Reviewerly/exHarmony huggingface dataset:
Automatic and intelligent content visualization system based on deep learning and genetic algorithm

Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use.
Research on Engineering Vehicle Target Detection in Aerial Photography Environment based on YOLOX

The aerial target detection of engineering vehicles such as excavators is the key technology for UAV inspection of optical cable lines and petroleum pipelines. It is proposed to apply the deep learning YOLOX (You Only Look Once version X) target detection algorithm to the target detection of engineering vehicles in aerial images. Based on the Pytorch deep learning framework, through the production of engineering vehicle aerial photography data sets, simulation has realized the target detection of engineering vehicles, and its target recognition AP@0.5:0.95 value reaches 59.38%. Based on the same data set and training conditions, its detection accuracy and speed surpass classic algorithms such as YOLOv4 and YOLOv5. The simulation results can provide a certain reference for the research of aerial inspection and maintenance of underground pipeline facilities.
SHARIF: Solid Pod-Based Secured Healthcare Information Storage and Exchange Solution in Internet of Things

The recent development has enlightened health informatics on the Internet of medical Things (IoT) 5.0. Healthcare services have seen greater acceptance of information and communications technology (ICT) in recent years; in light of the increasing volume of patient data, the traditional way of storing data in physical files has eventually moved to a digital alternative such as electronic health record (EHR). However, conventional healthcare data systems are plagued with a single point of failure, security issues, mutable logging, and inefficient methods to retrieve healthcare records. Social linked data (Solid) has been developed as a decentralized technology to alter digital data sharing and ownership for its users radically. However, Solid alone cannot address all the security issues posed to data exchange and storage. Present research combines two decentralized technologies, Solid ecosystem and blockchain technology, to tackle all potential security issues using solidity-based smart contracts, thereby providing a secure patient-centric design for the complex under developing EHR data exchange.
Unlocking digital archives: cross-disciplinary perspectives on AI and born-digital data

Co-authored by a Computer Scientist and a Digital Humanist, this article examines the challenges faced by cultural heritage institutions in the digital age, which have led to the closure of the vast majority of born-digital archival collections. It focuses particularly on cultural organizations such as libraries, museums and archives, used by historians, literary scholars and other Humanities scholars. Most born-digital records held by cultural organizations are inaccessible due to privacy, copyright, commercial and technical issues. Even when born-digital data are publicly available (as in the case of web archives), users often need to physically travel to repositories such as the British Library or the Bibliothèque Nationale de France to consult web pages. Provided with enough sample data from which to learn and train their models, AI, and more specifically machine learning algorithms, offer the opportunity to improve and ease the access to digital archives by learning to perform complex human tasks. These vary from providing intelligent support for searching the archives to automate tedious and time-consuming tasks. In this article, we focus on sensitivity review as a practical solution to unlock digital archives that would allow archival institutions to make non-sensitive information available. This promise to make archives more accessible does not come free of warnings for potential pitfalls and risks: inherent errors, "black box" approaches that make the algorithm inscrutable, and risks related to bias, fake, or partial information. Our central argument is that AI can deliver its promise to make digital archival collections more accessible, but it also creates new challenges - particularly in terms of ethics. In the conclusion, we insist on the importance of fairness, accountability and transparency in the process of making digital archives more accessible.
Open-set 3D Object Detection

3D object detection has been wildly studied in recent years, especially for robot perception systems. However, existing 3D object detection is under a closed-set condition, meaning that the network can only output boxes of trained classes. Unfortunately, this closed-set condition is not robust enough for practical use, as it will identify unknown objects as known by mistake. Therefore, in this paper, we propose an open-set 3D object detector, which aims to (1) identify known objects, like the closed-set detection, and (2) identify unknown objects and give their accurate bounding boxes. Specifically, we divide the open-set 3D object detection problem into two steps: (1) finding out the regions containing the unknown objects with high probability and (2) enclosing the points of these regions with proper bounding boxes. The first step is solved by the finding that unknown objects are often classified as known objects with low confidence, and we show that the Euclidean distance sum based on metric learning is a better confidence score than the naive softmax probability to differentiate unknown objects from known objects. On this basis, unsupervised clustering is used to refine the bounding boxes of unknown objects. The proposed method combining metric learning and unsupervised clustering is called the MLUC network. Our experiments show that our MLUC network achieves state-of-the-art performance and can identify both known and unknown objects as expected.
Optimizing Implementations of Non-Profiled Deep Learning-Based Side-Channel Attacks

