Reviewerly/exHarmony huggingface dataset:
Distributed Ledger and Decentralised Technology Adoption for Smart Digital Transition in Collaborative Enterprise

Digital transformation of Collaborative Enterprise (CE), both in terms of planning and implementation, relies on new business models and innovative technologies. One of such technologies is Distributed Ledger Technology (DLT) which deploys a decentralised approach requiring no need for intermediaries, thereby facilitating decentralised enterprise operations. Accordingly, this article provides a systematic review and theoretical background of the current state and applications of DLTs within CEs by exploring how this disruptive technology can digitally transform CE practices. Grounded on the review of existing literature, a comprehensive characteristics, application, governance, stakeholders, and emerging security threats faced during DLT adoption and mitigation strategies are presented. More importantly, a model is proposed based on the factors that inhibit DLT adoption in CE. Findings from this study provide implications to support smart digital transition in CE for smart enterprise operations.
An efficient differential evolution algorithm based on orthogonal learning and elites local search mechanisms for numerical optimization

Differential evolution (DE) is an efficient stochastic algorithm for solving global numerical optimization problems. To effectively relieve the stagnation and premature convergence problems in DE, this paper presents an efficient DE variant, abbreviated as OLELS-DE, by designing orthogonal learning and elites local search mechanisms. More specifically, the stagnation or premature convergence phenomenon will be detected by monitoring the best individual's update condition during the evolution, then a population diversity estimation technique is utilized to distinguish between these two conditions empirically. To recover the population's evolution vitality according to the classification results, the enhanced orthogonal learning scheme is employed by selecting two different groups of individuals for constructing the orthogonal experimental design procedure. Moreover, the elites local search method is developed by selecting several well-performing elite individuals based on the Gaussian distribution model to further assist the former orthogonal learning mechanism. This scheme is designed to enhance the exploitation ability by searching the regions around elite individuals. The parameters and strategies in above two mechanisms are designed on the expectation of balancing the local exploitation and global exploration capabilities. The optimization performance of proposed OLELS-DE algorithm is evaluated based on 30 benchmark functions from CEC2014 test suite and is compared with eight state-of-the-art DE variants. As it was anticipated, the incorporation of orthogonal learning and elites local search mechanisms helps OLELS-DE have significantly better or at least comparable performance to the adopted DE competitors.
A two-tier Blockchain framework to increase protection and autonomy of smart objects in the IoT

In recent years, the Internet of Things paradigm has become pervasive in everyday life attracting the interest of the research community. Two of the most important challenges to be addressed concern the protection of smart objects and the need to guarantee them a great autonomy. For this purpose, the definition of trust and reputation mechanisms appears crucial. At the same time, several researchers have started to adopt a common distributed ledger, such as a Blockchain, for building advanced solutions in the IoT. However, due to the high dimensionality of this problem, enabling a trust and reputation mechanism by leveraging a Blockchain-based technology could give rise to several performance issues in the IoT. In this paper, we propose a two-tier Blockchain framework to increase the security and autonomy of smart objects in the IoT by implementing a trust-based protection mechanism. In this framework, smart objects are suitably grouped into communities. To reduce the complexity of the solution, the first-tier Blockchain is local and is used only to record probing transactions performed to evaluate the trust of an object in another one of the same community or of a different community. Periodically, after a time window, these transactions are aggregated and the obtained values are stored in the second-tier Blockchain. Specifically, stored values are the reputation of each object inside its community and the trust of each community in the other ones of the framework. In this paper, we describe in detail our framework, its behavior, the security model associated with it and the tests carried out to evaluate its correctness and performance.
Breast Cancer Histopathological Image Classification Using Stochastic Dilated Residual Ghost Model

A new deep learning-based classification model called the Stochastic Dilated Residual Ghost (SDRG) was proposed in this work for categorizing histopathology images of breast cancer. The SDRG model used the proposed Multiscale Stochastic Dilated Convolution (MSDC) model, a ghost unit, stochastic upsampling, and downsampling units to categorize breast cancer accurately. This study addresses four primary issues: first, strain normalization was used to manage color divergence, data augmentation with several factors was used to handle the overfitting. The second challenge is extracting and enhancing tiny and low-level information such as edge, contour, and color accuracy; it is done by the proposed multiscale stochastic and dilation unit. The third contribution is to remove redundant or similar information from the convolution neural network using a ghost unit. According to the assessment findings, the SDRG model scored overall 95.65 percent accuracy rates in categorizing images with a precision of 99.17 percent, superior to state-of-the-art approaches.
Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users

