Reviewerly/exHarmony huggingface dataset:
CloudArcade: A Blockchain Empowered Cloud Gaming System

By rendering game scenes on the remote cloud and delivering real-time video to end devices via the Internet, cloud gaming enables players to access game services anytime anywhere despite the hardware capacity of their terminals. However, as a commercial service, the state-of-the-art payment models for cloud gaming are still in their preliminary stages. In this paper, we reveal the shortages of existing cloud gaming pricing models and propose CloudArcade, a token-based cloud gaming system that employs blockchain-empowered cryptocurrency as a payment method for the players using the cloud gaming services. By using cryptocurrency, CloudArcade provides a transparent and resource-aware pricing method, it also enables a time irrelevant silent payment on the floating price to protects users' payment. These features eliminate the quality of experience degradation caused by the spot price in the traditional dynamic pricing model on the QoE-aware service pricing. We also employ the payment channel in CloudArcade to improve the system performance. Discussions on service pricing criteria are put forward, open issues about token issuing and malicious resource speculation are also reviewed. We believe the design of CloudArcade can show a good generality on other QoE-aware and human-centered applications.
User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation

Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. Using minimal-labor (UIs) to guide the annotation is promising, but challenges remain on best harmonizing the mask prediction with the UIs. To address this, we propose the user-guided domain adaptation (UGDA) framework, which uses prediction-based adversarial domain adaptation (PADA) to model the combined distribution of UIs and mask predictions. The UIs are then used as anchors to guide and align the mask prediction. Importantly, UGDA can both learn from unlabelled data and also model the high-level semantic meaning behind different UIs. We test UGDA on annotating pathological livers using a clinically comprehensive dataset of 927 patient studies. Using only extreme-point UIs, we achieve a mean (worst-case) performance of \(96.1\%\) (\(94.9\%\)), compared to \(93.0\%\) (\(87.0\%\)) for deep extreme points (DEXTR). Furthermore, we also show UGDA can retain this state-of-the-art performance even when only seeing a fraction of available UIs, demonstrating an ability for robust and reliable UI-guided segmentation with extremely minimal labor demands.
Inferring sandstone grain size using spectral datasets: An example from the Bresnahan Group, Western Australia

Remotely sensed hyperspectral datasets were integrated with petrographic data to map the distribution of sedimentary rocks in order to infer grain size variations within the siliciclastic, Paleoproterozoic Bresnahan Group in Western Australia. Finer sandstones have greater compositional variation compared to coarser sandstones, with higher modal proportions of mudstone intraclasts, K-feldspar, and muscovite laths, and lower modal quartz. Partial Least Squares modelling indicates a link between visible to shortwave infrared spectral data collected on samples and their average grain size. Specific electronic absorptions are important for defining this relationship, particularly in the blue-green visible spectra, wavelengths related to water or hydroxyl bonds, along with certain wavelengths in the shortwave infrared (SWIR) spectra. The results from field samples were compared to airborne hyperspectral datasets which showed broad scale regional variations in composition could be discerned from processed white mica abundance maps and images that use spectral bands 2350 nm and 2200 nm. The remotely sensed compositional variations are relatable to changes in sandstone grain size. Diagenetic alteration and surface weathering may influence the absorption spectra, although the effect of the latter can be reduced by using processed white mica abundance maps. Overall, the results show that airborne hyperspectral datasets, particularly the SWIR part of the spectra, can be useful to remotely map spatial compositional variations associated with sandstone grain size. This shows the method can be used to remotely map siliciclastic successions in other sedimentary basins by aiding recognition of grain size trends and significant stratal surfaces associated with changes in composition. However, the important wavelengths may differ due to other controls on sandstone composition such as uplift history of the source region, changes in sediment dispersal, and transport mechanisms.
Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks

Requirements Engineering (RE) is a means to reduce the risk of delivering a product that does not fulfill the stakeholders' needs. Therefore, a major challenge in RE is to decide how much RE is needed and what RE methods to apply. The quality of such decisions is strongly based on the RE expert's experience and expertise in carefully analyzing the context and current state of a project. Recent work, however, shows that lack of experience and qualification are common causes for problems in RE. We trained a series of Bayesian Networks on data from the NaPiRE survey to model relationships between RE problems, their causes, and effects in projects with different contextual characteristics. These models were used to conduct (1) a post-mortem (diagnostic) analysis, deriving probable causes of suboptimal RE performance, and (2) to conduct a preventive analysis, predicting probable issues a young project might encounter. The method was subject to a rigorous cross-validation procedure for both use cases before assessing its applicability to real-world scenarios with a case study.
3D Fluid Flow Reconstruction Using Compact Light Field PIV

