Reviewerly/exHarmony huggingface dataset:
Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification

Authors:
raw_author_name: Tommaso Boccato
raw_affiliation_string: Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
raw_author_name: Nicola Toschi
raw_affiliation_string: Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States

Abstract: Background The willingness to trust predictions formulated by automatic algorithms is key in a wide range of domains. However, a vast number of deep architectures are only able to formulate predictions without associated uncertainty. Purpose In this study, we propose a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different networks similar to the original one at each forward pass. Methods We combine our method with a tunable rejection-based approach that employs only the fraction of the data, i.e., the share that the model can classify with an uncertainty below a user-set threshold. We test our model in a large cohort of brain images from patients with Alzheimer's disease and healthy controls, discriminating the former and latter classes based on morphometric images exclusively. Results We demonstrate how combining estimated uncertainty with a rejection-based approach increases classification accuracy from 0.86 to 0.95 while retaining 75% of the test set. In addition, the model can select the cases to be recommended for, e.g., expert human evaluation due to excessive uncertainty. Importantly, our framework circumvents additional workload during the training phase by using our network “turned into Bayesian” to implicitly investigate the loss landscape in the neighborhood of each test sample in order to determine the reliability of the predictions. Conclusion We believe that being able to estimate the uncertainty of a prediction, along with tools that can modulate the behavior of the network to a degree of confidence that the user is informed about (and comfortable with), can represent a crucial step in the direction of user compliance and easier integration of deep learning tools into everyday tasks currently performed by human operators.
An unrestricted Arnold’s cat map transformation

Authors:
raw_author_name: Erhan Gökçay
raw_affiliation_string: Department of Software Engineering, Atilim University, Ankara, Turkey
raw_author_name: Hakan Tora
raw_affiliation_string: Department of Electrical-Electronics Engineering, Atilim University, Ankara, Turkey

Abstract: Abstract The Arnold’s Cat Map (ACM) is one of the chaotic transformations, which is utilized by numerous scrambling and encryption algorithms in Information Security. Traditionally, the ACM is used in image scrambling whereby repeated application of the ACM matrix, any image can be scrambled. The transformation obtained by the ACM matrix is periodic; therefore, the original image can be reconstructed using the scrambled image whenever the elements of the matrix, hence the key, is known. The transformation matrices in all the chaotic maps employing ACM has limitations on the choice of the free parameters which generally require the area-preserving property of the matrix used in transformation, that is, the determinant of the transformation matrix to be $$\pm 1.$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> <mml:mo>.</mml:mo> </mml:mrow> </mml:math> This reduces the number of possible set of keys which leads to discovering the ACM matrix in encryption algorithms using the brute-force method. Additionally, the period obtained is small which also causes the faster discovery of the original image by repeated application of the matrix. These two parameters are important in a brute-force attack to find out the original image from a scrambled one. The objective of the present study is to increase the key space of the ACM matrix, hence increase the security of the scrambling process and make a brute-force attack more difficult. It is proved mathematically that area-preserving property of the traditional matrix is not required for the matrix to be used in scrambling process. Removing the restriction enlarges the maximum possible key space and, in many cases, increases the period as well. Additionally, it is supplied experimentally that, in scrambling images, the new ACM matrix is equivalent or better compared to the traditional one with longer periods. Consequently, the encryption techniques with ACM become more robust compared to the traditional ones. The new ACM matrix is compatible with all algorithms that utilized the original matrix. In this novel contribution, we proved that the traditional enforcement of the determinant of the ACM matrix to be one is redundant and can be removed.
An Analytic Hierarchy Process Method to Evaluate Environmental Impacts of In Situ Oil Shale Mining

Authors:
raw_author_name: Xiaorong Wang
raw_affiliation_string: Beijing Zhonglu Consulting Co., Ltd., PetroChina Planning and Engineering Institute, Beijing 100089, China; School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China
raw_author_name: Shaolin He
raw_affiliation_string: Beijing Zhonglu Consulting Co., Ltd., PetroChina Planning and Engineering Institute, Beijing 100089, China
raw_author_name: Hongying Yuan
raw_affiliation_string: School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China
raw_author_name: Yang Song
raw_affiliation_string: Beijing Zhonglu Consulting Co., Ltd., PetroChina Planning and Engineering Institute, Beijing 100089, China

