Reviewerly/exHarmony huggingface dataset:
No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection

We propose an anomaly detection based image quality assessment method which exploits the correlations between feature maps from a pre-trained Convolutional Neural Network (CNN). The proposed method encodes the intra-layer correlation through the Gram matrix and then estimates the quality score combining the average of the correlation and the output from an anomaly detection method. The latter evaluates the degree of abnormality of an image by computing a correlation similarity with respect to a dictionary of pristine images. The effectiveness of the method is tested on different benchmarking datasets (LIVE-itW, KONIQ, and SPAQ).
Cross2Self-attentive Bidirectional Recurrent Neural Network with BERT for Biomedical Semantic Text Similarity

Estimating the similarity of biomedical sentence pair is an important component in such natural language processing (NLP) tasks as text retrieval and text summarization with great amount of biomedical information growing. Deep learning-based approaches have been successfully applied to the task, but they often rely on traditional pre-trained context-independent word embedding. Bidirectional Encoder Representations from Transformers (BERT) is recently employed to pre-train contextualized word/sentence representation models via bidirectional Transformers, outperforming the state-of-the-art for many NLP tasks. The mutual semantic influence between sentences is important for estimating semantic textual similarity, which is neglected in existing methods including BERT. On the other hand, biomedical corpora mainly consist of syntactic complex and long sentences. Owing to the above-mentioned issues, we proposed a hybrid architecture, integrating the pre-trained BERT and downstream bidirectional recurrent neural network (bi-RNN). The proposed model enhanced the sentence semantic representation via employing the self-attention instead of global attention to perform cross attention between sentences. Meanwhile, bi-RNN reduced redundant information in the output of BERT. Experimental results show that the best fine-tuned models consistently outperform previous methods and advance the state-of-the-art for clinical semantic textual similarity in OHNLP 2018 task 2, with up to 0.6% increase in Pearson correlation coefficient.
Fighting Deepfake by Residual Noise Using Convolutional Neural Networks

In the last few years, the easy access to images and videos shared online have been continuously increased. The generative adversarial networks using deep learning leads to create very realistic deepfake videos by playing with the digital content of images and videos. The spread of such deepfake videos on social media networks urged the international community to consider seriously its danger and accordingly encouraged the researchers around the world to develop powerful deepfake detection methods. Many approaches are available in the recent literature. In this paper, the proposed approach is based on exploiting the residual noise which is the difference between original image and its denoised version. The study of residual noise has shown effectiveness in deep-fake detection with regards to its distinctive and discriminative features which can be effectively captured by convolutional neural networks with transfer learning. The performance of our approach is evaluated on two datasets: low-resolution video sequences of the FaceForensics++ and high-resolution videos from Kaggle Deepfake Detection challenge (DFDC). The obtained results show relevant accuracy in comparison with other competitive methods.
A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data

Clustering data with both numeric and categorical attributes is of great importance as such data are ubiquitous in real-world problems. Multi-view learning approaches have proven to be more effective and having better generalisation ability compared to single-view learning in many problems. However, most of the existing clustering algorithms developed for mixed numeric and categorical data are single-view. In this research, we propose a novel multi-view clustering algorithm based on the k-prototypes (which we term Multi-view K-Prototypes) for clustering mixed data. To the best of our knowledge, our proposed Multi-view K-Prototypes is the first multi-view version of the well-known k-prototypes algorithm. To cluster the mixed data over multiple views, we present a novel representation prototype of cluster centres in the scenario of multiple views, and we also devise formulas for updating the cluster centres over each view. Then we propose the concept of consensus cluster centres to output the final clustering result. Finally, we carried out a series of experiments on four benchmark datasets to assess the performance of the proposed Multi-view K-Prototypes clustering. Experimental results show that the Multi-view K-Prototypes algorithm outperforms the seven state-of-the-art algorithms in most cases.
Efficient Algorithms For Storage Load Balancing Of Outsourced Data In Blockchain Network

