Reviewerly/exHarmony huggingface dataset:
Multi-view Based Pose Alignment Method for Person Re-identification

This paper proposes a Multi-View based Pose Alignment (MVPA) method for person re-identification (re-id). Most recent methods solve re-id as a matching process based on single image. However, when poses vary or viewpoints change, the performance seriously deteriorates. This paper aims to learn a representation insensitive to view and pose. Specifically, we establish a set of Multi-view based Person Pose Templates (MPPT) and propose a Pose-Guided Person image Generation (iPG2) model to synthesize multi-view and uniform-pose based images. The representation learned from multi-view images can significantly enhances the accuracy of re-id. We evaluate our method on two popular datasets, i.e., Market-1501 and DukeMTMC-reID. The results show that our framework promotes the performance of re-id a lot and surpass other methods.
Analysis of Actual Propagation Behavior of WannaCry Within an Intranet (Extended Abstract)

WannaCry is a ransomware that cannot only encrypt the victim’s data but propagate laterally in the victim’s local network. We present the results of analysis of some samples of WannaCry and actual propagation behavior of WannaCry in intranet environment.
Homotopy aware kinodynamic planning using RRT-based planners

This paper introduces a method for kinodynamic planning with homotopy class constraints, and proposes a homotopy class identifier that establishes a geometric relation between a trajectory and a union of convex partitions of the 2D robot workspace. The proposed identifier is shown to be invariant with respect to the trajectories that belong to the same homotopy class, in such a way that each class has its own unique signature. Furthermore, we show that the proposed homotopy class identifier can be easily incorporated in a RRT-based planner, without changing the planning algorithm, while restricting the solution trajectories to a designated homotopy class.
Face detection based on skin color extraction scheme

Quickly and accurately detecting a face in a picture is critical to subsequent face recognition applications. However, the complexity of actual images has a great influence on face detection. Face detection based on skin color extraction scheme can eliminate a large number of non-face background and determine the face area. Then, use the AdaBoost algorithm to detect the face accurately, so as to improve the speed and accuracy of the face detection system.
XSS Attack Detection and Prevention System Based on Instruction Set Randomization

Abstract As one of the most popular security vulnerabilities in Web applications, XSS has been widely researched and applied. This paper presents a XSS detection and prevention system aimed at solving the problem of website being attacked by increasingly sophisticated and severe XSS. The method proposed in this paper encodes the HTML/JavaScript keyword in the Web application by randomization and distinguishes the malicious attacks. The results show that this method not only can effectively detect and defend reflective and storage XSS attacks, but also it has better system response.
Boolean logic by convective obstacle flows

We present a potential new mode of natural computing in which simple, heat-driven fluid flows perform Boolean logic operations. The system comprises a two-dimensional single-phase fluid that is heated from below and cooled from above, with two obstacles placed on the horizontal mid-plane. The obstacles remove all vertical momentum that flows into them. The horizontal momentum extraction of the obstacles is controlled in a binary fashion, and constitutes the 2-bit input. The output of the system is a thresholded measure of the energy extracted by the obstacles. Due to the existence of multiple attractors in the phase space of this system, the input-output relationships are equivalent to those of the OR, XOR or NAND gates, depending on the threshold and obstacle separation. The ability to reproduce these logical operations suggests that convective flows might have the potential to perform more general computations, despite the fact that they do not involve electronics, chemistry or multiple fluid phases.
Coding Style of C Program from the Perspective of Software Engineering

Abstract Coding style is an important factor in measuring the program quality. The paper explores on the C program coding style of the program annotation writing, data declaration, program statement writing, and input/output design style from the perspective of software engineering. Good coding style plays an important role in improving the readability, testability and maintainability of the program.
Quantum Computing Circuits Based on Spin-Torque Qubit Architecture: Toward the Physical Realization of Quantum Computers

Quantum computers perform computations based on the principles of quantum mechanics, which enables faster speed and higher security than classical computers; they also have the ability to process a large amount of information via parallel processing. Some important concepts regarding quantum computers are qubits, quantum registers, quantum logic, quantum networks, quantum reversibility, quantum teleportation, quantum data compression, quantum cryptography, universal quantum computing, and quantum algorithms. Quantum computers rely on the basic quantum principles of superposition and entanglement. The time evolution of an arbitrary quantum state is computational ly more powerful than that of a digital logic state. Theoretical quantum computing based on the rotation of the qubits has proven that it is possible for quantum devices to address complex computing problems.
A framework for privacy assurance in a public video-surveillance system

