Reviewerly/exHarmony huggingface dataset:
Fast Content Adaptive Representation Selection in HEVC-Based Video Coding for Streaming Applications

Preparing the video content for adaptive video streaming and ensuring the best possible quality of experience (QoE) to end-users while minimizing the server memory consumption and needed computational power presents a challenging task. This paper presents a novel solution and notation of a model for the content-adaptive estimation of the bitrate for a certain spatial resolution for encoding with the High Efficiency Video Coding (HEVC) encoder. Compared to available solutions, the idea was to simplify the process of determining the bitrate ladder that takes into account the video content. Contrary to other solutions, the proposed solution does not require precoding or high computational power, thus it minimizes latencies so it can be used for real-time applications. The proposed model requires only the values of spatial and temporal information to determine the optimal bitrate ladder for a certain video sequence. The proposed solution consists of a representation selection model and a bitrate ladder model. The representation selection model is used to select the optimal bitrates for switching to higher spatial resolution. Pearson Linear Correlation Coefficient (PLCC) between the values estimated by the model and the measured values calculated for all video sequences used in the development of the model is 0.963 and for the test sequence is 0.975, confirming the accuracy of the model. In addition, the bitrate ladder model was developed to determine the bitrates for the desired quality levels. For the test sequence, the proposed model achieves a PLCC value of 0.9175, and the mean difference between estimated and measured values is 1.826 %.
AR application for education

This paper presents the application of augmented reality, AR, for education. The application is intended for high school students with the aim of raising the user experience that enhances the adoption of materials using smartphones. The AR application is based on the visualization of a eukaryotic cell in the form of a virtual 3D model. The research was conducted in the Classical Gymnasium in Zagreb in the first grade.
Semantic Metadata for Image Files

Deep neural networks used in industry applications usually work the best when they are trained using supervised learning given that there is a lot of data available, that the data is from the same distribution as the data from the production environment and given that the labels are of a high quality. Large amounts of image data is available on the Internet, but it is not useful for machine learning applications in raw format. This concept goes through the process of image data acquisition from public sources on the Internet and automatic data labeling using existing computer vision deep learning models for object detection and instance segmentation. It sets up baseline labels that way and touches the problem of versioning labels from different sources for the same image and the same task. Eventually, it assigns machine learning semantic metadata to the given image. Furthermore, it goes beyond baseline automatic labels and proposes concept to refine them in both automatic and manual ways. With the semantic metadata available, it is shown how to make use of that semantic metadata to synthetically create even more image data and labels that deep learning depends on.
REVOLVER: A Zero-Step Execution Emulation Framework for Mitigating Power Side-Channel Attacks on ARM64

Software and hardware vulnerabilities to power side-channel attacks (SCA) are hard to detect and mitigate in systems already deployed in-the-field, because they require specialized equipment and aligned power traces. In this paper, we present REVOLVER, a software-based framework that performs zero-step execution emulation and generates power traces with instruction-level resolution. REVOLVER is a hybrid emulator, because part of it runs on the system that it emulates, an actual ARM64 platform, and evaluates the power consumption of its emulated instructions using actual measurements from on-chip low-frequency power sensors. Such sensors are already present on many system-on-chips (SoCs). To improve the accuracy of the collected traces, REVOLVER repeats the execution of the instructions in a zero-step fashion. To demonstrate the capabilities of our framework, we show that AES keys can be recovered by Correlation Power Analysis (CPA) on traces acquired using REVOLVER, which proves experimentally that there is a leaking power side-channel in the examined system that could potentially be exploited by power SCAs. Moreover, we show how REVOLVER can be used by a security engineer not only to identify software and hardware vulnerabilities to power SCAs, but also to design and evaluate mitigation strategies.
Remarks on rank one Drinfeld modules and their torsion elements

We consider modules generated by torsion elements of a fixed normalized rank one Drinfeld module of generic characteristic. The behavior of these modules seems to be governed by a vector space whose dimension is bounded by the genus of the ground field.
Night-time image dehazing using deep hierarchical network trained on day-time hazy images