Abstract: The great demand for oil shale resource development and the corresponding threats to the environment have resulted in the urgent need to assess the impact of oil shale in situ mining on the environment. In this paper, based on an analysis method developed by the previous literature and the Delphi technique, three secondary indicators and ten tertiary indicators were selected. The weights of the indicators at all levels were subsequently calculated. The results identify environmental capacity, groundwater risk and prevention, and cleaner production as having the largest weights in the indicator system. Following this, the typical three-level indicators with the largest weight and content related to carbon emissions were introduced in detail, and combined with the calculated weight of each indicator, the comprehensive evaluation index method was used to determine the comprehensive evaluation model for the environmental impact of oil shale in situ mining. The comprehensive evaluation model value, PA, of the target layer (the primary indicator) under two different heating methods, combustion heating and electric heating, were then calculated based on the oil shale in situ mining area in Fuyu City, Jilin Province, China. This study introduces carbon-emission-related elements into the three-level indicator evaluation calculation model, which is a more accurate and advanced comprehensive evaluation method.
Digital vulnerability: People-in-prison, videoconferencing and the digital criminal justice system

Authors:
raw_author_name: Carolyn McKay
raw_affiliation_string: University of Sydney Law School, University of Sydney, New South Wales, Australia

Abstract: Since the COVID-19 pandemic forced prisons into extended lockdowns, people-in-prison have been increasingly reliant on remote access technologies such as videoconferencing to interact with the outside world from their carceral context. During the pandemic, both corrective services and criminal courts found that these communication technologies could adequately facilitate court appearances, legal conferencing, health services and visitations. Now, despite the lessening panic regarding COVID-19, there are suggestions that authorities are keen to continue, if not increase, the usage of videolinks meaning that people-in-prison will experience decreasing human contact. The argument is that COVID-19 “successes” are being used as an excuse to cement remote hearings and visits. This points to a future of absolute confinement uninterrupted by “inconvenient” physical court attendance or human visitors. This article will focus on the “digital vulnerability” of people-in-prison, that is, the intersection between the vulnerabilities of incarcerated people with the use of remote communication technologies such as videoconferencing or videolinks, and the digital inequalities that may be provoked or solved. The article will draw on qualitative research – fieldwork interviews with judicial officers, lawyers and associated criminal justice professionals – that reveal critical perspectives on the impacts of digital transformation on vulnerable people-in-prison. For instance, when asked about the vulnerabilities of people-in-prison, one defence lawyer (DL1) expressed the view that “no one cares about defendants” or their vulnerabilities, nor their abilities to engage with videolinks, comprehend or play any role in their own remote legal matters. We analyse our source materials through a framework of vulnerability theory and the developing concept of digital vulnerability. In this way, we seek to offer new knowledge regarding prison digital transformation, specifically the relationship between digital technologies in correctional environments and the vulnerable incarcerated population.
Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization

Authors:
raw_author_name: Weiyi Chen
raw_affiliation_string: College of Weapon Engineering, Naval University of Engineering, Wuhan 430033, China
raw_author_name: Bing Fu
raw_affiliation_string: College of Weapon Engineering, Naval University of Engineering, Wuhan 430033, China
raw_author_name: Zhonghong Wu
raw_affiliation_string: College of Weapon Engineering, Naval University of Engineering, Wuhan 430033, China
raw_author_name: Lingjun Hao
raw_affiliation_string: Naval Research Academy, Beijing 100161, China

Abstract: Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.
Multilayer Perceptron-Based Error Compensation for Automatic On-the-Fly Camera Orientation Estimation Using a Single Vanishing Point from Road Lane

Authors:
raw_author_name: Xingyou Li
raw_affiliation_string: Electrical and Computer Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea
raw_author_name: Hyoungrae Kim
raw_affiliation_string: Future Vehicle Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea
raw_author_name: Vijay Kakani
raw_affiliation_string: Integrated System Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon 22212, Republic of Korea
raw_author_name: Hakil Kim
raw_affiliation_string: Electrical and Computer Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea

Abstract: This study introduces a multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation, leveraging a single vanishing point and road lane lines within a steady-state framework. The research emphasizes cameras with a roll angle of 0°, predominant in autonomous vehicle contexts. The methodology estimates pitch and yaw angles using a single image and integrates two Kalman filter models with inputs from image points (u, v) and derived angles (pitch, yaw). Performance metrics, including avgE, minE, maxE, ssE, and Stdev, were utilized, testing the system in both simulator and real-vehicle environments. The outcomes indicate that our method notably enhances the accuracy of camera orientation estimations, consistently outpacing competing techniques across varied scenarios. This potency of the method is evident in its adaptability and precision, holding promise for advanced vehicle systems and real-world applications.
I-CVSSDM: IoT Enabled Computer Vision Safety System for Disaster Management

Authors:
raw_author_name: Parameswaran Ramesh
raw_affiliation_string: Anna University, Chennai
raw_author_name: Shabana Parveen M
raw_affiliation_string: Sri Sairam Engineering College
raw_author_name: Deepak Athipan A M B
raw_affiliation_string: Anna University, Chennai
raw_author_name: Bhuvaneswari P T V
raw_affiliation_string: Anna University, Chennai

Abstract: INTRODUCTION: Around the world, individuals experience flooding more frequently than any other natural calamity.&#x0D; OBJECTIVES: The motivation behind this research is to provide an Internet of Things (IoT)-based early warning assistive system to enable monitoring of water logging levels in flood-affected areas. Further, the SSD-MobiNET V2 model is used in the developed system to detect and classify the objects that prevail in the flood zone.&#x0D; METHODS: The developed research is validated in a real-time scenario. To enable this, a customized embedded module is designed and developed using the Raspberry Pi 4 model B processor. The module uses (i) a pi-camera to capture the objects and (ii) an ultrasonic sensor to measure the water level in the flood area.&#x0D; RESULTS: The measured data and detected objects are periodically ported to the cloud and stored in the cloud database to enable remote monitoring and further processing.&#x0D; CONCLUSION: Also, whenever the level of waterlogged exceeds the threshold, an alert is sent to the concerned authorities in the form of an SMS, a phone call, or an email.
Efficient Top-k Frequent Itemset Mining on Massive Data

Authors:
raw_author_name: Xiaolong Wan
raw_affiliation_string: School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Harbin, Heilongjiang, China
raw_author_name: Xixian Han
raw_affiliation_string: School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Harbin, Heilongjiang, China

Abstract: Abstract Top- k frequent itemset mining (top- k FIM) plays an important role in many practical applications. It reports the k itemsets with the highest supports. Rather than the subtle minimum support threshold specified in FIM, top- k FIM only needs the more understandable parameter of the result number. The existing algorithms require at least two passes of scan on the table, and incur high execution cost on massive data. This paper develops a prefix-partitioning-based PTF algorithm to mine top- k frequent itemsets efficiently, where each prefix-based partition keeps the transactions sharing the same prefix item. PTF can skip most of the partitions directly which cannot generate any top- k frequent itemsets. Vertical mining is developed to process the partitions of vertical representation with the high-support-first principle, and only a small fraction of the items are involved in the processing of the partitions. Two improvements are proposed to reduce execution cost further. Hybrid vertical storage mode maintains the prefix-based partitions adaptively and the candidate pruning reduces the number of the explored candidates. The extensive experimental results show that, on massive data, PTF can achieve up to 1348.53 times speedup ratio and involve up to 355.31 times less I/O cost compared with the state-of-the-art algorithms.
The Changing Face of Free Speech: A Study of Article 19 in the Digital Age in India

Authors:
raw_author_name: Tushar Sharma
raw_affiliation_string: LLM Student, The West Bengal National University of Juridical Sciences (NUJS), Kolkata, West Bengal.

