Reviewerly/exHarmony huggingface dataset:
Information Literacy in Place-Based Interdisciplinary Teaching and Learning

This chapter explores the role of information literacy in virtual or hybrid place-based interdisciplinary courses. Whether teaching as a guest lecturer or as a co-instructor, I infuse information literacy competencies into assignments, relying on the Framework for Information Literacy for Higher Education. Four of the six frames of the Framework map especially well to interdisciplinary teaching and learning: information has value, authority is contested and contextual, research as inquiry, and searching as strategic exploration. Through searching in special collections and archives and integrating digitized primary sources into research projects students engage in a virtual exploration of place, becoming familiar with it through digitized primary sources. At the same time, the interdisciplinary approach helps students gain a critical perspective on information production and preservation.
Use a UAV System to Enhance Port Security in Unconstrained Environment

Ensuring maritime port security—a rapidly increasing concern in a post-9/11 world—presents certain operational challenges. As batteries and electric motors grow increasingly lighter and more powerful, unmanned aerial vehicles (UAVs) have been shown to be capable of enhancing a surveillance system’s capabilities and mitigating its vulnerabilities. In this paper, we looked at the current role unmanned systems are playing in port security and proposed an image-based method to enhance port security. The proposed method uses UAV real-time videos to detect and identify humans via human body detection and facial recognition. Experiments evaluated the system in real-time under differing environmental, daylight, and weather conditions. Three parameters were used to test feasibility: distance, height and angle. The findings suggest UAVs as an affordable, effective tool that may greatly enhance port safety and security.
Hierarchical Relevance Determination based on Information Criterion Minimization

Abstract This paper addresses the issue of hierarchical relevance determination (HRD), which boils down to determining all degrees of freedom in a supervised mixture distribution automatically. Such relevance determination is useful for wide range of machine learning applications. However, it is difficult to solve the HRD task because its objective function includes $$L_{0}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math> norm such as the number of models in the mixture and the optimal features to use. Our contribution is twofold. As the main contribution, we formally defined the HRD problem and subsequently proposed a solution strategy, the SICM (sequential in formation criterion minimization) algorithm. The SICM algorithm enables us to continuously minimize an information criterion such as AIC or BIC, both of which includes the number of parameters, and therefore it enables us to determine all the degrees of freedom automatically. As another contribution, we realized a concrete implementation of the ideas, and tested this on actual data. Experiments using a hierarchical model which is constructed using the SICM algorithm have revealed that SICM has capability of constructing interpretable and highly accurate model.
Impossible Differential Analysis on 8-Round PRINCE

PRINCE is a lightweight block cipher, which was proposed by Borghoff et al. in Asiacrypt 2012. Various cryptanalytic techniques have been employed to evaluate the security of PRINCE. In 2017, Ding et al. constructed a 4-round impossible differential based on some observations on M′ operation and launched impossible differential attacks on 6- and 7-round PRINCE and the underlying PRINCEcore. In this paper, we explore the differential distribution table (DDT) of the S-box employed in PRINCE and construct a more detailed DDT which contains the input/output values corresponding to each differential. Taking advantage of the table, we compute the subkeys instead of guessing them. With this technique, we extend the impossible differential attacks of PRINCE and PRINCEcore to eight rounds. The corresponding computational and complexities are \( 2^{110.7} \) and \( 2^{62.26} \) encryptions, respectively, which are much less than exhaustive search. And the data complexities are \( 2^{64} \) and \( 2^{60} \) chosen plaintexts, respectively.
Research on Gaussian Plume Model of Gas Diffusion in Coal Mine Roadway Based on BP Neural Network Optimized by Genetic Algorithm

Abstract Intelligent monitoring and control of harmful gas in a coal mine is an important aspect of the building on intelligent and green mine. It is a hot spot of artificial intelligence research in the coal mine to study the new technology of dynamic prediction of harmful gas by using artificial intelligence, big data and other interdisciplinary fusion methods, and gradually replace the artificial monitoring work. By researching the gas diffusion theory and the characteristics of the coal mine environment, this paper uses the Gaussian plume model to simulate the gas diffusion law of coal mine roadway, and puts forward a gas prediction model optimized by genetic algorithm and BP neural network. The algorithm takes the absolute error between the predicted output of gas source term and the field measured value as the peculiar fitness value and optimizes it. The results show that the optimized BP neural network can obtain the best concentration prediction value for short time and a small error. Compared with the traditional gas prediction method in coal mine, this method has the advantages of small calculation amount and high prediction accuracy, and it can improve the reliability of the model by continuous machine learning combined with daily monitoring, which provides a new technical solution for the intelligent detection of environmental information in coal mine roadway.
Pervasive and perplexing pies

