Reviewerly/exHarmony huggingface dataset:
Robust Shadow Detection by Exploring Effective Shadow Contexts

Effective contexts for separating shadows from non-shadow objects can appear in different scales due to different object sizes. This paper introduces a new module, Effective-Context Augmentation (ECA), to utilize these contexts for robust shadow detection with deep structures. Taking regular deep features as global references, ECA enhances the discriminative features from the parallelly computed fine-scale features and, therefore, obtains robust features embedded with effective object contexts by boosting them. We further propose a novel encoder-decoder style of shadow detection method where ECA acts as the main building block of the encoder to extract strong feature representations and the guidance to the classification process of the decoder. Moreover, the networks are optimized with only one loss, which is easy to train and does not have the instability caused by extra losses superimposed on the intermediate features among existing popular studies. Experimental results show that the proposed method can effectively eliminate fake detections. Especially, our method outperforms state-of-the-arts methods and improves over $13.97%$ and $34.67%$ on the challenging SBU and UCF datasets respectively in balance error rate.
An Improved GWR Approach for Exploring the Anisotropic Influence of Ore-Controlling Factors on Mineralization in 3D Space

Spatial non-stationarity is a common geological phenomenon, and the formation of orebodies is a typical non-stationary process. Therefore, a quantitative study of the non-stationary relationships between mineralization and its controlling factors in 3D space is of great significance for metallogenic prediction. Geographically weighted regression (GWR) is an effective method for exploring spatial non-stationarity by measuring the nearness between factors in the data. However, non-stationarity is affected not only by distances but also by factors related to direction. Traditional GWR cannot address the non-stationarity that arises from differences in direction. To address this issue, we propose an improved GWR method to characterize the directional characteristics of non-stationary relationships by introducing a direction weight to the GWR. The anisotropic influence of factors can be obtained by comparing the performance of models with different weights on direction terms. A case study of the Xiadian and Dayingezhuang gold deposits, Jiaodong Peninsula, Eastern China was carried out to verify the anisotropic nature of ore-controlling factors. First, multi-collinearity and OLS (ordinary least squares) diagnosis for the variables were performed and the necessity of the non-stationarity exploration was demonstrated. Second, GWR was applied to explore the spatial non-stationarity of the relationships among the variables by comparing the global R2 value with that of OLS, evaluating the local R2, values testing the t-statistic values, analyzing and comparing the spatial autocorrelation of residuals with that of OLS, and calculating the spatial stationary index of the parameter estimates of explanatory variables. Third, the improved GWR method was applied, and the directional characteristics of the non-stationary relationship were analyzed. Finally, the anisotropic influence of the controlling factors on mineralization was validated by comparing the performance of the improved GWR model with different bandwidths and different kernels, and the importance of the direction of the fault zone to mineralization was further verified.
When Video Classification Meets Incremental Classes

With the rapid development of social media, tremendous videos with new classes are generated daily, which raise an urgent demand for video classification methods that can continuously update new classes while maintaining the knowledge of old videos with limited storage and computing resources. In this paper, we summarize this task as Class-Incremental Video Classification (CIVC) and propose a novel framework to address it. As a subarea of incremental learning tasks, the challenge of catastrophic forgetting is unavoidable in CIVC. To better alleviate it, we utilize some characteristics of videos. First, we decompose the spatio-temporal knowledge before distillation rather than treating it as a whole in the knowledge transfer process; trajectory is also used to refine the decomposition. Second, we propose a dual granularity exemplar selection method to select and store representative video instances of old classes and key-frames inside videos under a tight storage budget. We benchmark our method and previous SOTA class-incremental learning methods on Something-Something V2 and Kinetics datasets, and our method outperforms previous methods significantly.
Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System

Deep learning (DL) architectures have been successfully used in many applications including wireless systems. However, they have been shown to be susceptible to adversarial attacks. We analyze DL-based models for a regression problem in the context of downlink power allocation in massive multiple-input-multiple-output systems and propose universal adversarial perturbation (UAP)-crafting methods as white-box and black-box attacks. We benchmark the UAP performance of white-box and black-box attacks for the considered application and show that the adversarial success rate can achieve up to 60% and 40%, respectively. The proposed UAP-based attacks make a more practical and realistic approach as compared to classical white-box attacks.
Shard Manager

