Reviewerly/exHarmony huggingface dataset:
A Visual Detection Method for Foreign Objects in Power Lines Based on Mask R-CNN

The high-voltage power lines and transmission towers are large in volume, large in number, and wide in coverage, so they are easily attached to foreign objects, which may cause failure of the transmission line. The existing object detection methods are susceptible to weather and environmental factors, and the use of neural networks for target detection can achieve good results. Therefore, this article uses MASK R-CNN as the basic network detection method for detecting foreign objects in the transmission network. The experimental results show that compared with the traditional target detection method, the method adopted in this article has achieved good results in the speed, efficiency, and recognition precision of foreign object detection. In the future, image processing operations can be performed for complex backgrounds of transmission lines to improve recognition effect.
A lightweight and compromise‐resilient authentication scheme for IoTs

Abstract Internet of Things (IoTs) connects billions of devices through the Internet having billion of data points. The security and privacy of those data points are very important and considered as a major concern. Due to the resource constraints nature of IoTs, the security solutions for IoTs must be secure and lightweight in terms of processing and storage. However, many existing security solutions specifically in the field of authentication are not suitable for IoTs due to the computation involved. Alternatively, the lightweight existing solutions are vulnerable to various attack(s). In this paper, a lightweight and compromise‐resilient authentication (LCA) scheme for IoT is proposed. The proposed LCA scheme is lightweight because it uses lightweight hash and XOR operations. The security analysis manifests that the proposed scheme is compromise resilient against numerous security attacks. The proposed LCA scheme is compared with existing authentication schemes based on execution time, security, and computation cost. The outcomes show that the proposed LCA scheme is more secure and robust as compared to the existing authentication schemes proposed for IoTs.
Type-Independent Characterization of Spacelike Separated Resources

Quantum theory describes multipartite objects of various types: quantum states, nonlocal boxes, steering assemblages, teleportages, distributed measurements, channels, and so on. Such objects describe, for example, the resources shared in quantum networks. Not all such objects are useful, however. In the context of spacelike separated parties, devices which can be simulated using local operations and shared randomness are useless, and it is of paramount importance to be able to practically distinguish useful from useless quantum resources. Accordingly, a body of literature has arisen to provide tools for witnessing and quantifying the nonclassicality of objects of each specific type. In the present Letter, we provide a framework which subsumes and generalizes all of these resources, as well as the tools for witnessing and quantifying their nonclassicality.
The senatorial governance of Bitcoin: making (de)centralized money

In recent years the development of cryptocurrencies and wider implementations of blockchain technology have been valourized as digitally decentralized networks that dissipate control evenly among their peers. With Bitcoin, the first blockchain-based cryptocurrency, monetary policy is enacted via software built through an open source consensus model. This promotes a techno-decentralist ideology that promises to democratize societies by eradicating centralized points of control in economic systems. Contrastingly, this paper demonstrates how Bitcoin's production process operates through strict authoritative channels. The overall political framework for altering the Bitcoin code is described as senatorial governance: a (de)centralized model of bureaucratic parties who compete to change the monetary policy (codified rules) of the protocol. This model shows how Bitcoin is not an autonomous system but is assembled and maintained via human discretion.
Evaluating the Effectiveness of Climate Change Adaptations in the World’s Largest Mangrove Ecosystem

The Sundarbans is the world’s largest coastal river delta and the largest uninterrupted mangrove ecosystem. A complex socio-ecological setting, coupled with disproportionately high climate-change exposure and severe ecological and social vulnerabilities, has turned it into a climate hotspot requiring well-designed adaptation interventions. We have used the fuzzy cognitive maps (FCM)-based approach to elicit and integrate stakeholders’ perceptions regarding current climate forcing, consequent impacts, and efficacy of the existing adaptation measures. We have also undertaken climate modelling to ascertain long-term future trends of climate forcing. FCM-based simulations reveal that while existing adaptation practices provide resilience to an extent, they are grossly inadequate in the context of providing future resilience. Even well-planned adaptations may not be entirely transformative in such a fragile ecosystem. It was through FCM-based simulations that we realised that a coastal river delta in a developing nation merits special attention for climate-resilient adaptation planning and execution. Measures that are likely to enhance adaptive capabilities of the local communities include those involving gender-responsive and adaptive governance, human resource capacity building, commitments of global communities for adaptation financing, education and awareness programmes, and embedding indigenous and local knowledge into decision making.
Simulating Photo-realistic Snow and Fog on Existing Images for Enhanced CNN Training and Evaluation

