Reviewerly/exHarmony huggingface dataset:
Improved Model Structure with Cosine Margin OIM Loss for End-to-End Person Search

End-to-end person search is a novel task that integrates pedestrian detection and person re-identification (re-ID) into a joint optimization framework. However, the pedestrian features learned by most existing methods are not discriminative enough due to the potential adverse interaction between detection and re-ID tasks and the lack of discriminative power of re-ID loss. To this end, we propose an Improved Model Structure (IMS) with a novel re-ID loss function called Cosine Margin Online Instance Matching (CM-OIM) loss. Firstly, we design a model structure more suitable for person search, which alleviates the adverse interaction between the detection and re-ID parts by reasonably decreasing the network layers shared by them. Then, we conduct a full investigation of the weight of re-ID loss, which we argue plays an important role in end-to-end person search models. Finally, we improve the Online Instance Matching (OIM) loss by adopting a more robust online update strategy, and importing a cosine margin into it to increase the intra-class compactness of the features learned. Extensive experiments on two challenging datasets CUHK-SYSU and PRW demonstrate our approach outperforms the state-of-the-arts.
Camera viewpoint learning for person reidentification

Camera viewpoint has a great influence on reidentification (re-ID) for its diversity. However, there are few methods in response to it, which limits the development of the re-ID community. We analyze the influence of camera viewpoint on re-ID to obtain both visualization and quantitative results, indicating the potential to solve this problem. On this basis, we propose mean and variance loss function (MVL) and camera ID classifier (CIC). MVL reduces the gap among the mean and variance of the feature vectors of images acquired by different cameras, so that the distribution of image feature vectors under different cameras is similar. CIC filters out the images, which have the same camera id with the query to avoid recognition interference. The experiments are conducted on Market1501 and DukeMTMC-reID datasets. We show that MVL and CIC can mitigate the negative impact of the camera viewpoint for re-ID and further improve the accuracy of re-ID. In the Market1501 dataset, rank-1 [mean average precision score (mAP)] accuracy is improved from 88.1 (71.1) % to 90.0 (73.9) % for ResNet-50. In the DukeMTMC-reID dataset, rank-1 (mAP) accuracy is improved from 77.0 (58.9) % to 83.9 (64.1) % for ResNet-50.
Feature Selection by Using DE Algorithm and k-NN Classifier

Feature selection (FS) in data mining is one of the most important preprocessing steps before starting analysis processes. In addition to more accurate estimation of the results by the FS process, unnecessary resource utilization can be avoided by removing unnecessary attributes. One of the most commonly used data prediction and classification methods in data mining is k Nearest Neighbor algorithm (k-NN). k-NN classifier can classify data swiftly. In this study, FS process with Differential Evolution (DE) algorithm has been realized by using sample datasets. During the cost accounting of candidate solutions of DE algorithm, k-NN classifier has been used. The results obtained with DE algorithm have been compared to Correlation based feature selection, Information gain based feature selection and Learner based feature selection methods that are commonly used in classification problems. The results showed that the proposed method could successfully perform FS process.
Considering cultural probes in HCI4D/ICTD research

This is a forum for perspectives on designing for communities marginalized by economics, social status, infrastructure, or policies. It will discuss design methods, theoretical and conceptual contributions, and methodological engagements for underserved communities. --- Nithya Sambasivan, Editor
Application of Majorization-Minimization Method to Chan --- Vese Algorithm in the Image Segmentation Problem

The purpose of the study was to modify Chan --- Vese algorithm in order to overcome its shortcomings, such as high computational complexity and the use of approximations. In the considered modification, optimization is carried out by the majorization-minimization method, the main idea of which is to reduce the complexity of the problem using the majority function. Due to the proposed optimization method, it is possible to use the Heaviside step function and Dirac delta function. This enabled the same or better saturation levels when optimization is done by the graph cut method in a smaller number of iterations, which reduced the operation time. The proposed algorithm was tested on a Caltech101 dataset. The algorithm is general, does not depend on the subject area and does not require prior training. This allows it to be used as the basis for a wide range of image segmentation algorithms.
A Study for the Identification of a Full-Body Gesture Language for Enabling Natural User Interaction

Most proposals addressing gesture recognition consider two main stages: gesture identification in a source and association between gestures and meanings. About the semantic meaning of gestures, it is important to consider that due to their cultural and linguistic specificity each of them may be mapped to many concepts and vice versa, making them ambiguous and incompletely defined. From the HCI perspective, there is work in the literature presenting elicitation studies on which researchers find gestures sets to allow user interaction with tailored applications under determined contexts. In this paper, we present a full-body language for enabling gesture-based interaction in different contexts. For this purpose, 70 users were asked to provide the gestures they would use as commands within different existing applications, keeping awareness of the relationship between the tasks they were asked to do on the applications and abstract tasks, resulting on 980 gestures which were compared for generating a reduced set of 68 gestures consisting on their graphic representation, a textual description, an anthropometric characterizations for each of them, and generic labels. Interpretation and insights obtained during the experiment are also reported.
Monte Carlo analysis of gathering and scattering behavior generated by gravitational particle swarm algorithm