Sharding is widely used to scale an application. Despite a decade of effort to build generic sharding frameworks that can be reused across different applications, the extent of their success remains unclear. We attempt to answer a fundamental question: what barriers prevent a sharding framework from getting adopted by the majority of sharded applications?
The use of incentives to promote technical debt management

When developing software, it is vitally important to keep the level of technical debt down since, based on several studies, it has been well established that technical debt can lower the development productivity, decrease the developers' morale and compromise the overall quality of the software, among others. However, even if researchers and practitioners working in today's software development industry are quite familiar with the concept of technical debt and its related negative consequences, there has been no empirical research focusing specifically on how software managers actively communicate and manage the need to keep the level of technical debt as low as possible. This study aims to understand how software companies give incentives to manage technical debt. This is carried out by exploring how companies encourage and reward practitioners for actively keeping the level of technical debt down add whether the companies use any forcing or penalising initiatives when managing technical debt. As a first step, this paper reports the results of both an online survey providing quantitative data from 258 participants and interviews with 32 software practitioners. As a second step, this study sets out to specifically provide a detailed assessment of additional and in-depth analysis of technical debt management strategies based on an encouraging mindset and attitude from both managers and technical roles to understand how, when and by whom such strategies are adopted in practice. Our findings show that having a technical debt management strategy (specially based on encouragement) can significantly impact the amount of technical debt related to the software. The result indicates that there is considerable unfulfilled potential to influence how software practitioners can further limit and reduce technical debt by adopting a strategy based explicitly on an encouraging mindset from managers where they also specifically dedicate time and resources for technical debt remediation activities.
A regional attention-based detector for SAR ship detection

Automatic ship detection in Synthetic Aperture Radar (SAR)imagery has been playing a significant role in the field of marine monitoring. But great challenges still exist in real-time application. Despite the exciting progress made by deep-learning techniques, most detectors failed to yield locations of fairly high quality, especially for small objects under the complicated background. To alleviate the above problem, the author proposes a single-stage detector based on the attention mechanism. First, we degenerate pixel-level semantic segmentation into box-level segmentation to suppress background interference. The attention map generated from weak segmentation roughly locates the region of interest through automatic learning. Besides, it has a top-down feature pyramid structure embedded with the multi-branch fusion module. With more detailed features and richer semantic information, it can detect multi-scale and multi-directional targets more effectively. Experiments on the SAR ship dataset have achieved a promising result.
AI robustness analysis with consideration of corner cases

Corner cases are a set of high-risk data in deep learning (DL) systems, which could lead to incorrect and unexpected behaviors. To study corner cases' influence on DL models' robustness and stability, this paper implemented a research with corner case description and detection, as well as DL model's robustness measurement. Firstly, a corner case descriptor based on surprise adequacy was introduced for corner case data detection, its high values are proved useful to reflect incorrect behaviors in classification. Then, based on the proposed corner case descriptor, training dataset was updated by removing data having high possibility to be corner case, and utilized for model retraining. A practical robustness analysis method was applied to measure the robustness radius of both the original and the retrained DL models. Through experiments on MNIST data and industrial vision inspection data, an interesting phenomenon is found that, with consideration of corner case data removing in training data, DL models' robustness can be improved to some extent.
Trustworthiness Requirements: The Pix Case Study

The advent of socio-technical, cyber-physical and artificial intelligence systems has broadened the scope of requirements engineering, which must now deal with new classes of requirements, concerning ethics, privacy and trust. This brings new challenges to Requirements Engineering, in particular regarding the understanding of the non-functional requirements behind these new types of systems. To address this issue, we propose the Ontology-based Requirements Engineering (ObRE) method, which aims to systematize the elicitation and analysis of requirements, by using an ontology to conceptually clarify the meaning of a class of requirements, such as privacy, ethicality and trustworthiness. We illustrate the working of ObRE by applying it to a real case study concerning trustworthiness requirements.
A Trustworthy Safety Inspection Framework Using Performance-Security Balanced Blockchain

