Reviewerly/exHarmony huggingface dataset:
Emergence of sequential dynamical invariants in central pattern generators from auto-organized constraints in their sequence time intervals

Motor coordination by the nervous system involves generating and coordinating muscle sequential activity. Such activity must be robust to produce effective motion that relies on patterned motor events ordered in a specific sequence and, at the same time, flexible in timing and duration to adapt to intrinsic and environmental circumstances. Motor neural circuits produce the signaling that muscles use for their sequential action. Even in rhythmic repetitive motion such as walking, breathing and chewing, variability in the timing of the events that build the sequence can be observed within the overall ordered rhythm of the motion. In this context, central pattern generators (CPGs) are key neural circuits to study the cycle-by-cycle balance between the robustness of the sequential order of neural events and the irregularity of the intracycle intervals that shape a rhythmic sequence. Recently, the presence of sequential dynamical invariants in the form of robust cycle-by-cycle relationships between specific sequence time intervals was unveiled in living CPGs. Variability of other sequence intervals remains unrelated and coexists with the presence of the invariants. Sequential dynamical invariants have been proposed to underlie the cycle-by-cycle CPG coordination. In this work, we used a minimal CPG circuit building block of two interacting inhibitory model neurons to assess the emergence of coordinated variability in sequential neural activity. We studied the conditions that produce sequential dynamical invariants in the circuit using intrinsically irregular neuron models and symmetric and asymmetric network topologies. We quantified the circuit irregularity and related it to the presence of invariants under different configurations. We found the conditions to propagate and sustain coordinated variability between time intervals, and thus our study illustrates the auto-organized constraint-based mechanisms for motor sequence coordination that shape sequential dynamical invariants. This is the first model study in which sequential dynamical invariants are observed considering only the intrinsic variability of CPG neurons without external stimuli. Finally, we discuss possible applications of this research in the context of autonomous robotics and artificial intelligence.
A novel technique for image steganography based on maximum energy seam

Abstract Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represents the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stego-image, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of images and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.
Clustering-based attack detection for adversarial reinforcement learning

Abstract Detecting malicious attacks presents a major challenge in the field of reinforcement learning (RL), as such attacks can force the victim to perform abnormal actions, with potentially severe consequences. To mitigate these risks, current research focuses on the enhancement of RL algorithms with efficient detection mechanisms, especially for real-world applications. Adversarial attacks have the potential to alter the environmental dynamics of a Markov Decision Process (MDP) perceived by an RL agent. Leveraging these changes in dynamics, we propose a novel approach to detect attacks. Our contribution can be summarized in two main aspects. Firstly, we propose a novel formalization of the attack detection problem that entails analyzing modifications made by attacks to the transition and reward dynamics within the environment. This problem can be framed as a context change detection problem, where the goal is to identify the transition from a “free-of-attack” situation to an “under-attack” scenario. To solve this problem, we propose a groundbreaking “model-free” clustering-based countermeasure. This approach consists of two essential steps: first, partitioning the transition space into clusters, and then using this partitioning to identify changes in environmental dynamics caused by adversarial attacks. To assess the efficiency of our detection method, we performed experiments on four established RL domains (grid-world, mountain car, carpole, and acrobot) and subjected them to four advanced attack types. Uniform, Strategically-timed, Q-value, and Multi-objective. Our study proves that our technique has a high potential for perturbation detection, even in scenarios where attackers employ more sophisticated strategies.
Who spies on whom? Unravelling the puzzle of state-sponsored cyber economic espionage

Traditional conceptions of state-sponsored cyber economic espionage suggest that countries with different product profiles should experience high levels of espionage between them. However, this is not what we observe empirically. Incidence of economic espionage tends to be prevalent between countries with similar product and manufacturing profiles. This suggests that we may be missing critical parts of what drives state-sponsored cyber economic espionage. To help unravel this puzzle, I develop a novel theoretical framework that proposes that because attackers seek to maximize the expected utility of stolen information, they target countries that possess similar productive capabilities as themselves. Consequently, countries with dissimilar product profiles should avoid targeting each other for espionage. I test this argument using data on states’ product complexities and cyber economic espionage for a global sample of countries in a dyadic analytical framework. The results robustly show that for any pair of countries, as the complexities of their products diverge, they become significantly less likely to aim espionage attempts at each other. This study thus contributes new insights to explain why cyber economic espionage appears restricted to only a small number of advanced economies. It also illustrates the utility of large-N dyadic approaches in studying state-sponsored cyber espionage.
Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification

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

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

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

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

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

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

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

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

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.
The key: Abstraction, embodiment, and proper distance within the virtual home

The emergence of virtual reality (VR) humanitarian filmmaking as a genre over the past ten years has generated a large body of critical debate around the efficacy and ethics of VR as a tool for generating empathy towards marginalised communities. Whilst numerous studies have indicated the potential for VR to impact empathy levels of end users, there have been recurrent critiques of the power dynamics of VR production, as well as the value of empathy as a means of producing social change. Lacking in these discussions has been a detailed consideration of VR aesthetics and the extent to which stylistic strategies impact audience positioning. Through the example of the animated VR experience The Key (Celine Tricart, 2019), this article will explore experience design in the context of ethical debates around humanitarian VR. As an interactive, narrative experience that addresses themes of loss and displacement, The Key can be productively analysed in relation to both VR ethics and wider cultural understandings of home and belonging. Responding to ethical debates around proximity within immersive experiences, the article will examine aesthetic strategies within The Key for ensuring what Roger Silverstone has labelled “proper distance” between the user and the virtually represented space. Through its use of visual abstraction and simplification, as well as the limited physical interaction it affords with its virtual world, the virtual home of The Key will be understood as a site of resistance to universalising narratives of home, one which invites critical reflection on the factors that determine our access to shelter.