Particle Imaging Velocimetry (PIV) estimates the fluid flow by analyzing the motion of injected particles. The problem is challenging as the particles lie at different depths but have similar appearances. Tracking a large number of moving particles is particularly difficult due to the heavy occlusion. In this paper, we present a PIV solution that uses a compact lenslet-based light field camera to track dense particles floating in the fluid and reconstruct the 3D fluid flow. We exploit the focal symmetry property in the light field focal stacks for recovering the depths of similar-looking particles. We further develop a motion-constrained optical flow estimation algorithm by enforcing the local motion rigidity and the Navier-Stoke fluid constraint. Finally, the estimated particle motion trajectory is used to visualize the 3D fluid flow. Comprehensive experiments on both synthetic and real data show that using a compact light field camera, our technique can recover dense and accurate 3D fluid flow.
Intention-aware motion planning with road rules

We present an approach for intention-aware motion planning in an autonomous driving scenario, where a vehicle aims to traverse a road segment as quickly as possible, while constrained by road rules encoded in syntactically co-safe linear temporal logic. We show that by combining the RRTx algorithm with trajectory prediction using Mixed Observable Markov Decision Processes (MOMDP), we can achieve least-violating behavior wrt. mission completion time and the road rules, while ensuring that the likelihood of collisions remains below a user specified threshold. We illustrate the validity of our approach using simulations of a variety of traffic scenarios.
Multi-domain Transfer Learning for Text Classification

Leveraging data from multiple related domains to enhance the model generalization performance is critical for transfer learning in text classification. However, most existing approaches try to separate the features into shared and private spaces regardless of correlations between domains, resulting in the inadequate features sharing among certain most related domains. In this paper, we propose a generic dual-channels multi-task learning framework for multi-domain text classification, which can capture global-shared, local-shared, and private features simultaneously. Our novel framework incorporates Adversarial network and Mixture of experts into a neural network for multi-domain text classification, which is very useful for sharing more features among domains. The extensive experiments on the real-world text classification data-sets across 16 domains demonstrate our proposed approach achieves better results than five state-of-the-art techniques.
R-MnasNet: Reduced MnasNet for Computer Vision

In Deep Learning, Convolutional Neural Networks (CNNs) are widely used for Computer Vision applications. With the advent of new technology, there is an inevitable necessity for CNNs to be computationally less expensive. It has become a key factor in determining its competence. CNN models must be compact in size and work efficiently when deployed on embedded systems. In order to achieve this goal, researchers have invented new algorithms which make CNNs lightweight yet accurate enough to be used for applications like object detection. In this paper, we have tried to do the same by modifying an architecture to make it compact with a fair trade-off between model size and accuracy. A new architecture, R-MnasNet (Reduced MnasNet), has been introduced which has a model size of 3 MB. It is trained on CIFAR-10 [4] and has a validation accuracy of 91.13%. Whereas the baseline architecture, MnasNet [1], has a model size of 12.7 MB with a validation accuracy of 80.8% when trained with CIFAR-10 dataset. R-MnasNet can be used on resource-constrained devices. It can be deployed on embedded systems for vision applications.
Skeleton-Based Sleep Posture Recognition with BP Neural Network

Human sleep postures are inextricably linked to health, which can be used as a pivotal indicator of disease prevention and treatment. To obtain a machine learning model for analyzing the human sleep postures, a new approach is proposed to efficiently recognize the types of sleep postures based on skeleton extraction. Four typical sleep postures, i.e., lying in the supine, prone, left lateral and right lateral, are classified with the method of extraction of key points relation feature as well as the direct coordinate feature, which can extract features of skeleton correctly and effectively. Furthermore, the presented method is applied to a specific scenario, which is utilized for monitoring sleep postures of patients who suffered from Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) by making the detailed classification of supine posture. The effectiveness of the proposed framework was validated quantitatively and qualitatively. The performance of the extensive comparison experiments demonstrate that the proposed approach is superior and achieves the state-of-the-art.
Optimal Multi-Agent Coverage and Flight Time with Genetic Path Planning

When generating 3D maps with unmanned aerial vehicles (UAVs), it is important for the mapping algorithm to have good coverage of the environment. It is also important, especially when planning paths for multiple agents, to have loop closures along each flight path and with other agents. Because multirotor UAVs are limited in flight time, the flight paths must be limited in length. Generating a good flight path to map a new environment can be difficult and tedious because of the free-form nature of a flight path. To solve this problem, we propose using a genetic algorithm designed to maximize total area coverage while minimizing flight time and enforcing sufficient loop closures. The natural ability of genetic algorithms to rapidly explore a design space is advantageous when solving complex free-form problems like path planning.
Perversities or Problems in the Rise of Peer Production with Knowledge Socialism: Collegiality, Collaboration, Collective Intelligence

