Reviewerly/exHarmony huggingface dataset:
A Label Embedding Method via Conditional Covariance Maximization for Multi-label Classification

Multi-label classification has revealed broad applications and outstanding importance in machine learning and data mining. In the multi-label domain, in addition to the problems including large instance size and high-dimensional features which need to be addressed urgently, the dimensionality of label space is increasing rapidly, leading to high computational overhead and curse of dimensionality. For tackling this issue, a typical technique called label space dimensionality reduction (LSDR) has been proposed, whose various corresponding methods are responsible for converting the high-dimensional label space into a low-dimensional one, including label embedding (LE) and label selection (LS), and then solve a low-dimensional classification or regression problem efficiently. In this paper, we focus on LE approaches, whose performance greatly depends on a preferable LE criterion. We investigate the conditional covariance operator, which originally is a nonlinear kernel-based dependence between two random vectors, formulated as a trace operation including label Gram matrix and feature Gram inverse matrix. We simplify this operator with linear label kernel matrix, which is then maximized under orthonormal constraints, resulting in an eigenvector problem. Therefore, our compression and recovery matrices correspond to those eigenvectors with several top-ranked eigenvalues. Our proposed novel LE method based on conditional covariance maximization is termed as ML-CCM for short. The experiments on four public benchmark data sets demonstrate that our proposed technique is statistically superior to three state-of-the-art LE methods on the basis of two performance evaluation metrics.
Multi-core Adaptive Merging of the Secondary Index for LSM-Based Stores

NoSQL databases have gained great popularity recently. Most of them use the Log Structured Merge (LSM) tree which provides fast write throughput and fast lookup of primary keys. Nevertheless, searching by non-key attributes is very slow because the entire LSM-tree must be scanned. To overcome this problem, the secondary index can be used. Typically, all items in the database are equally covered by the secondary index. However, this is not effective in big data stores where some items are queried very often and some never. To solve this problem, adaptive merging has been introduced. The key idea is to create a secondary index adaptively as a side-product of query processing. Consequently, the database is indexed partially depending on the query workload. The paper considers the adaptive merging of the secondary index in LSM-based stores. In this approach, the secondary index can be initiated at an arbitrary moment. Thereafter, only the requested data are inserted into the secondary index. They are retrieved from the independent immutable files created during the index initialization in a parallel way. The method can work in the dynamic database environment where database modifications interleave with user queries. The experiments show that the proposed approach outperforms traditional methods by about 30%.
Temporal Semantic Attention Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis aims to predict the polarity of sentiment towards a specific aspect in the context. In this paper, we propose the Temporal Semantic Attention Network (TSAN) model for ABSA tasks, which comprising a Global Semantic Feature Network for feature extraction and an Interact Dual Attention module to capture the dependencies of text-target interaction. Experiments on four ABSA benchmark datasets validates the effectiveness of our modules in extracting aspect-level sentiment features.
How Does the System Perceive Me? — A Transparent and Tunable Recommender System

We present a transparent and tunable recommender system using neural networks in which the user's preference for each tag calculated from his or her rating history is extracted as a user feature, and latent knowledge about the relationship between an item and a tag is extracted as an item feature. To improve user satisfaction with recommender systems, researchers have been focusing not only on a system's recommendation accuracy but also on its transparency, novelty, and serendipity as evaluation indices. Furthermore, the degree of user involvement in the recommendation process has been shown to substantially affect user satisfaction. Therefore, we propose showing a tag cloud as the user's profile as captured by the system from the user's interaction history and providing the user with a way to tune the recommendation results so that user satisfaction can be improved.
Geometry Flow-Based Deep Riemannian Metric Learning

Deep metric learning (DML) has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks. Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance. However, these methods fail to preserve the geometric structure of data in the embedding space, which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning. To alleviate these issues, by assuming that the input data is embedded in a lower-dimensional sub-manifold, we propose a novel deep Riemannian metric learning (DRML) framework that exploits the non-Euclidean geometric structural information. Considering that the curvature information of data measures how much the Riemannian (non-Euclidean) metric deviates from the Euclidean metric, we leverage geometry flow, which is called a geometric evolution equation, to characterize the relation between the Riemannian metric and its curvature. Our DRML not only regularizes the local neigh-borhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data. On several benchmark datasets, the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness.
A Blockchain-Based Data Authentication Algorithm for Secure Information Sharing in Internet of Vehicles

