Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network-Based Collaborative Filtering for Recommendation Systems

Collaborative filtering based recommender systems that predict the user preference based on their past interactions have been adopted by many online services. Matrix factorization that projects users and items into a shared latent space is one of the popular collaborative filtering techniques. Recently, a general neural network-based collaborative filtering (NCF) framework, employing generalized matrix factorization and multi-layer perceptron models termed as neural matrix factorization (NeuMF), was proposed for recommendation. Meanwhile, convolutional neural network (CNN) is a variation of a multi-layer perceptron commonly used in computer vision. CNN is also normally used to model user profiles and item descriptions for recommendation. In this work, the CNN was used differently, that is, to model the interaction between user and item features directly in the recommendation systems. More specifically, a special case of NCF that employs matrix factorization and CNN was proposed. The model used general matrix factorization to model latent feature interactions using a linear kernel and the CNN to learn the interaction function from data using a non-linear kernel. Experiments conducted on a public dataset, Movielens, demonstrated that the proposed model was superior when compared to the published NCF framework such as NeuMF and other state-of-the-art methods.
Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems

The paper suggests a randomized model for dynamic migratory interaction of regional systems. The locally stationary states of migration flows in the basic and immigration systems are described by corresponding entropy operators. A soft randomization procedure that defines the optimal probability density functions of system parameters and measurement noises is developed. The advantages of soft randomization with approximate empirical data balance conditions are demonstrated, which considerably reduces algorithmic complexity and computational resources demand. An example of migratory interaction modeling and testing is given.
Confusing terminology stunts the growth of cyber insurance

The lack of standard terminology in cyber insurance policies causes confusion for brokers and customers and is ultimately stunting market growth. And while demand for cyber insurance is growing, the confusion about the policies available, the cover provided and the differing approaches by insurers are deterring many clients.
Real-Time Human Body Pose Estimation for In-Car Depth Images

Over the next years, the number of autonomous vehicles is expected to increase. This new paradigm will change the role of the driver inside the car, and so, for safety purposes, the continuous monitoring of the driver/passengers becomes essential. This monitoring can be achieved by detecting the human body pose inside the car to understand the driver/passenger’s activity. In this paper, a method to accurately detect the human body pose on depth images acquired inside a car with a time-of-flight camera is proposed. The method consists in a deep learning strategy where the architecture of the convolutional neural network used is composed by three branches: the first branch is used to estimate the confidence maps for each joint position, the second one to associate different body parts, and the third branch to detect the presence of each joint in the image. The proposed framework was trained and tested in 8820 and 1650 depth images, respectively. The method showed to be accurate, achieving an average distance error between the detected joints and the ground truth of 7.6 pixels and an average accuracy, precision, and recall of 95.6%, 96.0%, and 97.8% respectively. Overall, these results demonstrate the robustness of the method and its potential for in-car body pose monitoring purposes.
The Method of Detection of Clay Minerals and Iron Oxide Based on Landsat Multispectral Images (as Exemplified in the Territory of Thai Nguyen Province, Vietnam)

Landsat multispectral images have been successfully used for discovering some mineral deposits in different regions of the world. Some minerals, including clay minerals and iron oxide, can be detected by multispectral surveys due to their spectral characteristics. This paper presents the results of the application of principal component analysis and Crosta technique for detecting accumulations of clay minerals and iron oxide based on a Landsat 8 Oli multispectral image of Thai Nguyen Province, north of Vietnam. The obtained results have demonstrated the feasibility and suitability of prompt detecting mineral deposits based on the remote sensing data. The image processing methods and facilities tested in this study can be used to create maps of distribution of clay minerals and iron oxide for effective and expedient prospecting and exploration for minerals.
A 15.2 TOPS/W CNN Accelerator with Similar Feature Skipping for Face Recognition in Mobile Devices

A low-power face recognition processor with similar feature skipping (SFS) and the tile-based clustering algorithm is proposed for high energy efficiency in mobile devices. For higher energy efficiency face recognition (FR) processor, this paper proposes two key features: 1) Tile-based clustering enables to reduce computation overhead of clustering. 2) SFS binary convolution core is proposed to increase energy efficiency, resulting in 15.2 TOPS/W energy efficiency. Implemented with 65 nm CMOS technology, the 6 mm2 FR processor achieves 0.26mW power consumption at 1 frames-per-second (fps) always-on face recognition in mobile devices.
Towards a Taxonomy of Text Mining Features

