When auditing financial reports based on this ledger, auditors are able to increase the number of samples they use, even up to including the entire data set without increasing their work-load or the time necessary to audit the integrity of the data. However, this introduces new technological and business risks that the auditor will need to assess.International Auditing Standards have not yet been revised in terms of how and when to use blockchain ecosystems and the risks that they create for external and fraud audits. This chapter will explain how and when to use blockchain technologies and identify the potential new risks that await the auditor. Since no definitive rules and regulations have yet been made, this study is based on the opinions of several professional bodies that are currently tackling bitcoin and other sub-tools that blockchain ecosystems are offering.
Face Recognition Based on DWT Feature for CNN

In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system based on the Viola-Jones face detection algorithm, discrete wavelet transform (DWT), facial image enhancement using histogram equalization (HE) algorithm, and deep convolution neural network. Extraction results of facial features using DWT are used directly to train the CNN network, this network composed of three convolution layers, two pooling layers, a fully-connected layer, and one softmax regression layer. The face recognition rate based on this network is 99.85% and 99.80%. The face recognition rate of the ORL face database and the AR face database based on this network achieved 99.85% and 99.80% respectively.
Redesign Me: Virtual Reality Experience of the Line of Life and Its Connection to a Healthier Self

Virtual Reality is used in various ways for creating a storytelling experience. It gives us the opportunity to imagine one's life events as a story, and in settings that are intended to aid the self, such as treatment of trauma, anxiety, phobia, etc. This paper discusses the ways that challenging experiences change the way people perceive their life narratives and form their memories. This paper suggests that virtual reality (VR) can be used for the exploration of alternative scenarios in order to see one's overall line of life in a new and healthier way. Considering the theoretical background of the narrative self, this research proposes a novel view of VR immersion as a medium for constructing a new storyline and attitude to the past. The approach would also influence attitudes regarding the present and future, and thus better shape the narrative of the self, which can lead to healthier life experiences.
Task Scheduling Algorithms in Cloud Computing: A Survey

Cloudcomputing has transformed the world of communication systems into a new modern way. A cloud system consists of a vast variety of resources like processors, memory and software that are distributed all around the world. Task scheduling has a critical part in the performance and service of the cloud system. Plenty of task scheduling algorithms exist whose aim is to intensify the overall quality of the system. This article investigates and categorizes these algorithms based on task scheduling metrics of the cloud environment.
Driver’s Emotional State-Based Data Anomaly Detection for Vehicular Ad Hoc Networks

The effectiveness of data for safe driving in the Vehicular Ad Hoc Network is the basis to improve vehicle safety. Unlike traditional information systems, data anomaly analysis of vehicle safety driving faces the diversity of data anomalies(associated with virus invasion, failed sensors, and noise jamming plague), and the randomness and subjectivity of the driving behavior. How to combine the characteristics of the vehicular network data and the sentiment analysis of the driver to provide effective real-time data anomaly detection has become an important issue in the application of the vehicular network. Aiming at the cooperative vehicular networks, this paper adopts a method based on driver's emotional state, and proposes Anomaly Detection Based on Driver's Emotional State (EAD) algorithm to realize real-time detection of data related to safe driving. Firstly, this paper defines the emotion recognition coefficient R_de and designs a driver's emotion quantification model to realize the characterization of the driver's driving style. Secondly, based on the emotion quantization model and vehicle driving state information, the data anomaly detection algorithm is designed based on Gaussian mixed model(GMM). Finally, combined with the application scenarios of cooperative vehicular networks, we conduct extensive experiments on real data set(NGSIM) to prove the EAD algorithm has good performance.
Geometric Estimation of Specificity within Embedding Spaces

Specificity is the level of detail at which a given term is represented. Existing approaches to estimating term specificity are primarily dependent on corpus-level frequency statistics. In this work, we explore how neural embeddings can be used to define corpus-independent specificity metrics. Particularly, we propose to measure term specificity based on the distribution of terms in the neighborhood of the given term in the embedding space. The intuition is that a term that is surrounded by other terms in the embedding space is more likely to be specific while a term surrounded by less closely related terms is more likely to be generic. On this basis, we leverage geometric properties between embedded terms to define three groups of metrics: (1) neighborhood-based, (2) graph-based and (3) cluster-based metrics. Moreover, we employ learning-to-rank techniques to estimate term specificity in a supervised approach by employing the three proposed groups of metrics. We curate and publicly share a test collection of term specificity measurements defined based on Wikipedia's category hierarchy. We report on our experiments through metric performance comparison, ablation study and comparison against the state-of-the-art baselines.
Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques

Knowledge Graphs (KGs) contain large amounts of structured information. Due to their inherent incompleteness, a process known as KG completion is often carried out to find the missing triples in a KG, usually by training a fact checking model that is able to discern between correct and incorrect knowledge. After the fact checking model has been trained and evaluated, it has to be applied to a set of candidate triples, and those that are considered correct are added to the KG as new knowledge. However, this process needs a set of candidate triples of a reasonable size that represents possible new knowledge, in order to be evaluated by the fact checking task and, if considered to be correct, added to the KG, enriching it. Current approaches for selecting candidate triples for their correctness checking either use the full set possible missing candidate triples (and thus provide no filtering) or apply very basic rules to filter out unlikely candidates, which may have a negative effect on the completion performance as very few candidate triples are filtered out. In this paper we present CHAI, a method for producing more complex rules that are able to filter candidate triples by combining a set of criteria to optimize a fitness function. Our experiments show that CHAI is able to generate rules that, when applied, yield smaller candidate sets than similar proposals while still including promising candidate triples.
On black-box optimization in divide-and-conquer SAT solving

Solving hard instances of the Boolean satisfiability problem (SAT) in practice is an interestingly nontrivial area. The heuristic nature of SAT solvers makes it impossible to know in advance how long it will take to solve any particular SAT instance. One way of coping with this disadvantage is the Divide-and-Conquer approach when an original SAT instance is decomposed into a set of simpler subproblems. However, the way it is decomposed plays a crucial role in the resulting effectiveness of solving. In the present study, we reduce the problem of choosing a proper decomposition to a stochastic pseudo-Boolean black-box optimization problem. Several optimization algorithms of different types were used to analyse a number of hard SAT-based optimization problems, related to SAT-based cryptanalysis of state-of-the-art stream ciphers. A meticulous computational study showed that some of the considered optimization algorithms perform much better than the others in the context of the problems from the considered class. It turned out that the obtained results also pose some cryptographic interest.
Knowledge Transfer based on Multiple Manifolds Assumption

Unsupervised domain adaptation is a popular but challenging problem setting. Existing unsupervised domain adaptation methods are based on the single manifold assumption, i.e., data are sampled from a single low-dimensional manifold, and thus may not well capture the complex characteristic of the real-world data. In this paper, we propose to transfer knowledge across domains under the multiple manifolds assumption that assumes the data are sampled from multiple low-dimensional manifolds. Specifically, we develop a multiple manifolds information transfer framework (MMIT). The proposed MMIT aims to transfer the multiple manifolds information, which is represented by the data manifold neighborhood structure, with the the best adaptation capacity. To do so, we propose to couple the multiple manifolds information transfer with the domain distribution discrepancy minimization in the adaptation procedure. Experimental studies demonstrate that MMIT achieves the promising adaptation performance on various real-world adaptation tasks.