Reviewerly/exHarmony huggingface dataset:
Visual Saliency Based Video Summarization: A Case Study For Preview Video Generation

A direction research to visual content-driven videos has been in facilitating a short preview of each video through summarization that largely contains short-duration sequence combination of each scene corresponding to stationary camera. This work aims at using visual saliency features to trace scene-change positions in the video. In the present work, visual saliency features are built using color and intensity information as features. Further, using accumulated difference measure (Forward and Backward) in saliency features has been used to filter out false-positive scene-change outcomes. The results have been found to be quite satisfactory and provide closed match to the exact scene-change positions in the video. Significant accuracy is observed with videos using stationary cameras. For moving or non-stationary camera, video summarization has always been a challenging issue. The proposed method has been successfully tested over visual content-driven videos ranging from underwater scenes, fight sequences to surveillance videos in generating preview video.
A continuous path planning approach on Voronoi diagrams for robotics and manufacturing applications

Coverage of an area is required for a large variety of robotics and manufacturing applications, such as environment monitoring, home cleaning, search and rescue operations, machining, delivery, additive manufacturing and even for 3D terrain reconstruction. In this work, we present highly flexible algorithms that can be used for coverage and graph traversal. Although our methods take advantage of variable-sized Voronoi cells, by which regular, irregular and complex geometries can be easily composed, it is not limited to Voronoi diagrams and can be applied for any connected graph. After the construction of the Voronoi diagram, an Eulerian graph is generated. Then, three algorithms are elaborated for the traversal of it. Two of those algorithms traverse the graph continuously, leading to Eulerian cycles. On the other hand, one algorithm requires switching positions from time to time, which we call fast travel. Additionally, we describe the phenomenon of traversing the same edge twice in a row, i.e., u turn, and draw a conclusion to emphasize the importance of this specific problem.
Continual Universal Object Detection

Object detection has improved significantly in recent years on multiple challenging benchmarks. However, most existing detectors are still domain-specific, where the models are trained and tested on a single domain. When adapting these detectors to new domains, they often suffer from catastrophic forgetting of previous knowledge. In this paper, we propose a continual object detector that can learn sequentially from different domains without forgetting. First, we explore learning the object detector continually in different scenarios across various domains and categories. Learning from the analysis, we propose attentive feature distillation leveraging both bottom-up and top-down attentions to mitigate forgetting. It takes advantage of attention to ignore the noisy background information and feature distillation to provide strong supervision. Finally, for the most challenging scenarios, we propose an adaptive exemplar sampling method to leverage exemplars from previous tasks for less forgetting effectively. The experimental results show the excellent performance of our proposed method in three different scenarios across seven different object detection datasets.
Utilisation of multiple current and legacy datasets to create a national minerals inventory: A UK case study

Mineral resources are vital for economic growth and maintaining quality of life. In order to maintain a steady, adequate, and sustainable supply of minerals it is important that appropriate policies exist at all levels of government. Sufficient information must be available to inform the land-use planning process and the development of minerals supply strategies. An important first stage for improving the security of supply of minerals is to identify the locations of a country's mineral resources and estimate the quantities that may be present. Whilst the existence of resources does not mean that they will be extracted, knowing what minerals can be found within a jurisdiction can facilitate the development of strategies for their sustainable management, and to inform the negotiation of trade relationships. The study presented here, undertaken within the framework of a project funded by the European Union, attempted to produce a dataset for all non-energy mineral (i.e. excluding fossils fuel and uranium) resources and reserves in the UK. For the UK and many other countries there is no statutory requirement for the compilation of mineral resource or reserve data and consequently this information is either absent or contained within a range of disparate sources. This paper presents a methodology for collating available data in a consistent way and for filling data gaps. The results show that it is feasible, for relevant organisations such as national geological surveys, to compile new and relevant data for minerals by using a combination of disparate sources such as legacy data, modern industry data and spatial analysis. This methodology is also applicable to other nations with similar situations to the UK regarding minerals data. The resultant inventory shows the UK is relatively well endowed with a wide range of mineral resources and has large quantities of certain minerals. However, resources of some minerals, on which many industrial and manufacturing processes rely, are either absent or unrecorded, particularly many metals that are required for new technologies. This study also identifies the challenges involved in compiling national mineral resource and reserve totals. Most significant are the large uncertainties, the assumptions required and the variety of codes and standards that exist for reporting resources and reserves data. Nevertheless, a new dataset for the UK's mineral resources and reserves is presented. This may encourage long-term quantitative systematic monitoring of resources and reserves at a national scale to provide a basis for the development of minerals policy, environmental protection policy and business strategy.
Combined spatial temporal based In-loop filter for scalable extension of HEVC

