Reviewerly/exHarmony huggingface dataset:
Quantitative Index Construction of Website Influence: Taking China Private Universities as Example

Abstract Website influence is of great value to the brand promotion of private colleges and universities. This study collects the link index and traffic index data of private university websites in China, conducts relevant analysis, sorts out the relationship between link indicators and traffic indicators, and interprets the strategies of these indicators for brand promotion of private colleges and universities combined with case analysis.
Application of update lifting morphological wavelet and non-negative matrix factorization for wheeled and tracked vehicles classification

Abstract This paper presents an available scheme based on update lifting morphological wavelet (ULMW) and Non-negative matrix factorization (NMF) for wheeled and tracked vehicles classification. The ULWM algorithm which utilizes the update operator, means the morphological filter to replace the linear filter can preserve the impulsive shape details in seismic signal. Meanwhile the NMF method can reduce the computation cost. The traditional linear wavelet analysis and statistical analysis are compared with the presented scheme. Experimental results demonstrate that the presented scheme achieves a promising performance on extracting impulsive features of seismic signal and recognizing ground moving target.
Security Through Transparency and Openness in Computer Design

Trust in information technology depends on the level of security promised by the software and hardware stack operating on a platform. Consumers rely on cybersecurity updates of the software and firmware running on their devices to keep their privacy and data protected from malicious use. Businesses and governments procure technology which they expect to run for long periods and be kept in line with the state of the art in security. Presently, however, neither consumer, nor business solutions provide sufficient transparency regarding potential cybersecurity risks stemming from either the software or hardware stack embedded in them. Businesses need transparency in order to plan sustainable long-term operations, while consumers need devices that can be easily maintained and repaired and which offer sufficient information regarding real or perceived safety or security hazards. In the quest for security, transparency is a key sociotechnical requirement which lies at the core of trust in computing. As one of the most important abstractions interfacing the hardware and the lowest level software, the instruction set architecture (ISA) is perhaps the most essential element in the path to trust through transparency. Currently, however, the market is dominated by two proprietary ISAs in a duopolistic configuration, and their implementations are controlled by two major companies. This status quo has impacted significantly the integrated circuit supply chain in terms of both diversity and transparency. This paper argues that open ISAs, such as RISC-V, would bring much-needed democratisation of microprocessor design while enabling higher levels of security through their modular design and extensibility. However, open ISAs are facing certain technical, organisations and legal challenges that require conceptual interdisciplinary thinking and coordinated legislative and regulatory response.
Towards robust classification detection for adversarial examples

In the field of computer vision, machine learning (ML) models have been widely used in various tasks to achieve better performance. ML models, however, do a poor job of identifying malicious inputs such as adversarial examples. Abuse adversarial examples can cause security threats in ML-based products or applications. According to the definition of adversarial examples, the feature distribution of adversarial examples and normal examples are different. Besides, classification results of adversarial examples are sensitive to additive perturbance while normal examples are robust. This provides a theoretical basis for detecting adversarial examples from its own distribution. In this paper, we summarized some adversarial attack methods and defense methods, and a detection method based on the robustness of the classification result is proposed. This detection method has relatively good performance on gradient-based adversarial attack methods and does not rely on the structure or other information of ML model, so the structure of ML models need not be modified, which has a certain significance in practical engineering.
Ensamble Learning: An Approach in Artificial Intelligence

Decades ago, in the field of machine learning and data mining, the development of methods of ensemble learning has received significant attention from science community. Machine integration techniques incorporate multiple learning acquisition skills, better performance of guesswork than you would find in any available learning skills alone. Combining multiple learning models is demonstrated in thought and experimentation providing better performance than single foundation students. In a book, mix learning algorithms form a dominant and high-level approach to high throughput performance, thus applied to real-world problems ranging from face to face emotional recognition through classification and medical diagnosis in financial forecasting.
Cloud Storage : A Way to Modify Data Storage in Advance

Cloud computing is the rising innovation. Cloud computing gives simple access and superior figuring on the information. Another significant test that today’s programming organizations face are capacity of information at reasonable cost and make accessible constantly. This paper gives the examination on prologue to distributed storage and virtual stockpiling design. Information stockpiling is a significant and important explore field in Cloud computing. We show how to improve the customary record stockpiling strategy dependent on eye OS Web working framework which figures it out document circulated capacity and deficiency tolerant control however HDFS innovation of Hadoop.
Exploring the Design Space of Efficient Deep Neural Networks

