Reviewerly/exHarmony huggingface dataset:
A Low-Latency Inference of Randomly Wired Convolutional Neural Networks on an FPGA

Convolutional neural networks (CNNs) are widely used for image processing tasks in both embedded systems and data centers. In data centers, high accuracy and low latency are desired for various tasks such as image processing of streaming videos. We propose an FPGA-based low-latency CNN inference for randomly wired convolutional neural networks (RWCNNs), whose layer structures are based on random graph models. Because RWCNNs have several convolution layers that have no direct dependencies between them, our architecture can process them efficiently using a pipeline method. At each layer, we need to use the calculation results of multiple layers as the input. We use an FPGA with HBM2 to enable parallel access to the input data with multiple HBM2 channels. We schedule the order of execution of the layers to improve the pipeline efficiency. We build a conflict graph using the scheduling results. Then, we allocate the calculation results of each layer to the HBM2 channels by coloring the graph. Because the pipeline execution needs to be properly controlled, we developed an automatic generation tool for hardware functions. We implemented the proposed architecture on the Alveo U50 FPGA. We investigated a trade-off between latency and recognition accuracy for the ImageNet classification task by comparing the inference performances for different input image sizes. We compared our accelerator with a conventional accelerator for ResNet-50. The results show that our accelerator reduces the latency by 2.21 times. We also obtained 12.6 and 4.93 times better efficiency than CPU and GPU, respectively. Thus, our accelerator for RWCNNs is suitable for low-latency inference.
Uncertainty SVM Active Learning Algorithm Based on Convex Hull and Sample Distance

In the process of traditional supervised learning, it is difficult to obtain large numbers of labeled samples and challenging to reduce the cost of data labeling. In response to this issue, and combining the convex-hull vector and the mechanism of support vector machine (SVM), an SVM active learning algorithm based on convex-hull vector and sample distance was proposed. Through the sample distance and convex-hull vector, the algorithm could actively select the samples most valuable to the current SVM classifier, i.e., the samples most likely to be support vectors. The experimental results demonstrated that with no negative impact on the classification accuracy, the proposed algorithm demanded significantly fewer labeled samples compared to random sampling, which reduced the sample labeling cost in learning, enhanced the SVM generalization performance, and increased the training speed.
Performance Measurement and Validation of the Distributed Digital Control Systems

The paper describes measurements and validation of a digital control system's performance (time) characteristic. Currently, in most cases, an industrial control (I&C) system is a distributed hybrid system combining hardware and software components that provide the necessary functionality. The I&C life cycle includes the stage of system tests to confirm its time characteristics concerning both speed (data processing time) in the system components and time (delay) of network data transfer between components. Various methods of time characteristic measurements are used. They depend on external conditions: experience and accepted practice and the properties of the measured parameters. In the work, we have researched the main statistical properties of the temporal characteristics of a typical I&C system for a Nuclear Power Plant (NPP). The measurements were carried out as with built-in system software as with an external testing agent. The results of both testings are discussed.
Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling

Recently, the necessity of multiple attention heads in transformer architecture has been questioned [1]. Removing less important heads from a large network is a promising strategy to reduce computation cost and parameters. However, pruning out attention heads in multihead attention does not evenly reduce the overall load, because feedforward modules are not affected. In this study, we apply attention head pruning on All-attention [2] transformer, where savings in the computation are proportional to the number of pruned heads. This improved computing efficiency comes at the cost of pruning sensitivity, which we stabilize with three training techniques. Our attention head pruning enables a considerably fewer number of parameters with a comparable perplexity for transformer-based language modeling.
Research on Security Evaluation Technology of Intelligent Video Terminal

The application of intelligent video terminal has spread in all aspects of production and life, such as urban transportation, enterprises, hospitals, banks, and families. In recent years, intelligent video terminals, video recorders and other video monitoring system components are frequently exposed to high risks of security vulnerabilities, which is likely to threaten the privacy of users and data security. Therefore, it is necessary to strengthen the security research and testing of intelligent video terminals, and formulate reinforcement and protection strategies based on the evaluation results, in order to ensure the confidentiality, integrity and availability of data collected and transmitted by intelligent video terminals.
The Impact of Continuous Code Quality Assessment on Defects