Deep learning plays a major role in the present video processing tools and algorithms. To​ alleviate the limitations of present in-loop filters in the scalable extension of HEVC (SHVC), a combined residual network (CResNet) in-loop filtering is proposed in this paper. The proposed CResNet in-loop filter exploits layer information available in the spatial temporal domain to restrain the visual artifacts like blocking and ringing. Particularly, the block information related to current and co-located blocks of the spatial and temporal base layer reference frames are considered to optimize the in-loop filtering. The proposed architecture has four convolution layers at the base layer and two convolution layers at the enhancement layer that significantly reduces the coding complexity and memory. Additionally to completely train the input content and also to enhance the in-loop filter performance, the on/off level control flag for coding tree Unit (CTU) is sensed using rate distortion optimization (RDO) approach. The experimental results demonstrate that the proposed architecture provides up to 6.2% to 7.2% reduction in bit rate and 1.01 dB improvement in PSNR compared to the standard SHVC codec.
Predicting Training Time Without Training

We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function. To do so, we leverage the fact that the training dynamics of a deep network during fine-tuning are well approximated by those of a linearized model. This allows us to approximate the training loss and accuracy at any point during training by solving a low-dimensional Stochastic Differential Equation (SDE) in function space. Using this result, we are able to predict the time it takes for Stochastic Gradient Descent (SGD) to fine-tune a model to a given loss without having to perform any training. In our experiments, we are able to predict training time of a ResNet within a 20% error margin on a variety of datasets and hyper-parameters, at a 30 to 45-fold reduction in cost compared to actual training. We also discuss how to further reduce the computational and memory cost of our method, and in particular we show that by exploiting the spectral properties of the gradients' matrix it is possible predict training time on a large dataset while processing only a subset of the samples.
Assessment and source identification of heavy metal contamination of groundwater using geospatial technology in Gadilam River basin, Tamil Nadu, India

Abstract The purpose of this study is to determine the groundwater quality of the Gadilam River basin on the basis of Geographic Information Technology (GIS) taxonomy evaluation and risk assessment and statistical analysis. The Gadilam River originates from the district of Kallakurichi and flows into the district of Viluppuram finally confluence at Bay of Bengal in the district of Cuddalore. Groundwater supplies are serving the entire drinking needs of the residents of the Gadilam River basin, and pollution of its toxic elements has been raised in recent years. A total of 120 groundwater samples, excluding reserved forest areas, were obtained from the Gadilam River basin. 50 samples of the Archaean formation, 34 samples of the Quaternary formation, 35 additional Tertiary formation samples and one Cretaceous formation sample. During the monsoon season (Nov. 2018), groundwater samples were taken, which is after the Gaja cyclone’s diastral effects. At the time of the sample acquisition, pH, EC and TDS were measured using the Elico field kit. Using the Atomic Absorption Spectrometer, the heavy metals Zn, Cr, Cu, Pb, Co, Fe, Mn and Ni were analyzed. The spatial distribution maps of the GIS shows that in all the samples the concentrations of chromium, lead, cobalt, nickel are high (above the permitted limit). In 93.40%of the area, the manganese concentrations are higher, dropping under the unauthorized cap for human use in compliance with WHO requirements. In addition, statistical analysis indicates that groundwater pollution of the roots of Pb and Cr is from foreign sources and geogenic origin cannot be avoided. The country's rocks and industrial effluents can trigger Zn and Co. The anthropogenic operations may be due to Cu. The taxonomy diagram showing all groundwater samples is classified as ‘near neutral high metal.’ Industrial waste, household sewage, has boosted the emission of heavy metals. Groundwater risk assessment study of heavy metals using SMCE approach reveals that dense residential area in the Archaean formation, lineaments intersection with river portion and sea shore area in Quaternary formation and nearby SIPCOT industry and Lignite mining area in Tertiary formation is noted in the ‘Extreme risk dangerous’ areas.
Asymptotics of representation learning in finite Bayesian neural networks*

Abstract Recent works have suggested that finite Bayesian neural networks may sometimes outperform their infinite cousins because finite networks can flexibly adapt their internal representations. However, our theoretical understanding of how the learned hidden layer representations of finite networks differ from the fixed representations of infinite networks remains incomplete. Perturbative finite-width corrections to the network prior and posterior have been studied, but the asymptotics of learned features have not been fully characterized. Here, we argue that the leading finite-width corrections to the average feature kernels for any Bayesian network with linear readout and Gaussian likelihood have a largely universal form. We illustrate this explicitly for three tractable network architectures: deep linear fully-connected and convolutional networks, and networks with a single nonlinear hidden layer. Our results begin to elucidate how task-relevant learning signals shape the hidden layer representations of wide Bayesian neural networks.
BCDM - A decision and operation model for blockchains