Abstract Decentralized storage of data is one of the typical applications in the blockchain network. However, most of the existing works neglected the storage balancing problem in the blockchain network, which has an immediate impact on the availability and stability of the network. Therefore, this paper proposes a storage balancing problem for non-local data storage in the blockchain network and proves that the problem is non-deterministic polynomial (NP)-hard. The criterion of the storage balance is established by a balanced coefficient in the proposed scheme. A heuristic matching algorithm (HMA), a genetic algorithm (GA) and a tabu search algorithm (TSA) are customized to solve the problem of imbalanced storage formalized in this paper. Compared with our previous algorithm fast matching algorithm (FMA), experimental results demonstrate that HMA achieves better performance in terms of accuracy, computation overhead and storage overhead. Specifically, the computation overhead of HMA is lower than that of FMA by 84.45% on average, whereas the storage overhead of HMA is lower than that of FMA by 32.26% on average. By using the initial solution of HMA, TSA achieves the highest accuracy among GA, TSA and moth-flame optimization (MFO). Meanwhile, by using the initial solution of FMA, TSA achieves the highest accuracy among GA, TSA and MFO.
Staging energy sources to extend flight time of a multirotor UAV

Energy sources such as batteries do not decrease in mass after consumption, unlike combustion-based fuels. We present the concept of staging energy sources, i.e. consuming energy in stages and ejecting used stages, to progressively reduce the mass of aerial vehicles in-flight which reduces power consumption, and consequently increases flight time. A flight time vs. energy storage mass analysis is presented to show the endurance benefit of staging to multirotors. We consider two specific problems in discrete staging - optimal order of staging given a certain number of energy sources, and optimal partitioning of a given energy storage mass budget into a given number of stages. We then derive results for a continuously staged case of an internal combustion engine driving propellers. Notably, we show that a multirotor powered by internal combustion has an upper limit on achievable flight time independent of the available fuel mass. Lastly, we validate the analysis with flight experiments on a custom two-stage battery-powered quadcopter. This quadcopter can eject a battery stage after consumption in-flight using a custom-designed mechanism, and continue hovering using the next stage. The experimental flight times match well with those predicted from the analysis for our vehicle. We achieve a 19% increase in flight time using the batteries in two stages as compared to a single stage.
Low-viewpoint forest depth dataset for sparse rover swarms

Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the prediction of scene depth information from a monocular camera for autonomous navigation. Motivated by the aim to develop a robot swarm suitable for sensing, monitoring, and search applications in forests, we have collected a set of RGB images and corresponding depth maps. Over 100000 RGB/depth image pairs were recorded with a custom rig from the perspective of a small ground rover moving through a forest. Taken under different weather and lighting conditions, the images include scenes with grass, bushes, standing and fallen trees, tree branches, leaves, and dirt. In addition GPS, IMU, and wheel encoder data were recorded. From the calibrated, synchronized, aligned and timestamped frames about 9700 image-depth map pairs were selected for sharpness and variety. We provide this dataset to the community to fill a need identified in our own research and hope it will accelerate progress in robots navigating the challenging forest environment. This paper describes our custom hardware and methodology to collect the data, subsequent processing and quality of the data, and how to access it.
Agricultural soils of the Animas River watershed after the Gold King Mine spill: An elemental spatiotemporal analysis via portable X‐ray fluorescence spectroscopy

Abstract In August 2015, 11.3 million L of heavy metal–contaminated water spilled into the Animas River from the Gold King Mine (Colorado, USA). National attention focused on water quality and agricultural production in areas affected by the spill. In response to local concerns, surface soil elemental concentrations were analyzed in three New Mexico agricultural fields to determine potential threats to agronomic production. Irrigated fields in the Animas watershed were scanned using portable X‐ray fluorescence (PXRF) spectrometry to monitor the spatiotemporal variability of Pb, As, Cu, and Cr. A total of 175 locations were scanned using PXRF before and after the growing season for 3 yr. The geostatistical model with the lowest RMSE was chosen as the optimal model. The lowest RMSE for the elements ranged from to 0.10 to 0.44 m for As, from 0.50 to 0.98 m for Cr, from 0.15 to 0.91 m for Cu, and from 0.14 to 0.44 m for Pb across the models selected. The spatial dependence between the measured values exhibited strong to moderate autocorrelation for all metals except for As, for which spatial dependence was strong to weak. Some areas in each field exceeded the New Mexico Environment Department soil screening limit of 7.07 mg As kg –1 . All sampling locations were below the screening limit at last sampling time in 2019. Mixed models used for temporal analysis showed a significant decrease only in As below the screening value at the end of the study. Results indicate that the agricultural soils were below the soil screening guideline values.
Leveraging the Defects Life Cycle to Label Affected Versions and Defective Classes

