Reviewerly/exHarmony huggingface dataset:
A Method to Extract Pedestrian Biological Features from Video for Open-World Re-Identification

Deep learning technology has promoted the rapid development of person reidentification (Re-ID). However, the existing re-ID methods are overly dependent on the appearance features of pedestrians’ clothing styles, colors, etc. These features cannot be used as pedestrian identification information because they are easily disguised and changed by people. In this paper, we extract the biological features of pedestrians based on pose estimation from the video frames. CSPNet and group convolution are used to our model. Simultaneously, in order to deal with the discontinuity of feature extraction caused by interference factors such as occlusion, exposure, and low resolution, a simple convolutional perception network named CoorNet7 is used to correct the output of the pose estimation model. Dynamic programming is used to measure the distance between feature vectors, and the space complexity of the algorithm is optimized. First, we conducted experiments on the open-world dataset PRCC, and the experimental results reached Rank -1: 41.11%. Then in order to test the model has strong generalization and robustness to the variables of the open-world complex factors. We verified the performance of the model on MARS, the largest re-ID dataset at present, and the experimental results show that our method achieves Rank-1: 79.5%, Rank-5: 86.6%, and mAP: 65.2%, good grades.
The Role of Creolized Texts in the Rhetorization of the Educational Process: Experience and Prospects

Purpose: In this article, we consider the problem of professional and personal development of philology students in university. The purpose of this study is to offer a theoretical ground for and to test the effectiveness of the methodology for the formation of students’ instrumental literacy based on the use of film fragments. Our specific objectives are to determine the psychological and pedagogical conditions for the use of creolized texts in professional training of future teachers of the Russian language and to study the peculiarities of students’ perception of films. Design/methodology/approach: The basis for our scientific and methodological research is the dialectical principle of universal communication and development, theoretical findings on the rhetorization of the modern educational process as a culture of thought and speech, and contemporary methodological ideas about the influence of multichannel resources on the transformation of pedagogical processes. The material of this research is the natural educational process and students’ classroom and extracurricular activities. The sample group researched was comprised of the students and faculty members from Moscow and Taganrog universities: teachers of philological departments aged 38–63 (18 people) and students of bachelor and master programs aged 18–23 (268 people). Findings: The novelty of our research is determined by (1), the development of the structure and psychological and pedagogical content of classes conducted both in traditional and online forms, and (2) a case study of situational tasks of linguomethodological content based on the perception and interpretation of fragments of films by philology students. Originality/value: Based on our study, we have substantiated and tested the effectiveness of the methodology for the formation of instrumental literacy of students using film fragments. It was concluded that the use of creolized texts in teaching philology students is promising and that the development of teaching tactics based on rhetorization techniques as a material for developing students’ professional skills is advisable.
Development of a Distributed Collaborative Research Tool for University-Industry Partnership

This research study aims at developing a collaborative research tool using distributed servers to enhance collaborative research among Universities and Industries to promote innovation. The Adaptive Software Development model was employed due to the innovative nature of the study. Requirements were gathered from key stakeholders to determine the system architecture and various models that supported the system development process. The testing procedure demonstrated that three (3) separately located servers representing University A, University B and Industry Players worked together as one unit such that all users could form Research Teams and collaboratively conduct research work on the platform to boost University-Industry partnership for innovation.
Porn Image Forensics: Image Classification, Forgery Detection and Localization

The manipulation of images has increased recently due to the development of advanced software tools that are used to modify the images for specific intentions like Cyberbullying, extortion, etc. In recent years pornographic images/videos are spread rapidly to exploit the individual personality by harassing through fake porn images. Therefore, the need for forensic analysis in images/videos is important to rebuild trust by using forgery detection techniques. We propose a three-step hierarchical procedure for classifying, detecting and localizing forged porn images and non-porn images. In the first step, we use ResNet50 pre-trained model for classifying the porn images. To detect porn forgery and non-porn forgery, we use LSTM-CNN model in the second step. Finally, we classify the forged object as copy-move or image splicing using template matching with SIFT algorithm. The performance of the model is better in terms of training and testing accuracy as compared to the existing models.
Mini-Batch Stochastic Three-Operator Splitting for Distributed Optimization

We consider a network of agents, each with its own private cost consisting of a sum of two possibly nonsmooth convex functions, one of which is composed with a linear operator. At every iteration each agent performs local calculations and can only communicate with its neighbors. The challenging aspect of our study is that the smooth part of the private cost function is given as an expected value and agents only have access to this part of the problem formulation via a heavy-tailed stochastic oracle. To tackle such sampling-based optimization problems, we propose a stochastic extension of the triangular pre-conditioned primal-dual algorithm. We demonstrate almost sure convergence of the scheme and validate the performance of the method via numerical experiments.
An Efficient and Rotation Invariant Fourier-Based Metric for Assessing the Quality of Images Created by Generative Models

