Reviewerly/exHarmony huggingface dataset:
Autonomous Path Planning of AUV in Large-Scale Complex Marine Environment Based on Swarm Hyper-Heuristic Algorithm

Autonomous underwater vehicles (AUVs) as an efficient underwater exploration means have been used to perform various marine missions. However, limited by the technologies of underwater acoustic communications and intelligent autonomy, the most current and advanced AUVs only perform a limited number of tasks in the small-scale area and the known underwater environment. Therefore, in this paper, a one path planning model was proposed combining the global path planning and the local path planning for the large-scale complex marine environment. More specifically, the B-spline curve was used to represent the smooth path for the requirement of kinematic constraints of AUVs. After considering the various constraints, such as the energy/time consumption, the turning radius limitation, the marine environment, and the ocean current, the path planning was abstractly modeled as a multi-objective optimization model with the time cost, the curvature cost, the map cost, and the ocean current cost. The swarm hyper-heuristic algorithm (SHH) with the online learning ability was proposed to solve this model with real-time performance and stability. The results showed that the proposed online learning SHH algorithm had obvious advantages in terms of time efficiency, stability, and optimal performance compared with the results of two traditional heuristic algorithms, both particle swarm optimization (PSO) and firefly algorithm (FFA). The time efficiency of the online learning SHH algorithm improved at least 20% compared with PSO and FFA. Featured Application.
Implicit information need as explicit problems, help, and behavioral signals

Information need is one of the most fundamental aspects of information seeking, which traditionally conceptualizes as the initiation phase of an individual’s information seeking behavior. However, the very elusive and inexpressible nature of information need makes it hard to elicit from the information seeker or to extract through an automated process. One approach to understanding how a person realizes and expresses information need is to observe their seeking behaviors, to engage processes with information retrieval systems, and to focus on situated performative actions. Using Dervin’s Sense-Making theory and conceptualization of information need based on existing studies, the work reported here tries to understand and explore the concept of information need from a fresh methodological perspective by examining users’ perceived barriers and desired helps in different stages of information search episodes through the analyses of various implicit and explicit user search behaviors. In a controlled lab study, each participant performed three simulated online information search tasks. Participants’ implicit behaviors were collected through search logs, and explicit feedback was elicited through pre-task and post-task questionnaires. A total of 208 query segments were logged, along with users’ annotations on perceived problems and help. Data collected from the study was analyzed by applying both quantitative and qualitative methods. The findings identified several behaviors – such as the number of bookmarks, query length, number of the unique queries, time spent on search results observed in the previous segment, the current segment, and throughout the session – strongly associated with participants’ perceived barriers and help needed. The findings also showed that it is possible to build accurate predictive models to infer perceived problems of articulation of queries, useless and irrelevant information, and unavailability of information from users’ previous segment, current segment, and whole session behaviors. The findings also demonstrated that by combining perceived problem(s) and search behavioral features, it was possible to infer users’ needed help(s) in search with a certain level of accuracy (78%).
An efficient privacy preserving protocol for dynamic continuous data collection

Past and ongoing decades have witnessed significant uplift in data generation due to ever growing sources of data. Collection and aggradation of such huge data have triggered serious concerns on privacy of data-owners' sensitive information. Catering this, several existing anonymization models proffer privacy-preserving data collection. However, the models put-forth either strict or unrealistic assumptions regarding leaders' selection (the concept of first and last leaders in data collection process). In this paper, we have identified and formally defined a privacy attack, Leader Collusion Attack (LCA); where first and second leaders may collude to breech individuals' privacy during data collection process. In this regard, we have proposed a novel k-anonymity based dynamic data collection protocol (presented single leader election) to mitigate LCA. Moreover, we have formally modelled and analysed the proposed protocol through HLPNs and demonstrated the mitigation of LCA. Experimentations on real-world datasets advocate the outperformance of our protocol over existing model in terms of better utility and privacy levels.
Delay-Optimal Scheduling of VMs in a Queueing Cloud Computing System with Heterogeneous Workloads

This paper studies virtual machine (VM) scheduling in a queueing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs&#x2019; delay requirements. The delay-optimal VM scheduling in such a cloud computing system is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queueing model that buffers the same type of VM jobs in one virtual queue. The queueing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. A min-min best fit (MM-BF) policy is used to schedule the jobs in different queues to minimize the remaining system resources, while a shortest-job-first (SJF) policy is used to buffer the job requests in each queue based on their job lengths in an ascending order. To avoid job starvation for the long-duration jobs in SJF-MMBF, we further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. Simulation results show that, SJF-MMBF and SJF-QMW achieve low delay performance in terms of average job completion time and high throughput performance in terms of job hosting ratio.
Robust vision-based tracking control of VTOL unmanned aerial vehicles