In this study, we propose a novel multimodal optimization (MMO) algorithm, gravitational particle swarm algorithm (GPSA). This replaces the global feedback term of a classical particle swarm optimization with a term that introduces inverse-square gravitational force between particles. We analyze GPSA's search behavior by Monte-Carlo simulation and evaluate its search performance for a one-dimensional MMO problem. It is shown that our GPSA's particles exhibit gathering and scattering behavior, based on which our GPSA was able to find 83% of the optimal solutions without using any algorithmic clustering procedures.
Shuffle Dense Networks for Biometric Authentication

In recent years, with the continuous development of artificial intelligence technology, biometric authentication based on computer vision technology have also been developed rapidly. In this paper, we propose a novel Shuffle dense networks (SDN) with combining ShuffleNet and DenseNet for biometric authentication. ShuffleNet is an extremely computation-efficient structure that can obtain more channels information. DenseNet makes full use of all hierarchical features, which can facilitate the flow of information. Specifically, dense skip connections is adopted for combining the low-level features and the high-level features to enhance the performance of the reconstruction and residual learning is applied for easing the difficulty of training the deep neural network. In addition, the grouped convolutions are introduced for reducing computational complexity and the number of parameters. What’s more, the shuffle dense connections are proposed for mitigating the grouped convolutions problem of lacking information exchange between the groups. The proposed method is evaluated quantitatively and qualitatively on four benchmark datasets, and the experimental results of super-resolution illustrated that our SDN achieves great performance over the state-of-the-art frameworks.
Identification of attitudes towards youth activity in the context of globalization

This article examines the problems arising in the process of globalization in the education of young people, and the need to attach importance to the media and national values in their elimination. Also, there are social surveys aimed at ensuring that young people are interested in literature, reading and visiting theaters and museums.
Accurate data identification in low signal-to-noise ratio series

In this paper we will present our unsupervised machine-learning-based method to filter signals with a low signalto-noise ratio. We will describe both the generalized version of the algorithm and a simplified example using a discrete, bidimensional signal. First, we will explain what unsupervised machine learning is and how a mathematical model can be trained to differentiate the useful signal from the noise. We will describe how we represent our data in a feature space, by extracting measurable properties of the signal and using them as features. Afterwards, we use a machine learning algorithm to group the signal into two different clusters, separating the noise from the true signal based on the aggregated differences between their features. The model is trained on data that is updated continuously and in real-time, with the need of minimal information beforehand. Secondly, we will detail the pipeline of our algorithm. The main focus of our work was to extract as many relevant features as possible from the signal. For this, we associated signal samples in spatially correlated groups, according to the Euclidean distance between them. Each group of signal samples forms a cloud of points, with each sample being one point. Furthermore, using the Minimum Volume Enclosing Ellipsoid algorithm, we construct an ellipsoid for each cloud of points. This was done in order to extract more useful information about the signal, based on its spatial distribution. Having that ellipsoid, we were able to use its geometrical properties as new features, such as its axis length, ratio and tilt angle. The next step in the filtering process was to concatenate all these features into a descriptor. A data-set was formed from the last n time steps, based on which the k-means algorithm clusters all the clouds in two different groups, based on their descriptors. Finally, in order to fairly evaluate the algorithm, we created a testing environment: a synthetic signal will be generated following basic patterns with random noise added. This will help by providing some useful metrics of accuracy in order to evaluate the proposed method.
Face Hallucination From New Perspective of Non-Linear Learning Compressed Sensing

The past decade has witnessed a prosperity of sparsity-inspired face hallucination methods that use sparse prior and instances to generate High-Resolution (HR) faces. However, they need numerous Low-Resolution (LR) and HR instance pairs and adopt approximate sparse coding, which will bring bias to the recovery and suffer from high computational burden. In this paper we advance a Single Face Image Hallucination (SFIH) method from a new perspective of Non-linear Learning Compressive Sensing (NLCS), which can recover HR faces from a surprisingly small number of HR faces. The nonlinear sparse coding of facial images is explored, and a Deep AutoEncoder (DAE) network is constructed for learning a kernel function from a single HR instance set. SFIH is then reduced to an analytic compressive recovery problem by reformulating linear sparse coding as a nonlinear DAE model. By exploring the nonlinear sparsity in the feature space, NLCS can accurately and rapidly recover HR facial images with large magnification factor and exhibit robustness to LR-HR instance pairs mapping. Some experiments are taken on realizing 3X, 6X, 9X amplification of face images, and the results prove its efficiency and superiority to its counterparts.
Development potential of information and communication technologies for formation of music understanding in primary school pupils

