Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Effectiveness of Offline RL for Dialogue Response Generation

A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different ways. This motivates use of sequence-level objectives for dialogue response generation. In this paper, we study the efficacy of various offline reinforcement learning (RL) methods to maximize such objectives. We present a comprehensive evaluation across multiple datasets, models, and metrics. Offline RL shows a clear performance improvement over teacher forcing while not inducing training instability or sacrificing practical training budgets.
Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images

In this work, we proposed a fully automatic computer-aided diagnostic system to help radiologists detect and segment Prostate cancer (PCa) lesions from multi-parametric Magnetic resonance imaging (mp-MRIs) and predict whether the segmented lesions are clinically significant or not (benign vs. malignant lesion). Our proposed system is based on deep learning methods: residual networks (ResNet) models (ResNet50, ResNet101V2, ResNet152V2), inception networks (InceptionNetV3), U-Net, and MultiResU-Net models to classify clinically significant cancer, and properly segment the prostate lesion and determine its GGG. We tested our fully automatic detection solution on two publicly available datasets: Radboudumc and ProstateX. We achieved encouraging results where the PCa lesion classification model AUROC exceeded 98.4% accuracy. On the other hand, MultiResU-Net Model achieved 98.34% accuracy for the PCa lesion segmentation, which competes with current state-of-the-art approaches.
A Framework to Detect Cyber-attacks against Networked Medical Devices (Internet of Medical Things): An Attack-Surface-Reduction by Design Approach

: Most medical devices in the healthcare system are not built-in security concepts. Hence, these devices' built-in vulnerabilities prone them to various cyber-attacks when connected to a hospital network or cloud. Attackers can penetrate devices, tamper, and disrupt services in hospitals and clinics, which results in threatening patients' health and life. A specialist can Manage Cyber-attacks risks by reducing the system's attack surface. Attack surface analysis, either as a potential source for exploiting a potential vulnerability by attackers or as a medium to reduce cyber-attacks play a significant role in mitigating risks. Furthermore, it is necessitated to perform attack surface analysis in the design phase. This research proposes a framework that integrates attack surface concepts into the design and development of medical devices. Devices are classified as high-risk, medium-risk, and low-risk. After risk assessment, the employed classification algorithm detects and analyzes the attack surfaces. Accordingly, the relevant adapted security controls will be prompted to hinder the attack. The simulation and evaluation of the framework is the subject of further research.
On the computation and comparison of specific absorption rate (SAR) in a skin tissue using analytical and numerical methods

Specific absorption rate (SAR) is a measure of safety and a requirement for portable radio frequency devices which are regulated mainly by the standards recommended by the Institute of Electrical and Electronics Engineers (IEEE) and International Commission on Non‐Ionizing Radiation Protection (ICNIRP). Accurate approximation of SAR is therefore important for safety as well as for compliance purposes. This article presents the analytical representation and derivation of the SAR from the basics of electromagnetic theory, Maxwell Equations, and law of conservation of energy in electromagnetic theory. It is shown, analytically, that for a time harmonic electromagnetic wave propagating in a homogeneous lossy medium having permittivity ϵ, permeability µ, and conductivity σ, the time rate change of electric and magnetic energy densities at steady state inside a given volume is zero. It is also shown that at the steady state, the net power flowing through a volume, bounded by a surface is equal to the power dissipated in that volume. Furthermore, this work demonstrates that at the steady state, SAR can be represented in terms of power density or in terms of power dissipated in the given volume. Moreover, calculation of SAR for a 1 mm × 1 mm × 0.01 m box having the dielectric properties of human skin was performed using the derived formulas and then compared with the numerical results, obtained using a full wave 3D simulation tool, HFSS. A good agreement was found between the analytical and numerical results.
Finite-time median-related group consensus over directed networks

ABSTRACT In this paper, we investigate a novel finite-time median-related group consensus problem, where the finial consensus value can be identified as a desired function of the median of initial states instead of the much studied average value. The underlying communication topology is modelled by a weighted dynamical directed network. A distributed control protocol is firstly introduced to ensure that the agents can reach a median-related consensus in finite time in a collaboration network, meaning that all edge-weights of the communication network are non-negative. We then generalise the results to cooperation–competition networks, where the communication network is divided into predetermined collaboration subnetworks allowing possibly negative weights. Effective group control protocols are designed to guarantee the median-related group consensus in finite time. Finally, numerical simulations are presented to illustrate the availability of our theoretical results.
The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice

Recent breakthroughs in generative artificial intelligence (GAI) and the emergence of transformer-based large language models such as Chat Generative Pre-trained Transformer (ChatGPT) have the potential to transform healthcare education, research, and clinical practice. This article examines the current trends in using GAI models in medicine, outlining their strengths and limitations. It is imperative to develop further consensus-based guidelines to govern the appropriate use of GAI, not only in medical education but also in research, scholarship, and clinical practice.
IOT Based Food Freshness Detection Using Deep Learning Techniques

Nowadays we must be more concerned about the quality of the food items we consume and feed our loved ones. To get a full nutrition & mineral value in food items, it’s better to consume the fresh food items to avoid unnecessary health problems. We have various methods to determine or detect the freshness quality of our food items like the traditional method of lab testing the food sample, using images of the food sample with various machine learning and deep learning algorithms and even using the smell from the food sample. Many machine learning models have been developed to detect the freshness of food samples, but they lack fast and accurate detection. We have developed an IOT model that can predict the freshness of the food samples with image and its smell in a more accurate level with less time using deep learning algorithms. We have combined two deep learning algorithms for image detection to obtain more accurate results. We classify the images of food samples using the Convolutional Neural Network and detect the affected areas using the Object Detection Algorithm YOLO. We also take the level of gas emitted by rotten food samples using sensors and detect the spoilage level using the Artificial Neural Network model. We can determine the affected or rotten areas by combining the value from both image data and gas emitted by the given food sample. The result can be viewed in an application through a browser.
THE COMPARISON OF MATCH FILTERING AND SPECTRAL INFORMATION DIVERGENCE METHODS FOR DETECTING OF WATER QUALITY

The quality of water resources, which are very important for environmental and human health, can now be examined together with remote sensing technology. Management of water resources is crucial for the development of countries. In our country, the quality of the water resources is getting worse due to the development of the industry and other factors. There are many spectral classification methods in remote sensing, and the applicability of two spectral classification methods is aimed to evaluate water quality in this study. Within the scope of this study, İmranlı region of Kızılırmak River was examined. High resolution satellite image in operation CHRIS Proba is provided free of charge from European Space Agency. The spectroradiometer measurement points were determined to obtain the water sample. From these points, local measurements were made with the spectroradiometer simultaneously with transition of the satellite. The quality evaluation was carried out by analyzing the water samples in the laboratory. As a result of measurements made with the spectroradiometer, reflectance from the measurements was resampled at the Chris PROBA band intervals and a reference for classification was established. Using this data, applicability of a spectral classification method, Match Filtering and Spectral Information Divergence, was tested in order to evaluate water quality.
Chaotic Behavior of Traffic-Flow Evolution with Two Departure Intervals in Two-Link Transportation Network

In this study, the influence of traveler's departure time choice in day-to-day dynamic evolution of traffic flow in a transportation network is investigated. Combining historical information and real-time information, a dynamic evolution model of traffic flow with a study period divided into two intervals is proposed for a simple two-link network. Then, the evolution of network traffic flow is investigated using numerical experiments. Three types of information are considered: (1) only historical information, (2) only real-time information, and (3) both historical and real-time information. The results show that the dynamic evolution of network traffic flow under the three types of information is similar. However, the possibility of chaos occurrence under both historical and real-time information is smaller than that under two individual types of information. When chaos occurs, the chaotic behavior in traffic-flow evolution under only real-time information is relatively less complex than that under the other two types of information.
An empirical study on the human role in merge conflict resolution

Modern software engineering often employs a version control system (VCS) to collaborate on a project, but collaboration inevitably leads to merge conflicts, hindering the automation of merging. Many attempts to predict, prevent and characterize conflicts have been made, but more research on the topic of merge conflict resolution needs to be conducted. Therefore, we test the feasibility of an approach that aims to improve the resolution of existing merge conflicts to add to this research field. To investigate the feasibility of the approach, we studied how often humans intervene in the resolution of merge conflicts for 8500 open-source projects from GitHub, containing a total of 119 794 conflicting merges, 217 712 conflicting files, and 297 126 conflicting chunks. We found that (i) 34.11 % of conflicting merges were resolved by picking one of the conflicting chunks in each conflict. Further, (ii) 46.29 % of conflicting files were resolved using the above method, and (iii) 80.63 % of conflicting chunks, i.e., single conflicts, were resolved using the abovementioned method. Additionally, we found that (iv) language or popularity of the projects does not influence the resolution process, but (v) the number of conflicts in a conflicting merge, respectively, in a conflicting file does play a role. We also studied two possible indicators, commit count and contributor count, on their predictive power on conflicting merges, and the results show that (vi) they are insignificant. We concluded that the approach to improve the resolution of conflicting merges is infeasible because the rate at which the conflicting merges were resolved by picking an existing chunk is too low in combination with performance problems concerning a growing number of conflicts.
Simulation of one dimensional open channel flows using the SPH model