This paper gives an overview of our ongoing work on the design space exploration of efficient deep neural networks (DNNs), specifically on the novel optimization perspectives that past work have mainly overlooked. We cover two complementary aspects of efficient DNN design: (1) static architecture design efficiency and (2) dynamic model execution efficiency. In the static architecture design, one of the major challenges of NAS is the low search efficiency. Different with current mainstream efficient search algorithm optimization, we identify the new perspective in efficient search space design. In the dynamic model execution, current major optimization methods still target at the model structure redundancy, e.g., weight/filter pruning, connection pruning, etc. We instead identify the new dimension of DNN feature map redundancy. By showcasing such new perspectives, further advantages could be potentially attained by integrating both current optimizations and our new perspectives.
Using End-to-End Deep Neural Networks for Diagnosing Drug Addiction from Speech Signals

In this paper, the idea of automatic drug addiction diagnosis from the speech voices is proposed, which provides a comfortable, secret, and remote test for diagnosis of the drug addiction. Moreover, we present an end-to-end approach for diagnosing drug addiction from speech signals based on a 1D Convolution Neural Network (CNN). The proposed method can deal with speech signals of any length as it splits the signal into overlapped frames using a sliding window. Moreover, to evaluate the proposed idea, a dataset was collected in collaboration with an addiction treatment center. Two different architectures are evaluated considering different input sizes. The performance of the proposed end-to-end approach in classifying sounds was assessed on the collected dataset. The experimental results have shown that the classification accuracy achieves 98.5%. Therefore, the proposed idea is applicable to controlled environments and situations.
Single Image Super-Resolution with Gradient Guidance

Recovering high-frequency image details such as edges and textures is a challenge of image super-resolution. To improve the reconstruction accuracy, image gradient maps are widely introduced as an additional input or a regularized term directly to existing methods. We argue that the best way to exploit gradient information is to learn from the training data. We propose a convolutional neural network for image super-resolution which is guided by image gradient maps. The gradient guidance provides a selective condition during super-resolution, leading to a more faithful super-resolved image. Our method is a flexible framework for image super-resolution, which can be easily incorporated into existing methods. Extensive benchmark evaluation shows that the proposed method achieves highly competitive performance, outperforming state-of-the-art performance in single image super-resolution.
The flow of converting from Synchronous design to Asynchronous Null Convention Logic design. Case study of S-box in AES encryption in secure router

In this paper, the flow of converting from synchronous to Null Convention Logic (NCL)-based asynchronous design is given. The asynchronous RTL code files in Verilog are synthesized to single rail netlists of D flip-flops, latches, combinational logic and special gates. This netlist is converted to a dual rail asynchronous netlist using NCL and then can be simulated and synthesized by other tools. The S-box which is considered as the most important component in Advanced Encryption Standard (AES) algorithm is chosen as a simple example to verify the design flow. In addition, this paper helps researchers see the power of the design tools aided by computer in asynchronous designs, and the choice to use it in the right context.
Strengthening Buffer Solution Learning Activities with Numbered Heads Together Models Equipped with a Combined Hand Out –Augmented Reality

Abstract This study aims to determine the comparison of the effect between the Numbered Heads Together (NHT) model equipped with Handout-AR and NHT model equipped with Microsoft PowerPoint on student learning activities on the buffer solution. The research sample was determined using the Cluster Random Sampling technique. The study data were obtained using documentation and observation techniques. The documentation technique was used to obtain data on the students’ midterm test results. The observation technique was used to obtain the data on the scores of student learning activities. The study data were analyzed using the Mann Whitney test. The results show that the application of the NHT model equipped with Handout-AR has a better effect on student learning activities on buffer solution material than the application of the NHT model equipped with Microsoft PowerPoint. These results are supported by the results of the Mann Whitney test and the average score of student learning activities from the two experimental classes. The data analysis using the Mann Whitney test produces asymp. Sig. 2-tailed value (0.000) greater than the significance level/a (0.050). The NHT/Handout-AR class has an average score of learning activities (87.6) which is greater than the NHT/Microsoft PowerPoint class (68.4).
Quantification of ocean-atmosphere energy exchange during super cyclone Amphan in the Bay of Bengal using Indian Ocean moored observatories