Two recent studies explicitly recommend labeling defective classes in releases using the affected versions (AV) available in issue trackers (e.g., Jira). This practice is coined as the realistic approach . However, no study has investigated whether it is feasible to rely on AVs. For example, how available and consistent is the AV information on existing issue trackers? Additionally, no study has attempted to retrieve AVs when they are unavailable. The aim of our study is threefold: (1) to measure the proportion of defects for which the realistic method is usable, (2) to propose a method for retrieving the AVs of a defect, thus making the realistic approach usable when AVs are unavailable, (3) to compare the accuracy of the proposed method versus three SZZ implementations. The assumption of our proposed method is that defects have a stable life cycle in terms of the proportion of the number of versions affected by the defects before discovering and fixing these defects. Results related to 212 open-source projects from the Apache ecosystem, featuring a total of about 125,000 defects, reveal that the realistic method cannot be used in the majority (51%) of defects. Therefore, it is important to develop automated methods to retrieve AVs. Results related to 76 open-source projects from the Apache ecosystem, featuring a total of about 6,250,000 classes, affected by 60,000 defects, and spread over 4,000 versions and 760,000 commits, reveal that the proportion of the number of versions between defect discovery and fix is pretty stable (standard deviation &lt;2)—across the defects of the same project. Moreover, the proposed method resulted significantly more accurate than all three SZZ implementations in (i) retrieving AVs, (ii) labeling classes as defective, and (iii) in developing defects repositories to perform feature selection. Thus, when the realistic method is unusable, the proposed method is a valid automated alternative to SZZ for retrieving the origin of a defect. Finally, given the low accuracy of SZZ, researchers should consider re-executing the studies that have used SZZ as an oracle and, in general, should prefer selecting projects with a high proportion of available and consistent AVs.
Truncating bar graphs persistently misleads viewers.

Data visualizations and graphs are increasingly common in both scientific and mass media settings. While graphs are useful tools for communicating patterns in data, they also have the potential to mislead viewers. In five studies, we provide empirical evidence that y-axis truncation leads viewers to perceive illustrated differences as larger (i.e., a truncation effect). This effect persisted after viewers were taught about the effects of y-axis truncation and was robust across participants, with 83.5% of participants across these 5 studies showing a truncation effect. We also found that individual differences in graph literacy failed to predict the size of individuals’ truncation effects. PhD students in both quantitative fields and the humanities were susceptible to the truncation effect, but quantitative PhD students were slightly more resistant when no warning about truncated axes was provided. We discuss the implications of these results for the underlying mechanisms and make practical recommendations for training critical consumers and creators of graphs.
Scalable Knowledge Graph Analytics at 136 Petaflop/s

We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as one of computing all-pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the Floyd-Warshall algorithm for distributed-memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (Distributed Accelerated Semiring All-Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4, 096nodes (24,576GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of $136\times 10^{15}$ floating-point operations per second (136petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70% of the best possible performance given our computation (in the single-precision tropical semiring or “min-plus” algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence-driven natural language processing workflows at scale.
Probabilistically Guaranteed Satisfaction of Temporal Logic Constraints During Reinforcement Learning

We propose a novel constrained reinforcement learning method for finding optimal policies in Markov Decision Processes while satisfying temporal logic constraints with a desired probability throughout the learning process. An automata-theoretic approach is proposed to ensure the probabilistic satisfaction of the constraint in each episode, which is different from penalizing violations to achieve constraint satisfaction after a sufficiently large number of episodes. The proposed approach is based on computing a lower bound on the probability of constraint satisfaction and adjusting the exploration behavior as needed. We present theoretical results on the probabilistic constraint satisfaction achieved by the proposed approach. We also numerically demonstrate the proposed idea in a drone scenario, where the constraint is to perform periodically arriving pick-up and delivery tasks and the objective is to fly over high-reward zones to simultaneously perform aerial monitoring.
Rolling Horizon Evolutionary Algorithms for General Video Game Playing