Haze influences the visibility in the images of the outdoor scenes and reduces the efficiency of different imaging algorithms. The problem becomes more complex for night-time hazy scenes. In this paper we introduce a novel strategy that employ a deep learning model trained with day-time image dehazing dataset that can achieve competitive results also for night time hazy scenes. We employ Deep Multi-Patch Hierarchical Network a recent deep learning approach that is trained on a realistic day-time image dehazing dataset. In our strategy, to generalize this approach also for night time hazy scene, we apply as a pre-processing step an effective color correction strategy [1]. The extensive qualitative evaluation demonstrate that our approach is competitive and in general yields better results for night time hazy scenes compared with several state-of-the-art dehazing techniques.
3D U-Net based method for fast segmentation of whole heart from CT images

There is an increasing number of clinical applications where deep learning plays an important role. Heart chamber segmentation enables delineation of anatomical structures of heart and it is a prerequisite for a wide range of clinical applications. 3D U-Net architecture consistently achieves the highest scores in various medical imaging challenges. NoNewNet architecture is a modification of the 3D U-Net architecture which was shown to outperform the original 3D U-Net and was recently implemented inside the NiftyNet package. In this paper we demonstrate that with the properly trained NoNewNet network and NiftyNet we can outperform the current state-of-the-art networks. The evaluation of the trained network was performed on 20 3D CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset using five-fold cross-validation. We experimentally prove that border size can significantly reduce inference time without affecting segmentation accuracy. Additionally, we provide the discussion of the effects of some of the NiftyNet configuration parameters on the performance of the network.
Hybrid Japanese Language Teaching Aid System with Multi-Source Information Fusion Mapping

Learning Japanese can enhance competitiveness in a globalized economy, and we address the problems of poor open-source Japanese language teaching, cumbersome teaching tasks, and a single teaching model. We propose a hybrid Japanese teaching aid system with multiple information fusion mapping, which can effectively improve the efficiency of Japanese teaching and reduce the tedious human teaching procedures. The system is divided into two branches of Japanese language recognition, namely, the Japanese text recognition branch and the Japanese voice sequence recognition branch. In the Japanese text recognition branch, we integrate attention mechanisms and long short-term memory networks as the basic network for Japanese character text recognition. In addition, we set up separate text feature recognition systems for Japanese computer writing and handwriting to prevent feature overlap problems. For Japanese voice sequence recognition, we used a combination of memory gating unit and encoder, based on the network still extending the structure of the deep neural network and using the residual structure connection in the gating unit to avoid the gradient disappearance problem. At the end of the system, we use a softmax layer to connect the text recognition and voice recognition networks to form a Japanese language teaching aid system. To verify the efficiency of our system, we selected the Japanese text recognition public dataset and voice recognition public dataset for experimental validation. To match the practical application of the system, we created our dataset based on the dataset standard and conducted experimental validation. To compare other Japanese recognition methods, we selected the six most representative Japanese recognition algorithms for experimental comparison. To ensure the balance of the experiments, each algorithm is trained in a separate experimental environment for modeling and tuning parameters. Experimental performance and the experimental results show that our method significantly outperforms the other methods and has better system stability.
Design and implementation of graphics module based on E9171

In order to promote the development of display technologies such as desktop computers and notebooks, a graphics module based on the E9171 display chip was designed. The hardware design and software adaptation method of the graphics module are introduced, and the performance of the module is tested. The 3DMark11 performance test results show that the performance of the graphics card is about 50% higher than that of the previous generation. The performance-to-power ratio of the graphics card is excellent, which can meet the application of most embedded occasions, and is a very good realization of graphics module.
Multi-label text classification based on graph attention network and self-attention mechanism

Multi-Label Text Classification (MLTC) is a task to assign documents to one or more class labels. Most of the latest approaches are based on deep learning. However, among the various types of methods, there are still defects in capturing fine-grained information in the text and in modeling the dependencies between labels. In recent years, neural network model-based approaches have made great progress, among which the BERT language model can capture bidirectional contextual information well but cannot capture label-specific semantic information, so we propose a new multi-label text classification framework LAGAT by combining attention mechanism for feature learning. This framework is devoted to mining inter-label dependencies by constructing graphs of label-specific text semantic information and using the graph attention network (GAT) to learn the relative importance between labels by updating the weights on the edges, thus mining the inter-label dependencies. In further experiments, we confirm that the GAT is weaker than the self-attention mechanism in mining text labels by aggregating neighborhood information at moderate label size, and propose an improved model framework LA-Trans, which also utilizes label association relations with text semantic information, focuses on the target region and ignores irrelevant information, and achieves the performance of state of the arts.
Operational interpretation of bipartite quantum correlation

