Reviewerly/exHarmony huggingface dataset:
Decompressor for hardware applications

The use of lossless compression in the application specificcomputersprovides such advantages as minimizedamount of memory, increased bandwidth of interfaces, reducedenergy consumption, and improvedself-testing systems. The article discusses known algorithms of lossless compression with the aim of choosing the most suitable one for implementation in a hardware-software decompressor. Among them, the Lempel-Ziv-Welch (LZW)algorithm makes it possible to perform the associative memory of the decompressor dictionary in the simplest way by using the sequential reading the symbols of the decompressed word. The analysis of the existing hardware implementations of the decompressors showed that the main goal in their development was to increase the bandwidth at the expense of increasing hardware costs and limited functionality. It is proposed to implement the LZW decompressor in a hardware modulebased on a microprocessor core with a specialized instruction set. For this, a processor core with a stack architecture was selected, which is developed by the authors for the tasks of the filegrammaranalyzing. Additional memory block for thedictionary storing and an input buffer which converts the byte stream of the packed file into a sequence of unpacked codes are added to it. The processor core instruction set isadjusted to both speed up decompression and reduce hardware costs. The decompressor is described bythe Very high-speedintegral circuit Hardware Description Language and is implemented in a fieldprogramable gate array (FPGA). At a clock frequency of up to two hundred megahertz,the average throughputof the decompressor is more than ten megabytes per second. Because ofthe hardware and software implementation, an LZW decompressor is developed, which has approximately the same hardware costs as that of the hardware decompressor andhas a lower bandwidth at the costs offlexibility, multifunctionality, which is provided by the processor core software. In particular, a decompressor of the GraphicInterchangeFormat files is implemented on the basis of this device in FPGA for the application of dynamic visualization of patterns on the embedded systemdisplay.
Modified dynamic opposite learning assisted TLBO for solving Time-Cost optimization in generalized construction projects

This study proposes an improved version of teaching learning-based optimization (TLBO) with a modified dynamic-opposition learning (MDOL) strategy, called modified dynamic oppositional learning TLBO (MDOLTLBO) to narrow the gap between research and practice for generalized time–cost optimization. Instead of opposite numbers, this study employs modified dynamic opposite points for the population initialization and generation jumping. This novel feature of the proposed algorithm is intended to diminish the randomness of the initial population and minimize the search effort required to achieve the optimal set of time–cost alternatives in the search space. An iterative-based varying weighting factor for MDOL is introduced to adjust the range of the search space with the aim of balancing the dynamicity and diversity of the algorithm. Hence, the MDOLTLBO algorithm can identify diversified and high-quality solutions based on non-dominated solutions. Four different case studies of real construction projects, ranging from 29 to 290 activities, were investigated to demonstrate the applicability of the proposed model. The experimental findings verify that the proposed algorithm is efficient, effective, and highly satisfying. Hence, the primary contribution of this study is MDOL approach that can successfully achieve high-quality solutions and good convergence speed for large-scale discrete time–cost optimization problems with generalized logical relationships.
Adversarial learning based residual variational graph normalized autoencoder for network representation

With the success of Graph Neural Networks (GNNs) on non-Euclidean data, some GNN-based approaches to network representation learning have emerged in recent years. Graph autoencoder (GAE) and variational GAE (VGAE) are unsupervised learning frameworks widely used to learn latent embedding representations based on graph-structured data. However, it performs poorly in link prediction, node clustering, and graph visualization task when isolated nodes are involved or when the raw data representation is weak. Based on the perspective of improving the capability of the latent embedding representation and the robustness of the model, we propose an Adversarial Learning based Residual Variational Graph Normalized Autoencoder (ARVGNA). We introduce a residual network to augment the raw data representation, use L2-normalization to derive better node embeddings, and add adversarial learning to promote the activation of the node representation and enhance the generalization of the model. The experiments on three benchmark datasets show that the proposed ARVGNA greatly outperforms the baselines in the tasks of link prediction, node clustering, and graph visualization, and exhibits strong competitive power and high robustness in network representation.
Customizing a teacher for feature distillation

