Reviewerly/exHarmony huggingface dataset:
A Study on ef fect of Advertisement Insertion to QoE in Video Streaming Services

Video streaming services such as Youtube and Netflix are one of the largest growing online services in the past few years. Currently, many video streaming service systems insert one or more advertisements in the middle of videos. However, such interruption of video viewing by the advertisement insertion forces users to view completely different videos which they are willing to and may be a cause of user’s quality of experience (QoE) degradation. To address the aforementioned issue, this study has done the experiment to evaluate the impact of multiple advertisement insertion into a single video to QoE. The experimental results showed that the user’s QoE increases when the advertisements are interesting to the user and there is no overlapped content in the multiple advertisements. We also discussed the maximum usage time to obtain sufficient utility from the perspective of service management based on two indicators: the profit obtained by an advertisement provider and the QoE of users.
Deep Transfer Learning-Based Intelligent Diagnosis of Malignant Tumors on Mammography

In this paper, we propose a deep transfer learning-based intelligent diagnosis approach for malignant tumors on mammography. An image segmentation algorithm is developed to remove the background, noise, and other redundancy in the image, for improving the learning efficiency. Based on the GoogleNet after training, we apply the transfer learning technique to the processed image. In this way, the accuracy of the classification model is improved. The experiment results show that the accuracy of our image segmentation algorithm is 100%, using only one-third of the data in training; the accuracy of our training approach is with the highest and average accuracy of 83% and 70%, respectively, by 2 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> iterations; and the area under the receiver operating characteristic curve is 0.77. These results are superior to those obtained by the existing methods.
Human Activity Recognition from RGB Video Streams Using 1D-CNNs

Human activity recognition is a challenging problem of classifying human activities based on video streams or accelerometer data. Most activity recognition methods use depth enabled data or multi-modal data to precisely predict movements. As majority of the real-world applications of activity recognition use RGB cameras, this paper aims at using RGB-only videos to classify actions. Motivated by recent advances of 1-Dimensional Convolutional Neural Networks (CNNs) to classify sequential data, this paper investigates the application of a deep 1D-CNN model for activity recognition as compared to a Bi-Directional Long Short-Term Memory (BLSTM) method. Instead of using raw RGB frames as input to the network, we utilize OpenPose API to detect 2D skeleton key points in video frames, which are then used to recognize human actions. This paper also addresses the challenges of training deep models with limited labeled data by using data augmentation and dynamic frame dropout techniques to increase the efficiency of the model and avoid overfitting. We verify the performance of our model on three popular and challenging benchmarks: UTD-MHAD, KTH and UCF-Sports. Extensive experiments demonstrate that our 1D-CNN model outperforms the BLSTM model and all the state-of-the-art methods consistently.
Vehicle High Beam Light Detection Based on Zoom Binocular Camera and Light Sensor Array

Abuses of high beam light cause numerous traffic accidents every year, which is a big threat to people's lives and property. Detection of high beam light in traffic surveillance will urge drivers use high beam light normatively, and reduce traffic accidents. Most existing researches focus on vehicle detection, vehicle classification, or vehicle license plate recognition, but research about high beam light detection is rare. In this paper, a high beam light detection system based on zoom binocular camera and light sensor array is developed. By combining the image processing and light sensor detection, vehicle with high beam light on can be captured with a low computational consumption.
Investigation of the student-professor interaction and self-learning ability for an aerospace engineering student

Based on the didactic experiences of the authors as professors of Aerospace Engineering, this article proposes a mathematical model of the learning of the students in Aerospace Engineering. Starting from the definition of a taxonomy of the student’s cognitive levels, a continuous-time model of the student’s learning dynamics is developed. To this end, various factors linked to the nature of the student are introduced, such as inertia in learning, forgetting of concepts, readiness to educational stimuli and self-learning skills. Based on the analysis of stability and the position of the equilibrium points of the newly developed model, the characterization of the different types of the student is obtained by outlining different skills in assimilating the educational stimuli or in exploiting self-learning skills. Finally, teaching strategies are proposed based on such analysis.
Data Visualization and Prediction of Goods Import Growth in Indonesia in 2010 - 2014