Secure communication between connected electric vehicles is critical for realizing the full potential of the Internet of Vehicles. However, the authentication and security of the information shared between vehicles remains a major challenge. In this work, we propose a blockchain-based data authentication algorithm to enable secure information sharing between electric vehicles. Our algorithm leverages the distributed ledger and consensus mechanism of blockchain technology to overcome limitations of traditional public key infrastructure schemes for large-scale vehicle networks. Each electric vehicle has a unique key pair and address on the blockchain network. Vehicles generate digital signatures using their private keys to share data, while recipients verify the signatures using corresponding public keys for authentication. Experimental results demonstrate that the proposed algorithm achieves high authentication success rates with acceptable latency and computation overhead. The algorithm provides benefits like decentralization, transparency and non-repudiation compared to existing approaches. Our work indicates the potential of blockchain to enhance security, trust and cooperation in Internet of Vehicles applications.
Pragmatic Study of Hate Speech in Some American Animated Movies

Language can be used to express positive thoughts and reflect positive perceptions of others. Language, on the other hand, might do the opposite. Hate speech is the language directed at a particular group of people. It incites hostility, violence, or discrimination against others based on a difference in race, religion, or gender, among others. People have become more interested in hate speech due to its impact on individuals and societies. It is argued that hate speech is common in American animated movies that are produced for the young and children. This renders such a phenomenon more dangerous. This study aims to answer the following questions: What are the motivations that incite hate speech in three different American animated movies? What are the pragmatic strategies used to convey it in the data under scrutiny? Which of these pragmatic strategies are the most prevalent? And has hate speech changed in terms of its motivations and pragmatic manifestation through time? This study develops an analytical framework be the basic apparatus for analyzing the data. This study uses Searle's theory (1975) and Culpeper's (1996, 2015) impoliteness. Three American animated movies from three different periods have been chosen: Cinderella (1950), Pocahontas (1995), and Zootopia (2016). The findings of this study have led to some conclusions. The most basic one is that each movie employs the same pragmatic strategies to convey hate speech, but its motivations are different.
A Study on Big Data Collecting and Utilizing Smart Factory Based Grid Networking Big Data Using Apache Kafka

In the Smart Factory environment of the 4 th industrial revolution, much data is generated from equipment, IoT sensors, and a wide range of manufacturing systems.As manufacturing sites are scattered around the world, information exchange between geographically remote factories is ever more necessary.Also, higher quality and effective management can be achieved by integrating and analyzing the collected and refined data and deriving organic results in the ever-rapidly changing manufacturing environment.However, as the main factory consists of a separate network with much data generated, it is highly difficult to gather all data into one and refine it.The most widely used method of data gathering at present has an architecture where data is linked through integration of the centrally configured solutions for data gathering and linkage.In other words, legacy systems most commonly used in manufacturing sites such as ERP, MES, WMS, etc. use the central system called ESB or EAI, to collect data with the SOA method for inter-system data linkage and collection and pass it on to another legacy system.The centralized method is not suitable for gathering and converging data generated from dozens or hundreds of different factories that are regionally dispersed or made up of independent networks and are also extremely vulnerable in terms of security and safety.This article aims to investigate how to stably and effectively exchange and collect data in geographically remote, independent networks using Apache Kafka, one of the big data ecosystems, and how to enable easy analysis of such data so that users can effectively utilize it.
Multiuser Personalized Ciphertext Retrieval Scheme Based on Deep Learning

With the rapid development of cloud computing technology and Internet of Things (IoT), enterprises and organizations tend to outsource local data to cloud servers and use searchable encryption (SE) technology to access and search encrypted data. However, the existing symmetric SE (SSE) schemes pay less attention to multiuser environments and users’ interest, which cause poor experience to users. In this article, we propose a multiuser personalized ciphertext search scheme (MPCS) by extending deep learning technology and SSE technology, which can achieve personalized retrieval and multiuser retrieval at the same time. MPCS achieves secure transmission of document keys and fine-grained access control by combining matrix decomposition and ciphertext policy attribute-based encryption (CP-ABE) technology, which assigns different private keys to each authorized user in the system and allows setting different access rights for different users. Second, we build an interest model for different users and design a user query update algorithm based on the attention mechanism to provide personalized ranking results, which improves the retrieval experience of users. In addition, the computation overhead of MPCS is lightweight, the size of ciphertext and key will not increase linearly with the number of attribute values, and MPCS supports lightweight document updates, which greatly reduces the computation overhead of the system. Formal security analysis verifies the security of MPCS, and simulation experiments on real data sets show that MPCS is feasible and efficient in practice.
Hardware and Software Design for Mobile Robot’s Navigation Over On-Road and Off-Road Curvature Paths