Ethereum is the largest public blockchain by usage. It applies an account-based model, which is inferior to Bitcoin's unspent transaction output model from a privacy perspective. Due to its privacy shortcomings, recently, several privacy-enhancing overlays have been deployed on Ethereum, such as non-custodial, trustless coin mixers and confidential transactions. In our privacy analysis of Ethereum's account-based model, we describe several patterns that characterize only a limited set of users and successfully apply these “quasi-identifiers” in address deanonymization tasks. Using Ethereum Name Service identifiers as ground truth information, we quantitatively compare algorithms in a recent branch of machine learning, the so-called graph representation learning, as well as time-of-day activity and transaction fee based user profiling techniques. As an application, we rigorously assess the privacy guarantees of the Tornado Cash coin mixer by discovering patterns of careless usage to link the mixing parties. To the best of our knowledge, we are the first to propose and implement Ethereum user profiling techniques based on quasi-identifiers.
Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation

Recently, micro-video sharing platforms such as Kuaishou and Tiktok have become a major source of information for people's lives. Thanks to the large traffic volume, short video lifespan and streaming fashion of these services, it has become more and more pressing to improve the existing recommender systems to accommodate these challenges in a cost-effective way. In this paper, we propose a novel concept-aware denoising graph neural network (named Conde) for micro-video recommendation. Conde consists of a three-phase graph convolution process to derive user and micro-video representations: warm-up propagation, graph denoising and preference refinement. A heterogeneous tripartite graph is constructed by connecting user nodes with video nodes, and video nodes with associated concept nodes, extracted from captions and comments of the videos. To address the noisy information in the graph, we introduce a user-oriented graph denoising phase to extract a subgraph which can better reflect the user's preference. Despite the main focus of micro-video recommendation in this paper, we also show that our method can be generalized to other types of tasks. Therefore, we also conduct empirical studies on a well-known public E-commerce dataset. The experimental results suggest that the proposed Conde achieves significantly better recommendation performance than the existing state-of-the-art solutions.
Dissolving the Dichotomies Between Online and Campus-Based Teaching: a Collective Response to The Manifesto for Teaching Online (Bayne et al. 2020)

Abstract This article is a collective response to the 2020 iteration of The Manifesto for Teaching Online . Originally published in 2011 as 20 simple but provocative statements, the aim was, and continues to be, to critically challenge the normalization of education as techno-corporate enterprise and the failure to properly account for digital methods in teaching in Higher Education. The 2020 Manifesto continues in the same critically provocative fashion, and, as the response collected here demonstrates, its publication could not be timelier. Though the Manifesto was written before the Covid-19 pandemic, many of the responses gathered here inevitably reflect on the experiences of moving to digital, distant, online teaching under unprecedented conditions. As these contributions reveal, the challenges were many and varied, ranging from the positive, breakthrough opportunities that digital learning offered to many students, including the disabled, to the problematic, such as poor digital networks and access, and simple digital poverty. Regardless of the nature of each response, taken together, what they show is that The Manifesto for Teaching Online offers welcome insights into and practical advice on how to teach online, and creatively confront the supremacy of face-to-face teaching.
Virtual reality as a promotion tool for small independent stores

Based on flow theory, we examined whether, compared to conventional online stores, 360-degree based virtual reality (VR) videos of a small independent store can enhance customers' store experience and their responses to the store through heightened flow state. Two types of stimuli creating different experiences were used for the experiments. The first stimulus type was a 360-degree VR video experience where respondents experienced the store of a small fashion retailer. The second was a website experience exploring the website of the same store. Results indicate that customers' VR experience (i.e., exploring the physical store in a VR setting), compared to website experience (i.e., exploring the same store's online store), enhanced their flow state, which, in turn, increased interest and visit intention toward the store. The findings suggest that 360-degree VR videos can be an effective customer acquisition tool for small retailers with limited resources because the software and hardware required to operate 360-degree VR videos are inexpensive and less complicated for small companies.
Can We Use Arbitrary Objects to Attack LiDAR Perception in Autonomous Driving?

