Miracle-dz/UniFAR-FTUs huggingface dataset:
An information-theoretic approach to side-channel analysis

Side-channels are unanticipated information flows that present a significant threatto security of systems. Quantitative analyses are required to measure the rate ofinformation leakage and the accuracy of information learned through side-channelattacks. To this end, the work presented in this thesis develops a general model ofa side channel, which is represented as a two-input-single-output system and specifiedby the probability distribution of the output conditioned on the inputs. For thismodel, three quantitative metrics are defined: capacity, leakage, and reliability rate.The thesis argues that capacity is an ill-suited metric for side channels and recommendsthe use of other two metrics to measure the leakage rate and accuracy of informationlearned, respectively. These metrics are used to analyze attacks employed invery different application areas: private communication detection in VoIP networks,packet schedulers in web communication, and timing attacks against modular multiplicationroutines used in public-key cryptosystems. The analyses presented in thisthesis enable us to: 1) determine system parameters and user behaviors that preserveprivacy, 2) compute the lifetime of private information, and 3) identify attackstrategies that leak most information. More importantly, they enable us to study theconditions under which existing countermeasures perform as expected and developinformation-theoretic countermeasures against side-channel attacks.
Neural representation dynamics reveal computational principles of cognitive task learning

During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass task representation learning. The formation of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain.
HeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline

The need for a more energy-efficient future is now more evident than ever. Energy disagreggation (NILM) methodologies have been proposed as an effective solution for the reduction in energy consumption. However, there is a wide range of challenges that NILM faces that still have not been addressed. Herein, we propose HeartDIS, a generalizable energy disaggregation pipeline backed by an extensive set of experiments, whose aim is to tackle the performance and efficiency of NILM models with respect to the available data. Our research (i) shows that personalized machine learning models can outperform more generic models; (ii) evaluates the generalization capabilities of these models through a wide range of experiments, highlighting the fact that the combination of synthetic data, the decreased volume of real data, and fine-tuning can provide comparable results; (iii) introduces a more realistic synthetic data generation pipeline based on other state-of-the-art methods; and, finally, (iv) facilitates further research in the field by publicly sharing synthetic and real data for the energy consumption of two households and their appliances.
Wigner function and entanglement dynamics of a two-atom two-mode nonlinear Jaynes-Cummings model

In this paper, a model in which two moving atoms interact with a two-mode field with nondegenerate two-photon transitions is studied in the presence of intensity-dependent coupling. To discuss the entanglement dynamics between subsystems as well as nonclassicality of the system, the time-dependent form of the state vector of the system is exactly obtained. The dynamics of entanglement between the atoms and the field is evaluated by the von Neumann entropy. To examine the degree of entanglement between the atoms, concurrence and negativity are obtained. Then, in order to understand the nonclassicality feature of the state vector of the system, the two-mode Wigner–Weyl quasi-probability distribution function is precisely derived. It is deduced from the numerical results that the domain and the maximum amount of the degree of entanglement and nonclassicality of the system can be appropriately tuned by suitably adopting the nonlinearity function and the field-mode structure parameters.
Polynomial Chaos Expansion for Probabilistic Uncertainty Propagation

Uncertainty propagation (UP) methods are of great importance to design optimization under uncertainty. As a well-known and rigorous probabilistic UP approach, the polynomial chaos expansion (PCE) technique has been widely studied and applied. However, there is a lack of comprehensive overviews and studies of the latest advances of the PCE methods, and there is still a large gap between the academic research and engineering application for PCE due to its high computational cost. In this chapter, latest advances of the PCE theory and method are elaborated, in which the newly developed data-driven PCE method that does not depend on the complete information of input probabilistic distribution as the common PCE approaches is introduced and improved. Meanwhile, the least angle regression technique and the trust region scenario are, respectively, extended to reduce the computational cost of data-driven PCE to accommodate it to practical engineering design applications. In addition, comprehensive comparisons are made to explore the relative merits of the most commonly used PCE approaches in the literature to help designers to choose more suitable PCE techniques in probabilistic design optimization.
A Tool for Composing Music via Graphic Scores in the style of Gy¨orgy Ligeti’s Artikulation using Self-supervised Representation Learning