Navigating a mobile robot through a curve path is a difficult task in both on-road and off-road navigation. This research provides a hardware and software platform to address this issue. Two types of research have been conducted in this paper. First, a hardware platform consisting of a Linear Actuator-based steering mechanism is implemented to hold the steering at the desired angle. Second, the curvature information from Google Maps path data is collected and used to improve the existing Pure Pursuit algorithm, which is known as the curve-aware Pure Pursuit (C-PP) algorithm, which navigates a mobile robot in both indoor and outdoor environments. The mobile robot is navigated using C-PP and steering is controlled by a Linear Actuator. The results taken in real-time and simulation show that the proposed algorithm for on-road and off-road environments has lower longitudinal and lateral RMSE values compared to the existing algorithms.
Semantically Enhanced Multi-Object Detection and Tracking for Autonomous Vehicles

Accurate ambient perception via multi-object detection and tracking is instrumental for autonomous vehicles. This article addresses two main challenges when operating solely on 3-D light laser detection and ranging (LiDAR) point clouds: the classification of objects with similar geometric structures and tracking under the commonplace setting of low-frequency sensing. First, we design a semantically enhanced feature aggregation module that fuses features learned from two branches with different resolutions and depths. Subsequently, the extracted semantic information combined with our proposed Margin Loss allows the re-identification module to extract time-invariant geometric features. These features are fused with the positional information provided by the detector by a cluster-based Earth's mover distance algorithm along with conflation to improve the tracking stability. Extensive experiments on nuScenes demonstrate that our proposed model outperforms the state-of-the-art methods for both LiDAR-based 3-D object detection and tracking. In particular, we report an increase of 1.1% in average multi-object tracking accuracy, as well higher mean average precision for detection by 6.2% and 7.5% on motorcycle and bicycle, respectively.
Efficient Defenses Against Output Poisoning Attacks on Local Differential Privacy

Local differential privacy (LDP) is a promising technique to realize privacy-preserving data aggregation without a trusted aggregator. Normally, an LDP protocol requires each user to locally perturb his raw data and submit the perturbed data to the aggregator. Consequently, LDP is vulnerable to output poisoning attacks. Malicious users can skip the perturbation and submit carefully crafted data to the aggregator, altering the data aggregation results. Existing verifiable LDP protocols, which can verify the perturbation process and prevent output poisoning attacks, usually incur significant computation and communication costs, due to the use of zero-knowledge proofs. In this paper, we analyze the attacks on two classic LDP protocols for frequency estimation, namely GRR and OUE, and propose two verifiable LDP protocols. The proposed protocols are based on an interactive framework, where the user and the aggregator complete the perturbation together. By providing some additional information, which reveals nothing about the raw data but helps the verification, the user can convince the aggregator that he is incapable of launching an output poisoning attack. Simulation results demonstrate that the proposed protocols have good defensive performance and outperform existing approaches in terms of efficiency.
Design and Analysis of Octa-phase Shift Keying Based Quantum Key Distribution System

This letter proposes a novel modulation signaling-based quantum key distribution (QKD) protocol that employs eight phases of the optical carrier, therefore named octa-phase shift keying QKD (8PSK-QKD). The proposed QKD protocol shares two key bits simultaneously and consequently exhibits increased spectral efficiency compared to existing modulation signaling-based QKD protocols. Different performance metrics, viz., probability of sifted keys ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P _{\text{sift}}$ </tex-math></inline-formula> ), quantum bit error rate, probability of sifted keys of Eve, and Eve’s error probability ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P _{\text{eve}}$ </tex-math></inline-formula> ), are determined using a novel analytical framework for a free-space optical channel under the combined effect of atmospheric turbulence and pointing error. To inspect the potential of the system, the 8PSK-QKD system is compared with the quadrature phase shift keying-based QKD (QPSK-QKD) and subcarrier intensity modulation binary phase shift keying-based QKD (SIM/BPSK-QKD) systems. Numerical results reveal that the 8PSK-QKD system follows the criteria of an acceptable QKD system even with transmitting two key bits at a time. Additionally, the 8PSK-QKD system outperform the SIM/BPSK-QKD and QPSK-QKD by offering better <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P _{\text{sift}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P _{\text{eve}}$ </tex-math></inline-formula> .
Attention is Needed for RF Fingerprinting