Verification and robustness testing of machine learning algorithms for autonomous driving is crucial. Due to the increasing complexity and quantity of those systems in a single vehicle, just driving the required distance with a newly developed vehicle is not feasible anymore: billions of hours on the street without failure are necessary to qualify for industry standards like ISO 26262. That is where simulation comes into play: machine learning algorithms are trained and evaluated on well known image data sets like KITTI or Cityscapes. But today's data sets mostly contain images taken under perfect weather conditions and therefore do not harden optical object detection algorithms against various weather conditions. This paper focuses on reusing these established and labeled data sets by augmenting them with adverse weather effects like snow and fog. Those effects are rendered physically correct and life like while being added to existing real world images. Thanks to easy parametrization the weather influences may be varied as necessary and allow for finely tuned learning and optimization processes. The weather effects are evaluated with regard to realism and impact on an established object detection algorithm. These newly created weather-influenced images may be used to validate or train new object detection algorithms.
Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification

In this paper, we propose a novel evolutionary algorithm for automatically creating neural network architectures. The proposed algorithm utilizes basic building blocks from existing advanced networks to initialize the population. During the evolutionary process, the algorithm generates new individuals (that is, new networks) which are constructed by making use of different blocks from different networks. Moreover, the encoding scheme, genetic operators and fitness function are well designed. As a result, the generated network architecture, which has a strong heterogeneous property, tends to integrate the merits of existing advanced networks. Experimental results show that the network architecture created by the proposed algorithm can achieve higher classification accuracy than the reference network architectures and the ones evolved by other state-of-the-art evolutionary topology design methods, demonstrating the effectiveness of the proposed algorithm on automatically creating network architectures.
A Study of LBPH, Eigenface, Fisherface and Haar-like features for Face recognition using OpenCV

Facial recognition is a major challenge in the field of computer vision. Here we have implemented various facial recognition algorithms like LBPH, Eigenface and Fisherface. Haar cascade has been used for facial identification. We trained the algorithms using the same data set and have got some insights, from which we have tried to identify which algorithm gives us the best results. Different algorithms are compared and their workings are discussed. At the end, tabular comparisons are provided. So that it would be easier to understand the difference between algorithms.
Explicit Constructions of MDS Self-Dual Codes

In this paper, we study self-dual codes over finite fields using tools from algebraic function fields in one variable. An algebraic geometry code of length n is defined using two divisors G and D = P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> + ⋯ + P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> . We characterize self-orthogonality of the genus zero code in terms of the divisors G, D and the value of a well-chosen derivative polynomial at points (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1≤i≤n</sub> . We explore the existence problem of MDS self-dual codes in the odd characteristic case, and we explicitly construct families of new MDS self-dual codes.
Attention Bridging Network for Knowledge Transfer

The attention of a deep neural network obtained by back-propagating gradients can effectively explain the decision of the network. They can further be used to explicitly access to the network response to a specific pattern. Considering objects of the same category but from different domains share similar visual patterns, we propose to treat the network attention as a bridge to connect objects across domains. In this paper, we use knowledge from the source domain to guide the network's response to categories shared with the target domain. With weights sharing and domain adversary training, this knowledge can be successfully transferred by regularizing the network's response to the same category in the target domain. Specifically, we transfer the foreground prior from a simple single-label dataset to another complex multi-label dataset, leading to improvement of attention maps. Experiments about the weakly-supervised semantic segmentation task show the effectiveness of our method. Besides, we further explore and validate that the proposed method is able to improve the generalization ability of a classification network in domain adaptation and domain generalization settings.
Modeling and Simulation of Robot Inverse Dynamics Using LSTM-Based Deep Learning Algorithm for Smart Cities and Factories

In smart cities and factories, robotic applications require high dexterity and security, which requires precise inverse dynamics model. However, the physical modeling methods cannot model the uncertain factors of the manipulator such as flexibility, joint clearance and friction, etc. As an alternative, artificial intelligence (AI) techniques have become increasingly popular in robotics for smart cities and factories. In this paper, deep learning neural network based on LSTM (Long Short-Term Memory) is adopted to predict the manipulator inverse dynamics. This study aims to summarize the influence of the hyper-parameter settings on model performance and to explore the applicability of the LSTM model to joint torque prediction of multiple degrees of freedom series manipulator. Furthermore, the feasibility of using only joint position as input data for torque prediction is verified. Simulation result has shown that, for the proposed deep learning architecture, the effects of the number of maximum epochs on model performance should be prioritized. The effects of the number of hidden nodes on model performance are limited, while prediction accuracy will deteriorate as the number of hidden layers increases. It is proved that it is feasible to predict inverse dynamics when input data is joint position only. The experimental results show that the training time increases with the increase of hidden layers, neurons and epochs.
Multi-scale multi-patch person re-identification with exclusivity regularized softmax

