Reviewerly/exHarmony huggingface dataset:
FP-AGL: Filter Pruning With Adaptive Gradient Learning for Accelerating Deep Convolutional Neural Networks

Filter pruning is a technique that reduces computational complexity, inference time, and memory footprint by removing unnecessary filters in convolutional neural networks (CNNs) with an acceptable drop in accuracy, consequently accelerating the network. Unlike traditional filter pruning methods utilizing zeroing-out filters, we propose two techniques to achieve the effect of pruning more filters with less performance degradation, inspired by the existing research on centripetal stochastic gradient descent (C-SGD), wherein the filters are removed only when the ones that need to be pruned have the same value. First, to minimize the negative effect of centripetal vectors that gradually make filters come closer to each other, we redesign the vectors by considering the effect of each vector on the loss-function using the Taylor-based method. Second, we propose an adaptive gradient learning (AGL) technique that updates weights while adaptively changing the gradients. Through AGL, performance degradation can be mitigated because some gradients maintain their original direction, and AGL also minimizes the accuracy loss by perfectly converging the filters, which require pruning, to a single point. Finally, we demonstrate the superiority of the proposed method on various datasets and networks. In particular, on the ILSVRC-2012 dataset, our method removed 52.09% FLOPs with a negligible 0.15% top-1 accuracy drop on ResNet-50. As a result, we achieve the most outstanding performance compared to those reported in previous studies in terms of the trade-off between accuracy and computational complexity.
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing

World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms. While learning world models for high-dimensional observations (e.g., pixel inputs) has become practicable on standard RL benchmarks and some games, their effectiveness in real-world robotics applications has not been explored. In this paper, we investigate how such agents generalize to real-world autonomous vehicle control tasks, where advanced model-free deep RL algorithms fail. In particular, we set up a series of time-lap tasks for an F1TENTH racing robot, equipped with a high-dimensional LiDAR sensor, on a set of test tracks with a gradual increase in their complexity. In this continuous-control setting, we show that model-based agents capable of learning in imagination substantially outperform model-free agents with respect to performance, sample efficiency, successful task completion, and generalization. Moreover, we show that the generalization ability of model-based agents strongly depends on the choice of their observation model. We provide extensive empirical evidence for the effectiveness of world models provided with long enough memory horizons in sim2real tasks.
Aerial Access Networks for Federated Learning: Applications and Challenges

Aerial access networks (AANs) and mobile edge computing (MEC) have been considered as key enablers of future networks. In this article, we investigate the application of MEC-empowered AANs (also known as aerial computing) for federated learning (FL), a promising technology for providing private and distributed solutions to mobile edge networks. We first introduce the fundamentals of AANs and FL, and illustrate the potential benefits of aerial FL networks. On this basis, we present important applications of AANs for FL. It is shown that distinctive characteristics such as flexible deployment and high mobility, when exploited cleverly, can provide various benefits for FL-enabled networks. Finally, major challenges and potential directions are highlighted.
RNS-Based Adaptive Compression Scheme for the Block Data in the Blockchain for IIoT

The Industrial Internet of Things (IIoT) is the essential component of Industry 4.0. Blockchain is a promising technology for secure data sharing and trustable cooperation between IIoT devices. However, the ever-growing transaction records make it difficult for the storage-limited IIoT devices to join the blockchain network. In this article, an adaptive compression scheme is proposed to decrease the storage volume on each node. In the scheme, the block body is compressed by representing the included transactions as their remainders stored in the distributed nodes. The original transaction could be recovered based on the Chinese remainder theorem. In particular, each node adapts its compression ratio according to its storage resource. The nodes storing more data have advantages in transaction recovery, introducing an incentive mechanism for efficient storage utilization. The theoretical analysis and simulation results show that the proposed scheme can achieve a high compression ratio with good service availability. The proposed scheme dramatically lowers the threshold for IIoT devices to join the blockchain network, which is important for the large-scale application of blockchain in Industry 4.0.
A Triple Real-Time Trajectory Privacy Protection Mechanism Based on Edge Computing and Blockchain in Mobile Crowdsourcing

