Reviewerly/exHarmony huggingface dataset:
Data Visualizations for the Internet of Things Operational Dashboard

The paper deals with the issue of visualization of data collected through sensors in the Internet of Things system through research of user perception of a smart building dashboard. The Internet of Things (IoT) system consists of four components - sensors, connectivity, data processing, and a user interface. The data collected in the Internet of Things system as such do not have much value for the user. When processed meaningfully and displayed correctly, they are a powerful tool for user action. The research aims to investigate the possibilities of data visualization through the design of the Building Management System (BMS) dashboard and to investigate the user understanding of such visualizations. The study explores the user experience of the diagrams design in the context of combining a smart system with the regulation and control of energy consumption. The final proposals for the dashboard visual format and conclusions related to user’s preferences were formed according to the results of the research.
Detecting Text-Bullying on Twitter Using Machine Learning Algorithms

The increasing use of social media leads to a large number of users-created unstructured text data. Due to the prevalence of social media, cyberbullying has become the main problem. Cyberbullying may cause many serious consequences on a person’s life and community. That is because of various cultures, intellectual and educational backgrounds. The distinction between offensive language and hate speech is an essential Challenge in Detecting noxious textual content. In our work, we proposed a method in order to automatically analyze that tweet on Twitter within binary labels: Offensive and non-offensive. Utilizing the tweets data set, then we implement analyses granting N-grams as a feature and comparative between Term Frequency-Inverse Document Frequencies (TFIDF), and we achieve in, Using Decision Tree Classifier, Multinomial NB (Naïve Bayes), Linear SVC (Support Vector Classifier), and AdaBoost Classifier, K-neighbors Classifier, and Logistic Regression machine learning models. Then tuning a model fitting the best results we get in (TFIDF). We achieve the best accuracy, 0.924 in the Linear SVC classifier, best F1 Score 0.942% in the same classifier, the best Precision 0.975 in the AdaBoost classifier, and Best Recall 0.977 in Multinomial NB. In the other model (Count-Vectorizer), we achieve Best accuracy 0.925 in Logistic Regression classifier, Best F1 Score 0.942% in the same classifier, best Precision 0.976 in AdaBoost classifier, and Best Recall 0.941 in Multinomial NB.
Time Insertion Functions

Time insertion functions as a tool to guarantee security of processes with respect to timing attacks are discussed and studied. We work with a security property called opacity and we investigate how it can be enforced by such functions. The time insertion function can alter time behaviour of original system by inserting some time delays to guarantee its security. We investigate conditions under which such functions do exist and also some of their properties.
A new ordered pooling network based on multi-scale fusion feature for medical image recognition

Aiming at the imbalance problem of Computed Tomography(CT) images diagnosis model under artificial intelligence assisted method, a new ordered pooling network based on multi-scale fusion feature for medical image recognition is proposed. Based on traditional CNN(Convolutional Neural Networks) architecture, the new overall neural network mainly consists of three parts: Multi-scale feature fusion layer, deep feature ordered pooling layer and feature recognition layer. Multi-scale feature fusion layer upsamples and merges different scale CNN features to achieve multi-scale deep feature fusion. Deep feature ordered pooling layer regards the fusion spatial deep convolution features as a continuous Weisfeiler–Lehman(WL) color, which makes a meaningful ordering to weaken the excessive spatial information feature , and improves the generalization capability. Feature recognition layer combines final sorted deep features with the softmax layer to realize CT images recognition for disease diagnosis. The experimental results show that the proposed method can extract the multi-dimensional features of the image better and effectively improve the accuracy of the approximate image recognition.
Design and Study of Circuits Using Reversible Logic

In VLSI systems design, reversible Logic has gained importance for Reducing power. This paper focuses on designing of sequential circuits and reducing the number of constant inputs - CI and the garbage outputs - GO. Basic reversible gates are used to build sequential circuits. D Flip flop, Latch and RAM cell are developed. All circuits are VHDL coded on Xilinx tool, simulated and verified. Reduction in CI of more than 33 percent and GO of more than 80 percent is achieved. This is achieved by properly reusing the outputs for configuring the constant input of other gates.
Multiple color medical images zero-watermark scheme based on quaternion generalized Fourier descriptor and QR code

