Reviewerly/exHarmony huggingface dataset:
Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies

This study utilized multivariate statistics such as hierarchical cluster and principal component analyses to monitor water and stream sediments pollution. Three Principal components loadings: PC1, PC2 and PC3 were generated from water samples data yielding eigen values of 20.563, 8.477, and 7.635 respectively. PC1 explained 28.56% of the total variance with high positive loadings for EC, turbidity, Br, Cl, SO42−, Ce, Dy, Er, Eu, La, Lu, Nd, Pr, Sm, Tb, Tm, Yb, Al, Ba, Bi, Co, Co, Cs, Mn, Th, Ti, U, V and Y. PC2 explained 11.77% of the total variance with high positive loadings for Hf and high negative factor loadings for Hg. PC3 explained 10.60% of the total variance and positively loaded for P, NO3, Cd, Ge, Ni, Pb, Sn, and Zn. PC1 is related to saline waters intrusion and geological nature of the aquifer. PC2 is controlled by high field strength element (HFSE) Hofnium (Hf) due to mineralization. PC3 contributed to pollution due to anthropogenic activities caused by chemical nitrogenous and phosphate fertilizer applications, to the soil and mineralogical constituents of the aquifer rock. Four principal components were identified in stream sediments and possessed eigen values of 12.29, 5.47, 3.19 and 2.10 respectively. PC1 explained 39.65% of the total variance and had significant positive loadings for Mo, Cu, Pb, Ni, Zn, Fe, U, Th, V, Ba, Ti, Sn, Nb, Be, Sc, Li, Zr, Rb and Hf. This suggest the occurrence of heavy mineral phases such as garnet, barite, zircon and monazite in mafic and ultramafic rocks and resistive heavy minerals. PC2 accounted for 17.65% of the total variance with moderate positive loadings for Mo, Cu, As and Ba, but with moderate negative loadings for Th, La and Ba. This component shows lithogeochemical input representative of weathering and mobility of sediments related mineralization. PC3 accounted for 10.29% of the total variance with positive high loadings for Mn, Bi and Y. PC3 may be related to anthropogenic activities such as agriculture, industry and urban life in the study area. PC4 accounted for 6.78% of the total variance with positive high loading for Ta. PC4 indicates the mineralization acquired by this component.
DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features

We present a pedestrian tracking algorithm, DensePeds, that tracks individuals in highly dense crowds (>2 pedestrians per square meter). Our approach is designed for videos captured from front-facing or elevated cameras. We present a new motion model called Front-RVO (FRVO) for predicting pedestrian movements in dense situations using collision avoidance constraints and combine it with state-of-the-art Mask R-CNN to compute sparse feature vectors that reduce the loss of pedestrian tracks (false negatives). We evaluate DensePeds on the standard MOT benchmarks as well as a new dense crowd dataset. In practice, our approach is 4.5 × faster than prior tracking algorithms on the MOT benchmark and we are state-of-the-art in dense crowd videos by over 2.6% on the absolute scale on average.
Ontology for Reactive Techniques in Digital Forensics

Techniques applied in response to detrimental digital incidents vary in many respects according to their attributes. Models of techniques exist in current research but are typically restricted to some subset with regards to the discipline of the incident. An enormous collection of techniques is actually available for use. There is no single model representing all these techniques. There is no current categorisation of digital forensics reactive techniques that classify techniques according to the attribute of function and nor is there an attempt to classify techniques in a means that goes beyond a subset. In this paper, an ontology that depicts digital forensic reactive techniques classified by function is presented. The ontology itself contains additional information for each technique useful for merging into a cognate system where the relationship between techniques and other facets of the digital investigative process can be defined. A number of existing techniques were collected and described according to their function - a verb. The function then guided the placement and classification of the techniques in the ontology according to the ontology development process. The ontology contributes to a knowledge base for digital forensics - essentially useful as a resource for the various people operating in the field of digital forensics. The benefit of this that the information can be queried, assumptions can be made explicit, and there is a one-stop-shop for digital forensics reactive techniques with their place in the investigation detailed.
Proof of Work as a Three-Sided Market

Blockchain technology is the distributed, decentralised ledger technology underlying Bitcoin and other cryptocurrencies. We apply Oliver Williamson's transactions cost analysis to the blockchain consensus mechanism. Blockchains reduce the costs of opportunism but are not 'trustless'. We show that blockchains are trust machines. Blockchains are platforms for three-sided bargaining that convert energy-intensive computation into economically-valuable trust.
A formal mapping between OPC UA and the Semantic Web