The differential deep learning analysis proposed by Timon is the first non-profiled side-channel attack technique that uses deep learning. The technique recovers the secret key using the phenomenon of deep learning metrics. However, the proposed technique made it difficult to observe the results from the intermediate process, while the neural network had to be retrained repeatedly, as the cost of learning increased with key size. In this paper, we propose three methods to solve the aforementioned problems and any challenges resulting from solving these problems. First, we propose a modified algorithm that allows the monitoring of the metrics in the intermediate process. Second, we propose a parallel neural network architecture and algorithm that works by training a single network, with no need to re-train the same model repeatedly. Attacks were performed faster with the proposed algorithm than with the previous algorithm. Finally, we propose a novel architecture that uses shared layers to solve memory problems in parallel architecture and also helps achieve better performance. We validated the performance of our methods by presenting the results of non-profiled attacks on the benchmark database ASCAD and for a custom dataset on power consumption collected from ChipWhisperer-Lite. On the ASCAD database, our shared layers method was up to 134 times more efficient than the previous method.
Generative model‐enhanced human motion prediction

The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD). Here, we formulate a new OoD benchmark based on the Human3.6M and Carnegie Mellon University (CMU) motion capture datasets, and introduce a hybrid framework for hardening discriminative architectures to OoD failure by augmenting them with a generative model. When applied to current state-of-the-art discriminative models, we show that the proposed approach improves OoD robustness without sacrificing in-distribution performance, and can theoretically facilitate model interpretability. We suggest human motion predictors ought to be constructed with OoD challenges in mind, and provide an extensible general framework for hardening diverse discriminative architectures to extreme distributional shift. The code is available at: https://github.com/bouracha/OoDMotion.
A Method for Resume Information Extraction Using BERT-BiLSTM-CRF

To solve the problem of low efficiency of electronic resume information extraction by artificial construction rules, a resume information extraction method based on named entity recognition is proposed, which extracted personal details such as graduation college, job intention and job skills from the resume into named entity recognition. Firstly, the TXT text in different formats of resume file is extracted for data cleaning and other preprocessing. The BERT language model based on multi-head self-attention mechanism is used to extract text features and obtain word granularity vector matrix. The BiLSTM neural network is used to obtain the context abstraction features of serialized text. Finally, using CRF to decode and annotate the global optimal sequence, the corresponding resume entity information is extracted. Experimental results show that the whole scheme can effectively extract electronic resume information, and the performance of the resume information extraction model based on BERT-BiLSTM-CRF is better than other models.
Handling data sparsity via item metadata embedding into deep collaborative recommender system

The tremendous growth in information over the last decade leads to information overwhelming problems for accessing personalized products. The recommender framework that retrieves user preferences on past interactions is known as collaborative filtering (CF). Although, CF is a prevalent technique amongst the techniques applied in the recommender environment. However, it suffers from many problems like information sparsity, scalability, cold-start, etc. Many investigations have been made to tackle some of these issues with the help of matrix factorization (MF) approaches. However, MF cannot handle the nonlinearity among the data. Deep learning is an advanced learning technique that has shown success in many applications such as image classification, computer vision, natural language processing, etc. Little work has been reported on deep learning techniques in the recommender domain. We propose an efficient deep collaborative recommender system that embeds item metadata to handle the nonlinearity in data and sparsity. The model consists of two stages, wherein the first stage, a neural network, is used to retrieve the data's nonlinear features through embedding vectors. These vectors are concatenated together and fed as input to the second stage of the model. The output of the model yields a partially observed rating. The input and the parameters are simultaneously optimized and updated to minimize errors. The proposed strategy is evaluated against the benchmark techniques on two well-known datasets. The exploratory outcomes signify our approach's exactitude and efficiency. Moreover, the missing values can also be recovered by propagating the embedding vectors from the input to the output layers.
Leveraging Blockchain for Multi-Operator Access Sharing Management in Internet of Vehicles

