Reviewerly/exHarmony huggingface dataset:
Deep Learning Approaches on Pedestrian Detection in Hazy Weather

Effectively detecting pedestrians in various environments would significantly improve driving safety for autonomous vehicles. However, the degrpted visibility and blurred outline and appearance of pedestrian images captured during hazy weather strongly limit the effectiveness of current pedestrian detection methods. To solve this problem, this article presents three novel deep learning approaches based on you only look once. The depth wise separable convolution and linear bottleneck skills were used to reduce the computational cost and number of parameters, rendering our network more efficient. We also innovatively developed a weighted combination layer in one of the approaches by combining multiscale feature maps and a squeeze and excitation block. Collected pedestrian images in hazy weather were augmented using six strategies to enrich the database. Experimental results show that our proposed methods can effectively detect pedestrians in hazy weather, significantly outperforming state-of-the-art methods in both accuracy and speed.
Model inversion attacks against collaborative inference

The prevalence of deep learning has drawn attention to the privacy protection of sensitive data. Various privacy threats have been presented, where an adversary can steal model owners' private data. Meanwhile, countermeasures have also been introduced to achieve privacy-preserving deep learning. However, most studies only focused on data privacy during training, and ignored privacy during inference.
SPGNet: Semantic Prediction Guidance for Scene Parsing

Multi-scale context module and single-stage encoder-decoder structure are commonly employed for semantic segmentation. The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path. In contrast, multi-stage encoder-decoder networks have been widely used in human pose estimation and show superior performance than their single-stage counterpart. However, few efforts have been attempted to bring this effective design to semantic segmentation. In this work, we propose a Semantic Prediction Guidance (SPG) module which learns to re-weight the local features through the guidance from pixel-wise semantic prediction. We find that by carefully re-weighting features across stages, a two-stage encoder-decoder network coupled with our proposed SPG module can significantly outperform its one-stage counterpart with similar parameters and computations. Finally, we report experimental results on the semantic segmentation benchmark Cityscapes, in which our SPGNet attains 81.1% on the test set using only 'fine' annotations.
DANet: Divergent Activation for Weakly Supervised Object Localization

Weakly supervised object localization remains a challenge when learning object localization models from image category labels. Optimizing image classification tends to activate object parts and ignore the full object extent, while expanding object parts into full object extent could deteriorate the performance of image classification. In this paper, we propose a divergent activation (DA) approach, and target at learning complementary and discriminative visual patterns for image classification and weakly supervised object localization from the perspective of discrepancy. To this end, we design hierarchical divergent activation (HDA), which leverages the semantic discrepancy to spread feature activation, implicitly. We also propose discrepant divergent activation (DDA), which pursues object extent by learning mutually exclusive visual patterns, explicitly. Deep networks implemented with HDA and DDA, referred to as DANets, diverge and fuse discrepant yet discriminative features for image classification and object localization in an end-to-end manner. Experiments validate that DANets advance the performance of object localization while maintaining high performance of image classification on CUB-200 and ILSVRC datasets.
CSI-FiSh: Efficient Isogeny Based Signatures Through Class Group Computations

In this paper we report on a new record class group computation of an imaginary quadratic field having 154-digit discriminant, surpassing the previous record of 130 digits. This class group is central to the CSIDH-512 isogeny based cryptosystem, and knowing the class group structure and relation lattice implies efficient uniform sampling and a canonical representation of its elements. Both operations were impossible before and allow us to instantiate an isogeny based signature scheme first sketched by Stolbunov. We further optimize the scheme using multiple public keys and Merkle trees, following an idea by De Feo and Galbraith. We also show that including quadratic twists allows to cut the public key size in half for free. Optimizing for signature size, our implementation takes 390 ms to sign/verify and results in signatures of 263 bytes, at the expense of a large public key. This is 300 times faster and over 3 times smaller than an optimized version of SeaSign for the same parameter set. Optimizing for public key and signature size combined, results in a total size of 1468 bytes, which is smaller than any other post-quantum signature scheme at the 128-bit security level.
Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed hybrid PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the hybrid PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed hybrid PSO-MFB algorithm is highly competitive in terms of path optimality.
Why ResNet Works? Residuals Generalize

