Reviewerly/exHarmony huggingface dataset:
Leukocyte nucleus segmentation method based on enhancing the saliency of saturation component

The accuracy of the leukocyte nucleus segmentation is an important preprocessing step in the leukocyte automatic analysis. However, different dyeing conditions or different illumination conditions may cause capturing different color leukocyte images in microscopic imaging system, which will result in the over-segmentation or under-segmentation of the leukocyte nucleus. A leukocyte nucleus segmentation method based on enhancing the saliency of the saturation component is proposed. While applying the set of calibration offset values [Formula: see text], [Formula: see text], and [Formula: see text] of the red (R), green (G), and blue (B) chrominance value on the blood smear microscopic images, it can enhance the saliency of the saturation component and the saliency of the leukocyte nucleus region increases the most obviously. The leukocyte nuclei are then segmented using Otsu’s histogram thresholding method. The experimental results show that the proposed algorithm outperforms the related algorithms in segmentation accuracy, over-segmentation rate, error rate, and relative distance error. It improves the accuracy, robustness, and universality further.
From Whole to Part: Reference-Based Representation for Clustering Categorical Data

Dissimilarity measures play a crucial role in clustering and, are directly related to the performance of clustering algorithms. However, effectively measuring the dissimilarity is not easy, especially for categorical data. The main difficulty of the dissimilarity measurement for categorical data is that its representation lacks a clear space structure. Therefore, the space structure-based representation has been proposed to provide the categorical data with a clear linear representation space. This representation improves the clustering performance obviously but only applies to small data sets because its dimensionality increases rapidly with the size of the data set. In this paper, we investigate the possibility of reducing the dimensionality of the space structure-based representation while maintaining the same representation ability. A lightweight representation scheme is proposed by taking a set of representative objects as the reference system (called the reference set) to position other objects in the Euclidean space. Moreover, a preclustering-based strategy is designed to select an appropriate reference set quickly. Finally, the representation scheme together with the k -means algorithm provides an efficient method to cluster the categorical data. The theoretical and the experimental analysis shows that the proposed method outperforms state-of-the-art methods in terms of both accuracy and efficiency.
Inferring Catchment in Internet Routing

BGP is the de-facto Internet routing protocol for interconnecting Autonomous Systems (AS). Each AS selects its preferred routes based on its routing policies, which are typically not disclosed. Due to the distributed route selection and information hiding, answering questions such as "what is the expected catchment of the anycast sites of a content provider at the AS-level, if new sites are deployed?", or "how will load-balancing behave if an ISP changes its routing policy for a prefix?", is a hard challenge. In this work, we propose a framework and methodology to infer the routing behavior in existing or hypothetical routing configurations, and provide new capabilities and insights for informative route inference (e.g., isolating the effect of randomness that is present in prior simulation-based approaches). The proposed framework can be useful in a number of applications: measurements/monitoring, traffic engineering, network planning, Internet routing models, etc.
Vadalog: Recent Advances and Applications

Vadalog is a logic-based reasoning language for modern AI applications, in particular for knowledge graph systems. In this paper, we present recent advances and applications, with a focus on the Vadalog language itself. We first give an easy-to-access self-contained introduction to Warded Datalog+/−, the logical core of Vadalog. We then discuss some recent advances: Datalog rewritability of Warded Datalog+/−, and the piece-wise linear fragment of Warded Datalog+/− that achieves space efficiency. We then proceed with some recent practical applications of the Vadalog language: detection of close links in financial knowledge graphs, as well as the detection of family-owned businesses.
Learning with Linear Mixed Model for Group Recommendation Systems

Accurate prediction of users' responses to items is one of the main aims of many computational advising applications. Examples include recommending movies, news articles, songs, jobs, clothes, books and so forth. Accurate prediction of inactive users' responses still remains a challenging problem for many applications. In this paper, we explore the linear mixed model in recommendation system. The recommendation process is naturally modelled as the mixed process between objective effects (fixed effects) and subjective effects (random effects). The latent association between the subjective effects and the users' responses can be mined through the restricted maximum likelihood method. It turns out the linear mixed models can collaborate items' attributes and users' characteristics naturally and effectively. While this model cannot produce the most precisely individual level personalized recommendation, it is relative fast and accurate for group (users)/class (items) recommendation. Numerical examples on GroupLens benchmark problems are presented to show the effectiveness of this method.
Library and Information Services on the Websites of National Libraries of Russia: Classification and Presentation Issues

