Reviewerly/exHarmony huggingface dataset:
RepDarkNet: A Multi-Branched Detector for Small-Target Detection in Remote Sensing Images

Recent years have seen rapid progress in target-detection missions, whereas small targets, dense target distribution, and shadow occlusion continue to hinder progress in the detection of small targets, such as cars, in remote sensing images. To address this shortcoming, we propose herein a backbone feature-extraction network called “RepDarkNet” that adds several convolutional layers to CSPDarkNet53. RepDarkNet considerably improves the overall network accuracy with almost no increase in inference time. In addition, we propose a multi-scale cross-layer detector that significantly improves the capability of the network to detect small targets. Finally, a feature fusion network is proposed to further improve the performance of the algorithm in the AP@0.75 case. Experiments show that the proposed method dramatically improves detection accuracy, achieving AP = 75.53% for the Dior-vehicle dataset and mAP = 84.3% for the Dior dataset, both of which exceed the state-of-the-art level. Finally, we present a series of improvement strategies that justifies our improvement measures.
“Nah, it’s just annoying!” A Deep Dive into User Perceptions of Two-Factor Authentication

Two-factor authentication (2FA) is a recommended or imposed authentication mechanism for valuable online assets. However, 2FA mechanisms usually exhibit user experience issues that create user friction and even lead to poor acceptance, hampering the wider spread of 2FA. In this article, we investigate user perceptions of 2FA through in-depth interviews with 42 participants, revealing key requirements that are not well met today despite recently emerged 2FA solutions. First, we investigate past experiences with authentication mechanisms emphasizing problems and aspects that hamper good user experience. Second, we investigate the different authentication factors more closely. Our results reveal particularly interesting preferences regarding the authentication factor “ownership” in terms of properties, physical realizations, and interaction. These findings suggest a path toward 2FA mechanisms with considerably better user experience, promising to improve the acceptance and hence, the proliferation of 2FA for the benefit of security in the digital world.
Minimum Cauchy Kernel Loss Based Robust Cubature Kalman Filter and Its Low Complexity Cost Version With Application on INS/OD Integrated Navigation System

This article focuses on addressing the non-linear state estimations problem of measurement noise with non-Gaussian distribution, which is critical for the performance of the inertial navigation system (INS)/odometer (OD) integrated navigation system. Although many existing robust filters have been proposed to address the non-Gaussian issue, their performance is not quite ideal for non-Gaussian noise in real applications, such as impulsive and noises from multimodal distributions. Meanwhile, the computational complexity burden impedes the application of robust non-linear algorithms in INS/OD integrated navigation systems. This paper proposes a minimal Cauchy kernel Cubature Kalman filter (MCK-CKF) to address the non-Gaussian problem in general non-linear systems. In addition, a simplified MCK-CKF (SMCK-CKF) is proposed to reduce the computational cost in INS/OD integrated navigation system, meanwhile ensuring outstanding performance similar to MCK-CKF. First, we derive the MCK-CKF based on the new cost function, which is obtained by a combination of weighted least square (WLS) and MCK. Then, for the INS/OD integrated navigation non-linear system, which has a linear measurement equation, the SMCK-CKF is established by introducing the approximate method to calculate the matrix inversion in the combination of Kalman filter measurement updated and MCK-CKF framework. The computational complexity of the CKF/MCK-CKF/SMCK-CKF is analyzed and compared. The powerful robustness for tackling various forms of non-Gaussian interference of MCK-CKF is validated by simulation on a typical non-linearity system. Furthermore, the vehicle’s INS/OD integrated navigation experiment demonstrates that the MCK-CKF/SMCK-CKF have remarkable robustness, and the SMCK-CKF has outstanding time efficiency.
A non-commutative Bayes' theorem

Using a diagrammatic reformulation of Bayes' theorem, we provide a necessary and sufficient condition for the existence of Bayesian inference in the setting of finite-dimensional $C^*$-algebras. In other words, we prove an analogue of Bayes' theorem in the joint classical and quantum context. Our analogue is justified by recent advances in categorical probability theory, which have provided an abstract formulation of the classical Bayes' theorem. In the process, we further develop non-commutative almost everywhere equivalence and illustrate its important role in non-commutative Bayesian inversion. The construction of such Bayesian inverses, when they exist, involves solving a positive semidefinite matrix completion problem for the Choi matrix. This gives a solution to the open problem of constructing Bayesian inversion for completely positive unital maps acting on density matrices that do not have full support. We illustrate how the procedure works for several examples relevant to quantum information theory.
Hypergraph Neural Networks for Hypergraph Matching

