Reviewerly/exHarmony huggingface dataset:
Boundary Focal Loss for Class Imbalanced Learning

The problem of class imbalance has become a severe change in data mining. While handling class imbalance, in addition to sampling strategies and cost-sensitive learning methods, designing loss functions is a crucial approach in neural network. The focal loss function had been proven to be an effective method. This paper proposes a novel loss function, named boundary focal loss (BFL) and validate it in three experiments. The experiment results show that BFL has better performance than cross-entropy and FL on imbalance dataset.
Practical Protection of Binary Applications via Transparent Immunization

In the past few years, massive data breach attacks on large organizations (e.g., Anthem Inc., Equifax) have compromised sensitive data of tens or even hundreds of millions of people. The 2017 Equifax data breach attack has compromised sensitive data of 148 million people and has costed Equifax $\$ 1.4$ billion as of May 2019. Unfortunately the average time to detect, contain a data breach was 206 days and 73 days respectively in 2019. There is a pressing need to develop practical and deployable capability to detect and block previously unseen, application specific cyberattacks on vulnerable binary applications in real-time. In this paper, we present AppImmu, a practical cyber defense system that can detect and block previously unknown cyber-attacks on vulnerable binary applications in real-time with no false positive. Given a potentially vulnerable ELF binary application, AppImmu can transparently and statically immunize it into an immunized version via binary rewriting. At run-time, AppImmu uses kernel level immunization based anomaly detection techniques to detect and block previously unknown cyberattacks on immunized binary applications without any prior knowledge of the attacks. We have successfully immunized real world large binary applications such as Apache Java execution environment, bash shell, Snort in Linux and have successfully detected and blocked real world data breach attacks (e.g., Apache Strut exploit used in 2017 Equifax data breach attack, Shellshock exploit) in true real-time. Our benchmark experiments show that AppImmu incurs less than 6% run-time overhead in overall system performance, 2.1% run-time overhead for applications under typical workload.
Head detection based on YOLOv3 image processing technology

With the advancement of science and technology, image processing technology is widely used. In this paper, we use computer technology to process the image data of the screenshots in the classroom monitoring to achieve the goal of extracting human heads. In this study, we selected screenshots of different size classrooms, camera views, and classroom lighting conditions. Combined with the PASCAL VOC dataset format, we applied the Labellmg-master image annotation tool to annotate the human head. The traditional YOLOv3 algorithm misses and repeats detections. Its accuracy and recall rates are poor. Therefore, we propose an optimization algorithm for classroom monitoring via head detection based on the YOLOv3 algorithm. The main improvements are the clustering the dataset labels with the Kmeans algorithm, increasing the size of the output feature map, and optimizing the loss function. Experiments were conducted with both the YOLOv3 and optimized YOLOv3 algorithms. By comparing the experimental results, we can conclude that the optimized YOLOv3 algorithm can improve the detection accuracy by 6% and recall rate by 11% on the test set, which has a high detection performance. This shows that image data processing technology has a better application effect in classroom monitoring.
On Non-linear Trajectory Tracking via Variety-based Background Subtraction Models

Trajectory tracking wherein the trajectories can be described by polynomials in the spatial coordinate space is considered. A background subtraction model is extended to include an algebraic variety-based low rank constraint that captures the trajectory. The optimization is solved via the Iteratively Reweighted Least Squares method, adapted to the new signal model. The convergence of the Variety-based Background Subtraction Iterative Reweighted Least Squares (VBSI) algorithm is investigated, where a boundedness analysis is conducted. It is proved that the sequence of estimates generated by VBSI is bounded; furthermore, any limit point of that sequence is a stationary point of the optimization problem. An aggregated error function is shown to converge to zero. A bound on the estimation error is derived that characterizes the relationship between performance and system parameters. Simulation results show that the proposed algorithm achieves excellent recovery of the key matrices in both noisy and noiseless scenarios, and typically converges in 20 iterations. Furthermore, a performance improvement of 2.4 dB with regards to the Mean Square Error is observed over prior methods. Finally, the proposed technique is applied to real video data and shown to be effective in estimating non-linear trajectories.
Deep learning based stereo cost aggregation on a small dataset