With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5 G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.
A Comprehensive Survey on Topic Modeling in Text Summarization

Topic modeling is the statistical model for discovering hidden topics or keywords in a collection of documents. Topic modeling is also considered a probabilistic model for learning, analyzing, and discovering topics from the document collection. The most popular techniques for topic modeling are latent semantic analysis (LSA), probabilistic latent semantic analysis (pLSA), latent Dirichlet allocation (LDA), and the recent deep learning-based lda2vec. LDA is most commonly used in extractive multi-document summarization to determine whether the extracted sentence reflects the concept of the input document. In this paper, we will try to explore various multi-document summarization techniques that use LDA as a topic modeling method for improving final summary coverage and to reduce redundancy. Finally, we compared LDA and LSA using the Genism toolkit, and our experiment results show that LDA outperforms LSA if we increase the number of features considered for sentence selection.
Access Control and Privacy-Preserving Blockchain-Based System for Diseases Management

In many developing countries, the healthcare sector is facing several challenges, mainly due to the lack of personal, institutions, and medications in public health systems. Over the past decade, information and communication technology has proved its ability to improve medical quality, reduce costs, and promote data security. Developing countries can exploit these technologies to improve the healthcare process and ensure remote health monitoring, especially in rural areas. The Internet of Things and smart medical devices are widely used to provide remote patient monitoring. Current systems are based on centralized communication with cloud servers. However, this architecture increases several security and privacy risks. The adoption of a distributed architecture is required to overcome these issues. In this article, we describe a Blockchain-based system for securing Internet-of-Things (IoT) healthcare devices. In addition to data encryption, we propose to use Blockchain technology to enhance security and privacy in healthcare systems. The system is intended to allow remote patient monitoring, particularly for chronic diseases that necessitate regular monitoring. Three important characteristics were taken into account: security, scalability, and processing time. The security concerns are ensured by using the re-encryption proxy in conjunction with Blockchain to encrypt data and control access to it. To ensure Blockchain scalability, data are stored in an InterPlanetary file system (IPFS) off-chain database. We use an Ethereum Blockchain based on proof of authority (PoA) to speed up the data storage. In comparison to existing methods, the experimental system has shown a significant improvement in the security of healthcare systems.
A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and a systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on two real-life public trajectory datasets to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.
Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments

The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed simulation environments and are hard to test against conventional planning approaches. Furthermore, the integration and deployment of these approaches into real robotic platforms are not yet completely solved. In this paper, we present Arena-bench, a benchmark suite to train, test, and evaluate navigation planners on different robotic platforms within 3D environments. It provides tools to design and generate highly dynamic evaluation worlds, scenarios, and tasks for autonomous navigation and is fully integrated into the robot operating system. To demonstrate the functionalities of our suite, we trained a DRL agent on our platform and compared it against a variety of existing different model-based and learning-based navigation approaches on a variety of relevant metrics. Finally, we deployed the approaches towards real robots and demonstrated the reproducibility of the results. The code is publicly available at github.com/ignc-research/arena-bench.
Power Side-Channel Leakage Assessment Framework at Register-Transfer Level

