Reviewerly/exHarmony huggingface dataset:
RRCNet: Rivet Region Classification Network for Rivet Flush Measurement Based on 3-D Point Cloud

In the aircraft manufacturing industry, rivet inspection is a vital task for the aircraft structure stability and aerodynamic performance. In this article, we propose a novel framework for fully automated rivet flush measurement, which is the key step in rivet inspection task. To efficiently perform rivet flush measurement, we first develop a mobile 3-D scanning system to automatically capture the 3-D point cloud of the aircraft skin surface. Subsequently, rivet regions are extracted through point cloud processing techniques. Instead of relying on handcrafted features, we propose a novel data-driven approach for rivet point extraction via a deep-learning-based technique. Our algorithm takes a scanned point cloud of the aircraft skin surface as input and produces a dense point cloud label result for each point, distinguishing as rivet point or not. To achieve this, we propose a rivet region classification network (RRCNet) that can input the 2-D representations of a point and output a binary label indicating that the point is rivet or nonrivet point. Moreover, we design a field attention unit (FAU) to assign adaptive weights to different forms of 2-D representations via the attention mechanism in convolutional neural networks. The extracted rivet regions can then be used to perform rivet flush measurement. The abovementioned components result in a fully automatic contactless measurement framework of aircraft skin rivet flush. Several experiments are performed to demonstrate the priority of the proposed RRCNet and the effectiveness of the presented rivet flush measurement framework.
A Graph Learning Based Approach for Identity Inference in DApp Platform Blockchain

Current cryptocurrencies, such as Bitcoin and Ethereum, enable anonymity by using public keys to represent user accounts. On the other hand, inferring blockchain account types (i.e., miners, smart contracts or exchanges), which are also referred to as blockchain identities, is significant in many scenarios, such as risk assessment and trade regulation. Existing work on blockchain deanonymization mainly focuses on Bitcoin that supports simple transactions of cryptocurrencies. As the popularity of decentralized application (DApp) platform blockchains with Turing-complete smart contracts, represented by Ethereum, identity inference in blockchain faces new challenges because of user diversity and complexity of activities enabled by smart contracts. In this paper, we propose I <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> GL, an identify inference approach based on big graph analytics and learning to address these challenges. Specifically, I <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> GL constructs a transaction graph and aims to infer the identity of nodes using the graph learning technique based on Graph Convolutional Networks. Furthermore, a series of enhancement has been proposed by exploiting unique features of blockchain transaction graph. The experimental results on Ethereum transaction records show that I <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> GL significantly outperforms other state-of-the-art methods.
Beyond Model-Level Membership Privacy Leakage: an Adversarial Approach in Federated Learning

With the rise of privacy concerns in traditional centralized machine learning services, the federated learning, which incorporates multiple participants to train a global model across their localized training data, has lately received signifi-cant attention in both industry and academia. However, recent researches reveal the inherent vulnerabilities of the federated learning for the membership inference attacks that the adversary could infer whether a given data record belongs to the model’s training set. Although the state-of-the-art techniques could successfully deduce the membership information from the centralized machine learning models, it is still challenging to infer the membership to a more confined level, user-level. In this paper, We propose a novel user-level inference attack mechanism in federated learning. Specifically, we first give a comprehensive analysis of active and targeted membership inference attacks in the context of the federated learning. Then, by considering a more complicated scenario that the adversary can only passively observe the updating models from different iterations, we incorporate the generative adversarial networks into our method, which can enrich the training set for the final membership inference model. The extensive experimental results demonstrate the effectiveness of our proposed attacking approach in the case of single-label and multi-label.
Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving

Driveable area detection is a key component for various applications in the field of autonomous driving (AD), such as ground-plane detection, obstacle detection and maneuver planning. Additionally, bulky and over-parameterized networks can be easily forgone and replaced with smaller networks for faster inference on embedded systems. The driveable area detection, posed as a two class segmentation task, can be efficiently modeled with slim binary networks. This paper proposes a novel binarized driveable area detection network (binary DAD-Net), which uses only binary weights and activations in the encoder, the bottleneck, and the decoder part. The latent space of the bottleneck is efficiently increased (×32→×16 downsampling) through binary dilated convolutions, learning more complex features. Along with automatically generated training data, the binary DAD-Net outperforms state-of-the-art semantic segmentation networks on public datasets. In comparison to a full-precision model, our approach has a ×14.3 reduced compute complexity on an FPGA and it requires only 0.9MB memory resources. Therefore, commodity SIMD-based AD-hardware is capable of accelerating the binary DAD-Net.
The cybersickness susceptibility questionnaire