Game-playing evolutionary algorithms, specifically rolling horizon evolutionary algorithms (RHEA), have recently managed to beat the state of the art in win rate across many video games. However, the best results in a game are highly dependent on the specific configuration of modifications introduced over several papers, each adding additional parameters to the core algorithm. Furthermore, the best previously published parameters have been found from only a few human-picked combinations, as the possibility space has grown beyond exhaustive search. This article presents the state of the art in RHEA, combining all modifications described in the literature, as well as new ones. We then use a parameter optimizer, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -tuple bandit evolutionary algorithm, to find the best combination of parameters in 20 games from the general video game Artificial Intelligence (AI) framework. Furthermore, we analyze the algorithm’s parameters and some interesting combinations revealed through the optimization process. Finally, we find new state of the art solutions on several games by automatically exploring the large parameter space of RHEA.
Soft Thresholding Attention Network for Adaptive Feature Denoising in SAR Ship Detection

Recently, synthetic aperture radar (SAR) ship detection is used in many applications within the marine field, such as fishery management, traffic control, and urgent rescue operations. Meanwhile, deep learning-based methods have bought new capabilities for ship detection in SAR images on account of high accuracy and robustness. However, several challenges remain to be addressed: 1) the shapes of the ships in SAR images have a relatively extreme aspect ratio comparing to the target objects in the optical images, and 2) complex background and clutter noise result in adverse effects for the network to extract prototypical SAR target features, which limit the ship detection performance. To address these issues, this paper proposes two effective approaches to augment the feature extraction ability of the network. Firstly, IOU (Intersection over Union) K-means is carried out to settle the extreme aspect ratio problem. The IOU K-means, as a preprocessing step, clusters a set of aspect ratios from datasets that are suitable for ship detection. Secondly, we embed a soft thresholding attention module (STA) in the network to suppress the impact of noise and complex background. The comparison results with several state-of-the-art object detection algorithms confirm the efficiency and feasibility of proposed approaches.
Validation of Architectural Requirements for Tackling Cloud Computing Barriers: Cloud Provider Perspective

Although researchers and organisations have recognised cloud computing obstacles in the prior cloud adoption stage, few efforts have been made to investigate and address cloud barriers in depth during cloud implementation projects. The current technical cloud barriers faced by governments, such as a lack of cloud service availability and flexibility, a lack of application of service-level agreements (SLAs), and deficiencies in data security and privacy, require a proper strategic solution to elevate the role of enterprise architecture. This research designed and measured architectural requirements to overcome common technical cloud barriers. The architectural requirements were guided through The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM). The research employed a qualitative approach through three case studies to address the role of cloud providers. The findings of this research provide validation and overall support of the proposed value propositions with 12 identification-confirmed, newly emerged and designed architectural requirements, each of which can successfully contribute to overcoming barriers to government cloud projects. The elicited architectural requirements presented in the matrix are focused on the most important cloud constructs with different levels of emphasis.
Analog Sensing and Leap Motion Integrated Remote Controller for Search and Rescue Robot System

In the age of modern technological capabilities, robotic systems play a vital role in making our lives more comfortable. Robotic systems are becoming an essential part of human life due to their efficiency, multi-functionality, and high performance. In recent years, search and rescue robot systems are used tremendously for helping to rescuers in a natural disaster. Every year, millions of people are affected by disasters in different parts of the world. Disasters can be manmade or natural, such as building collapse, earthquakes, avalanches, volcanic activity, wildfires, and floods, etc. This witnesses the importance of sensor-based search and rescue robot systems in the emergency and also disaster management field. A variety of sensing and wireless technologies are used for remote and vision controlling in the emergency field. The use of these sensing and wireless technologies, the rescuers instead of going inside in the disaster area, can control the robot systems remotely when calamity occurs. A remote controller is used in the remote control of modern robotic systems. In this paper, we design an analog sensing and Leap Motion integrated remote controller for search and rescue robot system. The controller consists of an Arduino Mega Pro MINI, joystick module, Leap Motion controller, radio module, USB hub, potentiometer, power switch, button, USB type B female, Ii-on battery and Ii-on charger. The remote controller has the ability to help the rescuers to remote control in the disaster area from far away. The rescuers also could save their lives by using this remote controller. The main objective of this paper is to design a multi-functional and easy to control remote controller for search and rescue robot system.