Residual connections significantly boost the performance of deep neural networks. However, few theoretical results address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks. This article studies the influence of residual connections on the hypothesis complexity of the neural network in terms of the covering number of its hypothesis space. We first present an upper bound of the covering number of networks with residual connections. This bound shares a similar structure with that of neural networks without residual connections. This result suggests that moving a weight matrix or nonlinear activation from the bone to a vine would not increase the hypothesis space. Afterward, an O(1 / √N) margin-based multiclass generalization bound is obtained for ResNet, as an exemplary case of any deep neural network with residual connections. Generalization guarantees for similar state-of-the-art neural network architectures, such as DenseNet and ResNeXt, are straightforward. According to the obtained generalization bound, we should introduce regularization terms to control the magnitude of the norms of weight matrices not to increase too much, in practice, to ensure a good generalization ability, which justifies the technique of weight decay.
Design Features of Embodied Conversational Agents in eHealth: a Literature Review

Embodied conversational agents (ECAs) are gaining interest to elicit user engagement and stimulate actual use of eHealth applications. In this literature review, we identify the researched design features for ECAs in eHealth, the outcome variables that were used to measure the effect of these design features and what the found effects for each variable were. Searches were performed in Scopus, ACM Digital Library, PsychINFO, Pubmed and IEEE Xplore Digital Library, resulting in 1284 identified articles of which 33 articles were included. The agents speech and/or textual output and its facial and gaze expressions were the most common design features. Little research was performed on the agent's looks. The measured effect of these design features was often on the perception of the agent's and user’s characteristics, relation with the agent, system usage, intention to use, usability and behaviour change. Results show that emotion and relational behaviour seem to positively affect the perception of the agents characteristics and that relational behaviour also seems to positively affect the relation with the agent, usability and intention to use. However, these design features do not necessarily lead to behaviour change. This review showed that consensus on design features of ECAs in eHealth is far from established. Follow-up research should include more research on the effects of all design features, especially research on the effects in a long-term, daily life setting, and replication of studies on the effects of design features performed in other contexts than eHealth.
An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection

Various attacks have emerged as the major threats to the success of a connected world like the Internet of Things (IoT), in which billions of devices interact with each other to facilitate human life. By exploiting the vulnerabilities of cheap and insecure devices such as IP cameras, an attacker can create hundreds of thousands of zombie devices and then launch massive volume attacks to take down any target. For example, in 2016, a record large-scale DDoS attack launched by millions of Mirai-injected IP cameras and smart printers blocked the accessibility of several high-profile websites. To date, the state-of-the-art defense systems against such attacks rely mostly on pre-defined features extracted from the entire flows or signatures. The feature definitions are manual, and it would be too late to block a malicious flow after extracting the flow features. In this work, we present an effective anomaly traffic detection mechanism, namely D-PACK, which consists of a Convolutional Neural Network (CNN) and an unsupervised deep learning model (e.g., Autoencoder) for auto-profiling the traffic patterns and filtering abnormal traffic. Notably, D-PACK inspects only the first few bytes of the first few packets in each flow for early detection. Our experimental results show that, by examining just the first two packets in each flow, D-PACK still performs with nearly 100% accuracy, while features an extremely low false-positive rate, e.g., 0.83%. The design can inspire the emerging efforts towards online anomaly detection systems that feature reducing the volume of processed packets and blocking malicious flows in time.
Blockchain technology in the future of business cyber security and accounting

This study looks into the current, and potential uses of Blockchain technology in business, specifically in Accounting and in cybersecurity. We relate Blockchain uses to current concerns within cybersecurity and accounting. We review the literature that includes topics such as Big Data in Accounting, blockchain’s use in financial security and cybersecurity, and its use in financial accounting though the use of ledger technology and also as a system of tracking financial misconduct. We also review the Department of Homeland Security plan for cybersecurity over the next few years to understand what the US Government plans because of the importance of cybersecurity development. We show that Blockchain impacts auditing in different ways that will change the profession drastically. We also find that blockchain should be effectively implemented into different aspects of cybersecurity, and accounting, such as Auditing and general accounting procedures.
PhaseNet 2.0: Phase Unwrapping of Noisy Data Based on Deep Learning Approach