Graphic scores are powerful and expressive symbolic music notations which are promising for music generation in multi-modal settings. However, it is a challenging task to decipher the relationship between the graphic scores and their corresponding musical pieces to explicitly use the creative mapping between them in generative settings. In this work, we connect graphic score and audio domains using self-supervised representation learning to reveal the mapping be-tween these modalities, and utilise this technique to compose music using graphic scores in the universe of Gy¨orgy Ligeti’s Artikulation, which is a well-known electronic piece. To ex-periment with and disseminate this approach, we have built an interactive web application in Hugging Face Spaces, designed using the Gradio SDK.
Causes And Potential Treatment For Altered Muscle Metaboreflex Control Of Ventricular Vascular Interactions In Heart Failure

CAUSES AND POTENTIAL TREATMENT FOR ALTERED MUSCLE METABOREFLEX CONTROL OF VENTRICULAR VASCULAR INTERACTIONS IN HEART FAILURE by JOSEPH MANNOZZI December 2020 ADVISOR: Dr. Donal O’Leary MAJOR: Physiology DEGREE: Doctor of Philosophy Muscle Metaboreflex Activation occurs as a result of metabolic accumulation within active skeletal muscle that stimulates type III and IV afferents. This reflex in healthy subjects causes increased ventricular contraction, tachycardia, enhanced central blood volume mobilization, and 2 mediated vasodilation as a means to increase mean arterial pressure and thereby improve perfusion pressure of active skeletal muscle. However, to date no study has evaluated the interaction between the observed changes in ventricular and vascular dynamics or how the reflex impacts contraction relaxation dynamics before and after induction of heart failure Furthermore, no study has evaluated the impact of chronic selective ablation of skeletal muscle afferents responsible for reflex activation. In this study we addressed three questions: 1) Does muscle metaboreflex activation increase effective arterial elastance and maintain ventricular vascular coupling before and after induction of heart failure, 2) Are ventricular contraction and relaxation dynamics influenced by muscle metaboreflex activation and what is the effect of heart failure, 3) What is the contribution of TRPV1 expressing skeletal muscle afferents to muscle metaboreflex activation. We used a chronically instrumented canine model in a
Enhancing COVID-19 Diagnosis Through a Hybrid CNN and Gray Wolf Optimizer Framework

—Covid-19 is an infectious respiratory disorder brought about using a brand-new coronavirus first found in 2019. The severity of symptoms can vary from mild to life-threatening. No vaccine or specific treatment has been developed to address Covid-19. Hence the most effective preventive measure is to practice social distancing and adhere to good hygiene practices. Medical imaging and convolutional neural networks are used in Covid-19 research to quickly identify infected individuals and detect changes in the lung tissue of those infected. Convolutional neural networks can be used to analyze chest CT scans, detecting potential signs of infection like ground-glass opacities, which indicate the presence of Covid-19. This article introduces a powerful framework for classifying COVID-19 images utilizing a hybrid of CNN and an improved version of Gray Wolf Optimizer. To demonstrate the efficiency of the projected framework, it is verified on a standard dataset and compared with other methods, with results indicating its superiority over the others.
Combination of Adaptive Neuro Fuzzy Inference System and Machine Learning Algorithm for Recognition of Human Facial Expressions

—A face recognition system's initial three processes are face detection, feature extraction, and facial expression recognition. The initial step of face detection involves colour model skin colour detection, lighting adjustment to achieve uniformity on the face, and morphological techniques to maintain the necessary face region. To extract facial characteristics such the eyes, nose, and mouth, the output of the first step is employed. Third-step methodology using automated face emotion recognition. This study's goal is to apply the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm to increase the precision of the current face recognition systems. For the purpose of removing noise and unwanted information from the data sets, independent data sets and a pre-processing technique are built in this study based on color, texture, and shape, to determine the features of the face. The output of the three-feature extraction process is given to the ANFIS model as input. By using our training picture data sets, it has already been trained. This model receives a test image as input, then evaluates the three aspects of the input image, and then recognizes the test image based on correlation. The determination of whether input has been authenticated or not is made using fuzzy logic. The proposed ANFIS method is compared to the existing methods such as Minimum Distance Classifier (MDC), Support Vector Machine (SVM), Case Based Reasoning (CBR) with the following quality measure like error rate, accuracy, precision, recall. Finally, the performance is analyzed by combining all feature extractions with existing classification methods such as MDC, KNN (K-Nearest Neighbour), SVM, ANFIS and CBR. Based on the performance of classification techniques, it is observed that the face detection failures are reduced, such that overall accuracy for CBR is 92% and it is 97% in ANFIS.
A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles

Heuristic optimization algorithms have been proved to be powerful in solving nonlinear and complex optimization problems; therefore, many effective optimization algorithms have been applied to solve optimization problems in real-world scenarios. This paper presents a modification of the recently proposed Gaining–Sharing Knowledge (GSK)-based algorithm and applies it to optimize resource scheduling in the Internet of Vehicles (IoV). The GSK algorithm simulates different phases of human life in gaining and sharing knowledge, which is mainly divided into the senior phase and the junior phase. The individual is initially in the junior phase in all dimensions and gradually moves into the senior phase as the individual interacts with the surrounding environment. The main idea used to improve the GSK algorithm is to divide the initial population into different groups, each searching independently and communicating according to two main strategies. Opposite-based learning is introduced to correct the direction of convergence and improve the speed of convergence. This paper proposes an improved algorithm, named parallel opposition-based Gaining–Sharing Knowledge-based algorithm (POGSK). The improved algorithm is tested with the original algorithm and several classical algorithms under the CEC2017 test suite. The results show that the improved algorithm significantly improves the performance of the original algorithm. When POGSK was applied to optimize resource scheduling in IoV, the results also showed that POGSK is more competitive.
Facial Image Generation from Bangla Textual Description using DCGAN and Bangla FastText

—The synthesis of facial images from textual descriptions is a relatively difficult subfield of text-to-image synthesis. It is applicable in various domains like Forensic Science, Game Development, Animation, Digital Marketing, and Metaverse. However, no work was found that generates facial images from textual descriptions in Bangla; the 5th most spoken language in the world. This research introduces the first-ever system to generate facial images from Bangla textual descriptions. The proposed model comprises two fundamental constituents, namely a textual encoder, and a Generative Adversarial Network(GAN). The text encoder is a pre-trained Bangla text encoder named Bangla FastText which is employed to transform Bangla text into a latent vector representation. The utilization of Deep Convolutional GAN (DCGAN) allows for the generation of face images that correspond to text embedding. Furthermore, a Bangla version of the CelebA dataset, CelebA Bangla is created for this study to develop the proposed system. CelebA Bangla contains images of celebrities, their corresponding annotated Bangla facial attributes and Bangla Textual Descriptions generated using a novel description generation algorithm. The proposed system attained a Fr´echet Inception Distance (FID) score of 126.708, Inception Score(IS) of 12.361, and Face Semantic Distance(FSD) of 20.23. The novel text embedding strategy used in this study outperforms prior work. A thorough qualitative and quantitative analysis demonstrates the superior performance of the proposed system over other experimental systems.
Deep Learning-based Intrusion Detection: A Novel Approach for Identifying Brute-Force Attacks on FTP and SSH Protocol

—As networks continue to expand rapidly, the number and diversity of cyberattacks are also increasing, posing a significant challenge for organizations worldwide. Consequently, brute-force attacks targeting FTP and SSH protocols have become more prevalent. IDSes offer an essential tool to detect these attacks, providing traffic analysis and system monitoring. Traditional IDSes employ signatures and anomalies to monitor information flow for malicious activity and policy violations; however, they often struggle to effectively identify unknown or novel patterns. In response, we propose a novel intelligent approach based on deep learning to detect brute-force attacks on FTP and SSH protocols. We conducted an extensive literature review and developed a metric to compare our work with existing literature. Our findings indicate that our proposed approach achieves an accuracy of 99.9%, outperforming other comparable solutions in detecting brute-force attacks.
Epitopes and Mimotopes Identification Using Phage Display for Vaccine Development against Infectious Pathogens

Traditional vaccines use inactivated or weakened forms of pathogens which could have side effects and inadequate immune responses. To overcome these challenges, phage display has emerged as a valuable tool for identifying specific epitopes that could be used in vaccines. This review emphasizes the direct connection between epitope identification and vaccine development, filling a crucial gap in the field. This technique allows vaccines to be engineered to effectively stimulate the immune system by presenting carefully selected epitopes. Phage display involves screening libraries of random peptides or gene/genome fragments using serum samples from infected, convalescent, or vaccinated individuals. This method has been used to identify epitopes from various pathogens including SARS-CoV-2, Mycobacterium tuberculosis, hepatitis viruses, H5N1, HIV-1, Human T-lymphotropic virus 1, Plasmodium falciparum, Trypanosoma cruzi, and Dirofilaria repens. Bacteriophages offer advantages such as being immunogenic carriers, low production costs, and customization options, making them a promising alternative to traditional vaccines. The purpose of this study has been to highlight an approach that encompasses the entire process from epitope identification to vaccine production using a single technique, without requiring additional manipulation. Unlike conventional methods, phage display demonstrates exceptional efficiency and speed, which could provide significant advantages in critical scenarios such as pandemics.
Co-seismic and post-seismic deformation for the 1995 Colima–Jalisco and 2003 Tecomán thrust earthquakes, Mexico subduction zone, from modelling of GPS data

