Reviewerly/exHarmony huggingface dataset:
Side-Channel Analysis of the Random Number Generator in STM32 MCUs

The hardware random number generator (RNG) integrated in STM32 MCUs is intended to ensure that the numbers it generates cannot be guessed with a probability higher than a random guess. The RNG is based on several ring oscillators whose outputs are combined and post-processed to produce a 32-bit random number per round of computation. In this paper, we show that it is possible to train a neural network capable of recovering the Hamming weight of these random numbers from power traces with a higher than 60% probability. This is a 4-fold improvement over the 14% probability of the most likely Hamming weight.
Web Page Ranking Using Web Mining Techniques: A Comprehensive Survey

Due to the exponential growth of Internet users and traffic, information seekers depend highly on search engines to extract relevant information. Due to the accessibility of a large amount of textual, audio, video etc., contents, the responsibility of search engines has increased. The search engine provides relevant information to Internet users concerning their query, based on content, link structure, etc. However, it does not guarantee the correctness of the information. The performance of a search engine is highly dependent upon the ranking module. The performance of the ranking module is dependent upon the link structure of web pages, which analyze through Web structure mining (WSM) and their content, which analyzes through Web content mining (WCM). Web mining plays a vital role in computing the rank of web pages. This article presents web mining types, techniques, tools, algorithms, and their challenges. Further, it provides a critical comprehensive survey for the researchers by presenting different features of web pages, which are essential to check their quality. In this work, authors presented different approaches/techniques, algorithms and evaluation approaches in previous researches and identified some critical issues in page ranking and web mining, which provide future directions for the researchers working in the area.
Tackling temporal-dynamic service composition in cloud manufacturing systems: A tensor factorization-based two-stage approach

Service composition (SC) is a critical matching process in cloud manufacturing (CMfg) mainly to organize geographically-scattered manufacturing services for manufacturing tasks. Currently, most of the existing CMfg SC approaches assume that cloud service data are always static without considering the dynamics in cloud environments. Meanwhile, existing approaches rely heavily on easy-to-get dense historical service data for SC. However, adapting such ideal approaches to fast-changing temporal-dynamic cloud environments possibly leads to unsatisfactory matching results. This paper proposes a hybrid two-stage approach to deal with temporal-dynamic CMfg SC problem by incorporating tensor factorization (TF). The approach has two stages: temporal-dynamic prediction (Stage I) and many-objective SC optimization (Stage II). For Stage I, a non-negative TF based CMfg SC predictor is proposed to obtain average attribute values of candidate services in desired time slots easily and conveniently. The predictor extracts temporal-dynamic QoS values of candidate services into low-dimensional latent factor space and build a low-rank approximation. Based on the predicted service data, Stage II optimizes temporal-dynamic CMfg SC solutions by an improved many-objective evolutionary optimizer, GrEA-X. GrEA-X is reinforced with multiple variation operators and a modified mating pool selection procedure to enhance its exploration ability in a given time limit. Numerical experiments are then carried out to validate the efficacy of the proposed approach. Based on these results, we confirm the superior performance of our approach over other benchmarks.
Interest Evolution-driven Gated Neighborhood aggregation representation for dynamic recommendation in e-commerce

Recommender system as an effective method to reduce information overload has been widely used in the e-commerce field. Existing studies mainly capture semantic features by considering user-item interactions or behavioral history records, which ignores the sparsity of interactions and the drift of user preferences. To cope with these challenges, we introduce the recently popular Graph Neural Networks (GNN) and propose an Interest Evolution-driven Gated Neighborhood (IEGN) aggregation representation model which can capture accurate user representation and track the evolution of user interests. Specifically, in IEGN, we explicitly model the relational information between neighbor nodes by introducing the gated adaptive propagation mechanism. Then, a personalized time interval function is designed to track the evolution of user interests. In addition, a high-order convolutional pooling operation is used to capture the correlation among the short-term interaction sequence. The user preferences are predicted by the fusion of user dynamic preferences and short-term interaction features. Extensive experiments on Amazon and Alibaba datasets show that IEGN outperforms several state-of-the-art methods in recommendation tasks.
An underwater target recognition method based on improved YOLOv4 in complex marine environment

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Simplified-attention Enhanced Graph Convolutional Network for 3D human pose estimation