Video surveillance systems can be found today almost in any public areas and, on one hand, they are used to ensure security. On the other hand, most of the time there are no security threats. In this situation, the system is actually violating our right to privacy without assuring any security. This paper presents a framework for privacy assurance in a public video-surveillance system. A new processing block is introduced: the privacy enforcer. The privacy enforcer automatically detects in real-time faces of people in a video recording and ensures privacy by scrambling the pixels position around the head. The scrambling is done by a three-step circular pixel shift algorithm. The scrambling is reversible in order to recover the original recording if a security threat is detected.
Toward an Efficient Computation of Log-Likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering

This work presents an unsupervised learning algorithm, using the mesh method for computing the log-likelihood function. The multinomial Dirichlet distribution (MDD) is one of the widely used methods of modeling multicategorical count data with overdispersion. Recently, it has been shown that traditional numerical computation of the MDD log-likelihood function either results in instability or leads to long run times that make its use infeasible in case of large datasets. Thus, we propose to use the mesh algorithm that involves approximating the MDD log-likelihood function based on Bernoulli polynomials. Moreover, we extend the mesh algorithm approach for computing the log-likelihood function of a more flexible distribution, namely the multinomial generalized Dirichlet (MGD). We demonstrate the efficiency of this method in statistical inference, i.e., maximum likelihood estimation, for fitting finite mixture models based on MDD and MGD as efficient distributions for count data. Through a set of experiments, the proposed approach shows its merits in two real-world clustering problems, namely natural scenes categorization and facial expression recognition.
Single-quantum interferometry: which-way versus which-phase information stored in an ancillary quantum system

In interferometers, the more information about the quanta's path available in an ancillary quan- tum system (AQS), the less visibility the interference has. By use of Shannon entropy, we try to compare the amount of which-phase information with the amount of which-way information stored in the AQS of two-path interferometers with symmetric beam merging. We show that the former is lower than or equal the latter if the bipartite system of the single-quanta and the AQS is initially prepared in a pure state and the interaction between the two parts is unitary. Especially when there exists symmetry, the equality holds. No which-way information is obtained by the measurement that we use for extracting the which-phase information and vice versa. In order to verify the results experimentally, we propose assembling a new single-photon interferometer.
Going Deeper into Colorectal Cancer Histopathology

The early diagnosis of colorectal cancer (CRC) traditionally leverages upon the microscopic examination of histological slides by experienced pathologists, which is very time-consuming and rises many issues about the reliability of the results. In this paper we propose using Convolutional Neural Networks (CNNs), a class of deep networks that are successfully used in many contexts of pattern recognition, to automatically distinguish the cancerous tissues from either healthy or benign lesions. For this purpose, we designed and compared different CNN-based classification frameworks, involving either training CNNs from scratch on three classes of colorectal images, or transfer learning from a different classification problem. While a CNN trained from scratch obtained very good (about 90%) classification accuracy in our tests, the same CNN model pre-trained on the ImageNet dataset obtained even better accuracy (around 96%) on the same testing samples, requiring much lesser computational resources.
Improvements on diagnostic assessment questionnaires of Maturity Level Management with feature selection

In the last few years, several new tools addressing maturity level management have been proposed, e.g. diagnostic assessment questionnaires (DAQ). In practice, the usage of questionnaires presents some drawbacks related to subjectivity, time cost, and applicant bias. Moreover, the questionnaires may present a large number of questions, as well as part of them redundant. Another important fact of real-life application of DAQs concerns the usage of multiple questionnaires, increasing the shortcoming impacts. To pave the way to a more convenient tool to support and facilitate the achievement of organizational strategies and objectives, we proposed an intelligent reduction of DAQs by the use of single-label and multilabel feature selection. In this paper, we reduced four DAQs (Risk Management, Infrastructure, Governance and Service Catalogs) with our proposal in comparison to different feature selection algorithms (χ2, Information Gain, Random Forest Importance and ReliefF). The reduction was driven by a machine learning prediction model towards ensuring the new subset of question grounded in the same obtained score result. Results showed that removing irrelevant and/or redundant question it was possible to increase the model fitting even reducing about one-third of the questions with the same predictive capacity.
Expert survey and analysis of the results

