Reviewerly/exHarmony huggingface dataset:
Cybersecurity maturity model for the protection and privacy of personal health data

This paper proposes a cybersecurity maturity model to assess the capabilities of medical organizations to identify their level of maturity, prioritizing privacy and personal data protection. There are problems such as data breaches, the lack of security measures in health information, and the poor capacity of organizations to handle cybersecurity threats that generate concern in the health sector as they seek to mitigate risks in cyberspace. The proposal, based upon C2M2 (Cybersecurity Capability Maturity Model), incorporates practices and controls which allow organizations to identify security gaps generated through cyberattacks on sensitive health patient data. This model seeks to integrate the best practices related to privacy and protection of personal data in the Peruvian legal framework through the Administrative Directive No. 294-MINSA and the personal data protection Act No. 29733. The model consists of 3 evaluation phases. 1. Assessment planning; 2. Execution of the evaluation; 3. Implementation of improvements. The model was validated and tested in a public sector medical organization in Lima, Peru. The preliminary results showed that the organization is at Level 1 with 14% of compliance with established controls, 34% in risk, threat and vulnerability management practices and 19% in supply chain management. These the 3 highest percentages of the 10 evaluated domains.
Implementation of CNN Heterogeneous Scheme Based on Domestic FPGA with RISC-V Soft Core CPU

Field Programmable Gate Array (FPGA) has the characteristics of low power consumption, high performance and flexibility. Research on FPGA neural network acceleration is emerging, but most of the researches are based on foreign FPGA devices. In order to improve the current situation of domestic FPGA, a novel Convolutional neural networks (CNNs) accelerator for domestic FPGA equipped with lightweight RISC-V soft core is proposed. The peak performance of the proposed accelerator reaches 153.6 GOP/s, occupying only 14K LUTs (Look-Up-Table), 32 DRMs (Dedicated RAM Modules) and 208 APMs (Arithmetic Process Modules). The proposed accelerator has enough computing power for most of the Edge-AI applications and embedded systems, providing a possible AI inference acceleration solution for domestic FPGA.
A Fully-Connected and Area-Efficient Ising Model Annealing Accelerator for Combinatorial Optimization Problems

The combinatorial optimization problem is ubiquitously in our daily life and typically inefficient for modern Von Neumann architecture-based computer. Targeting for various combinatorial optimization problems, this paper presents a 10K-bit area-efficient architecture of the domain specific accelerator based on fully-connected Ising model using an FPGA platform. The proposed system is based on simulated annealing algorithm with a spin preselection scheme to prevent the system to be trapped in the local minimum and increase the convergence efficiency, which is more easily and efficiently to be hardware implemented. Using max-cut problem as the experiment benchmark, the proposed hardware architecture achieves an acceleration of 50,000 × compared with the software simulation result.
SAUST: A Scheme for Acceleration of Unstructured Sparse Transformer

Transformer achieves impressive results on many AI tasks. However, it also introduces a huge amount of computation. Pruning is a promising method to reduce the computation load by generating sparse transformer models. To avoid load imbalance caused by computing involved in zero elements, previous works explore structured pruning combined with hardware acceleration. However, tight constraints in structured pruning usually make training much harder and reach a lower sparsity level in the end. This paper proposes SAUST, a scheme that exploits the high sparsity level of unstructured pruning and addresses the load imbalance problem using both hardware and software methods. FPGA implementation shows that SAUST can achieve 3.35x and 2.76x execution time speedup compared to two state-of-the-art references on hardware accelerators.
Monitoring Metrics for Load Balancing over Video Content Distribution Servers

Cloud computing and video streaming services have been in constant expansion in recent years. Along with it, the demand for computing resources has also increased significantly. In this context, monitoring the use of these resources is crucial to maintain a satisfactory level of Quality of Service and, consequently, Quality of Experience, especially in video transmission services. This work discusses a new method of monitoring resources and quality of service metrics on content servers involving CPU utilization and server throughput, which is obtained in a distributed way. For that, a distributed collector system that is based on a modified version of the ring election algorithm is developed to retrieve the Quality of Service metrics in each server. Evaluation experiment results show that there are no performance gains on the system such as the content loading faster for the user, there are however, improvements in terms of the whole system scalability. The greater the number of servers for monitoring, the better the approach is compared to the traditional method of monitoring resources through request and response.
Using End-to-end Multitask Model for Simultaneous Language Identification and Phoneme Recognition

