Reviewerly/exHarmony huggingface dataset:
Power Measurement-Based Vulnerability Assessment of IoT Medical Devices at Varying Countermeasures for Cybersecurity

A method for the vulnerability assessment of Internet-of-Things (IoT) medical transducers cybersecurity at varying the power countermeasures is proposed. The implemented countermeasures reinforce the most popular cryptographic algorithm, advanced encryption standard (AES), running on a low-cost, low-energy, and low-performance smart transducer for IoT medical devices. The proposed method is based on attacks standardized in the smart-card industry: differential power analysis (DPA) and correlation power analysis (CPA). Adopting a methodically rigorous framework, a boost for the study of power countermeasures was offered for IoT medical transducers. An experimental comparison of security performance among different power countermeasures was realized. Each countermeasure increases the number of power traces needed to discover the AES secret key. Random delay, random SBox, and masking strengthen the AES of 1.3, 208, and 318 factors, respectively. The masking countermeasure was by far the most effective.
Delaunay Triangulation Based Wide-Area Visualization of Electric Transmission Grids

This paper presents a Delaunay triangulation based technique to develop planar visualizations to better show the overall flow of power in large-scale electric grids. One of the challenges with analyzing large-scale electric grids is maintaining an understanding of the overall electric grid state, including the grid's flow pattern. The paper's algorithm addresses this by first aggregating the buses into groups. Then a Delaunay triangulation is used to create a planar graph, followed by a mapping of the transmission line flows onto this graph. Finally the flows are visualized with dynamically sized line thicknesses and/or flow arrows. Results are demonstrated using several example systems ranging in size from seven to 82,000 buses.
RPC: Representative possible world based consistent clustering algorithm for uncertain data

Clustering uncertain data is an essential task in data mining and machine learning. Possible world based algorithms seem promising for clustering uncertain data. However, there are two issues in existing possible world based algorithms: (1) They rely on all the possible worlds and treat them equally, but some marginal possible worlds may cause negative effects. (2) They do not well utilize the consistency among possible worlds, since they conduct clustering or construct the affinity matrix on each possible world independently. In this paper, we propose a representative possible world based consistent clustering (RPC) algorithm for uncertain data. First, by introducing representative loss and using Jensen–Shannon divergence as the distribution measure, we design a heuristic strategy for the selection of representative possible worlds, thus avoiding the negative effects caused by marginal possible worlds. Second, we integrate a consistency learning procedure into spectral clustering to deal with the representative possible worlds synergistically, thus utilizing the consistency to achieve better performance. Experimental results show that our proposed algorithm outperforms existing algorithms in effectiveness and performs competitively in efficiency.
The Effect of Clustering with a minimum Pattern of Features Extraction for Person Recognition

In personal image recognition algorithms, two effective factors govern the system’s evaluation, recognition rate and size of the database. Unfortunately, the recognition rate proportional to the increase in training sets. Consequently, that increases the processing time and memory limitation problems. This paper’s main goal was to present a robust algorithm with minimum data sets and a high recognition rate. Images for ten persons were chosen as a database, nine images for each individual as the full version of the training data set, and one image for each person out of the training set as a test pattern before the database reduction procedure. The proposed algorithm integrates Principal Component Analysis (PCA) as a feature extraction technique with the minimum means of clusters and Euclidean Distance to achieve personal recognition. After indexing the training set for each person, the clustering of the differences is determined. The recognition of the person represented by the minimum mean index; this process returned with each reduction. The experimental results show that the recognition rate is 100% despite reducing the training sets to 44%, while the recognition rate decrease to 70% when the reduction reaches 89%. The clear picture out is the results of the proposed system support the idea of the redaction of training sets in addition to obtaining a high recognition rate based on application requirements.
Heterogeneous Dual-Core Overlay Processor for Light-Weight CNNs

Convolutional neural networks (CNNs) have achieved extensive success on miscellaneous artificial intelligence applications such as image classification and object detection. A plethora of models emerge with different operators and architectures, gradually shifting attention from accuracy to efficiency in terms of speed and power, since VGG-like architecture from early stage has significant redundancy. Light-weight CNNs are proposed to reduce computation complexity and parameter amount. MobileNets, one typical example of light-weight CNNs, adopt depthwise separable convolution, while others such as SqueezeNet alter model topology to spare computation power.
Input-Output Balanced Framework for Long-Tailed Lidar Semantic Segmentation