Discriminative feature learning is critical for person re-identification. To obtain abundant visual information from the input person image, we first propose a novel network that extracts multi-scale patch-level deep features. Then, we propose an improved softmax loss function for learning more compact and more discriminative feature vectors. Specifically, we integrate feature pyramid blocks and region-level global average pooling functions into the feature extraction network, introduce the well-established normalization techniques in face recognition algorithms into person re-ID, and penalize the redundancy in feature vectors by minimizing the l1,2 norm of the weight matrix in the softmax layer. Experiments on three large-scale datasets under the standard settings show the effectiveness of the proposed method. Moreover, we report our cross-domain re-ID results by training re-ID models on source datasets and testing them on other target datasets.
Self-Admitted Technical Debt Removal and Refactoring Actions: Co-Occurrence or More?

Technical Debt (TD) concerns the lack of an adequate solution in a software project, from its design to the source code. Its admittance through comments or commit messages is referred to as Self-Admitted Technical Debt (SATD). Previous research has studied SATD from different perspectives, including its distribution, impact on software quality, and removal. In this paper, we investigate the relationship between refactorings and SATD removal. By leveraging a dataset of SATD and their removals in four open-source projects and by using an automated refactoring detection tool, we study the co-occurrence of refactorings and SATD removals. Results of the study indicate that refactorings are more likely to co-occur with SATD removals than with other commits, however, in most cases, they belong to different quality improvement activities performed at the same time.
Face Authenticity: An Overview of Face Manipulation Generation, Detection and Recognition

In recent years, there has been an exponential increase in photo and video manipulation by easy-to-use editing tools (e.g., Photoshop). Especially, ‘face digital manipulations’ (e.g., face swapping) is a critical issue for automated face recognition systems (AFRSs) as it detrimentally effects the AFRS’ performance. Also, the advent of powerful deep learning methods has led to realistic face sample generation and manipulation. Despite recent advances in face manipulation detection techniques, manipulations-aware AFRSs and face synthetic sample/manipulation generation, detecting sophisticated face manipulations is still a challenge to human examiners and existing technologies. Devising more effective universal face manipulation detectors and manipulations-aware AFRSs will immensely improve the trust in biometric applications and digital communications. This paper presents an overview of the recent technologies on face manipulation generation, detection, recognition, and databases. Also, potential future research directions and challenges are discussed.
An ANFIS‐based model for solar energy forecasting and its smart grid application

In the present work, a model underlying the principle of adaptive neural‐fuzzy inference system (ANFIS) architecture is proposed. This research investigates the ANFIS‐based model performance to forecast global solar energy under different weather conditions, namely, clear sky, hazy sky, hazy and cloudy sky, and cloudy sky; and for distinct climate zones using salient meteorological parameters. Power generation from solar photovoltaic (PV) may be affected by different factors, such as cloud cover, topographical locations, time, and seasonal variations. Therefore, accurate forecasting of PV power is an important factor for system reliability and robustness. In this work, the proposed model is implemented for smart grid applications in forecasting short‐term PV power in a composite climate zone. Finally, the accuracy of the model is compared with other approaches, namely, support vector machine, feedforward neural network, multivariate adaptive regression splines, generalized regression neural network, linear neural network, fuzzy logic‐based model, and empirical models based on multiple regression analysis using statistical performance indicators. The overall analysis reveals that the ANFIS‐based model outperforms the other models in terms of accuracy.
Sketch-Based Image Retrieval With Multi-Clustering Re-Ranking

