Reviewerly/exHarmony huggingface dataset:
The Simulated User Impairment Testing (SUIT) Protocol and Toolbox for Digital Artifacts

User testing involving people with varying abilities and impairments are two of the key ingredients of universal design that can help to increase the accessibility and usability of an ICT artifact. However, recruiting adequate participants during the design and development process can be difficult, time-consuming and resource intensive. We suggest mitigating these challenges by employing user simulation as additional quality assessment measure throughout the design and development process of a digital ICT artifact. In the present paper, we present a protocol and a toolbox for Simulated User Impairment Testing (SUIT) that enables non-impaired designers and developers to simulate the perception of people with some of the most common impairments related to cognitive, visual, auditory, vocal, mobility-related and motor-related functioning. We compile a list of possible tools and methods that are readily available or easily obtainable in a production and design setting. Moreover, we complement this list with relevant user examples for each category, as well as examples of the most common barriers. Finally, we point out how the Simulated User Impairment Testing (SUIT) toolbox can be used to add an additional layer of quality assessment during the design and development process of a digital artifact and argue that it cannot replace the user testing part.
Bounded Asymmetric Gaussian Mixture-Based Hidden Markov Models

Hidden Markov models (HMMs) have been widely applied in machine learning to model diversified and heterogeneous time series data. In this chapter, integration of the bounded asymmetric Gaussian mixture model (BAGMM) into the framework of HMM is proposed, which uses BAGMM to model the emission probabilities, which are also known as observation probabilities. The transition probabilities of HMM must be discrete, but emission probabilities can be modeled by any continuous distribution. This modified HMM (BAGMM-HMM) is motivated by the proven capability of BAGMM and its benefits over Gaussian mixture models (GMMs). The complete inference and parameter learning of BAGMM-HMM are proposed. The validation involves several real-world applications, such as occupancy estimation and human activity recognition. The proposed model is compared with three existing mixture model-based HMMs in a similar setting for all the applications. It is examined from all experiments and results that BAGMM-HMM has good modeling capabilities and outperforms other comparable mixture-based HMMs.
Robust Blind Image Watermarking Using Coefficient Differences of Medium Frequency between Inter-Blocks

The existing discrete cosine transform (DCT) differential quantization robust watermarking has poor robustness against JPEG compression, crop, and combined attacks. Aiming at such issue, a pair of adjacent block coefficients are selected to reduce the offset and improve the robustness of the watermarking. Firstly, at adjacent positions of neighboring blocks, the differences of medium frequency coefficients are calculated, then the differences are used to divide regions. Experimental results show that this method is more robust to various attacks than the existing DCT differential quantization robust watermarking. JPEG compression attack increased by 2%, cropping attack increased by 4.4%, and combined attack increased by 9%.
Mining Mixed Data Bases Using Machine Learning Algorithms

We discuss numerical algorithms (based on metric spaces) to explore data bases (DB) even though such DBs may not be metric. The following issues are treated: 1. Determination of the minimum equivalent sample, 2. The encoding of categorical variables and 3. Data analysis. We illustrate the methodology with an experimental mixed DB consisting of 29,267 tuples; 15 variables of which 9 are categorical and 6 are numerical. Firstly, we show that information preservation is possible with a (possibly) much smaller sample. Secondly, we approximate the best possible encoding of the 9 categorical variables applying a statistical algorithm which extracts the code after testing an appropriate number of alternatives for each instance of the variables. To do this we solve two technical issues, namely: a) How to determine that the attributes are already normal and b) How to find the best regressive function of an encoded attribute as a function of another. Thirdly, with the transformed DB (now purely numerical) it is possible to find the regressive approximation errors of any attribute relative to another. Hence, we find those attributes which are closer to one another within a predefined threshold (85%). We argue that such variables define a cluster. Now we may use algorithms such as Multi-Layer Perceptron Networks (MLPN) and/or Kohonen maps (SOM). The classification of a target attribute “salary” is achieved with a MLPN. It is shown to yield better results than most traditional conceptual analysis algorithms. Later, by training a SOM for the inferred clusters, we disclose the characteristics of every cluster. Finally, we restore the DB original values and get visual representations of the variables in each cluster, thus ending the mining process.
Development of Shape Feature Analysis Algorithms for Hangeul Font Recommendation

