Reviewerly/exHarmony huggingface dataset:
On an Empirical Study

Face recognition is an interesting topic in biometrics research, which can be divided into two sub-problems: face detection followed by face recognition. The application of face recognition in real life situations and pose variations still remains a challenge. The aim of this paper is to evaluate and compare various systems of face recognition based on speed and high accuracy Machine Learning algorithms. The Support Vectors Machine is a strong algorithm for mutli-classification. The feed- forward Neural Network is a popular one. Recently, Deep Learning is becoming a very important subset of machine learning due to its high level of performance across many types of data, in particular using Convolutional Neural Networks (CNNs). A large colored Face Database is used to evaluate these three proposed and adapted architectures. The results are competitive.
Multidimensional Nature of Students’ Cognitive Activity: Experience on Practical Implementation

Purpose: The purpose of the research is to test the effectiveness of the model of technology for developing students’ cognitive activities in the framework of extracurricular work of the university, which combines interactive teaching techniques and innovative methods that lead to the implementation of the research project. The subject of the study is the cognitive activity of students in university extracurricular work. The hypothesis was that this technology: Design/Methodology/Approach: Theoretical analysis of scientific literature on pedagogy and psychology on the problem of the use of educational and pedagogical technologies for the development of cognitive activity allowed us to study the factors that motivate this activity and subsequent actions for its implementation in the scientific sphere. Findings: The results of the experimental study showed a significant increase of students’ cognitive activity in the course of extracurricular work under the influence of the development technology, which strengthens their internal motivation to the research process, and not only to the result, develops their creativity, desire for freedom in research activities, and promotes the growth of their professional interests. It is proved that internal motivation is associated with creativity as the ability to produce constructive ideas in cognitive activity, and the individual self-realization manifests the multidimensional cognitive activity of students. Originality/Value: The scientific novelty of the research is the development and implementation of a model of the innovative technology for the development of students’ cognitive activity in extracurricular work, which provides pedagogical and psychological development of cognitive activity taking into account its components (motivation, well-being, activeness, mood and self-realization).
Implementing Culturally Relevant Relationships Between Digital Cultural Heritage Objects

A vocabulary of culturally relevant relationships – CRR - between cultural heritage objects in Library, Archive, and Museum (LAM) was proposed with the aim of interlinking digital collections using the Linked Open Data (LOD) technologies. The CRR vocabulary was proposed to be used by culture curators, teachers, historians, etc. to enable them to interlink such digital resources to provide a richer context and to reveal new senses of such resources. This paper aims at testing and evaluating the CRR vocabulary, as follows. Wikipedia articles about remarkable cultural heritage objects as the painting Mona Lisa and the book Dom Casmurro are used to create RDF triples where the heritage object is the subject, relationships of the CRR vocabulary are the predicates, and links found in each Wikipedia article to other LAM digital objects, or other Web resources are the objects of each triple. The RDF graphs thus generated are presented and discussed. The necessity of improvements in the proposed CRR vocabulary is outlined and suggestions of these changes are proposed.
High Capacity Steganography on Float-Point Number with Single Precision

Nowadays, the research on modern image steganography mainly focuses on the spatial domain and jpeg transform domain. And the image elements that used to carry the message bits are all integers without exception. However, with the in-depth study of High Dynamic Range (HDR) images, single-precision float-point numbers have gained more and more attention. The float-point number has more redundant bits than integer, which makes it can hide more secret information and more suitable for steganography. In this paper, based on the IEEE754 standard, we employ the luminance channel of HDR image as the steganography domain which is named the mantissa spatial domain. Based on this new steganography domain, we propose a novel adaptive steganography scheme that can realize satisfactory security under high payload such as 0.75bpp. Experimental results show that the proposed scheme achieves better performance on resisting the state-of-the-art steganalysis method than existing schemes and make a good balance between capacity and security.
Fault-tolerant Subgraph Matching on Aligned Networks

In the context of networks curated from real world data, an important problem is that of aligning entities across multiple data sources. Methods for performing alignment are often error-prone due to lack of information and noise within the data itself. When performing subgraph matching on the resulting aligned networks, these alignment errors often lead to catastrophic results. In this paper, we propose a fault-tolerant algorithm for subgraph matching on aligned networks which takes into account potential alignment errors.
MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data

