Reviewerly/exHarmony huggingface dataset:
Detection and Removal of Moving Object Shadows Using Geometry and Color Information for Indoor Video Streams

The detection and removal of moving object shadows is a challenging issue. In this article, we propose a new approach for accurately removing shadows on modern buildings in the presence of a moving object in the scene. Our approach is capable of achieving good performance when addressing multiple shadow problems, by reducing background surface similarity and ghost artifacts. First, a combined contrast enhancement technique is applied to the input frame sequences to produce high-quality output images for indoor surroundings with an artificial light source. After obtaining suitable enhanced images, segmentation and noise removal filtering are applied to create a foreground mask of the possible candidate moving object shadow regions. Subsequently, geometry and color information are utilized to remove detected shadow pixels that incorrectly include the foreground mask. Here, experiments show that our method correctly detects and removes shadowed pixels in object tracking tasks, such as in universities, department stores, or several indoor sports games.
STN-Homography: Direct Estimation of Homography Parameters for Image Pairs

Estimating a 2D homography from a pair of images is a fundamental task in computer vision. Contrary to most convolutional neural network-based homography estimation methods that use alternative four-point homography parameterization schemes, in this study, we directly estimate the 3 × 3 homography matrix value. We show that after coordinate normalization, the magnitude difference and variance of the elements of the normalized 3 × 3 homography matrix is very small. Accordingly, we present STN-Homography, a neural network based on spatial transformer network (STN), to directly estimate the normalized homography matrix of an image pair. To decrease the homography estimation error, we propose hierarchical STN-Homography and sequence STN-homography models in which the sequence STN-Homography can be trained in an end-to-end manner. The effectiveness of the proposed methods is demonstrated based on experiments on the Microsoft common objects in context (MSCOCO) dataset, and it is shown that they significantly outperform the current state-of-the-art. The average processing time of the three-stage hierarchical STN-Homography and the three-stage sequence STN-Homography models on a GPU are 17.85 ms and 13.85 ms, respectively. Both models satisfy the real-time processing requirements of most potential applications.
Observing Cyber Security Incident Response: Qualitative Themes From Field Research

Cyber security increasingly focuses on the challenges faced by network defenders. Cultural and security-driven sentiments about external observation, as well as publication concerns, limit the ability of researchers to understand the context surrounding incident response. Context awareness is crucial to inform design and engineering. Furthermore, these perspectives can be heavily influenced by the targeted sector or industry of the research. Together, a lack of broad contextual understanding may be biasing approaches to improving operations, and driving faulty assumptions in cyber teams. A qualitative field study was conducted in three computer security incident response teams (CSIRTs) and included perspectives of government, academia, and private sector teams. Themes emerged and provide insights across multiple aspects of incident response, including information sharing, organization, learning, and automation. The need to focus on vertical integration of issues at different levels of the incident response system is also discussed. Future research will build upon these results, using them to inform technology advancement in CSIR settings.
An exploration of primary school students’ perceived learning practices and associated self-efficacies regarding mobile-assisted seamless science learning

This study aimed to explore the relationships between students' perceived learning practices and associated self-efficacies regarding mobile-assisted seamless science learning. The learning practices for mobile-assisted seamless science learning questionnaire was developed with three scales that denote learning supported by mobile technology. These scales included authentic learning, self-directed learning, and collaborative learning with information and communication technology. Associated self-efficacies promoted by mobile-assisted seamless science learning could include authentic problem-solving, creative thinking and academic self-efficacy. A sample of 312 primary school students from China responded to the mobile-assisted seamless science learning questionnaire. Confirmatory factor analyses indicated that the mobile-assisted seamless science learning questionnaire had high reliability and validity. The path analysis results analysed via a structural equation modelling technique implied that primary school students' perceived self-directed learning and authentic learning were important as positive predictors for authentic problem-solving efficacy, which may function as a partial mediation variable in the effect of students' perceptions of learning practices on the academic self-efficacy of mobile-assisted seamless science learning. The findings highlighted that learning practices (i.e. authentic learning and self-directed learning) and authentic problem-solving efficacy are both indispensable and mutually reinforcing. This can provide some insights for promoting mobile learning in science learning in the future.
Proximal Mean-Field for Neural Network Quantization