Abstract Although pie charts remain a highly popular genre for visualizing data, evident partly by their continued use in corporate annual reports, designers and theorists and have long critiqued and even denigrated them. This conflicted relationship with pie charts has resulted largely from their paucity of data, labeling difficulties, and perceptual weaknesses. Like many primary data design genres, however, pie charts attained their conventional status as a result of a long evolutionary process, initiated in the later nineteenth century by experimentation, hybrid forms, and data-rich designs. After the subsequent simplification and minimizing effects of twentieth-century modernism, pie charts have begun to experience a reemergence of innovation with digital data design, which has reintroduced hybridity into pie charts, vastly enlarged their data domain, and addressed at least some of their perceived weaknesses.
Creating with Constraints

Abstract Musicians have long framed their creative activity within constraints, whether imposed externally or consciously chosen. As noted by Leonard Meyer, any style can be viewed as an ensemble of constraints, requiring the features of the artwork to conform with accepted norms. Such received stylistic constraints may be complemented by additional, voluntary limitations: for example, using only a limited palette of pitches or sounds, setting rules to govern repetition or transformation, controlling the formal layout and proportions of the work, or limiting the variety of operations involved in its creation. This chapter proposes a fourfold classification of the limits most often encountered in music creation into material (absolute and relative), formal, style/genre, and process constraints. The role of constraints as a spur and guide to musical creativity is explored in the domains of composition, improvisation, performance, and even listening, with examples drawn from contemporary composers including György Ligeti, George Aperghis, and James Tenney. Such musical constraints are comparable to self-imposed limitations in other art forms, from film (the Dogme 95 Manifesto) and visual art (Robert Morris’s Blind Time Drawings) to the writings of authors associated with the Oulipo (Ouvroir de littérature potentielle) such as Georges Perec and Raymond Queneau.
Steganographic Information Hiding Method based on Continuous Wavelet Transform

This article considers continuous wavelet transforms as a method of steganographic embedding of confidential information in an image. The relevance of steganography is shown by the example of hiding information in a jpg file. The paper shows data hiding algorithm, the theoretical justification of the presented method, Matlab implementation of the presented algorithm and analysis of the results of the study.
The Development Dilemma and Countermeasures of Strong Artificial Intelligence in Meeting Human Emotional Needs

Artificial intelligence, as a new technological science involving cognitive level, is gradually entering our daily life, and it is developing faster than imagined. In the long process of human evolution, society has long been deeply imprinted in genes as a natural attribute. But people’s socialization with machines has just begun, and they are full of expectations for communication with AI and even emotional dialogue. However, when the artificial intelligence robot without life-span concept appears in our life, the AI with simulation emotion and excellent ability also brings the ethical problems of life and death concept, controllability, emotionalization, social concept, specificity, authenticity and man-machine boundary. Just as Asimov’s three laws of robots, human society should guide and restrict the development direction of artificial intelligence according to the applicable objects, and avoid possible out of control situations, which requires some relatively specific guiding principles to lead. We hope to see the glory of technology and wisdom embodied by AI, but at the same time, we should be more alert to the huge dilemma of AI development brought by robots once they enter the reverse lane of human emotional needs.
Adaptive density based data mining technique for detection of abnormalities in traffic video surveillance

Detection of abnormal events in a traffic scene is a highly challenging task due to vast field of view, continuous stream of video data, various object interactions and complex events in Video Surveillance. Hence, this research proposes novel schemes using machine learning approach to detect abnormal events such as illegal U-turn, presence of pedestrian in driving region, wrong side driving and frequent lane change. Recently, Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular method that has been used for clustering the trajectory datasets. The existing Density Based Clustering approach used for Abnormal detection in traffic scene uses random selection of cluster radius (Eps) and minimum points (minpts) needed to form a cluster. This random selection is time consuming and inefficient clustering results in accuracy reduction in abnormal detection. So, Adaptive Density based Spatial Clustering of Applications with Noise (ADBSCAN) is proposed for the detection of abnormal events based on spatial temporal information relating to individual objects which determines the optimal values for the cluster radius (Eps) using the slope calculation of the K-d plot. Gaussion Mixture Model (GMM) is used for obtaining the moving foreground regions and region-based tracking is used for the identification of the objects in successive frames. The centroid of the region is calculated using image moments. If there is an occlusion between the vehicles then vehicle identification number (Id no) is used to differentiate them. The main advantage in this technique is clustering/labelling the normal pattern without the help of manual intervention. The effectiveness of ADBSCAN is experimentally evaluated using a real time benchmark video traffic dataset and it found that it gives better accuracy in detecting anomalies than the state-of-the-art techniques.
Enabling Medical Research Through Privacy-Preserving Data Markets

