Reviewerly/exHarmony huggingface dataset:
Network Schema Preserving Heterogeneous Information Network Embedding

As heterogeneous networks have become increasingly ubiquitous, Heterogeneous Information Network (HIN) embedding, aiming to project nodes into a low-dimensional space while preserving the heterogeneous structure, has drawn increasing attention in recent years. Many of the existing HIN embedding methods adopt meta-path guided random walk to retain both the semantics and structural correlations between different types of nodes. However, the selection of meta-paths is still an open problem, which either depends on domain knowledge or is learned from label information. As a uniform blueprint of HIN, the network schema comprehensively embraces the high-order structure and contains rich semantics. In this paper, we make the first attempt to study network schema preserving HIN embedding, and propose a novel model named NSHE. In NSHE, a network schema sampling method is first proposed to generate sub-graphs (i.e., schema instances), and then multi-task learning task is built to preserve the heterogeneous structure of each schema instance. Besides preserving pairwise structure information, NSHE is able to retain high-order structure (i.e., network schema). Extensive experiments on three real-world datasets demonstrate that our proposed model NSHE significantly outperforms the state-of-the-art methods.
Innovation under pressure: Implications for data privacy during the Covid-19 pandemic

The global Covid-19 pandemic has resulted in social and economic disruption unprecedented in the modern era. Many countries have introduced severe measures to contain the virus, including travel restrictions, public event bans, non-essential business closures and remote work policies. While digital technologies help governments and organizations to enforce protection measures, such as contact tracing, their rushed deployment and adoption also raises profound concerns about surveillance, privacy and data protection. This article presents two critical cases on digital surveillance technologies implemented during the Covid-19 pandemic and delineates the privacy implications thereof. We explain the contextual nature of privacy trade-offs during a pandemic and explore how regulatory and technical responses are needed to protect privacy in such circumstances. By providing a multi-disciplinary conversation on the value of privacy and data protection during a global pandemic, this article reflects on the implications digital solutions have for the future and raises the question of whether there is a way to have expedited privacy assessments that could anticipate and help mitigate adverse privacy implications these may have on society.
TarMAC: Targeted Multi-Agent Communication

We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments. This targeting behavior is learnt solely from downstream task-specific reward without any communication supervision. We additionally augment this with a multi-round communication approach where agents coordinate via multiple rounds of communication before taking actions in the environment. We evaluate our approach on a diverse set of cooperative multi-agent tasks, of varying difficulties, with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to 3D indoor environments, and demonstrate the benefits of targeted and multi-round communication. Moreover, we show that the targeted communication strategies learned by agents are interpretable and intuitive. Finally, we show that our architecture can be easily extended to mixed and competitive environments, leading to improved performance and sample complexity over recent state-of-the-art approaches.
Ethics of blockchain

Purpose The purpose of this paper is fourfold: first, to provide the first systematic study on the ethics of blockchain, mapping its main socio-technical challenges in technology and applications; second, to identify ethical issues of blockchain; third, to propose a conceptual framework of blockchain ethics study; fourth, to discuss ethical issues for stakeholders. Design/methodology/approach The paper employs literature research, research agenda and framework development. Findings Ethics of blockchain and its applications is essential for technology adoption. There is a void of research on blockchain ethics. The authors propose a first theoretical framework of blockchain ethics. Research agenda is proposed for future search. Finally, the authors recommend measures for stakeholders to facilitate the ethical adequacy of blockchain implementations and future Information Systems (IS) research directions. This research raises timely awareness and stimulates further debate on the ethics of blockchain in the IS community. Originality/value First, this work provides timely systematic research on blockchain ethics. Second, the authors propose the first research framework of blockchain ethics. Third, the authors identify key research questions of blockchain ethics. Fourth, this study contributes to the understanding of blockchain technology and its societal impacts.
LEARNED STEP SIZE QUANTIZATION

Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy. Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured. Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters. This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.
An Overview of Bag of Words;Importance, Implementation, Applications, and Challenges