In recent years, new fonts have been developed with widely varying designs. The number of fonts with many design elements, including standardized fonts, is increasing. It is very important to apply fonts effectively according circumstance because fonts play a large role as a medium that can effectively convey information through text. However, there are difficulties in choosing an appropriate font owing to the diversity of media in everyday life. Thus, a system that recommends fonts based on various purposes is required to solve this problem. The development of such a system requires a recommendation system for similar fonts based on shape feature analysis. In this study, therefore, we define shape properties in the contour data of fonts, and developed algorithms to measure them automatically. In the future, they can be used in a system designed to recommend similar fonts according to their measured property values. To the best of our knowledge, the present work is the first to analyzed and numerically represented the shapes of Hangul fonts, and various font types can be defined and classified numerically based on the results presented. This study is expected to allow font users to choose a font that has appropriate shapes for a given design layout by using the formative shape properties to emphasize the meaning to be delivered, thus improving communication efficiency.
Region Contrastive Camera Localization

Visual camera localization is a well-studied computer vision problem and has many applications. Recently, deep convolutional neural networks have begun to be utilized to solve six-degree-of-freedom (6-DoF) camera pose estimation via scene coordinate regression from a single RGB and it outperforms the traditional methods. However, most existing works do not consider scene variations such as viewpoint, light, scale, etc. In this work, we propose a region contrastive representation learning approach for camera localization. The proposed approach maps image features from different camera views of the same 3D region to a similar point in the learned feature space. In contrast with that, it pushes visual features of other regions to distant points. Our method improves the existing camera localization methods and achieves state-of-the-art results on indoor 7-Scenes and outdoor Cambridge Landmarks datasets. Moreover, to demonstrate the efficiency of the proposed approach, we perform an ablation study on different settings with and without region segmentation and contrastive representation learning on a baseline regression network.
A Novel Approach for Cladding Identification and Reconstruction of Remanufacturing Blade Based on Point Cloud Curvature Image

A novel approach to cladding identification and model reconstruction based on point cloud curvature image is proposed for intelligent and precise machining of remanufacturing blades. The transformation and region matching of unorganized location point cloud to curvature image is accomplished by Triangulation-based 3D linear interpolation and projected boundary polygons. Compared with the artificially extracted cladding boundary, the average deviation of identified cladding boundary is 0.0686 mm and the variance is less than 0.01. In addition, the normal angle weights adjusted by the bilateral filtering redirection algorithm are used to extend and equalize the curvature, which can excellently enrich the texture features and connect the local interrupted boundaries of the curvature image. With the combination of adjusted curvature and the multi-directional projection method of the normal sub-region, the cladding region of the complex full-profile blade is precisely segmented. Moreover, the Biharmonic spline surface is interpolated according to the dense point cloud of the unworn region, and the average deviation of the reconstructed model is 0.0098 mm compared to the theoretical numerical model. Furthermore, the identification and reconstruction methods are applied to guide the abrasive belt grinding and polishing experiments of remanufacturing blade cladding. Average deviations of the machined cladding surface and cladding boundary are 0.0128 mm and 0.0088 mm, respectively. The above results fully demonstrate that the presented methods can ensure the integrity of the machined surface and effectively avoid over-grinding.
Increasing Trust and Fairness in Machine Learning Applications within the Mortgage Industry

The integration of machine learning in applications provides opportunities for increased efficiency in many organisations. However, the deployment of such systems is often hampered by the lack of insight into how their decisions are reached, resulting in concerns about trust and fairness. In this article, we investigate to what extent the addition of explainable AI components to ML applications can contribute to alleviating these issues. As part of this research, explainable AI functionality was developed for an existing ML model used for mortgage fraud detection at a large international financial institution based in the Netherlands. A system implementing local explanation techniques was deployed to support the day-to-day work of fraud detection experts working with the model. In addition, a second system implementing global explanation techniques was developed to support the model management processes involving data-scientists, legal experts and compliance officers. A controlled experiment using actual mortgage applications was carried out to measure the effectiveness of these two systems, using both quantitative and qualitative assessment methods. Our resultsÂ show that the addition of explainable AI functionality results in a statistically significant improvement in the levels of trust and usability by its daily users. The explainable AI system implementing global interpretability was found to considerably increase confidence in the ability to perform the processes focused on compliance and fairness. In particular, bias detection towards demographic groups successfully aided in the identification and removal of bias towards applicants with a migration background.
Investigating the FAIRness of Science and Technology Open Data: A Focus in the Scandinavian Countries

