Reviewerly/exHarmony huggingface dataset:
On the Optimization Models for Automatic Grouping of Industrial Products by Homogeneous Production Batches

We propose an optimization model of automatic grouping (clustering) based on the k-means model with the Mahalanobis distance measure. This model uses training (parameterization) procedure for the Mahalanobis distance measure by calculating the averaged estimation of the covariance matrix for a training sample. In this work, we investigate the application of the k-means algorithm for the problem of automatic grouping of devices, each of which is described by a large number of measured parameters, with various distance measures: Euclidean, Manhattan, Mahalanobis. If we have a sample with the composition known in advance, we use it as a training (parameterizing) sample from which we can calculate the averaged estimation of the covariance matrix of homogeneous production batches using the Mahalanobis distance. We propose a new clustering model based on the k-means algorithm with the Mahalanobis distance with the averaged (weighted average) estimation of the covariance matrix. We used various optimization models based on the k-means model in our computational experiments for the automatic grouping (clustering) of electronic radio components based on data from their non-destructive testing results. As a result, our new model of automatic grouping allows us to reach the highest accuracy by the Rand index.
Pedagogical Considerations for Mobile-Based Augmented Reality Learning Environments

As teaching methods become increasingly digitized, mobile augmented reality has taken a leading position in education due to its affordances, for instance the anywhere-anytime accessibility, the availability of augmented reality applications for students’ smartphones, and the effective and collaborative capabilities. Although there is a growing number of studies investigating the use of augmented reality in various subjects, a set of guidelines describing learning theories and pedagogical considerations regarding the development and use of mobile-based augmented reality environments is clearly needed. This paper first reviews empirical studies in the domain of learning with mobile-based augmented reality and then proposes situated cognition as a pedagogical framework for the design and use of this technology in educational contexts.
Decision Framework and Detailed Analysis on Privacy Preserving Smart Contract Frameworks for Enterprise Blockchain Applications

Blockchains are globally gaining traction and gradually disrupting the traditional transactional eco-systems by eliminating the non-value adding parties in the value chain. Although blockchains enables digital currency transactions, distributed consensus models and provenance, the problem of scalability, security and privacy has to be solved for the blockchains to be utilized in its full potential. Typically all the transactions recorded in blockchain are visible to all the participants. Even though some blockchain frameworks offers private transactions they still lack transactional privacy and confidentiality. Privacy preserving smart contracts is an emerging field which guarantees the privacy of transactions during runtime and ensures confidentiality as well. In this paper we analyze various frameworks and methodologies and propose a systematic way of choosing the right privacy preserving smart contract framework for enterprise needs and requirements.
A reformulated Shapley-like value for cooperative games with interval payoffs

In this paper, a new value for cooperative interval games is proposed which may remedy the disadvantages of the interval Shapley-like value and of the improved interval Shapley-like value introduced by Han et al. (2012). Moreover, it is shown that the reformulated interval value uniquely satisfies the properties of efficiency, indifference null player, symmetry, and additivity.
Behave or Be Detected! Identifying Outlier Sequences by Their Group Cohesion

Since the amount of sequentially recorded data is constantly increasing, the analysis of time series (TS), and especially the identification of anomalous points and subsequences, is nowadays an important field of research. Many approaches consider only a single TS, but in some cases multiple sequences need to be investigated. In 2019 we presented a new method to detect behavior-based outliers in TS which analyses relations of sequences to their peers. Therefore we clustered data points of TS per timestamp and calculated distances between the resulting clusters of different points in time. We realized this by evaluating the number of peers a TS is moving with. We defined a stability measure for time series and subsequences, which is used to detect the outliers. Originally we considered cluster splits but did not take merges into account. In this work we present two major modifications to our previous work, namely the introduction of the jaccard index as a distance measure for clusters and a weighting function, which enables behavior-based outlier detection in larger TS. We evaluate our modifications separately and in conjunction on two real and one artificial data set. The adjustments lead to well reasoned and sound results, which are robust regarding larger TS.
Research on infrared passive ranging algorithm based on unscented Kalman filter and modified spherical coordinates

Abstract Target range detection is one of the key technologies for intelligent unmanned vehicle to work reliably and efficiently. Infrared passive ranging can estimate the relative distance information of the target in real time by measuring the angle information with noise. It has many advantages such as low cost, good real-time performance and so on, which has a good application prospect in the range detection. In this paper, the mathematical model of infrared passive ranging of intelligent unmanned vehicle based on modified spherical coordinates (MSC) is established, and the algorithm flow based on unscented Kalman filter (UKF) is given. Finally, the simulation experiment and analysis are carried out. The results show that: in the problem of target range detection of intelligent unmanned vehicle, the method adopted has high ranging accuracy and good stability.
Artificial intelligence for pathology

