Reviewerly/exHarmony huggingface dataset:
A Gather Accelerator for GNNs on FPGA Platform

Graph Neural Networks (GNNs) have emerged as the state-of-the-art deep learning model for representation learning on graphs. GNNs mainly include two phases with different execution patterns. The Gather phase, depends on the structure of the graph, presenting a sparse and irregular execution pattern. The Apply phase, acts like other neural networks, showing a dense and regular execution pattern. It is challenging to accelerate GNNs, due to irregular data communication to gather information within the graph. To address this challenge, hardware acceleration for Gather phase is critical. The purpose of this research is to design and implement an FPGA-based accelerator for Gather phase. It achieves excellent performance on acceleration and energy efficiency. Evaluation is performed using a Xilinx VCU128 FPGA with three commonly-used datasets. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA P100 GPU, our work achieves on average 101.28× speedup with 75.27× dynamic energy reduction and average 12.27× speedup with 45.56× dynamic energy reduction, respectively.
Impact of Patch Extraction Variables on Histopathological Imagery Classification Using Convolution Neural Networks

Pathologists visually examine cell morphology by observing the biopsy slides under a microscope through different magnifying factors, which is time-consuming and error-prone. In this regard, computer-aided whole slide image (WSI) analysis is necessary to help pathologists reduce time effort, and human error. With the recent advances in deep learning for computer vision, convolutional neural networks (ConvNets) have gained attention in the medical domain and have shown significant progress in whole slide image classification. Existing deep learning approaches work by feeding ConvNet with small patches extracted from WSIs. However, it is unknown how the size of the extracted patches and the magnifying factor of the WSI affect the performance of the ConvNet. Therefore, we construct several datasets by extracting patches from stomach histopathological imagery, varying the size of the patches and the magnifying factor of the WSIs. Densely Connected Convolutional Neural Network (DenseNet) is used to classify dysplasia, malignant, and benign patches. We observe the impact of the patch extraction variables using precision and recall. This study shines a light on why these factors would affect the model performance concerning data representation and provides a guideline for histopathological imagery data extraction methods.
Unmanned Aerial vehicles (UAV) Path Planning Approaches

With a rising industry, unmanned aerial vehicles (UAVs) provide a significant commercial opportunity for industries and numerous entrepreneurs as a means of replacing humans in dangerous missions and in hostile situations. The use of UAVs is rapidly growing in which energy-efficient path planning is a major issue. In this research article, a detailed classification of the entire path planning approaches for a UAV in terms of energy efficiency has been discussed, moreover, a detailed comparison between different path planning approaches is also provided. Multiple considerations are to be considered for an ideal path planning which includes path completeness, optimality. The execution of path planning becomes a challenging task in terms of trajectory generation and also requires careful consideration.
Optimal Bilateral Filtering of CT Images

In this paper a general optimization algorithm is proposed for tuning the parameters of a bilateral filter when processing Computed Tomography images, containing Additive White Gaussian Noise. The Peak Signal to Noise Ratio and the Structural Similarity Index Measure are the target parameters during the optimization with the explicit aim of achieving their maximums. The operation of the optimal configuration of the bilateral filter is compared with the results from filtering of the same images with the Gaussian and average filters. Positive results are obtained and the proposed optimization is considered applicable not only for Computed Tomography images, but also for Magnetic Resonance Imaging, multispectral and hyperspectral images.
Evaluation strategies at University for students with Dyslexia: a pilot study supported by face emotion recognition

This paper describes the final assessment of a student with SLD who uses compensatory tools to verify the effectiveness of the strategies adopted to support the construction of an inclusive assessment environment. The proposed case study is part of long-term research conducted by the University of Macerata. New technologies for analyzing emotional feedback based on the analysis of facial expressions collected from both students and professors during the exam supported the observation. Results show how the application of the emotional recognition system allows researchers to reflect on the evaluation process, on the teacher&#x2019;s perception about compensatory tools and, more generally, on accommodation exams strategies for students with DSA.
An Energy-Efficient Task Scheduling Strategy Based on Improved Fireworks Algorithm in Heterogeneous Cloud

