Reviewerly/exHarmony huggingface dataset:
Bayesian Approximation Filtering With False Data Attack on Network

Very often, a measurement is transmitted through network systems before it is available for filtering. The network systems, designed with several communication channels, are prone to cyber-attacks. The cyber-attack often injects false data to alter the original measurement. This article develops a modified Bayesian approximation filtering method for nonlinear filtering with measurements altered due to cyber-attack. The proposed development is within the scope of nonlinear Gaussian filtering. It considers the false data to have either additive or multiplicative effect over the original measurement. Subsequently, two modified measurement models are introduced to model the possibility of false data stochastically. Then, the traditional nonlinear Gaussian filtering method is redesigned for the modified measurement models to deal with the false data attack. The proposed modification is applicable to any of the existing nonlinear Gaussian filters, such as extended Kalman filter, unscented Kalman filter, cubature Kalman filter, and Gauss–Hermite filter. The simulation results show an enhanced estimation accuracy for the proposed modification over the traditional nonlinear Gaussian filtering in the presence of false data.
How Do Home Computer Users Browse the Web?

With the ubiquity of web tracking, information on how people navigate the internet is abundantly collected yet, due to its proprietary nature, rarely distributed. As a result, our understanding of user browsing primarily derives from small-scale studies conducted more than a decade ago. To provide an broader updated perspective, we analyze data from 257 participants who consented to have their home computer and browsing behavior monitored through the Security Behavior Observatory. Compared to previous work, we find a substantial increase in tabbed browsing and demonstrate the need to include tab information for accurate web measurements. Our results confirm that user browsing is highly centralized, with 50% of internet use spent on 1% of visited websites. However, we also find that users spend a disproportionate amount of time on low-visited websites, areas with a greater likelihood of containing risky content. We then identify the primary gateways to these sites and discuss implications for future research.
A Novel Real-time Anti-spam Framework

As one of the most pervasive current modes of communication, email needs to be fast and reliable. However, spammers and attackers use it as a primary channel to conduct illegal activities. Although many approaches have been developed and evaluated for spam detection, they do not provide sufficient accuracy. This deficiency results in significant economic losses for organizations. In this article, we first propose a framework for creating novel spam filters using Keras to combine a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) classification models. We then use this framework to introduce a specific solution applicable to realistic scenarios involving dynamic incoming email data in real-time. This solution takes the form of a real-time content-based spam classifier. We evaluate its performance concerning accuracy, precision, recall, false-positive, and false-negative rates. Our experimental results show that our approach can significantly outperform existing solutions for real-time spam detection.
Vehicle Detection in Aerial Imaginary using a Miniature CNN Architecture

Wide area surveillance systems (WAS), which have used since years, are becoming easily accessible and widely used with the developing camera and unmanned aerial vehicle (UAV) technologies. In addition to traditional image processing methods, deep learning networks are also frequently used in object detection in wide area images. Although vehicle detection methods in WAS provide high accuracy, they also involve significant computational complexity and require hardware with intensive processing power. In this study, a low complexity and high accuracy hybrid vehicle detection method based on convolutional neural network (CNN) with support of morphological operations is presented. Results on the COWC dataset show that the proposed architecture can detect vehicles with precision of 96% even though the number of neural network parameters is just above a hundred thousands. A comparison on number of parameters with respect to commonly used neural networks reveal that the proposed architecture uses significantly lower number of parameters. Performance evaluation and the comparison of the proposed method with YOLO-v4 show the success of the presented miniature CNN network.
Convolutional neural network implementations using Vitis AI

Recently, Xilinx has provided a field programmable gate array (FPGA)-based Vitis AI development environment, which is a deep learning framework to accel-erate AI operations and to seek a suitable neural network construction for a target application. We have implemented convolutional neural networks of three types onto the Vitis AI development environment and then we have evaluated their performance, power consumption, design man-hours, and so on. Results confirmed the Vitis AI benefits. Most notably, the FPGA platform power consumption is 4.96 times less than that of a GPU.
Computationally easy, spectrally good multipliers for congruential pseudorandom number generators

Abstract Congruential pseudorandom number generators rely on good multipliers , that is, integers that have good performance with respect to the spectral test. We provide lists of multipliers with a good lattice structure up to dimension eight and up to lag eight for generators with typical power‐of‐two moduli, analyzing in detail multipliers close to the square root of the modulus, whose product can be computed quickly.
Image Emotion Recognition Using Visual and Semantic Features Reflecting Emotional and Similar Objects