In this paper we propose the Cybersickness Susceptibility Questionnaire (CSSQ), which predicts the likeliness of a user experiencing cybersickness related symptoms when exposed to virtual reality (VR). The items on this questionnaire were compiled from several previous works that have demonstrated the influence of various biological, chemical and psychological factors on the human senses and nervous system.
Governing Online Speech: From 'Posts-As-Trumps' to Proportionality and Probability

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Efficient Shapley Explanation for Features Importance Estimation Under Uncertainty

Complex deep learning models have shown their impressive power in analyzing high-dimensional medical image data. To increase the trust of applying deep learning models in medical field, it is essential to understand why a particular prediction was reached. Data feature importance estimation is an important approach to understand both the model and the underlying properties of data. Shapley value explanation (SHAP) is a technique to fairly evaluate input feature importance of a given model. However, the existing SHAP-based explanation works have limitations such as 1) computational complexity, which hinders their applications on high-dimensional medical image data; 2) being sensitive to noise, which can lead to serious errors. Therefore, we propose an uncertainty estimation method for the feature importance results calculated by SHAP. Then we theoretically justify the methods under a Shapley value framework. Finally we evaluate our methods on MNIST and a public neuroimaging dataset. We show the potential of our method to discover disease related biomarkers from neuroimaging data.
Deep snippet selective network for weakly supervised temporal action localization

Temporal action localization has been a hot topic in video analyzation. In this paper, we propose a novel method called deep snippet selective network (DSSN) to address two key problems in weak supervision for temporal action localization, which are separability and integrality. Specifically, we employ two erasing branches to ensure the integrality, which can force the network to select other complementary snippets by erasing the most discriminative snippets. It is worth mentioning that a ternary mask is utilized to provide erasing branches with a background prior to enhance the separability of the model. Besides, we design a background suppression branch to further reduce the effect of background snippets. Extensive experiments on dataset THUMOS’14 and ActivityNet show the effectiveness of our method.
Improving emergency preparedness to cascading disasters: A case‐driven risk ontology modelling

Abstract With the acceleration of urbanization, cascading disaster risks (CDR) as a typical risk mode have become the main threat to cities. After experiencing several cascading disasters, such as typhoon Lekima, how to clarify the basic features of CDR and achieve risk modelling has turned to be increasingly significant for building resilient city. However, the complexity of CDR brings about the difficulty to quickly map such risk mode depending entirely on expertise. Therefore, this paper attempts to provide a CDROntology system built by concepts and relations, and make full use of the historical cases to drive the modelling of the target CDR with case‐based reasoning. Firstly, we describe the basic structure and content of CDR and give a three‐level CDROntology system with the explanation of modelling primitives. Then, taking CDROntology system as the basis, a case‐driven selection process is proposed to provide the modelling source for the target CDR. Furthermore, set covering and manual correction methods are adopted to model the evolutionary risk chain and the specific risk scenario of the target case. Finally, a case study is given to illustrate the use of CDROntology system and case‐driven method for building a predictive risk model in typhoon‐triggered cascading disasters.
On the Price of Concurrency in Group Ratcheting Protocols

Post-Compromise Security, or PCS, refers to the ability of a given protocol to recover—by means of normal protocol operations—from the exposure of local states of its (otherwise honest) participants. While PCS in the two-party setting has attracted a lot of attention recently, the problem of achieving PCS in the group setting—called group ratcheting here—is much less understood. On the one hand, one can achieve excellent security by simply executing, in parallel, a two-party ratcheting protocol (e.g., Signal) for each pair of members in a group. However, this incurs $$\mathcal {O}(n)$$ communication overhead for every message sent, where n is the group size. On the other hand, several related protocols were recently developed in the context of the IETF Messaging Layer Security (MLS) effort that improve the communication overhead per message to $$\mathcal {O}(\log n)$$ . However, this reduction of communication overhead involves a great restriction: group members are not allowed to send and recover from exposures concurrently such that reaching PCS is delayed up to n communication time slots (potentially even more). In this work we formally study the trade-off between PCS, concurrency, and communication overhead in the context of group ratcheting. Since our main result is a lower bound, we define the cleanest and most restrictive setting where the tension already occurs: static groups equipped with a synchronous (and authenticated) broadcast channel, where up to t arbitrary parties can concurrently send messages in any given round. Already in this setting, we show in a symbolic execution model that PCS requires $$\varOmega (t)$$ communication overhead per message. Our symbolic model permits as building blocks black-box use of (even “dual”) PRFs, (even key-updatable) PKE (which in our symbolic definition is at least as strong as HIBE), and broadcast encryption, covering all tools used in previous constructions, but prohibiting the use of exotic primitives. To complement our result, we also prove an almost matching upper bound of $$\mathcal {O}(t\cdot (1+\log (n/t)))$$ , which smoothly increases from $$\mathcal {O}(\log n)$$ with no concurrency, to $$\mathcal {O}(n)$$ with unbounded concurrency, matching the previously known protocols.
Combining Multi-task Learning with Transfer Learning for Biomedical Named Entity Recognition