Compressing large Neural Networks (NN) by quantizing the parameters, while maintaining the performance is highly desirable due to reduced memory and time complexity. In this work, we cast NN quantization as a discrete labelling problem, and by examining relaxations, we design an efficient iterative optimization procedure that involves stochastic gradient descent followed by a projection. We prove that our simple projected gradient descent approach is, in fact, equivalent to a proximal version of the well-known mean-field method. These findings would allow the decades-old and theoretically grounded research on MRF optimization to be used to design better network quantization schemes. Our experiments on standard classification datasets (MNIST, CIFAR10/100, TinyImageNet) with convolutional and residual architectures show that our algorithm obtains fully-quantized networks with accuracies very close to the floating-point reference networks.
Joint Learning of Semantic Alignment and Object Landmark Detection

Convolutional neural networks (CNNs) based approaches for semantic alignment and object landmark detection have improved their performance significantly. Current efforts for the two tasks focus on addressing the lack of massive training data through weakly- or unsupervised learning frameworks. In this paper, we present a joint learning approach for obtaining dense correspondences and discovering object landmarks from semantically similar images. Based on the key insight that the two tasks can mutually provide supervisions to each other, our networks accomplish this through a joint loss function that alternatively imposes a consistency constraint between the two tasks, thereby boosting the performance and addressing the lack of training data in a principled manner. To the best of our knowledge, this is the first attempt to address the lack of training data for the two tasks through the joint learning. To further improve the robustness of our framework, we introduce a probabilistic learning formulation that allows only reliable matches to be used in the joint learning process. With the proposed method, state-of-the-art performance is attained on several benchmarks for semantic matching and landmark detection.
Lie detection using extreme learning machine: A concealed information test based on short‐time Fourier transform and binary bat optimization using a novel fitness function

Abstract Lie detection is one of the major challenges that is being faced by the forensic sciences. Identification of lie on the basis of a person's mental behavior is a tedious task. Brain‐computer interface is one such medium which provides a solution to this problem by displaying visual stimuli and recording subject's brain responses. A P300 response is elicited whenever a person comes across a familiar stimuli in a series of rare stimuli. This P300 response is used for the lie detection method. In the proposed concealed information test, acquired signals are preprocessed to discard noise. Then, short‐time Fourier transform method is applied to extract features from the preprocessed electroencephalogram signals. To avoid the curse of dimensionality and to reduce computational overhead, binary bat algorithm is applied, which helps in choosing optimal subset of features. The obtained set of features is given as an input to the extreme learning machine classifier for training of guilty and innocent samples. The performance of the system is assessed using 10‐fold cross‐validation. The resultant accuracy obtained from the proposed lie detection system is 88.3%. The system has provided efficient results in contrast with most of the state‐of‐the‐art lie detection methods.
Energy and Profit-Aware Proof-of-Stake Offloading in Blockchain-based VANETs

In Vehicular Ad-hoc NETworks (VANET) users do not necessarily trust each other and in some cases they may introduce dubios information in the network. Centralized approaches to improve the credibility of information do not easily scale, require trusting the service provider and have higher network delay. Blockchain solutions are promising in the area but they need consensus on the credibility of new information, which requires the participants solve computational puzzles in a competitive manner. To cope with vehicles' limited computational resources and mobility, we propose brokerage mechanism to decide whether to execute the validation locally, or to offload it to an edge or cloud infrastructure. We define a Satisfiability Modulo Theories (SMT) method to enable participants to decide whether to take part in the validation and whether to offload it considering the state of the infrastructure, energy efficiency, the offloading cost and the computation reward. Our method obtains 77.7% higher profit and consumes 39.2% less energy in comparison with the case where no offloading is allowed.
Application of Cuckoo Search algorithm to Loading Pattern Optimization problems

The Loading Pattern Optimization (LPO) is related to important goals in a Nuclear Power Plant (NPP) operation such as the extension of the cycle according to safety margins. The LPO is a combinatorial problem of relevance and interest for Nuclear Engineering. Optimization metaheuristics have been efficient in solving the LPO. The recent metaheuristic Cuckoo Search (CS) is based on the brood parasitism of some cuckoo species, combined with the behavior of the Lévy flight of some birds. In the present work the results of the application of CS to the LPO using IAEA-3D and BIBLIS-2D benchmarks are presented, as well as the application of CS in the optimization of 7th cycle of Angra 1 NPP, in Brazil. The results are compared to the metaheuristics Artificial Bee Colony and Population-Based Incremental Learning. Statistical analyses show that CS is the most robust algorithm for the set of instances selected for tests.
Domain-Adaptive Single-View 3D Reconstruction