Based on the in-situ subsurface thermal and salinity measurements from the Ocean Moored buoy network for Northern Indian Ocean (OMNI) during the Super Cyclone Amphan in the Bay of Bengal during 14–18 May 2020, we have identified that the ocean-atmosphere interaction is limited to 80 m, the depth of the pycnocline. Using the OAEE-TS algorithm and dynamic changes in the sea subsurface temperature, salinity and precipitation as inputs, we have calculated the water vapor generation rates for a range of wind speeds. Based on the salinity changes, the water vapor generation rates for cyclone period-averaged wind speeds of 9, 16 and 25 m/s were 0.86, 4.33 and 11.24 kg/m2/h, respectively. For the same wind speeds, based on the Ocean Heat Content (OHC) changes, ∼76, 86 and 95 % of the OHC changes were in the form of latent heat. The identified evaporation rate parameters along with the previous study results are presented as model which could be used as inputs for ocean-atmosphere coupled models and tropical cyclone intensification studies in the Bay of Bengal.
Research on English Teaching Reform Based on Information Technology

Abstract With the popularization and application of information technology, college teaching has been greatly improved. Information technology and equipment have been effectively applied in the teaching of colleges and universities, which have great influence on the traditional teaching methods. English teaching in colleges and universities is an important part of the basic curriculum. In the information-based teaching reform, English teaching and information technology should be well combined to achieve a new breakthrough in English teaching. Based on the analysis of the characteristics of information technology, this paper puts forward the application strategies and ideas of information technology in English teaching reform.
Heuristic Function Evolution For Pathfinding Algorithm in FPGA Accelerator

A* is an informed pathfinding algorithm that depends on an accurate heuristic function to search for the shortest path. A complex pathfinding problem requires a well-informed heuristic function to efficiently process all data and compute the next move. Hence, designing good heuristic functions for specific domains becomes the primary research focus on pathfinding algorithms optimization. However, designing new heuristic functions is time consuming and difficult. Evolutionary Heuristic A* (EHA*) search proposed to have a self-evolving heuristic function to reduce the engineering efforts on heuristic functions design. The Genetic Algorithm is one of the most popular and efficient optimization techniques that is based on the Darwinian principle of survival of the fittest. It has been successfully applied on many complex real world applications including VLSI circuit partitioning, Travelling Salesman Problem (TSP), and robotic designs. Although the Genetic Algorithm is proved to be efficient on solving complex problems, the amount of computations and iterations required for this method is enormous. Therefore, we propose a hardware accelerator architecture for EHA* that is implemented on a Field Programmable Gate Array(FPGA) by employing a combination of pipelining and parallelization to achieve better performance. Moreover, the proposed Genetic Algorithm accelerator can be customized in terms of the population size, number of generations, crossover rates, and mutation rates for flexibility. The FPGA accelerator proposed in this paper achieves more than 8x speed up compared to the software implementation.
Infrared Image Sequence Small Target Recognition Method Based on Wavelet Transform Domain

In order to reduce the recognition time of small target in infrared image sequence, a small target recognition method based on wavelet transform domain is designed. Firstly, the noise of infrared image is analyzed, and the image is denoised. Then the infrared image is decomposed by wavelet transform domain. Finally, the small target recognition of infrared image sequence is completed based on wavelet transform domain. Experimental results show that the proposed method not only shortens the recognition time, but also improves the accuracy of small target recognition.
Scene classification of remote sensing image based on compound pruning

Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with filter elasticity and batch normalization scaling factor, a compound pruning method of convolution neural network is proposed. Only the superparameter of pruning rate needs to be adjusted during training. in the process of training, the performance of the model can be improved by means of transfer learning. In this paper, algorithm tests are carried out on NWPU-RESISC45 remote sensing image data to verify the effectiveness of the proposed method. According to the experimental results, the proposed method can not only effectively reduce the number of model parameters and computation, but also ensure the accuracy of the algorithm in remote sensing image classification.
Adapting to Movement Patterns for Face Recognition on Mobile Devices

Facial recognition is becoming an increasingly popular way to authenticate users, helped by the increased use of biometric technology within mobile devices, such as smartphones and tablets. Biometric systems use thresholds to identify whether a user is genuine or an impostor. Traditional biometric systems are static (such as eGates at airports), which allow the operators and developers to create an environment most suited for the successful operation of the biometric technology by using a fixed threshold value to determine the authenticity of the user. However, with a mobile device and scenario, the operational conditions are beyond the control of the developers and operators.In this paper, we propose a novel approach to mobile biometric authentication within a mobile scenario, by offering an adaptive threshold to authenticate users based on the environment, situations and conditions in which they are operating the device. Utilising smartphone sensors, we demonstrate the creation of a successful scenario classification. Using this, we propose our idea of an extendable framework to allow multiple scenario thresholds. Furthermore, we test the concept with data collected from a smartphone device. Results show that using an adaptive scenario threshold approach can improve the biometric performance, and hence could allow manufacturers to produce algorithms that perform consistently in multiple scenarios without compromising security, allowing an increase in public trust towards the use of the technology.
Lloyd-based Approach for Robots Navigation in Human-shared environments