The purpose of this study is to identify and analyse services on the websites of national libraries of Russia. The author presents definition of the concept “library (library and information) service” and highlights the essence of these services. The article analyses how the services are presented on the official websites of national libraries: the Russian State Library (RSL), the National Library of Russia (NLR) and the Presidential Library named after B. Yeltsin (PL). The author uses the following methods: analysis of texts of library sites, comparison, generalization, classification analysis. RSL names the services on the main page of the website and provides the list of fee-based services. NLR also demonstrates services twice, but more complete list presents on the main page of the site. PL does not provide the full list of services on the website, but lists some services in the section “Access to Resources” (also, the site presents “The Price List of fee-based services (works)”. Electronic services of national libraries of Russia are allocated. The author characterizes classification of services on the website of each national library. Analysis of these services shows that they are not presented comprehensively (most fully in the RSL), and the services are called and classified by libraries in different ways. Users accessing different national libraries are not always able to understand exactly what services are described there. The article proposes classification of library and information services developed by the author. The classification is based on two attributes: the object that is requested by the user (document, reference, etc.), and the place of service. This classification can be used by both national and other libraries.
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies

Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pre-trained NMT model to a new, unrelated language without shared vocabularies. We relieve the vocabulary mismatch by using cross-lingual word embedding, train a more language-agnostic encoder by injecting artificial noises, and generate synthetic data easily from the pre-training data without back-translation. Our methods do not require restructuring the vocabulary or retraining the model. We improve plain NMT transfer by up to +5.1% BLEU in five low-resource translation tasks, outperforming multilingual joint training by a large margin. We also provide extensive ablation studies on pre-trained embedding, synthetic data, vocabulary size, and parameter freezing for a better understanding of NMT transfer.
Language features in extractive summarization: Humans Vs. Machines

This paper presents a comparative statistical analysis of the language features most commonly used for Automatic Text Summarization (ATS), namely: Parts of Speech (PoS) (unigrams and bigrams), sentiments (by token and sentence), and Rhetorical Structure Theory (RTS) relations. The analyses were carried out on both human-made and machine-made summaries, in order to determine whether current ATS systems capture the same kind of information as humans do. Our results show that there are some marked differences between machine and human-made summaries, which at times may seem counterintuitive. For instance, named entities were usually frequent in machine-made summaries, but not in human-made ones. Similarly, words perceived to hold a “neutral” sentiment were systematically favored by machines, but not always by humans.
On the most extended interval-valued intuitionistic fuzzy modal operators from both types

The most extended (by the moment) interval-valued intuitionistic fuzzy modal operators from both types are introduced.A theorem for equivalence of two of them is proved.
A unified catalog of earthquake hypocenters and magnitudes at volcanoes in Alaska—1989 to 2018

First posted May 15, 2019 For additional information, contact: Contact Information,Volcano Science CenterU.S. Geological Survey4210 University DriveAnchorage, AK 99508 The Alaska Volcano Observatory (AVO) has maintained an earthquake catalog since 1989 that now contains over 120,000 hypocenters and magnitudes that occurred near Alaskan volcanoes. Since 1989 the seismic instrumentation and data acquisition and processing techniques have undergone numerous changes as computer systems and seismic processing software have advanced and evolved. In this report we recalculate earthquake hypocenters and magnitudes in a consistent manner between October 1989 and January 2018. The new hypocenters and magnitudes are archived at the AVO within an AQMS database and in the compressed UNIX tar file that accompanies this publication.
Image quality and super resolution effects on object recognition using deep neural networks

Real-time object recognition systems are critical for several UAV applications since they provide fundamental semantic information of the aerial scene. In this study, we describe how image quality limits object detection frame-works such as YOLO which can distinguish 80 different object classes. This paper will focus on vehicles such as cars, trucks and buses. Pristine high-resolution images are degraded using different blurring functions, spatial resolution, reduced image contrast, additive noise and lossy compression. Object recognition results are significantly better after applying an image super-resolution algorithm to realistically simulated under-sampled imagery.
deWristified

