Reviewerly/exHarmony huggingface dataset:
Once For All Skip: Efficient Adaptive Deep Neural Networks

In this paper, we propose a new module, namely once for all skip (OFAS), for adaptive deep neural networks to efficiently control the block skip within a DNN model. The novelty of OFAS is that it only needs to compute once for all skippable blocks to determine their execution states. Moreover, since adaptive DNN models with OFAS cannot achieve the best accuracy and efficiency in end-to-end training, we propose a reinforcement learning-based training method to enhance the training procedure. The experimental results with different models and datasets demonstrate the effectiveness and efficiency in comparison to the state of the arts. The code is available at https://github.com/ieslab-ynu/OFAS.
Pedestrian Reidentification Algorithm Based on Local Feature Fusion Mechanism

In the application of pedestrian reidentification, misjudgment is often caused by low video resolution, illumination variation, and background interference. In order to solve these problems, this study proposes a pedestrian reidentification algorithm based on local feature fusion. Taking advantage of the inherent structure of the human body, we pay attention to pedestrian parts with prominent features and ignore other parts with interference information. Feature extraction is carried out for detected pedestrian parts with significant features, and new fusion features are generated. By calculating distance measurement between image features, pedestrians are classified and recognized. Experimental results show that the accuracy of the proposed algorithm is superior to that of other comparison algorithms on the datasets of Market1501, Duke, and CUHK03. It is proved that the proposed algorithm has a good pedestrian reidentification effect.
An unsupervised monocular image depth prediction algorithm using Fourier domain analysis

Aiming at the problems of high cost and low accuracy of scene details during the depth map generation in 3D reconstruction, we propose an unsupervised monocular image depth prediction algorithm based on Fourier domain analysis. Generally speaking, small-scale images can better display depth details, while large-scale images can more reliably display the depth distribution value of the entire image. In order to take advantage of these complementary properties, we crop the input image with different cropped image ratios to generate multiple disparity map candidates, and then use Fourier frequency domain analysis algorithms to fuse disparity mapping candidates into left and right disparity maps. At the same time, we propose a loss function based on MSSIM to compensate the difference between left and right views and realize unsupervised monocular image depth prediction model training. Experimental results show that our method has good performance on the KITTI dataset.
Application of Coastal Protection Structure for Mangrove Rehabilitation &amp; Rejuvenation of West Coast Johor. Case Study: Tanjong Labuh, Batu Pahat, Johor.

Abstract Tanjung Labuh beach was a muddy beach along the Malacca Strait and the shoreline was the land-sea border. The shoreline has an irregular existence and shifts because of human activities and nature, resulting in erosion and sediment eradication on the shore. The aim of this study focuses on identify the coastline changes from the image taken by using drone and to analyse the data from the image by using ArcGIS mapping software and also to determine soil properties, coastal properties and analyse the movement of sediment transport. DJI Phantom 4 Pro (UAV) is a method used to identify the shoreline changes in study area. Measuring large-scale land recovered by land survey methods such as the complete station and GPS however demands a great deal of time and job investment. Analysis such as specific gravity, hydrometer test and sieve analysis will be carried out to obtain results on the characteristic of sediment for identified coastal erosion. The result shows during the low tide between November 2020 and May 2021 the shoreline changes are visible. This research would allow organizations or some other group to shed light on problems and the construction of this coastal structure can give a significant impact on the ecosystem of mangrove area.
Runout detection in cricket using IOT

Cricket is a sport that has a huge fan following worldwide. During a cricket match, a runout occurs when a batsman tries to run between wickets, and the opponent team tries to get the ball to the wicket before the batsman crosses a line near the wicket. A batsman who leaves his area while the ball is in play can be marked as runout without scoring. The referee's decision plays a vital role in determining the game's outcome. Human conclusions are prone to error, the necessity for intelligent technology examination is challenging. Replaying the live video is the current method for finding the runout but it has some limitations, whether the bat is grounded or slightly in the air are things that need to be considered. The main objective of the work is to solve the problem stated above for detecting the runout using the capacitive proximity sensor. A series of capacitive proximity sensors will be placed on the crease line to determine whether a particular runout is legal or illegal based on the interpretation of the signal by the bat. An alert will be sent to the umpire using IOT when a runout is detected.
Information-Communication Strategy in Special Library: The Main Aspects for Development

The article is devoted to the trend of expanding the range of information products and services prepared by special libraries, which leads to the need to bring information about them to interested target audiences. The article considers the marketing aspects of the implementation of the information and communication strategy, the use of the modeling method is justified, also presented the main stages of the development of a communication strategy.
An NSCT Image Denoising Method Based on Genetic Algorithm to Optimize the Threshold