The article is devoted to the educational and developmental potential of information and communication technologies for the formation of understanding of music in primary school students at the music lesson. Psychological characteristics of younger school age – inquisitiveness, emotionality, mental activity, immediacy, openness, trustworthiness, their belief in the undeniable authority of the teacher and willingness to fulfill any of his tasks are favorable conditions for affirmation of broad social motives at this age responsibility, understanding of the need for training etc. The possibility of a teacher in music lessons using various computer programs: MIDI, Musical Instruments Digital Interface, Garage Band, FlexiMusic Composer, FL Studio, Finale, Sibelius. The use of modern computer technology in music lessons in primary school makes learning bright, unforgettable, interesting for the student, contributing to the formation of cognitive skills in students, forming an emotional-positive attitude to the arts. After all, the use of information and communication technologies in the classroom increases the level of understanding of music in primary school students. Various and multifaceted psychological and pedagogical tasks of informatization of the educational process in primary school students are highlighted, namely: development of communication skills; correction of mental and emotional states; development of the emotional sphere of the child's personality; formation of selfpresentation skills; setting up an emotionally-supportive atmosphere in the classroom. The use of information and communication technologies in music lessons contributes to the educational and development potential of primary school students to form an understanding of music, namely: motivation; individualization and differentiation; cognitive development of the child; forming a culture of educational activity and information culture. Thus, the problem of educational and development potential of primary school students by means of information and communication technologies does not lose its relevance. The introduction of information and communication technologies into the educational process for forming the understanding of music in primary school students in a music lesson is a complex process that is caused by individual psychological characteristics, personal experience, emotional and sensual sphere. Key words: educational and development potential, information and communication technologies, understanding, primary school students, music lesson.
Abstract Generation System for Chinese Articles and Reviews of 3C Products

The Internet has become popular and convenient. Product articles and reviews are written by people on digital media platforms such as Facebook, PTT, Mobile01, and Apple Daily News. Most people read many articles and reviews on digital media when they want to buy a product. However, an overwhelming number of articles and reviews of products is available on the Internet, and a prospective buyer can become confused. People should organize these articles and reviews before deciding on whether to buy a product or not. Therefore, it is essential to summarize the available data and provide customers useful information.Many researchers have investigated this task, but most studies have been focused on English reviews. This work focuses on Chinese articles and reviews in digital media, and propose a system designed to summarize data from digital media. When a user is interested in a product, the system extracts features and opinion words of the product from review articles and uses these features to identify sentences highly related to the product. After obtaining these sentences, the approach in this work selects top 20 important sentences to form the summary of the product, which is presented to the user. This work conducts several experiments to compare the effectiveness of TextRank, Luhn's method, and the proposed approach. Among them, the approach proposed in this work exhibits the best performance.
Motion Analysis of Kick Mechanism Using in Muay Thai Matial Art

Abstract The purposes of this research were to analyse speed of basic Muay Thai skills which consists of using feet and knee. By using the human motion-tracking cameras to create 3D animations that is associated with an image from a video camera and biomechanics analysis. The subjects group consisted of 5 professional boxers from Kiat Moo 9 Muay Thai camp by using purposive sampling method. The instrument used of this research was Qualisys Trach Managa (Motion Capture System). It was used to collect data motion capture and to analyses velocity Muay Thai basic skills. Statistics used for this research were arithmetic mean and standard deviation. The results showed that the average speed of basic Muay Thai skills include, Diagonal Kick, Straight Foot-thrust, Sideways Foot-thrust, Straight Knee-kick and Diagonal Knee-kick has 17.15, 6.01, 5.28 6.29 and 6.42 m/s, respectively. Standard deviation has 2.90, 1.47, 1.24, 1.01 and 1.75 respectively.
Image Alignment using Norm Conserved GAT Correlation

This paper describes a new area-based image alignment technique, norm conserved GAT (Global Affine Transformation) correlation. The cutting-edge techniques of image alignment are mostly feature-based, such well-known techniques as SIFT, SURF, ASIFT, and ORB. The proposed technique determines affine parameters maximizing ZNCC (zero-means normalized cross-correlation) between warped and reference images. In experiments using artificially warped images subject to rotation, blur, random noise, a few kinds of general affine transformation, and a simple 2D projection transformation, we compare the proposed technique against the feature-based ORB (Oriented FAST and Rotated BRIEF), the competing area based ECC (Enhanced Correlation Coefficient), the original GAT correlation, and the GPT (Global Projection Transformation) correlation techniques. We show a very promising ability of the proposed norm conserved GAT correlation by discussing the advantages and disadvantages of these techniques with respect to both ability of image alignment and computational complexity.
On Neural Network Training from Noisy Data using a Novel Filtering Framework