Recent progress in generative image modeling is leading to a new era of high-resolution fakes visually indistinguishable from real life images. However, the development of metrics capable of discerning whether images are synthetic or not runs behind the race of achieving the best generator, thus bringing potential threats. We propose a rotation invariant metric capable of distinguishing real and generated image datasets and we call it CSD (Circular Spectrum Distance) due to its circular nature and its inherent relation to the Fourier Spectrum. Its performance is analysed on a whole brain MRI dataset. CSD has similar behavior to FID during training but requires smaller batch sizes and is faster to compute.
A Novel Xrf Based Lithological Classification in the Tarkwaian Paleo Placer Formation Using Smote-Xgboost

Geological processes such as weathering and metamorphism normally lead to the destruction of primary physical features on rocks, making it difficult to accurately classify lithologies during manual core logging. As a result, manual core logging has become a subjective and inconsistent process. Machine learning techniques have exhibited tremendous success in lithological classification, however, their performance has encountered significant drawbacks, especially in highly imbalanced data domain and complex geological terrain. Due to the increasing demand for accurate and timely lithological classification, this study proposes an objective hybrid approach based on synthetic minority oversampling technique and extreme gradient boosting (SMOTE-XGB) for lithological classification within the Tarkwaian palea placer formation using assay data acquired through X-Ray Fluorescence (XRF) analysis. To further evaluate the performance of the proposed approach, four other intelligent approaches were developed for comparative purposes using accuracy (Acc), area under the curve (AUC), precision, sensitivity and F1 score as evaluation metrics. The statistical results have justified the proposed SMOTE-XGB as a more efficient approach over the other machine learning models adopted in this study. By applying SMOTE to the imbalanced dataset, the results showed a significant increase in the classification performance of the minority classes, especially the mineralised lithologies. To this end, this study has demonstrated that the application of SMOTE to the imbalanced dataset can improve the prediction efficiency of machine learning models.
Abnormal Human Behavior Detection System in Video Surveillance Systems

The abnormal behavior detection, in terms of importance has become a necessity in the visual surveillance systems. The main problem is the ambiguity in difference between the characteristics of abnormal and normal behavior which its definition is usually different according to previous context of images. In this research, three approaches are used. In the first approach, we use a standard Convolutional Automatic Encoder (CAE). After evaluation, it was found that standard CAE problem is that it does not take into account the temporal aspect of the image sequence. In the second approach, we use an automatic encoder that can learn spatio-temporal structures in dataset. In third approach, the complex LSTM cells are used for the further improvement. The results of experiments show that proposed methods have better performance compared to many of previous conventional methods and its efficiency in identifying the abnormal behavior is very competitive compared to previous methods.
Gesturing with Smart Wearables: An Alternate Way to User Authentication

A method of alternate user authentication that relies on sensory data from a smartwatch has been explored in this paper. This attempt to beef up the authentication security was made by taking the user-defined hand gesture into account while wearing a smartwatch. Eventually, the preset hand gesture would work similar way to the password-based authentication scheme. In our experiment, we recorded the 3D coordinate values measured by the accelerometer and gyroscope over a set of gestures. We experimented with 50 gesture samples comprising of five different gesture patterns and ten repeated samples for each pattern. We developed an Android WearOS smartwatch app for sensor data collection, implemented our method of sensor data processing, and performed a series of experiments to demonstrate the potential of this method to achieve high accuracy.
New Educational Requirements of Youth in the Digital Economy of the Future: Innovation and Risks Versus Stability and Return on Investment

The paper aims to identify the new requirements of young people for education in the digital economy of the future and, considering these requirements, identify the attractiveness of higher education programs on training digital personnel to work with breakthrough technologies among young people in Central Asia and Russia. The authors conduct a review and comparative analysis of relevant statistics to identify common patterns and conditions of the involvement of youth in higher education in Central Asia and Russia. The authors apply the SWOT analysis to assess the attractiveness of higher education programs to train digital personnel to work with breakthrough technologies (artificial intelligence, Internet of Things, Big Data, and robots) among young people in Central Asia and Russia. The research identifies new requirements of young people for education in the digital economy of the future. Young people are not ready to take high risks in learning innovative educational programs and prefer stability and a high probability of return on investment in training. Growing government funding for higher education programs to train the digital workforce for breakthrough technologies (artificial intelligence, Internet of Things, Big Data, and robots) in Central Asia reduces the attractiveness of higher education for young people. The indicated problem can be solved by increasing the number of knowledge-intensive jobs (in the labor market) and providing guarantees of employment in the profession for university graduates.
Cloud as a Platform for W-Machine Didactic Computer Simulator