As an effective way to acquire accurate information about the driving environment, LiDAR perception has been widely adopted in autonomous driving. The state-of-the-art LiDAR perception systems mainly rely on deep neural networks (DNNs) to achieve good performance. However, DNNs have been demonstrated vulnerable to adversarial attacks. Although there are a few works that study adversarial attacks against LiDAR perception systems, these attacks have some limitations in feasibility, flexibility, and stealthiness when being performed in real-world scenarios. In this paper, we investigate an easier way to perform effective adversarial attacks with high flexibility and good stealthiness against LiDAR perception in autonomous driving. Specifically, we propose a novel attack framework based on which the attacker can identify a few adversarial locations in the physical space. By placing arbitrary objects with reflective surface around these locations, the attacker can easily fool the LiDAR perception systems. Extensive experiments are conducted to evaluate the performance of the proposed attack, and the results show that our proposed attack can achieve more than 90% success rate. In addition, our real-world study demonstrates that the proposed attack can be easily performed using only two commercial drones. To the best of our knowledge, this paper presents the first study on the effect of adversarial locations on LiDAR perception models' behaviors, the first investigation on how to attack LiDAR perception systems using arbitrary objects with reflective surface, and the first attack against LiDAR perception systems using commercial drones in physical world. Potential defense strategies are also discussed to mitigate the proposed attacks.
Decentralized Federated Learning Framework for the Neighborhood

The fast-growing trend of Internet of Things (IoT) has provided its users with opportunities to improve user experience such as voice assistants, smart cameras, and home energy management systems. Such smart home applications often require large numbers of diverse training data to accomplish a robust model. As single user may not have enough data to train such a model, users intent to collaboratively train their collected data in order to achieve better performance in such applications, which raise the concern of data privacy protection. Existing approaches for collaborative training need to aggregate data or intermediate model training updates in the cloud to perform load forecasting, which could directly or indirectly cause personal data leakage, alongside with significant communication bandwidth and extra cloud service monetary cost.
VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning

In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, while an untrusted central server aggregates the encrypted models to obtain an updated global model. Prior work has demonstrated how to verify the correctness of aggregation in such a setting. However, such verification relies on strong assumptions, such as a trusted setup among all users under unreliable network conditions, or it suffers from expensive cryptographic operations, such as bilinear pairing. In this paper, we scrutinize the verification mechanism of prior work and propose a model recovery attack, demonstrating that most local models can be leaked within a reasonable time (e.g., <inline-formula><tex-math notation="LaTeX">$98\%$</tex-math></inline-formula> of encrypted local models are recovered within 21 h). Then, we propose <small>VerSA</small> , a verifiable secure aggregation protocol for cross-device federated learning. <small>VerSA</small> does not require any trusted setup for verification between users while minimizing the verification cost by enabling both the central server and users to utilize only a lightweight pseudorandom generator to prove and verify the correctness of model aggregation. We experimentally confirm the efficiency of <small>VerSA</small> under diverse datasets, demonstrating that <small>VerSA</small> is orders of magnitude faster than verification in prior work.
Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.
Integrating Part-Object Relationship and Contrast for Camouflaged Object Detection

Object detectors that solely rely on image contrast are struggling to detect camouflaged objects in images because of the high similarity between camouflaged objects and their surroundings. To address this issue, in this paper, we investigate the role of the part-object relationship for camouflaged object detection. Specifically, we propose a Part-Object relationship and Contrast Integrated Network (POCINet) covering both search and identification stages, where each stage adopts an appropriate scheme to engage the contrast information and part-object relational knowledge for camouflaged pattern decoding. Besides, we bridge these two stages via a Search-to-Identification Guidance (SIG) module, in which the search result, as well as decoded semantic knowledge, jointly enhances the features encoding ability of the identification stage. Experimental results demonstrate the superiority of our algorithm on three datasets. Notably, our algorithm raises Fβ of the best existing method by approximately 17 points on the CPD1K dataset. The source code will be released soon.
Joint Unsupervised and Supervised Training for Multilingual ASR