Visual sentiment analysis has a lot of applications, including image captioning, opinion mining, and advertisement; however, it is still a difficult problem and existing algorithms cannot produce satisfactory results. One of the difficulties in classifying images into emotions is that visual sentiments are evoked by different types of information - visual and semantic information where visual information includes colors or textures, and semantic information includes types of objects evoking emotions and/or their combinations. In contrast to the existing methods that use only visual information, this paper shows a novel algorithm for image emotion recognition that uses both information simultaneously. For semantic features, we introduce an object vector and a word vector. The object vector is created by an object detection method and reflects existing objects in an image. The word vector is created by transforming the names of detected objects through a word embedding model. This vector will be similar among objects that are semantically similar. These semantic features and a visual feature made by a fine-tuned convolutional neural network (CNN) are concatenated. We perform the classification by the concatenated feature vector. Extensive evaluation experiments using emotional image datasets show that our method achieves the best accuracy except for one dataset against other existing methods. The improvement in accuracy of our method from existing methods is 4.54% at the highest.
EDUBOT-A Chatbot For Education in Covid-19 Pandemic and VQAbot Comparison

A chatbot is software that is programmed and designed to simulate conversation with human users through Artificial Intelligence. During this pandemic, where students are confined to their homes with limited guidance from teachers, this chatbot provides a human-like interface that can solve academic queries of students, thus encouraging students to learn in an interesting manner. This paper demonstrates and presents the design and development of Student Chatbot software integrated with a user website that handles students' queries through defined intents. The paper covers the chatbot system with Recurrent Neural Network (RNN) for dealing language part, Convolutional Neural Network (CNN) to deal with the image part, Dialogflow, illustrating with precision the intent and entity representation and keyword matching techniques used by introducing an artificial brain into “Web-Based Bot”. The aim to have a balanced user interface that is easy to access and customized to all target users. Also two Visual Question Answering chatbots ie visualdialog.cloudcv and CloudCV are compared to find which performs better. It was found that chatbot serves accurate for education.
Key Encapsulation Mechanism with Tight Enhanced Security in the Multi-user Setting: Impossibility Result and Optimal Tightness

For Key Encapsulation Mechanism (KEM) deployed in a multi-user setting, an adversary may corrupt some users to learn their secret keys, and obtain some encapsulated keys due to careless key managements of users. To resist such attacks, we formalize Enhanced security against Chosen Plaintext/Ciphertext Attack (ECPA/ECCA), which ask the pseudorandomness of unrevealed encapsulated keys under uncorrupted users. This enhanced security for KEM serves well for the security of a class of Authenticated Key Exchange protocols built from KEM.
A Heterogeneous RISC-V Processor for Efficient DNN Application in Smart Sensing System

Extracting features from sensing data on edge devices is a challenging application for which deep neural networks (DNN) have shown promising results. Unfortunately, the general micro-controller-class processors which are widely used in sensing system fail to achieve real-time inference. Accelerating the compute-intensive DNN inference is, therefore, of utmost importance. As the physical limitation of sensing devices, the design of processor needs to meet the balanced performance metrics, including low power consumption, low latency, and flexible configuration. In this paper, we proposed a lightweight pipeline integrated deep learning architecture, which is compatible with open-source RISC-V instructions. The dataflow of DNN is organized by the very long instruction word (VLIW) pipeline. It combines with the proposed special intelligent enhanced instructions and the single instruction multiple data (SIMD) parallel processing unit. Experimental results show that total power consumption is about 411 mw and the power efficiency is about 320.7 GOPS/W.
Forensic analysis of anti‐forensic file‐wiping tools on Windows

This paper presents forensic analysis of anti-forensic file-wiping tools on the Windows platform. The goal is to identify and extract the evidence of the tools used to wipe files and the files wiped by them on the Windows operating system. To achieve this goal, we analyzed the changes made by these tools to metadata structures of Windows file systems during file wiping. We also analyzed Registry keys and .lnk files to collect the evidence. Our experiments used four file-wiping tools (SecureDelete v1.0, Secure Eraser v5.2, PC Shredder v1.1, and Blank and Secure v5.88) to wipe files on three Windows file systems (FAT32, exFAT and NTFS). The results suggest that FAT32 and exFAT file system directory structures and $MFT entries of NTFS file system can confirm the use of wiping tools, identify these tools and provide the remnants of the wiped files. Also, $LogFile and $UsnJrnl files of NTFS file system, and Windows Registry keys provide detailed evidence of wiping tools used and the files wiped by them. We also found that the contents of resident and non-resident alternate data streams, $LogFile and $UsnJrnl files, and Windows Registry keys are not wiped by these tools. Finally, this study makes many recommendations, highlights the limitations of the work and points out the future scope.
Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap

The domain shift problem is an important issue in automatic cell detection. A detection network trained with training data under a specific condition (source domain) may not work well in data under other conditions (target domain). We propose an unsupervised domain adaptation method for cell detection using the pseudo-cell-position heatmap, where a cell centroid becomes a peak with a Gaussian distribution in the map. In the prediction result for the target domain, even if a peak location is correct, the signal distribution around the peak often has a non-Gaussian shape. The pseudo-cell-position heatmap is re-generated using the peak positions in the predicted heatmap to have a clear Gaussian shape. Our method selects confident pseudo-cell-position heatmaps using a Bayesian network and adds them to the training data in the next iteration. The method can incrementally extend the domain from the source domain to the target domain in a semi-supervised manner. In the experiments using 8 combinations of domains, the proposed method outperformed the existing domain adaptation methods.
Visual-Textual Attentive Semantic Consistency for Medical Report Generation

Automatic report generation on medical radiographs have recently gained interest. However, identifying diseases as well as correctly predicting their corresponding sizes, locations and other medical description patterns, which is essential for generating high-quality reports, is challenging. Although previous methods focused on producing readable reports, how to accurately detect and describe findings that match with the query X-Ray has not been successfully addressed. In this paper, we propose a multi-modality semantic attention model to integrate visual features, predicted key finding embeddings, as well as clinical features, and progressively decode reports with visual-textual semantic consistency. First, multi-modality features are extracted and attended with the hidden states from the sentence de-coder, to encode enriched context vectors for better decoding a report. These modalities include regional visual features of scans, semantic word embeddings of the top-K findings predicted with high probabilities, and clinical features of indications. Second, the progressive report decoder consists of a sentence decoder and a word decoder, where we propose image-sentence matching and description accuracy losses to constrain the visual-textual semantic consistency. Extensive experiments on the public MIMIC-CXR and IU X-Ray datasets show that our model achieves consistent improvements over the state-of-the-art methods.
Voxel-Level Importance Maps for Interpretable Brain Age Estimation

Brain aging, and more specifically the difference between the chronological and the biological age of a person, may be a promising biomarker for identifying neurodegenerative diseases. For this purpose accurate prediction is important but the localisation of the areas that play a significant role in the prediction is also crucial, in order to gain clinicians' trust and reassurance about the performance of a prediction model. Most interpretability methods are focused on classification tasks and cannot be directly transferred to regression tasks. In this study, we focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We interpret its predictions by extracting importance maps, which discover the parts of the brain that are the most important for brain age. In order to do so, we assume that voxels that are not useful for the regression are resilient to noise addition. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We average the importance maps of the subjects and end up with a population-based importance map, which displays the regions of the brain that are influential for the task. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature, highlighting that the hippocampus and the ventricles are the most relevant regions for brain aging.
Cross-Temporal Snapshot Alignment for Dynamic Networks

In this paper, we study the task of cross-temporal snapshot alignment for dynamic networks. The goal of this task is to match equivalent nodes across temporal snapshots of a given dynamic network. Previous static network alignment methods ignore the non-stationary nature of networks, while existing dynamic counterparts focusing on two separate evolving networks lose sight of the problem of aligning two snapshots of the same dynamic network. To alleviate these issues, we propose a Cross-Temporal Snapshot Alignment model (CTSA), which maps nodes from different snapshots into the same semantic space and makes the equivalent nodes in the source and target snapshots to be aligned locate as closely as possible. Our CTSA model utilizes graph neural networks to embed nodes for each snapshot by aggregating the local structural information, and integrates the self-attention based encoders to model the dependencies in different snapshots over time. Additionally, to improve the alignment performance of the model, we contrive a novel positional embedding learning method, which takes into account both the ordering information of input representation sequences at each time step and the graph information of each network snapshot. Experimental results on real-world dynamic networks demonstrate that our model outperforms the state-of-the-art baselines.
Design of Online Handwritten Mathematical Expression Recognition System Based on Gated Recurrent Unit Recurrent Neural Network