Phase unwrapping is an ill-posed classical problem in many practical applications of significance such as 3D profiling through fringe projection, synthetic aperture radar and magnetic resonance imaging. Conventional phase unwrapping techniques estimate the phase either by integrating through the confined path (referred to as path-following methods) or by minimizing the energy function between the wrapped phase and the approximated true phase (referred to as minimum-norm approaches). However, these conventional methods have some critical challenges like error accumulation and high computational time and often fail under low SNR conditions. To address these problems, this paper proposes a novel deep learning framework for unwrapping the phase and is referred to as “PhaseNet 2.0”. The phase unwrapping problem is formulated as a dense classification problem and a fully convolutional DenseNet based neural network is trained to predict the wrap-count at each pixel from the wrapped phase maps. To train this network, we simulate arbitrary shapes and propose new loss function that integrates the residues by minimizing the difference of gradients and also uses L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> loss to overcome class imbalance problem. The proposed method, unlike our previous approach PhaseNet, does not require post-processing, highly robust to noise, accurately unwraps the phase even at the severe noise level of -5 dB, and can unwrap the phase maps even at relatively high dynamic ranges. Simulation results from the proposed framework are compared with different classes of existing phase unwrapping methods for varying SNR values and discontinuity, and these evaluations demonstrate the advantages of the proposed framework. We also demonstrate the generality of the proposed method on 3D reconstruction of synthetic CAD models that have diverse structures and finer geometric variations. Finally, the proposed method is applied to real-data for 3D profiling of objects using fringe projection technique and digital holographic interferometry. The proposed framework achieves significant improvements over existing methods while being highly efficient with interactive frame-rates on modern GPUs.
IoU-aware single-stage object detector for accurate localization

Single-stage object detectors have been widely applied in many computer vision applications due to their simpleness and high efficiency. However, the low correlation between the classification score and localization accuracy in detection results severely hurts the average precision of the detection model. To solve this problem, an IoU-aware single-stage object detector is proposed in this paper. Specifically, IoU-aware single-stage object detector predicts the IoU for each detected box. Then the predicted IoU is multiplied by the classification score to compute the final detection confidence, which is more correlated with the localization accuracy. The detection confidence is then used as the input of the subsequent NMS and COCO AP computation, which substantially improves the localization accuracy of model. Sufficient experiments on COCO and PASCOL VOC datasets demonstrate the effectiveness of IoU-aware single-stage object detector on improving model's localization accuracy. Without whistles and bells, the proposed method can substantially improve AP by 1.7%–1.9% and AP75 by 2.2%–2.5% on COCO test-dev. And it can also substantially improve AP by 2.9%–4.4% and AP80, AP90 by 4.6%–10.2% on PASCAL VOC. The source code will be made publicly available.
A memory-based Grey Wolf Optimizer for global optimization tasks

Grey Wolf Optimizer (GWO) is a new nature-inspired metaheuristic algorithm based on the leadership and social behaviour of grey wolves in nature. It has shown potential to solve several real-life applications, but still for some complex optimization tasks, it may face the problem of getting trapped at local optima and premature convergence. Therefore, in this study, to prevent from these drawbacks and to get a more stable sense of balance between exploitation and exploration, a new modified GWO called memory-based Grey Wolf Optimizer (mGWO) is proposed. In the mGWO, the search mechanism of the wolves is modified based on the personal best history of each individual wolves, crossover and greedy selection. These strategies help to enhance the global exploration, local exploitation and an appropriate balance between them during the search procedure. To investigate the effectiveness of the proposed mGWO, it has been tested on standard and complex benchmarks given in IEEE CEC 2014 and IEEE CEC 2017. Furthermore, some real engineering design problems and multilevel thresholding problem are also solved using the mGWO. The results analysis and its comparison with other algorithms demonstrate the better search-efficiency, solution accuracy and convergence rate of the proposed mGWO in performing the global optimization tasks.
Augmented Reality Marketing: A Technology-Enabled Approach to Situated Customer Experience