In order to solve the defect that the threshold value of the NSCT transform method is too large or the real signal coefficients are directly lost during image denoising, an adaptive threshold method of genetic algorithm is used to optimize the NSCT image denoising method. The genetic algorithm is used to generate the initial population, and the genetic operator is determined by selection, crossover, and mutation operations to achieve NSCT threshold optimization. The obtained optimized NSCT threshold is used to process different directions. The coefficients of different scales are processed by using NSCT inverse transform to obtain the denoised image. The results of the case analysis show that the proposed method is used to denoise the image, the peak signal-to-noise ratio of the image after denoising is higher than 30 dB, the image contains rich edge information and detailed information, and the denoising performance is superior.
Face Recognition using Nearest Neighbour and Nearest Mean Classification Framework : Empirical Analysis, Conclusions and Future Directions

Abstract Human Face recognition algorithms have made huge progress in the last decade. In this manuscript, we have presented an approach for the implementation of a face recognition system in a successful manner by varying pose, scale, lighting, and age variation. The different empirical analysis was performed with various datasets for face detection and face identification. Face identification system detects efficiently segments and recognizes face in a cluttered sequence under varying pose, lighting and age variations. From this experimental analysis morphological model outperformed k-NNC, NMC based closest mean classifier and informative knowledge distillation with fairly reasonable accuracy. Three proposed methods on the basis of an efficient way of handling the face recognition problems. The morphological method outperformed well when compared with k-NNC, NMC based closest mean classifier a proposed method, and another innovative method named Informative knowledge Distillation. The morphological method is suitable for large datasets where occlusion, pose variation, age variations, and different expression of images.
Programmable Implementation and Blockchain Security Scheme Based on Edge Computing Firework Model

In cloud computing, since the program runs in cloud, it can be written in programming language and maintained only in the cloud after compilation. Due to the heterogeneous nature of the edge node platform, many tasks are migrated from the cloud to the edge terminal. It is not easy to realize the programming under the edge computing, and the maintenance cost is also high. At the same time, because the programmable is a high-risk activity, it has high security requirements. In order to solve this problem, this paper designs a programmable and blockchain security scheme based on the edge computing firework model, realizes the programming of the internet of things (IoT) gateway firework node under the edge computing, and appreciates the safe transmission and storage of programmable data through the blockchain system. The experimental results show that this scheme not only facilitates the user's programming, enhances the real-time performance, and saves the data transmission cost, but also ensures the security and reliability of the system.
Descriptive Complexity of Reversible Languages Having Finitely Many Reduced Automata

Reversible forms of computations are often interesting from an energy efficiency point of view. When the computation device in question is an automaton, it is known that the minimal reversible automaton recognizing a given language is not necessarily unique, moreover, there are languages having arbitrarily large reversible recognizers possessing no nontrivial “reversible” congruence. Building atop on our earlier result, we show that the corresponding decision problem is [Formula: see text]-complete, and that even in the case when there are only finitely many such reversible recognizers, the largest one among them can be exponentially larger than the minimal automaton. Both results hold for the case of binary alphabets.
CSLE: A Cost-sensitive Learning Engine for Disk Failure Prediction in Large Data Centers

As the principal failure in data centers, disk failure may pose the risk of data loss, increase the maintenance cost, and affect system availability. As a proactive fault tolerance technology, disk failure prediction can minimize the loss before a failure occurs. Whereas, a weak prediction model with a low Failure Detection Rate (FDR) and high False Alarm Rate (FAR) may substantially increase the system cost due to inadequate consideration or misperception of the misclassification cost. To address these challenges, we propose a cost-sensitive learning engine CSLE for disk failure prediction, which combines a two-phase feature selection based on Cohen's D and Genetic Algorithm, a meta-algorithm based on cost-sensitive learning, and an adaptive optimal classifier for heterogeneous and homogeneous disk series. Experimental results on real datasets show that the AUC of CSLE is increased by 2%-42% compared with the commonly used rank-sum test. CSLE can reduce the misclassification cost by 52 %-96 % compared with the rank model. Besides, CSLE has a better pervasiveness than the traditional prediction model, it can reduce both the misclassification cost and the FAR by 16%-70% for heterogeneous disk series, and increase the FDR by 3%-29% for homogeneous disk series.
Neural Network for Spam Recognition in Short Message Services as an Instructional Application for Students of Vocational Education and Training

