Reviewerly/exHarmony huggingface dataset:
Types of Lightweight Cryptographies in Current Developments for Resource Constrained Machine Type Communication Devices: Challenges and Opportunities

Machine-type communication devices have become a vital part of the autonomous industrial internet of things and industry 4.0. These autonomous resource-constrained devices share sensitive data, and are primarily acquired for automation and to operate consistently in remote environments under severe conditions. The requirements to secure the sensitive data shared between these devices consist of a resilient encryption technique with affordable operational costs. Consequently, devices, data, and networks are made secure by adopting a lightweight cryptosystem that should achieve robust security with sufficient computational and communication costs and counter modern security threats. This paper offers in-depth studies on different types and techniques of hardware and software-based lightweight cryptographies for machine-type communication devices in machine-to-machine communication networks.
Exemplar-Based, Semantic Guided Zero-Shot Visual Recognition

Zero-shot recognition has been a hot topic in recent years. Since no direct supervision is available, researchers use semantic information as the bridge instead. However, most zero-shot recognition methods jointly model images on the class level without considering the distinctive character of each image. To solve this problem, in this paper, we propose a novel exemplar-based, semantic guided zero-shot recognition method (EBSG). Both visual and semantic information of each image is used. We train visual sub-model to separate each image from the other images of different classes. We also train semantic sub-model to separate this image from the other images described with different semantics. We concatenate the outputs of visual and semantic sub-models to represent images. Image classification model is then learned by measuring visual similarity and semantic consistency of both source and target images. We conduct zero-shot recognition experiments on four widely used datasets. Experimental results show the effectiveness of the proposed EBSG method.
Data-Driven Digital Integrity Verification

Abstract Images and videos are by now a dominant part of the information flowing on the Internet and the preferred communication means for younger generations. Besides providing information, they elicit emotional responses, much stronger than text does. It is probably for these reasons that the advent of AI-powered deepfakes, realistic and relatively easy to generate, has raised great concern among governments and ordinary people alike.
Identifying Literary Characters in Portuguese

We introduce the problem of identifying characters in literary text, and mention some specific issues that are special for Portuguese, in the context of presenting DIP, a shared task to foster work in the area and produce resources for computational literature studies in Portuguese. We describe how the task is organized, the resources that will be created, and how we plan to evaluate the results produced by the participant systems.
A Study on Medical Image Compression Techniques based on Huffman Coding and Discrete Wavelet Transform (DWT)

In emergency situations, different types of medical images must be transferred from one place to another, and larger-sized images are difficult to transfer because they are time-consuming and take up more storage space. The size of the images that belongs to a single patient is very large, and it includes the resolution factor as well as diagnostic images. As a result, effective compression techniques for these medical images are required. Therefore, this paper has studied and analyzed the previous methods for the medical image compression using Huffman Coding & Discrete Wavelet Transform (DWT). This paper is also highlighted the strength and the weaknesses of Huffman Coding and Discrete Wavelet Transform (DWT) along with the parameters measurement that used for image compression.
Predicting HER2 scores from registered HER2 and H&amp;E images

Human epidermal growth factor (HER2) is a predictive and prognostic biomarker whose degree of presence in breast cancer informs prognosis and therapeutic decision making. In clinical practice, it is routinely assessed using immunohistochemical (IHC) staining. A pathologist assigns a score from 0 to 3+ depending on the intensity and distribution of staining – 0 or 1+ scores are classified as HER2 negative, 3+ scores as HER2 positive, and 2+ as equivocal. Unfortunately, variations in HER2 staining and the subjectivity in scoring can lead to inaccuracies. Therefore, we sought to develop an automated method to predict HER2 scores from HER2 and H&E slide images. Our database consisted of 52 adjacent HER2 and H&E tissue sections. Positive regions on HER2 were segmented using a previously developed method. Using 13-fold cross-validation, a truncated Resnet18 was then trained to classify extracted patches using HER2 score as labels for positive regions and a score of 0 for negative regions. Using the same folds, attentionbased multiple instance learning was used to aggregate learned patch embeddings into overall slide-level embeddings, which were subsequently classified. The preliminary method achieved 88% accuracy on 0/1+ and 85% accuracy on 2+ and 3+. Subsequent preliminary experiments qualitatively demonstrate that identified positive regions from HER2 can successfully be transferred over to H&E via image registration. Furthermore, applying the proposed method to predict HER2 score from H&E demonstrates that attention is paid to HER2 positive regions on H&E. Results provide preliminary evidence that HER2 can be localized and therefore scored using H&E images alone.
OpenWaters: Photorealistic Simulations For Underwater Computer Vision