Power side-channel (PSC) attacks received significant attention over the past two decades due to their effectiveness in breaking mathematically strong cryptographic implementations. However, most existing PSC assessment frameworks apply only to post-silicon implementations; this is unfavorable to the industry due to the lack of flexibility in fixing the design and the high cost/time penalty incurred in redoing the entire design cycle. This article presents the register transfer level (RTL)-power analysis tool (PAT) framework to perform a technology-independent PSC assessment of cryptographic (pre- and post-quantum) hardware at the RTL stage. Performing assessment at the RTL gives designers the utmost flexibility to quickly apply the countermeasures locally. RTL-PAT can also serve as a front-end sign-off framework for PSC leakage, allowing a designer to make changes in the early design stage, which would otherwise be difficult/time-consuming to perform in subsequent design stages. Furthermore, RTL-PAT can analyze both FPGA and ASIC design flows for standalone IPs and SoCs. In this article, we present the efficacy of RTL-PAT on several cryptographic implementations. The results are presented for standalone IPs, which include different AES implementations (Galois field, lookup table, pipelined, and threshold implementation) and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PRESENT</monospace> cipher. We also analyze a large-scale SoC, which includes the post-quantum SABER implementation and AES. The results show that the framework effectively identifies the leaky modules and validates the efficacy of PSC countermeasures implemented in the RTL. The obtained RTL-PAT assessment results are validated with the post-silicon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -statistics assessment as well.
Construct Validity in Software Engineering

Empirical research aims to establish generalizable claims from data. Such claims may involve concepts that must be measured indirectly by using indicators. Construct validity is concerned with whether one can justifiably make claims at the conceptual level that are supported by results at the operational level. We report a quantitative analysis of the awareness of construct validity in the software engineering literature between 2000 and 2019 and a qualitative review of 83 articles about human-centric experiments published in five high-quality journals between 2015 and 2019. Over the two decades, the appearance in the literature of the term construct validity increased sevenfold. Some of the reviewed articles we reviewed employed various ways to ensure that the indicators span the concept in an unbiased manner. We also found articles that reuse formerly validated constructs. However, the articles disagree about how to define construct validity. Several interpret construct validity excessively by including threats to internal, external, or statistical conclusion validity. A few articles also include fundamental challenges of a study, such as cheating and misunderstanding of experiment material. The diversity of topics included as threats to construct validity calls for a more minimalist approach. Based on the review, we propose seven guidelines to improve how construct validity is handled and reported in software engineering.
Domain Adaptation for Semi-Supervised Ship Detection in SAR Images

Current synthetic aperture radar (SAR) ship detectors achieve excellent performance with sufficient samples while encountering degraded results when the sensors and imaging conditions change. The mismatch of view, shape, and illumination inevitably result in the variations of feature distribution between source domain and target domain, which will lead to detection performance degradation. Therefore, devising a detector with well transferability to new domains remains a challenging issue. To this end, this letter proposes a novel domain adaptive YOLOv5 framework for cross-domain SAR ship detection, which is composed of the following keypoints: 1) a cross-domain co- attention feature correlation module, which models spatial and semantic interdependencies by capturing pixel correspondence between source and target domain in a bidirectional way; 2) a multilevel feature alignment module, which constrains the inter-domain difference of features from different scales by inserting three domain classifiers; and 3) teacher–student mutual learning, which makes full use of unlabeled target data and iteratively generates higher-quality pseudo-labels, thus further improving a teacher model with narrowed domain gap. Model performance is evaluated on three SAR ship datasets, and comprehensive results demonstrate the superiority of our method on multiple domain transfer scenarios, i.e., cross resolution, cross-sensor adaptation, and cross-resolution adaptation under the same sensor.
Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things

Cognitive radio-based Internet of Things (CR-IoT) network provides a solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in CR-IoT network, which has been investigated extensively based on deep learning (DL). Despite the unique advantages of DL in spectrum sensing, the black-box and unexplained properties of deep neural networks may lead to many security risks. This article considers the fusion of traditional interference methods and data poisoning which is an attack method on the training data of a machine learning tool. We propose a new adversarial attack for reducing the sensing accuracy in DL-based spectrum sensing systems. We introduce a novel design of jamming waveform whose interference capability is reinforced by data poisoning. Simulation results show that significant performance enhancement and higher mobility can be achieved compared with traditional white-box attack methods.
A Systematic Literature Review on Text Generation Using Deep Neural Network Models