In this study, shallow water equations with source terms were numerically solved using the smoothed particle hydrodynamics (SPH) method. The proposed model can incorporate the inflow and outflow boundaries. In addition, bed discontinuity was modelled using the fluid particles without any extra bed particles. Artificial viscosity to smoothen the numerical oscillations in the water surface profile was considered. The sensitivity and convergence analyses were used to determine the optimal model parameters. Seven different kinds of open channel flow in prismatic channels were simulated to demonstrate the model’s capabilities. The numerical accuracy was quantified in terms of L2 error norm. Comparison with earlier results shows that the SPH model can be used to compute steady and unsteady open channel flows with or without bed discontinuity.
Improving Voice Conversion for Dissimilar Speakers Using Perceptual Losses

The rising trend of using voice as a means of interacting with smart devices has sparked worries over the protection of users' privacy and data security. These concerns have become more pressing, especially after the European Union's adoption of the General Data Protection Regulation (GDPR). The information contained in an utterance encompasses critical personal details about the speaker, such as their age, gender, socio-cultural origins and more. If there is a security breach and the data is compromised, attackers may utilise the speech data to circumvent the speaker verification systems or imitate authorised users. Therefore, it is pertinent to anonymise the speech data before being shared across devices, such that the source speaker of the utterance cannot be traced. Voice conversion (VC) can be used to achieve speech anonymisation, which involves altering the speaker's characteristics while preserving the linguistic content.
Autonomous Underwater Docking using Flow State Estimation and Model Predictive Control

We present a navigation framework to perform autonomous underwater docking to a wave energy converter (WEC) under various ocean conditions by incorporating flow state estimation into the design of model predictive control (MPC). Existing methods lack the ability to perform dynamic rendezvous and autonomously dock in energetic conditions. The use of exteroceptive sensors or high performing acoustic sensors have been previously investigated to obtain or estimate the flow states. However, the use of such sensors increases the overall cost of the system and expects the vehicle to navigate close to the seafloor or other landmarks. To overcome these limitations, our method couples an active perception framework with MPC to estimate the flow states simultaneously while moving towards the dock. Our simulation results demonstrate the robustness and reliability of the proposed framework for autonomous docking under various ocean conditions. Furthermore, we conducted laboratory trials with a BlueROV2 docking with an oscillating dock and achieved a greater than 70% success rate.
Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation

There exist complex patterns behind the decision-making processes of different individuals across different environments. For instance, in a social recommender system, various user behaviors are driven by highly entangled latent factors from two environments, i.e., consuming environment where users consume items and social environment where users connect with each other. Un-covering the disentanglement of these latent factors for users can benefit in enhanced explain-ability and controllability for recommendation. However, in literature there has been no work on social recommendation capable of disentangling user representations across consuming and social environments. To solve this problem, we study co-disentangled representation learning across different environments via proposing the curriculum co-disentangled representation learning (CurCoDis) model to disentangle the hidden factors for users across both consuming and social environments. To co-disentangle joint representations for user-item consumption and user-user social graph simultaneously, we partition the social graph into equal-size sub-graphs with minimum number of edges being cut, and design a curriculum weighing strategy for subgraph training through measuring the complexity of subgraphs via Descartes’ rule of signs. We further develop the prototype-routing optimization mechanism, which achieves co-disentanglement of user representations across consuming and social environments. Extensive experiments for social recommendation demonstrate that our proposed CurCoDis model can significantly outperform state-of-the-art methods on several real-world datasets.
Active Noise Attenuation Using Lqg/ltr Control

– This paper presents a control system design for active noise cancellation using “Linear Quadratic Gaussian / Loop Transfer Recovery” (LQG/LTR). An implementation by an equipment like a headset is proposed, where both active and passive attenuation techniques are used. In this paper, the active technique is applied to attenuate low-frequency acoustic noise. The design objective is not only the active attenuation of acoustic noise, but also the sound audition at frequency band of the human voice. Results obtained from digital simulations are presented, which is considering the existence of a noise in the environment located near the turbine in a hydroelectric plant. In this noisy environment, the noise is attenuated and the equipment allows the user to hear human voices.
Model-Free H∞ Optimal Tracking Control of Constrained Nonlinear Systems via an Iterative Adaptive Learning Algorithm