Import is one of the trade process conducted between two countries, the import process is done by a country to meet the needs of the country that is not meet by the ability of the country. Indonesia also imports many items such as fuel or rice. Therefore, to reduce the level of imports, we need to know which sector has the most import rate and whether the level of imports in that sector is increasing or not, so Indonesia can save the country's foreign exchange reserves.Import data Indonesia displayed in the form of visualization of data created using the method of Visual Data Mining (VDM) and using Power BI as a tools. Indonesian Import Prediction is done to know the growth of Indonesia import rate by using Linear Regression method and using SPSS tools.In a study of the growth of import levels in Indonesia, Indonesia has an import rate that increased between 8-20%, but in some periods experienced an increase and decrease caused by other factors such as consumption expenditure, currency exchange rates and state revenues.
Development of A Wearable Device of Measuring Viewing Distances in Smartphone Use

A WDMD, a glasses-like device, was created to measure the viewing distances of reading on smartphones to alarm uses when their eyes are too close to the phone displays. It is consisted of a Micro-controller (Arduino Nano board based on the ATmega328), an ultrasonic sensor (HC-SR04), a MicroSD card adaptor compatible with Arduino, two 3v-coin batteries to offer the electric power, and an SD card to store the distance data. The HC-SR04 is rotated downward about 31 degrees for measuring the viewing distances of most populations because the lines of human viewing sights were not perpendicular to the mobile phones. An experiment was performed subsequently to test the precision of the WDMD. The outcomes showed that the final WDMD could measure the distances with better precision because errors were less than 0.43 cm.
Способы реализации клиент-серверных приложений потоковой передачи данных на основе

In this article discusses ways to implement client-server data streaming applications.The main data transmission protocols TCP and UDP are described, with an indication of their characteristics.The author provides the necessary requirements for the implementation of data streaming and describes their key advantages and limitations based on TCP and UDP protocols within the framework of this task.In the article, based on the results of the study, the author presents an algorithm that allows solving the identified limitations and increasing the performance of data streaming software implemented on the basis of the TCP protocol.As a result, it was concluded that the presented algorithm allows you to reduce the cost of developing network code, increase the efficiency of data streaming, and minimize the delays that occur with standard data streaming based on the TCP protocol.
Analysis of Word Dependency Relations and Subword Models in Abstractive Text Summarization

Abstractive text summarization is an important task in natural language processing. As many texts are becoming available in the digital world at very high speeds, people begin to need automated systems to summarize such bulk data in a condensed form that only holds the necessary information. With recent advances in deep learning techniques, abstractive text summarization has gained even more attention. Attention-based sequence-to-sequence models are adapted for this task and achieved state-of-the-art results. Additional mechanisms like pointer/generator and coverage have become the standard mechanisms for abstractive summarization. On top of these common approaches, we observe the effects of integrating word dependency relations and using subword models. We show that word dependency relations increase the performance. We also present that subword usage is another viable option to be included for models as well.
Performance Characterization of MPI_Allreduce in Cloud Data Center Networks

Advancements in hardware architecture and system design have enabled the transformational pivot towards cloud-computing. The rising cost of on-premise, vertical scaling, and maintenance combined with the rise of workload heterogeneity have fueled this paradigm shift. Furthermore, increasingly demanding use-cases for storage and High-Performance Computing (HPC) as well as the emergence of new workloads such as machine learning and big-data have motivated research into network traffic analysis, resource disaggregation, job scheduling, and orchestration in a bid to reduce total cost while maintaining high performance. A prime focus of this research is the communication performance of collectives, which comprise a significant portion of the communication of many of the aforementioned workloads. In this paper, we characterize the performance of MPI_Allreduce, which is used extensively in HPC and Deep-Learning (DL) training workloads in cloud environments. We demonstrate several key insights including that the Ring algorithm performs better than the Rabenseifner algorithm under conditions of high congestion and packet drops. We further illustrate detailed performance analysis under different assumptions and provide recommendations on how to address issues that can manifest.
A Questionnaire Study on the Use of Access Control in Industrial Systems

Industrial systems have traditionally been kept isolated from external networks. However, business benefits are pushing for a convergence between the industrial systems and new information technology environments such as cloud computing, as well as higher level of connectivity between different systems. This makes cybersecurity a growing concern for industrial systems. In strengthening security, access control is a fundamental mechanisms for providing security in these systems. However, access control is relatively immature in traditional industrial systems, as compared to modern IT systems, and organizations' adherence to an established cybersecurity standard or guideline can be a deciding factor for choices of access control techniques used. This paper presents the results of a questionnaire study on the usage of access control within industrial system that are being developed, serviced or operated by Swedish organizations, contrasted to their usage of cybersecurity standards and guidelines. To be precise, the article focuses on two fundamental requirements of cybersecurity: identification and authentication control, and presents related findings based on a survey of the Swedish industry. The goal of the study is breaching the gap between the current state and the requirements of emerging systems with regards to access control.
Accelerated Degradation Test Design and Degradation Trajectory Prediction of Radar Gain Control Module