Abstract Operational interpretations of quantum resource measures lie at the heart of quantum information processing technologies. In this paper, we show that the bipartite quantum correlation in terms of Wigner-Yanase skew information, is the minimal failure probability of a particular state ensemble discrimination task via the associated least square measurement. We then derive a general relation between quantum coherence with respect to quantum operation and discriminating probability specific to least square measurement. Furthermore, we provide a wave-particle duality relation between bipartite quantum correlation and which-path information.
A bidirectional-LSTM method based on temporal features for deep fake face detection in videos

The technology of deep fake face detection in videos is particularly important in the field of internet and finance. In order to solve the significant problem of the spread of misinformation, it is essential that deep fake face detection methods robust manipulation. This paper proposes a Bidirectional-LSTM method based on temporal features for deep fake face detection in videos. Bidirectional-LSTM method is one of deep learning methods, which has positive effect on exploiting the temporal information in videos. Facial expressions and muscle movements play an important role in an individual’s speaking style. The continuity features of temporality suffer from the working process of the fake face in videos so that deep fake detection can be used for the fake face videos. Compared with previous methods such as SVM, BP neural network, the proposed method takes less time to train the model and have a good performance on accuracy. The experimental results show that the accuracy reaches 82.65% on DFDC data set. The proposed method has an excellent effect on the robustness in detecting compressed videos with noise interference.
Implementation of multiple object tracking for tracking pedestrians

Multiple object tracking is complex and this essay will analyse a method to build flexible tracking system, contributing to people detection. The system can perform automatic detection using Kalman filter’s prediction and update function, and bboxOverlapRatio calculates the cost of matching detection and track. Then, referring to the cost calculated, Munkres’ version of the Hungarian algorithm matches detection and track. Also, the method uses customized non-maximum suppression and approach to discover and eliminate false alarm tracks. The simulation tracks people from a moving car, and finally, under common circumstances, the method can help detect people successfully and rapidly. Although, there are still some problems existing, like that it can’t recognize the static people and the distance between pedestrians and car will influence the result. The method still provides new ideas of detection and is useful to solve the problems of shelter among objects from one another, the effect of motion, including vagueness and capture.
Design and implementation of network teaching system based on P2P technology

With the rapid development of computer network technology and digital technology, network education has entered a new era of digitalization, intelligence and individualization. New communication technologies and network teaching methods provide more network education resources and bring unprecedented development to network teaching construction. In recent years, people's demands have been rising. They are no longer satisfied with browsing and downloading texts, but need media resources that can be used and interacted in real time. Streaming media is born from this and has developed rapidly. Streaming media is limited by bandwidth in the process of transmission, and cannot maintain a large number of clients to connect at the same time. For this problem, this paper adopts the P2P mode to improve the parallel speed. On the basis of the experiment, the P2P network topology of the abstract layer of the network is proposed, and a P2P-based network teaching system is established. The system has been developed and tested, and has been applied in the relevant school local area network. Experiments show that the system solves the limitation of the resources of a single server in the traditional system. It makes reasonable use of idle resources and greatly improves the capability of the whole system.
Study on the efficiency of A, IDA, and JPS algorithm

Different path-finding algorithms are chosen in different environments, according to their advantages and disadvantages. This study investigates the characteristics of three of the popular heuristic path-finding algorithms — A*, IDA* and JPS. The investigation focuses on their performance of time efficiency, memory efficiency and performance on whether could give out optimal solution, and was carried out in 2D orthogonal grids. These algorithms are shown to be affected by density of barriers in grids, complexity of routes, map size and whether the map has boundary. This study analyzed the data of performance of three algorithms based on these aspects and concluded the environments that are suitable for each algorithm. In general, A* was found to be more universal when navigating, JPS was less universal and IDA* has the most limited universality.
Design and implementation of unified authentication platform for large campus network

