Miracle-dz/UniFAR-FTUs huggingface dataset:
Computation Offloading in Energy Harvesting Systems via Continuous Deep Reinforcement Learning

As a promising technology to improve the computation experience for mobile devices, mobile edge computing (MEC) is becoming an emerging paradigm to meet the tremendous increasing computation demands. In this paper, a mobile edge computing system consisting of multiple mobile devices with energy harvesting and an edge server is considered. Specifically, multiple devices decide the offloading ratio and local computation capacity, which are both in continuous values. Each device equips a task load queue and energy harvesting, which increases the system dynamics and leads to the time-dependence of the optimal offloading decision. In order to minimize the sum cost of the execution time and energy consumption in the long-term, we develop a continuous control based deep reinforcement learning algorithm for computation offloading. Utilizing the actor-critic learning approach, we propose a centralized learning policy for each device. By incorporating the states of other devices with centralized learning, the proposed method learns to coordinate among all devices. Simulation results validate the effectiveness of our proposed algorithm, which demonstrates superior generalization ability and achieves a better performance compared with discrete decision based deep reinforcement learning methods.
Online Scheduling with Redirection for Parallel Jobs

An important component of High Performance Computing (HPC) clusters is the job scheduling algorithm, which decides the allocation and the scheduling of the jobs in the system. Such scheduling algorithms need to be scalable to confront the growth both in size and in complexity of the modern clusters. We propose in this paper a new algorithm for scheduling parallel jobs with redirection. Specifically, our algorithm redirects the jobs whose execution affects significantly an important number of other jobs. A redirected job is stopped and restarted from the beginning in a dedicated part of the cluster. We show the effectiveness of our method through an intensive experimental campaign of simulations of production cluster log traces.
Understanding Solvers' Continuance Intention in Crowdsourcing Contest Platform: An Extension of Expectation-Confirmation Model

The great potential of crowdsourcing contest is bringing the issue of how to sustain solvers’ participation intention to be a hot topic in research and practice. This study uses the framework of Expectation-confirmation model to explain solvers’ continuance intention. Due to the uncertainties inherent in crowdsourcing contest, trust, a salient psychological belief, should be taken into account with the Expectation-confirmation model framework to predict solvers’ continuance intention. In addition, the intensive demand of intelligence and competition indicate interaction and fairness as two crucial factors for solvers to achieve expectation, thus suggesting that they may have influence on the confirmation level. Corresponding to these challenges, this study integrates platform trust, interaction, and perceived fairness into an extended Expectation-confirmation model to examine solvers’ continuance intention. Using a sample of 306 solvers, empirical results show that satisfaction, perceived benefits, and platform trust, which are positively associated with confirmation, are three significant antecedents of solvers’ continuance intention. Confirmation is further found to be significantly determined by interaction and perceived fairness. These findings provide some implications in both theory and practice for understanding the process of triggering sustained intention with an Expectation-confirmation model framework in crowdsourcing contest.
Optimization-Based Control Allocation for Driving/Braking Torque Vectoring in a Race Car *

Most of recent researches on the automotive field focus on autonomous vehicles. These vehicles are equipped with conventional chassis systems. The goal is to control the vehicle’s traction, brakes, and front steering. This paper discusses the importance of advanced chassis systems, as driving/braking torque vectoring, for both autonomous and non-autonomous vehicles, especially in a race mode. Reliable co-simulation results show that expanding the vehicle’s potential leads to high performances and safety with respect to severe situations when optimal control allocation is ensured. Therefore, future passenger cars shall not only be equipped with additional sensors, but also by advanced systems along with adequate control algorithms.
FusAtNet: Dual Attention based SpectroSpatial Multimodal Fusion Network for Hyperspectral and LiDAR Classification

With recent advances in sensing, multimodal data is becoming easily available for various applications, especially in remote sensing (RS), where many data types like multispectral imagery (MSI), hyperspectral imagery (HSI), LiDAR etc. are available. Effective fusion of these multisource datasets is becoming important, for these multimodality features have been shown to generate highly accurate land-cover maps. However, fusion in the context of RS is non-trivial considering the redundancy involved in the data and the large domain differences among multiple modalities. In addition, the feature extraction modules for different modalities hardly interact among themselves, which further limits their semantic relatedness. As a remedy, we propose a feature fusion and extraction framework, namely FusAtNet, for collective land-cover classification of HSIs and LiDAR data in this paper. The proposed framework effectively utilizses HSI modality to generate an attention map using "self-attention" mechanism that highlights its own spectral features. Similarly, a "cross-attention" approach is simultaneously used to harness the LiDAR derived attention map that accentuates the spatial features of HSI. These attentive spectral and spatial representations are then explored further along with the original data to obtain modality-specific feature embeddings. The modality oriented joint spectro-spatial information thus obtained, is subsequently utilized to carry out the land-cover classification task. Experimental evaluations on three HSILiDAR datasets show that the proposed method achieves the state-of-the-art classification performance, including on the largest HSI-LiDAR dataset available, University of Houston (Data Fusion Contest - 2013), opening new avenues in multimodal feature fusion for classification.
Optimized Convolutional Neural Network architecture for UAV navigation within unstructured trail