In this paper, we present OpenWaters, a real-time open-source underwater simulation kit for generating photorealistic underwater scenes. OpenWaters supports creation of massive amount of underwater images by emulating diverse real-world conditions. It allows for fine controls over every variable in a simulation instance, including geometry, rendering parameters like ray-traced water caustics, scattering, and ground-truth labels. Using underwater depth (distance between camera and object) estimation as the use-case, we showcase and validate the capabilities of OpenWaters to model underwater scenes that are used to train a deep neural network for depth estimation. Our experimental evaluation demonstrates depth estimation using synthetic underwater images with high accuracy, and feasibility of transfer-learning of features from synthetic to real-world images.
Facemask Wearing Alert System Based on Simple Architecture With Low-Computing Devices

The Covid-19 epidemic has been causing heavy losses to humanity in terms of population, economy, and political stability. To deal with outbreaks of the pandemic, countries have been racing to develop vaccines and issue many regulations for people in daily life. Wearing a facemask in public is mandatory and will be severely punished if violated. In addition to the above mandatory regulations, it is necessary to develop tools for early warning when the human does not wear the facemask in public places such as offices, schools, supermarkets, train stations, etc. This paper proposed a facemask wearing alert system based on a simple convolutional neural network (CNN) operating on low-computing devices. This system works in two stages: face detection and facemask classification. In the first stage, it uses a face detection network with the main benefit of convolution, separable depthwise convolution, and double detectors layer to extract face region of interest (RoI). Then, this image area will go through a facemask classification network that exploits the advantages of convolution, separable depthwise convolution, and skip connection layers to classify facemask wearing (Mask or NoMask). The proposed networks are trained and evaluated on benchmark datasets. Along with simple designs, optimizing network parameters without ignoring accuracy, the system works in real-time at 33.17 and 26.18 frames per second (FPS) on an Intel Core I7-4770 CPU &#x0040; 3.40 GHz (Personal Computer - PC) and a Nvidia Maxwell GPU (Jetson Nano device), respectively. The demo video can be found here <uri>https://bit.ly/3yUgb8f</uri>.
Self-organizing map based differential evolution with dynamic selection strategy for multimodal optimization problems

Many real-world problems can be classified as multimodal optimization problems (MMOPs), which require to locate global optima as more as possible and refine the accuracy of found optima as high as possible. When dealing with MMOPs, how to divide population and obtain effective niches is a key to balance population diversity and convergence during evolution. In this paper, a self-organizing map (SOM) based differential evolution with dynamic selection strategy (SOMDE-DS) is proposed to improve the performance of differential evolution (DE) in solving MMOPs. Firstly, a SOM based method is introduced as a niching technique to divide population reasonably by using the similarity information among different individuals. Secondly, a variable neighborhood search (VNS) strategy is proposed to locate more possible optimal regions by expanding the search space. Thirdly, a dynamic selection (DS) strategy is designed to balance exploration and exploitation of the population by taking advantages of both local search strategy and global search strategy. The proposed SOMDE-DS is compared with several widely used multimodal optimization algorithms on benchmark CEC'2013. The experimental results show that SOMDE-DS is superior or competitive with the compared algorithms.
Research and Application of Terminal Vulnerability Mining System of Electric Power Company Based on Artificial Intelligence

In the era of Energy Internet, prefecture and municipal power supply companies are faced with a large number of underlying terminal equipment. Traditional manual detection of vulnerabilities has the disadvantages of low batch processing efficiency and unstable vulnerability detection rate. This article combines vulnerability mining and artificial intelligence, using bidirectional LSTM network. Feature learning is used as the core algorithm of the automatic vulnerability mining module, and has been applied in practice in the world-class urban power distribution network demonstration project in the ancient city of Suzhou. The application results show that the terminal vulnerability mining system based on artificial intelligence can greatly improve the automatic discovery ability and analysis efficiency of terminal equipment high-risk vulnerabilities, and can effectively improve the defects of traditional methods that cannot be batched and modularized.
On the Algorithmic Complexity of Digital Image Processing Filters with Winograd Calculations

This paper analyzes the computational complexity of methods of digital filtering of images by linear spatial filters. A comparison of the direct method of realization of two-dimensional filtering and the method of realization of two-dimensional filters by Winograd is carried out. It is shown that the best result using the Winograd method is obtained for averaging Gaussian filters and for symmetrical filters of a general form with different coefficients. The Winograd method application for Gauss filter reduces the number of multiplication operations 3 times at an increase of several addition operations 1.84 times. For symmetric general filters with different coefficients, multiplication operations are reduced to 2.12 at a rise in the number of addition operations 2 times.
Loss functions for Style Transfer with CycleGAN