Recently, text mining has received special attention from both researchers and practitioners, since it enables the development of intelligent and automated services. Text mining has been influenced by different disciplines like computer science, statistics, computational linguistics and library and information sciences. However, text mining features that evolved in one particular discipline are often unknown or rarely used in the other disciplines. No scientific feature framework exits which facilitates costly feature engineering and evaluation. Therefore, we aim to develop a novel text mining feature taxonomy, which helps researchers and practitioners to develop, refine, compare and evaluate their text mining studies. In this research in progress paper, we focus on laying the foundation for our taxonomy development by presenting our first two research cycles. Here, we were aiming for diversity, not completeness. We derived five dimensions and classified different text features accordingly to provide a deeper understanding.
Crime Alert! Crime Typification in News Based on Text Mining

In this paper we detailed a multinomial classification-based methodology that combines different algorithms (SVM and MLP) with document representations (Tf Idf vectorization and Doc2vec embedding) and: (i) can distinguish between crime-related news and not-crime related news and; (ii) allows the assignment of each crime-related news to its corresponding crime type. With a F1-score of 84% achieved by the MLP with Doc2vec approach, it can be concluded that it is possible to answer the question of how the crimes are committed (what types of crime are perpetrated) and, in this way, offer a thermometer to citizens about criminal activity in a given territory, as reported by news articles.
Active Embedding Search via Noisy Paired Comparisons

Task : for a given user, estimate a preference vector w ∈ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> in a similarity embedding of items.
C-ASP: Continuous ASP-Based Reasoning over RDF Streams

The ability to perform complex reasoning over data streams has recently become an important area of research in the Semantic Web community. Most of SPARQL-inspired engines have limitations in capturing sophisticated user requirements and dealing with complex reasoning tasks. To address these challenges, we propose and implement C-ASP, a reasoning system based on the Answer Set Programming (ASP) system Clingo and extended to handle continuous reasoning requests over RDF streams. We provide the syntax of the C-ASP language, as well as a set of examples in order to illustrate its expressive power. In addition, we present preliminary experimental results showing C-ASP performances.
On the Complexity of Cloud and IoT Integration: Architectures, Challenges and Solution Approaches

Cloud Computing and the Internet of Things (IoT) shift from trend technologies to well established and broadly accepted means to foster business development and service quality. For utilising their full potential in the context of complex systems, applications based on these technologies often have to be properly integrated resulting in major challenges. In this paper, we provide means for better understanding possible occurrences of integration challenges when establishing Cloud and IoT systems. We briefly present several existing Cloud and IoT architectures and a survey on existing integration challenges. Based on these results, we derived an overall integration architecture as a supporting tool for the indication of the different integration challenges, which is presented in a short and full version due to the overall complexity. At last, some general approaches for integration are discussed.
Imagining the Future Academic Library Collection

This special issue of Collection Management on “Imagining the Future Academic Library Collection” includes 23 articles representing a range of views about library collections trends. This introductory essay summarizes the articles and identifies some common themes.
A Counting-Based Approach to Scalable Micro-service Deployment

Deploying a cloud-based distributed application created from the composition of micro-services is a challenging problem. It mandates the resolution of a resource allocation problem accounting for resource utilization and network load. But it also imposes security requirements such as the selection of suitable technology stacks to protect the communication channels. Both sets of decisions are intimately related as hosting decisions affect the cost or feasibility of security measures under consideration. This paper revisits the problem and focuses on a scalable approach suitable to deploy large distributed applications. Specifically, it introduces a counting-based model to deliver solutions for hundreds of services within short computation times. The essence is to side-step some of the difficulties by focusing first and foremost on deciding how many services of each type need to be deployed at each location and postponing the instance connectivity problem to a post-optimization phase. Empirical results demonstrate the scope of the improvements and illustrate the performance to expect as a function of instance sizes.
Diffusion Variational Autoencoders.

Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to approximate a homeomorphism between the data distribution and the sampling space. This is a construction that strives to define the data manifold. A major obstacle to VAEs and GANs, however, is choosing a suitable prior that matches the data topology. Well-known consequences of poorly picked priors are posterior and mode collapse. To our knowledge, no existing method sidesteps this user choice. Conversely, $\textit{diffusion maps}$ automatically infer the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism (i.e. decoder/generator). We propose a method that combines these approaches into a generative model that inherits the asymptotic guarantees of $\textit{diffusion maps}$ while preserving the scalability of deep models. We prove approximation theoretic results for the dimension dependence of our proposed method. Finally, we demonstrate the effectiveness of our method with various real and synthetic datasets.
Enfrentamento, Dificuldades e Práticas de Autocuidado de Pacientes com Doença Renal Crônica Submetidos à Diálise Peritoneal

Objective: To understand the coping, difficulties and self-care practices used by persons with chronic renal failure, submitted to peritoneal dialysis treatment, by a synthesis of primary and eligible studies, found in the literature.Results: Selected nine articles from the databases based on the inclusion and exclusion criteria.There are four in the Portuguese language, two in the English language and three in the Spanish language.The results were discussed in specific categories.Conclusion: The experience of a new moment of the individual´s life affected by Chronic Renal Disease, which consequently leads him to Peritoneal Dialysis Treatment.This disease brings major changes in the affected individual´s life style.The nursing professional who will be the main intermediator for the success in this treatment process should very well consider this whole situation.This health professional is who should give support for the family of the ill person and lead the patient to receive a treatment with all the care needed to reach a physical and emotional well-being in its new stage of life.
Ligt: An LLOD-Native Vocabulary for Representing Interlinear Glossed Text as RDF.

The paper introduces Ligt, a native RDF vocabulary for representing linguistic examples as text with interlinear glosses (IGT) in a linked data formalism. Interlinear glossing is a notation used in various fields of linguistics to provide readers with a way to understand linguistic phenomena and to provide corpus data when documenting endangered languages. This data is usually provided with morpheme-by-morpheme correspondence which is not supported by any established vocabularies for representing linguistic corpora or automated annotations.Interlinear Glossed Text can be stored and exchanged in several formats specifically designed for the purpose, but these differ in their designs and concepts, and they are tied to particular tools, so the reusability of the annotated data is limited. To improve interoperability and reusability, we propose to convert such glosses to a tool-independent representation well-suited for the Web of Data, i.e., a representation in RDF. Beyond establishing structural (format) interoperability by means of a common data representation, our approach also allows using shared vocabularies and terminology repositories available from the (Linguistic) Linked Open Data cloud.We describe the core vocabulary and the converters that use this vocabulary to convert IGT in a format of various widely-used tools into RDF. Ultimately, a Linked Data representation will facilitate the accessibility of language data from less-resourced language varieties within the (Linguistic) Linked Open Data cloud, as well as enable novel ways to access and integrate this information with (L)LOD dictionary data and other types of lexical-semantic resources. In a longer perspective, data currently only available through these formats will become more visible and reusable and contribute to the development of a truly multilingual (semantic) web.
Impossibility of perfectly-secure one-round delegated quantum computing for classical client

Blind quantum computing protocols enable a client, who can generate or measure single-qubit states, to delegate quantum computing to a remote quantum server protecting the client's privacy (i.e., input, output, and program). With current technologies, generations or measurements of single-qubit states are not too much burden for the client. In other words, secure delegated quantum computing is possible for ``almost classical" clients. However, is it possible for a ``completely classical" client? Here we consider a one-round perfectly-secure delegated quantum computing, and show that the protocol cannot satisfy both the correctness (i.e., the correct result is obtained when the server is honest) and the perfect blindness (i.e., the client's privacy is completely protected) simultaneously unless BQP is in NP. Since BQP is not believed to be in NP, the result suggests the impossibility of the one-round perfectly-secure delegated quantum computing.
The digital Wild West: on social entrepreneurship in extended reality

Purpose Our central thesis is that the dynamic, immersive and agile nature of extended reality (XR) both provides an unusually fertile ground for the development of alternative forms of governance and essentially necessitates this development by contrast with relatively inagile institutions of public governance. Design/methodology/approach I take an epistemologically aware, systems-theoretic perspective in my analysis to properly tease out the relevant micro-, meso- and macro-structures; their direct interactions; and their entanglements. Findings The challenges presented by rapidly advancing XR may require much more agile forms of governance than are available from public institutions, even under widespread algorithmic governance. Social entrepreneurship in blockchain solutions may very well be able to meet some of these challenges, as we show. Originality/value There are very few systems-aware, epistemological analyses of social entrepreneurship utilizing algorithms versus public algorithmic governance and none that focus on how these two channels of social action interact with developments in XR.
Redescription Mining for Learning Definitions and Disjointness Axioms in Linked Open Data