Multi-task learning approaches have shown significant improvements in different fields by training different related tasks simultaneously. The multi-task model learns common features among different tasks where they share some layers. However, it is observed that the multi-task learning approach can suffer performance degradation with respect to single task learning in some of the natural language processing tasks, specifically in sequence labelling problems. To tackle this limitation we formulate a simple but effective approach that combines multi-task learning with transfer learning. We use a simple model that comprises of bidirectional long-short term memory and conditional random field. With this simple model, we are able to achieve better F1-score compared to our single task and the multi-task models as well as state-of-the-art multi-task models.
(Non-)Interacting with conversational agents

Conversational agents (CAs) such as Siri, Alexa, and Google Assistant are increasingly penetrating everyday life. From a Human-Computer Interaction (HCI) perspective, designing CAs that appropriately support the way they are used within daily life is still challenging. While initial design guidelines for human-AI interaction exist, we still know little about how users actually perceive CAs within their daily lives and what aspects motivate their usage of such tools. Within our research, we therefore conducted an interview study with 29 participants to uncover daily positive and negative experiences with CAs. By revealing how users currently perceive CAs, we identify quality criteria that could inform their future design. By evaluating these criteria with respect to existing research discourses about user experience (UX) guidelines for CAs, we contribute to the field by extending these guidelines from an end-user's perspective.
Multiple maneuvering extended targets detection by 3D projection and tracklet association

In this paper, the five fundamental issues of target extension, target maneuvering, closely spaced targets, weak targets, and dense clutter are tackled simultaneously in the framework of a new tracking approach that is capable of effectively detecting multiple closely-spaced maneuvering weak and extended targets in densely cluttered environment. The proposed technique finds its foundation in the track-before-detect (TBD) paradigm, proven to be effective in tracking weak targets in presence of dense clutter. The approach consists of two stages. In the first stage, a 3-dimensional projection based TBD algorithm allows addressing the problem of target extension, weak targets, and dense clutter. Straight line tracklets are obtained at the end of the first stage. In the second stage, tracklets association is performed to obtain the target trajectories. The issue of closely spaced targets and target maneuvering is addressed in this second stage. To illustrate the effectiveness of the proposed approach, synthetic data, real data from a netted radar system, and real data from a high resolution radar are processed both with the proposed algorithm and with several existing algorithms. The obtained results reveal that the proposed method can outperform the competing approaches in various scenarios.
Political instability and political terror: Global evidence on persistence

We test the hypotheses that fundamental characteristics in regional proximity, landlockedness, religious‐domination, legal origin, and income levels affect cross‐country differences in the persistence in political terror and political instability in 163 countries for the period 2010–2015. The empirical evidence is based on generalised method of moments. The hypotheses are that the following are associated with comparatively higher levels of persistence in political terror and political instability: regions with predominantly low income countries (Hypothesis 1); landlockedness (Hypothesis 2); Christian‐orientation (Hypothesis 3); French civil law (Hypothesis 4); and Low income (Hypothesis 5). The tested hypotheses are largely invalid. Only Hypotheses 5 and 2 are robustly investigated in the light of concerns about instrument proliferation. Hypothesis 2 is valid for political terror but not for political instability while Hypothesis 5 is neither valid for political instability nor for political terror.
Improved Safety-First A-Star Algorithm for Autonomous Vehicles

The research focus of this paper is to improve the traditional A-star algorithm to meet the needs of autonomous vehicles for path safety and smoothness. First, the improved algorithm considers the factor of obstacle distance in the heuristic function. This allows the algorithm to strike a balance between path length and path security, as well as avoiding searching for redundant nodes that are too close to obstacles. Besides, the expansion mode of the 8-connection of the traditional A-star algorithm is improved to the 20-connection, so that the sharpness of turning at the corner can be greatly reduced. And because there is enough safety distance between the planned path and obstacles, the path smoothing can be performed to meet the vehicle dynamics. The improved safety-first A-star algorithm is compared with the traditional A-star algorithm in various scenes. The experimental results prove that the security of the improved safety-first A-star algorithm is greatly enhanced.
Security and Issues of M-Banking: A Technical Report