Effective teaching of how computers work is essential for future computer engineers and requires fairly simple computer simulators used in regular students’ education. This article shows the evaluation of alternative architectures (platform-based and serverless) for cloud computing as a working platform for a simple didactic computer simulator called W-Machine. The model of this didactic computer is presented at the microarchitecture level, emphasizing the design of the control unit of the computer. The W-Machine computer simulator allows students to create both new instructions and whole assembly language programs.
Ordered Ensemble Classifier Chain for Image and Emotion Classification

Ensemble techniques play a significant role in the enhancement of machine learning models; hence, they are highly applicable in multi-label classification, a more complex form of classification compared to binary or multi-class classification. Classifier chain is the most prevalent and oldest technique that utilizes correlation among labels for solving multi-label classification problems. The ordering of class labels plays a significant role in the performance of the classifier chain; however, deciding the order is a challenging task. A more recent method, ensemble of classifier chains (ECC) solves this problem by using multiple CC’s with a different random order of labels for each CC as the base classifier. However, it requires at least ten CCs, and it is computationally expensive. Improving the prediction accuracy with less than ten CCs is a challenging task that this paper addresses and proposes a classifier chain’s ensemble model termed Ecc_Wt_Rase. It uses a weighted ensemble of only four classifier chains. The performance of Ecc_Wt_Rase is compared with the traditional CC and ECC over three standard multi-label datasets, belonging to image and emotion (music) domains using four performance parameters. On the one hand, Ecc_Wt_Rase reduces the computational cost and, on the other hand, improves the classification accuracy. The improvement in Hamming loss is approximately 6%, which is exceptional for multi-label classification; the training time is also reduced by approximately 40%, as the number of CCs in the proposed model is four, compared to ten in traditional ECC.
The World in Transition

In order to be able to classify the challenges for achieving digital excellence, we first take a look at the big picture. How has our world developed into a VUCA world? What are the challenges that go hand in hand with this development? What impact does digital Darwinism have on our industry? How can the opportunity and threat situation be determined for your own company? For this purpose, a systematic analysis of the macro-environment is carried out using the PESTEL-analysis. The aspects that need to be taken into account in the development of digital excellence are systematically worked out.
Sampling for Scientific Data Analysis and Reduction

With exascale supercomputers on the horizon, data-driven in situ data reduction is a very important topic that potentially enables post hoc data visualization, reconstruction, and exploration with the goal of minimal information loss. Sophisticated sampling methods provide a fast approximation to the data that can be used as a preview to the simulation output without the need for full data reconstruction. More detailed analysis can then be performed by reconstructing the sampled data set as necessary. Other data reduction methods such as compression techniques can still be used with the sampled outputs to achieve further data reduction. Sampling can be achieved in the spatial domain (which data locations are to be stored?) and/or temporal domain (which time steps to be stored?). Given a spatial location, data-driven sampling approaches take into account its local properties (such as scalar value, local smoothness etc.) and multivariate association among scalar values to determine the importance of a location. For temporal sampling, changes in the local and global properties across time steps are taken into account as importance criteria. In this chapter, spatial sampling approaches are discussed for univariate and multivariate data sets and their use for effective in situ data reduction is demonstrated.
A Data-Driven Method for Diagnosing ATS Architecture by Anomaly Detection

Autonomous Transport System (ATS) architectures enable a wide range of new applications and bring significant benefits to transport systems. However, during the design stage, errors of the architecture can have an impact on the smooth implementation of the ATS, which will endanger the normal operation of the transport systems. To ensure a high autonomy of the ATS architecture, i.e., “functionally evolvable, logically reconfigurable and physically configurable”, the detection of ATS architecture design errors is essential. This paper aims to fill the research gap in the existing research on diagnosing or evaluating ATS architectures. Inspired by word embedding models in natural language processing communities, we propose a data-driven approach to diagnose ATS architectures without prior knowledge or rules. We use an architecture embedding model to generate vector representations of ATS architectures, then train the model through negative sampling of the training dataset to identify the features of abnormal ATS architecture. Finally, we employ the trained model to classify structural errors of the test dataset generated from the ATS architecture. The experimental results show that the proposed method gains a relatively good effect of classifying with an average accuracy of 79.3%, demonstrating the effectiveness of the method.
Energy-Efficient and Fair IoT Data Distribution in Decentralised Federated Learning