Optical motion capture systems have been used intensively to obtain human body poses. However, there still exist several problems. First is the dislocation problem caused by joints being too close together. The second is the joint lost problem. Restricted by severe self-occlusions, cameras may not capture the target joints. Given this observation, we investigate the high-level constraints over human poses to solve these two problems. In this work, we present a Simplified-attention Enhanced Graph Convolutional Network (SaEGC-Net) to extract both spatial and temporal features from monocular videos flexibly. The SaEGC-Net for 3D human pose estimation is U-shaped and involves the Cascaded Spatial-Temporal Graph Convolutional (CST-GC) blocks and the Simplified Spatial-Temporal Attention (SST-Att) blocks, allowing for drawing long-range dependencies between unconnected joints by graph topologies and attention mechanism, respectively. Specifically, the CST-GC block embeds two predefined graph structures into a convolutional network, incorporating discriminative features from distant joints. The proposed SST-Att block disregards redundant information by sharing part of the attention map, which is highly lightweight. It also considers dimension-expanded joint relationships to maintain the diversity of dependence. To evaluate the effectiveness of our method, we conduct extensive experiments on two datasets: Human3.6M and our own dataset FDU-Motion. Results demonstrate that our model achieves excellent performance and can competently handle the above two problems. Also, ablation studies show that our network's submodules can better exploit the motion information of the human body.
Leveraging user interaction signals and task state information in adaptively optimizing usefulness-oriented search sessions

Current information retrieval (IR) systems still face plenty of challenges when applied in addressing complex search tasks (CSTs) that trigger multi-round search iterations. Existing relevance-oriented optimization algorithms and metrics are limited in helping users find documents that are useful for completing CSTs, rather than merely topically relevant. To address this gap, our work aimed to characterize CSTs from a process-oriented perspective and develop a state-based adaptive approach to simulating and evaluating search path recommendations. Based on the data collected from 80 journalism search sessions, we first extracted intention-based task states from participants' annotations to characterize temporal their temporal cognitive changes in searching and validated the state labels with expert assessments. Built upon the state labels and state distribution patterns, we then developed a simulated adaptive search path recommendation approach, aiming to help users find needed useful documents quicker. The results demonstrate that 1) different types of CSTs can be differentiated based on their distinct state distribution and transition patterns; 2) After a small number of iterative training, our adaptive recommendation algorithm can consistently outperform the best possible performance from individual participants in terms of the useful-based search efficiency across all CSTs. Going beyond traditional static viewpoint of task facets and relevance-focused evaluation approach, our work characterizes CSTs with a dynamic perspective and develops a domain-specific adaptive search algorithm that can help users find useful documents quicker and learn from online search logs. Our findings can facilitate future exploration of adaptive search path adjustments for similar types of CSTs in other domains and work task scenarios.
Inductive Risk, Understanding, and Opaque Machine Learning Models

Abstract Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes.
A New Chaotic-Based RGB Image Encryption Technique Using a Nonlinear Rotational 16 × 16 DNA Playfair Matrix

Due to great interest in the secure storage and transmission of color images, the necessity for an efficient and robust RGB image encryption technique has grown. RGB image encryption ensures the confidentiality of color images during storage and transmission. In the literature, a large number of chaotic-based image encryption techniques have been proposed, but there is still a need for a robust, efficient and secure technique against different kinds of attacks. In this paper, a novel RGB image encryption technique is proposed for encrypting individual pixels of RGB images using chaotic systems and 16 rounds of DNA encoding, transpositions and substitutions. First, round keys are generated randomly using a logistic chaotic function. Then, these keys are used across different rounds to alter individual pixels using a nonlinear randomly generated 16×16 DNA Playfair matrix. Experimental results show the robustness of the proposed technique against most attacks while reducing the consumed time for encryption and decryption. The quantitative metrics show the ability of the proposed technique to maintain reference evaluation values while resisting statistical and differential attacks. The obtained horizontal, vertical and diagonal correlation is less than 0.01, and the NPCR and UACI are larger than 0.99 and 0.33, respectively. Finally, NIST analysis is presented to evaluate the randomness of the proposed technique.
Internet of Things resource monitoring through proactive fault prediction