Hypergraph matching is a useful tool to find feature correspondence by considering higher-order structural information. Recently, the employment of deep learning has made great progress in the matching of graphs, suggesting its potential for hypergraphs. Hence, in this paper, we present the first, to our best knowledge, unified hypergraph neural network (HNN) solution for hypergraph matching. Specifically, given two hypergraphs to be matched, we first construct an association hypergraph over them and convert the hypergraph matching problem into a node classification problem on the association hypergraph. Then, we design a novel hypergraph neural network to effectively solve the node classification problem. Being end-to-end trainable, our proposed method, named HNN-HM, jointly learns all its components with improved optimization. For evaluation, HNN-HM is tested on various benchmarks and shows a clear advantage over state-of-the-arts.
Achieving Fine-Grained Data Sharing for Hierarchical Organizations in Clouds

Cloud computing has become an increasingly popular option for users to store and share data. Encryption prior to outsourcing data to the cloud is the best way to protect data security and privacy; however, it hinders sharing of the data that was encrypted. In addition, users in many real-world organizations (e.g., enterprises) have multiple level structures and a higher-level user should have the privilege to decide which data can be shared with a lower-level user. Most solutions in the literature suffer from inefficiency or inflexibility in tackling this problem. In this article, we propose a fine-grained hierarchical data sharing (FHDS) scheme in clouds. With FHDS, the data owner can encrypt data with his public key, and then selectively share encrypted data with users in a hierarchy; if necessary, the users can disseminate the owner's data to their subordinates in the lower levels by generating access keys. In particular, the higher-level users could puncture the keys with some tags such that the part of the owner's data which is labeled by the punctured tags will not be accessible to the lower-level users. The proposed scheme is provable secure under our security model and performance analyses show the efficiency of the scheme.
Centralized bidding mechanism of demand response based on blockchain

In future power systems with the characteristics of high-elastic, demand response (DR) is considered to be an essential way for improving system stability and awakening demand-side resources. However, all the DR programs in China are implemented in a centralized model, which is easier for supervision, but hard to deal with the challenges of data credibility, privacy protection, transaction efficiency, and so on. Therefore, a novel blockchain-based framework for the DR program is proposed to deal with the problems. On this basis, the bidding transaction process based on the repeated verification mechanism is designed, and the effectiveness of blockchain in DR is analyzed. Then, the smart contract functions involved in DR bidding transactions and subsidy settlement are customized. The feasibility of the DR bidding mechanism is illustrated by the simulation results on the platform of Remix IDE.
Blockchain tools for socio-economic interactions in local communities

Abstract Blockchain technology is generating interest in novel applicative fields such as co-production of public services. Our CommonsHood project is a “wallet app” that uses the Blockchain as a tool to support sustainability of the local economy. Its tokenization mechanism allows everyone to create new types of cryptographic tokens on the Blockchain in order to digitalize assets, augment the availability of local liquidity, and incentivize cooperative socio-economic interactions. This article analyzes a concrete application of CommonsHood for innovating local development policies and service co-production in the tourism sector. We examine this application using Linders’s analytical framework for information and communications technology (ICT)-enabled co-production of services (2012). We show the advantages our project brings for local policies on tourism development, and we discuss the benefits and costs of using the Blockchain in that context. We argue that the observed case study covers different types of digitally enabled co-production of services, and that it can be defined as a case of Governance as a Platform. We also argue that well-established analytical frameworks for ICT-enabled co-production of services need to be expanded in order to account for the new affordances enabled by the Blockchain technology, namely the creation and transaction of digital values, which represent a paradigm change in how we understand the Internet and digital co-production.
Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion

Local path planning is a key task for the motion planners of autonomous vehicles since it commands the vehicle across its environment while avoiding any obstacles. To perform this task, the local path planner generates a trajectory and a velocity profile, which are then sent to the vehicle’s actuators. This paper proposes a new local path planner for autonomous vehicles based on the Attractor Dynamic Approach (ADA), which was inspired by the behavior of movement of living beings, along with an algorithm that takes into account four acceleration policies, the ST dynamic vehicle model, and several constraints regarding the comfort and security. The original functions that define the ADA were modified in order to adapt it to the non-holonomic vehicle’s constraints and to improve its response when an impact scenario is detected. The present approach is validated in a well-known simulator for autonomous vehicles under three representative cases of study where the vehicle was capable of generating local paths that ensure the security of the vehicle in such cases. The results show that the approach proposed in this paper is a promising tool for the local path planning of autonomous vehicles since it is able to generate trajectories that are both safe and efficient.
Deep Learning-based Continuous Authentication for an IoT-enabled healthcare service

The Internet of Things (IoT) has introduced a new dimension to the Internet in the last decade; nonetheless, security, particularly attacks on authentication, continue to be a significant concern in IoT. The majority of research endeavours consider external attacks that originate from outside of an IoT network. Their authentication mechanisms authenticate users at the outset of a session. However, a device or user within the network may be a more significant threat than the external attacker due to their accessibility. An intruder during the session can physically grasp any IoT device and impersonate it. Therefore, the suggested security system continuously authenticates legitimate users inside a session. The system takes data from users and authenticates them using a Deep Learning-based Long Short-Term Memory classification algorithm. There are 3.5 percent false acceptances and 2.4% false rejections for the security system. The research also compared the suggested approach to other current security techniques.
Recent Developments in Arabic Conversational AI: A Literature Review

Conversational AI is one of the most active research areas in AI, and it has gained more attention from academia as well as industry.Given recent advancements in several conversational AI systems in addition to the availability of several datasets, the aim of this study is to explore the landscape of Arabic textbased conversational AI systems.In this work, we provide a thorough review of recent Arabic conversational AI systems.We group them into three categories based on their functionality: (1) question-answering (QA) systems, (2) task-oriented dialogue systems (DS), and (3) chatbots.Furthermore, we describe the common datasets used in building and evaluating conversational AI systems in Arabic.Few surveys have targeted the conversational AI field for the Arabic language, and we aim to cover this gap with this study.Our contribution focuses on reviewing and analyzing the literature in the field and highlighting future research directions towards human-like conversational AI systems in Arabic.
Procrustean Training for Imbalanced Deep Learning

Neural networks trained with class-imbalanced data are known to perform poorly on minor classes of scarce training data. Several recent works attribute this to over-fitting to minor classes. In this paper, we provide a novel explanation of this issue. We found that a neural network tends to first under-fit the minor classes by classifying most of their data into the major classes in early training epochs. To correct these wrong predictions, the neural network then must focus on pushing features of minor class data across the decision boundaries between major and minor classes, leading to much larger gradients for features of minor classes. We argue that such an under-fitting phase over-emphasizes the competition between major and minor classes, hinders the neural network from learning the discriminative knowledge that can be generalized to test data, and eventually results in over-fitting. To address this issue, we propose a novel learning strategy to equalize the training progress across classes. We mix features of the major class data with those of other data in a mini-batch, intentionally weakening their features to prevent a neural network from fitting them first. We show that this strategy can largely balance the training accuracy and feature gradients across classes, effectively mitigating the under-fitting then over-fitting problem for minor class data. On several benchmark datasets, our approach achieves the state-of-the-art accuracy, especially for the challenging step-imbalanced cases.
A Portable Sign Language Collection and Translation Platform with Smart Watches Using a BLSTM-Based Multi-Feature Framework

Continuous sign language recognition (CSLR) using different types of sensors to precisely recognize sign language in real time is a very challenging but important research direction in sensor technology. Many previous methods are vision-based, with computationally intensive algorithms to process a large number of image/video frames possibly contaminated with noises, which can result in a large translation delay. On the other hand, gesture-based CSLR relying on hand movement data captured on wearable devices may require less computation resources and translation time. Thus, it is more efficient to provide instant translation during real-world communication. However, the insufficient amount of information provided by the wearable sensors often affect the overall performance of this system. To tackle this issue, we propose a bidirectional long short-term memory (BLSTM)-based multi-feature framework for conducting gesture-based CSLR precisely with two smart watches. In this framework, multiple sets of input features are extracted from the collected gesture data to provide a diverse spectrum of valuable information to the underlying BLSTM model for CSLR. To demonstrate the effectiveness of the proposed framework, we test it on an extremely challenging and radically new dataset of Hong Kong sign language (HKSL), in which hand movement data are collected from 6 individual signers for 50 different sentences. The experimental results reveal that the proposed framework attains a much lower word error rate compared with other existing machine learning or deep learning approaches for gesture-based CSLR. Based on this framework, we further propose a portable sign language collection and translation platform, which can simplify the procedure of collecting gesture-based sign language dataset and recognize sign language through smart watch data in real time, in order to break the communication barrier for the sign language users.
Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance

Distributed surveillance systems have the ability to detect, track, and snapshot objects moving around in a certain space. The systems generate video data from multiple personal devices or street cameras. Intelligent video-analysis models are needed to learn dynamic representation of the objects for detection and tracking. Can we exploit the structural and dynamic information without storing the spatiotemporal video data at a central server that leads to a violation of user privacy? In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from graph sequences: (1) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. (2) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user’s data to server. (3) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. We design an appropriate aggregation mechanism of secure aggregation primitives that can protect the security and privacy in federated learning with scalability. Experiments on four video camera datasets as well as simulation demonstrate that Feddy achieves great effectiveness and security.
SDNN: Symmetric deep neural networks with lateral connections for recommender systems

The recommender system is the key approach to alleviate the data explosion problem. Recently, with the rapid development of deep learning, there are several researches of employing deep neural networks (DNNs) on recommender systems. Most of these methods tend to capture the complex mapping relations between user-item representation and matching score via DNNs. These methods are mainly a pyramid structure which maps relations into low-dimensional space and then predicts the result by logistic regression. However, partial relations may be linearly indivisible in low-dimensional space. As we know, data that are hard to be separated in low-dimensional space can become much easier after being mapped into a high-dimensional space. Hence, motivated by the ladder network, we propose a Symmetric Deep Neural Networks (SDNN) with lateral connections, which can learn relations in both high-dimensional and low-dimensional spaces simultaneously. Moreover, considering that deep neural network is very inefficient in catching low-rank relations between users and items, we further combine SDNN with an improved deep matrix factorization model into a unified framework, and name this new model DualCF. Extensive experiments on three benchmark datasets are conducted and the results verify the effectiveness of SDNN and DualCF over state-of-the-art models for implicit feedback prediction.
Ask&amp;Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Query

Text-based image retrieval has seen considerable progress in recent years. However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description. In this work, we introduce the partial-query problem and extensively analyze its influence on text-based image retrieval. Previous interactive methods tackle the problem by passively receiving users’ feedback to supplement the incomplete query iteratively, which is time-consuming and requires heavy user effort. Instead, we propose a novel retrieval framework that conducts the interactive process in an Ask-and-Confirm fashion, where AI actively searches for discriminative details missing in the current query, and users only need to confirm AI’s proposal. Specifically, we propose an object-based interaction to make the interactive retrieval more user-friendly and present a reinforcement-learning-based policy to search for discriminative objects. Furthermore, since fully-supervised training is often infeasible due to the difficulty of obtaining human-machine dialog data, we present a weakly-supervised training strategy that needs no human-annotated dialogs other than a text-image dataset. Experiments show that our framework significantly improves the performance of text-based image retrieval. Code is available at https://github.com/CuthbertCai/Ask-Confirm.
Auction based cost‐efficient resource allocation by utilizing blockchain in fog computing

Abstract In the IoT‐cloud environment, the growing amount of spawn data may limit performance in terms of communication latency, network traffic, processing power, and energy usage. The introduction of fog computing extends the cloud services nearer to the edge of the network. Since these lightweight fog servers are not able to fulfill the demand of every user node and process each offloaded task due to the limited computation resources. Accordingly, an efficient resource management scheme is required to proficiently handle fog resources. The profit‐driven nature of both the fog service providers and user nodes increases the possibility of malicious activity while resource trading for their advantages or to privilege a bunch of devices. In this article, we designed a trusted and fair incentive mechanism that encourages buyers and sellers to trade by leveraging the benefits of blockchain and smart contracts. Especially, a combinatorial double auction employed market model is proposed which satisfies different economical properties such as individual rationality, budget balance, and truthfulness. Blockchain‐driven decentralized fog environments prevent the tampering of trade‐related information by the malicious nodes. Simulation results indicate that the proposed combinatorial double auction significantly improves the network utilization by improved winner determination and pricing model.