Self-supervised training has shown promising gains in pretraining models and facilitating the downstream finetuning for speech recognition, like multilingual ASR. Most existing methods adopt a 2-stage scheme where the self-supervised loss is optimized in the first pretraining stage, and the standard supervised finetuning resumes in the second stage. In this paper, we propose an end-to-end (E2E) Joint Unsupervised and Supervised Training (JUST) method to combine the supervised RNN-T loss and the self-supervised contrastive and masked language modeling (MLM) losses. We validate its performance on the public dataset Multilingual LibriSpeech (MLS), which includes 8 languages and is extremely imbalanced. On MLS, we explore (1) JUST trained from scratch, and (2) JUST finetuned from a pretrained checkpoint. Experiments show that JUST can consistently outperform other existing state-of-the-art methods, and beat the monolingual baseline by a significant margin, demonstrating JUST’s capability of handling low-resource languages in multilingual ASR. Our average WER of all languages outperforms average monolingual baseline by 33.3%, and the state-of-the-art 2-stage XLSR by 32%. On low-resource languages like Polish, our WER is less than half of the monolingual baseline and even beats the supervised transfer learning method which uses external supervision.
Cross-Modal Hybrid Feature Fusion for Image-Sentence Matching

Image-sentence matching is a challenging task in the field of language and vision, which aims at measuring the similarities between images and sentence descriptions. Most existing methods independently map the global features of images and sentences into a common space to calculate the image-sentence similarity. However, the image-sentence similarity obtained by these methods may be coarse as (1) an intermediate common space is introduced to implicitly match the heterogeneous features of images and sentences in a global level, and (2) only the inter-modality relations of images and sentences are captured while the intra-modality relations are ignored. To overcome the limitations, we propose a novel Cross-Modal Hybrid Feature Fusion (CMHF) framework for directly learning the image-sentence similarity by fusing multimodal features with inter- and intra-modality relations incorporated. It can robustly capture the high-level interactions between visual regions in images and words in sentences, where flexible attention mechanisms are utilized to generate effective attention flows within and across the modalities of images and sentences. A structured objective with ranking loss constraint is formed in CMHF to learn the image-sentence similarity based on the fused fine-grained features of different modalities bypassing the usage of intermediate common space. Extensive experiments and comprehensive analysis performed on two widely used datasets—Microsoft COCO and Flickr30K—show the effectiveness of the hybrid feature fusion framework in CMHF, in which the state-of-the-art matching performance is achieved by our proposed CMHF method.
Literature Review of Deep Network Compression

Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focus on reducing the complexity of models while maintaining their powerful performance. In this paper, we present an overview of popular methods and review recent works on compressing and accelerating deep neural networks. We consider not only pruning methods but also quantization methods, and low-rank factorization methods. This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings.
A Novel Post-Quantum Blind Signature for Log System in Blockchain

In recent decades, log system management has been widely studied for data security management.System abnormalities or illegal operations can be found in time by analyzing the log and provide evidence for intrusions.In order to ensure the integrity of the log in the current system, many researchers have designed it based on blockchain.However, the emerging blockchain is facing significant security challenges with the increment of quantum computers.An attacker equipped with a quantum computer can extract the user's private key from the public key to generate a forged signature, destroy the structure of the blockchain, and threaten the security of the log system.Thus, blind signature on the lattice in post-quantum blockchain brings new security features for log systems.In our paper, to address these, firstly, we propose a novel log system based on post-quantum blockchain that can resist quantum computing attacks.Secondly, we utilize a post-quantum blind signature on the lattice to ensure both security and blindness of log system, which makes the privacy of log information to a large extent.Lastly, we enhance the security level of lattice-based blind signature under the random oracle model, and the signature size grows slowly compared with others.We also implement our protocol and conduct an extensive analysis to prove the ideas.The results show that our scheme signature size edges up subtly compared with others with the improvement of security level.