Continuous monitoring of internet of things (IoT) resources generates a stream of monitoring data and a proactive model works based on the information extracted from this data. Such proactive models often suffer from lack of the availability of relevant data as well as the non-stationary distribution of data. In this work, an incremental deep neural network (DNN) is employed as a predictive model to predict the maintenance notification as instances of data are incrementally added to the existing pool of data. The presented model further addresses the existing challenges through a distribution preserving technique for effective sampling and early detection of the change in data distribution. The proposed model is tested over different benchmark datasets and it shows a fair amount of accuracy in predicting faults. It improves the accuracy at least by 5%–10% in comparison to the existing state-of-the-art methods and the recent fault prediction approaches such as deep small-world neural network (DSWNN), attention octave convolution residual networks (AOC-ResNet50), and distributed log mining using ensemble learning for fault prediction (DLME). The results further demonstrate that the approach is adaptive to imbalance and non-stationary distribution of the monitoring data.
LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks

Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications. However, a hitherto overlooked problem in SP-NNs is that the underlying silicon photonic devices suffer from intrinsic optical loss and crosstalk noise, the impact of which accumulates as the network scales up. Leveraging precise device-level models, this paper presents the first comprehensive and systematic optical loss and crosstalk modeling framework for SP-NNs. For an SP-NN case study with two hidden layers and 1380 tunable parameters, we show a catastrophic ~84% drop in inferencing accuracy due to optical loss and crosstalk noise.
Parameter analysis for sigmoid and hyperbolic transfer functions of fuzzy cognitive maps

Abstract Fuzzy cognitive maps (FCM) have recently gained ground in many engineering applications, mainly because they allow stakeholder engagement in reduced-form complex systems representation and modelling. They provide a pictorial form of systems, consisting of nodes (concepts) and node interconnections (weights), and perform system simulations for various input combinations. Due to their simplicity and quasi-quantitative nature, they can be easily used with and by non-experts. However, these features come with the price of ambiguity in output: recent literature indicates that changes in selected FCM parameters yield considerably different outcomes. Furthermore, it is not a priori known whether an FCM simulation would reach a fixed, unique final state (fixed point). There are cases where infinite, chaotic, or cyclic behaviour (non-convergence) hinders the inference process, and literature shows that the primary culprit lies in a parameter determining the steepness of the most common transfer functions, which determine the state vector of the system during FCM simulations. To address ambiguity in FCM outcomes, we propose a certain range for the value of this parameter, $${\uplambda }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> , which is dependent on the FCM layout, for the case of the log-sigmoid and hyperbolic tangent transfer functions. The analysis of this paper is illustrated through a novel software application, In-Cognitive , which allows non-experts to define the FCM layout via a Graphical User Interface and then perform FCM simulations given various inputs. The proposed methodology and developed software are validated against a real-world energy policy-related problem in Greece, drawn from the literature.
Adaptive multi-view multiple-means clustering via subspace reconstruction

Clustering is a notable research topic, but it is still challenging when facing massive multi-view data from different ways or multiple feature extractors. The crucial problem is how to promote cooperative learning between views via subspace reconstruction while preserving the underlying geometric structure of data. Moreover, most existing methods habitually utilize K-means to achieve the final results, which is not conducive to dealing with intricate non-convex patterns in multi-perspective data. Based on the above consideration, in this paper, we present a fresh multi-view clustering approach called Adaptive Multi-view Multiple-Means Clustering via Subspace Reconstruction(AM2CSR). AM2CSR aims to simultaneously capture compatible, complementary, geometric, and discrimination information among multiple views. Subsequently, a low-rank restriction is forced on the low-dimensional representation to reduce redundancy, and K-Multiple-Means(KMM) is adopted as the clustering technique to achieve satisfying results. Additionally, an effective iteration updating method with a convergence guarantee is applied to settle the optimization matter of AM2CSR. Extensive empirical experiments on eight benchmark datasets exhibit the superiority of AM2CSR.
Earthquake Prediction Using Deep Neural Networks

Reliable prediction of earthquakes has numerous societal and engineering benefits. In recent years, the exponentially rising volume of seismic data has led to the development of several automatic earthquake detection algorithms through machine learning approaches. In this study, we propose a fully functional and efficient earthquake detector cum forecaster based on deep neural networks of long-short-term memory (LSTM) units. The model captures inherent temporal characteristics of earthquake data. For illustration, we consider an earthquake catalog from the Himalaya and its neighboring regions. The proposed LSTM model shows satisfactory performance for small to medium-sized earthquakes. We also implement a baseline artificial neural network (ANN) model to perform a suitable comparison. It is observed that both ANN and LSTM models fail to produce desired result for large events.
Vehicle detection in foggy weather based on an enhanced YOLO method