Regular safety inspection is critical to reduce safety risk in industry. Applying the consortium blockchain technology to safety inspection can ensure the effectiveness of the inspection process and tracing of problems. However, there are two major issues when using conventional consortium blockchain. It is challenging to guarantee the authenticity of the retrieved data source, and meanwhile, achieving a balance between performance and security is not easy. Hence, this article proposes a blockchain-based performance-security balanced safety inspection framework (PSB-SIF), in which a safety inspection box is designed to ensure the authenticity of the inspector’s identity while inspection logic is executed automatically via smart contracts. In addition, this article also proposes a novel credit scoring-based Byzantine fault-tolerant (BFT) consensus algorithm, named safety inspection BFT consensus algorithm (SIBFT), which is used to balance the performance and security of consensus network in a safety inspection. We evaluate the proposed approach by comparing with the solutions using RAFT, Practical BFT (PBFT), and SIBFT consensus algorithms in terms of throughput, transaction latency, scalability, and security of PSB-SIF. The evaluation results show that PSB-SIF is efficient for all these quality metrics.
STENet: A hybrid spatio-temporal embedding network for human trajectory forecasting

In this paper, we present a hybrid spatio-temporal embedding network (named as STENet) for human trajectory forecasting, which is built upon a GAN-based hierarchical framework. Differently from traditional approaches that only use LSTM for trajectory modeling, we exploit the 1D Convolutional Neural Network (1D-CNN) to embed position features at multiple temporal scales. Moreover, we propose a two-stage graph attention mechanism, which can better describe mutual interactions among pedestrians in the crowd. Additionally, group influences at every time step are taken into account as well. The overall framework is designed using a hierarchical manner, and trained using the Wasserstein distance. We carry out our experiments on the ETH and the UCY datasets. The corresponding results demonstrate the effectiveness of the proposed framework.
Understanding in-app advertising issues based on large scale app review analysis

In-app advertising closely relates to app revenue. Reckless ad integration could adversely impact app quality and user experience, leading to loss of income. It is very challenging to balance the ad revenue and user experience for app developers. Towards tackling the challenge, we conduct a study on analyzing user concerns about in-app advertisement. Specifically, we present a large-scale analysis on ad-related user feedback. The large user feedback data from App Store and Google Play allow us to summarize ad-related app issues comprehensively and thus provide practical ad integration strategies for developers. We first define common ad issues by manually labeling a statistically representative sample of ad-related feedback, and then build an automatic classifier to categorize ad-related feedback. We study the relations between different ad issues and user ratings to identify the ad issues poorly scored by users. We also explore the fix durations of ad issues across platforms for extracting insights into prioritizing ad issues for ad maintenance. (1) We summarize 15 types of ad issues by manually annotating 903 out of 36,309 ad-related user reviews. From a statistical analysis of 36,309 ad-related reviews, we find that users care most about the number of unique ads and ad display frequency during usage. (2) Users tend to give relatively lower ratings when they report the security and notification related issues. (3) Regarding different platforms, we observe that the distributions of ad issues are significantly different between App Store and Google Play. (4) Some ad issue types are addressed more quickly by developers than other ad issues. We believe the findings we discovered can benefit app developers towards balancing ad revenue and user experience while ensuring app quality.
Calibrating rectangular interferometer meshes with external photodetectors

Multiport interferometer meshes can be used to implement unitary transformations on input vectors of light in both the classical and quantum domain. In practice, the phase-shifters in a mesh photonic circuit must be calibrated to compensate for phase errors due to fabrication variations. Calibration using photodetectors external to the mesh has been demonstrated for triangular meshes, but not rectangular meshes. Here, we propose an algorithm for the calibration of rectangular meshes using only external photodetectors and simulate it to evaluate its feasibility.
Monocular Pedestrian Orientation Recognition Based on Capsule Network for a Novel Collision Warning System