Increasing numbers of blockchain applications will be faced with groundbreaking technologies. In this context, two research questions arise. First, deciding which use cases are profitable for business transformation and, second, ascertaining how the blockchain technology can be mapped and operated analogous to an IT Service Manangement approach. We believe that the introduction and application of Blockchain concepts must be considered in the context of the overall IT ecosystem of an organization. Therefore, we will provide a comprehensive decision and operating model, called BCDM, for the implementation of blockchains that will allow us to answer these two questions.
Automated Medical Image Analysis in Digital Mammography

Labeling and categorizing pixels in medical images is to obtain meaningful regions, which is called medical image segmentation process and/or is to detect and localize target objects as medical abnormalities of the images, which is called image detection process. Both of these processes divide an image into different parts/regions with common features that are the most important stages for computer-aided medical image analysis, in particular related to the human tumors or blood vessels issues. The analysis of these issues is often technically difficult due to tumor-tissue similarity, tumor-surrounding tissues, partial volume effect, numerous variations in tumor shape, depth, size, and location of the mass; distribution of vessels within the tumor and related vessels, etc. However, because of the vitality of the problem, the medical analysis is usually done manually by experts that its disadvantages include high computational time and high cost. To meet these challenges, computer-aided methods are proposed to analyze medical images with high accuracy without the need for user intervention. In other words, the presented methods in medical image analysis have reduced the need for manpower and eliminated human error. The main purpose of this work is to review and discuss findings concerning the analysis of medical mammography images and retinal fundus images. Also, detection and segmentation methods in medical images will be reviewed. Then, technical researches in the diagnosis of breast tumors and retinal blood vessels are discussed.
Qualities, Challenges and Future of Genetic Algorithms

Genetic algorithms, computer programs that simulate natural evolution, are increasingly applied across many disciplines. They have been used to solve various optimisation problems from neural network architecture search to strategic games, and to model phenomena of adaptation and learning. Expertise on the qualities and drawbacks of this technique is largely scattered across the literature or former, motivating an compilation of this knowledge at the light of the most recent developments of the field. In this review, we present genetic algorithms, their qualities, limitations and challenges, as well as some future development perspectives. Genetic algorithms are capable of exploring large and complex spaces of possible solutions, to quickly locate promising elements, and provide an adequate modelling tool to describe evolutionary systems, from games to economies. They however suffer from high computation costs, difficult parameter configuration, and crucial representation of the solutions. Recent developments such as GPU, parallel and quantum computing, conception of powerful parameter control methods, and novel approaches in representation strategies, may be keys to overcome those limitations. This compiling review aims at informing practitioners and newcomers in the field alike in their genetic algorithm research, and at outlining promising avenues for future research. It highlights the potential for interdisciplinary research associating genetic algorithms to pulse original discoveries in social sciences, open ended evolution, artificial life and AI.
Intelligent Amalgamation of Blockchain Technology with Industry 4.0 to Improve Security

The first industrial revolution back in the mid-eighteenth century changed the production techniques and a new social-economic structure came into existence. This industrial revolution 1.0 increased production on a large scale. Then, in the early nineteenth century, industry 2.0 changed the production style by mass production assembly lines with electricity. Car production reached new heights by industry 2.0. Then, in the twentieth century, the third industrial revolution emerged, which partially automated the production by integrating electronics and information technology in it. First, programmable logical controller by the gift of industry 3.0 revolution. Now we are witnessing industry 4.0, which completely changed the process by digitization and connecting all the actors of the process by digital means. It is based on the fusion of the production process with information and communication technology. Physical systems after connecting with cyber power are more intelligent than ever before. The industrial production process can communicate with logistics units without human intervention. It is not easy for all businesses to adopt this industrial revolution. The challenges of adapting industry 4.0 for businesses are implementing the data transparency, decentralized access and decision-making process, interconnectivity of all the stakeholders, ease of data exchange, automation, traceability, and maintaining privacy and security of data being exchanged between the concerned peers of the network. These hurdles can be easily removed by integrating blockchain technology with the industry 4.0 approach. Blockchain is an emerging technology that will the inevitable component of all the technological advancements in the upcoming days. The most important factor for its consideration is the security provided by this technology. It uses cryptographic techniques and a consensus mechanism, which makes it secure and tamper-proof. Industry 4.0 can be integrated with blockchain to achieve unbreakable security for all the data which flows between different actors of the businesses. Other than security, this technology also provides transparency in data and a decentralized approach for a collaborative environment. In this chapter, various security preferences of industry 4.0 and how blockchain technology can be used to cater to the security requirements of this industrial revolution are discussed. Finally, after exploring challenges in the implementation of blockchain for industry 4.0, some suggestions are enumerated to guide researchers and companies for future development perspectives.
RFDoc: Memory Efficient Local Descriptors for ID Documents Localization and Classification