The communication protocol OPC UA is one of the most important IIoT enablers within the automation domain. OPC UA not only aims to provide interoperability on the transport layer, but also interoperability of the semantic layer shall be addressed based on so-called Companion Specifications. However, the lack of OPC UA formal semantics makes automatic validation of OPC UA data models impossible. Another drawback is the shortage of available tools for OPC UA, such as an implementation of the query engine for the specified OPC UA query language. In this paper we provide a formal translation of OPC UA models to the Semantic Web standard OWL, thus making OPC UA implicit semantics, that is described in the documentation, explicit, by means of OWL axioms. Moreover, we outline how this mapping can be used to offer validation and querying of OPC UA data models based on already existing Semantic Web technology.
Designing multi-scale maps: lessons learned from existing practices

Mapping applications display multi-scale maps where zooming in and out triggers the display of different maps at different scales.Multi-scale maps strongly augmented the potential uses of maps, compared to the traditional single-scaled paper maps.But the exploration of the multi-scale maps can be cognitively difficult for users because the content of the maps can be very different at different scales.This paper seeks to identify the factors in the design of map content and style that increase or decrease the exploration cognitive load, in order to improve multi-scales map design.We studied sixteen existing examples of multi-scale maps to identify these factors that influence a fluid zooming interaction.Several different analyses were conducted on these sixteen multiscale maps.We first conducted a guided visual exploration of the maps, and a detailed study of the scales of the maps, to identify general trends of good practices (e.g. the WMTS standard that defines zoom levels is widely used) and potential ways of improvement (e.g. a same map is often used at multiple successive zoom levels).Then, we focused on the visual complexity of the multi-scale maps by analyzing how it varies, continuously or not, across scales, using clutter measures, which showed a peak of complexity at zoom level 12 of the WMTS standard.Finally, we studied how buildings and roads are subject to abstraction changes across scales (e.g. at what zoom level individual buildings turn into built-up areas), which can be one of the causes of exploration difficulties.We identified some good practices to reduce the impact of abstraction changes, for instance by mixing different levels of abstraction in the same map.
Classification and Spectral Mapping of Stationary and Moving Objects in Road Environments Using FMCW Radar

In order to establish a reliable map of the road environment, this paper aims to classify the stationary and moving objects unlike the previous researches which generally focus on object recognition.The characteristics of the radar signals of stationary and moving objects were analyzed and the relation between the slope of pattern in radar time-frequency spectrum and relative velocity of the object was described mathematically.To discriminate the stationary and moving objects, the difference between the measured velocity by the slope and the velocity of the ego-vehicle was proposed as a feature.The statistical characteristics of stationary and moving objects according to the proposed feature were modeled using Gaussian model.To investigate the performance of the proposed method, the similarity between modeling of stationary and moving objects was quantified.Additionally, the receiver operating characteristics (ROC) curve and the correlation coefficient between the proposed feature and the ground-truth feature map was applied to verify the performance.
Autonomic Resource Allocation Mechanism for Service-based Cloud Applications

The user's request changes dynamically in service-based cloud applications, which requires optimal amount of computing resources to meet service-level agreements (SLAs). The existing server-side resource allocation mechanisms have limitations in provisioning the required resources to handle the incoming load on the basis of user's requests. To overcome the aforementioned situation, cloud computing provides ample amount of computing resources to meet the SLAs. There are possibilities that cloud resources might not be properly utilized and might suffer over and under utilization. In this study, the authors have proposed an autonomous resource allocation mechanism, that automatically provisions (allocate and de-allocate) the required computing resources as per the load. The primary goal of this study is to improve the virtual resource utilization and response time with respect to the existing methods. The proposed model leverages the simple heuristic with response time and the number of virtual machines as the parameters. Finally, the results have shown that the response time and resource utilization have been improved.
Learning Residual Flow as Dynamic Motion from Stereo Videos

We present a method for decomposing the 3D scene flow observed from a moving stereo rig into stationary scene elements and dynamic object motion. Our unsupervised learning framework jointly reasons about the camera motion, optical flow, and 3D motion of moving objects. Three cooperating networks predict stereo matching, camera motion, and residual flow, which represents the flow component due to object motion and not from camera motion. Based on rigid projective geometry, the estimated stereo depth is used to guide the camera motion estimation, and the depth and camera motion are used to guide the residual flow estimation. We also explicitly estimate the 3D scene flow of dynamic objects based on the residual flow and scene depth. Experiments on the KITTI dataset demonstrate the effectiveness of our approach and show that our method outperforms other state-of-the-art algorithms on the optical flow and visual odometry tasks.
Student Readiness and Perception of Tablet Learning in Higher Education in the Pacific- A Case Study of Fiji and Tuvalu

