Reviewerly/exHarmony huggingface dataset:
Multi-step Bayesian Optimization-based intelligent task planning for an ozone observation satellite

This paper presents an intelligent task planning strategy for an Earth Observing Satellite (EOS) monitoring the Ozone distribution using multi-step Bayesian Optimization (BO). Significant improvement in observation performance can be achieved by implementing an offset-pointing strategy using single-step or classical BO compared to the simple nadir-pointing approach, which does not consider the possible future outcomes when deciding the current action. To account for future considerations, this paper presents a modified Continuous Belief Tree Search (CBTS) algorithm that enables a non-myopic multi-step look-ahead optimization in the continuous action domain. The design utilizes a Control-effort based Upper Confidence Bound function as the acquisition function for BO. Simulation results demonstrate that the multi-step BO is capable of significantly reducing the model uncertainty compared to the single-step BO. Additionally, using BO provides a superior way to determine the first actions than obtaining them randomly. Moreover, sampling the training data set provides a way to reduce the computational time without reducing the computation tree size.
Quantum covariance via quantum information

Abstract For a quantum state and a set of observables, S. Luo found a matrix inequality linking the quantum covariance and the quantum Fisher information defined via the symmetric logarithmic derivatives. In this paper, we provide the corresponding matrix inequality based on the Wigner–Yanase skew information.
Autonomous Navigation and Mapping of Snake Robot for Urban Search and Rescue

In an Urban Search and Rescue situation, under extreme time pressure, rescue workers have to locate and extract the trapped people in collapsed structures. Due to the lack of medical treatment, food, and water, the victim's mortality rate dramatically increases over time. Rescue operations for both rescue workers and victims might be as dangerous as the initial event. For such situations, snake robots which are inspired by their biological counterparts, are shown to be a good option in the literature, to help the rescue workers in positioning the victims or delivering life-saving drugs to extend the life of the victims for some time. However, current research mainly focuses on mechanical design, control mechanisms, and gait generation. To alleviate this concern, we have integrated state-of-the-art methods to develop an autonomous snake robot that can navigate in an unknown environment while also generating a 3D map, to provide a better idea of the environment to the rescue workers. A simulated maze environment is implemented and demonstrated by using the CoppeliaSim simulation, running on Robot Operation System (ROS) and Linux OS. The simulation result shows the effectiveness of the proposed autonomous navigation system for the snake robot to plan an obstacle-free path from the robot's current position to the goal position without an apriori knowledge of the environment.
Optimal Design and Simulation of a Rooftop Photovolataic System in Faculty of Engineering Guilan University

In this paper, simulation and study the rooftop photovoltaic system on building roofs of the faculty of engineering Guilan university, Iran. It is now clear that conventional plants heavily contribute to greenhouse gas emissions, so they are not environmentally friendly. Renewable energy sources are an alternative solution. In this study, the design of a grid-connected photovoltaic system was simulated. PV panels and the number of inverters are the main elements of the PV power system. According to the regional weather data and university ceiling structure, shadow analysis, calculation concerning the total number of panels, number of strings, and number of panels per string, and their optimal angles are done. PVSYST software is used for the proposed photovoltaic system design
Comparison Between ANSYS Fluent and Solidworks Internal Flow Simulation for Analysis of A Fuzzy Logic Controller-Based Heating/Cooling System in A Mobile Robot Design

Nowadays, Computational Fluid Dynamics (CFD) software's have become important tools to study the behavior of fluids in the design phase of robots in various applications. This includes fluid behavior around underwater remotely operated vehicles, aerodynamic characteristics of mobile robots/drones in extreme outdoor environments, and internal flow simulation to determine the heating/cooling time of an enclosed space and estimate the system's energy requirements. For this paper, two CFD software's; ANSYS Fluent and Solidworks 2022 are used to conduct a CFD analysis on the heating/cooling of the internal space of a mobile robot design aimed to operate in extreme temperatures ranging from −40 °C to 50 °C. Heating time is determined by using a constant power magnitude of 8138683 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> emitted from four different heating elements located at different parts of the robot body and two fans rotating at 1800 rpm. Moreover, the cooling time of the internal space is evaluated based on fans operating at 6000 rpm over the electronics and three outlets that direct the warm air to the outside environment. For both software's, the same boundary conditions were applied to the robot body to obtain fair results for comparison. Based on the simulation results, the desired temperature of 8 °C from an initial temperature of −40 °C was reached within 44 s for ANSYS Fluent and 650 s for Solidworks. For cooling, 8 °C was obtained within 6 s for ANSYS Fluent and 24 s for Solidworks. The difference in the results between the two software's is mainly due to the method in which the fluid domain is defined. In the case of Solidworks, it considers the material thickness on the boundary between solid/fluid regions.
Deep Learning-based Feature Fusion for Action Recognition Using Skeleton Information