Abstract: In the digital age, India’s narrative of free speech has encountered both evolution and turbulence. Rooted in the democratic ethos, Article 19 of the Indian Constitution safeguards this freedom, reflecting the country’s commitment to upholding a citizen’s right to express. With the proliferation of online platforms—ranging from social media to news portals—the citizens have found dynamic avenues to articulate their viewpoints. As Nelson Mandela once said, “To be free is not merely to cast off one’s chains but to live in a way that respects and enhances the freedom of others.” However, this freedom has its pitfalls in the digital realm: the rapid dissemination of misinformation and the perils of divisive rhetoric. This research delves into the protective umbrella of Article 19 of the Constitution, emphasizing its pivotal role in safeguarding expression. The current landscape necessitates a balance, a careful navigation between the unrestricted flow of opinions and the dangers of digital misinformation. Issues of censorship, both governmental and self-imposed, further complicate this balance. As the world envisages the future of India’s digital discourse, it becomes paramount to ensure that the sanctity of free speech, as championed by Article 19, is preserved, yet responsibly exercised. Conclusively, the paper underscores the imperative of navigating the nuanced balance between unbridled digital expression and the responsibilities accompanying it, all through the lens of Article 19.
Optical Extreme Learning Machines with Atomic Vapors

Authors:
raw_author_name: Nuno A. Silva
raw_affiliation_string: Center for Applied Photonics, Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Rua do Campo Alegre, 4169-007 Porto, Portugal; Departamento de Física e Astronomia, Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
raw_author_name: Tiago D. Ferreira
raw_affiliation_string: Center for Applied Photonics, Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Rua do Campo Alegre, 4169-007 Porto, Portugal; Departamento de Física e Astronomia, Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal

Abstract: Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the output layer, the approach has the potential to speed up the training process and the capacity to turn any physical system into a computing platform. Yet, requiring strong nonlinear dynamics, optical solutions operating at fast processing rates and low power can be hard to achieve with conventional nonlinear optical materials. In this context, this manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine leveraging their enhanced nonlinear optical properties. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level, paving opportunities for energy-efficient computing solutions.
On monitorability of AI

Authors:
raw_author_name: Roman V. Yampolskiy
raw_affiliation_string: Computer Science and Engineering, University of Louisville, Louisville, KY, USA

Abstract: Abstract Artificially intelligent (AI) systems have ushered in a transformative era across various domains, yet their inherent traits of unpredictability, unexplainability, and uncontrollability have given rise to concerns surrounding AI safety. This paper aims to demonstrate the infeasibility of accurately monitoring advanced AI systems to predict the emergence of certain capabilities prior to their manifestation. Through an analysis of the intricacies of AI systems, the boundaries of human comprehension, and the elusive nature of emergent behaviors, we argue for the impossibility of reliably foreseeing some capabilities. By investigating these impossibility results, we shed light on their potential implications for AI safety research and propose potential strategies to overcome these limitations.
Analysis of Visual and Vestibular Information on Motion Sickness in Flight Simulation

Authors:
raw_author_name: Ahmad Javaid
raw_affiliation_string: Immersive Technologies Lab, School of Interdisciplinary Engineering and Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan
raw_author_name: Adnan Maqsood
raw_affiliation_string: Computational Aeronuatics Lab, School of Interdisciplinary Engineering and Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan

Abstract: Virtual reality (VR) is in its nascent technological advancement and market diffusion stages. Interestingly, the scientific exploration concerning the impact of non-isometric mapping disparities within visual–vestibular stimuli on motion sickness remains deficient. This investigation focuses on scrutinizing the visual–vestibular implications for motion sickness within the context of flight simulation. The developed motion platform, offering specific pitch and roll ranges of ±16 and ±17 degrees, respectively, was employed to induce varying ratios of simulated visual–vestibular cues. Involving a cohort of five participants, the study exposed them to two prevalent simulated mission profiles, subsequently assessing their motion sickness symptoms. Sixty responses were analyzed using the subjective assessment of the Simulator Sickness Questionnaire (SSQ). The findings reveal a reduction in cybersickness severity with congruent visual–vestibular stimuli in proportion to the variance observed among visual–vestibular coupling ratios. A comparative analysis of SSQ sub-categories demonstrates that disorientation holds the most significance in the hierarchy of motion sickness contributors, followed by oculomotor discomfort, with nausea manifesting as the least influential. This study can lead to situation awareness analysis by integrating VR-based flight-simulation setups in the formal training of pilots and UAV operators.