Wireless service providers (WSPs) are seeking cooperation to share both resources (spectrum, infrastructures) and costs. In particular, cooperation is critical for vehicle-to-everything (V2X) applications that value quality-of-service foremost. However, cooperation is inefficient without strong trust bases. Recently, blockchain arises as a promising solution to decentralized trust, owing to its transparency and immutability. Therefore, we propose a WSP cooperation framework based on blockchain, which keeps a shared ledger on resource utilization. In this paper, we first study the WSP selection problem of vehicles by evolutionary game. Then, we make the blockchain public to vehicles to maintain its decentralization and enhance its security by crowdsourcing idle computing resources from vehicles. To deal with the mobility of vehicles, as well as to improve the consensus efficiency, we propose delegated proof-of-work (DPoW) scheme, which introduces delegate nodes for participating in the blockchain consensus on behalf of the vehicles. The alternating direction method of multipliers (ADMM) optimization framework is further used to derive optimal consensus parameters including block size, computing resource demand of delegate nodes, and prices paid for vehicles. We conduct extensive simulations to show the effectiveness of the proposed methods.
Coded Distributed Computing With Partial Recovery

Coded computation techniques provide robustness against straggling workers in distributed computing.However, most of the existing schemes require exact provisioning of the straggling behavior and ignore the computations carried out by straggling workers.Moreover, these schemes are typically designed to recover the desired computation results accurately, while in many machine learning and iterative optimization algorithms, faster approximate solutions are known to result in an improvement in the overall convergence time.In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation.We then extend this approach to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery, where the results of subtasks computed by the workers are coded before being communicated.Numerical simulations on a large linear regression task confirm the benefits of the proposed scheme in terms of the trade-off between the computation accuracy and latency.
Comparative Analysis of the Results of Algorithms for Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy Using Deep Learning

In this article, has been developed a convolutional neural network algorithm based on Echo to automatically classify cardiomyopathy. This algorithm allows to classify dilated (DCM) cardiomyopathy and hypertrophic cardiomyopathy (HCM) with an accuracy of 98. 2%. With the improvement of this approach, it is possible to develop an intelligent system and expert systems that will help to make decisions aimed at early detection, classification and diagnosis of symptoms of heart diseases.
Modeling of Hyperparameter Tuned Deep Learning Model for Automated Image Captioning

Image processing remains a hot research topic among research communities due to its applicability in several areas. An important application of image processing is the automatic image captioning technique, which intends to generate a proper description of an image in a natural language automated. Image captioning is a recently developed hot research topic, and it started to receive significant attention in the field of computer vision and natural language processing (NLP). Since image captioning is considered a challenging task, the recently developed deep learning (DL) models have attained significant performance with increased complexity and computational cost. Keeping these issues in mind, in this paper, a novel hyperparameter tuned DL for automated image captioning (HPTDL-AIC) technique is proposed. The HPTDL-AIC technique encompasses two major parts, namely encoder and decoder. The encoder part utilizes Faster SqueezNet with the RMSProp model to generate an effective depiction of the input image via insertion into a predefined length vector. At the same time, the decoder unit employs a bird swarm algorithm (BSA) with long short-term memory (LSTM) model to concentrate on the generation of description sentences. The design of RMSProp and BSA for the hyperparameter tuning process of the Faster SqueezeNet and LSTM models for image captioning shows the novelty of the work, which helps to accomplish enhanced image captioning performance. The experimental validation of the HPTDL-AIC technique is carried out against two benchmark datasets, and the extensive comparative study pointed out the improved performance of the HPTDL-AIC technique over recent approaches.
A Full-Reference Quality Assessment Metric for Fine-Grained Compressed Images

