Reviewerly/exHarmony huggingface dataset:
Differentially Private Combinatorial Cloud Auction

Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">auction</i> has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this article, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differentially private</i> combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time.
Partitioned iterated function systems by regression models for head pose estimation

Abstract Head pose estimation represents an important computer vision technique in different contexts where image acquisition cannot be controlled by an operator, making face recognition of unknown subjects more accurate and efficient. In this work, starting from partitioned iterated function systems to identify the pose, different regression models are adopted to predict the angular value errors (yaw, pitch and roll axes, respectively). This method combines the fractal image compression characteristics, such as self-similar structures in order to identify similar head rotation, with regression analysis prediction. The experimental evaluation is performed on widely used benchmark datasets, i.e., Biwi and AFLW2000, and the results are compared with many existing state-of-the-art methods, demonstrating the robustness of the proposed fusion approach and excellent performance.
Silent Speech and Emotion Recognition from Vocal Tract Shape Dynamics in Real-Time MRI

We propose a novel deep neural network-based learning framework that understands acoustic information in the variable-length sequence of vocal tract shaping during speech production, captured by real-time magnetic resonance imaging (rtMRI), and translate it into text. In an experiment, it achieved a 40.6% PER at sentence-level, much better compared to the existing models. We also performed an analysis of variations in the geometry of articulation in each sub-regions of the vocal tract with respect to different emotions and genders. Results suggest that each sub-regions distortion is affected by both emotion and gender.
Single low-light image enhancer using Taylor expansion and fully dynamic convolution

Most preexisting deep learning-based enhancers are incapable of adjusting brightness of enhanced images, due to constant convolutional kernels. To address this issue, we propose an enhancer based on Taylor expansion and fully dynamic convolution, which can flexibly adjust the level of the brightness. In this study, the retinex model is first modified to serve as a framework for the proposed enhancer. Next, Taylor expansion and the attention mechanism are applied to construct a backbone network based on the modified retinex model. Subsequently, a strategy of fully dynamic convolution is proposed to flexibly adjust the degree of the brightness. Specifically, a weight-bias learning network is designed to dynamically generate weight matrices which are fed to the backbone network to perform the dynamic convolution. Furthermore, local mean and variance are used as a supplemental term for our loss function to improve the performance of the proposed enhancer, while a method of simulating realistic low-light images is used for synthesizing training data to suppress noise. Comprehensive experiments demonstrate satisfactory performance of the proposed enhancer in improving the clarity of low-light images and adjusting the degree of the brightness flexibly. • A novel enhancer is proposed, which can enhance low-light images, suppress noise, and adjust the brightness of enhanced images. • Taylor expansion is applied to integrate features to improve the performance of the proposed network. • A strategy of full dynamic convolution is explored to flexibly adjust the brightness of enhanced images. • The local mean and variance are added to loss function to further improve the performance of the proposed network.
Design and analysis of depth cues on depth perception in interactive mixed reality simulation systems

Abstract Implementing an interactive mixed reality simulation system is an economical and risk‐free method for skill training. However, research to enhance the accuracy of mixed reality simulation has always been a challenge. One of the key indicators of a simulation's accuracy is the user's depth perception. Adding depth cues to the simulation scene can effectively increase the accuracy of depth perception. Therefore, in this study, we designed five depth cues to find the one most beneficial for depth perception: (1) depth bar, (2) location ball, (3) fixed window, (4) 3D wireframe, and (5) movable silhouettes. These depth cues were tested using an experimental system with two sets of experimental settings. The first experiment compared the five depth cues at five different viewing distances and showed that the movable silhouettes benefited depth perception accuracy the most. The second experiment compared depth perception accuracies among three different horizontal tilt settings, showing that depth perception accuracy is dependent on the horizontal tilt. Furthermore, different depth perception accuracies were obtained with and without depth cues, proving that the effects from the horizontal tilts were independent of depth cues. The results of these studies provide references for designing interactive mixed reality simulation systems.
Aircraft Classification Using RADAR from small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations

This work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE) with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.
AI Assisted Fan Failure Prediction using Workload Fingerprinting

Server fans are one of the most critical parts of the data center server’s thermal system solution and a significant failure cause of many electronic systems. A fan inside the computer chassis is used to draw cooler air from outside and expel exhausted air from inside to adjust a server’s thermal range of electronic components. Fan failure can reduce cooling efficiency and result in unexpected throttling, overheating, and thermal shutdown. This paper demonstrates an AI-assisted methodology that predicts fan failure by developing a probabilistic model that correlates fan speed to workload fingerprint representing resource consumption patterns in the spatio-temporal domain. We use training data from many workloads that execute on many machines of similar configurations and record resource utilization statistics along with environmental parameters with a configurable sliding window. The algorithm evaluates the phase distribution using CPU utilization features and corresponding fan speeds to characterize the expected behavior of phases within individual phase boundaries. This allows us to explain abnormal fan speeds due to CPU’s ambient temperature, voltage, and peak hour workload demands. The paper also presents a novel simulator that helps generate artificial datasets covering various edge cases to evaluate the prediction algorithm’s performance.
A Fuzzy Multi-class Novelty Detector for Data Streams Under Intermediate Latency