The education of Vocational Education and Training (VET) students on various topics related to the programming of personalized electronic devices beyond a strong theoretical foundation requires a broad hands-on experience as well. This paper proposes such a hands-on didactic application that can be integrated with the design of the extension of the curriculum of the Specialty &#x201C;Technical Applications of Informatics&#x201D; for the course &#x201C;Computer Programming&#x201D; of the Greek VET educational system. In particular, this paper explores how a program that identifies unsolicited (spam) Short Message Services (SMSs) using modern Natural Language Processing (NLP) techniques can be integrated into the teaching practice. First, we cover the theoretical background for the necessity of the intervention and the overall framework that is required to understand and ensure the correctness of the techniques used. Then, we demonstrate how the implementation of an artificial neural network can be used effectively to solve this particular problem. To achieve all the above, we have designed and proposed a teaching scenario that on the one hand approaches the new advanced communication technologies, and, on the other hand, operates within the cognitive limits of secondary technical education.
A novel strategy for fine-grained semantic verification of civil aviation radiotelephony read-backs

During the flight of the aircraft, the pilot must repeat the instruction sent by the controller, and the controller must further confirm these read-backs, in this way to further ensure the safety of air transportation. However, fatigue, tension, negligence and other human factors may prevent the controller from realizing read-back errors in time, which is a huge hidden danger for the safety of civil aviation transportation. This paper proposes a novel strategy to implement fine-grained semantic verification of radiotelephony read-backs by introducing interaction layer and attention mechanism at the output of BiLSTM model. Compared with the traditional two-channel verification strategy, the interaction layer is added to obtain fine-grained semantic matching relation representation, rather than connecting the BiLSTM output vectors to obtain the overall semantic representation of the sentence. And by adding attention layer, the new strategy can capture the potential semantic relation between the read-backs and the instructions, which is applicable to non-standard diction and abbreviated read-backs in real radiotelephony communications. Extensive experiments are conducted and the results show that the proposed new strategy is more effective than the traditional method for read-backs checking, and the average test accuracy of the new strategy based on the Chinese ATC radiotelephony read-backs corpus can reach 93.03%.
Multidimensional Blockchain as Robust Distributed Ledger

The paper observes the problem of scaling robust distributed ledgers based on blockchain technology. A solution for the problem is presented - multidimensional blockchain. A model for protocol implementing ideal multidimensional blockchain is constructed. It is proven that multidimensional blockchain and its corresponding protocol GUC-implement robust distributed ledger.
Study of the optimal active control of a trombone

In the brass instrument family, the sound can be modified or attenuated using a mute, which is usually inserted in the bell of the instrument. The objective of this paper is to study the principle and the technological feasibility of an active mute using loudspeakers placed in front or around the instrument bell. This mute must reduce the acoustic power emitted by the instrument while avoiding any impact on its playability. At this stage, an optimal control is considered and no real-time controller is implemented. Results show that an active control placed outside the trombone is theoretically feasible and can be efficient to reduce the acoustic power up to 2000 Hz by placing a ring of sources around the bell and a source in front of the trombone. The instrument input impedance is very slightly affected by the control. In accordance with numerical simulations, the experiment showed that placing a control speaker very close in front of the bell of the instrument modifies the pressure field of the instrument in such a way that it allows to obtain a power attenuation greater than the predicted one. The control is technologically achievable but requires a high power for the closest source.
Joint Classification of IoT Devices and Relations in the Internet with Network Traffic

With the rapid growth and popularization of Internet of Things (IoT), more and more devices are deployed in homes, enterprises, cities, etc. The existed methods to classify types and relations of devices are usually two separate tasks. So, it is difficult to quickly provide attributes of devices in the smart network for operators at the same time. At this situation, the paper presents a framework JCIDR for Joint Classification of IoT Device and Relations In the Internet with Network Traffic. By fusing the numerical features and binary image features of traffic, the devices and relations of devices can be recognized simultaneously. The experiment is carried out in a real IoT environment and the accuracy of JCIDR is over 86% with about half time reduction. Therefore, JCIDR could provide operators with a fast, easy, low-cost network device monitoring method without professional equipment or protocols.
Application of power system vector model in substation operation and maintenance period

In the face of the continuous improvement of intelligent application level of power industry, the sharp increase of operation and maintenance workload, put forward higher requirements for power network operation capacity. The emergence of power system vector model data provides a new opportunity for power grid digital transformation. This paper analyzes and compares the vector model graphics platform comprehensively from the aspects of parsing speed, graphics effect, operation efficiency, integrity, storage space and expansibility. In addition, the paper illustrates the application of vector model data in substation operation and maintenance from three aspects of power equipment operation state analysis, maintenance simulation and asset management.
Differential Expression of the Genes Coding for Adipokines and Epithelial Cell Polarity Components in Women With Low and High Mammographic Density