Continuous Code Quality Assessment (CCQA) tools promise that increasing code quality leads to fewer defects, i.e., that software quality from the user view can be increased by increasing quality of the product. Currently, there is limited evidence on that application of CCQA tools, such as, SonarCloud (SC), during software development actually reduces the amount of defects over time. In this paper we study five open-source projects that adopt SonarCloud (SC) for CCQA and we compare frequencies of defect reports before and after adoption of SC. For only one project (Apache Ratis), we find a statistically significant decrease of defects after adoption of the tool. After closer investigation we find, that this decrease is likely just a coincidence and not caused by the adoption of SC and adherence to its code quality recommendations. In general, we find no evidence for that application of a CCQA tool increases product quality.
CREATION OF A «TREE OF RESEARCH IN SPECIAL PEDAGOGY» AS A MEANS OF FORMING GENERAL AND PROFESSIONAL COMPETENCIES OF PEDAGOGICAL COLLEGE STUDENTS STUDYING IN THE SPECIALTY 44.02.04 SPECIAL PRESCHOOL EDUCATION.

The article is devoted to the formation of readiness for professional activity of students of the pedagogical college. The author characterizes the activity of the laboratory for the support of special childhood as a form of educational activity and consecrates in detail the lesson on extracurricular activities to create a tree of research in special pedagogy with students of the specialty "Special preschool education". The topic under consideration will be of interest to specialists in the field of vocational education.
Comparison between a Whole and Separated Feature Information for Acute Lymphoblastic Leukemia (ALL) Classification

Acute lymphoblastic Leukemia (ALL) is dangerous cancer in which the infected blood cells disturb the blood and bone marrow. It attacks the body's immune and the ability of bone marrow to produce white blood cells have diminished. This research aims to classify the ALL image using the whole feature information. We proposed a method to decrease the image's size using the whole co-occurrence matrix to represent the object. The research performances have produced 90.77%, 96,67%, and 95.38% for the maximum accuracy, sensitivity, and specificity. This research has also compared to separate channels, which are red, green, and blue. Our novel method shows that the whole feature information has yielded higher accuracy, sensitivity, and specificity than the others, which are the red, green, as well as blue channels. Furthermore, this research has a novelty, i.e., to prove that the whole feature information method is better for the implementation system. Additionally, this research contributes by proposing a method about whole feature information for the implementation system.
Investigation and Optimization Research on Online and Offline Blended Learning of Higher Vocational English from the Perspective of CoI Algorithm

Abstract With the development of information technology, online and offline blended learning has become a universal learning form. In order to investigate and optimize the online and offline blended learning of English in higher vocational colleges, this paper introduces the CoI theory to analyze blended learning. First, design the questionnaire based on the three elements of the CoI theory. After analyzing the results of the questionnaire, conduct related experiments. In order to ensure the accuracy of the results, an experimental group and a control group are set up. After three months, most of the students in the experimental group have improved their performance. The experimental results show that blended learning has a certain positive effect on the improvement of vocational students’ English performance. In addition, it is found through research that there are certain differences in the promoting effect of blended learning on English scores under different scores. Therefore, this article puts forward some suggestions for blended learning.
Development of MATTERED (modul of heat and temperature based on android) for senior high school

Abstract Physics is one of the subjects considered troublesome and unique by understudies, so it requires learning media to be able to get the concept of physics. This study points to create a learning module, to be specific MATTERED on Android-based Temperature and Heat for high school understudies. MATTERED is an android-based interactive module with respect to the concept of temperature and heat which is created within the frame of interactive multimedia. This research design uses Research &amp; Development (R&amp;D). MATTERED wasat that point created and approved by material specialists and media specialists, and tried on physics instructors and 90 understudies of class 11 th grade in three high schools within the city of Surakarta, Central Java, Indonesia. Based on the comes about of the approval by the material and media master validator, a score of 87.44% was gotten, and the MATTERED trial on instructors and students each obtained a score of 95.03% and 96%, where all appraisals were included within the exceptionally great category. Based on this, it demonstrates that MATTERED is doable to be utilized in learning physics in class for students of class 11 th grade.
Advanced QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm and Its Application for Neural Network