B. Cosenza-Muralles ,1,2 C. DeMets ,1 B. Márquez-Azúa,3 O. Sánchez,4 J. Stock,5 E. Cabral-Cano4 and R. McCaffrey6 1Department of Geoscience, University of Wisconsin-Madison, Madison, WI 53706, USA. E-mail: cosenzamural@wisc.edu 2Instituto de Investigación en Ciencias Fı́sicas y Matemáticas, Escuela de Ciencias Fı́sicas y Matemáticas, Universidad de San Carlos de Guatemala, 01012 Ciudad de Guatemala, Guatemala 3Departamento de Estudios Socio Urbanos, Universidad de Guadalajara, 44100 Guadalajara, Jalisco, México 4Instituto de Geofı́sica, Universidad Nacional Autónoma de México, Ciudad Universitaria, México CDMX 04510, México 5Caltech Seismological Laboratory, California Institute of Technology, Pasadena, CA 91125, USA 6Department of Geology, Portland State University, Portland, OR 97201, USA
SEE&TELL: Controllable Narrative Generation from Images

We propose a visual storytelling framework with a distinction between what is present and observable in the visual storyworld, and what story is ultimately told. We implement a model that tells a story from an image using three affor-dances: 1) a fixed set of visual properties in an image that constitute a holistic representation its contents, 2) a variable stage direction that establishes the story setting, and 3) incremental questions about character goals. The generated narrative plans are then realized as expressive texts using few-shot learning. Following this approach, we generated 64 visual stories and measured the preservation, loss, and gain of visual information throughout the pipeline, and the willingness of a reader to take action to read more. We report different proportions of visual information preserved and lost depending upon the phase of the pipeline and the stage direction’s apparent relatedness to the image, and report 83% of stories were found to be interesting.
The performance bounds of an optical communication system using irradiance profile modulation

Abstract We assess the performance bounds of an optical communication system that uses irradiance profile modulation. This modulation is based on the four different orders of vortex beams. To this end, we find the turbulence induced average irradiance profiles of Gaussian, Bessel–Gaussian and modified Bessel–Gaussian vortex beams on the receiver plane. Each one is then cross correlated against the free space equivalents. Plotting the cross-correlation coefficients, it becomes possible to identify the borders of correct decision and error regions, thus, deduce the performance bounds of such a system. When measured in terms of structure constant, i.e. the turbulence strength and the propagation length being fixed to 3 km, it is seen that the error region extends beyond the structure constant values of and higher. There seem to be some variations with the beam type and the order of the vortex beam. The performance of Bessel–Gaussian vortex beam comes out to be slightly better than the rest.
Role of Artificial Intelligence and Business Decision Making

— Artificial Intelligence (AI) has emerged as a transformative technology with profound implications for various sectors, including business. In recent years, AI has revolutionized decision-making processes by providing organizations with advanced analytical capabilities, enabling them to extract valuable insights from vast amounts of data. The application of AI in businesses may force the sector to rely on quicker, less expensive, and more accurate marketing techniques. By utilizing the AI in marketing strategies, a business owner may increase audience reaction and build a strong online brand that can compete with others. In addition to marketing, it has the capacity to remodel a business with fresh concepts. Additionally, it provides solutions for challenging problems, aiding in the enormous business growth. The study's primary goal is to investigate how artificial intelligence and decision-making are deployed in business and tried to explore how AI is being used to enhance decision-making processes and how it is changing business models. The study reveals that the role of artificial intelligence in business decision making is transformative, offering significant advantages in terms of efficiency, accuracy, and innovation. AI-powered systems enable businesses to process and analyze vast amounts of data efficiently, leading to quicker and more informed decision making. Overall, the integration of AI in business decision making has the potential to drive organizational success and shape the future of business practices.