Most of the existing zero-watermark algorithms for medical images are only applicable to grayscale images, and when they protect the copyright of a large number of medical images, the repetitive operation will bring a lot of time and storage costs. Therefore, to solve the above problems, this paper proposes a zero-watermark scheme for multiple color medical images based on quaternion generalized Fourier descriptor(QGFD) and QR code. QGFD is used to calculate the feature invariants of each color medical image, then the representative features of each image are selected to form the feature matrix, and binarize it to get the binary feature image, finally, the zero-watermark image can be obtained by performing an XOR operation on the scrambled copyright image and feature image. The experimental results show that the proposed scheme can not only effectively resist common attacks, geometric attacks and joint attacks, but also can effectively reduce the time and storage cost of copyright protection for a large number of medical images.
Improved Adaptive Threshold Segmentation of Ultrasound Medical Images

research-article Share on Improved Adaptive Threshold Segmentation of Ultrasound Medical Images Authors: Sana Khalid Beijing University of Chemical Technology, China Beijing University of Chemical Technology, ChinaView Profile , Haijiang Zhu Beijing University of Chemical Technology, China Beijing University of Chemical Technology, ChinaView Profile , Tengfei Yang Beijing University of Chemical Technology, China Beijing University of Chemical Technology, ChinaView Profile Authors Info & Claims IMIP '21: 2021 3rd International Conference on Intelligent Medicine and Image ProcessingApril 2021 Pages 1–6https://doi.org/10.1145/3468945.3468946Online:27 September 2021Publication History 0citation56DownloadsMetricsTotal Citations0Total Downloads56Last 12 Months56Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Unsupervised Anomaly Detection for Financial Auditing with Model-Agnostic Explanations

Explainable Artificial Intelligence (AI) has emerged to be a key component for Black-Box Machine Learning (ML) approaches in domains with a high demand for transparency. Besides medical expert systems, which inherently need to be interpretable, transparent, and comprehensible as they deal with life-changing decision tasks, other application domains like financial auditing require trust in ML as well. The European General Data Protection Regulation (GDPR) also applies to such highly regulated areas where an auditor evaluates financial transactions and statements of a business. In this paper we propose an ML architecture that shall help financial auditors by transparently detecting anomalous datapoints in the absence of ground truth. While most of the time Anomaly Detection (AD) is performed in a supervised manner, where model-agnostic explainers can be easily applied, unsupervised AD is hardly comprehensible especially across different algorithms. In this work we investigate how to dissolve this: We describe an integrated architecture for unsupervised AD that identifies outliers at different levels of granularity using an ensemble of independent algorithms. Furthermore, we show how model-agnostic explanations can be generated for such an ensemble using supervised approximation and Local Interpretable Model-Agnostic Explanations (LIME). Additionally, we propose techniques for explanation-post-processing that allow explanations to be selective, receiver-dependent, and easily understandable. In a nutshell, our architecture paves the way for model-agnostic explainability for the task of unsupervised AD. It can further be transferred smoothly to other unsupervised ML problems like clustering problems.
QualFace: Adapting Deep Learning Face Recognition for ID and Travel Documents with Quality Assessment

Modern face recognition biometrics widely rely on deep neural networks that are usually trained on large collections of wild face images of celebrities. This choice of the data is related with its public availability in a situation when existing ID document compliant face image datasets (usually stored by national institutions) are hardly accessible due to continuously increasing privacy restrictions. However this may lead to a leak in performance in systems developed specifically for ID document compliant images. In this work we proposed a novel face recognition approach for mitigating that problem. To adapt deep face recognition network for document security purposes, we propose to regularise the training process with specific sample mining strategy which penalises the samples by their estimated quality, where the quality metric is proposed by our work and is related to the specific case of face images for ID documents. We perform extensive experiments and demonstrate the efficiency of proposed approach for ID document compliant face images.
Individual Verifiability and Revoting in the Estonian Internet Voting System

Individual verifiability remains one of the main practical challenges in e-voting systems and, despite the central importance of this property, countries that sought to offer it to their voters faced repeated security problems. In this note, we revisit this property in the context of the IVXV version of the Estonian Internet voting system, which has been deployed for the Estonian municipal elections of 2017 and for the Estonian and European parliamentary elections of 2019. We show that a compromised voter device can defeat the individual verifiability mechanism of the current Estonian voting system. Our attack takes advantage of the revoting option that is available in the Estonian voting system, and only requires to compromise the voting client application: it does not require compromising the mobile device verification app, or any server side component. This issue, which has been confirmed by the IVXV system designers, adds to an increasingly long list of failures to offer genuine individual verifiability in Internet voting systems deployed for government elections. It prompts for reinforced caution regarding the evidences that are offered regarding the verifiability of voting systems, especially when the verifiability is a property on which is based the decision to deploy a voting system in government elections.
Exploring Emotional Reappraisal and Repression through Acoustic Mood Self-Tracking