Knowledge distillation is a method to train a lightweight network by transferring class probability knowledge from a cumbersome teacher network. However, transferring only the class probability knowledge would limit the distillation performance. Therefore, several approaches have been proposed to transfer the teacher's knowledge at the feature map level. In this paper, we revisit the feature distillation method and have found that the larger the teacher's architecture/capacity becomes, the more difficult it is for the student to imitate. Thus, the feature distillation method is unable to achieve its full potential. To address this, a novel end-to-end distillation framework, termed Customizing a Teacher for Feature Distillation (CTFD), is proposed to train a teacher to be more compatible with its student. In addition, we apply the customized teacher to three feature distillation methods. Moreover, data augmentation is used as a trick to train the student to improve its generalization performance. Extensive empirical experiments and analyses are conducted on three computer vision tasks, including image classification, transfer learning, and object detection, to substantiate the effectiveness of the proposed method.
Public key authenticated searchable encryption against frequency analysis attacks

To retrieve data in form of encryption, Boneh et al. proposed a primitive called the public key encryption with keyword search (PEKS). Unfortunately, PEKS is inherently vulnerable to the keyword guessing attack (KGA). To resist such an attack, Huang et al. introduced an authenticated version of PEKS (i.e., PAEKS), in which the sender not only encrypts the keyword, but also uses his/her secret key to authenticate it. Since then, many PAEKS schemes have emerged to improve the security model of the PAEKS system. However, none of these PAEKS schemes consider frequency analysis attacks (FAA). This attack works by tabulating the frequency distribution of trapdoors generated by user when searching, i.e., placing trapdoors embedded with the same keyword in the same category and recording the number of trapdoors in each category. These frequencies are then compared with the priori knowledge of the frequency of keyword searched by user to extract the keyword embedded in the trapdoor, thereby corrupting the user's search privacy. This work formally analyzes how frequency analysis attacks lead to the leakage of keyword information of the trapdoor in the PAEKS system. Then, to resist against this attack, we propose the first PAEKS scheme with frequency analysis attack resistance to protect the user's search privacy. In order to simplify the management of the sender/receiver's public keys, we further propose our second PAEKS scheme based on the first scheme. Finally, we show that both of these two schemes are proven secure under static assumptions.
Complex exponential graph convolutional networks

Graph convolutional networks based on spectral methods have recently achieved considerable success in processing non-Euclidean structured data such as graphs. In this paper, we propose the complex exponential graph convolutional network (CEGCN), which is a novel spectral convolutional architecture for performing deep learning on graphs. Specifically, we design a complex exponential polynomial filter with powerful expressive ability and whose zeros can be changed to prevent the over-smoothing issue. Furthermore, a CEGCN variant, called CEGCN-hp, combines the graph Fourier transform-based and high-pass filters to capture the high-frequency components in the graph spectral domain. We perform a spectral analysis to illustrate the motivation and expressive power of the proposed model. Experimental results show that our model matches or outperforms various state-of-the-art baselines on three downstream tasks: semi-supervised node classification, community detection, and graph classification. Our implementation is available online.1
Wear‐free indoor fall detection based on RFID and deep residual networks

Summary Since falls of the elderly can easily cause serious health problems in daily life, fall detection has received the attention of researchers. Traditionally, wearable sensors have been used to detect whether a person has fallen. However, wearable sensors may bring inconvenience to users' activities and affect user experience. In this paper, a fall detection approach based on RFID is proposed. In the proposed approach, non‐contact passive tags are used to construct an array of tags. Fall detection will be performed without the user wearing the device. The RSSI and phase data are collected when the reader queries the tags. Furthermore, an action segmentation algorithm is designed to quickly extract human action information based on the short‐term variance change of the phase signal. Subsequently, a deep residual network is built to classify fall movements and daily movements. Experiments show that the system can handle differences among users and locations and has an excellent performance in terms of recognition accuracy and efficiency, with an average accuracy rate of 96.77%.
Hybrid computer aided diagnostic system designs for screen film mammograms using <scp>DL</scp>‐based feature extraction and <scp>ML</scp>‐based classifiers