Deep learning (DL) has been used in many computer vision tasks including stereo matching. However, DL is data hungry, and a large number of highly accurate real-world training images for stereo matching is too expensive to acquire in practice. The majority of studies rely on large simulated datasets during training, which inevitably results in domain shift problems that are commonly compensated by fine-tuning. This work proposes a recursive 3D convolutional neural network (CNN) to improve the accuracy of DL based stereo matching that is suitable for real-world scenarios with a small set of available images, without having to use a large simulated dataset and without fine-tuning. In addition, we propose a novel scale-invariant feature transform (SIFT) based adaptive window for matching cost computation that is a crucial step in the stereo matching pipeline to enhance accuracy. Extensive end-to-end comparative experiments demonstrate the superiority of the proposed recursive 3 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> CNN and SIFT based adaptive windows. Our work achieves effective generalization corroborated by training solely on the indoor Middlebury Stereo 2014 dataset and validating on outdoor KITTI 2012 and KITTI 2015 datasets. As a comparison, our bad-4.0-error is 24.2 that is on par with the AANet (CVPR2020) method according to the publicly evaluated report from the Middlebury Stereo Evaluation Benchmark.
Single/Multiple Fragments with NUN

This paper investigates the Korean 'what/how about' construction.In Korean, unlike English, the wh-question word 'what/how' is typically phonologically suppressed and the particle '-(N)UN' is used, corresponding to the English preposition 'about'.Departing from Chung's (2021) recent work on this construction, this paper makes three claims.First, three distinct uses of this construction indicate that '-(N)UN' can be not only a contrastive topic marker but also a (contrastive) focus marker.Second, the underlying representation of this construction is that the pre-'(N)UN' part has a clausal structure that undergoes elision, leaving behind single or multiple fragment remnants.Meanwhile, the post-'(N)UN' part has ettehkey toy(-ess)-ni 'what happened'/ etteh-ni 'how'/ ettehkey ha-l ke-ni 'how do you cope with', where ettekey or ettehaasks a polar or wh-constituent question relating to the pre-'(N)UN' clause.Third, the formation of single or multiple fragment remnants is achieved via Move & Delete, where more specifically Move is the operation of scrambling widely available to the Korean language.
SM9-based Anonymous Search over Encrypted Data

To prop standardized commercial cryptography up, a series of commercial cryptography standards (SM) have be announced. There exist SM-based constructions for digital signature and encryption up to date while it is still unknown how to design SM-based searchable encryption (SE) for better data retrieval. If applying SM9 (which fits SE structure) into traditional SE schemes, it incurs data source leakage due to insider attacks. Harmonizing SM9 and searchable encryption is, therefore, a challenge especially the privacy issue is considered. In our paper, we first explore the privacy of sender in SE and construct a newly cryptographic primitive named Privacy-preserving Encryption with Sender Search (PES) via a combination of identity-based encryption (IBE). With an idea of additionally introducing the sender’s secret key, PES can resist the sender identity leakage. We present an SM9-based Anonymous Searchable Encryption (SM9-based AES) scheme on top of PES and prove its security in the formal security models. Through algorithm implementation, the proposed SM9-based AES is demonstrated to be cost-efficient with reasonable computation time.
A Hybrid Key Schedule Algorithm Based on Blum Blum Shub Generator and Chaotic Systems

Encryption algorithms are now becoming an important part of our daily lives. Therefore, being able to design effective encryption algorithms has become a hot research topic in computer science studies. An integral component of an encryption algorithm is key scheduling algorithms. In this study, a new approach that can be used as a key scheduling algorithm is proposed. The proposed approach has an innovative aspect that combines the advantages of deterministic and true random number generators. It is thought that this new hybrid approach, both successfully providing statistical analysis and having an unpredictable structure, will be a reason for preference for possible practical applications to be designed in the future.
Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene

For an automated driving system to be robust, it needs to recognize not only fixed signals such as traffic signs and traffic lights, but also gestures used by traffic police. With the aim to achieve this requirement, this paper proposes a new gesture recognition technology based on a graph convolutional network (GCN) according to an analysis of the characteristics of gestures used by Chinese traffic police. To begin, we used a spatial–temporal graph convolutional network (ST-GCN) as a base network while introducing the attention mechanism, which enhanced the effective features of gestures used by traffic police and balanced the information distribution of skeleton joints in the spatial dimension. Next, to solve the problem of the former graph structure only representing the physical structure of the human body, which cannot capture the potential effective features, this paper proposes an adaptive graph structure (AGS) model to explore the hidden feature between traffic police gesture nodes and a temporal attention mechanism (TAS) to extract features in the temporal dimension. In this paper, we established a traffic police gesture dataset, which contained 20,480 videos in total, and an ablation study was carried out to verify the effectiveness of the method we proposed. The experiment results show that the proposed method improves the accuracy of traffic police gesture recognition to a certain degree; the top-1 is 87.72%, and the top-3 is 95.26%. In addition, to validate the method’s generalization ability, we also carried out an experiment on the Kinetics–Skeleton dataset in this paper; the results show that the proposed method is better than some of the existing action-recognition algorithms.
Student’s Perception of Higher Education in Technical Universities and Its Connection With Subsequent Professional Development (MSTU, MADI and MIREA - Comparative Analysis) (Ending Part 4)