Correlated speech tasks can learn related information from each other to enhance the performance. In this paper, a unified model based on end-to-end DNN and multi-task learning is proposed, which can simultaneously realize language identification and phoneme recognition. As a special feature of this model, we use the syllable, a larger unit in linguistics, to model temporal patterns and multiple cues to improve the robust and accuracy of language identification/phoneme recognition, including phonetic, prosodic and phonotactic. These experimental results on three speech corpuses shown that the unified model and multiple cues achieves the accuracy improvements. In addition, the experimental results on NIST LRE07 shown that our method outperforms the other methods in the task of language identification.
FENet: An Efficient Feature Excitation Network for Video-based Human Action Recognition

Human action recognition is a challenging task due to the need for modeling complex temporal and motion information. The mainstream methods to solve this problem are mostly based on 3D CNNs. However, 3D CNNs are computationally intensive and challenging to deploy on resource-limited devices. Besides, most methods rarely consider the redundancy in feature maps, resulting in more storage requirements. To tackle this issue, we propose a feature excitation network (FENet) based on 2D CNN framework. Specifically, we first establish a 2D backbone focuses on reducing spatial and channel redundancy in feature maps. Then we design a feature excitation model (FEM) to motivate features from multiple perspectives by using attention mechanism. Afterward, we do a series of experiments to find the optimal combination of 2D CNN and FEM. Extensive experiments demonstrate the effectiveness of our proposed method on the action recognition task.
Research on quantitative inference acceleration technology of Convolutional Neural Network for ARM Platform

With the rapid development of the Internet of Things, the advantages of edge computing such as low latency, high availability and high real-time are constantly highlighted. Applications with high computing consumption like convolutional neural network are also constantly deployed on mobile edge.However, the deployment of convolutional neural networks on mobile terminals is limited due to the high computing density, high parallelism, many floating point operations and relatively limited computing resources on mobile terminals.This article in order to realize convolutional neural network optimization and its deployment, on the mobile end for convolution the parameters of the neural network to load the file, choose after dynamic network weight cutting method cutting out the redundant weight and quantification model of network weights fixed point, design convolution kernels offline coding scheme, in order to reduce the floating-point computation and storage consumption in the process of convolution operation.The convolution layer algorithm is designed to process the input and output and complete the convolution operation of the input and the convolution kernel. SIMD instruction design provided by ARMCPU is used to optimize the convolution layer from the bottom operation, that is, NEON instruction set is used to share as much computation as possible in the convolution calculation module to reduce the number of convolution cycles.Give full play to the performance of SIMD instructions.Experiments confirm the acceleration effect of this optimization.
Score-CAMpp: class activation map based on logarithmic transformation

Score-CAM is the first algorithm based on gradient-free model, which can provide convincing explanation for the decision made by neural network. Score-CAM use the confidence score to express the weight of activation maps. Compared with the gradient-based algorithm, Score-CAM can not only accurately locate a single object, but also show better performance when locating multiple similar objects. However, Score-CAM not only contains a lot of non-target information, but also the feature map has poor selectivity to feature areas. Therefore, we propose a Score-CAM m based on logarithmic transformation, namely Score-CAMpp. Firstly, the deep features of images are extracted by the deep neural network. And we need to process the extracted feature map by logarithmic transformation to enhance the effective area of the feature map. Then, the transformed feature map is used as a mask, multiplied by the input image and sent to the network to obtain the weight. Finally, the weight and the transformed feature map are linearly weighted and summed. The experimental results show that the proposed algorithm is superior to Score-CAM in visualization and data on evaluation standard.
Improved Vit via knowledge distallation on small datasets

The transformer-based models have driven revolutionary advances in natural language processing tasks (NLP), and its application in image classification has also achieved corresponding results. The pure transformer-based sequences of image patches can obtain performance comparable to the current optimal convolutional network. But high-performance visual converters use large-scale infrastructure to pre-train hundreds of millions of images, limiting their applications. In this work, given on the transformer model for classification tasks based on training on small datasets (CIFAR-10 and CIFAR-100), we introduced a teacher-student strategy for transformers. It relies on a distillation token to ensure that students learn from the teacher through attention. Especially when using the convnets as a teacher network (we achieve around 90.0% accuracy on CIFAR-100), we obtain results comparable to the convnets.
Graph-based Model Using Text Simplification for Readability Assessment

Previous studies show that readability assessment can be useful for text simplification, whereas we argue that text simplification can also play an important role in the evaluation of text readability. In this work, we propose a text graph network to learn the simplified relation between documents. Specifically, we first establish the parallel simplification corpora by preprocessing. Then, we can obtain the simplified relation between documents contained in parallel simplification corpora. We explore our idea by adding edges between documents (nodes) with simplified relation. The extensive experimental results on public datasets demonstrate the effectiveness of our method.
Handwriting recognition in Tibetan based on active learning strategy

