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HARMONI is the first light visible and near-IR integral field spectrograph for the ELT. It covers a large spectral range from 470nm to 2450nm with resolving powers from 3300 to 18000 and spatial sampling from 60mas to 4mas. It can operate in two Adaptive Optics modes - SCAO (including a High Contrast capability) and LTAO - or with NOAO. The project is preparing for Final Design Reviews. The integral field spectrograph is a key sub-system of HARMONI instrument, which forms the 2D spectral image and projects it onto the scientific detector. It has 40 operational modes with different platescales and gratings covering the band of 811-2450 nm with three resolution grades. In each of this configurations the as-built spectrograph wave-front error is strictly limited. We perform the sensitivity analysis for measurable and unknown errors and build the errors budget on this basis. Then we correct the values for the actual technological limits and perform a three-stage Monte-Carlo analysis combined with simulation of a few specific effect as the holographic grating wavefront error. Eventually, we show that it is possible to reach the target image quality in terms of the wavefront error and spectral resolution for the entire sub-system with practically feasible tolerances on design parameters., Comment: 20 pages, 10 figures, 1 table

In this paper, we introduce Mellin-Steklov exponential samplingoperators of order $r,r\in\mathbb{N}$, by considering appropriate Mellin-Steklov integrals. We investigate the approximation properties of these operators in continuousbounded spaces and $L^p, 1 \leq p < \infty$ spaces on $\mathbb{R}_+.$ By using the suitablemodulus of smoothness, it is given high order of approximation. Further, we present a quantitative Voronovskaja type theorem and we study the convergence results of newly constructed operators in logarithmic weighted spaces offunctions. Finally, the paper provides some examples of kernels that support the our results.

We consider an imaging system tasked with estimating the angular distance between two incoherently-emitting sub-Rayleigh-separated point sources, without any prior knowledge of the centroid or the constellation and with a fixed collected-photon budget. It was shown theoretically that splitting the optical recording time into two stages -- focal-plane direct imaging to obtain a pre-estimate of the centroid, and using that estimate to center a spatial-mode sorter followed by photon detection of the sorted modes -- can achieve 10 to 100 times lower mean squared error in estimating the separation. In this paper, we demonstrate this in proof-of-concept, using a programmable mode sorter we have built using multi-plane light conversion (MPLC) using a reflective spatial-light modulator (SLM) in an emulated experiment where we use a single coherent source to characterize the MPLC to electronically piece together the signature from two closely-separated quasi-monochromatic incoherent emitters., Comment: 7 pages, 7 figures in main paper, 3 pages, 8 figures in supplementary material

Artificial intelligence (AI) signals the beginning of a revolutionary period where technological advancement and social change interact to completely reshape economies, work paradigms, and industries worldwide. This essay addresses the opportunities and problems brought about by the AI-driven economy as it examines the effects of AI disruption on the IT sector and information technology education. By comparing the current AI revolution to previous industrial revolutions, we investigate the significant effects of AI technologies on workforce dynamics, employment, and organizational procedures. Human-centered design principles and ethical considerations become crucial requirements for the responsible development and implementation of AI systems in the face of the field's rapid advancements. IT education programs must change to meet the changing demands of the AI era and give students the skills and competencies they need to succeed in a digital world that is changing quickly. In light of AI-driven automation, we also examine the possible advantages and difficulties of moving to a shorter workweek, emphasizing chances to improve worker productivity, well-being, and work-life balance. We can build a more incslusive and sustainable future for the IT industry and beyond, enhancing human capabilities, advancing collective well-being, and fostering a society where AI serves as a force for good by embracing the opportunities presented by AI while proactively addressing its challenges., Comment: Submitted and accepted for CSCE'24: July 22-25, 2024