This study addresses the problem of robust tracking control of vertical take-off and landing (VTOL) unmanned aerial vehicles through a vision-based control technique. The main objective is to design a new theoretical result for vision-based tracking of a moving target in the presence of external disturbances. In particular, the proposed control framework is able to simultaneously guarantee a saturated thrust input, and measurable and bounded desired angular velocity and acceleration while dealing with time-varying disturbances in the dynamics of the system. Considering different sets of visual features, two control schemes are proposed, where the features are defined based on perspective image moments. Simulation studies are conducted in perturbed conditions to verify the effectiveness of the proposed controllers.
Spectral modeling and spectral impacts on the performance of mc-Si and new generation CdTe photovoltaics in warm and sunny climates

This paper presents an analysis of spectral impacts on mc-Si and new generation CdTe in two distinct regions of Brazil: Florianópolis-SC (27°S, 48°W) in the South, and Assu-RN (5°S, 37°W) in the Northeast. As utility-scale PV power plants are progressively being deployed in the Brazilian Northeast, the need for evaluation of the spectral effects of local blue-biased spectra on the energy yield of different PV technologies arises, as well as the need for spectral correction of field peak-power measurements done during commissioning and system performance tests. Considering the high cost of adequate spectral measurement equipment, this paper proposes a new approach for the estimation of spectral impacts without the need for in loco measurements. The proposed methodology consists of the use of satellite data acquired from NASA's Giovanni platform as inputs for the SMARTS 2.9.5 spectra model. Results for measured spectra showed that, for both locations, new generation CdTe (i.e. First Solar Series 4 and 6) has significant spectral gains: up to 10% for Assu-RN (Northeastern Brazil) and 2% for Florianópolis-SC (Southern Brazil). A seasonal variation could also be detected for the Florianópolis site, with lower spectral gains for CdTe - and higher for mc-Si - close to the Southern Hemisphere’s winter solstice, due to higher air mass values and lower precipitable water content of the atmosphere. The proposed method using Giovanni data and SMARTS spectra modeling produced very similar spectra to those measured for clear days in the field at both sites, yielding good results for the calculation of spectral factors for both mc-Si and CdTe. For days with a higher diffuse fraction the results were not as satisfactory, as expected. The proposed method was also applied to instantaneous measurements for three different times (and AM values) of the day for Florianópolis: 9:00, 12:00 and 15:00, yielding satisfactory results for IV curve spectral correction.
Digitizing Invoice and Managing VAT Payment Using Blockchain Smart Contract

Nowadays, the authenticating procedure for transactions is still complicated, and the current Value-Added Tax (VAT) administration system works as centralized server, which consists of high-risk attacks from hackers. Therefore, only a few countries use digital technologies to calculate and manage the VAT payment despite of their benefits. In this paper, by combining decentralized storage network (DSN) with the smart contract (SC), we propose a new model based on Blockchain technology to authenticate the transaction, calculate Value-Added Tax and approve VAT payment. This system runs in a host computer (host PC) for encrypting and decrypting data. The smart contract is implemented in Remix Integrated Development Environment (IDE) based on the Ethereum platform. Experiential results show that the new model not only saves the cost for authenticating transactions but also protects the data from hacker’s attacks due to consensus property of the Blockchain technology.
Heuristic swarm intelligent optimization algorithm for path planning of agricultural product logistics distribution

In the current agricultural product consumption market, the consumer’s demand for agricultural products tends to be diversified and individualized, and they need stricter links of cold chain logistics distribution of agricultural products. The cold chain logistics of fresh agricultural products is also the business of high energy consumption and high carbon emission in the logistics industry. The contradictory relationship between it and the “low-carbon economy” advocated today leads to the necessity to consider the relationship between economic benefit and environmental impact in the process of rapid development. Based on this, this paper, from the perspective of low-carbon economy, based on the analysis of the necessity of optimizing the distribution path of cold chain logistics of agricultural products, constructs a model for this and puts forward a swarm intelligence optimization algorithm-particle swarm optimization (PSO) algorithm. The algorithm is improved from inertia weight, convergence factor, learning factor, and population size and so on. The optimized PSO algorithm is compared with the traditional PSO algorithm. Finally, the simulation results show that the improved algorithm can be used to optimize the distribution path of cold chain logistics of agricultural products effectively, and the specific optimization countermeasures are put forward according to the actual situation.
Continuously reasoning about programs using differential Bayesian inference