We discuss a novel method to train a neural network from noisy data, using Optimal Transport based filtering. We show a comparative study of this methodology with three other filters: the Extended Kalman filter, the Ensemble Kalman filter, and the Unscented Kalman filter, that can also be used for the purpose of training a neural network. We empirically establish that Optimal Transport based filter performs better than the other three filters with respect to root mean square error measure, for non-Gaussian noise in the output. We demonstrate the efficacy of utilizing the Optimal Transport based filtering for neural network training in the context of predicting Mackey-Glass chaotic time series data.
Development of face presentation function of operator for a telepresence robot

We are developing telepresence robots that play outside with children in remote areas. Therefore, we propose a robot that reproduces the face of the operator. Since the visibility of the liquid crystal display is low outside, we use the face of the robot imitating a person. However, if the face of the operator is always presented, it will be exposed to the public, which is a problem. The purpose of this research is to develop a robot which presence the operator’s face only when necessary.
Hardness of learning DNFs using halfspaces

The problem of learning t-term DNF formulas (for t = O(1)) has been studied extensively in the PAC model since its introduction by Valiant (STOC 1984). A t-term DNF can be efficiently learnt using a t-term DNF only if t = 1 i.e., when it is an AND, while even weakly learning a 2-term DNF using a constant term DNF was shown to be NP-hard by Khot and Saket (FOCS 2008). On the other hand, Feldman, Guruswami, Raghavendra and Wu (FOCS 2009) showed the hardness of weakly learning a noisy AND using a halfspace – the latter being a generalization of an AND, while Khot and Saket (STOC 2008) showed that an intersection of two halfspaces is hard to weakly learn using any function of constantly many halfspaces. The question of whether a 2-term DNF is efficiently learnable using 2 or constantly many halfspaces remained open.
Complexity in Business Systems Automation Requirements Engineering Social and Power Effects in Systems Engineering Projects in Brazil

A large number of business automation systems projects in developing countries fail. These projects typically begin with the analysis of clients’ needs using a process called “requirements engineering” (RE). The paper postulates that social complexity, especially in relation to power dynamics, loads important factors onto RE. Using data from Brazilian automation systems projects, factor analysis techniques are employed to confirm the importance of socio-political features of RE in these projects. The first contribution of this paper is to present empirical findings on RE complexity in a Latin American country, a region under-represented in the literature. Secondly, it explores critical social power features during RE. Results validate a complexity model which has received little attention in the systems development literature. The study demonstrates important social effects in automation projects in less developed regions and opens the way for further research into these effects thereby improving currently high failure rates.
Towards a Safer Social Media – Submissions to the Ministry of Information and Technology, Government of India, on the Draft Information Technology Intermediary Guidelines (Amendment) Rules, 2018

The Ministry of Electronics and Information Technology, Government of India, has sought comments from the general public on the draft Information Technology (Intermediary Guidelines) Amendment Rules, 2018. This is my public submission to the Ministry.
ECC$$^2$$: Error Correcting Code and Elliptic Curve Based Cryptosystem

With the fast development of quantum computation, code-based cryptography arises public concern as a candidate for post quantum cryptography. However, the large key-size becomes a drawback such that the code-based schemes seldom become practical. Algebraic geometry codes was considered to be a good solution to reduce the size of keys, but its special structure results in lots of attacks. In this paper, we propose a public key encryption scheme based on elliptic codes which can resist the known attacks. By choosing the rational points carefully, we build elliptic codes that can resist Minder’s attack. We apply the list decoding algorithm to decryption thus more errors beyond half of the minimum distance of the code could be correct, which is the key point to resist other known attacks for AG code based cryptosystems.
Fuzzy string implementation matching on android-based encyclopedia and anatomy quiz

Abstract Encyclopedia anatomy is an android application to introduce anatomy, equipped with a search feature, multiple choice quizzes and guess image quizzes. Due to some anatomical term is uptake from foreign languages and frequently typing errors by a smartphone user, then search feature in this application implement fuzzy string matching method which uses Levenshtein distance algorithm. However, the results obtained are still not accurate, then added method of splitting and indexing, so the results are more accurate and search process fast enough. Due to more simple than a book and supported by a good appearance, this application can get a positive response from the users.