As an important part of radar receiver, radar gain control module determines whether the radar system can work normally and reliably. Therefore, it is necessary to estimate its life reliably. Whereas, there are few designs for radar degradation tests and the traditional accelerated life test method is no longer suitable for long life analog integrated circuit. In this paper, an environment test system for multi-stress loading is built, and the power control board and the fixed board of the radar gain control module are designed and made based on the idea of accelerated degradation test. The test data are obtained through the temperature cycle accelerated degradation test, and the eigenvectors are extracted which can reflect the performance degradation trajectory of the radar gain control module. The extreme learning machine combining a kernel based on grey wolf optimization is used to fit the degradation trajectory, and it is compared with the grid search method, the extreme learning machine with hidden layers and several other regression methods. The results show that, the accelerated degradation test and the extreme learning machine combining a kernel based on grey wolf optimization proposed in this paper can satisfy the performance evaluation of high reliability electronic products and have high precision and great application value.
An improved unified online multi-object tracking algorithm combined with attention mechanism

At present, the mainstream paradigm of multi-target tracking is still tracking-by-detection, which includes two parts: the detector for locating the target and the appearance embedding model for data association. Most methods implement the two modules separately, without considering the relationship between them. However, the biggest problem of this two-stage methods is the large amount of calculation, leading to slow running speed. In this paper, we build a unified online multi-object tracking system. By integrating the object detector and the apparent embedding model into the same shared model, we can get the bounding box and the embedding model simultaneously, so as to reduce the network complexity and speed up the operation. To further improve the performance of the detector, we add an attention mechanism to weight each dimension of the output channel, so as to highlight the important foreground information and ignore the influence of the background as much as possible. The experimental results demonstrate that we can achieve competitive results on MOT16 dataset, and the best trade-off between accuracy and speed.
New Ultrasound Image Watermarking Enhancement algorithm for cardiovascular disease

The primary goal of this article is to implement a new space-frequency-domain picture enhancing method. Algorithms often have low contrast, although algorithmic algorithms solve this issue. After that, the heuristic image search method is employed to search for the picture of the cost matrix. A study published in the journal Computer Assisted Tomography concluded that when an image-processing technique called a spatial-frequency-domain image ultrasound imaging algorithm is used, ultrasound image watermarking of cardiovascular disease have improved contrast and sharpness, leading to better middle-edge visibility, increased detection accuracy, and improvement in ultrasound of cardiovascular disease. Also, the sharpness of the image border is kept intact. A study published in e studied the effectiveness of the ultrasound imaging method, which is based on spatial frequency domain image enhancement, in clinical applications. More energy efficient yields more grain, sharper, more contrast, and more information.
MODBUS: A Target for Covert Communication in Iot Devices

Internet of Things (IoT) is a part of Cyber Science that has been gaining popularity exponentially.IoT are generally referred to as smart devices since they carry out their operations with minimal human intervention.The IoT devices are connected to each other via a device such as a centralized modem.Through this method, IoT helps provide an easier life for its consumers.Even so, these smart devices are flawed and face privacy challenges and can be exploited at the physical level to obscurely perform information exchange that they are not intended to do.This is known as a covert channel.By definition, a covert channel is some form of a medium which is used to exploit the functionalities of an overt channel to secretly send and receive messages which they are not originally programmed to do so.Following this definition, "MODBUS Protocol" was chosen to be used as a communication protocol in a Master-Slave model for a covert channel.The MODBUS protocol uses a Master and Slave system model where the Master sends functional instructions to the slaves and the slaves return the output corresponding to the instruction.By exploiting this feature of the Master-Slave architecture, we have built a covert channel wherein the receiver maps each character of the covert message into an instruction and sends it to the slave and the slave strips off the data in that instruction and sends it to the intended receiver, where the receiver maps the instruction back to the character and prints out the message.
Provable Data Possession (PDP) and Proofs of Retrievability (POR) of Current Big User Data