Providing researchers and other users access to data can accelerate knowledge discovery and enhance research transparency and reliability. In this context, the FAIR vision was formulated with the goal to optimize data sharing and reuse by humans and machines. In this paper, we investigate Scandinavian open data portals using FAIR data principles. We review and analyze the current state of datasets categorized as "Science and Technology" since in our view, such data is particularly relevant for reusability. Additionally, this study aims to further highlight and understand any specific challenges related to suitability of scientific data according to FAIR principles. Our findings highlight that, while the possibilities of open data from the science and technology domain are very promising, there are still a plethora of challenges we've discovered, and that need to be tackled in order to truly leverage the benefits.
Social Intelligence Design for Social Computing

Social intelligence Design (SID) is about the impact and significance of technology in our lives, work, home, and on the move. Social Intelligence is defined as the ability of people to relate to, understand and interact effectively with others. The central question is how it can be empowered using emerging technologies. In the information society, new technologies have huge impact on the way people work, interact, and collaborate. They influence the ways they develop personal relationships, as well as enhancing interpersonal communication and professional performance. At the same time, these technologies might amplify miscommunication and bring about new threats and fears. A notorious example is a flaming war, a barrage of postings containing abusive personal attacks, insulting, or chastising replies to other people, which has not been so disastrous before the networked society. Moreover, they provide effective channels for spreading misinformation, catfishing or grooming potential victims, thus amplifying the damaging effects of social misdemeanours. In this paper we address the issues of both beneficial and damaging impacts of emerging technologies on social intelligence and suggest ways of addressing them in the context of social intelligence design (SID). SID is focused not only on the technology design but also on the cognitive, social and organizational context of its use. In this paper we take a holistic approach to emerging technologies inspired by Artificial Intelligence research, bearing in mind their real-life significance [1].
Practical Multipartite Entanglement Distribution in Noisy Channels

Entanglement among multiple parties is vital for practical quantum information applications. Astwo kinds of typical multipartite entangled state, the W and Greenberger-Horne-Zeilinger (GHZ)states have attracted a lot of attention. In this paper, we propose two practical linear optical schemes for multipartite entanglement distribution in noisy channels. Using the time-bin or spatial-mode degree of freedom (DoF) encoding, the influence of the noise will be eliminated and perfect W or GHZ state can be achieved among different nodes in a deterministic way. Moreover, we construct quantum circuits, which can be implemented with current technology. These results contribute to long-distance quantum communication both in theoretical and experimental points of view.
Rank-Based Filter Pruning for Real-Time Deep Uav Trackers

UAV tracking is an emerging task and has wide potential applications in such as agriculture, navigation, entertainment and public security. However, the limitations of computing resources, battery capacity, and maximum load of UAV hinder the deployment of DL-based tracking algorithms on UAV. In contrast to deep learning trackers, discriminative correlation filters (DCF)-based trackers stand out in the UAV tracking community because of their high efficiency. However, their precision is usually much lower than trackers based on deep learning. Model compression is a promising way to narrow the gap (i.e., effciency, precision) between DCF- and deep learning- based trackers, which has not caught much attention in UAV tracking community. In this paper, We propose the P-SiamFC++ tracker, which is the first to use rank-based filter pruning to compress the SiamFC++ model, achieving a remarkable balance between efficiency and precision. Our method is general and may encourage further studies on UAV tracking with model compression in the future. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and Vistrone2018, show that P-SiamFC++ tracker significantly outperforms state-of-the-art UAV tracking methods.
An Approach for the Robust Machine Learning Explanation Based on Imprecise Statistical Models

An approach for a robust modification of some explanation methods by using imprecise statistical models is proposed. The imprecise models produce sets of probability distributions such that every distribution from the sets can be a candidate for constructing a robust explanation model. Two types of explanation models will be considered and modified. The first type is based on modification of the factual local explanation method like LIME where the linear approximation is implemented to explain the black-box model. This type of models aims to search for important features which impact on the prediction of an example of interest. The second type uses counterfactual explanation which is extended to be robust. We assume that outcomes of the black-box model compose a set of values, for example, the class probabilities as a prediction vector of an input example. An example of the imprecise contaminated model is given. It is shown how the maximin optimization problem implementing the explanation method is reduced to the standard linear or quadratic optimization problems.
Attracting Future Students’ Attention by an UX-Optimized Website