The issues of organizing an expert survey and carrying out statistical processing and analysis of the results are considered. The experts are the fifth-year students undergoing training at the Department of Management and Informatics «Moscow Power Engineering Institute» of the National Research University. The goal of the survey is revealing the disciplines that are most useful for employment in their specialty. We discuss the special features of the survey and a concept of «work in the specialty», with due regard for statistical reliability of the results. Data of written questionnaire gained in 2018 were processed and analyzed using cluster analysis (construction of dendrograms and application of the K -means method) and non-parametric statistical criteria (Friedman and Mann – Whitney – Wilcoxon). Data processing is implemented in the program STATISTICA. The analysis is carried out to reveal significant differences between the educational courses and assess the degree of consistency of the respondents to divide them into clusters that unite the students with similar judgments. Data analysis revealed that experts’ estimates in 2018 are in fairly good agreement with the estimates of previous studies; among the respondents there are three coalitions corresponding to the training modules «Software», «Management Theory», «Data Analysis»; the overall consistency of students in the two groups is very low (and, on the contrary, high in the identified clusters); grades are homogeneous and do not depend on training groups (and employment – unemployment of the respondents). The obtained results allow us to address a number of important questions regarding the ways of improving the educational process, e.g., to optimize yearly course hours for different educational modules.
Learning word hierarchical representations with neural networks for document modeling

Word embedding models treat words with equal status, which leads to the neglect of hierarchical semantic relationships between words (e.g., ‘green’ – ‘color’ and ‘cat’ – ‘mammal’). To build a hierarchical structure of words from raw text data, we propose an unsupervised model to learn word hierarchical representations (WHR), which are extended from word representations. Globally, WHRs can describe a word with several other words representing the basic attributes. The WHR model is an extended continuous bag-of-words (CBOW) neural language model with perceptual grouping and attention mechanisms. We further use WHRs to generate document representations, that are more expressive than some widely used document models, such as latent topic and deep learning models. Experimental results demonstrated that our model outperforms state-of-the-art baselines in terms of document retrieval, document classification, and sentiment analysis.
Path Planning of Mobile Robot Using Traveling Salesman Problem Modeled for Genetic Algorithm

One of the major requirement of automation is to reduce or eliminate human hands, even in extreme situations like rescue of humans from disaster where robots are employed in large numbers. The robots in such instances need to be proficient in identifying the targets and tiding through hazardous environment without collection. Literature has several techniques of path planning of a robot to reach the destination without collision. In this paper, path planning of a robot is attempted using genetic algorithm which is modelled as a traveling salesman problem (TSP). The basics of the TSP is that when the robot deviates from the designed path, the TSP algorithm is executed for several iterations to reach the destination optimally. The algorithm is tested on a robot hardware called iRobot (ICreate) which is interfaced with ARM Cortex M3 controller. The iRobot was able to track the destination successfully by avoiding obstacles and by making rotation of 90°. Improvement are being worked out to implement more optimal rotations.
Dual-Cycle Deep Reinforcement Learning for Stabilizing Face Tracking

In this paper, we propose a dual-cycle deep reinforcement learning (DCDRL) method for stabilizing face tracking. Unlike most existing face tracking approaches which require per-frame annotations and dense facial landmarks are usually quite costly to annotate manually, our DCDRL aims to learn a robust face tracking policy by only using weakly-labeled annotations that were sparsely collected from raw video data. Motivated by the fact that facial landmarks in videos are usually coherent along with the forward and backward playing orders, we formulate the face tracking problem as a dual-cycle Markov decision process (MDP) by defining two agents for the forward-cycle and the backward-cycle accordingly. Specifically, both agents reason with the MDP policies by interacting in tuples of states, state transitions, actions and rewards during the MDP processes. Moreover, we carefully design a consistency-check reward function to track along until the target and back again it should arrive the start position in the reverse order. With the designed function, each policy generates a sequence of actions to refine the tracking routing by accumulating the maximal scalar rewards. This typically enforces the temporal consistency constraint on consecutive frames for reliable tracking outcomes. Experimental results demonstrate the robustness of our DCDRL versus many severe challenging cases especially in uncontrolled conditions.
A Decomposition Approach for Complex Gesture Recognition Using DTW and Prefix Tree

Gestures are effective tools for expressing emotions and conveying information to the environment. Sequence matching and machine-learning based algorithm are two main methods to recognize continuous gestures. Machine-learning based recognition systems are not flexible to new gestures because the models have to be trained again. On the other hand, the computational time that matching methods required increases with the complexity and the class of the gestures. In this work, we propose a decomposition approach for complex gesture recognition utilizing DTW and prefix tree. This system can recognize 100 gestures with an accuracy of 97.38%.
Brokered Agreements in Multi-Party Machine Learning

Rapid machine learning (ML) adoption across a range of industries has prompted numerous concerns. These range from privacy (how is my data being used?) to fairness (is this model's result representative?) and provenance (who is using my data and how can I restrict this usage?).
A mean test on the autocorrelation function of a chaotic signal aiming to support the statistical independence sampling distance