We present an experimental demonstration of an ideal direct imaging coronagraph design capable of achieving the quantum limits of exoplanet detection and localization by using spatial mode filtering. Our benchtop experimental implementation performs a forward and inverse pass through a free-space programmable spatial mode sorter configured to isolate photons in a point spread function (PSF)-adapted mode basis. During the forward pass, the fundamental mode is rejected, effectively eliminating light from an on-axis point-like star. On the inverse pass, the remaining modes are coherently recombined, enabling direct imaging of a faint companion. Our experimental system is able to localize an artificial exoplanet at sub-diffraction distances from its host star with a 1000:1 star-planet contrast ratio. The ability to resolve faint companions of a host star at sub-diffraction scale is crucial to further the discovery of exoplanets predicted to reside in the sub-diffraction regime. These exoplanets are currently beyond the reach of state-of-the-art coronagraphs, which typically have an inner working angle (IWA) larger than the diffraction scale. Furthermore, our coronagraph architecture is potentially capable of measuring higher-fidelity spectrographs of exoplanets using spatial-spectral mode demultiplexing., Comment: 11 pages, 13 figures

Vibration sensors are essential in acquiring seismic activity for an accurate earthquake assessment. The state-of-the-art sensors can provide the best signal quality and the highest bandwidth; however, their high cost usually hinders a wide range of applicability and coverage, which is otherwise possible with their basic and cheap counterparts. But, their poor quality and low bandwidth can significantly degrade the signal fidelity and result in an imprecise analysis. To address these drawbacks, in this study, we propose a novel, high-quality, and full bandwidth seismic signal synthesis by transforming the signal acquired from an inferior sensor. We employ 1D Operational Generative Adversarial Networks (Op-GANs) with novel loss functions to achieve this. Therefore, the study's key contributions include releasing a new dataset, addressing operational constraints in seismic monitoring, and pioneering a deep-learning transformation technique to create the first virtual seismic sensor. The proposed method is extensively evaluated over the Simulated Ground Motion (SimGM) benchmark dataset, and the results demonstrated that the proposed approach significantly improves the quality and bandwidth of seismic signals acquired from a variety of sensors, including a cheap seismic sensor, the CSN-Phidgets, and the integrated accelerometers of an Android, and iOS phone, to the same level as the state-of-the-art sensor (e.g., Kinemetrics-Episensor). The SimGM dataset, our results, and the optimized PyTorch implementation of the proposed approach are publicly shared.

Cyberbullying is a significant concern intricately linked to technology that can find resolution through technological means. Despite its prevalence, technology also provides solutions to mitigate cyberbullying. To address growing concerns regarding the adverse impact of cyberbullying on individuals' online experiences, various online platforms and researchers are actively adopting measures to enhance the safety of digital environments. While researchers persist in crafting detection models to counteract or minimize cyberbullying, malicious actors are deploying adversarial techniques to circumvent these detection methods. This paper focuses on detecting cyberbullying in adversarial attack content within social networking site text data, specifically emphasizing hate speech. Utilizing a deep learning-based approach with a correction algorithm, this paper yielded significant results. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.15%, and 91% respectively. Additionally, the LSTM model's performance surpassed that of previous studies., Comment: 10 pages, 8 figures, 3 tables, under reviewing

In the evolving field of Explainable AI (XAI), interpreting the decisions of deep neural networks (DNNs) in computer vision tasks is an important process. While pixel-based XAI methods focus on identifying significant pixels, existing concept-based XAI methods use pre-defined or human-annotated concepts. The recently proposed Segment Anything Model (SAM) achieved a significant step forward to prepare automatic concept sets via comprehensive instance segmentation. Building upon this, the Explain Any Concept (EAC) model emerged as a flexible method for explaining DNN decisions. EAC model is based on using a surrogate model which has one trainable linear layer to simulate the target model. In this paper, by introducing an additional nonlinear layer to the original surrogate model, we show that we can improve the performance of the EAC model. We compare our proposed approach to the original EAC model and report improvements obtained on both ImageNet and MS COCO datasets., Comment: This paper is accepted for publication at IEEE SIU conference, 2024