A great deal of recent advances in medical research are leveraging machine learning and artificial intelligence techniques. A major impediment to these advances is the reluctance of patients to share personal health data due to privacy concerns and lack of trust. In this paper, we propose a new privacy-preserving data-market platform to tackle this problem. Our novel crowd sourcing platform uses zero-knowledge proofs and blockchains to verify the validity of patients’ contributions, while providing strong privacy guarantees for these contributions.
Unsupervised Keyphrase Extraction from Scientific Publications

We propose a novel unsupervised keyphrase extraction approach that filters candidate keywords using outlier detection. It starts by training word embeddings on the target document to capture semantic regularities among the words. It then uses the minimum covariance determinant estimator to model the distribution of non-keyphrase word vectors, under the assumption that these vectors come from the same distribution, indicative of their irrelevance to the semantics expressed by the dimensions of the learned vector representation. Candidate keyphrases only consist of words that are detected as outliers of this dominant distribution. Empirical results show that our approach outperforms state-of-the-art and recent unsupervised keyphrase extraction methods.
Automated Simple Analysis Attack

In this paper we discuss simple side channel analysis attack against our Montgomery <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$kP$</tex> implementation. As long as the attempt to extract the key is done by a visual inspection of the recorded traces the attack is not successful, but when we automated the search for differences in the shape of the traces it becomes successful. In addition we show that the attack becomes even easier when the design works at higher frequencies, using state of the art FPGAs.
Single-Station Passive Tracking Algorithm for Joint Azimuth and Doppler Frequency

Given that Bearing-Only the location algorithm is time-consuming, not strictly accurate and can-not work in real-time when facing targets at a constant speed along a straight line in two dimension. So this paper proposes a combined radiation target Doppler frequency information and adopts the square root central difference Kalman Filter (SRCDKF) algorithm in a bid to achieve effective tracking and positioning of the system. The simulation results show that the proposed algorithm in the system model solves the problem that extended Kalman filter (EKF) and Particle Filter (PF) algorithms can-not converge and performs well in filtering accuracy, convergence, and stability.
Application of fuzzy logic methods for controlling buildings HVAC equipment

Abstract This article represents an application of fuzzy logic methods for control purposes of heating, ventilating and air conditioning (HVAC) of modern buildings. Analysis of the main parameters of space comfort leads to the justification of fuzzy logic methods and definition of research goals. This research suggested a composition of linguistic variables and knowledge database for a virtual room air conditioning model which was simulated in MATLAB (Simulink) environment. The analysis of the modelling results proved that fuzzy methods demonstrate acceptable control capabilities and can be successfully implemented in practice. The work has been carried out within the course of studying the prospects of development of building management and automation (BMS) in the future.
Emotions in Online Gambling Communities: A Multilevel Sentiment Analysis

In this study, we analyzed whether interaction dynamics are related to emotional expressions within online gambling communities. As data, we used 8452 comments posted on Reddit gambling communities. The data were analyzed with sentiment analysis tool VADER and multilevel regression analysis. Results showed that comments were more positive when they were directed to other users and made by users with more interactive commenting behavior. Comments were less positive in those discussions that were most active and in those that mainly involved replies to other users. We also found that more positive posts received more positive commenting and negative posts received more negative comments. Overall, the activity and interactivity of communication and emotional correlation are associated with positive emotional expression in online communication. For negative emotions, we found evidence only for emotional correlation. Future studies should explore how interaction dynamics together with more contextual factors shape emotional expressions within online communities.
Compound or phrase or in between? Testing linguistic criteria for compoundhood in English