Human abnormal physical and psychological behaviors, such as high level of stress, may result in negative impacts on work and life, if not handled efficiently. However, the continuous collection of behavioral data from questionnaires is not feasible, as is often the case for the natural downside of survey data gathering. Thanks to the proliferation of mobile sensors, it brings compelling opportunities for us to more deeply analyze human behavior. In this work, we ask the question of detecting anomalies in human physical and psychological behaviors from multivariate temporal data from multi-modal sensors. In the past decades, many efforts have been made in developing anomaly detection methods, but there remain several challenges in this specific domain problem: 1) data contains missing values at random positions. 2) data from multiple sensors is of multi-resolution and multivariate. 3) human behaviors are correlated to each other, thus it poses a multi-label problem. 4) the available labeled instances are limited, which requires the semi-supervised learning setting. 5) the frequency of anomaly occurrence is much smaller than that of normal instances, leading to imbalance problems. We propose a novel framework MBead to resolve these concerns. MBead consists of three key components: reweighted autoencoder to capture the dependency across temporal domain and multiple modalities, relevance learning module to learn the pairwise relations among labeled instances, and temporal prediction module to detect the anomalies while trained in semi-supervised settings. Extensive experiments show our MBead outperforms seven state-of-art baselines on three tasks of behavior anomaly detection: stress, affect, and work performance.
Open Distributed Infrastructure Management - ODIM

The evolution of the Telecom network to 5G and Edge Computing requires IT compute, storage and networking infrastructure from multiple vendors to be deployed across potentially thousands of geographically distributed and diverse points of presence, including central offices, cell tower huts, wiring closets as well as traditional data centers. This sharply contrasts with the comparatively few homogeneous hyper-scale data centers of the cloud service providers. Telecom operators are demanding open, scalable infrastructure management for this distributed multi-vendor infrastructure based on widely adopted industry standards. In this paper, we are addressing these unique challenges through an open-source, Distributed Management Task Force (DMTF) Redfish®[1] based Resource Aggregation function, helping create a vibrant open-source infrastructure management community around the Resource Aggregator will allow in the building of next-generation telecommunications and other highly distributed networks.
TADS: Transformation of Anomalies in Data Streams

Detecting anomalies, in many cases, is only the first step. Often, it is merely the first step in a process that seeks to recover from an anomalous situation so that the system is back to a normal state. Manual analysis is a possible approach; however, this can be time consuming and error prone. Ideally, it would be desirable to have an automated means for resolving anomalies, which is invoked whenever one or more anomalies are detected. In this paper, we present a tool, TADS (Transformation of Anomalies in Data Streams), that creates automated recommendations based upon the observed streaming data. This is accomplished by utilizing the recently introduced concept of dynamic action rules with the Massive Online Analysis (MOA) data streaming platform. In experimental results, we demonstrate that TADS is able to correctly generate recommendations 100% of the time for over half the experiments and over 90% for all but two experimental conditions. In addition, only seconds are required to generate the personalized recommendation for each anomaly. Hence, the results indicate that TADS is a viable approach for correcting anomalies within a streaming environment.
The evolution of conceptual sphere of Robert Bridges

The analysis of the individual writing style is a dynamically developing direction of modern linguistics. An integral part of individual style is the metaphorical system of the author. Metaphors are one of the most accurate markers of individual style, since they give a perspective on personality and mental worldview of the writer. The study of metaphor allows describing the nature of the imagery world of the author or his conceptual sphere. The subject of this research the evolution of the conceptual sphere of Robert Bridges, a prominent representative of English poetry of the late XIX &amp;ndash; early XX centuries. The goal consists in elucidation of peculiarities of the author's conceptual sphere. The research methodology leans on the method of studying metaphor developed by N. V. Pavlovich. The article explores the questions of individual writing style, placing emphasis on the dynamics of imagery system of R. Bridges. The scientific novelty consists in revealing the mental worldview in each period of the author&amp;rsquo;s work, outlining the author's conceptual sphere, and describing the nature of transformations in the author's imagery system throughout his literary path. The acquires results demonstrate the characteristics of conceptual sphere of the author's early and mature lyrics, as well as determine stable and dynamic parameters of his mental worldview. It is established that the structure of conceptual sphere seeks to preserve the fundamentals of the author's view of the world, as well embraces the changes taking place in the poet's worldview.
Document Embedding using piped ELM-GAN Model

