Reviewerly/exHarmony huggingface dataset:
Control-Implications-Based Side-Channel Monitoring for Embedded Systems

Monitoring of embedded-system anomalies which influence the regulation of physical-world processes is examined. Specifically, detection of such anomalies using remote measurements of the controlled process itself - which we refer to as a controlled-process side channel - is studied. A physics-guided anomaly detection algorithm is proposed, which decomposes the measurement signal into patterned and ambient responses, and exploits sparsity in both components. Detailed simulations of two controlled-process side channels on an autonomous underwater vehicle, namely a motion guidance system and a sonar transmitter, are undertaken to assess the methodology. Our preliminary findings suggest that controlled process side channels may be sufficient for monitoring anomalies, when traditional side channel signals are difficult to measure.
People Counting System Using OpenCV Algorithms and Edge Computing for Safety Management

The necessity of ensuring people's safety in urban conditions during gathering in a certain indoor or outdoor area, as well as during the operation of technical equipment, requires measurement of their number, weights, the distance between them, density and many other parameters. The collection of those parameters and analysis allows you to ensure the optimal numerical value of people per area and not exceed the critical peak value, ensuring the safety of people's health and life. However, automating those processes requires stable network bandwidth and enormous computing resources for data transferring to the data center and its processing, as well as applying artificial intelligence algorithms to the data and sending the results back to the end-user. In this paper, to avoid the disadvantages of cloud computing, we consider Raspberry Pi with a camera module as an edge device to develop an automated people-counting system based on using OpenCV algorithms and Edge Computing for Safety Management. The proposed system can be used for indoor or outdoor areas, during scaffolding works and lift's operation for controlling an optimal number of people. As a result of the application the edge computing, network bandwidth and computing resources of the data center have been optimized.
Machine Learning Methods for Phishing Attacks: Survey

The basis of cybersecurity is an understanding of the mechanisms of social engineering. This increases the effectiveness in combating this type of manipulation. One of them is phishing. Phishing attacks actively exploit the human factor to collect credentials or distribute malware. Machine learning has been successfully used to detect and prevent phishing. The purpose of this study is to review machine learning methods and the results of previous studies to prevent phishing attacks. Further studies will be planned based on this work.
Feasibility Analysis of the Prediction of High Power Electromagnetic Environment from Antenna Current Response

How to predict the characteristics of high power electromagnetic (HPEM) environment surrounding the electronic victims is of vital importance for taking effective activities to be protected. Hence, this article tried to utilize the measured antenna response data excited by HPEM to predict the electric field wave of HPEM according to the combination of the system identification model and artificial intelligence method. The train and test data are generated by MoM method, which takes simple line antenna as the targeted antenna and double-exponential waves with multiple parameters as HPEM environments respectively. The classical system identi-fication method output-error (OE) model and nonlinear autoregressive model with external input (NARX) neural network (NARX-NN) are introduced to establish the inverse model of the trans-fer function from current response to HPEM. The OE prediction accuracy 68% and NARX-NN's 72.4% demonstrate that it is feasible to predict HPEM environment by using antenna response test data.
User Authentication using Behavioural Biometrics with Machine Learning

People relying on personal devices for personal information is the new normal. As the ecosystem of such devices is more connected, they have developed and become highly dynamic and convoluted. Current user authentication solutions in action like Password and PIN might not be reliable enough for user data security. This paper examines how user behavioural recognition can mitigate user authentication challenges. We have used two open-source datasets of user behavioural biometrics (touch dynamics and periocular traits). Later, fusion was performed at the feature level and incorporated distance measure techniques (Euclidean, Manhattan, Cosine, Minkowski) to determine whether the user was genuine or an imposter. We have also performed multi-data processing methods such as Principal Component Analysis (PCA) & Linear Discriminant Analysis (LDA). Our proposed model achieved recognition accuracy of 94.30% using the median statistical index, Manhattan distance measure, LDA feature reduction, and KNN-supervised machine learning technique.
Learn How to Prune Pixels for Multi-View Neural Image-Based Synthesis

Image-based rendering techniques stand at the core of an immersive experience for the user, as they generate novel views given a set of multiple input images. Since they have shown good performance in terms of objective and subjective quality, the research community devotes great effort to their improvement. However, the large volume of data necessary to render at the receiver's side hinders applications in limited bandwidth environments or prevents their employment in real-time applications. We present LeHoPP, a method for input pixel pruning, where we examine the importance of each input pixel concerning the rendered view, and we avoid the use of irrelevant pixels. Even without retraining the image-based rendering network, our approach shows a good tradeoff between synthesis quality and pixel rate. When tested in the general neural rendering framework, compared to other pruning baselines, LeHoPP gains between 0.9 dB and 3.6 dB on average.
Natural Face Anonymization via Latent Space Layers Swapping