Now a day, Mobile banking playing a very vital role in everybody's life. Recently nobody has to stand in a queue at a bank counter for availing banking services since mobile banking had made it very easy. Sitting at a remote location using smartphone customers can avail banking services. It has many benefits; on the other hand, it has some demerits also. Hackers can easily trick bank customers using social engineering and cyberstalking to have their money. Cyber-criminals are hard to trace, so this opportunity is fully utilized by hackers. In this paper, a thorough review has been done on various types of scams that are taking place frequently on mobile or online banking. This paper mainly focuses on the increasing number of online fraud cases related to the banking industry. Hence, awareness programs are required among bank customers to prevent or avoid different types of online fraud..
Web Design for Distance Learning Indonesian Language BIPA

BIPA (Bahasa Indonesia bagi Penutur Asing) is an Indonesian learning program for Foreign Speakers. Apart from being a medium for learning and conveying various information about Indonesia, BIPA teaching can also introduce Indonesian culture to foreign speakers. From year to year, international speakers who learn Indonesian continue to grow. This was accompanied by an increase in the number of BIPA providers in Indonesia. To support the acceleration of this growth, digital service is needed to make it easier to find native speaker tutors and adjust material according to the needs of foreign speakers. This digital service is a website that can bring international speakers and Indonesian language tutors wherever they are. This website is named BIPALOKA, which is a digital platform that bridges language lovers around the world who want to learn Indonesian from the community of Professional Indonesian tutors. The method used in making the BIPALOKA website is a waterfall model, which in the process is made in stages starting from analysis, planning, implementation, checking, deploying, and ending with maintenance.
Spatio-Temporal Slowfast Self-Attention Network For Action Recognition

We propose Spatio-Temporal SlowFast Self-Attention network for action recognition. Conventional Convolutional Neural Networks have the advantage of capturing the local area of the data. However, to understand a human action, it is appropriate to consider both human and the overall context of given scene. Therefore, we repurpose a self-attention mechanism from Self-Attention GAN (SAGAN) to our model for retrieving global semantic context when making action recognition. Using the self-attention mechanism, we propose a module that can extract four features in video information: spatial information, temporal information, slow action information, and fast action information. We train and test our network on the Atomic Visual Actions (AVA) dataset and show significant frame-AP improvements on 28 categories.
Fast Multiscale Superpixel Segmentation for SAR Imagery

Superpixel segmentation is essential to the rapid information extraction from synthetic aperture radar (SAR) imagery. In this letter, we propose a fast multiscale superpixel segmentation method based on the minimum spanning tree (MST), which can generate all scales of superpixels accurately in real time. Therefore, our method has the ability to segment SAR imagery with different scales efficiently and is meaningful for applications that require different levels of SAR image details. Experimental results on two real SAR images demonstrate that our proposed superpixel segmentation method can capture the image information of different levels, resulting in better hierarchical segmentation performance in comparison with other state-of-the-art methods.
Feature-Enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon Cancer

Mining histopathological and genetic data provides a unique avenue to deepen our understanding of cancer biology. However, extensive cancer heterogeneity across image- and molecular-scales poses technical challenges for feature extraction and outcome prediction. In this study, we propose a feature-enhanced graph network (FENet) for genetic mutation prediction using histopathological images in colon cancer. Unlike conventional approaches analyzing patch-based feature alone without considering their spatial connectivity, we seek to link and explore non-isomorphic topological structures in histopathological images. Our FENet incorporates feature enhancement in convolutional graph neural networks to aggregate discriminative features for capturing gene mutation status. Specifically, our approach could identify both local patch feature information and global topological structure in histopathological images simultaneously. Furthermore, we introduced an ensemble strategy by constructing multiple subgraphs to boost the prediction performance. Extensive experiments on the TCGA-COAD and TCGA-READ cohort including both histopathological images and three key genes’ mutation profiles (APC, KRAS, and TP53) demonstrated the superiority of FENet for key mutational outcome prediction in colon cancer.
A tale from the trenches

Cognitive biases are hard-wired behaviors that influence developer actions and can set them on an incorrect course of action, necessitating backtracking. While researchers have found that cognitive biases occur in development tasks in controlled lab studies, we still don't know how these biases affect developers' everyday behavior. Without such an understanding, development tools and practices remain inadequate. To close this gap, we conducted a 2-part field study to examine the extent to which cognitive biases occur, the consequences of these biases on developer behavior, and the practices and tools that developers use to deal with these biases. About 70% of observed actions that were reversed were associated with at least one cognitive bias. Further, even though developers recognized that biases frequently occur, they routinely are forced to deal with such issues with ad hoc processes and sub-optimal tool support. As one participant (IP12) lamented: There is no salvation!