Document Embedding methods are an impressive task in each machine learning or neural network based natural language processing task. This task is entitled by representation learning and knowledge representation, too. In ultimate the target of this task, each document outputs a representation format of text documents in order to be understandable for machine. Literature reviews in representation learning, shows that document embedding methods for text is weaker in compare with representation of image or signal. Also, in compare to other data like as image or signal, representation of text has more challenges. By this, this paper we suggested a piped process of Generative Adversarial Neural Network and Extreme Learning Machine technique for document embedding. The experimental results show that document embedding using this combination of Generative Adversarial Networks and Extreme learning machines is comparative with other available methods of document embedding.
Research on telemetry data compression technology based on inter frame differential adaptive run length encoding

Abstract Aiming at the problem of massive historical telemetry data storage in flight test, a lossless compression method based on adaptive interval run length encoding is proposed. Aiming at the problem of low compression efficiency of traditional run length encoding algorithm for word data, by studying the storage characteristics of telemetry data, this algorithm automatically identifies the frame format of telemetry data, and carries out longitudinal run length adaptive interval encoding for inter frame differential data to improve the compression efficiency. The test results show that the compression ratio of the improved algorithm is improved by 58.1% and 1.5% compared with the traditional run length encoding algorithm and the inter frame differential lateral run length encoding algorithm.
Feature Selection in Jump Models

Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account. We propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings where the number of features is large compared to the number of observations and the underlying states differ only with respect to a subset of the features. We develop and implement a coordinate descent algorithm that alternates between selecting the features and estimating the model parameters and state sequence, which scales to large data sets with large numbers of (noisy) features. We demonstrate the usefulness of the proposed framework by comparing it with a number of other methods on both simulated and real data in the form of financial returns, protein sequences, and text. The resulting sparse jump model outperforms all other methods considered and is remarkably robust to noise.
An Overview of Entity Relation Extraction

Abstract Plenty of digital text is generated and shared in nowadays life, which constitutes many unstructured text resources. An automatically information extraction method is highly in demand as a large amount of useful information can be stored and output through it. In this paper, several basic concepts about entity relation extraction are introduced, including word embedding, positional embedding and convolutional neutral networks. Several types of relation extraction are also discussed: supervised relation extraction, relation extraction using distant supervision and relation extraction using few-shot approach. In addition, the existing challenges and problems are discussed, like overlapped triples, wrong labeled data and the difficulties that few-shot learning approaches are now facing.
Assessing the variability of posterior probabilities in Gaussian model-based clustering

We propose a variant of the bootstrap to assess the variability of posterior probabilities arising from Gaussian model-based clustering. The bootstrap variant uses predictions based on out-of-bootstrap-sample observations and then constructs confidence intervals for the posterior probabilities using the percentile method. The methodology outperforms the multivariate Delta method approach when comparing empirical coverage probabilities on simulated data. The proposed and multivariate Delta methods are also illustrated on the well-known Iris data set.
CONSERVATIVE AND PROGRESSIVE COMPONENTS IN SCIENCE

The development of science, proceeding at a higher and higher speed, leads to the creation of new concepts, theories, and ideas. They constitute a progressive component of science. However, scientific development does not mean that everything that was accepted earlier has to be given up. New elements may be acquired and exist together with the old ones. Such old elements constitute a conservative component of science. That is why modern science shows itself as a wholeness constituted by the above-mentioned components.
Scale-Aware Network with Attentional Selection for Human Pose Estimation

Human pose estimation is a fundamental yet challenging task in computer vision. Human pose estimation from a single image is a challenging problem due to the limited information of 2D images and the large variations in configuration and appearance of body parts. Recent works has largely improved the result of human pose estimation because of the development of convolutional neural network. However, there still exists many difficult cases, such as occluded keypoints, complex background and scale variations of human body keypoints, which cannot be well dealt with. In this paper, we design a novel scale-aware network with attentional selection that extracts multi-scale semantic information and meaningful features. Specifically, we propose a Feature Pyramid Supervision Module (FPSM), which can improve the estimation accuracy of scale variations. Meanwhile, a Spatial and Channel Attention Module (SCAM) is designed for recalibrating the spatial and channel features. Based on the proposed algorithm, we achieve state-of-the-art result on LSP dataset and make competitive performance on MPII Human Pose dataset.
Efficient Parameter Aggregation in Federated Learning with Hybrid Convergecast