Hu, Pei Pan, Jeng-Shyang Chu, Shu-ChuanThe QUasi-Affine TRansformation Evolution (QUATRE) was first proposed by Meng et al. in 2016. It has the characteristics of few parameters and fast convergence. This paper brings two methods to improve its solution quality. The opposite position and comprehensive learning greatly advance the ability of jumping out of local traps when the QUATRE falls into stagnation. Their performance is verified by 23 benchmark functions. In the end, they succeed to train the parameters of neural network and predict the long-term traffic flow in Qingdao.
Classification of the Energy Production Potential of Water Lens Solar Concentrators Using Machine Learning

Abstract Assessing the potential of renewable energy sources for buildings in neighborhoods becomes a crucial task in the early planning stage. Integrating solar energy equipment into urban buildings poses many challenges, such as uncertainties and the complexity of urban built agglomeration. Due to the time-consuming solar energy potential assessment process and lack of knowledge of urban actors, a reliable framework is required to predict buildings’ solar energy potential. This research presents a comprehensive machine learning data processing framework to predict output energy of Water Lenses (WL) based on buildings specifications and relationship to the neighbourhood. The research used a raw dataset consisting of 7000 sample buildings in different situations by applying 12 years of climatic conditions in Tallinn, Estonia. The results were entered into a Supervised Machine Learning process and the Gaussian Naive Bayes technique was used for classification of building features to be implemented with solar systems. Finally, the process was measured by a confusion matrix that showed 80% accuracy of ML output predictions in the urban context.
Insights to Computational Intelligence Techniques for Computer Vision

This modern world, driven by technological advancement and inventions, is moving toward automation of processes, technology, manufacturing, etc. We have self-driving cars, and robots are used in production and so on. Computer vision has a huge role to play in it. With the advancement in computational intelligence, it has been possible to create complex algorithms and tools which have brought a revolution in the field of computer vision. In this chapter, we are going to look at the most recent developments in the field of computer vision. Computer vision itself is a vast field, so we will be sticking to object detection, which is one of the main tasks of computer vision. It also forms the base for further developments in this field. The paper looks back at the inspiration that led to the idea of computer vision, its history, and how it has developed over the years; these are covered in brief in the chapter. Various algorithms form the majority of the portions of the section. Starting from the basics, the paper slowly and gradually builds the base and move on to understand more complex recent algorithms that are being used in real-life applications. These help build the understanding, which can be applied for research as shown with a case study. Finally, the paper looks at some of the latest innovations in the field of computer vision, those that have been developed based on these concepts.
Speaker Identification using Triplet Loss Function Combined with Clustering Techniques

Speaker identification plays a critical role in many applications like robotics specially the applications that focus on humanoid robotics. The speaker identification includes comparing unknown utterances against pre-stored utterances of speakers. In general, the encoded features are stored from the pre-known speakers database and 1:N comparisons between the extracted encoded features of the unknown utterances and the pre-stored N known speakers are implemented. Different techniques can be used for these types of comparisons of which cosine similarity is the most used one. However, the more the number of the pre-stored known speakers, the longer the execution time the model will need to finish these comparisons, and hence it may not be suitable for real-time applications. In this paper, we combined previously published Triple Neural Network for speaker identification with clustering techniques on the speakers dataset. We employed different clustering techniques and presented two different methods for comparing unknown utterances against pre-stored utterances. The obtained results showed a significant enhancement in the comparisons time with a few reductions in the obtained accuracy. The proposed approach provided a framework that can represent a trade-off between execution time and obtained accuracy.
The cat’s eye target recognition based on fully convolutional residual network and visual saliency

In order to improve the accuracy and speed of cat-eye target recognition, a cat-eye target recognition method combining fully convolutional residual network and visual saliency is proposed. This method is based on semantic segmentation. Firstly, the collected active and passive images were preprocessed, and saliency detection is used to enhance the cat-eye target in the different image. Second, the active image is segmented by the full convolutional residual network to achieve background suppression and target enhancement, and the fusion result graph containing the cat-eye target region is obtained by the and operation of the two results. Finally, the candidate target area of the cat-eye target Recognition, to extract real cat-eye target. The experimental results show that the proposed method has high accuracy and fast running time, and can be used for real-time detection.
The Development of Learning Media Based on Augmented Reality for Industrial Metrology and Quality Control Courses