Several recent research efforts have shown that privacy of handwritten information is vulnerable to inference threats that employ zero-permission motion sensors commonly found on wrist-wearables (e.g., smart watches and fitness bands) as information side-channels. While the adversary model in these earlier efforts have been reasonable and the proposed inference (or threat) frameworks themselves are practical and have technical merit, the related empirical evaluations suffer from several significant shortcomings, such as, use of specialized sensor hardware and highly constrained or restrictive experimental procedures, to name a few. As a result, it is hard to estimate the practical feasibility of these threats from existing research results in the literature, and thus, the extent to which end-users must be concerned about the possibility of such attacks in real-life. To answer the above question, this paper replicates some of the well-known wrist motion-based handwriting inference frameworks in the literature in order to (re)evaluate their success or accuracy in natural, unrestricted handwriting scenarios and settings by employing commercially available wrist-wearables. The results of these extensive replication and (re)evaluation studies highlight several characteristics in motion data corresponding to natural handwriting scenarios, which were either not observed or ignored by earlier efforts, and contribute to poor inference accuracy of the corresponding frameworks. In summary, accurate and practical handwriting inference using motion data (side-channeled) from consumer-grade wrist-wearables is difficult primarily due to unique and/or inconsistent handwriting behavior observed in natural writing.
A Weighted Fourier and Wavelet-Like Shape Descriptor Based on IDSC for Object Recognition

This article presents an effective shape descriptor with a property of fast matching. This descriptor, called IDSC-wFW (a weighted Fourier and wavelet-like descriptor based on inner distance shape context), first rewrites shape histograms of IDSC descriptors, changing the histogram belonging to a point to the histogram belonging to a field, and sets the histogram of a field as a one-dimensional signal, then transforms this one-dimensional signal by using a Fourier transform and a transform similar to Haar wavelet. Finally, the two transform results are linearly combined to form a new descriptor. This new descriptor requires only a distance-based measure method during the matching stage. Experimental results on three well-known databases show that this new descriptor not only obtains accurate retrieval results but also runs fast.
Presenting a Medical Expert System for Diagnosis and Treatment of Nephrolithiasis

Expert systems aim to employ expert skills for non-expert person. These designs simulate intellectual and practical performance of human making the performance of expert systems close to that of an expert human. Various expert systems have been proposed in medicine, thus this area is attracting attention. Most problems in modern medicine are very complicated and there is no logic reason for accurate decision making. To this end, doctors decide arbitrarily and variably. On the other hand, large volume of medical information makes decision making more difficult while modern technologies add to volume of information and make problems more difficult. Considering these problems, there is a great challenge in medical diagnosis which requires decision-making support systems. In this paper, an expert system is presented for diagnosis and treatment of nephrolithiasis in which knowledge required for diagnosis and treatment is stored as rules in the system knowledge base. If experts are absent, diagnosis and treatment can be done reliably. Expert systems can be used as decision support by the users. However, currently, they cannot replace experts.
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

While graph kernels (GKs) are easy to train and enjoy provable theoretical guarantees, their practical performances are limited by their expressive power, as the kernel function often depends on hand-crafted combinatorial features of graphs. Compared to graph kernels, graph neural networks (GNNs) usually achieve better practical performance, as GNNs use multi-layer architectures and non-linear activation functions to extract high-order information of graphs as features. However, due to the large number of hyper-parameters and the non-convex nature of the training procedure, GNNs are harder to train. Theoretical guarantees of GNNs are also not well-understood. Furthermore, the expressive power of GNNs scales with the number of parameters, and thus it is hard to exploit the full power of GNNs when computing resources are limited. The current paper presents a new class of graph kernels, Graph Neural Tangent Kernels (GNTKs), which correspond to infinitely wide multi-layer GNNs trained by gradient descent. GNTKs enjoy the full expressive power of GNNs and inherit advantages of GKs. Theoretically, we show GNTKs provably learn a class of smooth functions on graphs. Empirically, we test GNTKs on graph classification datasets and show they achieve strong performance.
Adaptive Masked Proxies for Few-Shot Segmentation

Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method that constructs the final segmentation layer weights from few labelled samples. It utilizes multi-resolution average pooling on base embeddings masked with the label to act as a positive proxy for the new class, while fusing it with the previously learned class signatures. Our method is evaluated on PASCAL-$5^i$ dataset and outperforms the state-of-the-art in the few-shot semantic segmentation. Unlike previous methods, our approach does not require a second branch to estimate parameters or prototypes, which enables it to be used with 2-stream motion and appearance based segmentation networks. We further propose a novel setup for evaluating continual learning of object segmentation which we name incremental PASCAL (iPASCAL) where our method outperforms the baseline method. Our code is publicly available at https://github.com/MSiam/AdaptiveMaskedProxies.
Cybersecurity Culture in Computer Security Incident Response Teams: Investigating difficulties in communication and coordination