A thorough and holistic scene understanding is crucial for autonomous vehicles, where LiDAR semantic segmentation plays an indispensable role. However, most existing methods focus on the network design while neglecting the inherent difficulty, i.e, imbalanced data distribution in the realistic dataset (also named long-tailed distribution), which narrows down the capability of state-of-the-art methods. In this paper, we propose an input-output balanced framework to handle the issue of long-tailed distribution. Specifically, for the input space, we synthesize these tailed instances from mesh models and well simulate the position and density distribution of LiDAR scan, which enhances the input data balance and improves the data diversity. For the output space, a multi-head block is proposed to group different categories based on their shapes and instance amounts, which alleviates the biased representation of dominating category during the feature learning. We evaluate the proposed model on two large-scale datasets, i.e, SemanticKITTI and nuScenes, where state-of-the-art results demonstrate its effectiveness. The proposed new modules can also be used as a plug-and-play, and we apply them on various backbones and datasets, showing its good generalization ability.
K-Means Clustering via a Nonconvex Optimization Approach

Clustering is one of the basic tasks in machine learning and data mining. Euclidean minimum-sum-of-squares clustering problem is probably the most common clustering model. It consists in finding k cluster centers or representatives so as the sum of squared Euclidean distances from a set of points and their closest centers is minimized. The problem is known to be nonconvex and NP-hard even in the planner case. In this paper, we propose a new DC programming approach to the problem. First, we cast the original nonconvex problem as a continuous optimization problem with the objective function represented as a DC function. We then devise a solution algorithm, resting upon the global optimality conditions and global search scheme for DC minimization problems proposed by A.S. Strekalovsky. We implement the developed algorithm and compare it with k-means clustering algorithms on generated datasets.
Topic Modeling and Progression of American Digital News Media During the Onset of the COVID-19 Pandemic

Currently, the world is in the midst of a severe global pandemic, which has affected all aspects of people’s lives. As a result, there is a deluge of COVID-related digital media articles published in the United States, due to the disparate effects of the pandemic. This large volume of information is difficult to consume by the audience in a reasonable amount of time. In this article, we develop a natural language processing (NLP) pipeline that is capable of automatically distilling various digital articles into manageable pieces of information, while also modeling the progression topics discussed over time in order to aid readers in rapidly gaining holistic perspectives on pressing issues (i.e., the COVID-19 pandemic) from a diverse array of sources. We achieve these goals by first collecting a large corpus of COVID-related articles during the onset of the pandemic. After, we apply unsupervised and semi-supervised learning procedures to summarize articles, then cluster them based on their similarities using the community detection methods. Next, we identify the topic of each cluster of articles using the BART algorithm. Finally, we provide a detailed digital media analysis based on the NLP-pipeline outputs and show how the conversation surrounding COVID-19 evolved over time.
An overview of soft computing techniques on Review Spam Detection

Individuals that work with web services commonly focus on internet-based feedback to make decisions about shopping via online, booking an apartment, renting banquette halls etc., because people believe that making a decision which is based on the views of others, can contribute in making the right decision about their purchases. While publishing negative reviews or comments, brings financial benefit to manufacturers via online review websites and also increases the review spam happenings. Sentimental misinformation in feedback has therefore become a significant challenge for people to make decisions about their purchase and thus decrease the credibility of review on websites. The malicious spam identification of views is therefore an important activity in the fields of machine learning (NLP- Natural Language Processing). The conventional bag-of-words model is used to recognize the text analysis characteristics of most of the current research on review spam detection and to apply traditional machine learning models such as Support Vector Machines, Random forest, Decision tree and Naive Bayes as classifiers. With increase in the effective applications of Soft computing, the spam detection framework has achieved improved efficiency over traditional machine learning and works well with unique models and provides better solutions to complicated real-life scenarios. This paper discusses the overview of soft computing techniques in spam detection.
ActuBoard: An Open Rapid Prototyping Platform to integrate Hardware Actuators in Remote Applications

Prototyping is an essential step in developing tangible experiences and novel devices, ranging from haptic feedback to wearables. However, prototyping of actuated devices nowadays often requires repetitive and time-consuming steps, such as wiring, soldering, and programming basic communication, before HCI researchers and designers can focus on their primary interest: designing interaction. In this paper, we present ActuBoard, a prototyping platform to support 1) quick assembly, 2) less preparation work, and 3) the inclusion of non-tech-savvy users. With ActuBoard, users are not required to create complex circuitry, write a single line of firmware, or implementing communication protocols. Acknowledging existing systems, our platform combines the flexibility of low-level microcontrollers and ease-of-use of abstracted tinker platforms to control actuators from separate applications. As further contribution, we highlight the technical specifications and published the ActuBoard platform as Open Source.
Pipa: Privacy-preserving Password Checkup via Homomorphic Encryption

