Reviewerly/exHarmony huggingface dataset:
A Collaborative Filtering Algorithm Fusing Category Preference and Spatiotemporal Information

In order to overcome the disadvantages of the traditional collaborative filtering algorithm and reduce the user interest caused by the rarity of user interest matrix, this paper proposes a novel collaborative filtering algorithm based on user experience, which integrates category preference and spatiotemporal information. The algorithm combines the similarity of category preference with the traditional similarity, and introduces the time and location factors into the collaborative filtering algorithm. In addition, the algorithm also analyzes and calculates the time similarity and location similarity, and uses the comprehensive weight to obtain the user similarity. The research results show that the algorithm breaks through the limitations of the traditional collaborative filtering algorithm and fundamentally improves the accuracy of recommendation.
Learning to Represent Human Motives for Goal-directed Web Browsing

Motives or goals are recognized in psychology literature as the most fundamental drive that explains and predicts why people do what they do, including when they browse the web. Although providing enormous value, these higher-ordered goals are often unobserved, and little is known about how to leverage such goals to assist people’s browsing activities. This paper proposes to take a new approach to address this problem, which is fulfilled through a novel neural framework, Goal-directed Web Browsing (GoWeB). We adopt a psychologically-sound taxonomy of higher-ordered goals and learn to build their representations in a structure-preserving manner. Then we incorporate the resulting representations for enhancing the experiences of common activities people perform on the web. Experiments on large-scale data from Microsoft Edge web browser show that GoWeB significantly outperforms competitive baselines for in-session web page recommendation, re-visitation classification, and goal-based web page grouping. A follow-up analysis further characterizes how the variety of human motives can affect the difference observed in human behavioral patterns.
From Coarse to Fine: Hierarchical Multi-scale Temporal Information Modeling via Sub-group Convolution for Video Action Recognition

In the video action recognition task, it is essential to model the temporal information. Since different actions have different durations, capturing multi-scale temporal features is very crucial. In this paper, we propose a multi-scale modeling (MSM) module to exploit temporal information for action recognition, which is composed of a multi-scale temporal convolution (MTC) block and a multi-scale hierarchical convolution (MHC) block. MTC uses convolutions of multiple temporal depths to capture features of different temporal scales to enhance the connection between frames. MHC employs group convolution to obtain more fine-grained multi-scale features in the channel dimension. In MHC, the convolutions are formulated as a hierarchical structure to expand the receptive fields as well as help implement a series of sub-group convolutions, which can help to realize the modeling of distinctive and long-term information. The two components of MSM are complementary in temporal modeling. Finally, we evaluated our method on several action recognition benchmarks including Kinetics, UCF10l, and HMDB51, and obtained competitive results, which verified the effectiveness of the proposed method in temporal modeling,
Aesthetics and Ordering in Stacked Area Charts

Stacked area charts are a common visualisation type for sets of time series. Yet, they are also known to be challenging to read, in particular if the time series exhibit much fluctuation or even abrupt changes. In this paper, we introduce a novel approach to improving the layout of stacked area charts by means of reordering the time series in the stack. This approach breaks down into two parts: First, we gather aesthetic criteria and define associated quality metrics for stacked area charts. Second, we use these quality metrics together with a new algorithm called UpwardsOpt to find orderings of the stacked time series that optimise a chart’s aesthetic properties. The produced orderings guarantee optimality in the sense that no better result can be obtained by moving any individual time series to a different position in the stack. In a benchmark study, we show that our algorithm can increase the layout quality up to 25%–50% over the state-of-the-art approach at the expense of longer runtimes. All datasets and an open source implementation of our algorithm are provided to facilitate their reuse and the reproducibility of our results.
New Practical Teaching Model Design for mutualized practical works based on low cost ICT, IoT and AI developed solutions

Recently, Information and Communication Technology (ICT) plays a crucial role in teaching-learning, particularly tracking, trainings, knowledge management and knowledge organization. The aim of this article is to propose a new practical teaching model design for practical works based on ICT, IoT and AI Tools. This new model will allow students to acquire skills and knowledges outside their field of training related to their course. The challenge of this teaching model is to teach more practical works by adopting the learning by doing approach, with a minimum cost effect.
The Expansion Complexity of Ultimately Periodic Sequences Over Finite Fields

The expansion complexity is a new figure of merit for cryptographic sequences. In this paper, we present an explicit formula of the (irreducible) expansion complexity of ultimately periodic sequences over finite fields. We also provide improved upper and lower bounds on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> th irreducible expansion complexity when they are not explicitly determined. In addition, for some infinite sequences with given nonlinear complexity, a tighter upper bound of their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> th expansion complexity is given.
Realization of a Natural Logic in a Database System