Industrial metrology and quality control courses are compulsory subjects Mechanical Engineering Department students, Faculty of Engineering, State University of Malang. The learning media used are still conventional using modules so that the attractiveness of students is less. An alternative learning media is needed to increase student attractiveness, namely augmented reality-based engineering learning media. The objectives of this research and development include developing products in the form of application-based learning media with the application of augmented reality technology for industrial metrology and control courses and knowing the level of student attractiveness regarding the application-based learning media used. The validation results obtained of material expert validation shows a percentage of 88.75%, media expert validation shows a percentage of 86.25%, and attractiveness students on small groups show a percentage of 95.69 %. It can be concluded that the learning media of augmented reality-based engineering measurement in industrial metrology and quality control courses is very feasible to be used in the learning process.
Touch Your Own Device! A COVID-Safe Alternative to Multi-touch Interactions with Public Touchscreens

Touchscreens are a widely used medium for interacting with digital applications in public and they are found in various contexts including entertainment, culture, travel, marketing, etc. However, in the current times of COVID-19 pandemic there are safety concerns about the use of these devices, as touching a common surface may increase the risk of transmission. Thus, there is a need for alternative means for interacting with public interactive kiosks in a usable and effective way. In this paper we propose and evaluate a universal solution to safer interactions with public touchscreens using the smartphone as a touchpad controller. Users have to scan a QR code, and then use their device as a controller to interact with the public system though touch and multi-touch interactions (tap, drag and two-finger gestures). We have setup a user study, where we compare the usability and user experience of smartphone control to the traditional touchscreen input and we examine the users’ intention and opinion about the proposed approach. Our study indicates that the use of the mobile device as a controller has the potential to be a safe and practical alternative to direct touch input for public interactive kiosks, and reveals some usability issues that need further consideration.
On the convex characterisation of the set of unital quantum channels

Abstract In this paper, we consider the convex structure of the set of unital quantum channels. To do this, we introduce a novel framework to construct and characterise different families of low-rank unital quantum maps. In this framework, unital quantum maps are represented as a set of complex parameters on which we impose a set of constraints. The different families of unital maps are obtained by mapping those parameters into the operator representation of a quantum map. For these families, we also introduce a scalar measuring their distance to the set of mixed-unitary maps. We consider the particular case of qutrit channels which is the smallest set of maps for which the existence of non-unitary extremal maps is known. In this setting, we show how our framework generalises the description of well-known maps such as the antisymmetric Werner–Holevo map but also novel families of qutrit maps.
Design of a High Concurrency Payment System in Bank

The development of online banking payment system has promoted the transformation and upgrading of traditional banking industry and attracted numerous customers. At the same time, the access of high-traffic customers puts forward higher requirements on the performance of the server that hosts these millisecond data and high concurrent response. Based on the technology of load balancing, on the basis of in-depth study, based on the independent training node and the prediction model of load balancing algorithm, this paper designs a set of high concurrency bank payment system. Through the historical data training prediction model, this paper predicts the response time of the new request, and according to the forecast of each server response time, assign the request which has the least time corresponding server nodes, thus improving the cluster request distribution balance, reducing system response time. The realization of high concurrency response of the bank payment system meets the demand of large flow customer visit.
Application of College English Mobile Learning Model in the Field of "5G" Technology

With the progress of society and the continuous development of 5G communication technology, mobile learning has gradually developed. Compared with traditional classroom learning, mobile learning relies on Internet technology, wireless communication technology as the means, smart phones, tablet computers and other carriers for mobile online learning. Based on the analysis of the characteristics of 5G technology field and mobile learning, this paper puts forward the characteristics of 5G mobile learning model in college English learning and the strategies to improve the timeliness of learning. It conforms to the interest characteristics of college students in today's times, which can stimulate students' learning initiative, finally realize students' autonomous learning, and solve problems by means of network at the same time, so as to cultivate students' ability to use English comprehensively.
Analysis and Design of College Sports Information System Based on Network

research-article Analysis and Design of College Sports Information System Based on Network Share on Authors: Jingyi Shan Tianjin University of Traditional Chinese Medicine, China Tianjin University of Traditional Chinese Medicine, ChinaView Profile , Bao Han Tianjin University of Traditional Chinese Medicine, China Tianjin University of Traditional Chinese Medicine, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2852–2857https://doi.org/10.1145/3482632.3487528Online:22 November 2021Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access