This study aims to identify the factors related to developing a cybersecurity culture at an organizational context and the difficulties faced in communicating and cooperating within a CSIRT. Specifically, our aim is to identify: 1) The issues which may limit the communication and the coordination of incident management process inside a CSIRT, 2) the issues which may limit the cooperation from top management to employees and reverse and 3) approaches towards addressing the issues that limit the communication and the cooperation of a CSIRT. The research was conducted using an online survey and study participants were experts within the existing CSIRT community. In total, 25 participants responded to the questionnaire, from 23 different countries in the world. The questions of the survey queried the personal knowledge and experience of participants regarding CSIRTs. In our analysis, issues such as communication, cooperation, coordination, trust and information sharing are discussed as crucial factors that affect the development of a cybersecurity culture. Several issues and weaknesses in terms of communication, coordination and cooperation within CSIRT are outlined and a set of recommendations and key elements are defined.
Blockchain Based MFA Solution: the use of hydro raindrop MFA for information security on WordPress websites

Goal: The present work aims to present how the use of a blockchain two-factor authentication solution 2FA on a page developed on WordPress can contribute to the information security regarding user authentication.&#x0D; Design/Methodology/Approach: The research method employed is characterized as an exploratory research, since all the analysis is based on the theoretical reference data available on the subject. A field research was carried out in relation to the implementation of the multi-factor authentication plugin Hydro Raindrop MFA, which uses blockchain technology offered by The Hydrogen Technology Corporation and the Project Hydro platform over the Ethereum network. Thus, this paper sought to present and conceptualize some of the technologies used, pointing out their contribution to information security.&#x0D; Results: The main results showed that the use of decentralized technology, such as blockchain and the Hydro Raindrop Plugin, can contribute considerably in the process of user authentication, which may strengthen the safeguard of the information and assets of individuals and organizations by inhibiting or reducing the possibility of successful a hacker attack. This solution is at the forefront of innovation with regard to data security because it uses advanced blockchain technology. It might contribute in a satisfactory way to the preservation of critical data and information that are the core value of many organizations of the industry 4.0.&#x0D; Limitations of the investigation: This research was limited to analyzing how the implementation of the Hydro Raindrop multi-factor authentication solution on a WordPress page can be beneficial to ensure information security.&#x0D; Practical implications: This study’s findings can contribute to entities interested in cybersecurity. As a suggestion for future works, analyses of plugins or similar solutions available on the market in distinct types of websites, or performance comparisons between them, may be relevant to contribute to scientific research.&#x0D; Originality/Value: This work can contribute in an innovative way to scientific research, since it addresses a recently created solution that uses blockchain technology as its basis for a safer method of authentication.
Visual Exploration of Time Series Anomalies with Metro-Viz

This demo presents a novel data visualization solution for exploring the results of time series anomaly detection systems. When anomalies are reported, there is a need to reason about the results. We introduce Metro-Viz -- a visual tool to assist data scientists in performing this analysis. Metro-Viz offers a rich set of interaction features (e.g., comparative analysis, what-if testing) backed by data management strategies specifically tailored to the workload. We show our tool in action via multiple time series datasets and anomaly detectors.
A Holistic Approach for Query Evaluation andResult Vocalization in Voice-Based OLAP

We focus on the problem of answering OLAP queries via voice output. We present a holistic approach that combines query processing and result vocalization. We use the following key ideas to minimize processing overheads and maximize answer quality. First, our approach samples from the database to evaluate alternative speech fragments. OLAP queries are not fully evaluated. Instead, sampling focuses on result aspects that are relevant for voice output. To guide sampling, we rely on methods from the area of Monte-Carlo Tree Search. Second, we use pipelining to interleave query processing and voice output. The system starts providing the user with high-level insights while generating more fine-grained results in the background. Third, we optimize speech output to maximize the user's information gain under speaking time constraints. We use a maximum-entropy model to predict the user's belief about OLAP results, after listening to voice output. Based on that model, we select the most informative speech fragments (i.e., the ones minimizing the distance between user belief and actual data). We analyze formal properties of the proposed speech structure and analyze complexity of our algorithm. Also, we compare alternative vocalization approaches in an extensive user study.
Desire-Driven Reasoning for Personal Care Robots

In this paper, in order to reduce the burden of caring for individuals who are bedridden, we present a method of reasoning about the objects and actions that could satisfy their physiological needs. First, we introduce a method of representing knowledge about everyday objects in terms of their properties and functions. Based on this representation, we then propose a desire-driven reasoning approach that bridges the gap between physiological desires and robot actions. This can also deal with issues caused by uncontrolled domains, including incomplete knowledge and dynamic environments. Finally, we evaluate the proposed method by applying our newly developed KUT-PCR personal care robot to real household scenarios.