Natural logics are formal logics whose sentences resemble simplified natural language. As such they are suitable for knowledge bases. The offered logical proof rules apply directly to the natural logic sentences, ensuring explainability of query inferences. We describe a natural logic, NaturaLog, suited for ontology-structured knowledge bases, and explain how it can be implemented in a database system for conducting deductive querying.
Improving Parametric PCA Using KL-divergence Between Gaussian-Markov Random Field Models

Parametric PCA is a dimensionality reduction based metric learning method that uses the Bhattacharrya and Hellinger distances between Gaussian distributions estimated from local patches of the KNN graph to build the parametric covariance matrix. Later on, PCA-KL, an entropic PCA version using the symmetrized KL-divergence (relative entropy) was proposed. In this paper, we extend this method by replacing the Gaussian distribution by a Gaussian-Markov random field model. The main advantage is the incorporation of the spatial dependence by means of the inverse temperature parameter. A closed form expression for the KL-divergence is derived, allowing fast computation and avoiding numerical simulations. Results with several real datasets show that the proposed method is capable of improving the average classification performance in comparison to PCA-KL and some state-of-the-art manifold learning algorithms, such as t-SNE and UMAP.
Compare Network with Task-dependent Embeddings for Few-shot Learning

Few-shot Learning, a recent research hotspot in computer vision, attempts to distinguish unseen classes with a few labeled samples. Although significant progress has been made in this field, difficulties still exist in promoting classification accuracy. To tackle this issue, we proposed an effective and novel approach called compare network with task-dependent embeddings for few-shot learning, which includes three modules: embedding module, self-attention module and compare module. With the features extracted by the embedding module, the self-attention module, a use of attention mechanisms as component, can fully capture the internal relevance of features in the context of a certain task so as to learn more discriminative and adaptive task-dependent embeddings by taking a view of the entire task. And the compare module, the direct use of attention mechanisms as metrics, can efficiently evaluate the similarity score between query and discriminative task-dependent embeddings. The effectiveness of our architecture has been verified on two standard classification benchmark, namely the miniImageNet and Omniglot. Experimental results show that our method achieved very competitive results when compared to the state-of-the-art method in terms of few-shot tasks and realized consistent improvements over baselines.
Bibliography and the doctrine of bibliosphere: the state and prospects of development

The article is devoted to the analysis of the methodological potential of the main provisions of the Doctrine of Bibliosphere, developed by A. V. Sokolov. It is noted that its source is the concept of Noosphere by V. I. Vernadsky, which has been developed in the works of N. N. Moiseev. The basis of the Doctrine is a number of provisions. The first one concerns the nature of bibliosphere. However, in different works by A. V. Sokolov, it is defined in different ways, e.g. as part of a social space, as a national system of infrastructural book and communication systems that have developed at the industrial stage of man-made civilization, as a part of the document sphere, as a supersystem of book and communication systems that ensure the reproduction, preservation and further development of the national book culture. The second proposition concerns Bibliologos, or Book Mind, as the governing principle of Bibliosphere. The content of the third provision is devoted to the identification of the conflict between book and digital culture. The fourth and final statement is aimed at revealing the humanistic nature of Bibliosphere. It is based on ideas on the humanistic nature of book. The existing contradictions are analyzed. Theу include, first of all, the impossibility of the direct transfer of terminology and basic provisions of the theory of Noosphere to library and bibliographic concepts due to the difference in the scale of these theories (Noosphere and Bibliosphere), as well as the applied nature of the latter. Secondly, the contradiction highlighted by A. V. Sokolov is considered as a local problem caused by the transition to new technologies. Its resolution is not of a key nature and does not determine the vector of development of librarianship and bibliography. Third, the author questions the validity of identifying Bibliologos as a governing principle within the framework of a theory based on the principles of scientific rationality, which excludes the appeal to mysticism. Fourth, it is noted that the humanistic nature of librarianship and bibliography is given not by the nature of the book, which is indifferent, but by library and library and bibliographic activities aimed at the humanistic development of society. As a result of the analysis of Doctrine, the author comes to the conclusion that the idea of creating a theory, the main provisions and methodology of which allow us comprehensive consideration of social, information, technological and ethical (humanistic) aspects of librarianship and bibliography is relevant for modern library and bibliographic science in the context of the transition to a new model of organizing librarianship and bibliography. The analysis of the Doctrine on Bibliosphere has shown that it can potentially become such a theory, provided that the contradictions and unresolved issues identified by us are successfully resolved. At the same time, from a methodological point of view, it is fundamental to return to the positions of materialism and dialectics.
A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR

In today’s time, software quality assurance is the most essential and costly set of activities during software development in the information technology (IT) industries. Finding defects in system modules has always been one of the most relevant problems in software engineering, leading to increased costs and reduced confidence in the product, resulting in dissatisfaction with customer requirements. Therefore, to provide and deliver an efficient software product with as few defects as possible on time and of good quality, it is necessary to use machine learning techniques and models, such as supervised learning to accurately classify and predict defects in each of the software development life cycle (SDLC) phases before delivering a software product to the customer. The main objective is to evaluate the performance of different machine learning models in software defect prediction applied to 4 NASA datasets, such as CM1, JM1, KC1, and PC1, then de-terminate and select the best performing model using the MCDM: VIKOR multi-criteria decision-making method.
A Novel Approach to Enhance Image Security using Hyperchaos with Elliptic Curve Cryptography

Information security dominate the world. All the time we connect to the internet for social media, banking, and online shopping through various applications. Our priceless data may be hacked by attackers. There is a necessity for a better encryption method to enhance information security. The distinctive features of Elliptic Curve Cryptography (ECC) in particular the key atomity, speedy ciphering and preserving bandwidth captivating its use in multimedia encipher. An encryption method is proposed by incorporating ECC, Secure Hash Algorithm – 256 (SHA-256), Arnold transform, and hyperchaos. Randomly generated salt values are concatenated with each pixel of an image. SHA-256 hash is imposed which produces a hash value of 32-bit, later used to generate the key in ECC. Stronger ciphering is done by applying Arnold’s transformation and hyperchaos thereby achieved more randomness in image. Simulation outcomes and analysis show that the proposed approach provides more confidentiality for color images.
Classification Auction Motorcycle and Car In South Jakarta District Attorney Using Naïve Bayes

The Prosecutor’s Office, as an institution, plays a role in enforcing the law, particularly in the field of prosecution. Goods used or resulting from a criminal act will be confiscated and then confiscated by the state. As a result of goods obtained as a result of a criminal act, said goods are to be auctioned off. According to the Minister of Finance Regulation Number 27 / PMK.06 / 2016 regarding auction implementation instructions, an auction is an open sale of goods to the public with the highest price bid in writing or orally, and it can be done online or offline. The South Jakarta District Prosecutor’s Office must determine the grade of the items being auctioned. However, determining the quality to be categorized took a month. As a result, research is required to categorize the quality of the items being auctioned. The need to classify auctions, particularly motorcycles and cars, to reduce the time spent classifying and classifying vehicles. The standard condition is used to classify auction items. This grade applies to the categories of an engine, exterior, and interior car, vehicle year, letter in the form of SNTK, BPKB, and vehicle keys. The Naive Bayes method is used in this study to classify auction items at the South Jakarta District Prosecutor’s Office. The accuracy value of this study is 74%, 44% precision, 50% recall, and 44% f1-score for motor vehicles and 57%, 44% precision, 50% recall, and 44% f1-score for car vehicles.
An Image-Enhanced Topic Modeling Method for Neuroimaging Literature

Topic modeling based on neuroimaging literature is an important approach to aggregate world-wide research findings for decoding brain cognitive mechanism, as well as diagnosis and treatment of brain and mental diseases, artificial intelligence researches, etc. However, existing neuroimaging literature mining only focused on texts and neglects brain images which contain a large amount of topic information. Following the writing and reading habits combining images with texts, we present in this paper an image-enhanced LDA (Latent Dirichlet Allocation), which extracts literature topics from both neuroimaging images and full texts. Combining topics from fMRI brain regions activation images with topics from full texts to model neuroimaging literatures more accurately. On the one hand, topics related brain cognitive mechanism can be pertinently extracted from activated brain images and their descriptions. On the other hand, topics from activated brain images can be integrated with topics from full text to model neuroimaging literature more accurately. The experiments based on actual data has preliminarily proved effectiveness of proposed method.
Distributed Task Privacy for Aggregation Using Linear Codes