This article concluded the presentation of the results obtained during a comparative study of students of technical universities: MSTU, MADI and MIREA. These results were received, using a sociological questionnaire with closed-type questions. The questionnaire contained 9 main thematic blocks: 1. Professional choice; 2. Profession; 3. Study and education; 4. Educating; 5. Professional and personal competence; 6. Teachers; 7. Communication of students; 8. Humanitarian knowledge in the training of an engineer; 9. Intelligentsia. The article is primarily devoted to the presentation of the results of a comparative study of technical universities: MSTU, MADI and MIREA. In it we will present data obtained using the last four blocks of the sociological questionnaire: 6. Teachers; 7. Communication of students; 8. Humanitarian knowledge in the training of an engineer; 9. Intelligentsia. At the same time, we also want to supplement them with the results of our previous studies of MSTU students. In particular, we will continue to consider the psychological characteristics found in students studying at the Bauman Moscow State Technical University, at the faculties of Aerospace and Engineering Business and Management.
A Multi-View DCNN Based Method for Breast Cancer Screening

Breast cancer is the most widespread cancer amongst women worldwide for cancer diagnoses in the world. In the first place, screening mammography is widely acknowledged to be the most effective imaging tool which allows to detect breast cancer early. Early detection is particularly associated with a decrease of incidence and mortality rate. However the limiting factors with the use of mammography are i) the fact that mammogram is difficult to interpret due to the high density of breast tissue, ii) the workload of radiologists; the situation is even worse because of the double reading process, and iii) false positive recalls are many times accompanying with needless tests and biopsies. We propose to set up a multi-view based design of a deep convotutional neural network in order to carry out mammography screening task - the fulfilled network can extract distinctive characteristics from Medio-Lateral Oblique (MLO) and Cranial Caudal (CC) mammography's views for each breast (a set of 4 images). We test it on a subset selected from the open Digital Database for Screening Mammography (DDSM) using exams (each exam is composed of 4 images). We demonstrate that our approach can outperforms existing ones both in the matter of prediction accuracy and false positive rate reduction. Our method accomplishes a 97.77% specificity rate and a 98.7% accuracy rate.
Double echo state network with multiple reservoirs for time-series prediction

With the rise of neural networks, Echo state network (ESN) is also added to the queue of the neural network. ESN is a powerful form of reservoir computation, is a new type of recurrent neural network and only need to train the output weights. ESN overcomes the problems of the traditional recursive neural network such as too complicated training algorithm, slow convergence speed and easy to fall into local minimum.In order to reflect different learning tasks need different status updating methods and appropriate reservoir are constructed for a given task, an improved leaky integrator echo state network is proposed. The improved model, named Double Echo State Network with Multiple Reservoir(DMESN) for Time-Series Prediction. It uses multiple sub-reservoir to build a total reservoir and extends it in depth. Then the batch gradient descent method is utilized to optimize the DM-ESN parameters. Finally, the proposed method is applied to the time-series prediction. Simulation results show that the DM-ESN model can greatly improve the accuracy and stability.
Maximum Similarity Directed Subgraph Searching Algorithm on Subjective Logic

The determination of similar subgraphs has always been a difficult point in the search of directed graphs. Existing algorithms are difficult to accurately describe the similarity of subgraphs. Therefore, we proposes a novel search algorithm for maximal similar directed subgraph based on subjective logic. The algorithm is based on isomorphic nodes, based on the similarity argument of subjective logic for graphics containing a few nodes, greedy algorithm is adopted to gradually expand the scale of similar subgraphs until it is lower than the preset threshold. Compared with other related algorithms, this algorithm makes use of subjective logic’s ability to accurately describe realistic arguments and uncertain reasoning, which makes the judgment of subgraph similarity more accurate.
Deeply Multi-channel guided Fusion Mechanism for Natural Scene Text Detection