Generative Adversarial Networks has been used in many fields now, and it is particularly essential in the field of computer vision. In respect of image-to-image translation, CycleGAN is an important part. In this paper, CycleGAN is used to translate portrait photographs to sketches, and ℓ1 loss, ℓ2 loss, perceptual loss and their combination losses are compared to find a cycle consistency loss function with better performance. After evaluating the generated image quality through two metrics: the peak signal to noise ratio (PSNR) and structural similarity (SSIM), the conclusion that combining the perceptual loss with ℓ1 and ℓ2 makes the generated images more structurally similar with their reference image is drawn.
Online malicious domain name detection with partial labels for large-scale dependable systems

Detecting malicious non-existent domain names (NXDomains) in a real-time manner is vitally important to the security of large-scale dependable systems. Existing detection methods are trained based on the assumption that the NXDomains, which cannot be recognized by the domain generation algorithm (DGA) archive, are benign. However, new types of malicious NXDomains are continuously generated, and the DGA archive cannot cover all of them, making the NXDomains partially labeled. Additionally, extracting all the features for distinguishing malicious and benign NXDomains is computationally inefficient and inappropriate for online detection in large-scale dependable systems. This work proposes a framework, PUFS, to train an accurate malicious NXDomain detection model according to partial labels and conduct efficient online detection for large-scale dependable systems. PUFS adopts a novel, simple, yet effective three-step strategy to combine PU learning and feature selection. We conduct extensive experiments using real-world data collected from a top-tier global online bank. PUFS achieves 99.19% of F1-Score, and improves the feature extraction efficiency by 1153%, making it suitable for online detection scenarios.
An integrated smart wheelchair model: IW Smart Wheelchair with Embedded Smart Home, Navigation and Obstacle Avoidance Systems

A smart wheelchair refers to a Powered Wheelchair (PW) with computers, sensors, and assistive technology attached. Our research is concerned with a Smart Wheelchair with sensing capability, designed to provide global accessibility. This paralyzing phenomenon affected many great individuals like Stephen Hawking and Max Brito. In this paper, wheelchair users have access to a variety of situations related to their everyday life. Our project attempts to make the lives of the people suffering from disabilities easier and simpler, helping them be more self-reliant and independent, which restores their confidence and restores their happiness. Moreover, this system is designed to make it easier for people with disabilities to move about freely without harming themselves or the environment. It helps them to control their home easily as well. It will help disabled people operate the wheelchair on their own. The technology of Android is a key to helping people interact with machines and tools in new ways. As a result, their problem can be addressed by using android technology to control the movement of a wheelchair. A wheelchair monitored and controlled by an application of android mobile is created to assist handicapped individuals by controlling the movement of a wheelchair in a different direction. The IW Smart Wheelchair model utilizes algorithms and systems that are meant to give safe navigation assistance through collision avoidance and also provides more flexibility for individuals with disabilities and for the elderly people to control the smart home. The system software's main function is to use the IW smart wheelchair model and sensors to control various devices such as doors, windows, lights, and temperature. The mobile unit and the microcontroller unit are the two main components of the system software. This paper covers our goals of the design for the IW Smart Wheelchair model and by extension the IW Smart Wheelchair, as well as the present state of the system prototype and future work plans.
Perceived Benefits of DevOps Implementation in Organization: A Systematic Literature Review

Nowadays, organizations are competing to accelerate the process of transforming business needs or business ideas into software applications. DevOps emerges to enable the development and operation team to work collaboratively and has gained much attention from various organizations to improve their software delivery process. Therefore, this study aims to gain further knowledge from various case studies on the perceived benefits of implementing the DevOps approach in organizations. A systematic literature review was conducted to summarizes the benefits that organizations perceived in implementing DevOps into benefit category mapping. From 446 potential related papers, 10 papers were selected by full-text review. This research found that there are nine benefit categories. The most benefit that organizations have perceived is frequent software deployment improvement, then followed by team productivity improvement and software quality improvement. With this DevOps benefit categories, it is hoped that researchers and practitioners can have an easy-to-understand understanding of the benefits that will be obtained if their organization wants to implement DevOps as their software development method.
Single image dehazing based on the fusion of multi-branch and attention mechanism