Information privacy in modern Internet of Things (IoT) environments is necessary for widespread adoption and public trust of collaborative data processing. Most current distributed privacy results ensure local node observations are not accessible by neighbouring nodes while still solving collaborative tasks. In this work we develop the distinct concept of distributed task privacy in unbounded public networks, where linear codes are used to create arbitrary hard limits on the number of nodes contributing to a distributed task. We accomplish this by wrapping local observations in a linear code and intentionally applying symbol errors or erasures prior to transmission. If many nodes join a distributed task, a proportional number of symbol errors and erasures are introduced into the aggregated code leading to decoding failure if the code's predefined symbol error/erasure limit is exceeded.
Innovative Approaches to Superset Asset Templates using Feature‐Based Product Line Engineering

Abstract This paper discusses our experience applying Feature‐based Product Line Engineering (PLE) to a product line of hardware modules designed for use in space‐based and airborne systems. Feature‐based PLE involves the automatic configuration of engineering artifacts (maintained as supersets) into product‐specific instances based on that product's feature selections. We observed a 40% reduction in non‐recurring engineering costs in this product line. The paper also discusses an innovative approach we used to extend the ability of the PLE configurator tool to interface with analysis and simulation tools for which no actual tool integration currently exists.
Using Hierarchical Transformers for Document Classification in Tamil Language

Riyaz Ahmed, M. Raghuraman, Bhuvan Briskilal, J.Document classification is used for various applications from spam detection in email to article classification. Recently document classification in Tamil has been gaining momentum due to increased data available in said language. One of the major advances in document classification is due to bidirectional encoder representations from transformers (also known as BERT), which uses transformer architecture and has been used effectively in many natural language processing problems like sentiment analysis and document classification for Tamil language. One of the main disadvantages of pre-trained BERT model is the number of tokens cannot be higher than 512; otherwise, it has to be retrained. Our implementation mitigates this issue by using hierarchical transformer architecture and is especially useful for resource poor languages like Tamil. We compare hierarchical transformer model and compared with classical machine learning algorithms and found recurrence over BERT shows substantial improvement over SVM, logistic regression and random forest, with a weighted average F1 score of 0.88 for news article classification.
Some aspects of ecological engineering in modern heating systems of cities

Abstract This article outlines the results of a study of some aspects of implementation and use of ecological engineering elements in the heat supply systems of large urban agglomerations of the Russian Federation. It considers important problems associated with the definition of the term ecological engineering, defines the elements of ecological engineering that can be introduced into the sphere of heat supply of large urban agglomerations. It provides the data on heat energy losses in some subjects of the Russian Federation with harsh climatic conditions, in which heat supply to the large cities lasts for over 9 months a year. The article also identifies the main challenges the heat supply systems used in large metropolitan agglomerations of the Russian Federation face related to the modernization and digital transformation of the heat supply sector of Russian cities. It designates the main problems and opportunities of ecological engineering, which will allow, if widely implemented, providing remote control of the operation of district heating systems, controlling their temperature regime and pressure in pipelines, carrying out the necessary engineering calculations of the functioning of the systems relative to the topography of a particular urban agglomeration. In addition, the study outlines important components of the digitalization of district heating systems in cities to ensure their efficient, effective and, most importantly, safe for the ecosystems of large urban agglomerations, functioning. It has been established that ecological engineering in the district heating industry of large urban agglomerations has to be directed at conservation and restoration of the reserves of natural thermal resources, as well as the natural environment as a whole, by reducing the intensity of operation and emissions into the atmosphere that are produced during the processing of fuel resources.
Super-Resolution by Latent Space Exploration: Training with Poorly-Aligned Clinical and Micro CT Image Dataset

This paper proposes a super-resolution (SR) method, for performing SR on a poorly-aligned dataset. Super-resolution methods commonly needs aligned low-resolution (LR) and high-resolution (HR) images for training. For obtaining paired LR and HR images in medical imaging, we need to align low and high-resolution data using image registration technology. However, since the hardness of aligning LR and HR images, the aligned LR-HR dataset is always low quality. Conventional SR methods always fail to train using poorly-aligned datasets since these methods need high-quality LR-HR datasets. To tackle this problem, we propose a two-step framework for SR using poorly-aligned datasets. In the first step, we decompose image representation into two parts: one is a content code that captures the image content; the other is a style code that captures the image style and anatomy difference between LR / HR images. To perform SR of a given LR image, we input the content code and a latent variable simultaneously into the SR network to obtain an SR result. In the second step, using the trained SR network and an LR image, we search for a content code, and a style code for generating the most proper SR image. This is conducted by searching for the best content code and the best style code by latent space exploration. We conducted experiments using a poorly-aligned clinical-micro CT lung specimen dataset. Experimental results illustrated the proposed method outperformed conventional SR methods by increasing SSIM from 0.309 to 0.312, and have much more convincing perceptual quality than conventional SR methods.