As a university it is more and more difficult to reach all target groups equally. Common problems like information overload, numerous institutions with same focuses or multi-channel-communication make it hard to gain the attention of the target group. This paper is four-fold: we present an overview of the state of art and the importance of the study (I), based on which we highlight the approach to user experience analysis. First, we identified the irritations in the course of an expert evaluation (II) and verified them within the test, including the target groups (III). Finally, based on the results, we were able to pro-vide recommendations for action to improve the UX and to be used for the conception of an intranet (IV).
Tell Me What You Like: Introducing Natural Language Preference Elicitation Strategies in a Virtual Assistant for the Movie Domain

Preference elicitation is a crucial step for every recommendation algorithm. Traditional interaction strategies for eliciting users' interests and needs range from button-based interfaces, where users have to select what they like among a set of fixed alternatives, to more recent conversational interfaces, where users have to reply to some questions formulated by the algorithm about their preferences. However, none of these strategies either mimics the dynamics of real-world open-ended interactions, or allows users to express their needs with an adequate level of expressiveness and control.In this paper, we present a strategy to introduce natural language preference elicitation in a virtual assistant for the movie domain. Our approach allows users to express preferences on objective movie features( e.g., actors, directors, etc.) that are extracted from a structured knowledge base, as well as on subjective features that are collected by mining user-written movie reviews. All the interaction happens in an open-ended fashion, which is completely guided by the user.In the experimental evaluation, we carried out a user study in the movie domain ($N=249$). Results show that users experience some difficulties in expressing their preferences in terms of subjective features. However, the combination of objective and subjective features often leads to better recommendations, at the cost of a slightly longer conversation. We have also identified the main challenges that arise when users talk to the virtual assistant by using subjective features, and this paves the way for future developments of our methodology
Class-Level Alignment Domain Adaptation Based on Optimal Transport

Domain adaptation is one of the most challenging tasks of modern data analysis. Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while labeled data are only available in the source domain. Previous researches minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To address this issue, this paper propose the model named Class-level Alignment Network (CLAN) based on optimal transport. We design a new updating strategy for training CLAN in an end-to-end manner. Cross-domain recognition experiments on five domain adaptation datasets demonstrate the validity of the proposed model. Compared with state-of-the-art domain adaptation methods, the proposed model achieved highlight performance.
An Exploration of Semi-supervised Text Classification

Good performance in supervised text classification is usually obtained with the use of large amounts of labeled training data. However, obtaining labeled data is often expensive and time-consuming. To overcome these limitations, researchers have developed Semi-Supervised learning (SSL) algorithms exploiting the use of unlabeled data, which are generally easy and free to access. With SSL, unlabeled and labeled data are combined to outperform Supervised-Learning algorithms. However, setting up SSL neural networks for text classification is cumbersome and frequently based on a trial and error process. We show that the hyperparameter configuration significantly impacts SSL performance, and the learning rate is the most influential parameter. Additionally, increasing model size also improves SSL performance, particularly when less pre-processing data are available. Interestingly, as opposed to feed-forward models, recurrent models generally reach a performance threshold as pre-processing data size increases. This article expands the knowledge on hyperparameters and model size in relation to SSL application in text classification. This work supports the use of SSL work in future NLP projects by optimizing model design and potentially lowering training time, particularly if time-restricted.
Is your Machine Better than You? You May Never Know

Artificial intelligence systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet, recent studies suggest that professionals sometimes doubt the quality of these systems and overrule machine-based prescriptions. This paper explores the extent to which a decision maker (DM) supervising a machine to make high-stake decisions can properly assess whether the machine produces better recommendations. To that end, we study a set-up, in which a machine performs repeated decision tasks (e.g., whether to perform a biopsy) under the DM’s supervision. Because stakes are high, the DM primarily focuses on making the best choice for the task at hand. Nonetheless, as the DM observes the correctness of the machine’s prescriptions across tasks, she updates her belief about the machine. However, the DM observes the machine’s correctness only if she ultimately decides to act on the task. Further, the DM sometimes overrides the machine depending on her belief, which affects learning. In this set-up, we characterize the evolution of the DM’s belief and overruling decisions over time. We identify situations under which the DM hesitates forever whether the machine is better, i.e., she never fully ignores but regularly overrules it. Moreover, the DM sometimes wrongly believes with positive probability that the machine is better. We fully characterize the conditions under which these learning failures occur and explore how mistrusting the machine affects them. Our results highlight some fundamental limitations in determining whether machines make better decisions than experts and provide a novel explanation for human-machine complementarity.