Majority of recent papers in the field of image matching introduces universal descriptors for arbitrary keypoints matching. In this paper we propose a data-driven approach to building a descriptor for matching local keypoints from identity documents in the context of simultaneous ID document localization and classification on mobile devices. In the first stage, we train features robust to lighting changes. In the second stage, we select the most best-performing and discriminant features and forms a set of classifiers which we called RFDoc descriptor. To address the problem of limited computing resources the proposed descriptor is binary rather than a real-valued one. To perform experiments we prepared a dataset of aligned patches from a subset of identity document types presented in MIDV datasets and made it public. RFDoc descriptor showed similar performance in complex document detection and classification on the test part of the MIDV-500 dataset to the state-of-the-art BEBLID-512 descriptor, which is more than 2.6 times less memory efficient than RFDoc. On a more complex MIDV-2019 dataset RFDoc showed 21% fewer classification errors.
Excogitating marine predators algorithm based on random opposition‐based learning for feature selection

Summary Obtaining precise information from a high‐dimensional dataset is one of the most difficult tasks as datasets contain more features and fewer samples. The high‐dimensionality of the dataset reduces predictive capability and increases the computational complexity of the analytical model. The widespread employment of meta‐heuristic methods to handle the challenge of high‐dimensional datasets has been exceptional in recent years. The marine predators algorithm (MPA) is a recently developed meta‐heuristic algorithm based on the “survival‐of‐the‐fittest” notion. This research critique overcomes the drawbacks of the existing MPA and proposes a feature selection model using random opposition‐based learning (ROBL). The searching for the optimum solution in a single direction of the traditional MPA reduces its performance. The incorporation of ROBL in the MPA enhances its ability to reconnoiter bigger search space. The proposed algorithm generates a new population based on the initial and random opposite population. The performance of ROBL‐MPA is inspected on six high‐dimensional microarray datasets. The results of the proposed ROBL‐MPA are compared to traditional MPA and opposition based MPA (OBL‐MPA). The proposed ROBL‐MPA outperforms traditional MPA based on several benchmark performance analysis tests.
QuSBT

Generating a test suite for a quantum program such that it has the maximum number of failing tests is an optimization problem. For such optimization, search-based testing has shown promising results in the context of classical programs. To this end, we present a test generation tool for quantum programs based on a genetic algorithm, called QuSBT (Search-based Testing of Quantum Programs). QuSBT automates the testing of quantum programs, with the aim of finding a test suite having the maximum number of failing test cases. QuSBT utilizes IBM's Qiskit as the simulation framework for quantum programs. We present the tool architecture in addition to the implemented methodology (i.e., the encoding of the search individual, the definition of the fitness function expressing the search problem, and the test assessment w.r.t. two types of failures). Finally, we report results of the experiments in which we tested a set of faulty quantum programs with QuSBT to assess its effectiveness. Repository (code and experimental results): https://github.com/Simula-COMPLEX/qusbt-tool
Phase Congruency Order-Based Local Structural Feature for SAR and Optical Image Matching

Automatic matching of synthetic aperture radar (SAR) and optical images is a fundamental task in many remote sensing applications. However, due to different imaging modalities, conventional matching methods provide limited performances. In this letter, based on the observation that structural features are maintained across different modality images, we propose a novel feature-based method to effectively address SAR and optical image matching. The proposed method is built on the phase congruency (PC) model and consists mainly of two stages. First, a modified version of the uniform nonlinear diffusion-based Harris (MUND-Harris) detector is introduced to extract the local features. Unlike the UND-Harris, MUND-Harris employs the PC instead of image intensity for feature extraction and thus obtain well-distributed and highly repeatable feature points. Second, a local structural descriptor, namely PC order-based local structural (PCOLS), is designed for the extracted points. PCOLS is constructed in a grouping manner and further encodes image structures with an adaptive descriptor structure, which provides robustness against modality variations including significant geometric and intensity differences. Experimental results obtained on several SAR and optical image pairs demonstrate the encouraging performance of the proposed method.