Recently academia and industry has growing interest in the sixth generation network, which aims to support a rich range of applications with higher capacity and greater coverage than existing 5G connections. One of such promising applications that can benefit from 6G is Decentralised Federated Learning, a privacy-preserving machine learning paradigm. Also, it relies heavily on peer-to-peer mobile connection among edge and mobile devices, instead of a powerful central server on the cloud. However, the data and device heterogeneity, and highly dynamic environment in mobile networks pose challenges to the performance of federated learning. In this paper, we propose a data redistribution phase that balances the data distribution on different participating devices to a certain degree, which can further increase the system performance in the training phase. To derive our method, we first model this problem as a bargaining game, the equilibrium of which is formalised as an optimisation problem. Then we propose two algorithms to solve it: a centralised one, and a decentralised one that each participant executes without centralised coordination. We further improve the energy efficiency of the decentralised algorithm by introducing several heuristics. We evaluate the proposed system with both simulation and DNN training tasks on large scale FEMNIST-based datasets.
Naïve Bayes’ classifiers in R

One of the very common theorems in classification algorithms is Bayes’ theorem. This theorem finds the probability of an event based on previous knowledge. In this chapter, we will learn about one of the simplest classification models based on Bayes’ theorem, called Naïve Bayes’ classifier, as it naively presumes independence of independent variables.
Two-for-One Conjoint: Bayesian Cross-Category Learning for Shared-Attribute Categories

Conjoint analysis is an ubiquitous market research tool. As normally implemented, each time you wish to understand consumer utilities for a product category, you run a conjoint study for that category. For example, if you require consumer preferences for yogurt features, you run a (choice-based) conjoint using choices between yogurts. In this research, we propose two conjoint methodologies. The first, based on transfer learning, has the researcher learn about a focal category (e.g. yogurt) by leveraging results from a conjoint study on a related category (e.g. ice cream). The second contribution is a data collection task that uses choice pairs between products in different categories. We demonstrate our approach using three category pairs (hiking jackets and sleeping bags, ice creams and yogurts, and televisions and computer monitors) where paired categories have fully or partially overlapping features. We show, with a Bayesian model, how out-of-sample prediction accuracy is improved when using out- of-category information, with both no data and sparse data from the focal category. We demonstrate the effects of out-of-category information on managerially relevant decisions such as market share predictions. Since our approach uses standard conjoint software with no modifications, it can be used immediately as a practical marketing research tool.
Statistical Relational Learning for Genomics Applications: A State-of-the-Art Review

This paper aims to review the state-of-the-art statistical relational learning models (SRL) in genomics. SRL deals with machine learning and data mining in relational domains where observations may be missing, partially observed, and noisy. This chapter introduces a background overview of various models, including probabilistic graphical models, Bayesian networks, dependency networks, Markov networks, first-order logic, and probabilistic inductive logic programming. This chapter also discusses the various statistical relational learning approaches, including probabilistic relational models, stochastic logic programs, Bayesian logic programs, relational dependency networks, relational Markov networks, and Markov logic networks. Finally, the last part of the paper focuses on the practical application of statistical relational learning techniques in genomics. The chapter concludes with a discussion on the limitations of current methods.
Neural Topic Modeling with Gaussian Mixture Model and Householder Flow

To overcome high computational cost suffered by statistical inference algorithms used in traditional topic models, recently, Variational AutoEncoder (VAE) frameworks have been proposed for topic modeling. However, the vanilla VAE model is originally introduced for unsupervised learning, which cannot meet more precise and customized requirements. In addition, the approximate posterior distribution in VAE is often selected as a Gaussian with a diagonal covariance matrix. This unimodal choice may hinder the ability of representation of latent space. In view of these limitations, in this paper, we propose to use Gaussian mixture model and Householder Flows for topic modeling under semi-supervised settings. We assume a document is associated with a mixture of classes, and a class is modeled as a multivariate Gaussian over latent topics. Specifically, an input document is encoded by a network into a discrete distribution, which not only serves the classifier for prediction, but also acts as mixing weights of Gaussian components. Another network is adopted to learn the parameters of Gaussian components. Additionally, Gaussian mixture is transformed by a Householder Flow to produce a more general posterior distribution. The effectiveness of the proposed model has been validated by the experiments performed on several standard datasets.