Machine learning is widely recognized as a key driver of technological progress. Artificial Intelligence (AI) applications that interact with humans require access to vast quantities of human image data. However, the use of large, real-world image datasets containing faces raises serious concerns about privacy. In this paper, we examine the issue of anonymizing image datasets that include faces. Our approach modifies the facial features that contribute to personal identification, resulting in an altered facial appearance that conceals the person's identity. This is achieved without compromising other visual features such as posture, facial expression, and hairstyle while maintaining a natural-looking appearance. Finally, Our method offers adjustable levels of privacy, computationally efficient, and has demonstrated superior performance compared to existing methods.
Is Algorithmic Personalized Pricing Unjust?

This contribution addresses the issue of justice in algorithmic personalized pricing by considering complex social interactions and determinants necessary to arrive at a conscientious ethical evaluation—hence, arriving at the conclusion that the burden of proof for the ethicality of algorithmic personalized pricing requires a reversal. Instead of assessing algorithmic personalized pricing as prima facie ethically neutral, I propose that there exist ample reasons to doubt this. Simply put, algorithmic personalized pricing is likely forcing consumers to engage in price-sensitive consumer behavior and share the respective data used to infer the consumers’ willingness-to-pay. However, data suggests that it is the most vulnerable that typically lack the resources to engage in long-term planning as well as careful and time-intensive price-sensitive consumption. In addition, algorithmic personalized pricing transfers some of the competitive action away from businesses and onto consumers if price-sensitivity is assessed in relative terms between customers. Such practice is likely to exacerbate inequalities and put those without the resources to engage in such competition at a further disadvantage.
Disentangled Feature Distillation for Light Field Super-Resolution with Degradations

Light field (LF) super-resolution has achieved remarkable results with the assumption of only downsampling. However, real-world LF scenes contain multiple degradation effects, which makes it difficult for existing methods to deal with hybrid distortions. In this paper, we propose a disentangled feature distillation framework for LF super-resolution with degradations. To reduce the learning difficulty, we propose a feature disentanglement mechanism to split the mixed reconstruction for both super-resolution and denoising into two single task learning processes. We also propose a feature enhancement strategy via knowledge distillation to transfer prior feature of each single reconstruction to our task of mixed reconstruction. Finally, the separate restored representations are fused to reconstruct a clean high-resolution LF. Experiments demonstrate the superior performance of our framework for different scale factors and noise levels. Additionally, our approach can also obtain excellent performance for joint super-resolution and deblurring, showing its gencralization for practical LF super-resolution applications.
High Accurate Cast Shadow Detection of Moving Vehicles in Low Illumination Environment

The shadow detection and removal of moving vehicles is a difficult task for the applications of computer vision i.e., vehicle detection, vehicle tracking, vehicle classification, vehicle counting, etc. because the motion of moving vehicles and their cast shadow is the same. Many researchers have published their work on shadow detection of moving vehicles on standard datasets or on the morning/afternoon duration dataset but less research published on shadow detection in low illumination and it is a very tricky task for various applications of computer vision. In this paper, the intensity ratio-based shadow detection method has been proposed along with negative transformation, a gamma correction image preprocessing technique on a self-generated dataset at the Vadodara-Mumbai highway in the evening time. The proposed method has been implemented on 500+ images of a self-generated dataset and the outcomes have been evaluated by shadow detection analysis matrices i.e., shadow detection rate, specificity (true negative rate), precision, F-measure, etc. in reference to ground truth which is obtained using ImageJ software. The average shadow detection rate is 80.18% and the average specificity is 93.96%. The proposed method is simple and robust against the shadow detection of moving vehicles in low illumination.
Classification and Detection of Banknotes using Machine Learning