Scene text detection methods have developed greatly in the past few years. However, due to the limitation of the diversity of the text background of natural scene, the previous methods often failed when detecting more complicated text instances (e.g., super-long text and arbitrarily shaped text). In this paper, a text detection method based on multi -channel bounding box fusion is designed to address the problem. Firstly, the convolutional neural network is used as the basic network for feature extraction, including shallow text feature map and deep semantic text feature map. Secondly, the whole convolutional network is used for upsampling of feature map and fusion of feature map at each layer, so as to obtain pixel-level text and non-text classification results. Then, two independent text detection boxes channels are designed: the boundary box regression channel and get the bounding box directly on the score map channel. Finally, the result is obtained by combining multi-channel boundary box fusion mechanism with the detection box of the two channels. Experiments on ICDAR2013 and ICDAR2015 demonstrate that the proposed method achieves competitive results in scene text detection.
Neural-Network Architecture Approach: An Automated Essay Scoring Using Bayesian Linear Ridge Regression Algorithm

Most people are relying on technology in various industries, such as education, where people are more likely to use technology as a platform for knowledge acquisition. In this study, the researchers proposed an Automated Essay Scoring or AES to help the teachers minimize the time in grading essay work of the student, prevent biased ratings and provide a feedback mechanism for the students. The study proposed a Neural-Network Approach Architecture and Bayesian Linear Ridge Regression Algorithm to improve the correctness and reliability of the AES system and dimensions we are going to use. We also use the new Hybrid Model or Hybrid Agile to have a detailed approach in developing our study. We used the dataset from the Hewlett Foundation, the Automated Student Assessment Prize (ASAP) from Kaggle. We also gather information in reliable sources and collect data to test the accuracy and reliability of our AES system.
Development and research of learning algorithms for neural networks with reinforcement in the gaming industry

This paper provides a test of the hypothesis about increasing the effectiveness of agents specialized for certain aspects over universal ones. Within the framework of the modified game environment Starcraft 2, a series of experiments was carried out, the analysis of the results of which was the conclusion about the confirmation of the hypothesis.
A DNN-Based Method for Extracting Promotional Media Elements from Urban Images

Companies and public organizations install billboard advertisements in various parts of urban spaces to promote their products and services. Because these billboards are installed in the real space, it has been pointed out that they cause a landscape pollution. In addition, there are so many billboards in urban spaces that meaningful advertising for consumers are buried. To address these problems, it is conceivable that billboards installed in the real space can be recognized through a camera of a smartphone. For example, automatically erase billboards from the image when we take a picture in an urban, or if advertising is of interest, lead the user to web pages of the product being promoted on billboards. However, to realize them, there are two limitations with the current environment surrounding billboards that must be mitigated. First, there are many billboards, especially in the urban space, and a model has not yet been developed to detect them on a large scale, regardless of their content. Second, an infrastructure to extract the elements on billboards has not been established. In this study, we construct object detection models to solve the first problem. The model targets images and detects them, regardless of their content. For the second problem, we construct extraction models that extract multiple elements from billboards such as &#x201C;genre,&#x201D; &#x201C;advertiser,&#x201D; and &#x201C;product name.&#x201D; Finally, we consider characteristics of each model, and present appropriate datasets and methods to construct each model.
Group Multi-Object Tracking for Dynamic Risk Map and Safe Path Planning

This paper studies the group multi-object tracking (MOT) problem in dynamic pedestrian environments, with intended application to safe navigation for autonomous vehicles. We complete a full autonomous vehicle navigation pipeline from object detection, tracking, grouping, to risk map generation and safe path planning. Our main contribution is to instantiate a group multi-object tracking algorithm, which provides the crucial grouped activity information, i.e. group position, group velocity, group size, to the risk map generator, and therewith produce a stable and robust risk map for the downstream safe path planner. Experimental results with real world data show the socially acceptable, robust and stable performance of the proposed algorithm over its individual MOT counterpart.
Two Kinds of Iteration

TODO: There are two kinds of iteration, but they are commonly conflated. The first is the evolution of a theory to become more suitable over time: design-focused iteration. The second is the evolution of an artifact (like code) to become more suitable over time: code-focused iteration. These two kinds of iteration ...
Evaluating the Performance of Synthetic Visual Data for Real-Time Object Detection

A method to produce synthetic visual data was developed for this study. The resulting synthetic imagery was used to train a YOLOv3 classification model to identify various species of sea turtles. The Blender library and Python were used to render and augment the images. Nine datasets were generated, each with different ratios of synthetic and authentic images. YOLOv3 was then trained and the performance resulting from each of the datasets was evaluated to measure which dataset produced the most confident model. The researchers optimized the classification model and demonstrated significant accuracy improvements, especially with models trained without authentic data. A dataset consisting of fully synthetic dataset performed best in testing, with an average confidence level of 9.68 percent greater than the fully authentic dataset.