In recent years, significant progress has been made in text generation. The latest text generation models are revolutionizing the domain by generating human-like text. It has gained wide popularity recently in many domains like news, social networks, movie script writing, poetry composition, to name a few. The application of text generation in various fields has resulted in a lot of interest from the scientific community in this area. To the best of our knowledge, there is a lack of extensive review and up-to-date body of knowledge of text generation deep learning models. Therefore, this paper aims to bring together all the relevant work in a systematic mapping study highlighting key contributions from various researchers over the years, focusing on the past, present, and future trends. In this work, we have identified 90 primary studies from 2015 to 2021 employing the PRISMA framework. We also identified research gaps that are further needed to be explored by the research community. In the end, we provide some future directions for researchers and guidelines for practitioners based on the findings of this review.
Self-Supervised Learning for Multimedia Recommendation

Learning representations for multimedia content is critical for multimedia recommendation. Current representation learning methods roughly fall into two groups: (1) using the historical interactions to create ID embeddings of users and items, and (2) treating multi-modal data as the side information of items to enrich their ID embeddings. Each user-item interaction offers the supervisory signal to optimize the representation learning by the traditional supervised learning paradigm. Due to the overlook of the multi-modal patterns (<inline-formula><tex-math notation="LaTeX">$e.g.$</tex-math></inline-formula>, co-occurrence of visual, acoustic, textual features in micro-videos a user saw before, and her behavioral features) hidden in the data, these methods are insufficient to create powerful representations and obtain satisfactory recommendation accuracy. To capture multi-modal patterns in the data itself, we go beyond the supervised learning paradigm, and incorporate the idea of self-supervised learning (SSL) into multimedia recommendation. Specifically, SSL consists of two components: (1) data augmentation upon multi-modal contents, where we design three operators &#x2014; feature dropout (FD), feature masking (FM), feature fine and coarse spaces (FAC) &#x2014; to generate multiple views of individual items; and (2) contrastive learning, which differentiates the views of an item from the others&#x2019; to distill additional supervisory signals. Clearly, SSL enables us to explore and exhibit the underlying relations among modalities, thereby resulting in powerful representations. We denote the generic framework by <i>Self-supervised Learning-guided Multimedia Recommendation</i> (SLMRec). Extensive experiments are performed on three real-world datasets, showing that SLMRec achieves significant improvements over several state-of-the-art baselines like LightGCN [1], MMGCN [2]. Further analysis shows how SSL affects recommendation performance.
Deep Multiple Instance Learning for Automatic Breast Cancer Assessment Using Digital Mammography

Breast cancer causes serious public health problems; it is the most common cancer among women worldwide. Screening and early detection of signs of breast cancer increase the chance of survival. Early diagnosis is a crucial task for radiologists and physicians. Therefore, many Computer-Aided Detection and Diagnosis (CADx) systems are being developed to ensure the survival of radiologists&#x2019; decisions. In this paper, we describe a framework to automate assessment of suspicious regions, detected in screening mammography, without having carried out additional examinations, especially unnecessary biopsies in the case where the suspect regions are benign tumors. The setup of the proposed framework is ordered as follows: Regions of interest (ROIs) have been segmented using a modified K-means algorithm; the Bi-dimensional empirical mode decomposition (BEMD) algorithm is applied to derive many layers (Bi-dimensional Intrinsic Mode Function BIMF) from ROIs. Then, textural features are extracted from the obtained ROIs. First, directly from segmented ROI, second from the ROI and its sub-layers (BIMFs + Residue). The features extracted in the second time have been grouped into a bag descriptive of the ROI under consideration. This bag is the input parameter of the classification algorithm based on the support vector machine (which has been confirmed to be beneficial for the classification of breast cancer). The average obtained sensitivity, specificity, accuracy and AUC rates, are, respectively 98.60%, 98.65%, 98.62%, and 98.23%. Generally, the experimental results in: INbreast, DDSM, and MIAS datasets demonstrate the robustness and the efficiency of the developed framework compared to previous works in the literature and have shown a significant advance.