Abstract Mammography is most popular imaging method used often in predicting breast cancer within women above age of 38 years. Various computer‐assisted algorithms have been employed for classifying breast masses as normal or malignant using screen film mammographic (SFM) images. In present research work, exhaustive experimentations have been carried out with nine deep learning‐based convolutional neural networks (CNNs) belonging to the three different categories of CNN architectures including (a) simple convolution‐based series models, that is VGG16 and VGG19 (b) simple convolution‐based directed acyclic graph (DAG) model, that is GoogleNet and (c) dilated convolution‐based DAG models, that is ResNet18, ResNet50, Inceptionv3, XceptionNet, ShuffleNet and MobileNet‐V2, for binary classification of the mammographic masses with SFM images. The experimental work has been carried out using 518 mammographic images taken from DDSM dataset with 208 images ϵ benign class and 310 images ϵ malignant class, respectively. The encoder‐decoder based semantic segmentation network model, that is ResNet50 has been used for the segmentation of mammographic masses from SFM images. The segmented masses images obtained from the ResNet50 model are subjected to classification experiments. To design a robust hybrid classifier design, that is, deep learning (DL)‐based feature extraction and machine learning (ML)‐based classification, the first step is to obtain an optimal DL‐based feature extractor for classification task. The optimal feature set obtained by the best performing CNN Model, that is, VGG 19 has been subjected to correlation‐based feature extraction and ML‐based classifiers including (i) adaptive neuro fuzzy classifier‐linguistic hedges (ii) principal component analysis‐ support vector machine classifier and (iii) GA‐SVM classifier to yield an optimal hybrid computer aided diagnostic (CAD) system design. It is found that hybrid CAD system using VGG19 as feature extractor and ANFC‐LH as classifier yields 96% the highest classification accuracy. The other performance parameters yield values, that is 96% sensitivity, 96% specificity, 96% F‐score, 96% precision and 92% MCC, which indicates a best prediction of binary classification. The test images which were misclassified by these hybrid CAD system designs were analysed subjectively by experienced participating radiologist. The results obtained by present work suggests that proposed hybrid CAD system with VGG19 Network model acting as feature extractor and ANFC‐LH classifier can be employed for the differential diagnosis of benign as well as malignant mammographic masses using SFM images in a routine clinical setting.
The flip-flop neuron: a memory efficient alternative for solving challenging sequence processing and decision-making problems

Sequential decision-making tasks that require information integration over extended durations of time are challenging for several reasons, including the problem of vanishing gradients, long training times and significant memory requirements. To this end, we propose a neuron model fashioned after the JK flip-flops in digital systems. A flip-flop is a sequential device that can store state information of the previous history. We incorporate the JK flip-flop neuron into several deep network architectures and apply the networks to difficult sequence processing problems. The proposed architectures include flip-flop neural networks (FFNNs), bidirectional flip-flop neural networks (BiFFNNs), convolutional flip-flop neural networks (ConvFFNNs), and bidirectional convolutional flip-flop neural networks (BiConvFFNNs). Learning rules of proposed architectures have also been derived. We have considered the most popular benchmark sequential tasks like signal generation, sentiment analysis, handwriting generation, text generation, video frame prediction, lung volume prediction, and action recognition to evaluate the proposed networks. Finally, we compare the results of our networks with the results from analogous networks with Long Short-Term Memory (LSTM) neurons on the same sequential tasks. Our results show that the JK flip-flop networks outperform the LSTM networks significantly or marginally on all the tasks, with only half of the trainable parameters.
Refinement of ensemble strategy for acute lymphoblastic leukemia microscopic images using hybrid CNN-GRU-BiLSTM and MSVM classifier

Abstract Acute lymphocytic leukemia (ALL) is a common serious cancer in white blood cells (WBC) that advances quickly and produces abnormal cells in the bone marrow. Cancerous cells associated with ALL lead to impairment of body systems. Microscopic examination of ALL in a blood sample is applied manually by hematologists with many defects. Computer-aided leukemia image detection is used to avoid human visual recognition and to provide a more accurate diagnosis. This paper employs the ensemble strategy to detect ALL cells versus normal WBCs using three stages automatically. Firstly, image pre-processing is applied to handle the unbalanced database through the oversampling process. Secondly, deep spatial features are generated using a convolution neural network (CNN). At the same time, the gated recurrent unit (GRU)-bidirectional long short-term memory (BiLSTM) architecture is utilized to extract long-distance dependent information features or temporal features to obtain active feature learning. Thirdly, a softmax function and the multiclass support vector machine (MSVM) classifier are used for the classification mission. The proposed strategy has the resilience to classify the C-NMC 2019 database into two categories by using splitting the entire dataset into 90% as training and 10% as testing datasets. The main motivation of this paper is the novelty of the proposed framework for the purposeful and accurate diagnosis of ALL images. The proposed CNN-GRU-BiLSTM-MSVM is simply stacked by existing tools. However, the empirical results on C-NMC 2019 database show that the proposed framework is useful to the ALL image recognition problem compared to previous works. The DenseNet-201 model yielded an F1-score of 96.23% and an accuracy of 96.29% using the MSVM classifier in the test dataset. The findings exhibited that the proposed strategy can be employed as a complementary diagnostic tool for ALL cells. Further, this proposed strategy will encourage researchers to augment the rare database, such as blood microscopic images by creating powerful applications in terms of combining machine learning with deep learning algorithms.
Application of Deep Learning Techniques in Water Level Measurement: Combining Improved SegFormer-UNet Model with Virtual Water Gauge