To improve the performance of sketch-based image retrieval (SBIR) methods, most existing SBIR methods develop brand new SBIR methods. In fact, a re-ranking approach, which can refine the retrieval results of SBIR methods, is also beneficial. Inspired by this, in this paper, an SBIR re-ranking approach based on multi-clustering is proposed. In order to make the re-ranking approach invisible to users and adaptive to different types of image datasets, we made it an unsupervised method using blind feedback. Distinguished from the existing methods, this re-ranking approach uses the semantic information of three types of images: edge maps, object images (images with black background and natural images' foreground objects) and natural images themselves. With the initial retrieval results of an SBIR method, our approach first does the clustering operation for three types of images. Then, we utilize the clustering results to generate a cluster score for each initial retrieval result. Finally, the cluster score is used to calculate the final retrieval scores for the initial retrieval results. The experiments on different SBIR datasets are conducted. Experimental results demonstrate that, by implementing our re-ranking approach, the retrieval accuracy of a variety of SBIR methods is increased. Furthermore, the comparisons between our re-ranking method and the existing re-ranking methods are given.
CGR-GAN: CG Facial Image Regeneration for Antiforensics Based on Generative Adversarial Network

In this paper, a Computer-generated graphics (CG) facial image regeneration scheme for anti-forensics based on generative adversarial network (CGR-GAN) is proposed. The generator of CGR-GAN utilizes a deep U-Net structure, and its discriminator utilizes some stacked convolution layers. Besides, content loss and style loss are both designed to guarantee that the regenerated CG facial images (CGR) retain both the facial profile of the original CG and the characteristics of natural image (NI). Experimental results and analysis demonstrate that the CG facial images regenerated by the proposed anti-forensics scheme can achieve better visual quality compared with those of the existing CG facial image anti-forensics and domain adaptation methods, and it can strike a good balance between visual quality and deception ability.
Transfer learning-based discriminative correlation filter for visual tracking

Most Correlation Filter (CF)-based tracking methods can hardly handle occlusion or severe deformation, due to the lack of effective utilization of previous target information. To overcome this, we propose a novel Transfer Learning-based Discriminative Correlation Filter (TLDCF), which extracts knowledge from multiple previous tracking tasks and applies the knowledge for a new tracking task through Instance-Transfer Learning (ITL) and Probability-Transfer Learning (PTL). ITL applies knowledge of Gaussian Mixture Modelling (GMM) target representations and multi-channel filters learned in previous frames to directly train a new correlation filter. This improves the robustness of tracker for heavy occlusion and large appearance variations. Meanwhile, PTL encodes the spatio-temporal relationship predicted by Kalman Filter (KF) into a shared Gaussian prior to suppress huge location drift caused by similar targets. For optimization, we develop an efficient Alternating Direction Method of Multipliers (ADMM) based algorithm to calculate CFs on each independent channel in real time. Extensive experiments on OTB-2013 and OTB-2015 datasets well demonstrate the effectiveness of the proposed method. In particular, our method improves AUC score of the two datasets by 5.5% and 3.9% respectively compared to baseline, and achieves competitive performance against recent state-of-the-art deep trackers.
Conceptual analysis of Internet of Things use cases in Banking domain

Internet of Things (IoT) is an element of smart infrastructure and banking is one of the potential domains which can leverage a huge opportunity from IoT technology. Currently Automated Teller Machine (ATM), Point of service (POS) terminal, mobile banking acts as the edge for banking infrastructure. These digital edge systems in banking bring in one on one interactions with customers and their requirements which opens up opportunities for data acquisition, processing, analytics and decision making on that data. In this paper, banking, financial services and insurance (BFSI) based IoT application usage / use cases with concepts, current trends, opportunities and challenges will be discussed. Smart banking edge systems are needed to address the growing demands on BFSI requirements and IoT is one of the elements for Digital banking infrastructure that will meet the expectations.
Doly: Bengali Chatbot for Bengali Education

This Scientific Research paper is a procedure of an automated system "Doly: Bengali Chatbot" which gives a reply to a user query on behalf of a human for the education system in the Bengali language. This is an AI-based Chatbot, mainly based on machine learning algorithms and Bengali Natural Language Processing (BNLP). The machine gets embedded with this knowledge to identify the desired sentences and making a decision within itself, as a response to answer questions. There are many English Chatbot's which used in education, web query, banking sector & various sectors. In this research, we have propounded a complete data-driven retrieval based closed domain Chatbot which is easily colloquy in the Bengali language with the users. We've created the train function adapter to train the Doly by encoding (encoding="utf8") our corpus from bot data. An input adapter has been created to take input and for output, an output adapter has been created to generate automated responses to a user's input. We have also used a machine learning algorithm like search algorithm for finding an appropriate list of matching results from the corpus and use Naïve Bayesian algorithm to generate the right answer from data. The main aim of this Chatbot based system is to bridge the gap between the knowledge sources by providing instant replies to the questions and queries that have to ask in the Bengali language.