We present a Lloyd-based navigation solution for robots that are required to move in a dynamic environment, where static obstacles (e.g, furnitures, parked cars) and unpredicted moving obstacles (e.g., humans, other robots) have to be detected and avoided on the fly. The algorithm can be computed in real-time and falls in the category of the reactive methods. Moreover, we propose an extension to the multi-agent case that deals with cohesion and cooperation between agents. The goodness of the method is proved through extensive simulations and, for the single agent navigation in human-shared environment, also with experiments on a unicycle-like robot.
Forse non tutti sanno che...

The article illustrates the characteristics of the Sistema bibliotecario Nazionale (SBN) whose management is currently entrusted to the Istituto per il catalogo unico delle biblioteche italiane e per le informazioni bibliografiche (ICCU) and which groups about one third of the over 18,000 Italian libraries. After having analyzed the variety of bibliographic records, assessed their quality, that of the catalog and of the services provided, he proposes a re-foundation of SBN by the university library services, following the example of what has already been done by nine southern universities with Share Catalogue.
Experiences with Virtual Reality at Secondary Schools: Is There an Impact on Learning Success?

This chapter examines the effects of immersive virtual reality (VR) learning units on the learning success of secondary school students. In a three-step research approach an immersive VR prototype that was designed to achieve specific learning goals in the field of mathematics was developed and validated with pedagogues. In a second step the usability of this prototype was tested with 20 secondary school students. Finally, a field experiment was conducted with 67 secondary school students to evaluate the impact of the VR learning environment on the achievement of measurable learning goals. The analysis showed no difference in learning success between a conventional mathematics lesson and the VR learning environment. The subjects of both experimental group (with VR) and control group (conventional lesson) achieved short and medium-term learning success. Some tendencies with respect to differences by gender and by short- versus long-term learning success lack statistical relevance, but can be seen as indications for further research to investigate the potential of immersive learning.
An Extension of Semi-supervised Boosting to Multi-valued Classification Problems

Generally, statistical learning using sufficient training data enables a highly accurate classification. However, it is sometimes difficult to collect sufficient training data for constructing an accurate classifier. In particular, for classification problems, correct labels corresponding to each feature vector are required. Therefore, semi supervised learning that uses not only labeled training data but also a large amount of unlabeled data for acquiring an accurate classifier has recently received attention. In a semi-supervised learning setting, if the distribution of labeled data is biased in each category set, it is difficult to estimate the correct labeling for unlabeled data. Consequently, the classification accuracy is degraded owing to the incorrect labeling. SemiBoost is a type of semi supervised learning method that avoids the above problem and has high performance. However, this method is a binary classification method and cannot be extended directly to handle multi-valued classification problems. In this paper, we propose a method to extend SemiBoost to enable it to perform multi-valued classification by introducing to it the concept of the error correcting output code (ECOC) method. Using the proposed method enables a more accurate labeling for unlabeled data. To verify the effectiveness of our proposed method, we conducted simulation experiments by using the data from the UCI Machine Learning Repository. The experimental results showed that the proposed method is effective for biased data. In addition, the classification results when the ratio of bias data was changed are shown and discussed.
Design of RF Robot Autonomous Obstacle Avoidance System Based on Deep Learning

In order to make the obstacle avoidance performance of the radio frequency robot more intelligent, this research designed an autonomous obstacle avoidance system for the radio frequency robot based on deep learning. The system is composed of five modules. The sensor module mainly adopts the complementary measurement scheme of ultrasonic sensor unit and infrared sensor unit, while adding angle measurement sensor unit; communication module includes CAN communication unit and serial communication unit; drive module uses DC motor as power, The selected DC motor uses Jinli Motor JRK-365S as the driving motor of the obstacle avoidance system; the obstacle detection module is mainly based on image processing for obstacle detection; the sample library module is designed based on deep learning, and the system is constructed for obstacle avoidance through BP neural network algorithm Sample library. On this basis, through the combination of hardware and software in the module, the autonomous obstacle avoidance of radio frequency robot is realized. The experimental results show that the system has a strong obstacle avoidance ability.