In recent years, convolutional neural networks have made significant progress in the field of image dehazing, but most methods still include the problems of incomplete dehazing and color distortion. An end-to-end dehazing network combining transfer learning and attention feature fusion mechanism is proposed to respond to these problems. First, we build a transfer learning sub-network by introducing a pre-training model. Secondly, two attention feature fusion molecular networks are used to assist the transfer learning network to obtain more accurate feature information. Finally, we use the tail convolution operation to achieve end-to-end dehazing processing, and the resulting graph is further obtained. The experimental results show that, compared with advanced algorithms, the dehazing effect of this algorithm is closer to clear images in both synthetic data sets and real data sets, and effectively solves the problem of color distortion.
The Role of Hyperparameter Optimization in Fine-Tuning of Cnn Models

The immense popularity of convolutional neural network (CNN) models has led to a growing interest in their hyperparameters optimization. Finding hyperparameter values leading to an optimally trained CNN is a complex and time-consuming task often requiring repetitive numerical experiments. Thus, a lot of attention is currently paid to the development of methods aiming to adjust the hyperparameters for specific CNN models and specific classification tasks. Existing optimization methods often provide hyperparameters that lead to good image classification results, but they do not indicate which hyperparameters are worth optimizing, what should be their value ranges, and whether it is reasonable to use a subset of training data for the optimization process. This work is focused on the optimization of hyperparameters during transfer learning. The goal is to investigate the influence of optimization methods and the selection of hyperparameters on the performance of fine-tuned models. In our experiments, we ranked the importance of hyperparameters and identified hyperparameter ranges that lead to optimally trained CNNs. With the a priori knowledge about the hyperparameter space taken into consideration we also compared four hyperparameter optimization methods: grid search, random search, Bayesian optimization, and Asynchronous Successive Halving Algorithm (ASHA) and investigated the possibility of fine-tuning hyperparameters using a subset of the training data. By optimizing the hyperparameters, the CNN's classification accuracy improved by up to 6\%. Furthermore, we noted that class-balancing in a subset of data used for parameter optimization plays a key role in establishing optimal set of hyperparameters for CNN training. Achieved results show that hyperparameter optimization is task and dataset-specific.
Construction of a Smart Library Subject Precise Service Platform Based on User Needs

Under the development and support of digital, networked and intelligent technologies (IT), university wisdom libraries adopt superb IT, sophisticated big data resource allocation, fine supervised wisdom RFD management system and exquisite supervised wisdom service system to provide a wisdom environment to meet users’ personalized needs and promote the transformation of libraries to a new form of wisdom. University intelligent libraries are still in their infancy and development stages in today’s world; the infrastructure has not been fully developed, the number of intelligent devices, the types of intelligent services, and the level of intelligence have all been developed but are still in their infancy and development stages, and the level of intelligence has not yet reached a significant level, and the level of intelligence has not yet reached a significant level. This paper develops the construction of a precise service platform for wisdom library disciplines based on users’ needs. In this paper, we propose a hybrid continuous ant colony optimization (BACO) method, and further propose a BACO-based multidoor image segmentation bun type for facial image segmentation. The experiments show that the proposed method has good performance and can lay the foundation for the next step of facial recognition and even the construction of smart library to ensure the accurate service of university smart library.
Bi-Level Selection via Meta Gradient for Graph-Based Fraud Detection

Graph Neural Networks (GNNs) have achieved remarkable successes by utilizing rich interactions in network data. When applied to fraud detection tasks, the scarcity and concealment of fraudsters bring two challenges: class imbalance and label noise. In addition to overfitting problem, they will compromise model performance through the message-passing mechanism of GNNs. For a fraudster in a neighborhood dominated by benign users, its learned representation will be distorted in the aggregation process. Noises will propagate through the topology structure as well. In this paper, we propose a Bi-Level Selection (BLS) algorithm to enhance GNNs under imbalanced and noisy scenarios observed from fraud detection. BLS learns to select instance-level and neighborhood-level valuable nodes via meta gradient of the loss on an unbiased clean validation set. By emphasizing BLS-selected nodes in the model training process, bias towards majority class (benign) and label noises will be suppressed. BLS can be applied on most GNNs with slight modifications. Experimental results on two real-world datasets demonstrate that BLS can significantly improve GNNs performance on graph-based fraud detection.
Modeling Characteristics in the Design of E-Collaboration Systems

Among the many ways to do business, e-collaboration system is gradually widely used by virtue of its advantages. Yet, designing e-collaboration systems still proves to be a great challenge due to the incomplete and inaccurate design specifications. This paper presents a characteristic modeling approach to facilitate e-collaboration system design. This approach transforms e-collaboration requirements descriptions into four types of design models. The coordination, communication, collaboration, and connectivity characteristics of e-collaborative systems are firstly addressed in these design models. In general, these models are designed to provide a more comprehensive and accurate system specification than the original requirement descriptions. The approach intends to provide a systematical approach for modeling e-collaboration processes to support e-collaboration systems design.