One of the main characteristics of Data Streams is the arrival of examples on a continuous way, which can change over time. An important feature of these algorithms is novelty detection, which makes it possible to identify new classes, which may arise, even if their true labels are not yet available and update the decision model accordingly. Some approaches await the arrival of the true labels of the examples already classified to use this information in updating and maintaining the decision model. This time is called latency. Most classification algorithms, though, assume that the true labels are available immediately after the example is classified. Other methods in the literature deal with the existence of infinite latency and update their decision models in an unsupervised manner. In this work, we focus on a scenario rarely addressed in the literature, where the real labels will be available after a certain time, called intermediate latency. In addition, aiming at a more flexible learning to deal with the imprecision inherent to the data streams, we use fuzzy set theory concepts. Therefore, we propose a method called Enhanced Fuzzy Classifier with Multi-class Novelty Detection for Data Streams (EFuzzCND) that uses fuzzy clustering to perform classification and novelty detection in data streams. We compared our approach with one that is well known in the literature that uses hard clustering in order to show the main advantages and disadvantages of using fuzzy clustering in data streams classification.
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning

Link prediction for knowledge graphs aims to predict missing connections between entities. Prevailing methods are limited to a transductive setting and hard to process unseen entities. The recently proposed subgraph-based models provide alternatives to predict links from the subgraph structure surrounding a candidate triplet. However, these methods require abundant known facts of training triplets and perform poorly on relationships that only have a few triplets. In this paper, we propose Meta-iKG, a novel subgraph-based meta-learner for few-shot inductive relation reasoning. Meta-iKG utilizes local subgraphs to transfer subgraph-specific information and to rapidly learn transferable patterns via meta-gradients. In this way, we find the model can quickly adapt to few-shot relationships using only a handful of known facts with inductive settings. Moreover, we introduce a large-shot relation updating procedure to ensure that our model can generalize well to both few-shot and large-shot relations. We evaluate Meta-iKG on inductive benchmarks sampled from the NELL and Freebase, and the results show that Meta-iKG outperforms the currently state-of-the-art methods in both few-shot scenarios and standard inductive settings.
Trust Miscalibration Is Sometimes Necessary: An Empirical Study and a Computational Model

The literature on trust seems to have reached a consensus that appropriately calibrated trust in humans or machines is highly desirable; miscalibrated (i.e., over- or under-) trust has been thought to only have negative consequences (i.e., over-reliance or under-utilization). While not invalidating the general idea of trust calibration, a published computational cognitive model of trust in strategic interaction predicts that some local and temporary violations of the trust calibration principle are critical for sustained success in strategic situations characterized by interdependence and uncertainty (e.g., trust game, prisoner’s dilemma, and Hawk-dove). This paper presents empirical and computational modeling work aimed at testing the predictions of under- and over-trust in an extension of the trust game, the multi-arm trust game, that captures some important characteristics of real-world interpersonal and human-machine interactions, such as the ability to choose when and with whom to interact among multiple agents. As predicted by our previous model, we found that, under conditions of increased trust necessity, participants actively reconstructed their trust-investment portfolios by discounting their trust in their previously trusted counterparts and attempting to develop trust with the counterparts that they previously distrusted. We argue that studying these exceptions of the principle of trust calibration might be critical for understanding long-term trust calibration in dynamic environments.
Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations

Typically, nonlinear Support Vector Machines (SVMs) produce significantly higher classification quality when compared to linear ones but, at the same time, their computational complexity is prohibitive for large-scale datasets: this drawback is essentially related to the necessity to store and manipulate large, dense and unstructured kernel matrices. Despite the fact that at the core of training an SVM there is a simple convex optimization problem, the presence of kernel matrices is responsible for dramatic performance reduction, making SVMs unworkably slow for large problems. Aiming [at] an efficient solution of large-scale nonlinear SVM problems, we propose the use of the Alternating Direction Method of Multipliers coupled with Hierarchically Semi-Separable (HSS) kernel approximations. As shown in this work, the detailed analysis of the interaction among their algorithmic components unveils a particularly efficient framework and indeed, the presented experimental results demonstrate[, in the case of Radial Basis Kernels,] a significant speed-up when compared to the state-of-the-art nonlinear SVM libraries (without significantly affecting the classification accuracy).
Adaptation of Lazy-Theta* for UAS 3D path planning considering safety costs

This paper presents a 3D path planning solution for UAS to obtain a safe and smooth path. The safety plays an important role in the missions with UAS surrounding or entering the buildings. The heuristic search algorithms are good candidates to address this problem. The Lazy Theta* algorithm has been modified to foster safety and smoothness maintaining a distance from obstacles of the environment. Modifications to consider a new cost component related to the distance to the obstacles are performed. Tests in a realistic environment are shown to evaluate how the modified algorithm computes safer and smoother paths. Moreover, a comparison with the A* algorithm, the A* algorithm considering the new cost component and the original Lazy Theta* is presented.
LSB based image steganography using McEliece cryptosystem