Single-view 3D shape reconstruction is an important but challenging problem, mainly for two reasons. First, as shape annotation is very expensive to acquire, current methods rely on synthetic data, in which ground-truth 3D annotation is easy to obtain. However, this results in domain adaptation problem when applied to natural images. The second challenge is that there are multiple shapes that can explain a given 2D image. In this paper, we propose a framework to improve over these challenges using adversarial training. On one hand, we impose domain confusion between natural and synthetic image representations to reduce the distribution gap. On the other hand, we impose the reconstruction to be `realistic' by forcing it to lie on a (learned) manifold of realistic object shapes. Our experiments show that these constraints improve performance by a large margin over baseline reconstruction models. We achieve results competitive with the state of the art with a much simpler architecture.
Batching Soft IoU for Training Semantic Segmentation Networks

The majority of semantic segmentation networks generally employ cross-entropy as a loss function and intersection-over-union (IoU) as the evaluation metric for network performance. Employing IoU as a loss function can solve the mismatch issue between the loss function and the evaluation metric. We propose a Soft IoU training strategy based on mini-batch (mini-batch Soft IoU). Our work has two primary contributions: The first is to extend the IoU loss function to a multi-class segmentation network. The second is to collect various categories of the training samples in every mini-batch, which will ensure that the number of categories equals to the batch size at least. Our method breaks the randomness of the original mini-batch gradient descent (GD) strategy, advancing training samples in the mini-batch much more consistent with the distribution characteristics of the overall data. It solves the instability of IoU loss function. In addition, the experimental results on the PASCAL VOC2012 dataset reveal that our method effectively improves the segmentation accuracy of the network and attains significant improvements beyond state-of-the-art IoU loss function methods.
Prototype learning and collaborative representation using Grassmann manifolds for image set classification

Image set classification using manifolds is becoming increasingly more attractive since it considers non-Euclidean geometry. However, with the success of dictionary learning for image set classification using manifolds, how to learn an over-complete dictionary is still challenging. This paper proposes a novel prototype subspace learning method, in which a set of images is represented by a linear subspace and then mapped onto a Grassmann manifold. With this subspace representation, class prototypes and intra-class differences can be represented as principal components and variation subspaces, respectively. Isometric mapping further maps the manifolds into the symmetrical space via collaborative representation, which permits a closed-term solution. The proposed method is evaluated for face recognition, object recognition and action recognition. Extensive experimental results on the Honda, Extended YaleB, ETH-80 and Cambridge-Gesture datasets verify the superiority of the proposed method over the state-of-the-art methods.
Accelerate Learning of Deep Hashing With Gradient Attention

Recent years have witnessed the success of learning to hash in fast large-scale image retrieval. As deep learning has shown its superior performance on many computer vision applications, recent designs of learning-based hashing models have been moving from shallow ones to deep architectures. However, based on our analysis, we find that gradient descent based algorithms used in deep hashing models would potentially cause hash codes of a pair of training instances to be updated towards the directions of each other simultaneously during optimization. In the worst case, the paired hash codes switch their directions after update, and consequently, their corresponding distance in the Hamming space remain unchanged. This makes the overall learning process highly inefficient. To address this issue, we propose a new deep hashing model integrated with a novel gradient attention mechanism. Extensive experimental results on three benchmark datasets show that our proposed algorithm is able to accelerate the learning process and obtain competitive retrieval performance compared with state-of-the-art deep hashing models.
Spoken Language Identification Using ConvNets

Language Identification (LI) is an important first step in several speech processing systems. With a growing number of voice-based assistants, speech LI has emerged as a widely researched field. To approach the problem of identifying languages, we can either adopt an implicit approach where only the speech for a language is present or an explicit one where text is available with its corresponding transcript. This paper focuses on an implicit approach due to the absence of transcriptive data. This paper benchmarks existing models and proposes a new attention based model for language identification which uses log-Mel spectrogram images as input. We also present the effectiveness of raw waveforms as features to neural network models for LI tasks. For training and evaluation of models, we classified six languages (English, French, German, Spanish, Russian and Italian) with an accuracy of 95.4% and four languages (English, French, German, Spanish) with an accuracy of 96.3% obtained from the VoxForge dataset. This approach can further be scaled to incorporate more languages.
Customer’s Acceptance of Humanoid Robots in Services: The Moderating Role of Risk Aversion