Compressed image quality assessment (IQA) has been a crucial part of a wide range of image services such as storage and transmission. Due to the effect of different bit rates and compression methods, the compressed images usually have different levels of quality. Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels, however, they may perform poorly when quality differences of the compressed images are quite subtle. To better improve the Quality of Experience (QoE) and provide useful guidance for compression algorithms, we propose an FR-IQA metric for fine-grained compressed images, which estimates the image quality by analyzing the difference of structure and texture. Our metric is mainly validated on the fine-grained compression IQA (FGIQA) database and is tested on other commonly used compression IQA databases as well. The experimental results show that our metric outperforms mainstream FR-IQA metrics on the fine-grained compression IQA database and also obtains competitive performance on the coarse-grained compression IQA databases.
Single Image Super-Resolution with Arbitrary Magnification Based on High-Frequency Attention Network

Among various developments in the field of computer vision, single image super-resolution of images is one of the most essential tasks. However, compared to the integer magnification model for super-resolution, research on arbitrary magnification has been overlooked. In addition, the importance of single image super-resolution at arbitrary magnification is emphasized for tasks such as object recognition and satellite image magnification. In this study, we propose a model that performs arbitrary magnification while retaining the advantages of integer magnification. The proposed model extends the integer magnification image to the target magnification in the discrete cosine transform (DCT) spectral domain. The broadening of the DCT spectral domain results in a lack of high-frequency components. To solve this problem, we propose a high-frequency attention network for arbitrary magnification so that high-frequency information can be restored. In addition, only high-frequency components are extracted from the image with a mask generated by a hyperparameter in the DCT domain. Therefore, the high-frequency components that have a substantial impact on image quality are recovered by this procedure. The proposed framework achieves the performance of an integer magnification and correctly retrieves the high-frequency components lost between the arbitrary magnifications. We experimentally validated our model’s superiority over state-of-the-art models.
Analyzing Tail Latency in Serverless Clouds with STeLLAR

Serverless computing has seen rapid adoption because of its instant scalability, flexible billing model, and economies of scale. In serverless, developers structure their applications as a collection of functions invoked by various events like clicks, and cloud providers take responsibility for cloud infrastructure management. As with other cloud services, serverless deployments require responsiveness and performance predictability manifested through low average and tail latencies. While the average end-to-end latency has been extensively studied in prior works, existing papers lack a detailed characterization of the effects of tail latency in real-world serverless scenarios and their root causes. In response, we introduce STeLLAR, an open-source serverless benchmarking framework, which enables an accurate performance characterization of serverless deployments. STeLLAR is provider-agnostic and highly configurable, allowing the analysis of both end-to-end and per-component performance with minimal instrumentation effort. Using STeLLAR, we study three leading serverless clouds and reveal that storage accesses and bursty function invocation traffic are key factors impacting tail latency in modern serverless systems. Finally, we identify important factors that do not contribute to latency variability, such as the choice of language runtime.
DeepFake Detection Using Error Level Analysis and Deep Learning

The image recognition software is used in numerous distinctive industries that include entertainment and media. The deep learning (DL) algorithms have been of great help in the development of several techniques used for creating, altering, and locating any data. The deepfake method is a photo-faking technique that includes replacing two people's faces to an extent that it becomes very difficult to identify it with a naked eye. The convolution neural network (CNN) models including Alex Net and Shuffle Net are used to recognize genuine and counterfeit face images in this article. The technique analyzes the performance and working of all distinctive algorithms using the real/fake face recognition collection from Yonsei University's Computational Intelligence Photography Lab. The first step in the process starts by the normalizing of pictures then the Error Level Analysis is carried out before it is put into several difference CNN models. Then the in-depth features are extracted from the CNN models utilizing the Support Vector Machine and the K-nearest neighbor methods. The most perfect accuracy of 88.2% of Shuffle Net via KNN was analyzed while Alex Net's vector had the accuracy of 86.8%.