Advances in artificial intelligence (AI), especially in deep learning, improve pathological image analysis in basic, translational, and clinical research and in routine clinical practice. Deep learning is currently the dominant technique among the best solutions for many tasks in digital pathology. This chapter provides a general overview of different applications of deep learning in pathological image analysis, such as image classification, object detection, image segmentation, stain normalization, and image superresolution. It summarizes deep learning achievements and identifies the contributions in specific tasks. In addition, it discusses open challenges and potential directions of deep learning-based pathological image computing and presents barriers to clinical adoption of AI in digital pathology.
A Statistical Approach for Context-Awareness of Mobile Applications

Context-aware systems are able to sense and adapt to the environment. Mobile applications can benefit from context-awareness since they incur to context changes during their execution. A detailed understanding of the context is needed to know what a context-aware system should sense and adapt to. This paper introduces a statistical approach that helps in determining contextual situations that require adaptation. The approach starts from monitoring mobile context variables values, modeling their states, and deducing from these models a Markov chain model, where each state represents a contextual situation. Depending on transition probabilities and system quality at each state we can decide when it is necessary to apply context-awareness.
Real-time Abnormal Behavior Recognition and Monitoring System Based on Panoramic Video

Traditional monitoring systems are mostly monocular cameras, the main function of which is to play and store video in the monitoring area and to achieve the collection and judgment of the information in the video through artificial observation. Usually, emergencies that are not found and processed in time will cause serious situations. Compared with the monocular camera, the panoramic camera has a wide field of view and no blind spots. This system uses a panoramic camera to achieve 360-degree video acquisition, and uses deep learning to process the video to obtain the posture of the human body, and then processes it through the human behavior recognition network to identify the abnormal human behavior in real time. Through the dual warnings of local and remote clients and the artificial re-judgment of the received abnormal behavior video, the manager can timely find and handle abnormal behavior. This system not only saves labor costs, but also improves the utilization efficiency of video. The 360-degree all-round perspective provides the area manager with an immersive environment reproduction, allowing him to grasp the details of the scene in real time. This system provides virtual on-site conditions for decision-making and handling of emergencies, enabling early warning and minimizing losses.
Topic Diffusion Discovery based on Deep Non-negative Autoencoder

Researchers have been overwhelmed by the explosion of research articles published by various research communities. Many research scholarly websites, search engines, and digital libraries have been created to help researchers identify potential research topics and keep up with recent progress on research of interests. However, it is still difficult for researchers to keep track of the research topic diffusion and evolution without spending a large amount of time reviewing numerous relevant and irrelevant articles. In this paper, we consider a novel topic diffusion discovery technique. Specifically, we propose using a Deep Non-negative Autoencoder with information divergence measurement that monitors evolutionary distance of the topic diffusion to understand how research topics change with time. The experimental results show that the proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.
Sorting an array using the topological sort of a corresponding comparison graph

The quest for efficient sorting is ongoing, and we will explore a graph-based stable sorting strategy, in particular employing comparison graphs. We use the topological sort to map the comparison graph to a linear domain, and we can manipulate our graph such that the resulting topological sort is the sorted array. By taking advantage of the many relations between Hamiltonian paths and topological sorts in comparison graphs, we design a Divide-and-Conquer algorithm that runs in the optimal O(nlog⁡n) time. In the process, we construct a new merge process for graphs with relevant invariant properties for our use. Furthermore, this method is more space-efficient than the famous MergeSort since we modify our fixed graph only.
Nonconvex Haar-TV denoising

The anisotropic total variation (TV) denoising model suppresses noise for two-dimensional signals that are vertically and horizontally piecewise constant. However, two-dimensional signals may have sparse derivatives in other directions. We propose a modification of the classical anisotropic two-dimensional TV regularizer from a spectral point of view. In the frequency domain, the TV regularizer can be considered as penalizing the high-frequency component of original signals and promoting only low-frequency components. The classical anisotropic TV, which applies l1-norm on vertical and horizontal differences, suppresses high-frequency components of the signals. The proposed operator, named Haar total variation (Haar-TV), penalizes two-dimensional signals that have more varied high-frequency regions. Furthermore, we propose non-convex penalties based on the Haar-TV operator since non-convex penalties can preserve edges and thus enhance the quality of the estimation. We derive a condition that preserves the strong convexity of the total cost function so the global minimizer can be reached.
What Have Google’s Random Quantum Circuit Simulation Experiments Demonstrated About Quantum Supremacy?