This study examines racial disparities in violent arrest outcomes, challenging conventional methods through a nuanced analysis of Cincinnati Police Department data. Acknowledging the intricate nature of racial disparity, the study categorizes explanations into types of place, types of person, and a combination of both, emphasizing the impact of neighborhood characteristics on crime distribution and police deployment. By introducing alternative scenarios, such as spuriousness, directed policing, and the geo-concentration of racial groups, the study underscores the complexity of racial disparity calculations. Employing a case study approach, the analysis of violent arrest outcomes reveals approximately 40 percent of the observed variation attributed to neighborhood-level characteristics, with concentrated disadvantage neutralizing the influence of race on arrest rates. Contrary to expectations, the study challenges the notion of unintentional racism, suggesting that neighborhood factors play a more significant role than the racial composition in explaining arrests. Policymakers are urged to focus on comprehensive community development initiatives addressing socioeconomic inequalities and support the development of robust racial disparity indices. The study calls for nuanced explorations of unintentional racism and future research addressing potential limitations, aiming to enhance understanding of the complexities surrounding racial disparities in arrests.

This paper proposes a deep learning based solution for multi-modal image alignment regarding UAV-taken images. Many recently proposed state-of-the-art alignment techniques rely on using Lucas-Kanade (LK) based solutions for a successful alignment. However, we show that we can achieve state of the art results without using LK-based methods. Our approach carefully utilizes a two-branch based convolutional neural network (CNN) based on feature embedding blocks. We propose two variants of our approach, where in the first variant (ModelA), we directly predict the new coordinates of only the four corners of the image to be aligned; and in the second one (ModelB), we predict the homography matrix directly. Applying alignment on the image corners forces algorithm to match only those four corners as opposed to computing and matching many (key)points, since the latter may cause many outliers, yielding less accurate alignment. We test our proposed approach on four aerial datasets and obtain state of the art results, when compared to the existing recent deep LK-based architectures.

This study examines the impact of the COVID-19 pandemic on cybersecurity and data breaches, with a specific focus on the shift toward remote work. The study identifies trends and offers insights into cybersecurity incidents by analyzing data breaches two years before and two years after the start of remote work. Data was collected from the Montana Department of Justice Data Breach database and consisted of data breaches that occurred between April 2018 and April 2022. The findings inform best practices for cybersecurity preparedness in remote work environments, aiding organizations to enhance their defenses. Although the study's data is limited to Montana, it offers valuable insights for cybersecurity professionals worldwide. As remote work continues to evolve, organizations must remain adaptable and vigilant in their cybersecurity strategies.

Fileless malware predominantly relies on PowerShell scripts, leveraging the native capabilities of Windows systems to execute stealthy attacks that leave no traces on the victim's system. The effectiveness of the fileless method lies in its ability to remain operational on victim endpoints through memory execution, even if the attacks are detected, and the original malicious scripts are removed. Threat actors have increasingly utilized this technique, particularly since 2017, to conduct cryptojacking attacks. With the emergence of new Remote Code Execution (RCE) vulnerabilities in ubiquitous libraries, widespread cryptocurrency mining attacks have become prevalent, often employing fileless techniques. This paper provides a comprehensive analysis of PowerShell scripts of fileless cryptojacking, dissecting the common malicious patterns based on the MITRE ATT&CK framework., Comment: 10 pages, 1 figure

Cybercrime is a growing concern in West Africa due to the increasing use of technology and internet penetration in the region. Legal frameworks are essential for guiding the control of cybercrime. However, the implementation proves challenging for law enforcement agencies due to the absence of a dedicated and effective regional institutional follow-up mechanism. This study conducted a systematic literature review focusing on West Africa's prevalence of cybercrime, governing policies, regulations, and methodologies for combating cybercrime. West-Africa countries face significant cybercrime challenges, exacerbated by inadequate resources and a dearth of security experts. This study pinpoints potential cybercrime prevention strategies, such as leveraging the Triage framework and broadening research to cover pivotal areas like cyber aggression and cyberbullying. Our research findings highlight the urgency for policymakers and law enforcement agencies to devise more efficient prevention strategies and policies. Overall, this study provides invaluable insights into the state of cybercrime in West Africa to guide the formulation of potent prevention and intervention strategies.