The emerging introduction of humanoid robots in service encounters is becoming a reality in the present and the short-term. Owing to this unstoppable advance, there is a need to better understand customers’ perceptions and reactions toward humanoid agents in service encounters. To shed some light on this underexplored phenomenon, this research investigates how the interaction between robot and customer’s features may contribute to a successful introduction of this disruptive innovation. Results of an empirical study with a sample of 168 US customers reveal that customer’s perceptions of robot’s human-likeness increase the intentions to use humanoid service robots. Interestingly, customers’ risk aversion moderates this relationship. Specifically, the study found that highly risk-averse customers tend to avoid using humanoids when they are perceived as highly mechanical-like. The discussion highlights the main contributions of the research, which combine previous knowledge on human–robot interaction and risk aversion from a marketing approach. Managerial implications derived from the research findings and the avenues opened for further research are described at the end.
Dual CNN Models for Unsupervised Monocular Depth Estimation

The unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth. In unsupervised depth computation, the disparity images are generated by training the CNN with an image reconstruction loss. In this paper, a dual CNN based model is presented for unsupervised depth estimation with 6 losses (DNM6) with individual CNN for each view to generate the corresponding disparity map. The proposed dual CNN model is also extended with 12 losses (DNM12) by utilizing the cross disparities. The presented DNM6 and DNM12 models are experimented over KITTI driving and Cityscapes urban database and compared with the recent state-of-the-art result of unsupervised depth estimation.
Transforming the supply-chain management and industry logistics with blockchain smart contracts

Blockchain technology back in 2009 was mainly used for finance use cases due to the cryptocurrency support of the Bitcoin and the Ethereum networks. Nowadays with the emergence of business oriented distributed ledger frameworks we can find blockchain (BC) in almost every aspect of our everyday real-life transactions. Starting with supply chain, BC has been used to support and secure education, e-government, real estate, insurance, healthcare and other business cases. Regarding blockchain in supply chain, examples as the Walmart and IBM partnership shows that smart contracts (SC) and cryptocurrencies can disrupt the way suppliers, shippers, retailers and customers trust each other and interact. This paper describes how we applied BC technology in two real-life supply chain scenarios. The first one is for logging and tracing products, showing how we can use SCs to identify the ingredients of food products and how to uniquely identify the food product throughout its shipment from the factory to the customer who purchase it. Important aspects of this route is the transparency of the process as well as the verification of transport. The second one deals with how authentication works for users holding BC identities. The identity of a user is important in order to secure the network, allowing only permissioned parties to access it and perform actions on the data. As long as the network is private and immutable, authenticating and authorizing users is crucial for enforcing the end-to-end security to increase users' trust and protect the confidentiality of data. While exploring these two scenarios we define the actors, the transactions these actors can perform on the systems and we propose the SCs that have to be implemented for these use cases to take advantage of BC's unique characteristics, as immutability and non-repudiation. In addition, we provide sample template SCs for both scenarios.
Offline Handwritten Signature Verification using CNN inspired by Inception V1 Architecture

In the field of behavioral biometric, signature verification is most referenced procedure for authentication of a person. A signature is considered to be the “seal of approval” for verifying the approval of a user and remains the most preferred means of authentication. This verification system mainly aims at verifying the discriminating the forged signature (forged by an imposter) from the genuine signatures. In this paper, Convolutional Neural Networks (CNN) have been utilized to learn features from the pre-processed genuine signatures and forged signatures. The CNN used is inspired by Inception V1 architecture(GoogleNet). The architecture uses the concept of having different filters on same level so that the network would be wider instead of deeper. In this paper, the proposed model is tested on few publicly available datasets such as CEDAR, BH-Sig260 signature corpus, and UTSig.
An Attribute-Based Distributed Access Control for Blockchain-enabled IoT

In IoT, a flexible and trustworthy access control framework is of significance to ensure the security of lightweight IoT devices. The conventional centralized access control framework is no longer fit for the open and large-scale IoT environments. In this paper, we propose an attribute-based distributed access control framework (ADAC) for IoT using blockchain technology. The attributes, such as manufacturer and object-specified attribute, are considered in the proposed ADAC for more fine-grained access control in the open and lightweight IoT devices. Particularly, we design a smart contract system, which includes a subject contract (SC), an object contract (OC), an access control contract (ACC) and multiple policy contracts (PCs), to manage and access attributes of IoT devices for distributed and trustworthy access control (DTAC). SC and OC are responsible for managing subject attribute and object attribute information, respectively. PCs are used to manage access control policies. ACC performs authorization judgment by accessing attributes and policies. Finally, a case study is performed to demonstrate the workflow and show that ADAC could achieve fine-grained and flexible access control for IoT.