In federated learning, workers train local models with their private data sets and only upload local gradients to the remote aggregator. Data privacy is well preserved and parallelism is achieved. In large-scale deep learning tasks, however, frequent interactions between workers and the aggregator to transmit parameters can cause tremendous degradation of system performance in terms of communication costs, the needed number of iterations, the latency of each iteration and the accuracy of the trained model because of system “churns” (i.e., devices frequently joining and leaving the network). Existing research leverages different network topologies to improve the performance of federated learning. In this paper, we propose a novel hybrid network topology design that integrates ring (R) and n-ary tree (T) to provide flexible and adaptive convergecast in federated learning. Specifically, multiple participated peers within one-hop are formed as a local ring to adapt to device dynamics (i.e., “churns”) and carry out local cooperation shuffling; an n-ary convergecast tree is formed from local rings to the aggregator to assure the communication efficiency. Theoretical analysis shows the superiority of the proposed hybrid (R+T) convergecast design in terms of system latency as compared to existing topologies. Prototype-based simulation on CloudLab shows that the hybrid (R+T) design is able to reduce the rounds of iterations while achieving the best model accuracy under system “churns” as compared to the state of the art.
Detecting Discrete Cosine Transform-Based Digital Watermarking Insertion Area Using Deep Learning

Invisible digital watermarking, a technology for embedding information in digital content, is mainly used for copyright protection. In this paper, we proposed a method to identify images with invisible discrete cosine transform (DCT)-based watermarking and its vulnerability. Prior to watermarking, the images were normalized to 256 × 256 grayscale, and the network was generated through transfer learning; ResNet-18 was used to classify the input images as “watermarked” or “unwatermarked.” According to our results, the accuracy of the network, when classifying images into both classes, was as high as 99.80%. Furthermore, the testing accuracy of a network designed to detect the embedding location of the watermark in the frequency domain was 97.97%. It should be noted that the networks were ineffective when the DCT block size of the input images differed from that of the images in the training set.
Software Modernization Using Machine Learning Techniques

As software engineering techniques and practices continuously evolve, programs created with an older technology stack become harder and more costly to maintain. These software are often referred to as legacy code. Naturally, the need arises to make use of the newer and more effective technologies, making the legacy code easier to maintain and operate. However, companies rarely allocate the necessary resources to manually re-implement these systems as that would be highly time-consuming and extremely costly to spend exclusively for maintenance purposes. Thus, various code modernization approaches have been proposed and tools have been created to reduce the cost of re-implementation by semi-automatically translating legacy systems into a modern, more advantageous environment. However, the source and target languages may be so different in nature that making the generated code feel as natural as possible is often difficult. These linguistic differences frequently impose the emulation of certain features between the two languages, which may prove too difficult to automatically handle using conventional static analysis of the source code. To this end, in this paper we propose the novel method of using machine learning techniques to teach the transformer on how to effectively handle cases that would otherwise be very error-prone in practice. This way, the transformation tool can achieve both a high level of automation and the ability to generate precise, error free code.
Probabilistic Data Association for Orbital-Element Estimation Using Multistage Expectation–Maximization

Tracking space objects is important for managing space traffic and predicting collisions, but is difficult in part due to data association and orbit model uncertainty. Expectation–maximization (EM) is a commonly used tracking method that has not been widely considered for tracking space objects. The technique consists of iteratively computing data association probabilities with a set of current element estimates, and updating estimates of the elements by solving a nonlinear weighted least-squares regression problem where the weights are the data association probabilities. This paper demonstrates the use of EM for probabilistic data association and orbital-element estimation by applying the technique to simulated data from two angles-only tracking scenarios. In both scenarios, EM provides correct data associations and accurate maximum likelihood estimates of orbital elements. One scenario considers tracking a single object in clutter and quantifies the improvement of the orbital-element estimates and data associations as the detection probability increases. However, standard application of EM requires knowing the number of objects or may fail when a large number of objects are present. To address these issues, this paper employs a multistage version of EM that is applicable when there are a large and possibly unknown number of objects.
Regularity Conditions and Basic Properties

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