Reviewerly/exHarmony huggingface dataset:
Depthwise Separable Temporal Convolutional Network for Action Segmentation

Fine-grained temporal action segmentation in long, untrimmed RGB videos is a key topic in visual human-machine interaction. Recent temporal convolution based approaches either use encoder-decoder(ED) architecture or dilations with doubling factor in consecutive convolution layers to segment actions in videos. However ED networks operate on low temporal resolution and the dilations in successive layers cause gridding artifacts problem. We propose depthwise separable temporal convolution network (DS-TCN) that operates on full temporal resolution and with reduced gridding effects. The basic component of DS-TCN is residual depthwise dilated block (RDDB). We explore the trade-off between large kernels and small dilation rates using RDDB. We show that our DS-TCN is capable of capturing long-term dependencies as well as local temporal cues efficiently. Our evaluation on three benchmark datasets, GTEA, 50Salads, and Breakfast demonstrates that DS-TCN outperforms the existing ED-TCN and dilation based TCN baselines even with comparatively fewer parameters.
Auto-Colorization of Images: Fuzzy c-Means and SLIC Approaches

The task of conversion of grayscale images into colorful ones is a complex problem because the same intensity of light can correspond to different colors in three channel color models. This usually requires manual modifications to attain artifact-free quality. This paper looks into this problem and aims at the conversion of grayscale image to colorful ones. In the proposed approaches, support vector regression (SVR) is trained upon the features using SLIC and fuzzy c-means algorithm. Scores of trials and tests demonstrate that our algorithm gives an excellent performance in terms of quality, speed, and several feature learning benchmarks.
FEATURES OF STUDYING THEME «CORRUPTION AS A SOCIAL PHENOMENON» IN THE FRAMEWORK OF THE TRAINING COURSE «JURISPRUDENCE» IN CHEMICAL UNIVERSITY

The article discusses the peculiarities of studying corruption by students of a technical university from the standpoint of the relevance of its research component. The acquisition of knowledge of a legal nature is substantiated from the point of view of importance not only from the standpoint of the humanitarian discipline «jurisprudence», but also the significant situation of knowledge of legal aspects in any sphere of life. The authors assign a special role in the materials of the article to the dynamic component in the study of the student audience with a view to increasing interest in the humanitarian disciplines, where «jurisprudence» comes to the fore among the disciplines of the humanitarian block. Problematic issues of a social plan, in particular, the negative manifestation of the phenomenal and modern criteria elements of corruption schemes in the current modernity. According to the results of the study, the peculiarities of the procedures of judicial proceedings in relation to criminal acts in general, in particular, in the field of education, arouse keen interest among students. The article presents the effective positions of the research on the subject of active discussion and analysis of situations of corruption in the educational environment. The materials of the study of the disciplines of the humanitarian cycle in higher education are given according to the priority ranking among students.
Low Influence, Utility, and Independence in Differential Privacy: A Curious Case of (3 2)

We study the relationship between randomized low influence functions and differentially private mechanisms. Our main aim is to formally determine whether differentially private mechanisms are low influence and whether low influence randomized functions can be differentially private. We show that differential privacy does not necessarily imply low influence in a formal sense. However, low influence implies approximate differential privacy. These results hold for both independent and non-independent randomized mechanisms, where an important instance of the former is the widely-used additive noise techniques in the differential privacy literature. Our study also reveals the interesting dynamics between utility, low influence, and independence of a differentially private mechanism. As the name of this article suggests, we show that any two such features are simultaneously possible. However, in order to have a differentially private mechanism that has both utility and low influence, even under a very mild utility condition, one has to employ non-independent mechanisms.
UBHIC: Top-Down Semi-supervised Hierarchical Image Classification Algorithm

At present, most of hierarchical image classification methods are built under the premise that label of images are known. For unlabel image classification, the accuracy and efficiency can be improved. To solve this problem, this chapter proposes a top-down semi-supervised hierarchical image classification algorithm (UBHIC) with efficiency as the optimization objective. Firstly, the AP clustering is used for the batch of unlabel images, then the similarity between cluster centers and nodes is compared from top-down. Finally, the best classification path is determined by evaluating multiple classification paths. Experiments show that the UBHIC has better accuracy and efficiency than the classical methods.
Accelerated Mobile Pages: A Comparative Study

The rapid growth of mobile-phone industries has led webmasters to find alternative ways to develop web apps that are mobile-friendlier. Google company accomplished its mission to provide web pages that load almost instantly and are more accurate to mobile devices, through its Accelerated Mobile Pages (AMP) project. AMP technology has expanded to other industries like search engine optimization and e-commerce and this paper presents the benefits on these fields by using AMP.
Lightweight Algorithm for Preventing Intentional Fork in the Bitcoin Environment