Automatically recognizing Tibetan handwriting characters and digits has significant meaning for Tibetan information processing. This paper implemented a closed-loop feedback system of collection-training-recognition-retraining to recognize Tibetan handwriting by considering the lack of Tibetan handwritten sample data, inconsistent data formats, granularity, and other problems. A semi-automatic labeling system was proposed based on an active learning strategy to overcome the difficulty in data collection for small sample data and enhance the accuracy of the recognition model of Tibetan handwritten characters and digits. The recognition models, which utilized an improved LeNet-5 network structure, achieved 98.30% accuracy in handwritten digit recognition. Through this system, the model's accuracy will continue to improve after continuous iteration.
Lightweight of Supervised Person Re-identification via Knowledge Distillation

The current state-of-the-art supervised person re-identification has achieved excellent performance. However, these methods all use large models such as ResNet50 as the backbone, and the amount of parameters and computation is enormous, which can not be deployed on small devices. However, the accuracy of lightweight models with few parameters does not meet the demand. To solve this problem, we propose a simple and effective ReID model lightweight method based on knowledge distillation, which introduces knowledge distillation into person re-identification and achieves lightweight by transferring the prior knowledge of the large model to the small model. Our method uses a high-precision model as a teacher model to guide the training of a student model with fewer parameters. It reduces the number of model parameters and computation by making the performance of the student model approach that of the teacher model. Our compression results on the Market-1501 and DukeMTMC-ID datasets show that the compressed model can approach or even outperform the original model with far fewer parameters than the original model, based on only adding knowledge distillation training.
Attention Transfer in Self-Regulated Networks for Recognizing Human Actions from Still Images

Recognizing human actions from still images is a demanding computer vision task because of problems such as lack of temporal information, large within-class variations, cluttered backgrounds, and misleading objects that require highly discriminatory features. Transfer learning algorithms such as knowledge distillation and attentional transfer techniques provide the necessary skills to generate valuable features by maintaining former knowledge while new demonstrations are learned. Because the Residual networks have made significant advances in machine vision, most research to date focused on knowledge distillation and attention transfer in this architecture. But the self-regulated networks, which were recently introduced based on regulator modules and have better performance than ResNet networks in various computer vision tasks, have been less investigated. In order to recognize human actions in images, we propose the attention transfer framework in self-regulated networks. In addition to extracting representative features from images, the self-regulated module does not utilize annotations such as personal bounding boxes, object bounding boxes, and human-object interactions. To study the performance of our proposed method, extensive experiments were conducted on Stanford 40 and Pascal Voc 2012 datasets. Our best set of algorithms gains 93.17% on Stanford40 and 91.83% on Pascal Voc 2012. In this attention transfer framework, self-regulated networks are employed, which are based on the memory mechanism module.
Designing a High Performance and High-Profit P2P Energy Trading System Using a Consortium Blockchain Network

Renewable energy generating systems can be used to supply some or all of electricity needs, using technologies like solar, wind, or micro hydropower systems. Trading this kind of decentralized energy is essential to owners of these local systems. Regional p2p energy trading systems provide a solution for this issue. Due to expanding the concept of decentralization and blockchain-based trading models, some studies in recent years have proposed such models for local surplus energy trading. In this paper, we propose a distributed energy-trading framework based on a consortium blockchain for p2p energy trading energy of renewable energy systems. Our proposed model uses Jointgraph, a novel Byzantine fault-tolerance consensus algorithm, and a DAG-based consortium energy blockchain framework, which highly improves the performance of the trading model. Furthermore, we use Belief Distorted Nash Equilibrium (BDNE) for pricing strategy to increase the profitability of the system for both buyers and sellers. The implemented simulation confirms that the proposed framework outperforms similar p2p trading models in terms of both performance and profitability and can be used in real local energy trading systems.
Experimental evaluation and comparison of anti-pattern detection tools by the gold standard

Every symptom in the source code or design of software that violates object-oriented principles such as maintainability, reusability, and integrity is called an anti-pattern. Poor design or programming in the software development process can lead to anti-patterns that may cause further problems in software maintenance, so they should be removed by refactoring. The first and most crucial step in software refactoring is anti-pattern detection. Different approaches and tools have been proposed to do this, which provide different results in the same program due to the informal definition of anti-patterns. In this paper, four antipattern detection tools have been compared, namely Checkstyle, PMD, iPlasma, and Jspirit. These tools are implemented on opensource software systems that have been presented as a gold standard in previous studies in this field. The detection results of three anti-patterns are compared: Large Class, Long Method, and Feature Envy. By comparing tools output and the gold standard, we are sure our calculated precision and recall values are correct.
Improving performance of multi-label classification using ensemble of feature selection and outlier detection