Radio Frequency (RF) fingerprinting is a novel solution for identifying a unique radio from a large pool of devices by analyzing the subtle characteristics that are inherent in the radio waveform.Deep convolutional neural networks have been widely used to handle the RF fingerprinting task because of their exceptional capacity for representation learning.However, there are still challenges in employing deep convolutional neural networks, such as how to enable the model learn more robust and discriminative RF fingerprints.This paper aims to explore new model architectures to learn robust RF fingerprints.Hence we proposes a novel Dual Attention Convolution module that simultaneously learns channel attention and spatial attention to tune the RF fingerprints, enhancing the convolutional layers' potential for representation learning.Our proposed module is lightweight and plug-and-play.A number of convolutional neural networks can be equipped with our module, which enables them to extract robust and discriminative RF fingerprints.Our approach has been extensively tested through experimental trials, and the results have demonstrated its effectiveness.It is shown that the performance of convolutional neural networks on RF fingerprinting can be improved 1.5% on average, and DAConv-ResNet50 which combined ResNet50 and our Dual Attention Convolution module can achieve 95.6% recognition accuracy on 10 USRP X310.Our source code is available at https://github.com/zhangweifeng1218/Adaptive_RF_Fingerprinting.
Characterizing and Understanding Defense Methods for GNNs on GPUs

Graph neural networks (GNNs) are widely deployed in many vital fields, but suffer from adversarial attacks, which seriously compromise the security in these fields. Plenty of defense methods have been proposed to mitigate the impact of these attacks, however, they have introduced extra time-consuming stages into the execution of GNNs. These extra stages need to be accelerated because the end-to-end acceleration is essential for GNNs to achieve fast development and deployment. To disclose the performance bottlenecks, execution patterns, execution semantics, and overheads of the defense methods for GNNs, we characterize and explore these extra stages on GPUs. Given the characterization and exploration, we provide several useful guidelines for both software and hardware optimizations to accelerate the defense methods for GNNs.
Summarizing Students’ Free Responses for an Introductory Algebra-Based Physics Course Survey Using Cluster and Sentiment Analysis

In Physics Higher Education (PHE), Student Evaluation of Teaching (SET) surveys are widely used to collect students’ feedback on courses and instructions. In our research, we propose a more efficient way to summarize students’ free responses from the Student Assessment of their Learning Gains (SALG) survey [1], a form of the SET survey, of an algebra-based introductory physics course at a large Canadian research university. Specifically, we use cluster and sentiment analysis methods such as K-means [2] and Valence Aware Dictionary for sEntiment Reasoning (VADER) [3] to summarize students’ free responses. For cluster analysis, we extract popular keywords and summaries of responses in different clusters that reflect students’ dominant opinions toward each aspect of the course. Notably, we obtain an average silhouette coefficient of 0.480. In addition, we analyze sentiments in students’ free responses that are determined through applying VADER. Intriguingly, we see that VADER (micro F1 = 0.57, macro F1 = 0.55) can better classify responses with positive (F1 = 0.62) and neutral sentiment (F1 = 0.59). However, evident disagreements arise with negative sentiment responses (F1 = 0.42). In addition, our research suggests that some Likert-scale summaries deviate from the sentiment of free response summaries due to the limitations of Likert-scale responses. By creating various visualizations, we discover that Natural Language Processing (NLP) methods, such as cluster and sentiment analysis, effectively summarize students’ free responses, with several limitations.
An Adaptively Secure and Efficient Data Sharing System for Dynamic User Groups in Cloud

Cloud computing has been widely accepted as a computing paradigm to offer high-quality data services on demand. However, it suffers from various attacks as the cloud service provider and data owners are not in the same trusted domain. To support data confidentiality, existing cloud-based systems apply cryptographic tools to issue the decryption key to data users to share data in a controlled way. However, fine-grained cloud data sharing still faces many challenges, especially when dealing with dynamic user groups. In this paper, we introduce a secure and efficient cloud-based data-sharing system with fine-grained access control and dynamic user groups. Our system enjoys 1) adaptive security in prime-order groups, 2) forward secrecy against revoked user fetches data generated before being revoked, and 3) decryption key exposure resistance against the compromise of the frequently used decryption key, where the previous solutions only concentrate on one or two above-mentioned properties. More specifically, we introduce two timestamp management mechanisms that manage the timestamp in each ciphertext to support dynamic user groups with forward secrecy. By applying the proposed timestamp management mechanisms, we introduce two novel designs of attribute-based encryption schemes with formal definition and security analyses. The proposed schemes are adaptively secure in prime-order groups under a standard assumption and support decryption key exposure resistance. We conduct theoretical analysis and experimental simulation to demonstrate the outperformance of our solutions.