Most Bitcoin systems use blockchain technology to store all historical transaction data. Blockchain is a linked list structure but may be changed to a fork structure; a useful fork or an intentional fork. The useful fork may occur when the rules of the block are updated. On the other side, the intentional fork occurs when a miner has supercomputer properties that generate a set of blocks as a branch and does not publish this branch to the blockchain until the length of that branch is greater than the length of the main branch. When this fork occurs in the system, a set of blockchain transactions will be rollbacked, user waiting times increases, and miner rewards increases illegally. In this paper, the Lightweight algorithm is proposed to avoid the intentional fork issue and therefore rollback issue, users waiting time is reduced in the transaction process. The proposed algorithm is to make a single publisher in the system and divides the block construction into two phases. In the first phase, a miner generates a block and sends it to the memory pool. In the second phase, the memory pool will publish the block to the blockchain. The results show that the proposed algorithm has a good ability to avoid the intentional fork issue in the blockchain and the rollback issue thus minimize user waiting times.
The Impact of Interactive Visualization on Trade-off-Based Decision-Making Using Genetic Algorithm: A Case Study

This article presents a new visual interactive analysis method or VIAM that helps decision-maker (DM) to find the most favorable solution in the multi-criteria decision-making (MCDM) by evaluating the distribution of Pareto solutions (searched by genetic algorithm). The novelty of VIAM in comparison with others is that it is possible to re-orient search target. This might change the problem statement and consequently approach to the desired solution of DM. An application of VIAM for the multi-objective optimization of slider-crank mechanism (SCM) has been studied in order to demonstrate the efficiency of the method. The SCM design option obtained by VIAM has achieved the optimization of three criteria simultaneously such as power consumption, joint dynamic reaction, and mass. It is important to note that VIAM can be developed in the simple way, based on existing algorithms and it does not require the use of complex software.
Differential Fault Based Key Recovery Attacks on TRIAD

We demonstrate two fault based key recovery attacks – a bit-flipping fault attack, and a random fault attack, on the authenticated encryption stream cipher TRIAD. The attacks discussed in this paper are applicable mainly due to the simplicity of the TRIAD keystream function during the first few hundred rounds. We investigated the algebraic normal form (ANF) of the first 160 output keystream bits of TRIAD. The ANF of these output keystream bits shows specific patterns that are used in our fault attacks. We first use these patterns with a bit-flipping fault model combined with solving a system of low degree algebraic equations that requires 85 faults to recover the secret key of TRIAD, with a data complexity of $$2^{7.43}$$ . We then relax our assumptions by using a random fault model. The random fault model described in this paper is also combined with solving a system of low degree algebraic equations and requires on average 170 faults to recover the secret key of TRIAD with a data complexity of $$2^{8.01}$$ . For both of the attacks, the complexity of solving the low degree algebraic equations is negligible. We have not performed experiments on the actual hardware implementation, but we have verified all the attacks using simulation on TRIAD software implementation.
Particle Filter with improved MSV Resample for Manoeuvring Target Tracking

Aiming at the shortcomings of traditional resampling method in particle filtering, such as unstable filter performance, which is greatly affected by simulation parameters and lack of particles, the paper proposes an improved MSV resampling particle filter algorithm and applies it to the tracking problem of maneuvering targets under flicker noise for the first time. Compared with the traditional resampling, the improved MSV resampling is based on the same distribution characteristics for sampling, and the resampling loses less information, which can improve the stability and accuracy of filtering and improve the phenomenon of particle scarcity. Simulation results show that the proposed algorithm overcomes the shortcomings of traditional resampling without reducing computational efficiency.
Fractional-Order Image Segmentation for Security Surveillance

The enhancement of image processing techniques related to security surveillance issues is considered a pressing demand nowadays. Everything is now documented by digital images, out of which important information is extracted. In this work, fractional-order edge detection filters are employed in edge-based Active Contour segmentation technique for noisy surveillance images. The fractional-order filters add extra degree of freedom, allowing more details to be detected in images, and enhancing the quality of segmented noisy images. Two types of noise, Salt and Pepper noise as well as Gaussian noise, are applied to test the noise performance of the presented segmentation technique. The superiority of the fractional-based segmentation over the conventional integer-based one was proven visually and numerically using peak signal to noise ratio for both types of noise.
Feedback Knowledge Graph for Recommendation