Nowadays classification has become one of the most common techniques in machine learning. In classification, there are two types of data; single-label and multi-label. In multilabel datasets, one sample can have multiple labels at the same time. In recent years, classification of multi-label data has gained a lot of attention. Multi-label classification algorithms can be divided into 3 main parts: problem transformation methods, algorithm adaptation methods and ensemble methods. In problem transformation methods, classification of multi-label data is transformed to other fields. In algorithm adaptation methods, common single-label classification algorithms are changed so that they can deal with multi-label data. In third category, algorithms of two previous categories are combined together. Despite of many different proposed algorithms in this field, improvement of methods in terms of evaluation metrics has always been a challenge. Also, there is a lack of systems which can self-improve the base classifier. Thus, in this paper we try to present a novel ensemble system which can improve any classifier. The presented system has a novel structure which consists of two tree ensembles and each one has its own specific function. One of them has the task of removing noisy and outlier data with a novel approach and the other one has the task of removing noisy and redundant features. In one group some random samples are selected and in the other one, some random features are selected. If the evaluation metrics of the created child are improved, the algorithm can go to the next step and create its own child and if not, the parents create another child. Lastly, the results of these groups are combined together. The conducted experiments on 10 various datasets and 5 evaluation metrics show the superiority of the proposed method.
GAP: Fault tolerance Improvement of Convolutional Neural Networks through GAN-aided Pruning

Functionality and accuracy degradation imposed by Bit-Flip faults (BF) on the CNNs’ weights makes it vital first to evaluate neural networks’ behavior in the presence of faults in the design phase and then make use of these models, especially in safety-critical applications like autonomous vehicles. In the following paper, we propose a novel approach to improve the fault tolerance of convolutional neural networks by pruning their parameters with regard to their gradients instead of the more commonly used norm values. In particular, the gradient of the network’s parameters on an auxiliary classification task based on Generative Adversarial Network (GAN) is exploited to identify parameters of lower generalization. Due to the inherent efficiency that all pruning techniques introduce into the model, it is practical to use the ensembled of pruned models. In this paper, we evaluate the functionality of the naïve ensemble versus the ensemble of three differently pruned models. This idea comes from the Nversion Programming (NVP) concept of dependability literature. To assess the resiliency of our model, we conduct comprehensive random bit-flip fault injection experiments. Comparing the result of our approach to ordinary pruning techniques, we show that the classification accuracy is improved by 20% in the presence of a single fault in the network. Making an NVP ensemble of three VGG-16 GAN-aided pruned networks, we make a 10% further accuracy improvement in the presence of a single random fault in each base-learner for the CIFAR-10 dataset.
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ROUGE is a standard automatic evaluation metric based on N-gram for sequence-to-sequence tasks like abstractive summarization, while cross-entropy loss is an essential objective that optimizes at unigram level for neural network language models. In this paper we present differentiable N-gram objectives, attempting to alleviate the discrepancy between training and evaluating criteria. The novelty of our work is the objective does not ceil the number of matched sub-sequences by the ground truth count of N-gram in reference sequence and weights the matched sub-sequences equally. Therefore, our proposed objective can maximize the probabilistic weight of matched sub-sequences. We jointly optimize cross-entropy loss and the objective, providing decent evaluation scores enhancement including ROUGE over abstractive summarization datasets CNN/DM and XSum, outperforming competitive N-gram objectives.
A COMPARATIVE EVALUATION OF DEEP LEARNING METHODS IN DIGITAL IMAGE CLASSIFICATION

White Blood Cells are important in determining a person's overall health. The blood disease diagnosis includes characterization and identification of blood samples of a patient. Neural Networks (NN), Convolutional Neural Networks (CNN), and a mix of CNN and NN models are used in recent techniques to improve visual content understanding. From start to finish, The authors were driven to uncover remarkable characteristics in example photographs because of their expertise in medical image analysis. For blood cell classification, the overall performance of individual cell patches extracted using blood smear techniques has been excellent. These approaches, on the other hand, are incapable of dealing with the issue of multiple cells overlapping. Because of the blood cell overlapping pictures, the input image dimension is compressed, the classification time is reduced, as well as the network works better with more accurate parameter estimates. In this review, we are evaluating a detailed scientific comparison of some of the ways used to improve WBC classification. The authors will show some of the ways used to automatically classify their cells. The results of some of the tests used using available data, compared to blood cell classification techniques.