Abstract Vehicle detection is the key to driverless technology. For safety, driverless technology requires extremely high accuracy and real-time for vehicle detection in different situations. In this paper, we study an enhanced YOLO -based algorithm for vehicle detection in foggy weather conditions. We add a dehazing module in the YOLO model for more information restoration, which is built by the multi-scale retinex with color restoration (MSRCR). And the enhanced model is trained with the augmentation data processed MSRCR for more stable performance. We evaluate our method in the public dataset, the results show the enhanced YOLO model has better performance than conventional YOLO in vehicle detection in foggy weather.
NDMiner

Graph Pattern Mining (GPM) algorithms mine structural patterns in graphs. The performance of GPM workloads is bottlenecked by control flow and memory stalls. This is because of data-dependent branches used in set intersection and difference operations that dominate the execution time.
Exploring more concentrated and consistent activation regions for cross-domain semantic segmentation

The cross-domain semantic segmentation in urban scenes is challenging due to the gap between the source and the target domains. Although the existing methods achieve remarkable performance, these methods have some limitations. First, the region activated by the backbone network is dispersed. The dispersed feature activation region is difficult to help the feature distribution alignment between the two domains. Second, these methods ignore the inconsistent problem between the activation regions of the two domains. Intuitively, the consistent feature activation region between the two domains is beneficial to align the feature distribution. In this paper, we present a novel approach for cross-domain semantic segmentation. Specifically, we propose a kernel-based channel attention (KCA) module and a mutual information domain adaptation (MIDA) module. The KCA module models the relationship between channels by using explicit non-linear kernel mappings for generating KCA features that aim to obtain more concentrated activation regions of the category. The MIDA module is a strategy of the KCA feature distribution alignment between the two domains. Specifically, the MIDA module contains a mutual information loss function and two adversarial learning loss functions. The three loss functions work together to obtain more consistent activation regions of the KCA feature between the two domains. Finally, the aligned KCA feature is fused with the feature of backbone to guide the semantic segmentation. Furthermore, self-supervised learning and image translation methods are leveraged to learn a better cross-domain semantic segmentation model. Extensive experiments demonstrate that the proposed approach achieves competitive performance against the state-of-the-art methods on challenging synthetic-to-real tasks.
Classification of Breast Cancer Histopathological Images using DensNet201

Diagnosing and classifying breast cancer tumors is a rather complex activity for pathologists due to the heterogeneous nature of the tumor cells. The wide use of artificial intelligence (AI) and the rise of Deep Learning (DL) have led to promising results in terms of breast histopathology images classification. The outcomes depend largely on two main factors, namely, the number and quality of images. BreaKhis dataset shows an imbalance in the image classes distribution, thus generating the performance degradation of the classifier model due to a biased classification towards the majority class. In this paper, a Deep Convolution Generative Adversial Network (DCGAN) is applied to give the number of images consistence in the minority (benign) class with that of the majority (malignant) class. Data augmentation is a technique used later to create more data from the limited ones. The DenseNet201 pre-trained model is chosen and used with the concatenation of features from various DensNet blocks. Instead of considering all the layers of the pre-trained network, the features are extracted from the lower layers of DensNet201, via a global average pooling (GAP). These features are passed to the softmax classifier to classify breast cancer. The model is evaluated using a two-class BreaKhis, provided at four magnification levels 40x, 100x, 200x, and 400x. The proposed method yielded test accuracies of 96%, 95%, 88%, and 92% respectively for each magnification factor. As indicated in the results, the proposed method based on data augmentation by DCGAN and feature concatenation using DenseNet201 pre-trained models could produce an efficient prediction for breast cancer image classification.
Application of Machine Learning for Sentiment Analysis of Movies Using IMDB Rating

In today’s environment, the customer behavior related to the product is important. The rapid growth of social media has made it easier to collect information about our products. Billions of customers share information about their products on social media pages. Manually managing large amounts of social media information is difficult. However, we have proposed a framework for analyzing customer sentiment using data mining and machine learning algorithms. The proposed framework consists of several steps to processes the information and try to find the good accuracy of the classification. The proposed framework is used for sentiment analysis of movie using IMDB rating. In this paper, the data preprocessing and feature extraction are done using data mining techniques. The result analysis shows that the proposed framework can be useful for better decision-making.