Data-breach is not rare on Internet, and once it happens, web users may suffer from privacy leakage and property loss. In order to enable web users to conveniently check whether their confidential information is compromised in data-breach events while preserving their privacy, we design Pipa, which is essentially a special case of private set intersection protocol instantiated with homomorphic encryption. We choose the password checkup scenario for an entry point.
Explainable Artificial Intelligence (XAI) Supporting Public Administration Processes – On the Potential of XAI in Tax Audit Processes

Artificial Intelligence (AI) can offer significant potential for public administrations which – in Germany – are likely to face considerable skills shortages in the next few years. AI systems can especially support the automation of processes and thus disburden administrative staff. As transparency and fairness play a major role in administrative processes, explainable AI (XAI) approaches are expected to enable a proper usage of AI in public administration. In this article, we investigate the potential of XAI for the support of tax authority processes, especially the selection of tax audit target organizations. We illustrate relevant tax audit scenarios and present the potential of different XAI techniques which we currently develop in these scenarios. It shows that XAI can significantly support tax audit preparations resulting in more efficient processes and a better performance of tax authorities concerning their main responsibilities. A further contribution of this article lies in the exemplary application of XAI usage guidelines in the public administration context.
Parametric optimization of unmanned vehicle controller by PSO algorithm

For an unmanned vehicle, in difficult conditions, when spatial constraints seriously narrow the space of admissible states, the strategy of choosing a state space is more effective than sampling in the control space. Although this was obvious, the practical question is how to achieve it while meeting the stringent constraints of the vehicle’s dynamic feasibility. This article presents an unmanned vehicle control system based on the predictive integrated path model (MPPI) controller, deep convolutional neural network (CNN) for real-time scene understanding and particle swarm optimization (PSO) to find the vector of optimal cost function parameters. The method is based on the optimization of the cost function, which determines where the vehicle should move on the surface of the path.
Image generation from text with entity information fusion

Image generation from text is the task of generating new images from a textual unit such as word, phase, clause and sentence. It has attracted great attention in both the community of natural language processing and computer vision. Current approaches usually employ an end-to-end framework to tackle the problem. However, we find that the entity information, including categories and attributes of the images, are ignored by most approaches. Such information is crucial for guaranteeing semantic alignment and generating image accurately. For two pictures of the same category, the emphasis of the corresponding text description may be different, but the images generated by these two sentences should have some similarities and the generation process can learn from each other. Therefore, we propose two novel end-to-end frameworks to incorporate entity information in the process of image generation. For the first framework, an image representation is generated from entity labels using the variational inference mechanism and then fused with the representation generated from the corresponding sentence. Instead of fusing the images in high-dimensional space, images are inferred and fused in the latent space (the low-dimensional space) in the second framework, where computationally intensive upsampling modules are shared. Moreover, a novel metric (Entity Matching Score) is proposed to measure the degree of consistency of the generated image with its corresponding text description and the effectiveness of the metric has been proved by the generated samples in our experiments. Experimental results show that both the proposed frameworks outperform some state-of-the-art approaches significantly on two benchmark datasets.
From Mental Models to Machine Learning Models via Conceptual Models

Although much research continues to be carried out on modeling of information systems, there has been a lack of work that relates the activities of modeling to human mental models. With the increased emphasis on machine learning systems, model development remains an important issue. In this research, we propose a framework for progressing from human mental models to machine learning models and implementation via the use of conceptual models. The framework is illustrated by an application to a citizen science project. Recommendations for the use of the framework are proposed.
Impacts of Kernel Size on Different Resized Images in Object Recognition Based on Convolutional Neural Network

This paper focuses a study on impacts of kernel sizes on different resized image relying on convolutional neural network (CNN) for object recognition. Two sets of convolutional neural network (CNN) deep learning models: Conv573 and Conv3, based on shallow feed-forward-architecture network are employed here for feature extraction with comparative assessment purpose. The Conv573 refers to CNN with kernel size of 7×7, 5×5 and 3×3; whilst the Conv3 represents that of only 3×3 kernel size; where three and two convolutional layers of 3×3 kernel size are consecutively comparable to one convolutional layer of 7×7 and 5×5 kernel sizes. The experiments rely on different-resized CIFAR-10 image dataset, 50×50, 100×100 and 150×150 pixels for testing with the Conv573 and Conv3 models. For the purpose of bias reduction, recognition performance assessments depend on averages of precision, recall, F1 and accuracy rates, based upon 10-fold cross validation. The results indicate that the greater the size of an image is, the better the recognition accuracy would be based on Conv573, conversely for Conv3. 2.65% and 0.06% recognition accuracy improvement based on Conv573, whereas 0.39% and 1.76% performance decrease based on Conv3 are respectively yielded when resizing image from 50×50 to 100×100 and from 100×100 to 150×150. For 50×50 and 100×100 resized images, Conv3 yields 4.74% and 1.64% better averaged accuracy than Conv573; nevertheless, Conv573 generates 0.18% better averaged accuracy than Conv3 on 150×150 resized ones.
Joint object classification and turbulence strength estimation using convolutional neural networks