Significant growth and successful integration of mobile devices to the education landscape have garnered the introduction of mobile learning (mLearning) as a new technology-enabled educational tool. Mobile learning has become a new higher educational paradigm which makes learning more flexible and accessible. However, student readiness and their perceptions about educational use of tablet devices are still a concern particularly in the developing countries like the Pacific island countries (PICs). This article investigates these two important attributes of tablet devices in a first year blended course from a higher education institute in the Pacific with a sample of 43 students. The results revealed that the participants perceived that tablet devices were effective and innovative learning tools. The participants supported the idea of using tablet devices and were ready to incorporate them for learning provided there was training in place. This article finally outlines recommendations for the education sectors in the Pacific.
Object Placement Planning and optimization for Robot Manipulators

We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.
GA-Adaptive Template Matching for Offline Shape Motion Tracking Based on Edge Detection: IAS Estimation from the SURVISHNO 2019 Challenge Video for Machine Diagnostics Purposes

The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for performing angular resampling through Computed Order Tracking, a practice which is essential to highlight the machine spectral signature in case of non-stationary operational conditions. In these regards, the SURVISHNO 2019 international conference held at INSA Lyon on 8–10 July 2019 proposed a challenge about the estimation of the instantaneous non-stationary speed of a fan from a video taken by a smartphone, a pocket, low-cost device which can nowadays be found in everyone’s pocket. This work originated by the author to produce an offline motion-tracking of the fan (actually, of the head of its locking-screw) and obtaining then a reliable estimate of the IAS. The here proposed algorithm is an update of the established Template Matching (TM) technique (i.e., in the Signal Processing community, a two-dimensional matched filter), which is here integrated into a Genetic Algorithm (GA) search. Using a template reconstructed from a simplified parametric mathematical model of the features of interest (i.e., the known geometry of the edges of the screw head), the GA can be used to adapt the template to match the search image, leading to a hybridization of template-based and feature-based approaches which allows to overcome the well-known issues of the traditional TM related to scaling and rotations of the search image with respect to the template. Furthermore, it is able to resolve the position of the center of the screw head at a resolution that goes beyond the limit of the pixel grid. By repeating the analysis frame after frame and focusing on the angular position of the screw head over time, the proposed algorithm can be used as an effective offline video-tachometer able to estimate the IAS from the video, avoiding the need for expensive high-resolution encoders or tachometers.
Real Time Sentiment Analysis On Twitter Data Using Deep Learning(Keras)

Social media sites have emerged as one of the platforms to talk about issues and raise voice regarding almost anything going on in the world, with millions of people using social media sites every day, these sites enjoy tremendous popularity, and play a vital role in forming the opinion of the public. Due to this reason it becomes vital for any brand, political party, etc. to get a grasp of the prevailing sentiment among people, analyse it and create strategies to shape the opinion if needed. This is where sentiment analysis comes into play. It requires handling of huge amounts of data which is unstructured data which can be handled using deep learning, classifying algorithms.
Flexible character accuracy measure for reading-order-independent evaluation

The extraction of textual information from scanned document pages is a fundamental stage in any digitisation effort and directly determines the success of the overall document analysis and understanding application scenarios. To evaluate and improve the performance of optical character recognition (OCR), it is necessary to measure the accuracy of that step alone, without the influence of the processing steps that precede it (e.g. text block segmentation and ordering). Current OCR performance evaluation measures (based on edit distance) are strongly subjective as they need to first serialise the entire text in the documents – a process influenced heavily by the specific reading order determined (often wrongly, especially in cases of multicolumn and complex layouts) by processing steps prior to OCR. This paper presents a new objective and practical edit-distance-based character recognition accuracy measure which overcomes those limitations. It achieves its independence from the reading order by comparing sub-strings of text in a flexible way (i.e. allowing for ordering variations). The precision of the flexible character accuracy measure enables the effective tuning of complete digitisation workflows (as OCR errors are isolated and other steps can be evaluated and optimised separately). For the same reason, it also enables a better estimation of post-OCR (manual) correction effort required. The proposed character accuracy measure has been systematically analysed and validated under lab conditions as well as successfully used in practice in a number of high-profile international competitions since 2017.
Extreme Learning Machine Soft-Sensor Model With Different Activation Functions on Grinding Process Optimized by Improved Black Hole Algorithm