Steganography is the science of embedding secret data inside data so they can be sent to destination security. Encryption algorithms used to save information from sly activities when sent from one device to another over the wireless network. In this paper McEliece will be used to text encryption by goppa code, where McEliece cryptosystem is a public-key cryptosystem based on error-correcting codes; then embedding the ciphertext as steganography image by LSB method. This lead to save information from attackers.
Certificate Transparency for ECQV Implicit Certificates

ECQV implicit certificates are special public-key certificates, proposed for smaller size and faster verification. In resource-constrained environments such as Internet of Things (IoT), it’s common to replace X.509 certificates with ECQV implicit certificates for better efficiency. Google’s Certificate Transparency (CT) makes the certificate issuance be publicly-visible to everyone, to enhance the trustworthiness of Public Key Infrastructure (PKI). However, the conventional CT approach based on Signed Certificate Timestamp (SCT) will be a burden for ECQV certificate verifiers in resource-constrained environments. In this paper, we propose an efficient scheme, named ECQV Implicit Certificate Transparency (EICT), to integrate CT with implicit certificates following the same assumption and approach of ECQV implicit certificates. EICT obtains better performance than CT applied in traditional X.509 PKIs. We also conduct the security analysis and performance evaluation of the proposed scheme.
On Defensive Neural Networks Against Inference Attack in Federated Learning

Federated Learning (FL) is a promising technique for edge computing environments as it provides better data privacy protection. It enables each edge node in the system to send a central server a computed value, named gradient, rather than sending raw data. However, recent research results show that the FL is still vulnerable to an inference attack, which is an adversarial algorithm that is capable of identifying the data used to compute the gradient. One prevalent mitigation strategy is differential privacy which computes a gradient with noised data, but this causes another problem that is accuracy degradation. To effectively deal with this problem, this paper proposes a new digestive neural network (DNN) and integrates it into FL. The proposed scheme distorts raw data by DNN to make it unrecognizable then computes a gradient by a classification network. The gradients generated by edge nodes will be sent to the server to complete a trained model. The simulation results show that the proposed scheme has 9.31% higher classification accuracy and 19.25% lower attack accuracy on average than the differential private schemes.
Web-based tool for the decision making in photovoltaic/wind farms planning with multiple objectives

Decision-making in energy planning implies a compromise between several criteria. Technical, economic and environmental criteria, have been the most adopted. The lack of a system that, taking into account technical, economic and environmental criteria, proposes to the decision makers detailed and efficient solutions that satisfy their preferences system, in energy planning, based on the projection of photovoltaic/wind farms at national level, motivated the present study. Conceptual mathematical model and detailed mathematical model of the task were elaborated. The criteria taken into account by the model determined four objective functions: the maximization of the generated energy, the minimization of costs, the minimization of the impacted area by photovoltaic farms and the minimization of the impacted area by wind farms. NSGA-II algorithm was selected to serve as a solution method to the multi-objective problem obtained. A web-based tool was developed, which constitutes a decision-making support system in the planning of photovoltaic/wind farms at national level. The integration with geographic information systems allowed the estimation of the energy parameters of the model and led to the complete automation of the tool.
Error Mitigation in Quantum Measurement through Fuzzy C-Means Clustering

Recently, Quantum Computing is entered in the so-called Noisy Intermediate-Scale Quantum (NISQ) era, where devices characterized by a few number of qubits are potentially able to overcome classical computers in performing specific tasks. However, noise in quantum operators still limits the size of quantum circuits that can be run in a reliable way. Consequently, there is a strong need for error mitigation approaches aimed at increasing reliability in quantum computation and making this paradigm really useful and productive in real world applications. In this paper, a fuzzy method, such as Fuzzy C-Means (FCM) clustering, has been used, for the very first time, to support the identification of matrices for error mitigation in quantum measurement. As shown in experiments, mitigation matrices identified with the support of FCM are able to strongly reduce error in computation when compared to mitigation matrices conventionally identified, like those used by IBM in its quantum library named Qiskit.
The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.
Autonomous Decision-Making With Incomplete Information and Safety Rules Based on Non-Monotonic Reasoning

In this article we propose a decision process integrating Non-Monotonic Reasoning (NMR), embedded in a deliberative architecture. The NMR process uses Default Logic to implement goal reasoning, managing partially observable or incomplete information, allowing the design of default behaviours completed by the handling of specific situations, in order to manage the current mission objective as well as safety rules. We illustrate our approach through an application of an underwater robot performing a marine biology mission.
Computing with Integrated Photonic Reservoirs

The idea of using photonic systems as reservoirs to perform general-purpose computing was first introduced in 2008. Since then, a wide range of systems using either discrete or integrated optical components has been explored. In this chapter, we summarise a decade of research into integrated coherent photonic reservoirs. In these systems, information is carried by the intensity and the phase of light waves. Computations emerge from the way the light propagates inside the system, and the ways in which light that travels along different paths is mixed and transformed. We discuss the computational capabilities of these reservoirs and the trade-offs that can be made to optimise them. We also discuss the technological constraints that are encountered in building such systems and the ways these reflect on their design and training. Finally, we give an overview of recent approaches to combining multiple such reservoirs into larger and computationally more powerful systems.