Programs often evolve by continuously integrating changes from multiple programmers. The effective adoption of program analysis tools in this continuous integration setting is hindered by the need to only report alarms relevant to a particular program change. We present a probabilistic framework, Drake, to apply program analyses to continuously evolving programs. Drake is applicable to a broad range of analyses that are based on deductive reasoning. The key insight underlying Drake is to compute a graph that concisely and precisely captures differences between the derivations of alarms produced by the given analysis on the program before and after the change. Performing Bayesian inference on the graph thereby enables to rank alarms by likelihood of relevance to the change. We evaluate Drake using Sparrow—a static analyzer that targets buffer-overrun, format-string, and integer-overflow errors—on a suite of ten widely-used C programs each comprising 13k–112k lines of code. Drake enables to discover all true bugs by inspecting only 30 alarms per benchmark on average, compared to 85 (3× more) alarms by the same ranking approach in batch mode, and 118 (4× more) alarms by a differential approach based on syntactic masking of alarms which also misses 4 of the 26 bugs overall.
Unsupervised Neural Generative Semantic Hashing

Fast similarity search is a key component in large-scale information retrieval, where semantic hashing has become a popular strategy for representing documents as binary hash codes. Recent advances in this area have been obtained through neural network based models: generative models trained by learning to reconstruct the original documents. We present a novel unsupervised generative semantic hashing approach, Ranking based Semantic Hashing (RBSH) that consists of both a variational and a ranking based component. Similarly to variational autoencoders, the variational component is trained to reconstruct the original document conditioned on its generated hash code, and as in prior work, it only considers documents individually. The ranking component solves this limitation by incorporating inter-document similarity into the hash code generation, modelling document ranking through a hinge loss. To circumvent the need for labelled data to compute the hinge loss, we use a weak labeller and thus keep the approach fully unsupervised.
Performance Comparison of User Interface Devices for Controlling Mining Software in Virtual Reality Environments

Recently, many studies have been conducted to apply virtual reality (VR) technology to the mining industry. To accomplish this, it is necessary to develop user interface devices that can effectively control software. Most VR content in the mining industry requires precise device control for equipment operation or accident response. In this study, we compare the performance of four user interface devices (a 2D mouse, 2D &amp; 3D mice, a VR controller, and a Kinect (Microsoft) sensor and bend-sensing data glove) for controlling mining industry software in a VR environment. The total working time, number of device clicks and click accuracy, were analyzed based on 10 experimenters performing 3D orebody modeling, using each device in the VR environment. Furthermore, we conducted a survey to evaluate the ease of learning, ease of use, immersion and fatigue of each device after the experiment. The results show that the 2D mouse yields a high performance in terms of its working time, click accuracy, ease of learning and ease of use. However, the 2D mouse did not completely leverage the VR environment, owing to low user immersion. The Kinect sensor and bend-sensing data glove could control the software efficiently while maximizing user immersion. Our findings are expected to provide a useful reference for the future development of user interface devices in the mining industry.
Differential evolution using cooperative ranking-based mutation operators for constrained optimization

Differential evolution (DE) is a widely used heuristic algorithm for numerical optimization over continuous space. As the core operator, the mutation operator has a great effect on DE's performance. In this paper, we propose a cooperative ranking-based mutation strategy (CRM) for DE when solving constrained optimization problems (COPs). In CRM, two different ranking criteria are adopted in a cooperative way to move the population towards a feasible global optimum with a faster convergence speed. The first criterion is objective function value-based criterion which is applied for feasible solutions to converge towards the global optimum. The second criterion is overall constraint violation-based criterion which is applied for infeasible solutions to search for feasible regions. The proposed CRM is integrated into the basic DE and two advanced DE variants. The comparison results showed that the DE variants with CRM have a faster convergence speed than the non-CRM-based variants in the majority of test problems. This approach is a promising strategy to improve the search ability of DE variants when solving COPs.
Online learning control with Echo State Networks of an oil production platform