In this article, we present an original use of Redescription Mining (RM) for discovering definitions of classes and incompatibility (disjointness) axioms between classes of individuals in the web of data. RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse this process for providing definitions in terms of necessary and sufficient conditions to categories in DBpedia. Firstly, we recall the basics of redescription mining and make precise the principles of our definitional process. Then we detail experiments carried out on datasets extracted from DBpedia. Based on the output of the experiments, we discuss the strengths and the possible extensions of our approach.
Extreme Learning Machine with Elastic Net Regularization

Compared with deep neural learning, the extreme learning machine (ELM) can be quickly converged without iteratively tuning hidden nodes. Inspired by this merit, an extreme learning machine with elastic net regularization (ELM-EN) is proposed in this paper. The elastic net is a regularization method that combines LASSO and ridge penalties. This regularization can keep a balance between system stability and solution's sparsity. Moreover, an excellent optimization method, i.e., accelerated proximal gradient, is used to find the minimum of the system optimization function. Various datasets from UCI repository and two facial expression image datasets are used to validate the efficiency of our system. Final experimental results indicate that our ELM-EN requires less training time than multi-layer perceptron, and can achieve higher recognition accuracy than ELM and sparse ELM.
DIC/Moiré hybrid method based on regular patterns for deformation measurement

Digital Image Correlation (DIC) is a superior optical method to measure the surface deformation with a high accuracy. Currently, most researches on DIC are based on random patterns. In this paper, A DIC/Moiré hybrid method using regular patterns is proposed for deformation measurement. In this method, a Moiré fringe technique based on correlation coefficient is developed to provide accurate initial deformation estimation for DIC. Experimental results indicate a higher computational efficiency by the proposed method than the conventional DIC method. It is also found that the calculation accuracy increases using regular patterns. The advantage of obtaining accurate initial estimation by the DIC/Moiré hybrid method may enable potential application in deformation measurements.
Modeling task influences for saccade sequence and visual relevance prediction

Previous work from Wloka et al. (2017) presented the Selective Tuning Attentive Reference model Fixation Controller (STAR-FC), an active vision model for saccade prediction. Although the model is able to efficiently predict saccades during free-viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns (Yarbus, 1967). These factors are considered in previous Selective Tuning architectures (Tsotsos and Kruijne, 2014)(Tsotsos, Kotseruba and Wloka, 2016)(Rosenfeld, Biparva & Tsotsos 2017), proposing a way to combine bottom-up and top-down contributions to fixation and saccade programming. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-term memory in combination with stimulus-driven eye movement neuronal correlates. Initial theories and models of these influences include (Rao, Zelinsky, Hayhoe and Ballard, 2002)(Navalpakkam and Itti, 2005)(Huang and Pashler, 2007) and show distinct ways to process the task requirements in combination with bottom-up attention. In this study we extend the STAR-FC with novel computational definitions of Long-Term Memory, Visual Task Executive and a Task Relevance Map. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memory model by processing a hierarchy of visual features learned from salient object detection datasets. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting relevance maps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions.
A generalized approach for the fuzzy commitment scheme

Commitment Scheme is the way to hide the information and that information is revealed to the receiver in the later stage. The commitment scheme uses witness value to open the commitment. The role of witness value differentiates the fuzzy commitment scheme from a commitment scheme. Fuzzy commitment scheme allows the use of corrupted witness. In this paper, a generalized fuzzy commitment scheme is introduced that has the capability to reconstruct the secret from corrupted committed message and corrupted witness. The paper presents the fuzzy commitment scheme with the codes and analyzes the scheme with BCH and RS codes.
Modeling of HIV and AIDS in Indonesia Using Bivariate Negative Binomial Regression

Abstract The problem of HIV and AIDS in Indonesia is a frightening health problem with a number of cases that tend to increase each year. The aim of this research is to model the number of HIV and AIDS cases in Indonesia using bivariate negative binomial regression approach. Bivariate negative binomial regression is a regression method for modeling a pair of response variables in the form of count data with negative binomial distribution and correlating to each other. This research uses secondary data from the ministry of health in 2017 about the number of HIV and AIDS cases in Indonesia. From the results of this research, we obtained the deviance value of 38.9197 which was used to describe the goodness of fit test.