Handwritten input is an important input method for mobile devices. After developing for a long time, the technology of handwritten recognition is becoming more and more mature. However, the task of handwritten mathematical expression recognition is more complicated than general handwriting text recognition task. The large number of mathematical and alphabetic symbols and the complicated two-dimensional spatial structure increase the difficulty of handwritten mathematical expression recognition. In this paper, a system of integrated recognition without using grammar constraints was proposed for solving the problem of online handwritten mathematical expression recognition. In the proposed system, the sequences of features in stroke level are extracted from handwritten mathematical expression in chronological order, then the sequences of features are processed and recognized by the pre-training Gated Recurrent Unit (GRU) model with Connectionist Temporal Classification (CTC). Compared with the traditional grammar-driven recognition system of handwriting mathematical expression, the proposed system by passes accurate segmentation of mathematical expression sequence and the manual work for defining grammars. Therefore, the proposed system can reduce the time complexity of recognition algorithm of handwritten mathematical expression and improve the efficiency of recognition.
A convolutional neural network-based model for predicting lime utilization ratio in the KR desulfurization process

In the presented work, desulfurization process parameters and the lime utilization ratio were correlated by data-driven technique, and a convolutional neural network was applied to predict the lime utilization ratio in the Kambara Reactor (KR) desulfurization process. The results show that compared with the support vector regression model and random forest model, the convolutional neural network model achieves the best performance with correlation coefficient value of 0.9964, mean absolute relative error of 0.01229 and root mean squared error of 0.3392%. The sensitivity analysis was carried out to investigate the influence of process parameters on the lime utilization ratio, which shows that the lime weight and the initial sulfur content have the significant effect on the lime utilization ratio. By analyzing the influence of the lime weight on the lime utilization ratio under the current desulfurization process parameters, it can be concluded that decreasing the lime weight from 3256 kg to 2332 kg can increase the lime utilization ratio by about 5%.
Towards Complete Scene and Regular Shape for Distortion Rectification by Curve-Aware Extrapolation

The wide-angle lens gains increasing attention since it can capture a wide field-of-view (FoV) scene. However, the obtained image is contaminated with radial distortion, making the scene not realistic. Previous distortion rectification methods rectify the image in a rectangle or invagination, failing to display the complete content and regular shape simultaneously. In this paper, we rethink the representation of rectification results and present a Rectification OutPainting (ROP) method, aiming to extrapolate the coherent semantics to the blank area and create a wider FoV beyond the original wide-angle lens. To address the specific challenges such as the variable painting region and curve boundary, a rectification module is designed to rectify the image with geometry supervision, and the extrapolated results are generated using a dual conditional expansion strategy. In terms of the spatially discounted correlation, a curve-aware correlation measurement is proposed to focus on the generated region to enforce the local consistency. To our knowledge, we are the first to tackle the challenging rectification via outpainting, and our curve-aware strategy can reach a rectification construction with complete content and regular shape. Extensive experiments well demonstrate the superiority of our ROP over other state-of-the-art solutions.
An Unsupervised TCN-based Outlier Detection for Time Series with Seasonality and Trend

Outlier detection is challenging for time series with seasonality and trend due to the presence of local outliers. In this paper, we propose an online unsupervised deep learning based algorithm for outlier detection utilizing temporal convolutional neural network (TCN). In the proposed algorithm, firstly, the TCN network is trained using a novel loss function designed to address time series with seasonality and trend. Secondly, instead of a single global threshold for outlier detection for the entire time series, we define a set of thresholds computed based on the output of the TCN network, leading to robust detection of local outliers caused by the seasonality and the trend. The performance of the proposed algorithm is evaluated using synthetic time series. The results show that given 99% Precision, the proposed algorithm achieves at least 70% Recall and 80% F-score, which is much better than 43% Recall and 60% F-score achieved by the statistics-based seasonal extreme studentized deviate test (S-ESD) algorithm. Our algorithm also demonstrates better performance than that of the TCN based detection algorithm trained by the conventional loss function.
Q-learning-based navigation for mobile robots in continuous and dynamic environments

Autonomous collision-free navigation in dynamic environments is essential in many mobile robot applications. Reinforcement learning has the potential to automate the control design process and it has been successfully applied to path planning and mobile robot navigation. Obtaining effective navigation strategies with reinforcement learning is, however, still very time-consuming, especially in complex continuous environments where the robot can easily get trapped. In this paper, we propose a novel state space definition that includes information about the robot's most recent action. The proposed state variables for the target and nearby obstacles are binary and denote presence (or absence) within a corresponding region in the robot's frame of reference. In addition, we propose two heuristic algorithms that provide the robot with basic prior knowledge about a promising action in each state, which reduces the initial time-consuming blind exploration and thereby significantly shortens training time. We train a robot using our improved Q-learning approach to navigate in continuous environments in a high-fidelity simulator. In a series of experiments, we demonstrate the effectiveness of the proposed approach in terms of training time and solution quality compared to state-of-the-art approaches.