This analysis maps some operational perversities and structural problems at work in the historical contradictions between ‘knowledge socialism’ as it remains trapped within the workings of contemporary ‘knowledge capitalism’. It develops in three parts. First, it considers how the entrenched authorities of ‘knowledge capitalism’ have become an exploitative plutocratic elite, which operates as a ‘plutonomy’ to stall the coming of knowledge socialism. Second, it considers how the workings of democratic elections in many countries developing knowledge capitalist economies can be derailed from moving peacefully toward knowledge socialism as reactionary ‘illiberal capitalist’ forces clandestinely hijack the means of persuasion through disinformation, hacking, and virtual participants in cyber-subversion to spoof electoral systems, spread misinformation, and stop participation. And, third, it provisionally explores three different paths for the transition to knowledge socialism in ‘thought experiments’ to speculate about how that turn could unfold from the on-going creative destruction of knowledge capitalism.
Contribution of using mobile application on botanical fieldwork in primary school

The aim of the research is to examine the contribution of mobile application using in botanical fieldwork to the quality and durability of knowledge of the subject Nature and society compared to multimedia teaching in the fourth grade of primary school. The study involved 120 students, divided into two groups: control group, which taught on the principles of multimedia teaching with the help of digital herbarium and experimental group, which taught through botanical fieldwork, with the help of the mobile application PlantNet Plant Identification. A digital herbarium was placed on student's computer at school (in the computer classroom) and at home, and a mobile application on every student's mobile phone. Students studied and identified 30 plants, 13 forest and 17 cultivated plants. After the identification of the plants, the quality of students' knowledge was examined by a post-test, while the durability of knowledge was examined by a re-test. The results of the research showed that using of the mobile application in botanical fieldwork contributed to the higher quality and durability of students' knowledge compared to multimedia teaching at cognitive levels: analysis, evaluation and synthesis.
Statistical Shape Model: comparison between ICP and CPD algorithms on medical applications

Abstract The increasing availability of 3D anatomical models obtained from diagnostic images exploiting Reverse Engineering techniques allows the application of statistical analysis in the quantitative investigation of anatomical shapes variability. Statistical Shape Models are a well-established method for representing such variability, especially for complex forms like the anatomical ones. Not by chance, these models are widely used for medical applications, such as guiding segmentation of the diagnostic image and virtual reconstruction of incomplete anatomic region. The application of a statistical analysis on a set of shapes representing the same anatomical region essentially requires that shapes must be in correspondence, i.e. constituted by the same number of points in corresponding position. This work aims to compare two established algorithms, namely a modified version of the Iterative Closest Point and the non-rigid version of the Coherent Point Drift, to solve the correspondences’ problem in the construction of a Statistical Shape Model of the human cranium. The comparison is carried out on the models using the standard evaluation criteria: Generalization , Specificity and Compactness . The modified version of the Iterative Closest Point delivers a better Statistical Shape Model in terms of Generalization and Specificity , but not for Compactness , than the Coherent Point Drift-based model.
Weakly Supervised Metric Learning with Majority Classes for Large Imbalanced Image Dataset

In this paper, we strive to improve the classification performance of large multi-class imbalanced datasets, at low computational expense, by transforming the input data space through large margin nearest neighbor metric learning applied on limited samples of the majority classes only. The idea is to concentrate the majority data into smaller subspaces to prevent interference with the minority population. An iterative sum-based binary partitioning scheme divides the data into majority and minority classes based on the decreasing order of class-population. The entire transformed input space is applied for classification sans any sampling procedures that lead to loss of information. The experiments are performed on the Histogram of Gradient (HOG) features extracted from the highly imbalanced facial corpus LFW, with comparisons to the state of the art. Our weakly supervised model yields significantly improved results especially for the majority classes and noticeable improvement for the minority classes when used with distance or similarity classifiers.
The orienteering Problem with Replenishment

The planning of long-duration missions for resource-constrained agents is a longstanding challenge not only in field robotics research, but also in the fields of vehicle routing and logistics. To efficiently plan missions over a larger time scale than an agent's resource capacity natively allows, it is necessary to exploit the availability of recharging stations, where an agent can replenish its spent resources at the cost of time. This paper presents the Orienteering Problem with Replenishment, a novel extension of the orienteering problem, which can fully utilise the existence of recharging stations by not only revisiting such stations, but also intelligently managing the amount of time spent recharging at each stop. By leveraging the above mechanisms for overcoming the resource limitations of the platform, the proposed problem formulation allows for the planning of longer term missions. A suite of computational tests are performed to assess the proposed formulation, and its performance is analysed with respect to the impact of intelligent utilisation of the recharge points on reward acquisition. Finally, limitations of the proposed problem formulation are discussed, and avenues for future work are presented.
Defense against N-pixel Attacks based on Image Reconstruction