Quantum computing is of high interest because it promises to perform at least some kinds of computations much faster than classical computers. Arute et al. (Nature 574:505–511, 2019; Supplemental Information for Arute F et al. 2019a. File supp_info_41586_2019_1666_MOESM1_ESM.pdf. https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-019-1666-5/MediaObjects/41586_2019_1666_MOESM1_ESM.pdf. Accessed 6 February 2020, 2019) (informally, “the Google Quantum Team”) report the results of experiments that purport to demonstrate “quantum supremacy” – the claim that the performance of some quantum computers is better than that of classical computers on some problems. Do these results close the debate over quantum supremacy? We argue that they do not. In the following, we provide an overview of the Google Quantum Team’s experiments and then identify some open questions in the quest to demonstrate quantum supremacy.
Deep Learning-based Roman-Urdu to Urdu Transliteration

Attention-based encoder-decoder models have superseded conventional techniques due to their unmatched performance on many neural machine translation problems. Usually, the encoders and decoders are two recurrent neural networks where the decoder is directed to focus on relevant parts of the source language using attention mechanism. This data-driven approach leads to generic and scalable solutions with no reliance on manual hand-crafted features. To the best of our knowledge, none of the modern machine translation approaches has been applied to address the research problem of Urdu machine transliteration. Ours is the first attempt to apply the deep neural network-based encoder-decoder using attention mechanism to address the aforementioned problem using Roman-Urdu and Urdu parallel corpus. To this end, we present (i) the first ever Roman-Urdu to Urdu parallel corpus of 1.1 million sentences, (ii) three state of the art encoder-decoder models, and (iii) a detailed empirical analysis of these three models on the Roman-Urdu to Urdu parallel corpus. Overall, attention-based model gives state-of-the-art performance with the benchmark of 70 BLEU score. Our qualitative experimental evaluation shows that our models generate coherent transliterations which are grammatically and logically correct.
Personal laughter archives

We present our ongoing effort to capture, represent, and interact with the sounds of our loved ones' laughter in order to offer unique opportunities for us to celebrate the positive affect in our shared lived experiences. We present our informal evaluation of laughter visualizations and argue for applications in ubiquitous computing scenarios including Mobile Augmented Reality (MAR).
Open Government Data for Machine Learning Tax Recommendation

Taxpayers may be interested in overpayment and which group of taxpayers he or she belongs to. Government officials may be concerned with underpaying taxpayers for auditing purposes and group taxpayers in the rapidly changing society. Machine learning and data mining techniques have been applied to provide solutions to these taxation related queries. Classification algorithms allow predicting the tax bracket based on the taxpayers' attributes. The regression model allows to predict the tax estimate so that the overpayment or underpayment can be determined. Clustering algorithms group taxpayers so that they can be compared to the past year tax brackets. Finally, feature selection allows finding salient attributes to predict the tax and tax bracket. In this article, New York State's Open Tax Data is used to demonstrate the machine learning and data mining algorithms and identify issues of using them. Furthermore, various visualization techniques are to present the discovered information to both taxpayers and government officials.
How are Deep Learning Models Similar?

Deep learning (DL) has been successfully applied to many cutting-edge applications, e.g., image processing, speech recognition, and natural language processing. As more and more DL software is made open-sourced, publicly available, and organized in model repositories and stores (Model Zoo, ModelDepot), there comes a need to understand the relationships of these DL models regarding their maintenance and evolution tasks. Although clone analysis has been extensively studied for traditional software, up to the present, clone analysis has not been investigated for DL software. Since DL software adopts the data-driven development paradigm, it is still not clear whether and to what extent the clone analysis techniques of traditional software could be adapted to DL software.
Low-Rank Subspace Override for Unsupervised Domain Adaptation

Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification. Domain adaptation methods are used to improve these generalization properties. However, these techniques suffer either from being restricted to a particular task, such as visual adaptation, require a lot of computational time and data, which is not always guaranteed, have complex parameterization, or expensive optimization procedures. In this work, we present an approach that requires only a well-chosen snapshot of data to find a single domain invariant subspace. The subspace is calculated in closed form and overrides domain structures, which makes it fast and stable in parameterization. By employing low-rank techniques, we emphasize on descriptive characteristics of data. The presented idea is evaluated on various domain adaptation tasks such as text and image classification against state of the art domain adaptation approaches and achieves remarkable performance across all tasks.
Relevance of Grapheme’s Shape Complexity in Writer Verification Task