Various action recognition systems have been proposed, but most of them are not feasible to be used in real-time applications. Skeleton-based action recognition has a low computational cost and is not affected by background changes. As the pose estimation models are becoming faster (almost real-time), a model was created with only 1.8M parameters named DD-net, which uses the skeleton information to predict the action. Recently an improved version of the model came out and was named TD-net. The model is very rich with geometric-based features but lacks motion-based features. To overcome this we added two motion features in the model named acceleration and velocity. These features were created using second order Taylor's approximation, in a window around the current frame. The model accuracy was compared with DD-net, TD-net, and state-of-the-art algorithms using three different datasets. An increase in accuracy is observed for all three datasets (i.e 1.1% for SHERC, 1.7% for FPHAB and 2% for JHMDB) when compared with TD-net.
Applications of Hybrid Conditional Planning in Service Robotics

Hybrid planning computes task plans in full observability (a sequence of possible actuation actions) via the integration of low-level feasibility checks in classical planning approaches. Conditional planning extends the classical planning framework to account for plans under partial observability. We use a hybrid conditional planning approach to compute plans that include sensing and actuation actions in solving real-world problems by addressing uncertainties due to incomplete information at the planning phase. We generate a hybrid conditional plan of actions using parallel instances of a non-monotonic hybrid HCPLAN that supports defaults, integration of external computations, and non-deterministic actions. We show our hybrid conditional planning framework's HCPLAN applicability through service robotics scenarios with manipulation and navigation tasks. Furthermore, we evaluate the effect of parallel threads on the computation of hybrid conditional framework on different benchmark scenarios for the service robotics domains.
Housekeeping Using Multi-Agent Service Robotics

Service robotics in dynamic environments is a challenging yet exciting field that has witnessed incredible advances in the last few decades. The credit for these advancements goes to artificial intelligence and robotics technological progressions. The housekeeping domain is an example of a service robotics domain. A high-level task planner can compute a plan of action, while low-level functionalities like perception and motion planning can ensure feasibility. We propose a solution to the housekeeping problem that utilizes the expressiveness of action language C+ to formalize actions and compute an action plan. To check for feasibility in the real world, we execute the action plans in a physics simulator, GAZEBO. We also propose benchmark instances for the housekeeping domain and evaluate our planning and execution times approach. We report possible execution failures for the benchmark instances and offer plausible solutions, such as hybrid planning, plan execution monitoring, and conditional planning.
Detecting Online Grooming By Simple Contrastive Chat Embeddings

Protecting children against predators that use the internet as a medium to find their victims is essential, where the difficulty of monitoring online messaging platforms to avoid potential threats toward underage users is alarming. Online grooming detection requires a deep knowledge of predatory behaviour where an online conversation's positive or negative connotations rely on the context where it can change rapidly in an online chat. Therefore, it is essential to use robust feature vectors for predatory conversation detection. This research paper proposes a contrastive learning framework for feature extraction in a sentence-based manner where it can assign a feature vector to the conversation with misspellings using subword information. Also, it is vital to have a high detection rate of true positives to avoid potential non-wanted consequences. We propose a configuration of RoBERTa encoders and supervised SimCSE for training the SVM model, leading to a high rate of detecting relevant samples (predatory samples). Our proposed approach gains an F0.5-score of 0.96, an F1-score of 0.96, and an accuracy of 0.99 for predatory conversation detection that benchmarks the state-of-the-art. In order to improve the performance, we also perform experiments with various fusion approaches, where the sum fusion of all configurations obtains an accuracy of 0.99, an F_1-score of 0.97, and an F0.5-score of 0.98.
Deepfake Detection

This tutorial presents developments on the detection of Deepfakes, which are realistic images, audios and videos created using deep learning techniques. Deepfakes can be readily used for malicious purposes and pose a serious threat to privacy and security. The tutorial summarizes recent Deepfake detection techniques and evaluates their effectiveness with respect to several benchmark datasets. Our study finds that no single method can reliably detect all Deepfakes and, therefore, combining multiple methods is often necessary to achieve high detection rates. The study also suggests that more extensive and diverse datasets are needed to improve the accuracy of detection algorithms. A taxonomy of Deepfake detection techniques is introduced to aid future research and development in the field. We conclude by calling for the development of more effective Deepfake detection methods and countermeasures to combat this evolving and spreading threat.
Privacy-preserving Deep-learning Models for Fingerprint Data using Differential Privacy