In this paper, an $H_{\infty }$ optimal tracking controller for completely unknown discrete-time nonlinear systems with control constraints is obtained by using an iterative adaptive learning algorithm. An augmented system is established by integrating the tracking error system and the reference trajectory. As an identifier of the unknown systems, a neural network (NN) is introduced with asymptotic stability of the estimation error. An action–disturbance–critic NN structure is proposed to implement the iterative dual heuristic programming algorithm with convergence guarantee of the costate function and the control policy. Simulation results and comparisons are provided to illustrate the superior performance of the designed optimal tracking controller.
Application Defined Networks

With the rise of microservices, the execution environment of many cloud applications has become a set of virtual machines or containers connected by a flexible and feature-rich virtual network. We argue that the implementation of such virtual networks should be completely application-specific and not layered on top of general-purpose network abstractions from the Internet age. Such layering tends to more than double the latency and CPU usage of applications. We propose application-defined networks in which developers specify network functionality in a high-level language and a controller generates a custom distributed implementation that runs across available hardware and software resources. Experiments with a preliminary prototype suggest that, compared to the state of the art, ADN reduces latency by up to 20x and increases the throughput by up to 6x.
Controlling unfolding in type theory using extension types

—We present a new way to control the unfolding of definitions in dependent type theory. Traditionally, proof assistants require users to fix whether each definition will or will not be unfolded in the remainder of a development; unfolding definitions is often necessary in order to reason about them, but an excess of unfolding can result in brittle proofs and intractably large proof goals. In our system, definitions are by default not unfolded, but users can selectively unfold them in a local manner. We justify our mechanism by means of elaboration to a core theory with extension types —a connective first introduced in the context of homotopy type theory—and by establishing a normalization theorem for our core calculus. We have implemented controlled unfolding in the cooltt proof assistant, inspiring an independent implementation in Agda.
Solving a mixture model of two-phase flow with velocity non-equilibrium using WENO wavelet methods

Purpose The purpose of this work is to present the implementation of weighted essentially non-oscillatory (WENO) wavelet methods for solving multiphase flow problems. The particular interest is gas–liquid two-phase mixture with velocity non-equilibrium. Numerical simulations are carried out on different scenarios of one-dimensional Riemann problems for gas–liquid flows. Results are validated and qualitatively compared with solutions provided by other standard numerical methods. Design/methodology/approach This paper extends the framework of WENO wavelet adaptive method to a fully hyperbolic two-phase flow model in a conservative form. The grid adaptivity in each time step is provided by the application of a thresholded interpolating wavelet transform. This facilitates the construction of a small yet effective sparse point representation of the solution. The method of Lax–Friedrich flux splitting is used to resolve the spatial operator in which the flux derivatives are approximated by the WENO scheme. Findings Hyperbolic models of two-phase flow in conservative form are efficiently solved, as shocks and rarefaction waves are precisely captured by the chosen methodology. Substantial computational gains are obtained through the grid reduction feature while maintaining the quality of the solutions. The results indicate that WENO wavelet methods are robust and sufficient to accurately simulate gas–liquid mixtures. Originality/value Resolution of two-phase flows is rarely studied using WENO wavelet methods. It is the first time such a study on the relative velocity is reported in two-phase flows using such methods.
Pareto-Based Adaptive Resources Selection Model in Hybrid Cloud Environment

Hybrid cloud has found broad acceptance in organizations and in the field of research. Most of the organizations with its own data center maneuver a hybrid cloud model. The organization utilizes its private resources to compute its maximum load. But then move to the public cloud when infrastructure within its premises is inadequate to handle the peak workloads. The fluctuations in workloads lead to the widespread acceptance of pay-as-you-go cloud resource model. In this article, we describe the Adaptive Multiobjective Resource Selection Model (AMORSM) that automatically selects the resources from the private as well as public clouds for the execution of an application within user-specified deadline. The AMORSM is implemented and integrated with Gravitational Search Algorithm (GSA) enhanced with an adaptive elitist NSGA-II (Non-Dominated Sorting Genetic Algorithm-II). The proposed algorithm is used to accomplish multiple conflicting objectives such as minimum execution time, reduction in execution cost and energy consumption by obtaining Pareto frontier optimal solution. The hybrid elitist NSGA-II (A-NSGA2 with GSA) produces number of non-dominated solution satisfying the criteria, in order to facilitate wider preferences to the cloud consumer. It also uses adaptive acceleration coefficient to maintain the diversity in the frontiers. Our simulation demonstrates 32% improvement in performance and cost trade-off optimization, in hybrid cloud environment when compared with standard NSGA-II and SPEA multiobjective optimization algorithm.