Recognizing and identifying people, based on their physical and behavioral characteristics, have always had a wide range of applications, inciting researchers to propose dedicated human recognition systems for each human characteristic. These systems operate according to two different modes: identification mode, where the task is to assign one of the preregistered identities in the system to the human's sample read as input. The second mode is the verification (authentication), is a decision task stating if a human's sample read as input belongs really to the claimed identity. Handwriting has emerged as one of these behavioral features that attracted a lot of interests during the last decade. Many writer identification systems have been developed comparing to writer verification (authentication) systems. In this paper we propose an original approach based on the usage of the shape complexity to authenticate writers' identities. To this end, a local feature (grapheme) is considered, where the graphemes are generated automatically with a dedicated segmentation module. The Fourier Elliptic Transform was used to measure the shape complexity of the resulting graphemes. Only the top complex graphemes (K-Graphemes) were used to measure the similarity between a pair of handwritten samples. The approach was evaluated with 3 sets of 50 different writers of the BFL dataset, where we obtained a performance of almost 80% of good acceptance at 8% error rate. These results validate completely the relevance of the shape complexity in writer recognition tasks.
Cyberspace, Blockchain, Governance: How Technology Implies Normative Power and Regulation

Technologies and their inherent design choices create normative structures that affect governance. This chapter aims to illustrate how blockchain technology in particular introduces new norms into a legal framework. We first analyze the different forms of governance by distinguishing between old and new governance. With a view to code that functions as legal norms, Blockchain technology is particularly suited to create governance structures and mechanisms. However, one needs to be aware of the norms that are implicitly introduced into the legal system by a specific blockchain technology. We look at the blockchain technology that underlies cryptocurrencies such as Bitcoin. This blockchain introduces a decentralized, transparent, cryptographically locked and thus immutable shared ledger. In summary, these design choices have normative powers over the user and over user interaction. If this is indeed the case, then regulators have to actively assess newly introduced digital ledger technology and other technologies for their effect on the normative and legal system.
“Thaz how u kno ur dum”

Abstract This study analyzes language use among a group of gay men who participate on an online messageboard (OnYourKnees), focused on the attainment of a ‘dumb jock’ identity. Posters align with a series of qualities that largely conform to ideologies of American jock masculinity, but at the same time satirize those ideologies: in particular, many posters view as an integral quality of dumb-jock identity ‘dumbness:’ an unwillingness/inability to engage in scholarly/academic pursuits. The repeated citationality of dumbness as a positive quality creates a distinct identity-type that posters link with erotic desire. Orthographic variation contributes to the attainment and recognition of a jock identity: posters who identify as jocks are more likely to display non-standard American English spelling than those who do not. This study thus highlights the importance of orthographic variation in maintaining distinct identities among local communities, especially in a space where traditional ideologies of masculinity are recontextualized.
Tuberculosis CT Image Analysis Using Image Features Extracted by 3D Autoencoder

This paper presents an approach for the automated analysis of 3D Computed Tomography (CT) images based on the utilization of descriptors extracted using 3D deep convolutional autoencoder (AEC []) networks. Both the common flow of AEC model application and a set of techniques for overcoming the lack of training samples are presented in this work. The described approach was used for accomplishing the two subtasks of the ImageCLEF 2019: Tuberculosis competition [, ] and allowed to achieve the 2nd best performance in the TB Severity Scoring subtask and the 6th best performance in the TB CT Report subtask.
ThingGate: A Gateway for Managing Traffic of Bare-metal IoT Honeypot

The Internet of Things (IoT) malware keep evolving and utilize multiple vulnerabilities to infect IoT devices. Besides malware, human attackers also utilize various tools to access and collect variable information on the device. For instances, web UI of IP Cameras and routers are constantly searched and accessed if vulnerable. In order to observe and analyze such a variety of attacks in depth, there is an increasing need for bare-metal IoT devices as a honeypot, since it is costly to emulate device-specific vulnerabilities and complex functionalities from dedicated services. However, operating bare-metal IoT honeypots has unique technical challenges mostly coming from their low configurability as an embedded system. A bare-metal honeypot needs proper access control while it is allowing attackers to access its inside to some degree, such as filter out bricking commands and changes of critical configuration. From this observation, we propose ThingGate, a gateway for flexible operation of bare-metal IoT honeypot. ThingGate employs a man-in-the-middle proxy to control and manage inbound and outbound traffic of the bare-metal IoT honeypot. Moreover, it adds the functionality of web tracking, which is not provided by the web UI of the original devices. We evaluate ThingGate with seven bare-metal IoT devices and show that it successfully blocks unwanted incoming attacks, masks wireless access point information of the devices, and tracks attackers on the device web UI while showing high observability of various attacks exploiting different vulnerabilities.