With the development of cloud computing, energy consumption is getting bigger and bigger, and energy optimization has become one of the research focuses. Task scheduling is an efficient way to reduce the energy consumption of cloud. Most previous works ignored the heterogeneity of physical machines (PMs) configured with different CPUs and GPUs, which may result in high energy consumption in heterogeneous cloud. In this paper, an energy-efficient Task Scheduling Strategy based on Improved Fireworks Algorithm (TSS-IFWA) is proposed to reduce the energy consumption of the CPU/GPU heterogeneous cloud. First, a task scheduling model is constructed, in which the energy consumption of CPUs and GPUs are separately consid-ered. Then we obtain the task scheduling optimization problem of minimizing the energy consumption of heterogeneous cloud. Second, as traditional fireworks algorithm consumes massive computing resource and has a poor convergence performance, we improve a new calculation method of explosion amplitude of the fireworks algorithm. We also optimize the mutation strategy. Third, we utilize the improved fireworks algorithm to solve the task scheduling optimization problem. Specifically, the corre-sponding coding and decoding schemes of the fireworks algorithm are designed to initialize the fireworks individuals. We then obtain task scheduling results. Finally, TSS- IFWA is compared with traditional fireworks algorithm (FWA), round-robin algorithm (RR), and genetic algorithm (GA). The experimental results show that TSS- IFWA has a good performance in reducing energy consumption of heterogeneous cloud.
$PSeer$: Performance Prediction for Partially Co-located Distributed Deep Learning

This paper studies the problem of predicting the deep learning (DL) jobs&#x0027; training time which is the fundamental guide for training resources allocating and job scheduling. The existing prediction approaches, however, are greatly challenged for distributed deep learning (DDL) jobs due to the partial interference, namely, the resource contention that only occurs at partial time or locations of a job. We observe that the maximum difference in training time under different combinations of partial interference DDL can reach 2.2&#x00D7;. Thus partial interference is not a negligible factor for predicting the training time of DDL jobs. In this paper, we present PSeer, a partial interference-aware training time predictor for DDL workloads. It adopts a combined dual-submodel (the step number submodel and the step speed submodel), which is highly accurate in predicting by encoding the spatially and temporally varied partial interference into the model design. It is also lightweight: the step number submodel only requires the initial training loss value, and the step speed submodel can be constructed using the CPU and GPU resource utilization information of a few pre-trained steps. Empirical evaluation shows the prediction error generated by PSeer could achieve as low as 3.93&#x0025; when employing RFR in the training speed submodel, and the prediction overhead is &#x003C; 1ms.
A Deep Image Denoising Method at Transmit Electricity Surveillance Environment

Deep neural network has extraordinary advantages in the recognition and restoration of background pictures of transmit electricity surveillance environment. However, the existing network framework for identifying pictures and removing noise in the background pictures of transmit electricity surveillance environment still has very big problems, so our current challenge is to design a network framework suitable for identifying transmit electricity surveillance environment scenes and removing picture noise in the t transmit electricity surveillance environment. At present, most methods based on depth neural network are directly based on making poor quality images into good quality images to solve the problem of background pictures of transmit electricity surveillance environment, but this process will lead to the observation model that ignores the image degradation process, resulting in the difference between the final recognized image and our expected effect. In this paper, we make a deep image denoising algorithm about background pictures of transmit electricity surveillance environment. We can effectively calculate the iterative steps through the algorithm. So we expand the iteration into a DNN(deep neural network) on identifying the transmit electricity surveillance environment. The denoiser can make the most of the multi-scale redundancy of transmission line pictures. Therefore, we proposed network uses the excellent ability of deep neural network to remove image noise, but also uses the prior knowledge of image model under the background of power line. Through the node-to-node optimization training iteration, the network of different modules can be combined for optimization. The experimental results of several image recognition and optimization tasks in background pictures of transmit electricity surveillance environment show that our method can achieve very excellent experimental results.
Smart Task Reminder Model for People with Mild Dementia at Home

Assistive technologies (AT) for people with mild-to-moderate dementia (PwMMD) have evolved over the years and there is a growing awareness of the benefits to users and the need to identify applicable and effective methods for personalized implementations to cater to individual needs. As a result, in this paper, a wrist-worn pre-programmable personalized smart Global System for Mobile Communication (GSM) based task reminder system is proposed. The back-end of the system consists of a computer unit with an internet connection, a bulk SMS subscription service application, and a GSM modem connection. The front-end of the system consists of a user wrist-worn GSM module/receiver for receiving SMS task reminders from the back-end unit via the GSM network.
Abstractive Text Summarization via Stacked LSTM