Knowledge graph is wildly used in recommendation system to deal with the sparsity and lack of interactive information. The negative samples in most recommendation systems are obtained by negative sampling from the no interactive data. There are some false marks in negative sampling, and lack reasonable explanation. The real negative examples should exist in the user's interaction history, and user's preferences can be obtained from the feedback data (i.e.: ratings, reviews). In this paper, we use feedback data to find the negative samples, and propose a Feedback Knowledge Graph (FKG) which can give good explanation for the results. Specifically, the interactive data is divided into positive and negative samples by feedback information, and attention mechanism is introduced to aggregate information from different neighbors. Finally, we use a neural network to make interactive prediction of user items. Extensive experiments on two real-world datasets show that our model achieves good results.
A Robust Interacting Multiple Model Smoother with Heavy-Tailed Measurement Noises

Interacting multiple model (IMM) estimator has found an increasingly wide utilization in the filed of target tracking. Smoothing, uses all obtained measurements to estimate the previous state in order to provide a better estimation. However, IMM smoothers do not perform well and suffer severe performance degradation with some outliers existing in the measurement noises. This paper proposes a novel robust IMM smoother to deal with heavy-tailed measurement noises obeying the Student's t-distribution. The proposed smoother is derived from a forward filter combing with a backward filter. An example of maneuvering target tracking with heavy-tailed measurement noises verifies the effectiveness of the method.
Automatic Modulation Classification based on Wiener filter preprocessing and Cumulants

Wiener filter is the best linear filter under the minimum mean square error criterion. It can effectively eliminate the influence of noise, which may deal with the problem of Automatic modulation classification (AMC) performing poorly at low Signal-to-Noise Ratio (SNR). This paper explores how to use Wiener filter to improve the classification performance in the AMC, combining cumulant and Back Propagation (BP) neural network to classify Six kinds of digital modulation signals. The simulation results show that the AMC with Wiener filter preprocessing has a higher classification rate under low SNR (-10dB).
An Approach to Geometric Modeling Using Genetic Programming

In this work, we ‘derived’ the famous Pythagorean theorem from the measurements of the sides of right-angled triangles with machine learning. In classical Euclidean geometry, this result is proved with rigorous geometrical argument, but we have followed a data-driven approach and got the same result without entering a single step in the domain of geometry. We used symbolic regression with genetic programming to reach the model. As far as our knowledge goes, this result is a novel one and may open up a new avenue of applying machine learning tool in geometry. We have used Python programming language 3.7 and libraries such as DEAP (v1.2) and pygraphviz. The whole project can be found on https://github.com/snigdhasjg/Pythagorean-Triplate.git.
Weighted User Goal Sampling for Dialog Policy Learning

Abstract A deep reinforcement learning algorithm has been widely used for learning dialog policy in a task-oriented dialog system. Dialog agent collects training data and improves its policy by interacting with users. Interact with real users is time-consuming and not realistic. Consequently, we usually build a user simulator instead of a real user. At the beginning of each dialogue, the simulator will sample a user goal extract from training data. Then the simulator will communicate with the dialog agent to accomplish this user goal. The existing user simulator usually samples this user goal randomly. This will cause the dialog agent to waste a lot of time to learn what it already learned. To solve this problem, we propose two user goal weighting methods which give relatively large weight to the user goal the current dialog agent can’t accomplish. This lets the dialog agent pay more attention to those user goals. Experiment result in a movie-ticket booking task shows that the proposed weighted user goal sampling method can effectively accelerate the policy learning progress compares to the random sampling method.
Causal Features Extraction for Workpiece

Abstract In order to reduce the cost, computer vision technology is introduced into the measurement of workpiece size and shape on the factory production line. At present, the most widely used solution is the neural network model based on big data. However, the lack of data and the high cost of data processing also greatly limit the practical application of this aspect. The method of feature extraction brings challenges to the real-time, rotation invariance, and anti-noise of online detection. In this paper, firstly, Harris operator is used to extract feature points quickly. Then a two-layer scale space based on causality is constructed to filter the noise and project downward to obtain the robust feature position, which provides a basis for subsequent processing.
Scattering Environment Analysis of Quantum Radar for the Typical 2D target

Quantum radar may become a revolutionary technology in the battlefield of the electromagnetic environment. In this work, we proposed a simulational analysis of the quantum radar scattering environment for the typical 2D target. By following the recent set-up of a principle prototype, we gave the data in both monostatic and bistatic scattering scenarios based on our calculation of the quantum radar cross section. In particular, we used the convenient analysis formulas instead of the traditional numerical algorithm in this simulation. Finally, our results showed that both monostatic and bistatic scattering data are relatively significant so that this set-up of simulation may be useful for the experimental feasibility.
Development of Object Tracking System in Video Conference Applications for Distance Learning