In the dynamic urban landscape, where the interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This datasets is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

Suicide is recognized as one of the most serious concerns in the modern society. Suicide causes tragedy that affects countries, communities, and families. There are many factors that lead to suicidal ideations. Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations. As technology usage has increased, people share and express their ideations digitally via social media, chatbots, and other digital platforms. In this paper, we proposed a novel, simple deep learning-based model to detect suicidal ideations in digital content, mainly focusing on chatbots as the primary data source. In addition, we provide a framework that employs the proposed suicide detection integration with a chatbot-based support system., Comment: 5 pages, 6 figures, 4 tables, Under review in IEEE conference

Robust and real-time detection of faults on rotating machinery has become an ultimate objective for predictive maintenance in various industries. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearing fault detection as they can produce state-of-the-art detection performances under certain conditions. Despite such particular focus on the vibration signal, the utilization of sound, on the other hand, has been neglected whilst only a few studies have been proposed during the last two decades, all of which were based on a conventional ML approach. One major reason is the lack of a benchmark dataset providing a large volume of both vibration and sound data over several working conditions for different machines and sensor locations. In this study, we address this need by presenting the new benchmark Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF), which encapsulates sound and vibration data from two different motors operating under 1080 working conditions overall. Then we draw the focus on the major limitations and drawbacks of vibration-based fault detection due to numerous installation and operational conditions. Finally, we propose the first DL approach for sound-based fault detection and perform comparative evaluations between the sound and vibration over the QU-DMBF dataset. A wide range of experimental results shows that the sound-based fault detection method is significantly more robust than its vibration-based counterpart, as it is entirely independent of the sensor location, cost-effective (requiring no sensor and sensor maintenance), and can achieve the same level of the best detection performance by its vibration-based counterpart. With this study, the QU-DMBF dataset, the optimized source codes in PyTorch, and comparative evaluations are now publicly shared., Comment: 8 pages

We explore the potential of the Large Hadron Collider (LHC) in detecting a signal originating from the production of a heavy $SU(2)_R$ charged gauge boson that then decays into a top-bottom quark pair via the mediation of a right-handed neutrino, $p p \to W_R \to N_R \ell \to (\ell' t b)\ell$. Such a channel, that we study in the context of the minimal Left-Right Symmetric Model, contrasts with conventional smoking-gun signatures targeted experimentally and phenomenologically in which only light quarks are involved. We propose a selection strategy aimed at extracting such a top-bottom signal and we estimate the resulting sensitivity of the LHC to the model. Our results demonstrate the potential impact of such a search and we therefore urge the experimental collaborations to carry out a similar analysis in the light of present and future data., Comment: v1: 38 pages, 9 figures and 9 tables. v2: Matches published version in JHEP

Segmentation of three-dimensional (3D) point clouds is an important task for autonomous systems. However, success of segmentation algorithms depends greatly on the quality of the underlying point clouds (resolution, completeness etc.). In particular, incomplete point clouds might reduce a downstream model's performance. GRNet is proposed as a novel and recent deep learning solution to complete point clouds, but it is not capable of part segmentation. On the other hand, our proposed solution, GRJointNet, is an architecture that can perform joint completion and segmentation on point clouds as a successor of GRNet. Features extracted for the two tasks are also utilized by each other to increase the overall performance. We evaluated our proposed network on the ShapeNet-Part dataset and compared its performance to GRNet. Our results demonstrate GRJointNet can outperform GRNet on point completion. It should also be noted that GRNet is not capable of segmentation while GRJointNet is. This study1, therefore, holds a promise to enhance practicality and utility of point clouds in 3D vision for autonomous systems.