Women with extensive mammographic density (MD) are more likely to develop breast cancer than women with low MD because of a high epithelial component associated with a high proportion of stromal cells. To elucidate the biological association between high MD and risk of breast cancer, we compared the expression of a panel of genes coding for leptin, adiponectin, and some component of cell polarity and adherens junction complexes in dense and non-dense breast tissue.We interrogated a public dataset composed by 120 specimens of normal breast tissue with MD evaluation. The differential expression of the selected genes in the 2 MD subgroups was assessed by the Wilcoxon test, whereas Kruskal-Wallis test evaluated the differential expression of single genes in the fatty, epithelium, or nonfatty compartment. Spearman's correlation measured the relationship among genes in the subset with the highest epithelium proportion.In high MD, the expression level of PARD6B, CRB3, PATJ, LLGL2, CDH1, and MARVELD2 significantly lowered in tissues with the highest epithelium proportion, whereas, in low MD, the expression level of the genes increased with the increasing of the epithelium proportion. In the low MD subgroup, LEP correlated negatively with PRKCZ and DLG3, whereas, in high MD, such correlation was not observed.The expression of the genes governing cell polarity establishment and cell-cell adhesion assembly differed significantly in the epithelial component of dense and non-dense breasts. The correlation pattern between LEP and PRKCZ or DLG3 agrees with the role of leptin in cell polarity disruption.
Discussion on the Reconstruction of Electrical Engineering Undergraduate Teaching Scheme Facing the New Generation Power System

With the construction of a new generation of electric power systems with new energy as the main body, there are major changes in the traditional electrical engineering undergraduate teaching system. On the one hand, the dynamic operating characteristics of the power system dominated by new energy sources and dominated by power electronic equipment will be significantly different from the traditional power system dominated by synchronous generators. Therefore, there is an urgent need to reform the curriculum system to train electrical engineering professionals who can adapt to the future development trend of the power system. On the other hand, with the rapid advancement of advanced information technology in recent years, the application of cutting-edge technologies such as cloud computing, big data, and artificial intelligence in the field of electrical engineering has become more and more in-depth. Therefore, there is an urgent need to cultivate advanced technical experts who possess more advanced information theory and cybernetics knowledge and master the application of cutting-edge information technology in the field of electrical engineering. From the perspective of student training and professional construction needs, this paper systematically analyzes the lag of the traditional electrical engineering curriculum system, and analyzes the knowledge needs of electrical engineering students adapting to the development of the new generation of power systems. From the perspective of teaching schemes, curriculum systems, and evaluation systems, a plan for restructuring the teaching system was proposed. The work in this article can provide a reference for the training of college students majoring in electrical engineering, and it can also provide a more feasible improvement idea for the training of college students in other majors.
Performance of the 2D Coupled Map Lattice Model and Its Application in Image Encryption

The two-dimensional coupled map lattice (2D CML) model has been extensively employed as the basis component for designing various schemes in the cryptography system due to its complicated chaotic dynamic behavior. In this study, we analyze the chaotic characteristics of the 2D CML model, such as the Lyapunov exponent (LE), synchronization stability, bifurcation, and ergodicity. We then show that the chaotic sequences generated by the 2D CML model are random according to the NIST testing. Furthermore, we propose an image encryption scheme based on the 2D CML model and Singular Value Decomposition (SVD). In our scheme, the SVD method is used to reduce the image storage, and the Red, Green, and Blue channels of a color image will be encrypted through confusion and diffusion. The simulation results, as well as the results of the comparison with other schemes, demonstrate that our scheme possesses outstanding statistics, excellent encryption performance, and high security. It has great potential for ensuring the security of digital images in real applications.
Overview of Web Dawdler Outline and FKNN Utilizing Cluster-Based Secret Net

Everyone uses the world wide web for their everyday tasks. The WWW is divided into several side by sides, deep-net, and black net. It is dangerous to use the world wide web to obtain the deep or secret-net. In this chapter, the dawdler structure will be used for a faster and safer search. Various types of data will be gathered during the search process and kept in the data. The data categorization procedure will be used to determine the webpage's side by side. The fuzzy k-nearest neighbor approach is used in the arrangement procedure. The fuzzy k-nearest neighbor approach categories the results of the dawdling structure stored in the data. Data will be generated in the form of webpage addresses, page information, and other data by the dawdling structure. the fuzzy k-nearest neighbor arrangement result will result in web side by side data. In this chapter, to focus on many dataset tests, there is as much as 30% of the online surface net, 25% deep-net, 24.5% charter, and 30% secret web.