Abstract A growing trend over the last few years is storage outsourcing, where the concept of third-party data warehousing has become more popular. This trend prompts several interesting privacy and security issues. One of the biggest concerns with third-party data storage providers is accountability. This article, critically reviews two schemas/algorithms that allow users to check the integrity and availability of their outsourced data on untrusted data stores (i.e., third-party data storages). The reviewed schemas are provable data possession (PDP) and proofs of retrievability (POR). Both are cryptographic protocols designed to provide clients the assurance that their data are secure on the untrusted data storages. Furthermore, a conceptual framework is proposed to mitigate the weaknesses of the current storage solutions.
Preliminary Application of IoT and Block-chain Coupling Technology in the Management of Information Resources in the Whole Life Cycle of Coal Machinery Equipment

In order to solve the problems of poor sharing, weak transparency, and poor management of coal machinery equipment information resources, a whole life cycle information resource management method for coal machinery equipment incorporating the Internet of things and block-chain technology was proposed. Firstly, the key technologies of data acquisition, remote transmission and data processing in the environment of internet of things are studied to realize the information acquisition, transmission and storage of coal machinery equipment. Then, based on the decentralized, traceable and non tamperable characteristics of block-chain technology and the existing step-by-step intelligent production network in the research and development, production and use of coal machinery equipment, a block-chain model for the whole life cycle of coal machinery equipment is established, and the data of each link is equally connected according to the smart contract paradigm on the chain, so as to realize the data connection and sharing of the whole industrial chain and the complementary innovation technology of the industrial chain. Finally, the coupling model of the Internet of things and block-chain is established, and the challenges in the application of the coupling model in the coal machinery equipment's whole life cycle information resource management are analyzed and summarized. This study can strengthen the supervision of coal mining enterprises, realize the large-scale sharing of coal machine design and manufacturing resources, and protect the information security of data.
Applying Security-Awareness to Service-Based Systems

A service-based system (SBS) dynamically composes third-party services to deliver comprehensive functionality. As adaptive systems, SBSs can substitute equivalent services within the composition if service operations or workflow requirements change. Substituted services must maintain the original SBS quality of service (QoS) constraints. In this paper, we add security as a QoS constraint. Using a model problem of a SBS system created for self-adaptive system technology evaluation, we demonstrate the applicability of security assurance cases and service security profile exchange to build in security awareness for more informed SBS adaptation.
Visual tracking using multi-layer appearance approach

Abstract This paper investigated single target tracking of arbitrary objects. Tracking is a difficult problem due to a variety of challenges such as scale variation, motion, background clutter, illumination etc. To achieve better tracking performance under these severe conditions, this paper proposed covariance descriptor based on multi-layer instance search region. Our results show that the proposed approach significantly improves the performance in term of centre location error (in pixels) compared to covariance descriptor with using a fixed bounding box. From this work, it is believed that we have constructed a great solution in choosing best layer for this descriptor. This will be addressed in the next future work such as consider target motion during tracking.
Application of Computer Software in Pop Music Teaching

In recent years, with the rapid development of computer technology and multimedia technology, the information technology represented by it has been widely infiltrated into the teaching of various subjects, including the teaching of pop singing major in Chinese universities. Music is the art of sound and hearing. The particularity of music leads to the important position of information technology in the pop singing major. However, in terms of the application in the practical teaching of pop singing major, the audience of computer software teaching is not wide enough, and there are still many problems. This paper discusses the benefits of computer software to the teaching of pop music teachers, and puts forward the test method (a group of test questions to measure the actual situation of a certain educational phenomenon, so as to collect data for Research), mainly through the study and research of computer music software, this paper discusses some methods of using computer music software to assist pop singing teaching, so as to solve the shortcomings of traditional pop singing teaching methods. The purpose is to study the practicability of computer software teaching technology in pop singing major. This paper takes sibeliu, Cubase and logic computer music software as examples to explain the practical application of computer music software in the teaching of pop singing specialty, so that we can understand the practical methods of computer music software in pop singing teaching. This paper introduces the basic situation of some computer music software, the function and characteristics of computer music software, and the advantages of application in pop singing teaching. It also carries out the experimental analysis of the application of computer software teaching technology in the teaching of pop singing, and discusses the way and process of computer music software to assist the teaching of pop singing, experimental research shows that learners are most interested in "automatic accompaniment", accounting for 58.2%. The average score of the experimental group using Sibelius music teaching software is 78.66, and the average score of the control group using traditional teaching media is 74.32. The integration of this kind of software into classroom teaching can improve the enthusiasm of learners in learning pop singing major. The integration of computer software into pop singing teaching can not only expand the content of College pop singing teaching curriculum system, but also conform to the current wave of teaching reform widely carried out in Colleges and universities.