Flying within highly unstructured and unknown environments performs a big confrontation for general understanding of the view on-board a future platform for unmanned aerial vehicles (UAV) technology. In this work, an approach for automatic trail management within forest trail environment is presented. Our proposed approach generalizes successfully with high image resolutions allowing the UAV to manage in difficult natural conditions. A deep neural network is optimized to improve detection of forest paths for guiding UAVs, by using images of sufficiently high-resolution that are representative of UAV platform.
Adaptive Reinforcement Routing in Software Defined Vehicular Networks

The integration of learning architecture with SDN-based VANETs (SDVN) is beneficial for utilizing computing power by decoupling network management services from data transfer services. However, fast safety messages dissemination in a highly dynamic vehicular environment is a challenging and complex dilemma due to bi-directional traffic and the directional movement of vehicles. It is also challenging to get an effective solution against bottleneck situations and a reliable and fault-tolerant SDN network using clustering. So considering the features of adaptive learning, in this paper, we propose adaptive self-learning clustering algorithm with reinforcement routing in SDVN known as RL-SDVN. An Expectation-Maximization model is used to predict a vehicle's movement and further Q-learning model is used to route data packets, so that vehicles in the same cluster coordinate with each other to find optimum routes. We evaluate our experimental results by comparing our approach with the clustering and self-learning based schemes proposed in the past. The outcomes exhibit that the proposed scheme improved cluster stability and life-time of a cluster member vehicle with better performance in terms of low average transmission delay, and high throughput compared to the existing routing protocols used in this research.
GapFinder: Finding Inconsistency of Security Information From Unstructured Text

Textual data mining of open source intelligence on the Web has become an increasingly important topic across a wide range of domains such as business, law enforcement, military, and cybersecurity. Text mining efforts utilize natural language processing to transform unstructured web content into structured forms that can drive various machine learning applications and data indexing services. For example, applications for text mining in cybersecurity have produced a range of threat intelligence services that serve the IT industry. However, a less studied problem is that of automating the identification of semantic inconsistencies among various text input sources. In this paper, we introduce GapFinder, a new inconsistency checking system for identifying semantic inconsistencies within the cybersecurity domain. Specifically, we examine the problem of identifying technical inconsistencies that arise in the functional descriptions of open source malware threat reporting information. Our evaluation, using tens of thousands of relations derived from web-based malware threat reports, demonstrates the ability of GapFinder to identify the presence of inconsistencies.
Harnessing insights from an activity system – OSCEs past and present expanding future assessments

Abstract Objective Structured Clinical Examinations (OSCEs) are a dominant, yet problematic, assessment tool across health professions education (HPE). OSCEs’ standardised approach aligns with regulatory accountability, allowing learners to exchange exam success for the right to practice. We offer a sociohistorical account of OSCEs’ development to support an interpretation of present assessment practices. OSCEs create tensions. Preparing for OSCE success diverts students away from the complexity of authentic clinical environments. Students will not qualify and will, therefore, be of no use to patients without getting marks providing evidence of competence. Performing in a formulaic and often non patient-centred way is the price to pay for a qualification. Acknowledging the stultifying effect of standardising human behaviour for OSCEs opens up possibilities to release latent energy for change in medical education. In this imagined future, the overall object of education is refocused on patient care.
Developing a New Method for Psychological Investigation Using Text as Data

Over the past few decades, the study of psychology has been undergoing a methodological transformation. The increasing availability and quantity of real-world big data have prompted researchers to look for new ways to use these data to gain psychological insights. In this case study, I trace the process of developing a new method for testing psychological theories using corpora. Instead of bringing participants to the lab, I analyze statistical patterns of cooccurrence in naturally occurring texts obtained from a variety of sources, including political speeches, literature, and the Internet. The basic assumption I make is that these patterns reflect the representations and cognitive processes of their author. I present three different applications of the method and use them to describe how such data can be analyzed and used to answer a range of questions in psychology and social science. The first application examines the linguistic question of the relationship between word form and meaning. The second application identifies cognitive frames as they are found in text. The final application uses texts to measure the style of moral reasoning individuals apply in particular contexts. This case study provides insight into the process of developing new methodologies for hypothesis testing, as well as demonstrating how to formulate hypotheses that can be tested using corpora. In addition, several key pitfalls in the process of adapting statistical methods to new uses are identified and discussed.
Taxonomy and biology of two new species of gall midge (Diptera: Cecidomyiidae) infesting Sarcocornia quinqueflora (Chenopodiaceae) in Australian salt marshes.