This paper presents an analysis on two skew tent map chaotic signals mostly used in communications and cryptography applications. We inspect the autocorrelation function of tent map and how it relates to the statistical independence sampling distance. A statistical mean test on the autocorrelation function is presented in order to highlight the effect of the control tent map parameter when the autocorrelation function is computed for a distance value corresponding to statistical independence. The results are derived from a complex experimental study that included a Monte Carlo analysis on several statistical autocorrelation functions and simulations using double and extended precision.
A Robust and Effective Tracking Method in Remote Sensing Video Sequences

With the popularization of high resolution imaging technology and the progress of artificial intelligence, remote sensing target tracking in the aerial video plays a very important role in public security, such as antiterrorism efforts and military reconnaissance. As aerial video has rapid changes in orientations, low resolution, and multiple similar disruptors, and the main tracking methods generally have relatively low tracking performance in this research field, we develop a robust tracking method for remote sensing videos based on a saliency enhanced multi-domain convolutional neural network (SEMD). The process can be divided into two main stages: (1) in the offline pretraining stage, we combine the Least Squares Generative Adversarial Networks (LSGANs) with a rotation strategy to augment typical easily confused negative samples, which can improve the capacity to distinguish between target and the background. (2) in the online tracking process, a saliency module is embedded between convolutional layers and we optimize the arrangement of its functional sub-modules to boost the saliency of the feature map, which improve the network representation power for rapid dynamic changes in the target. Comprehensive evaluations of homemade datasets demonstrate that the proposed method can achieve high efficiency and accuracy results compared to state-of-the-art methods.
Syntax-Based Skill Extractor for Job Advertisements

In the context of extracting relevant skill-terms from job advertisements, we propose a syntax-based method for generating large amounts of machine-labelled text from a small amount of human-labelled data. This is then used to solve the vocabulary problem and significantly increase recall when detecting skills.
Fuzzy Logic Based Automatic Contrast Enhancement of Weather Degraded Images

A fuzzy logic based automatic contrast enhancement (CE) technique for grayscale images is presented in this paper. The proposed method generates results comparable with the ones obtained by using advanced CE techniques with less computational effort, being suitable for effective hardware implementations, in order to meet the specific requirements raised by the real time applications. It is based on the automatic adjustment of the transformation function generated by a set of fuzzy rules, depending on the statistics of the original histogram. Intensive experiments were conducted using low contrast bad weather driving scenes to verify the performance of the proposed method. The obtained results recommend it as an effective alternative to other computationally complex CE techniques.
Reception - A Deep Learning Based Hybrid Residual Network

Deep neural networks can be difficult to train and require extensive fine tuning for hyper-parameter optimization. In this paper a generalized deep convolutional hybrid network model is proposed, named Reception that not only can tackle problem of solving optimal kernel size but also have goodness of both ResNet and Inception. The proposed Reception module, compliments the learning of filters having small and large receptive fields. This allows the network to extract the tiniest of details as well as the broadest of shapes. Although this strategy increases the width of the network and the number of parameters, the depth requirement of the network reduces significantly. Moreover, the number of parameters are kept in line using a carefully crafted design. The model when used for classifying ships in satellite images achieves a mean test accuracy of 98.56% with standard deviation of 0.14 in 5-fold cross validation and F1-score of 0.99.
Extracting Information Cores with Multi-property Using a Multiobjective Evolutionary Algorithm

Recommender systems are efficient tools to alleviate the information overload problem. The information core extraction problem aims at finding a group of users who carry reliable, objective information to represent the system to provide recommendations. Many greedy algorithms have been proposed to deal with this problem, but most algorithms only focus on finding an information core to achieve accurate recommendation results, losing sight of other recommendation objectives with equivalent importance. To this end, we model the information core extraction problem as a multiobjective optimization problem, and propose a multiobjective evolutionary algorithm to extract information cores, labeled as MOEA-IC. MOEA-IC extracts information cores with multi-property at the same time, which can provide candidate solutions with different properties for decision makers. In the experiments, the performance of MOEA-IC is validated on three datasets with varying selection rates and compared with that of two existing greedy algorithms. The experimental results demonstrate the efficiency of MOEA-IC and show that the information core extracted by MOEA-IC can not only be helpful in providing accuracy recommendation, but also provide recommendations of good performance in terms of coverage and diversity criteria. The significance of this study is that the information-core-based recommendation is timesaving, and decision makers can choose information cores with different properties provided by MOEA-IC based on practical requirements.