Fingerprint recognition is a widely adopted biometric authentication method that leverages the unique characteristics of fingerprints to identify individuals. Its applications range from access control and authentication to forensic science, making the development of a robust, precise, and secure model for fingerprint recognition and analysis of paramount importance. Recently, deep learning and machine learning models have shown promise in this field, however, the use of these models raises significant privacy concerns as there is a potential for private fingerprint data to be compromised. This research aims to address these concerns by incorporating differential privacy techniques to protect the privacy of fingerprints. It provides evidence that the use of differential privacy technique leads to acceptable trade-off between preserving the privacy of fingerprints and accuracy of fingerprint recognition systems while maintaining robustness against model inversion attacks. With a noise multiplier of 0.01, the verification model attains a good accuracy of 89.32% at privacy budget ε = 1.2 , and less than 80% at lower privacy budget for the identification model.
Continuous Group Key Agreement with Flexible Authorization and Its Applications

Secure messaging (SM) protocols allow users to communicate se-curely over an untrusted infrastructure. Alwen et al. formally de-fined the key agreement protocol used in secure group messaging (SGM) as continuous group key agreement (CGKA) at CRYPTO 2020. In their CGKA protocol, all of the group members have the same rights and a trusted third party is needed. On the other hand, some SGM applications may have a user in the group who has the role of an administrator. When the administrator as the group manager (GM) is distinguished from other group members, i.e., in a one-to-many setting, it would be better for the GM and the other group members to have different authorities. We achieve this flex-ible authorization by incorporating a ratcheting digital signature scheme (Cremers et al. at USENIX Security 2021) into the existing CGKA protocol and demonstrate that such a simple modification allows us to provide flexible authorization. In Alwen et al.'s CGKA protocol, an external public key infrastructure (PKI) functionality as a trusted third party manages the confidential information of users, and the PKI can read all messages until all users update their own keys. In contrast, the GM in our protocol has the same role as the PKI functionality in the group, so no third party outside the group handles confidential information of users and thus no one except group members can read messages regardless of key updates. Our proposed protocol is useful in the creation of new applications such as broadcasting services.
Improving Application Artifact Inference Using Matched Sector Adjacency Weighting

Digital forensics investigators may need to infer the past presence of an application which was uninstalled prior to device seizure or media imaging. Some existing approaches use a catalog of fixed data block hashes to scan for evidence of a previously installed but now uninstalled application, but false positive sector-level matches may negatively impact algorithm performance. In an effort to get more accurate results when inferring prior application presence, we propose using a sector adjacency weighting method whereby we codify the intuition that an apparent application file remnant is more likely to be from the file of interest if we observe a run of contiguous matched sectors, and less likely if we observe scattered (singleton) matched sectors on the disk image. Test results show that the amount of false positive matching is reduced and the resulting inference is more accurate with the introduction of matched sector adjacency weighting.
Robot Autonomous Exploration and Map Building Method

How to enable effective autonomous exploration and construction maps in unknown indoor environments is one of the key issues in the field of mobile robots. The mobile robot autonomous exploration and map building system studied in this paper aims to further improve the autonomy of the indoor mobile robot, so that the robot can independently complete the exploration and map building task in the unknown indoor environment and realize the autonomous navigation and other functions according to the constructed environment map. An independent exploration and SLAM construction method based on the fast exploration random tree (RRT) is proposed, applying the RRT algorithm to the boundary exploration field. Through performing collision tests by randomly spreading the sampling points in the environment and adding the parameters of the adaptive step size, the boundary points are optimized and evaluated for the best explored boundary points. At the same time, using the combination of global RRT and local RRT construction method, it accelerates the exploration of the unknown environment, until throughout the whole environment. Experimental testing in a simulation environment, the experimental results show that the system works very well, and can independently go through the whole environment and complete the map drawing.
Genetic Drift and its Effects on the Performance of Genetic Algorithm(GA)