Aiming at predicting the key economic and technical indicators (Granularity and Ore content)in the grinding production process, the extreme learning machine (ELM) soft-sensor model with different activation functions on grinding process optimized by improved black hole (BH) algorithm was proposed. Based on the selected auxiliary variables for the soft-sensor model of the grinding, the KPCA method is used to reduce the dimensionality of the high-dimensional data. In order to investigate the influence of different activation functions on the prediction accuracy of the ELM model, seven continuous function (Arctan, Hardim, Morlet, ReLu, Sigma, Sin and Tanh) are used as the activation function of the ELM neural network to establish soft-sensor models respectively. For the shortcomings that ELM model weights and offset values are arbitrarily given so as to result in the low prediction accuracy and low reliability, an improved BH algorithm based on the golden sine operator and the Levy flight operator (GSLBH) was used to optimize the parameters of the ELM neural network. Simulation results show that the model has better generalization and prediction accuracy, and can meet the real-time control requirements of the grinding process.
PADVA: A Blockchain-Based TLS Notary Service

The TLS protocol is a de facto standard of secure client-server communication on the Internet. Unfortunately, the public-key infrastructure (PKI) deployed by TLS is a weakest-link system introducing hundreds of links (i.e., trusted entities). Consequently, an adversary compromising a single trusted entity can impersonate any website. Notary systems, based on multi-path probing, were early and promising proposals to detect and prevent such attacks. Unfortunately, despite their benefits, they are not widely deployed, mainly due to their long-standing unresolved problems. In this paper, we present Persistent and Accountable Domain Validation (PADVA), which is a next-generation blockchain-based TLS notary service. PADVA keeps notaries auditable and accountable, introduces service-level agreements and mechanisms to enforce them, relaxes availability requirements for notaries, and works with the legacy TLS ecosystem. We implemented and evaluated PADVA, and our experiments indicate its efficiency and deployability.
Corruption and FDI inflow to Nigeria: a nonlinear ARDL approach

Purpose This paper aims to empirically explore the asymmetric relationship between corruption control and foreign direct investment (FDI) in Nigeria. Design/methodology/approach The study utilized the non-linear autoregressive distributed lag (NARDL) bounds test technique for the time-series analysis covering the period 1984-2017. Findings The findings reveal that corruption inhibits FDI inflow and corruption control has asymmetric effects on FDI inflow to Nigeria. The coefficient of positive shock or changes in respect of corruption control is positive as well as statistically significant during the long run, while the coefficient of negative shock is negative, but statistically insignificant. This implies that improvement in corruption control encourages inflow of FDI to the country, whereas a decrease in corruption control has an insignificant effect. Practical implications Nigeria needs to intensify its corruption control efforts to effectively enhance the conduciveness as well as attractiveness of its business operating environment for FDI inflow. Originality/value This paper is among the first to use time-series analytical process to empirically verify the asymmetric association of corruption control and FDI inflow in Nigeria. In this regard, the insight generated by outcomes of the study will enable specific inferences to be drawn from the empirical findings by policy makers, academic researchers and business practitioners.
A Synthetic Recipe for OCR

Synthetic data generation for optical character recognition (OCR) promises unlimited training data at zero annotation cost. With enough fonts and seed text, we should be able to generate data to train a model that approaches or exceeds the performance with real annotated data. Unfortunately, this is not always the reality. Unconstrained image settings, such as internet memes, scanned web pages, or newspapers, present diverse scripts, fonts, layouts, and complex backgrounds, which cause models trained with synthetic data to break down. In this work, we investigate the synthetic image generation problem on a large multilingual set of unconstrained document images. Our work presents a comprehensive evaluation of the impact of synthetic data attributes on model performance. The results provide a recipe for synthetic data generation that will help guide future research.
Fast Free-viewpoint Video Synthesis Algorithm for Sports Scenes

In this paper, we report on a parallel free-viewpoint video synthesis algorithm that can efficiently reconstruct a high-quality 3D scene representation of sports scenes. The proposed method focuses on a scene that is captured by multiple synchronized cameras featuring wide-baselines. The following strategies are introduced to accelerate the production of a free-viewpoint video taking the improvement of visual quality into account: (1) a sparse point cloud is reconstructed using a volumetric visual hull approach, and an exact 3D ROI is found for each object using an efficient connected components labeling algorithm. Next, the reconstruction of a dense point cloud is accelerated by implementing visual hull only in the ROIs; (2) an accurate polyhedral surface mesh is built by estimating the exact intersections between grid cells and the visual hull; (3) the appearance of the reconstructed presentation is reproduced in a view-dependent manner that respectively renders the non-occluded and occluded region with the nearest camera and its neighboring cameras. The production for volleyball and judo sequences demonstrates the effectiveness of our method in terms of both execution time and visual quality.