The design of a control algorithm is difficult when models are unavailable, the physics are varying in time, or structural uncertainties are involved. One such case is an oil production platform in which reservoir conditions and the composition of the multiphase flow are not precisely known. Today, with streams of data generated from sensors, black-box adaptive control emerged as an alternative to control such systems. In this work, we employed an online adaptive controller based on Echo State Networks (ESNs) in diverse scenarios of controlling an oil production platform. The ESN learns an inverse model of the plant from which a control law is derived to attain set-point tracking of a simulated model. The analysis considers high steady-state gains, potentially unstable conditions, and a multi-variate control structure. All in all, this work contributes to the literature by demonstrating that online-learning control can be effective in highly complex dynamic systems (oil production platforms) devoid of suitable models, and with multiple inputs and outputs.
Direct linear and refraction-invariant pose estimation and calibration model for underwater imaging

Abstract Accuracy, detail, and limited time on site make photogrammetry a valuable means for underwater mapping. Imaging in such domains is subjected however to distortions which are caused by refraction of the incoming rays. As the literature shows, these distortions are depth-dependent, non-linear, and alter the standard single viewpoint geometry. To handle their effect, we derive in this paper a refraction-invariant representation and show that despite the pronounced distortions, such a model is attainable. We also show that its contribution is not only theoretical, as it also allows to estimate the pose parameters linearly and at a significantly improved accuracy. The paper then extends the model to calibrate the underwater-related system parameters and, again, demonstrates the ability to yield a linear model, to simplify the settings and requirements for calibration procedures, and most importantly to improve the accuracy of the system parameters by an order of magnitude or more. Experiments also show enhanced accuracy and stability of the model in the presence of high-level of noise. Thus, the paper provides an in-depth look into the geometrical modeling of underwater images and at the same time offers practical enhancement of the accuracies and requirements to reach them.
Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement

In this paper, we propose a novel underwater image enhancement method. Typical deep learning models for underwater image enhancement are trained by paired synthetic dataset. Therefore, these models are mostly effective for synthetic image enhancement but less so for real-world images. In contrast, cycle-consistent generative adversarial networks (CycleGAN) can be trained with unpaired dataset. However, performance of the CycleGAN is highly dependent upon the dataset, thus it may generate unrealistic images with less content information than original images. A novel solution we propose here is by starting with a CycleGAN, we add a pair of discriminators to preserve contents of input image while enhancing the image. As a part of the solution, we introduce an adaptive weighting method for limiting losses of the two types of discriminators to balance their influence and stabilize the training procedure. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on real-world underwater images.
Analysing trade-offs in frameworks for the design of smart environments

Smart Interactive Experiences (SIEs) are usage situations enabled by the Internet of Things that empower users to interact with the surrounding environment.The goal of our research is to define methodologies and software environments to support the design of SIEs; more specifically, we focus on design paradigms suitable for experts of given domains, who however might not be experts in technology.In this context, this paper discusses some trade-offs that we identified between six different dimensions that characterize the quality of software environments for SIE design.The trade-offs emerged from the analysis of data collected in an experimental study that compared three different design paradigms to understand in which measure each paradigm supports the creative process for SIE design.After reporting on the study procedure and the data analyses, the paper illustrates how the resulting trade-offs led us to identify alternatives for SIE design paradigms, and to structure on their basis a modular architecture of a software platform where the strengths of the three paradigms can be exploited flexibly, i.e., depending on the constraints and the requirements characterizing specific design situations.
Blockchain-Based Distributive Auction for Relay-Assisted Secure Communications

Physical layer security (PLS) is considered as a promising technique to prevent information eavesdropping in wireless systems. In this context, cooperative relaying has emerged as a robust solution for achieving PLS due to multipath diversity and relatively lower transmission power. However, relays or the relay operators in the practical environment are unwilling for service provisioning unless they are incentivized for their cost of services. Thus, it is required to jointly consider network economics and relay cooperation to improve system efficiency. In this paper, we consider the problem of joint network economics and PLS using cooperative relaying and jamming. Based on the double auction theory, we model the interaction between transmitters seeking for a particular level of secure transmission of information and relay operators for suitable relay and jammer assignment, in a multiple source-destination networks. In addition, theoretical analyses are presented to justify that the proposed auction mechanism satisfies the desirable economic properties of individual rationality, budget balance, and truthfulness. As the participants in the traditional centralized auction framework may take selfish actions or collude with each other, we propose a decentralized and trustless auction framework based on blockchain technology. In particular, we exploit the smart contract feature of blockchain to construct a completely autonomous framework, where all the participants are financially enforced by smart contract terms. The security properties of the proposed framework are also discussed.