Since machine learning and deep learning are largely used for image recognition in real-world applications, how to avoid adversarial attacks become an important issue. It is common that attackers add adversarial perturbation to a normal image in order to fool the models. The N-pixel attack is one of the recently popular adversarial methods by simply changing a few pixels in the image. We observe that changing the few pixels leads to an obvious difference with its neighboring pixels. Therefore, this research aims to defend the N-pixel attacks based on image reconstruction. We develop a three-staged reconstructing algorithm to recover the fooling images. Experimental results show that the accuracy of CIFAR-10 test dataset can reach 92% after applying our proposed algorithm, indicating that the algorithm can maintain the original inference accuracy on normal dataset. Besides, the effectiveness of defending N-pixel attacks is also validated by reconstructing 500 attacked images using the proposed algorithm. The results show that we have a 90% to 92% chance of successful defense, where N=1,3,5,10,and 15.
CORSICA: Cross-Origin Web Service Identification

Vulnerabilities in private networks are difficult to detect for attackers outside of the network. While there are known methods for port scanning internal hosts that work by luring unwitting internal users to an external web page that hosts malicious JavaScript code, no such method for detailed and precise service identification is known. The reason is that the Same Origin Policy (SOP) prevents access to HTTP responses of other origins by default.
A transpressional model for deformation patterns in northern part of dezful Embayment's oil fields in Zagros (Iran), using geo-information technologies

The Dezful Embayment Zone (DEZ) is a discrete structural unit hosting predominant oil fields in the Zagros Mountains. The northern part of the DEZ has been studied using Geo-information Techniques (GiT) to ascertain the role of surface and subsurface deformation patterns forming traps for oil fields in the region. The surface structures and outcropped rock types were studied using remote sensing techniques and detailed field investigations. Also, the subsurface studies are consist of: a) preparation of iso-potential surface maps from underground contour (UGC) maps and their 3D-sketches; b) preparation of 3D-perspectives from field surveying data and 2D seismic studies; c) the compilation of three cross sections; and d) compilation of a basement and aeromagnetic maps. The results of the subsurface and surface investigations revealed that activity along subsurface NW-SE orientated thrust faults caused to folding of sedimentary cover as fault related folding and then, the accumulation of hydrocarbons were taken placed in these structures in DEZ. Movement along these faults resulted in the forming of a network of braided pattern of elliptical shaped anticlinal oilfields with structural blocks at different depths. Thus, the thrust faults controlled the position, elliptical form and elevation of anticlines in the northern part of the DEZ. Finally, a complex consecutive transpressiontranstensional model, due to a shear regime, is proposed for the deformation pattern in the region. Whereas, the structural pattern of the southern part of the DEZ is a simple discrete folding and coeval thrusts which forming anticlinal oil traps at similar depths.
Kernelized-Likelihood Ratio Tests for Binary Phase-Shift Keying Signal Detection

In this article, kernelized-likelihood ratio tests (LRTs) for binary phase-shift keying (BPSK) signal detection based on the polynomial kernel function are proposed. Specifically, we kernelize the conventional LRT of BPSK signal detection using the so-called kernel trick, such that the inner product of the conventional LRT is replaced with proper polynomial kernel functions allowing for richer feature space to be deployed in the detection. We also derive computationally efficient recursive implementation structures for the proposed methods, resulting overall in six new detectors. With respect to the noise variance uncertainty (NVU), the proposed detectors can be divided into two general classes, namely i) constant false alarm rate (CFAR) and ii) semi-CFAR (S-CFAR) methods. To facilitate efficient operation under NVU, we also propose a new threshold-setting strategy to adjust the level of the proposed S-CFAR detectors. Additionally, we address the well-known energy detector (ED) under NVU and devise a new fixed-level ED formulation while also obtaining closed-form expressions for its false alarm and detection probabilities. Our extensive simulation results show that the proposed S-CFAR detectors outperform the state-of-the-art BPSK signal detectors with 2.4 dB signal-to-noise ratio (SNR) gain under practical worst-case NVU assumptions, while the performance gain is approximately 5.7 dB without NVU. In the case of the proposed CFAR detectors, the corresponding improvement in detection performance is approximately 1.8 dB.
UAV Mission Monitoring and Sequencing

A method is proposed to enhance the automation of Unmanned Aerial Vehicle operations. Waypoint-based missions are augmented with safety monitoring of the UAV as a whole and execution monitoring and control of each mission leg. The monitoring and control are implemented using a Behaviour Tree which is automatically generated from the waypoints. The approach exploits the modularity of Behaviour Trees to populate a standard mission template with modules appropriate to each mission. The result is a highly flexible and reconfigurable framework that is easy to use for those overseeing the flight. Simulated flight results in a simple inspection scenario are provided.