This paper is aimed at the problem of large campus networks with too many applications and different security levels for authentication. Based on discussing and analysis the characteristics and shortcomings of traditional authentication systems, this paper introduces the development trend of current authentication platforms. Moreover, it designed and implemented a unified campus identity authentication platform based on CAS. Judging from setting up an experimental platform for the trial operation and analysing and evaluating test data, this paper proves that the system is safe and feasible, simple, and efficient in access management and maintenance, so the system can be deployed in large-scale campus networks. The process of designing a unified authentication platform will analyze the campus network, design an authentication platform suitable for large campus networks based on the analysis results. The use of unified authentication in the case of multiple network applications is widespread, and it is because of these needs that numerous authentication technologies have been born. These authentication techniques are compared one by one to select suitable solutions, and finally the solutions are selected and then integrated for use, with the aim of complementing each other. Both Shiro and CAS used in this design can be used for authentication but they have their own advantages, and their integration can complement each other. A series of tests are required for this project to see if it meets the design requirements and to plan for follow-up maintenance.
Pyramid multi-view stereo with double self-attention and uncertainty awareness

We propose a novel model for 3D reconstruction. At present, most learning-based reconstruction methods process highresolution input images from coarse-to-fine manner, and have achieved good reconstruction results. However, these methods usually use the plane sweep volume with fixed depth hypothesis to construct the cost volume, which may miss the small boundary and the depth mutation region. We use the uncertainty estimation module to adapt to the uncertainty of depth prediction per-pixel. In addition, we introduce a double self-attention module into the feature extraction network, so that the network has the ability to capture the long-range dependent features related to depth inference tasks. Experiments were carried out on DTU datasets. The results show that our method achieves excellent performance.
Fraudulent SMS identification based on enhanced BERT

With the popularity of SMS services, telecommunication fraud incidents occur frequently, which seriously threaten people's life and property safety. The existing fraudulent SMS recognition techniques have the problems of feature selection relying on manual and low recognition. To solve this problem, a fraudulent SMS identification method (referred to as BERT-SIG) that combines BERT (bidirectional encoder representation from transformers) and Sigmoid is designed. The method performs automatic feature extraction by multi-headed attention mechanism, improves the quality of recognition by Sigmoid activation function, and performs the task of recognizing fraudulent SMS by fine-tuning to improve the efficiency of fraudulent SMS recognition. In the experiments, two open-source datasets, "Spam Email" and "E-Mail classification NLP", are used to check the persistence and robustness, and the BERT-SIG is compared with various machine learning models. The experimental results show that BERT-SIG has the best performance accuracy and F1 score, reaching 99.72% and 99.53% respectively, and can effectively identify fraudulent SMS.
Design of the Artificial Intelligence Vocal System for Music Education by Using Speech Recognition Simulation

At present, computer technology is not limited to the network but to many aspects of spread out, including artificial intelligence technology, signal processing technology, speech recognition technology, and so on which have been better developed, and music teaching has also obtained certain achievements. If the detected speech data have no other clutter in the speech, then the endpoint detection can achieve ideal results. The music education system includes the development of four dimensions: WEB terminal, mobile terminal, web interface, and database. Using the powerful functions of computers, such as computing functions and processing functions, combined with advanced intelligent technology and related equipment, the intelligent learning model based on computer technology is established. It can be applied in college teaching, combined with the actual needs of current college music teaching, corresponding to the curriculum and teaching plan issued by the school, combined with the design of online learning system and reasonable use of computer technology to build a vocal music education system. The intelligent system can improve the requirements of vocal music education management level, teaching organization ability, and teaching platform operation ability.
An Automatic Error Detection Method for Engineering English Translation Based on the Deep Learning Model

Accuracy of deep learning model translation is a key index to evaluate the application performance of engineering English translation. In this paper, an automatic error detection system for English translation is proposed. In the particular task of grammar detection, researchers have gradually shifted their attention from statistical methods to neural network methods. Three deep learning algorithm models are established, and the multitask performance of the model is better than that of the conditional random field model and the LSTM-CRF model. The reason is that the multitask learning model of auxiliary tasks is included to some extent, which solves the problem of data sparsity and enables the model to be fully trained even under the condition of uneven label distribution. Thus, it performs better than other models in the task of syntax error detection. It realizes the word spelling error check based on the dictionary and uses the thought of editing distance to prompt the word error found, which can automatically check a large number of translations. On the basis of analyzing the sentence structure characteristics of engineering English translation, this paper realizes the detection of subject-verb agreement errors and analyzes the main word of the subject corresponding to the predicate verb by constructing the syntactic structure tree of the sentence, so as to realize the judgment of subject-verb agreement errors.