A genetic algorithm (GA) is a meta-heuristic computation method that is inspired by Darwin's theory of evolution. GA has a promising future in optimization and search problems. It has caught the interest of researchers in the fields of data science, artificial intelligence, and mathematics among many others. GA depends on various operators which include parent selection, crossover, and mutation. The crossover and mutation operators incorporate diversity in the population. GA has a dependency on genetic diversity just like thriving species of any habitat. In the natural world, isolated species and small populations amplify genetic drift, increasing their chances of loss of alleles including beneficial ones. Existing research in GA has an emphasis on natural selection, however, another mechanism of evolution i.e., Genetic drift is not studied in GA. Genetic drift, like in nature, also affects genetic algorithms as it mimics natural processes. Genetic drift causes fixation of alleles and loss of diversity, making GA provide a sub-optimal solution. This research establishes the negative effects of demographic restrictions on the population as observed in the natural world. Subsequently establishes a link between research in biodiversity and evolution in the natural world to enhance the performance of GA in the digital world.
A Gradual Probabilistic Lambda Calculus

Probabilistic programming languages have recently gained a lot of attention, in particular due to their applications in domains such as machine learning and differential privacy. To establish invariants of interest, many such languages include some form of static checking in the form of type systems. However, adopting such a type discipline can be cumbersome or overly conservative. Gradual typing addresses this problem by supporting a smooth transition between static and dynamic checking, and has been successfully applied for languages with different constructs and type abstractions. Nevertheless, its benefits have never been explored in the context of probabilistic languages. In this work, we present and formalize GPLC, a gradual source probabilistic lambda calculus. GPLC includes a binary probabilistic choice operator and allows programmers to gradually introduce/remove static type–and probability–annotations. The static semantics of GPLC heavily relies on the notion of probabilistic couplings, as required for defining several relations, such as consistency, precision, and consistent transitivity. The dynamic semantics of GPLC is given via elaboration to the target language TPLC, which features a distribution-based semantics interpreting programs as probability distributions over final values. Regarding the language metatheory, we establish that TPLC–and therefore also GPLC–is type safe and satisfies two of the so-called refined criteria for gradual languages, namely, that it is a conservative extension of a fully static variant and that it satisfies the gradual guarantee, behaving smoothly with respect to type precision.
Autonomous Navigation and Mapping of Water Channels in a Simulated Environment Using Micro-Aerial Vehicles

Irrigation canal networks serve as the bedrock of agriculture sectors across the globe as they are the primary channel through which water runs from major sources to agricultural lands. However, the water-carrying capacity of these water channels significantly reduces over time because of erosion, structural deterioration, and silt accumulation. As a result, routine inspections are required to analyze and repair these water channels which necessitates automation because of the vast length of the channels. We present a framework that enables Micro-Aerial Vehicles(MAVs) not only to navigate in an unknown cluttered canal environment but also to provide a complete 3-Dimensional map for the inspection. The framework consists of three main components (mapping, path planning, and mission planner) that gradually explore the environment while solving for start to local goal queries. We use Octomap; an octree-based representation of the environment for mapping, and we extended the Informed Rapidly-exploring Random Tree (Informed-RRT*) for optimal path planning and replan paths with respect to the static nearby and dynamic obstacles perceived during the execution of the mission. A simulated 2,378 meters length of canal environment is implemented and demonstrated by using the Airsim simulation in the Unreal engine, running on Robot Operation System (ROS) and Linux OS. Results obtained show that the framework enables the MAV to navigate over a simulated canal environment and allows the MAV to map the 3D structure of the canal.
Role of Machine Learning in Power Analysis Based Side Channel Attacks on FPGA

The cloud-based devices face many threats these days due to shared resources like power. The attacker measures the power by using some remote sensors which are present in attacker tenants. These sensors get partial or full access to a power distribution network (PDN) and work as a backdoor for the attacker. In our research, we explored potential security issues which involved power analysis-based side-channel attacks (SCAs) on Field Programmable Gate Arrays (FPGAs). We have made three major contributions to our research paper. First, we have discussed the power analysis or power profiling of FPGA, which is dependent upon voltage fluctuations' leakage while performing some encryption tasks. The voltage fluctuations of the cryptographic module are measured by some physical source like an oscilloscope or remote source like delay line sensors. Second, we have discussed potential power analysis-based SCAs that used these measurements of voltage fluctuations to extract the secret key. Third, we have designed a framework based on machine learning (ML) and deep learning (DL) models to perform secret key predictions and successful attacks. Firstly, our custom convolutional neural networks (CNN) model has revealed all 16 bytes of the secret key and performed a successful attack with only 570 attack power traces. Secondly, the multi-layer perceptron (MLP) model has successfully attacked only using 3200 traces using the same framework. Overall we have achieved a better performance in terms of the required number of power traces for a successful attack, training time, prediction time, and attack time.