Recent advances in Augmented Reality (AR) technologies have led to a growing interest in their application for marketing strategy and practice – what we term Augmented Reality Marketing (ARM). However, despite emerging publications on the subject, managers and academics struggle to articulate how ARM delivers experiences that are valuable to customers in a way that is different from other marketing approaches. In this article, we review the emerging literature, and define ARM as a customer-facing interface for the application of digital marketing technologies in physical settings. Rooted in a class of ‘situated cognition’ theories from social psychology, we identify a unique set of digital affordances which ARM offers beyond extant marketing approaches in traditional media. By drawing on the key conceptual building blocks of situated cognition theory, we develop a framework of ARM experiences to synthesize current research and applications, and to suggest directions for future research.
Circuit Copyright Blockchain: Blockchain-Based Homomorphic Encryption for IP Circuit Protection

The fast development of Blockchain technology makes it widely applied in several fields of digital transactions, like e-government affairs and the protection of financial transactions. In this article, we propose a homomorphic encryption-based Blockchain for circuit copyright protection that effectively addresses the issues in the protection of circuit copyright transactions, such as low security of private data, low efficiency in transaction data storage, cooperation and supervision. First, we establish a homomorphic encryption-based mathematical model by utilizing Blockchain and intelligent contract, and next, the algorithms that include Blockchain generation, homomorphic chain encryption/decryption, and intelligent contract are designed. As the intelligent contract is correctly executed in Blockchain, a fully homomorphic encryption-based identity authentication protocol is tackled for Blockchain, given that it ensures the change operation of any third-party in Blockchain and realizes real-time verification. The system is apposite for circuit copyright protection in a blockchain network, due to the use of distributed identity authentication and real-time extensible storage improves the security and extensibility of blockchain-based circuit copyright protection. The experimental results show that the proposed algorithm has reduced the transmission cost and improved the efficiency of data storage and supervision. In addition, it is resilient to several common attacks (e.g., double-spending attacks), yet incurs low cost/overhead and has a higher level of security when compared to three other competing algorithms.
Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data

Early prediction of Alzheimer’s disease (AD) is crucial for delaying its progression. As a chronic disease, ignoring the temporal dimension of AD data affects the performance of a progression detection and medically unacceptable. Besides, AD patients are represented by heterogeneous, yet complementary, multimodalities. Multitask modeling improves progression-detection performance, robustness, and stability. However, multimodal multitask modeling has not been evaluated using time series and deep learning paradigm, especially for AD progression detection. In this paper, we propose a robust ensemble deep learning model based on a stacked convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This multimodal multitask model jointly predicts multiple variables based on the fusion of five types of multimodal time series data plus a set of background (BG) knowledge. Predicted variables include AD multiclass progression task, and four critical cognitive scores regression tasks. The proposed model extracts local and longitudinal features of each modality using a stacked CNN and BiLSTM network. Concurrently, local features are extracted from the BG data using a feed-forward neural network. Resultant features are fused to a deep network to detect common patterns which jointly used to predict the classification and regression tasks. To validate our model, we performed six experiments on five modalities from Alzheimer’s Disease Neuroimaging Initiative (ADNI) of 1536 subjects. The results of the proposed approach achieve state-of-the-art performance for both multiclass progression and regression tasks. Moreover, our approach can be generalized in other medial domains to analyze heterogeneous temporal data for predicting patient’s future status.
A semantic differential transaction approach to minimizing information redundancy for BIM and blockchain integration

Those attempting to integrate building information modeling (BIM) and blockchain soon encounter the enormous challenge of information redundancy. Storage of duplicated building information in decentralized ledgers already creates redundancy, and this is exacerbated as the BIM model develops and is utilized. This paper presents a novel semantic differential transaction (SDT) approach to minimizing information redundancy in the nascent field of BIM and blockchain integration. Whereas the conventional thinking is to store an entire BIM model or its signature code in blockchain, SDT captures local model changes as SDT records and assembles them into a BIM change contract (BCC). In this way, the version history of a BIM project becomes a chain of timestamped BCCs, and stakeholders can promptly synchronize BIM changes in blockchain. We test our approach in two pilot cases. The results show that SDT captures, in near real time, sequential and simultaneous BIM changes at <0.02% of the Industry Foundation Classes file size. We also prove model restoration from the lightweight BCCs in a small-scale BIM project. In addressing the fundamental issue of information redundancy in BIM and blockchain integration, this research can help the industry advance beyond the rhetoric to develop operable blockchain BIM systems.