In the past, there have been many models proposed for text summarization via sequence to sequence training (seq2seq), attention mechanism, and transformers. Although these methods achieve an advance regarding the performance, these models fail to create a more complex feature representation of the current input and consequently gain inferior performance for modeling the long staggered sentences and modeling the complex inter-sentence dependencies. In order to address this issue, we utilize a more complex feature representation for summarization via stacked LSTM. In this case, the main reason for stacking LSTM is to allow for greater model complexity. For a simple encoder, we stack layers to create a hierarchical feature representation with attention. We generate the text summaries for any test text in terms of predicting the target sequence. With the proposed method, we achieve a better performance compared to the existing state-of-the-art phrase-based system on the task of text summarization on gigaword dataset. Furthermore, Experimental results on this dataset show that our framework performs well in terms of various ROUGE scores.
A Deep Learning BERT-Based Approach to Person-Job Fit in Talent Recruitment

Although the widespread use of the Internet provides job recruiters with a larger pool to select the most qualified candidates, the tedious process of going over hundreds of resumes makes a fair and objective decision making more difficult. This paper proposes an end-to-end BERT-based framework to decrease the workload and expedite the shortlisting process of job applicants. Utilizing historical-records data of thousands failed and successful job applications, our model simulates the recruiters&#x2019; decision-making process by the state-of-the-art BERT algorithm. The results show that BERT outperforms a variety of models by a high margin.
PEAN: A Packet-level End-to-end Attentive Network for Encrypted Traffic Identification

Encrypted traffic identification is important to maintain the cybersecurity. Recently, as the SSL/TLS encryption protocols are widely used in modern Internet environment, how to identify the encrypted traffic become a big challenge. The traditional payload-based methods are usually used to identify the unencrypted traffic, but is no longer effective for the encrypted traffic. To solve the enrypted traffic identification problem, researchers tried to use machine learning methods to model the flow features of encrypted traffics and have made some progress. However the identification accuracy is still not high as these methods usually use the high-level hand-designed features which may loss a lot of important information. To overcome this limitation, in this paper, we design PEAN - a Packet-level End-to-end Attentive Network for encrypted traffic identification. PEAN uses the information such as raw bytes and length sequence as the model input rather than using the traditional hand-designed features. Then, we use an unsupervised network traffic pre-training model to better model the traffic bytes. A self-attention mechanism is also designed to better learn the deep relationship among traffic packets. Experiments on a real trace set demonstrate the effectiveness of PEAN.
Parallel Multithreaded Medical Images Filtering

The quality of medical images is paramount. Being of high grade, it guarantees the quality of medical diagnosis, treatment and quality of patient&#x2019;s life through the means of health care or using automate intelligent systems for medical diagnosing, treatment and monitoring. The paper presents the computational challenges in medical images processing. The great challenges are to propose parallel computational models and parallel program implementations based on the algorithms for medical images filtering. Parallel computational model based on two-dimensional filters is designed. The proposed parallel model is verified by multithreaded parallel program implementation. An investigation of the efficiency of medical images filters based on parallel multithreaded program implementation, applying two-dimensional filters on a given list of compressed jpeg medical images and generating output jpeg images for each type of applied filter. The applied filters are Brightness Control, horizontal and vertical filter of Sobel, Laplace and Blur. A number of experiments have been carried out for the case of dataset consisted of 162 whole mount slide images of Breast Cancer (BCa) specimens scanned at 40x and various number of threads. Parallel performance parameters execution time and speedup are estimated experimentally. The performance estimation and scalability analyses show that the suggested model has good scalability.
Federated Learning With Dynamic Staleness Correction for Privacy Protection in Vehicular Networks

Edge intelligence combines mobile edge computing(MEC) with artificial intelligence(AI), which has great potential for improving the security and efficiency of data-driven intelligent transportation systems (ITS) and emerging Internet Of Vehicles (IOV) services. However, when big data and AI empower ITS, the information security issue in vehicular networks needs to be paid more attention. In order to protect the privacy and security of vehicular training data, we propose a privacy protection federated learning scheme with staleness asynchronous update to overcome the differences caused by vehicle heterogeneity. Moreover, unlike the traditional weighted average on the server side only by the number of samples, our solution introduces dynamic time weights according to the calculation and communication capabilities of different vehicles, to make full use of the previously trained local model. The proposed scheme is able to reduce traffic load of the network and improve learning performance while enhancing security and privacy. Experiment results demonstrate that in terms of communication cost and model accuracy, the performance of the proposed asynchronous federated learning is better than the benchmark algorithm, and it also achieves good performance under NIID data.
Driving Gaze Behavior Prediction at S-Curve Based on Driver Experience using Machine Learning