Two new species of gall midge associated with two distinct galls on the succulent creeping shrub Sarcocornia quinqueflora are described from salt marshes in south-eastern Australia. The infestations caused by the new species hinder the growth of S. quinqueflora , the seeds of which are the major food of the critically endangered orange-bellied parrot Neophema chrysogaster. Asphondylia floriformis sp. n. Veenstra-Quah & Kolesik transforms leaf segments into flower-like galls, whereas Asphondylia sarcocorniae sp. n. Veenstra-Quah & Kolesik produces simple swellings on branches. Both galls have fungal mycelium growing in the apoplast of the gall tissue and lining the inner surface of the larval chamber where it is presumably grazed by the larva. Descriptions of the larvae, pupae, males, females and the geographical distribution of the two gall midges in south-eastern Australia are given.
Traffic-Aware Erasure-Coded Archival Schemes for In-Memory Stores

Redundancy schemes are introduced to in-memory stores to provide fault tolerance. To achieve good trade-off between access performance and memory efficiency, it is appropriate to adopt replication and erasure coding to keep popular and unpopular data, respectively. Within such a hybrid-redundancy in-memory store, an issue of redundancy transition from replication to erasure coding (a.k.a., erasure-coded archival) should be addressed for unpopular in-memory datasets, since caching workloads exhibit long-tail distributions and most in-memory data are unpopular. If data replicas are distributed across nodes in randomly-selected racks, then subsequent data-block-replica retrieval for erasure-coded archival will create cross-rack traffic, and final parity-block relocation will cause extra cross-rack communications. In this article, we propose an encoding-oriented replica placement policy - ERP - by incorporating an interleaved declustering mechanism. We design two traffic-aware erasure-coded archival schemes -TEA-TL and TEA-SL - for ERP-powered in-memory stores by taking into account temporal locality and spatial locality, respectively. With ERP in place, both TEA-TL and TEA-SL schemes embrace the following three salient features: (i) they alleviate cross-rack traffic raised by retrieving required data-block replicas; (ii) they improve rack-level load balancing by distributing replicas via load-aware primary-rack-selection approach; and (iii) they mitigate block-relocation operations launched to sustain rack-level and node-level fault-tolerance. We conduct quantitative performance evaluations using the YCSB benchmark. The empirical results show that both TEA-TL and TEA-SL schemes not only bring forth lower cross-rack traffic than the four candidate encoding schemes, but also exhibit superb archival-throughput and rack-level-balancing performance. In particular, within a group of comparative tests using the baseline configurations, TEA-TL and TEA-SL accelerate archival throughput by at least 36.3 and 70.8 percent, respectively; both TEA-TL and TEA-SL schemes improve rack-level load-balancing by a factor of more than 1.45x relative to the four candidate encoding schemes.
Passivity-Based Control Allocation of a Redundantly-Actuated Parallel Robotic Manipulator with a Point-Mass Payload

This paper examines the effect of control allocation on the passive input-output properties of a redundantly- actuated parallel robotic manipulator with a point-mass payload. In particular, it is shown that the mapping matrix used for control allocation is to satisfy a forward velocity kinematic constraint in order to preserve the manipulator’s passive inputoutput mapping from modified control torques in task space to the velocity tracking error of the payload. A method to generate the control allocation matrix using load-sharing parameters is proposed, and is shown to have a physically intuitive relationship to the control effort of the individual actuators. A numerical example of a cable-driven parallel robot is presented, which illustrates the intuitive nature of the proposed control allocation method compared to a pseudoinverse method in the literature.
Model Free Nonlinear Control with Finite-Time Estimation Applied to Closed-Loop Electrical Stimulation Induced Cycling

Model free or data-driven control methods are suitable for real-time applications that involve nonlinear systems with uncertainties. Human-machine interaction problems include parametric and non-parametric uncertainties that are hard to model. An alternative to develop complex models to account for these uncertainties is to exploit input-output data recorded from the human and machine to improve the performance of the combined system. In this paper, a motorized functional electrical stimulation (FES) cycling system is used to illustrate a data-driven approach that leverages past input-output data to generate an estimate of the system’s non-linearly parameterizable and uncertain dynamics. This estimate is computed using an estimation law motivated by a design tool from finite-time stability and used as an input into a feedback controller. The nonlinear controller that switches across the lower-limb muscle groups and an electric motor is designed to achieve a desired speed tracking objective. A Lyapunov-based stability analysis is used to prove an asymptotic result of the tracking and estimation errors.
Linear Model Predictive Control for Time Delay Systems*