On the basis of the look, people can easily differentiate banknotes and coin denominations. The coin currencies can be identified visually impaired people based on touch, but the note currencies cannot be identified easily as it has similar texture and appearance, it can be challenging for visually challenged people to distinguish the currencies. Demonetization has boosted the availability of fake cash in recent years. People face difficulty in distinguishing between real and fake banknotes because they are unaware of the security elements utilized in modern currencies. Additionally, these fake cash mislead persons who don't have proper vision. So, it becomes important to identify the denominations and detect fake and real banknotes in-order to avoid the problems caused due to these currencies or banknotes. This issue highlights the requirement for an accurate banknote identification model. By spotting the counterfeit currency, inflation and currency devaluation can be stopped. The suggested model aims to identify the denomination and categorize if a money note is real or fraudulent. The banknote denomination is determined using the machine learning algorithms like Support vector machines (SVM), decision trees classification (DTC), random forest classification (RFC), and K-NN (K-Nearest Neighbors) and the currency's authenticity is determined using a deep learning method called AlexNet.
From Bounded to Unbounded: Privacy Amplification via Shuffling with Dummies

In recent years, the shuffling model has been garnering attention in the realm of differential privacy (DP). This study focuses on the fact that the shuffling model follows bounded DP rather than unbounded DP. This characteristic causes a privacy issue in which participation itself is not protected. To address this issue, we propose a framework, called unbounded shuffling, which follows unbounded DP in addition to bounded DP under the trust assumption of the shuffling model. The main difference from the conventional shuffling model is the inclusion of dummies, which some users add to pose that perturbed records are sent by other users. We also analyze the privacy and utility of our proposed framework. The analysis shows that our framework achieves almost the same utility and privacy as that of the traditional shuffling model while guaranteeing unbounded DP. Additionally, we apply the technique of individual privacy accounting, which is built solely on unbounded DP, to stochastic gradient descent (SGD) using our framework. This approach approximately halves the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> of a baseline.
Mining Sequential Patterns with Timelines from Digital Health Data

Descriptive pattern mining is a useful tool in expansion of knowledge. One such area of descriptive pattern mining is that of sequential pattern mining. In sequential mining, items maintain an order of occurrence. In this paper, we present a digital health solution for mining sequential patterns from real-life healthcare data. Specifically, it is a non-trivial extension to the sequential mining algorithm PrefixSpan. Through an association of time, we find improved relevance of a pattern overall significance relative to a focal point. This is particularly useful in the medical domain, where significance of information varies depending on the time of its occurrence. For example, consider a time of being diagnosed with a disease. A condition occurring 16 years prior to the time of diagnosis provides less information than the same condition occurring 2 years prior to diagnosis. In traditional sequential mining, both conditions would equally contribute to support, despite their unequal value in describing causes of diagnosis. To resolve such issue, we provide an inclusion of two additional user-defined parameters to incorporate time within itemsets—namely, a timeline interval (describing the length of an interval, of which itemsets of different intervals are treated separately by their difference in time to a focal point), as well as a maximal window (denoting the maximal interval that disallows for any greater time difference than such interval). With timelines associated to itemsets, relevance of itemsets have improved interpretability for domain experts.
Anomaly Detection in Heterogeneous Time Series Data for Server-Monitoring Tasks

When conducting anomaly detection on server monitoring data, it is important to consider the heterogeneity of the data, which is characterized by the diverse and irregular nature of events. The event values can vary widely, encompassing both continuous and discrete values, and there may be a multitude of randomly occurring events. However, many commonly used anomaly detection methods tend to overlook or discard this heterogeneous data, resulting in a significant loss of valuable information. As such, we propose a novel method, called Heterogeneous Time Series Anomaly Detection (HTSAD), to overcome this difficulty. The approach introduces event gates in the Long Short-Term Memory (LSTM) model while using unsupervised learning to overcome the challenges mentioned above. The results of our experiments on real-world datasets show that HTSAD could achieve an f-score of 0.958, which demonstrates the effectiveness of our approach in detecting anomalies in heterogeneous time series data.
Reinforcement Learning for Orientation on the Lie Algebra

In this paper, we propose a novel framework for Reinforcement Learning on Lie algebra and show how it applies to learning the orientation of the robot's end effector in the task space. The proposed framework is suitable for model-free Reinforcement learning algorithms. Our research is motivated by the fact that in robotics, non-Euclidean data (e.g., orientation) is common in learning manipulation skills, yet neglecting the geometric meaning of such data affects learning performance and accuracy. In particular, our innovation is to apply policy parameterization and learning on the Lie algebra, then map back the learned actions to the hemisphere manifold. The proposed framework opens the door for some model-free Reinforcement learning algorithms designed for Euclidean space to learn non-Euclidean data without change. According to the best of our knowledge, this research work is the first effort in applying a policy parameterization in the context of Reinforcement learning on the Lie algebra of the hemisphere manifold. The results of our experiments provide evidence to support our hypothesis that learning orientation on the Lie algebra is more precise and leads to a superior solution than learning through the normalization of non-Euclidean data.
The Race to Robustness: Exploiting Fragile Models for Urban Camouflage and the Imperative for Machine Learning Security