Pedestrians are the most vulnerable road users, with around 23% of world road traffic fatalities.To prevent such traffic collisions, the Pedestrian Collision Warning System (PCWS) alerts the driver before an imminent collision.In order to protect worldwide pedestrians, the PCWS should take into account different pedestrian crossing behaviors and different road structures, especially pedestrians with risky behavior on unstructured environments.Since oblique crossing is the usual crossing way of pedestrians with risky behaviors, recognizing the pedestrian walking direction is one of the key factors to consider.However, the existent PCWS focuses more on detecting pedestrians rather than recognizing their walking direction.This paper highlights the safety of different kinds of pedestrians by presenting a novel approach used to estimate pedestrian's orientation from a single-frame.Next, the estimated orientation is integrated into the proposed PCWS system.Our method involves Capsule Network based technique to train on the pedestrian images.For this purpose, a new pedestrian orientation dataset taken from real city-scenes named SafeRoad was created, using a single camera mounted on a moving vehicle.TUD Multiview Pedestrian and Daimler datasets are thereafter used as a benchmark to evaluate the proposed approach.Experimental results show that Capsule Network exceeds significantly the accuracy performance on the three datasets compared to Convolutional Neural Networks algorithms.
Augmented two stream network for robust action recognition adaptive to various action videos

In video-based action recognition, using videos with different frame numbers to train a two-stream network can result in data skew problems. Moreover, extracting the key frames from a video is crucial for improving the training and recognition efficiency of action recognition systems. However, previous works suffer from problems of information loss and optical-flow interference when handling videos with different frame numbers. In this paper, an augmented two-stream network (ATSNet) is proposed to achieve robust action recognition. A frame-number-unified strategy is first incorporated into the temporal stream network to unify the frame numbers of videos. Subsequently, the grayscale statistics of the optical-flow images are extracted to filter out any invalid optical-flow images and produce the dynamic fusion weights for the two branch networks to adapt to different action videos. Experiments conducted on the UCF101 dataset demonstrate that ATSNet outperforms previously defined methods, improving the recognition accuracy by 1.13%.
YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.
Congruity of Virtual Reality In-Game Advertising

With virtual reality (VR) video game users beginning to see beta advertisements within game play, this conceptual article adds a needed digital and interactive marketing research foundation to the new construct of VR in-game advertising (IGA) activation. New consumer VR technology continues to disrupt traditional media as a $7.7 billion USD industry, that is expected to reach $57.55 billion by 2027. As such, marketing researchers must continue to evolve and understand the interdisciplinary VR research evolution as many VR users are likely to view IGA as intrusive. IGA and VR are not new constructs, but IGA within VR is unique as VR consumers have not yet experienced VR IGA intrusion. This article utilizes a sport marketing focus to provide an industry specific set of examples for the reader, however this article can be applied to broader fields including communications and interactive marketing. The main contributions of this article are 2-fold. First the development of a VR In-Game Advertising Congruity Framework is developed through a review of the literature and application to VR IGA in the topical areas of congruity of the IGA, interactivity of the IGA, intrusiveness of the IGA, realism of the experience, telepresence, brand awareness, and attitude toward the IGA. Secondly, a proper VR context definition of telepresence is provided through review of the literature that takes into account the interaction of a VR participant. This article aims to aid marketers in making informed IGA development decisions through strategic choice, via a centralized VR IGA congruity framework, that not only enhances brand awareness, but leaves participants with a favorable attitude toward the IGA to increase sales activation.
Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology

Self-supervised learning (SSL) has recently shown tremendous potential to learn generic visual representations useful for many image analysis tasks. Despite their notable success, the existing SSL methods fail to generalize to downstream tasks when the number of labeled training instances is small or if the domain shift between the transfer domains is significant. In this paper, we attempt to improve self-supervised pretrained representations through the lens of curriculum learning by proposing a hardness-aware dynamic curriculum learning (HaDCL) approach. To improve the robustness and generalizability of SSL, we dynamically leverage progressive harder examples via easy-to-hard and hard-to-very-hard samples during mini-batch downstream fine-tuning. We discover that by progressive stage-wise curriculum learning, the pretrained representations are significantly enhanced and adaptable to both in-domain and out-of-domain distribution data.We performed extensive validation on three histology benchmark datasets on both patch-wise and slide-level classification problems. Our curriculum based fine-tuning yields a significant improvement over standard fine-tuning, with a minimum improvement in area-under-the-curve (AUC) score of 1.7% and 2.2% on in-domain and out-of-domain distribution data, respectively. Further, we empirically show that our approach is more generic and adaptable to any SSL methods and does not impose any additional overhead complexity. Besides, we also outline the role of patch-based versus slide-based curriculum learning in histopathology to provide practical insights into the success of curriculum based fine-tuning of SSL methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>