Most computer vision algorithms for water level measurement rely on a physical water gauge in the image, which can pose challenges when the gauge is partially or fully obscured. To overcome this issue, we propose a novel method that combines semantic segmentation with a virtual water gauge. Initially, we compute the perspective transformation matrix between the pixel coordinate system and the virtual water gauge coordinate system based on the projection relationship. We then use an improved SegFormer-UNet segmentation network to accurately segment the water body and background in the image, and determine the water level line based on their boundaries. Finally, we transform the water level line from the pixel coordinate system to the virtual gauge coordinate system using the perspective transformation matrix to obtain the final water level value. Experimental results show that the improved SegFormer-UNet segmentation network achieves an average pixel accuracy of 99.10% and an Intersection Over Union of 98.34%. Field tests confirm that the proposed method can accurately measure the water level with an error of less than 1 cm, meeting the practical application requirements.
Effect of using 5G and cloud computing environment for independent college english vocabulary learning

Abstract An independent university refers to an institution of higher learning that has a bachelor's degree or higher education, and cooperates with social organizations other than state institutions or individuals to use non-state financial funds to establish and implement undergraduate education. While welcoming the arrival of the fifth-generation communication technology (5G) era, China's independent universities are also carrying out educational reforms to adapt to social development. 5G has created new opportunities for the development of students. Cloud computing refers to a system with extremely strong computing power formed through computer networks (mostly the Internet), which can store, collect relevant resources and configure them as needed to provide personalized services to users. Therefore, this paper studied and discussed the application of 5G and cloud computing in English vocabulary learning in independent universities. This paper explores the impact of 5G and cloud computing environments on independent college English vocabulary learning. With the assistance of 5G technology and a cloud computing environment, students' vocabulary mastery and application abilities have achieved certain improvements. Students' vocabulary learning can not only effectively break through the constraints of region and time, but also improve the interaction of learning and promote the communication and exchange between learners. This can not only deepen the understanding of vocabulary but also stimulate students' learning initiative and improve their learning ability and level. Given the current situation of English vocabulary acquisition, this paper used the fuzzy evaluation method to establish an evaluation model. The learning platform built by 5G and cloud computing technology has been utilized to study the effect of applying it to learners' vocabulary learning. In the questionnaire survey, 43.00% of the students are generally satisfied with the current English vocabulary teaching, 33.20% of the students think that their learning effect is average, 43.00% of the students think that the arrangement of teaching content is not reasonable, and 35.40% of the students think that the arrangement of class hours is generally reasonable. Before the experiment, the vocabulary of 68 and 65 students in the experimental group and the control group were between 1500–3500. Through the experiment, 67 students in the experimental group had vocabularies in the range of 3501–6500, while the vast majority of students in the control group still had vocabularies in the range of 1500–3500. In addition, through the experiment, the average total score of English test scores increased from 67 to 87 for the experimental group and from 69 to 72 for the control group. Based on the above data, it can be seen that the learning of English vocabulary through independent learning platforms built on 5G and cloud computing has a significant effect on the learning of English vocabulary of independent university students.
A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning-enabled Control Systems

Signal Temporal Logic (STL) has become a popular tool for expressing formal requirements of Cyber-Physical Systems (CPS). The problem of verifying STL properties of neural network-controlled CPS remains a largely unexplored problem. In this paper, we present a model for the verification of Neural Network (NN) controllers for general STL specifications using a custom neural architecture where we map an STL formula into a feed-forward neural network with ReLU activation. In the case where both our plant model and the controller are ReLU-activated neural networks, we reduce the STL verification problem to reachability in ReLU neural networks. We also propose a new approach for neural network controllers with general activation functions; this approach is a sound and complete verification approach based on computing the Lipschitz constant of the closed-loop control system. We demonstrate the practical efficacy of our techniques on a number of examples of learning-enabled control systems.