Transport is an essential activity that plays a key role worldwide, leading to a rise in infrastructure growth for sustainable growth. The speedy growth has directly led to an increase in the number of vehicles which demands the drivers to have a complete awareness while driving to reduce traffic accidents. For safe and comfortable driving, it would be helpful to detect the driver&#x0027;s attention using a gaze pattern. For this reason, we used Tobii Glass 2 to capture and record the gaze behavior from 10 drivers, each driving ten times on the S-Curve, and used the Extreme Gradient Boosting algorithm (XGBoost) to identify the essential features that influence the driver behavior. We developed Machine Learning models to predict the driver&#x0027;s gaze behaviors related to their experience. Our results showed the best performance for SVM with 93&#x0025; accuracy and 94&#x0025; Precision in classifying the driver performance based on experience while driving on S-Curve. Furthermore, this study established that while at the S-Curve, there was a significant difference between the Experienced and the Novice drivers on the gaze direction pattern. The Novice driver&#x0027;s gaze concentration was often diverted away and took a significant amount of time to return their gaze which is a danger to safe driving. Therefore, detecting the drivers&#x0027; gaze behavior is vital since failure by the drivers to distribute their attention effectively in driving situations leads to havoc.
E-book Circulation System Based on Blockchain

As the global trend of digitalization and automation constantly pushes our entire society forward, the library, as a gateway to both global knowledge and social culture, should by no means fall behind. However, instead of digitalizing the collections and book circulation processes, most libraries seem to focus their digitalization and automation endeavors on other services. We, on the other hand, would like to virtualize a library as operates on the very basis of electronic collections. Electronic books, often abbreviated to "E-books", are a better option that fits the reading habbit of many in the modern society. In creating a virtual library, we aim to design a user-friendly interface for easy, secure reader identity verification and e-book browsing and borrowing where no piracy can happen in case of the existence of a malicious user. We shall propose a new design based on the blockchain technology with smart contract that features distributed computing and data immutability. The new system is capable of solid user authentication as well as detialed e-book circulation history recording, and will offer the reader a smooth, comfortable e-book reading experience.
Trusted Sliding-Window Aggregation over Blockchains

Blockchain that continuously generates infinite transactions is widely applied to many decentralized applications. Applications generally focus more on the most recent transaction data to discover trends and make predictions, and thus there is an increasing demand for sliding-window aggregation over blockchains (e.g., a continuous query for the moving average of Bitcoin transaction volume over the last 24 hours). Blockchain submits transactions by block periodically, which makes it work well for sliding-window aggregation. However, the mutual distrust between blockchain nodes makes users consider both query efficiency and query authentication (e.g., simple payment verification (SPV) in Bitcoin). Aggregate B-tree can process sliding-window aggregation in a multi-query setting efficiently. In order to achieve authenticated sliding-window aggregation, a naive scheme may incorporate the Merkle tree into the aggregate B-tree, but that will complicate the index structure, and couple query logic and verification logic. In this paper, we propose a novel authenticated sliding-window aggregation scheme that separates query authentication from query processing. By designing a separate encoded Merkle tree, verification logic can authenticate query results of the aggregate B-tree by itself, without affecting query logic. We also develop an optimized scheme based on FiBA and software guard extensions (SGX), which further reduces aggregate and digest update costs. Security analysis and empirical study validate the robustness and practicality of the proposed scheme.
A Novel Feature Method for Fast Extraction of Mining Traffic

In recent years, the Internet has witnessed the emergence of mining behaviors, which have caused numerous problems, such as tremendous electricity consumption, bandwidth and computing resources consumption, and so on. Even mining behaviors have caused climate change. Therefore, effective detection and managing of mining behaviors make both financial and technical sense for many fields. In this paper, we analyzed the relationship between secondary miners and primary miners (mining pools) and studied the communication procedure between them, as most mining behaviors occur between these two parts. A fast traffic feature extraction approach and machine-learning detecting models are proposed to detect the mining behaviors rapidly. We designed a mining traffic collecting method and built a mining traffic dataset covering eight cryptocurrency mining types. Based on the collected traffic dataset, the proposed method is compared with other methods. Results show that our proposal is promising for detecting mining behaviors rapidly using traffic features.
Human Activity Recognition in Smart Home using Deep Learning Models

Applying deep learning to IoT data classification would yield deeper and more useful insights. IoT field is very wide and has several applications. In this paper we focus on smart home, especially human activity recognition within a house equipped with ambient sensors. Firstly, we describe ARAS Human Activity Dataset that are used in models training and testing. Secondly, we apply three deep learning models to it in order to classify the activities carried out by residents within the house. The deep learning models that we use in our experiments are : a Multilayer Perceptron (MLP), a Recurrent Neural Network(RNN) and Long short-term memory(LSTM). The results show best performances with MLP followed by RNN. In addition, it should be noted that there is a strong correlation between the frequency of activities and their recognition rate.