Adversarial Machine Learning (AML) represents the ability to disrupt Machine Learning (ML) algorithms through a range of methods that broadly exploit the architecture of deep learning optimisation. This paper presents Distributed Adversarial Regions (DAR), a novel method that implements distributed instantiations of computer vision-based AML attack methods that may be used to disguise objects from image recognition in both white and black box settings. We consider the context of object detection models used in urban environments, and benchmark the MobileNetV2, NasNetMobile and DenseNet169 models against a subset of relevant images from the ImageNet dataset. We evaluate optimal parameters (size, number and perturbation method), and compare to state-of-the-art AML techniques that perturb the entire image. We find that DARs can cause a reduction in confidence of 40.4% on average, but with the benefit of not requiring the entire image, or the focal object, to be perturbed. The DAR method is a deliberately simple approach where the intention is to highlight how an adversary with very little skill could attack models that may already be productionised, and to emphasise the fragility of foundational object detection models. We present this as a contribution to the field of ML security as well as AML. This paper contributes a novel adversarial method, an original comparison between DARs and other AML methods, and frames it in a new context - that of urban camouflage and the necessity for ML security and model robustness.
Proceedings 39th International Conference on Logic Programming

interpretation allows constructing sound static analysis tools by safely approximating program semantics.Frameworks for abstract interpretation typically provide an implementation of a specialized iteration strategy to compute an abstract fixpoint, as well as a number of abstract domains in order to approximate different program properties.However, the design and implementation of additional domains, as well as their combinations, is eventually necessary to successfully prove arbitrary program properties.We propose a rule-based methodology for rapid design and prototyping of new domains and combining existing ones, with a focus on the analysis of logic programs.We provide several examples for domains combining numerical properties and data types and apply them to proving complex program properties.
Research on Domain Knowledge Representation Techniques

Knowledge is the fruit of human's knowledge of the objective world in practice and the crystallization of wisdom. A knowledge base makes a knowledge-based system (or expert system) intelligent by structuring and sorting out knowledge in a specific field and storing, organizing, managing and using it in a computer using a scientific knowledge representation. Based on the research of knowledge base construction in securities industry, this paper first summarizes and explains several representative knowledge representation models. Then, it summarizes the application scenarios of common knowledge representation techniques in the fields of product design, robot control, and natural language processing. In addition, based on the investigation of the knowledge characteristics of the securities industry, a knowledge representation model of the securities industry based on 5W1lH is proposed to organize and manage the multimodal information resources and provide high-value information for user needs, while the knowledge representation technology of the mechanical industry based on hypergraph embedding is examined and the specific processes and application scenarios are summarized.
Classifier Guided Domain Adaptation for VR Facial Expression Tracking

Immersive Virtual Reality (VR) experience from headsets requires high-quality virtual facial expression animation. Existing approaches for facial expression animation require ground truth labels for facial expression prediction. However, current headset devices can only capture partially occluded facial images from the wearer due to the placement of the embedded cameras. To reduce the cost and effort of human annotation to collect ground truth labels of facial action units, synthetic data are widely used. In this paper, we focus on the problem of adapting the model trained on synthetic data to real data. We introduce a novel classifier-guided multi-branch domain adaptation model. Based on this model, we propose a new two-branch backpropagation design. The proposed model quantitatively outperforms existing domain adaptation methods on the synthetic data. Apart from quantitative evaluations, we conducted a subjective study to evaluate the quality of visual expression tracking on real data.
Crossing of the Dream Fantasy: AI Technique Application for Visualizing a Fictional Character's Dream

This research explores the creative potential of artificial intelligence (AI) technology in artistic practice by testing various AI tools for their usability and capability as mediums of artistic expression. The project focuses on visualizing a dream of Mulan, a classic Chinese female figure, using the predictable features of AI generative models, with the aim of exploring whether such methods can produce amazing results. The project employs a collaborative process that combines different AI platforms to generate a range of materials, which are then with subjectively integrated into Mulan's fantasy dreamscape. The main conclusion drawn from the project is that artists guide abstract concepts and provide micro-interference, while AI produces concrete components and variations. The findings suggest that AI tools have the potential to transform artistic creation modes, and collaborative art creation can result in unique and compelling artworks.