Self-tracking technologies are a dominant tool employed in Human-computer Interaction (HCI) in the realm of mental health research. The majority of these tools serve as mechanisms for self-reflection in individuals with affective disorders and are often used to inform medical treatment provided by caregivers. Mood tracking, in particular, has the potential to be expanded into other explorations beyond mere trend-lines of affective states; behaviors such as emotion reappraisal and repression could be measured and influenced as a means of improving user mental health outcomes. As self-tracking technologies face numerous challenges in the motivation and engagement of persons with mental illnesses, this position paper forwards an alternative approach demonstrated through “TENOR,” an acoustic mood-tracking application designed to increase user involvement through music creation and emotional recall.
An Augmented Reality Guided Capture Platform for Structured and Consistent Product Reference Imagery

Within e-commerce, vendors require high quality, consistent reference imagery to showcase products. Capturing correct images is difficult when photographic intent has to be conveyed through written instructions, as lack of real-time guidance leads to subjectivity in the interpretation of directions. Hiring professional photographers to conform to specific aesthetics can be expensive. The ubiquity of mobile devices and rapid improvements in augmented reality have facilitated the creation of experiences with in-situ guidance. We present Guided Capture, a mobile application utilizing augmented reality to enable structured capture of consistent user-generated content for reference imagery with real-time feedback. Results from user studies have shown that our system creates a streamlined feedback loop to reduce possible user error, scalable across large groups of people.
Feature Extraction for Image Matching in Wat Phra Chetuphon Wimonmangklararam Balcony Painting with SIFT Algorithms

The novel algorithms for feature extraction using in image matching for balcony painting in Wat Phra Chetuphon Wimonmangklararam was introduced in this research. The proposed techniques were carried out with the help of key points provided by SIFT algorithms. The suggested techniques were aimed at retrieving information from within an image. The Content-Based Image Retrieval (CBIR) method was used. In the matching procedure, the created key point and descriptors were used as input. The algorithms were tested in the Wat Phra Chetuphon Wimonmangklararam corridor. Traditional algorithms were compared to our proposed technique. In comparison to the previous approach, the accuracy obtained from the experiments showed that the suggested framework could efficiently provide the user with the correct descriptions. The proposed algorithms' results can be utilized to match the balcony painting at this temple, with an average accuracy of 95.98 percent.
Video steganography algorithm based on the relative relationship between DWT coefficients

H.264 is the commonly used standard for video compression, and is a lossy compression standard. This paper proposes a video steganography algorithm resisting to H.264 compression based on the relative relationship of DWT (Discrete Wavelet Transform) coefficients, which reduces the changes to individual coefficients. Firstly, the change of the low-frequency DWT coefficient difference between adjacent frames of video before and after H.264 compression is analyzed. Then, an appropriate coefficient difference threshold is selected according to the analysis result. Finally, the secret message bits are embedded by modulating the difference between the low-frequency DWT coefficients of adjacent frames. The experimental results show that the proposed algorithm can maintain high quality of the stego video, and resist to the H.264 compression well.
Quantum Newton’s Method for Solving the System of Nonlinear Equations

While quantum computing provides an exponential advantage in solving the system of linear equations, there is little work to solve the system of nonlinear equations with quantum computing. We propose quantum Newton’s method (QNM) for solving [Formula: see text]-dimensional system of nonlinear equations based on Newton’s method. In QNM, we solve the system of linear equations in each iteration of Newton’s method with quantum linear system solver. We use a specific quantum data structure and [Formula: see text] tomography with sample error [Formula: see text] to implement the classical-quantum data conversion process between the two iterations of QNM, thereby constructing the whole process of QNM. The complexity of QNM in each iteration is [Formula: see text]. Through numerical simulation, we find that when [Formula: see text], QNM is still effective, so the complexity of QNM is sublinear with [Formula: see text], which provides quantum advantage compared with the optimal classical algorithm.
STRUDEL: Self-training with Uncertainty Dependent Label Refinement Across Domains