Video conferences, or more popularly known as video calls, have grown increasingly popular in recent years, and they offer great potential benefits for the field of education, both reducing the costs of hosting educational activities and allowing content to be distributed quickly and efficiently regardless of distance within a more flexible timeframe. Still, there are potential areas of improvement for video conferences as a method of performing education, one of which is the addition of object tracking to video conferencing. Having a camera capable of following a teacher's movement allows for more interactive studying sessions to take place, as well as reducing reliance on camera operators. However, in order for such a video conferencing application to be useful in an organized academic effort, proper user interface should also be developed to accommodate the requirements of holding teaching-learning activities. The developed system demonstrates an attempt to tackle some of these interface requirements, focusing on basic features needed to perform an effective studying session with a WebRTC-based video conferencing application. The resulting web application is accompanied with an object tracking system based on the Viola-Jones algorithm, utilizing Arduino as the main component on the camera driver module.
Traffic Sign Classification Using ODENet

In the family of deep neural network models, deeper the model is, the longer it takes to predict and larger the memory space it utilizes. It is very much likely that use-cases have constraints to be respected, especially on embedded devices, i.e, low powered, memory-constrained systems. Finding a suitable model under constraints is repeated trial-and-error to find optimal trade-off. A novel technique known as Neural Ordinary Differential Equation Networks (ODENet) was proposed in NeurIPS2018, where instead of a distinct arrangement of internal hidden layers of a Residual Neural Network (RNN), they used parametrized derivatives of internal states in the neural system. Any differential equation solver can be used to calculate the final output. These models have constant depth and can trade between speed and accuracy. We propose a methodology for Traffic Sign Detection using ODENet and subsequently conclude that ODENets are more robust and perform better in comparison to ResNets. We also conclude that though training time is high in ODENets, they can trade-off between speed and accuracy when it comes to both training and testing.
Modeling and Interoperability Testing of Data Link Based on DEVS

As a key information technique, data link plays an important role in modern information-based warfare. Compared to traditional communication systems, data link has better interoperability and hence could promote the cooperation of all combat units. Interoperability is based on exchanging and utilizing data with the purpose of realizing tactical functions. Modeling and interoperability testing of data link have always been a great challenge. Taking protocol rules and tactical functions as testing objects, data link models are expressed in Discrete Event System (DEVS) Specification in this paper. Simulations show that the proposed methodology could perform typical rule and function test. Besides, it provides good effectiveness and scalability.
Research on Complex Robot Manipulation Tasks Based on Hindsight Trust Region Policy Optimization

Deep reinforcement learning (DRL) algorithms have make remarkable progress in robot manipulation task in recent years. However, the success of completing the task relies heavily on the special design of reward function which requires engineering experience or domain-specific knowledge. To avoid complex reward shaping and make robot learning more general, it's of great essential to study the sparse-reward environments. In this paper, we present two types of challenging goal-conditioned sparse-reward tasks with 7-DoF robot arm, one is a target reaching task with obstacles, and the other is the dynamic object task where the target object moves at a certain speed. Based on the Hindsight Trust Region Policy Optimization (HTRPO) algorithm proposed by our research group, we studied the control performance on the two types of tasks with continuous high-dimensional state space. The results show that HTRPO can achieve more stable strategic performance, higher success rate and sample efficiency compared with its baseline algorithm TRPO and HPG. However, there still remains challenges in solving the tasks with high moving speed.
Analysis and Optimization of Chamber-Loading Route of a Certain Type of Rifle

Aiming at the jamming failure in the development test of a certain type of rifle, the dynamic modeling and fault reproduction of the chamber-loading process are carried out by using the multi-rigid-body dynamics simulation software. It is concluded that the main reason is that when the number of ammunition in the magazine decreases, the pressure of the supporting spring decreases as the firing progresses, and the constraint of the guide part of the magazine on the ammunition increases. As a result, the constraint of the ammunition is premature at the initial stage of firing. For these reasons, the guide part of the magazine is optimized, and the optimized structure is simulated and analyzed. The simulation results show that after optimization, the lateral force time of the magazine on the bullet is delayed by 0.004s, and the trajectory of the bullet tip is reduced by 4.7mm compared with the lateral displacement. Therefore, the firing route is optimized.