This paper studies linear model predictive control of real matrix-valued single delay systems. The delay system is written as an abstract inflnite-dimensional control system which is then mapped into an infinite-dimensional discrete-time control system using Cayley-Tustin discretization. A constrained model predictive control (MPC) problem is formulated for the discrete-time system where a terminal penalty function is utilized to cast the infinite-horizon optimization problem into a finite-horizon one. The proposed MPC design is demonstrated on an example of constrained stabilization of a 2 × 2 system. We will demonstrate that the proposed discrete-time MPC law not only stabilizes the discrete-time system but can be utilized in stabilizing the original continuoustime system as well, which is due to several favorable properties of the Cayley-Tustin discretization.
A Hybrid Feature Extraction Network for Intrusion Detection Based on Global Attention Mechanism

The widespread application of 5G will make intrusion detection of large-scale network traffic a mere need. However, traditional intrusion detection cannot meet the requirements by manually extracting features, and the existing AI methods are also relatively inefficient. Therefore, when performing intrusion detection tasks, they have significant disadvantages of high false alarm rates and low recognition performance. For this challenge, this paper proposes a novel hybrid network, RULA-IDS, which can perform intrusion detection tasks by great amount statistical data from the network monitoring system. RULA-IDS consists of the fully connected layer, the feature extraction layer, the global attention mechanism layer and the SVM classification layer. In the feature extraction layer, the residual U-Net and LSTM are used to extract the spatial and temporal features of the network traffic attributes. It is worth noting that we modified the structure of U-Net to suit the intrusion detection task. The global attention mechanism layer is then used to selectively retain important information from a large number of features and focus on those. Finally, the SVM is used as a classifier to output results. The experimental results show that our method outperforms existing state-of-the-art intrusion detection methods, and the accuracies of training and testing are improved to 97.01% and 98.19%, respectively, and presents stronger robustness during training and testing.
Bus Mobile Payment Operation Analysis

Compared with the traditional payment method, bus mobile payment has incomparable advantages. The third-party payment platform plays a leading role in bus mobile payment field. This paper establishes a static mathematical model of the third-party payment platform's profit based on the status quo and relevant data, analyzes the development prospect of bus mobile payment, and finally gives feasible suggestions for the development of the third-party payment platform.
HybridSketch: A Memory-centric Precise Approach for Flow Measurement

As network bandwidth has rapidly developed, due to the high occupancy of memory and bandwidth required, the Sketch structure is favored by some researchers due to its limited memory usage and simple operation. But the accuracy will decrease when the Sketch system occupies less memory space. Traditional sketch algorithms and some other specially designed algorithms and structures are striving to improve accuracy. However, with the flow rate rapidly increasing, the on-chip memory will be the bottleneck of the system. Our network measurement system achieve good results focusing more on the memory usage. We proposes a hybrid method, HybridSketch, which focuses on the memory and precision of the system with mixing two measurement methods by quantitatively analyzing, modeling and allocating appropriate memory space to each method to achieve better results. Experimental results show that our method can provide $10 \times$ improvement in terms of precision, moreover, HybridSketch can provide the same level of precision with achieving $24 \times$ improvement in terms of memory size.
Estimation of Individual Fitness from Life-History Data

Fitness is a vague, poorly developed concept in ecology and evolutionary biology. Although it is generally recognized that fitness is determined by the complete survival and reproductive schedules of individual organisms, experimental studies have rarely attempted to integrate these into a single measure of individual fitness. Instead, particularly in studies of natural selection, components of fitness are measured as surrogates for fitness. In this article, we develop an integrated measure of fitness based on the philosophical underpinnings provided by the notion of propensity fitness, combined with classical demographic methods. We show how life-history data, collected for an individual, can be formatted as an age-structured population projection matrix, A(m). The dominant eigenvalue, λ(m), of this matrix is an estimate of that individual's propensity fitness. Using life-history data sets on European sparrowhawks and blue tits, we show that the interpretation of analyses of selection can shift radically using the integrated propensity fitness measure instead of components of fitness, such as lifetime reproductive output. Individual fitness, as formulated here, provides an integrated measure of performance that should prove useful in empirical studies in ecology and evolutionary biology.