In a recent paper, Kee et al. [ Appl. Opt. 59 , 9434 ( 2020 ) APOPAI 0003-6935 10.1364/AO.405663 ] use a multilayer perceptron neural network to classify objects in imagery after degradation through atmospheric turbulence. They also estimate turbulence strength when prior knowledge of the object is available. In this work, we significantly increase the realism of the turbulence simulation used to train and evaluate the Kee et al. neural network. Second, we develop a new convolutional neural network for joint character classification and turbulence strength estimation, thereby eliminating the prior knowledge constraint. This joint classifier–estimator expands applicability to a broad range of remote sensing problems, where the observer cannot access the object of interest directly.
A Book Recommender System Using Collaborative Filtering Method

Recommender systems are used to generate meaningful recommendations to users based on their preferences, which will be determined following several approaches. This work targets Arab readers by providing accurate and reliable results that match their needs and desirability. Eventually, it will enhance the reading experience for any Arab readers. The main approach is to filter the recommendations, and this can be achieved either by Content-Based filtering or by Collaborative Filtering. The collaborative filtering techniques presented in this paper compute the similarity matrix between items and users' ratings, and then evaluate the recommendations for users. The techniques cover User-Based and Item-Based Collaborative Filtering, as well as Matrix Factorization through an SVD algorithm. A comparison between these techniques is presented in terms of the fitting and testing time, and accuracy. The KNN-based algorithms showed better performance than the matrix factorization method with respect to fitting and testing time. However, the matrix factorization (SVD) algorithm had the best results in terms of accuracy.
A Survey on Phishing URL Detection Using Artificial Intelligence

Nowadays, a cyberattack is one of the most common security threats. The attackers generally try to steal confidential information using social engineering platforms. With the evolution of the internet in the last few years, phishing has also rapidly grown on the internet. By phishing, Internet users have a financial loss of billions of dollars per year. Phishing is a social engineering attack used to identify and steal the victim's personal information; it is generally done by e-mail, text messages, website spoofing, etc. Attackers send some attractive messages to the victims to misguide him/her and perform an attack, by using traditional methods; we cannot mitigate the ratio of the phishing attack. So, the researcher's focus on moving from the conventional approach to machine learning techniques to secure the use of the internet.
Finding Critical Users in Social Communities via Graph Convolutions

Finding critical users in social networks is an important issue. The criticalness of a user can be measured by the number of followers who will leave the community together when the user leaves. By taking a social community as a k-core, the problem of finding critical users is to find a set of nodes, U, of size b in a k-core that maximizes the number of nodes to be deleted from the k-core when all nodes in U are deleted. This problem is NP-hard. The state-of-the-art greedy algorithm is with no guarantee on the set of nodes U found. In this paper, we propose a neural network model, called Self-attentive Core Graph Convolution Network (SCGCN), to capture the hidden structure of the criticalness among node combinations that break the engagement of a specific social community. Supervised by sampling node combinations, SCGCN has the ability to inference the criticalness of unseen combinations of nodes. To further reduce the sampling and inference space, we propose a deterministic strategy to prune unpromising nodes on the graph. Our experiments conducted on many real-world graphs show that SCGCN significantly improves the quality of the solution compared with the state-of-the-art greedy algorithm.
Underwater Image Enhancement With Optimal Histogram Using Hybridized Particle Swarm and Dragonfly

Abstract Typically, underwater image processing is mainly concerned with balancing the color change distortion or light scattering. Various researches have been processed under these issues. This proposed model incorporates two phases, namely, contrast correction and color correction. Moreover, two processes are involved within the contrast correction model, namely: (i) global contrast correction and (ii) local contrast correction. For the image enhancement, the main target is on the histogram evaluation, and therefore, the optimal selection of histogram limit is very essential. For this optimization purpose, a new hybrid algorithm is introduced namely, swarm updated Dragonfly Algorithm, which is the hybridization of Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA). Further, this paper mainly focused on Integrated Global and Local Contrast Correction (IGLCC). The proposed model is finally distinguished over the other conventional models like Contrast Limited Adaptive Histogram, IGLCC, dynamic stretching IGLCC-Genetic Algorithm, IGLCC-PSO, IGLCC- Firefly and IGLCC-Cuckoo Search, IGLCC-Distance-Oriented Cuckoo Search and DA, and the superiority of the proposed work is proved.