We propose an unsupervised domain adaptation (UDA) approach for white matter hyperintensity (WMH) segmentation, which uses Self-TRaining with Uncertainty DEpendent Label refinement (STRUDEL). Self-training has recently been introduced as a highly effective method for UDA, which is based on self-generated pseudo labels. However, pseudo labels can be very noisy and therefore deteriorate model performance. We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty. STRUDEL is further improved by incorporating the segmentation output of an existing method in the pseudo label generation that showed high robustness for WMH segmentation. In our experiments, we evaluate STRUDEL with a standard U-Net and a modified network with a higher receptive field. Our results on WMH segmentation across datasets demonstrate the significant improvement of STRUDEL with respect to standard self-training.
Self-learning for Received Signal Strength Map Reconstruction with Neural Architecture Search

In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of sparse single-snapshot input measurements, in the case where data-augmentation by side deterministic simulations cannot be performed. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given (RSS) map. These ground-truth measurements along with the predictions of the model over a set of randomly chosen points are then used to train a second NN model having the same architecture. Experimental results show that signal predictions of this second model outperforms non-learning based interpolation state-of-the-art techniques and NN models with no architecture search on five large-scale maps of RSS measurements.
EVARS-GPR: EVent-Triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data

Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external influences are challenging. Therefore, predictions of a previously learned forecasting model might not be useful anymore. In this paper, we present EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (EVARS-GPR), a novel online algorithm that is able to handle sudden shifts in the target variable scale of seasonal data. For this purpose, EVARS-GPR combines online change point detection with a refitting of the prediction model using data augmentation for samples prior to a change point. Our experiments on simulated data show that EVARS-GPR is applicable for a wide range of output scale changes. EVARS-GPR has on average a 20.8% lower RMSE on different real-world datasets compared to methods with a similar computational resource consumption. Furthermore, we show that our algorithm leads to a six-fold reduction of the averaged runtime in relation to all comparison partners with a periodical refitting strategy. In summary, we present a computationally efficient online forecasting algorithm for seasonal time series with changes of the target variable scale and demonstrate its functionality on simulated as well as real-world data. All code is publicly available on GitHub: https://github.com/grimmlab/evars-gpr.
Design and Development of Sindhi Text Based CAPTCHAs for Regional Websites

Bots are created to use the resources maliciously on World Wide Web. The misuse of the resources could be prevented by employing CAPTCHAs. Several types of CAPTCHAs are being used against the bots (robot) attacks but text-based CAPTCHA type is the most popular being very secured and easy to use. Latin language based text CAPTCHAs can be found ubiquitously on Internet but English text based CAPTCHAs are already decoded by many researchers. Thus, a novel Sindhi language based text CAPTCHA was proposed for regional websites where Arabic style script was utilized. This scheme offered two fold benefits: first, the proposed scheme could easily be understood by averagely literate person; second, this scheme paved a way for Arabic style OCR developers to understand Sindhi language specific features and facilitate Sindhi text recognition in future. A survey was also conducted to analyze the usability and strength of proposed CAPTCHA.
Estimating the Number of Latent Topics Through a Combination of Methods

Estimating the number of latent topics is a prerequisite to using topic modeling algorithms such as LDA. A number of methods for this purpose are compared, including the de facto approach based on perplexity. Results from synthetic data show that a combination of methods yields better estimates than any single method, and in particular it provides an indicator of the reliability of the estimated number of topics, something no single method can do. In this paper, we extend the findings based on synthetic data over real data and introduce a technique, “Cosine inter-intra topics”, to assess the validity of the ground truth of real data. Moreover, we measure the reliability of estimated number of topics through the variance of methods. Results corroborate the ones from synthetic data, suggesting the approach generalizes to real text corpus.
An optimal control approach to particle filtering

We present a novel particle filtering framework for the continuous-time dynamical systems with continuous-time measurements. Our approach is based on the duality between estimation and optimal control, which allows for reformulating the estimation problem over a fixed time window into an optimal control problem. The resulting optimal control problem has a cost function that depends on the measurements, and the closed-loop dynamics under optimal control coincides with the posterior distribution over the trajectories for the corresponding estimation problem. By recursively solving these optimal control problems approximately as new measurements become available, we obtain an optimal control based particle filtering algorithm. Our algorithm uses path integrals to compute the weights of the particles and is thus termed the path integrals particle filter (PIPF). A distinguishing feature of the proposed method is that it uses the measurements over a finite-length time window instead of a single measurement for the estimation at each time step, resembling the batch methods of filtering, and improving fault tolerance. The efficacy of our algorithm is illustrated with several numerical examples.