In this paper, we present an empirical study on the definition of compounds in English, the graded nature of the phenomenon and its correlations with the commonly used linguistic criteria for compoundhood. We create a resource that includes a diverse set of nominal compounds identified by two trained independent annotators in sentences from the proceedings of the European Parliament. In addition, the annotators provide ratings on the compoundhood of the identified compounds, and ratings for the applicability of six prominent linguistic criteria of compoundhood for each item. We show the controversy of defining compounds in practice by comparing the annotations of two annotators, and the graded nature of compoundhood. By measuring the correlation between compoundhood and the six diverse linguistic criteria using machine learning techniques, we show that some linguistic criteria are stronger predictors of compoundhood than others.
An Evaluation Environment for User Studies in the Public Transport Domain

User studies to evaluate public transport systems are often hard to set up. While field tests provide important insight into real-world usability of public transport systems, they are also complex and expensive. Especially in early development stages of public transport related systems, field tests are not appropriate. However, usability of public transport systems is often depending on "real-life" context factors that are hard to reproduce in lab-based user studies. We have developed a mockup of a tram or train compartment that can be flexibly used to create a public transport experience in user studies. In this paper we will describe our experiences and recurring challenges with user studies in public transport, the design and set-up of our mockup, as well as give an insight into its applications in studies we conducted and lessons we learned.
Managing Information in the Case of Opinion Spamming

The rapid innovation of information and communication technology (ICT) within the last decades has come along with the development of new opportunities and challenges, new business models, new systems, etc. Companies like Amazon, Google or Alibaba shape business functions and percolate our daily life. Business operations go hand in hand with new types of user experiences when buying goes different than just traditional ways. Often it is not only the buying behavior but also the way recommendations are perceived as they may be readily available. This relates to opinion mining and, unfortunately, opinion spamming. We survey the issue of opinion spamming and fake review detection and focus on both sides of fake review groups, i.e., how to detect such groups but also how to set up a group that might be undetected with current methods. As both directions go hand in hand we elaborate on learning in both directions with the aim to improve related methods.
Read Ahoy

A digital/physical installation part a series of pilots developed for Habo Municipality, Sweden, in the context of a public co-design effort aimed at creating a shared understanding of the possibilities offered by digital transformation and the development of a connected city framework, “Read Ahoy!” provides children with a simple game-like challenge: find books randomly distributed in a number of locations by matching conceptual, spatial, aural, and verbal clues. Built as an embodied experience for library spaces, “Read Ahoy!” is narratively centered on a Viking crew in need of help after they have lost much of their precious cargo of books in a storm, on their way back after a trade expedition. The story grounds the challenge in tropes familiar to Swedish culture and gives children a playful setup and well-defined goals as they search for books. “Read Ahoy!” explores how children entering the school system search and make sense of information in a blended space, structurally recreating the way they customarily mix action in digital and physical space. Theoretically anchored in Benyon’s conceptualization of blended spaces, in Bates’ information seeking theory and information search tactics, and in Resmini and Lacerda’s formalization of information-based experience ecosystems, “Read Ahoy!” was designed and implemented as a low-budget end-of-year project for the students in the Master’s in Information Architecture and Innovation at Jönköping International Business School, Jönköping, Sweden, under the supervision of the authors. It was framed to meet the UN SDG4’s sub-targets on “Early childhood development” and “Universal Youth Literacy” and installed in Habo Library from June through August 2019 where it was used extensively by local children under the supervision of librarians during the summer. A full description of the installation and preliminary post-mortem reflections are offered in the paper.
The uncertainty and explainability in object recognition

In the object recognition task, due to changes in size, colour, illumination, position, viewing angle, and environmental background, great uncertainty is caused. The invariant recognition capability required to adapt to such diverse uncertainties has also become one of the most challenging goals of artificial intelligence, as only biometric systems can do this. The approach to dealing with uncertainty in the field of pattern recognition is nothing more than training with a large number of samples and powerful machine learning algorithms, and finally generating a classifier. Among them, knowledge representation has not only been ignored, but has even been considered redundant. But research from cognitive psychology and experimental psychology suggests that humans do not use such a huge mechanism. In contrast, we use more similar methods of symbolic artificial intelligence, such as representation, induction, reasoning, interpretation, and constraint propagation, to deal with uncertainty in object recognition. In this paper, the skeleton tree is used as the basic means to form the formal representation of the topological features and geometric features of the object, and based on the generalisation framework, the knowledge extraction of a small number of similar representations is used to form a generalised explicit representation of the knowledge about the object category. This generalised form representation can cope with uncertainties such as size, colour, shape, etc., and is significantly superior to current mainstream machine learning methods in terms of explainability and computational cost.