In the past fifteen years, the grow of using Bag of Words (BoW) method in the field of computer vision is visibly observed. In addition,-for the text classification and texture recognition, it can also be used in classification of images, videos, robot localization, etc. It is one of the most common methods for the categorization of text and objects. In text classification, the BoW method records the number of occurrences of each bag that is created for each instance type or word disregarding the order of the words or the grammar. And in visual scene classification it is based on clusters of local descriptors which are taken from the images disregarding the order of the clusters. The key idea is to generate a histogram for the words in the documents or the features in the images to represent the specified document or image. The BoW method is computationally and even conceptually is simpler than many other classification methods. For that reason, BoW based systems could record new and higher performance scores on common used benchmarks of text and image classification algorithms. This paper presents an overview of BoW, importance of BoW, how does it work, applications and challenges of using BoW. This study is useful in terms of introducing the BoW method to the new researchers and providing a good background with associated related works to the researchers that are working on the model.
Lightweight Mutual Authentication and Privacy-Preservation Scheme for Intelligent Wearable Devices in Industrial-CPS

Industry 5.0 is the digitalization, automation, and data exchange of industrial processes that involve artificial intelligence, industrial Internet of Things (IIoT), and industrial cyber-physical systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with the emergence of innovative healthcare applications. Similar to any other IIoT paradigm, I-CPS capable healthcare applications face numerous challenging issues. The resource-constrained nature of wearable devices and their inability to support complex security mechanisms provide an ideal platform to malevolent entities for launching attacks. To preserve the privacy of wearable devices and their data in an I-CPS environment, in this article we propose a lightweight mutual authentication scheme. Our scheme is based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities. After mutual authentication, the privacy risk associated with a patient data is predicted using an AI-enabled hidden Markov model. We analyzed the robustness and security of our scheme using Burrows-Abadi-Needham logic. This analysis shows that the use of lightweight security primitives for the exchange of session keys makes the proposed scheme highly resilient in terms of security, efficiency, and robustness. Finally, the proposed scheme incurs nominal overhead in terms of processing, communication and storage and is capable to combat a wide range of adversarial threats.
Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing

Shortcuts to adiabaticity are well-known methods for controlling the quantum dynamics beyond the adiabatic criteria, where counter-diabatic (CD) driving provides a promising means to speed up quantum many-body systems. In this work, we show the applicability of CD driving to enhance the digitized adiabatic quantum computing paradigm in terms of fidelity and total simulation time. We study the state evolution of an Ising spin chain using the digitized version of the standard CD driving and its variants derived from the variational approach. We apply this technique in the preparation of Bell and Greenberger-Horne-Zeilinger states with high fidelity using a very shallow quantum circuit. We implement this proposal in the IBM quantum computer, proving its usefulness for the speed up of adiabatic quantum computing in noisy intermediate-scale quantum devices.
An optimized technique for copy–move forgery localization using statistical features

Copy–Move Forgery Detection (CMFD) helps to detect copied and pasted areas in one image. It plays a crucial role in legal evidence, forensic investigation, defence, and many more places. In the proposed CMFD method, a two-step identification of forgery is presented. In step one, the suspected image will be classified into either one of two classes that are forged or authentic. Step two is carried out​ only if the suspected is classified as forged, then forged location will be identified using the block-matching procedure. Initially, the suspected image is decomposed into different orientations using Steerable Pyramid Transform (SPT); Grey Level Co-occurrence Matrix (GLCM) features are extracted from each orientation. These features are used to train Optimized Support Vector Machine (OSVM) as well as to classify. If the suspected image is categorized into forged, then the suspected grey image is converted into overlapping blocks, and from each block, GLCM features are extracted. The proper similarity threshold value and distance threshold value can locate the forged region using GLCM block features. The performance of the proposed method is tested using standard datasets CoMoFoD and CASIA Datasets. The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation. The OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).
Fuz-Spam: Label Smoothing-Based Fuzzy Detection of Spammers in Internet of Things

Nowadays, online spamming has already been a remarkable threat to contents security of Internet of Things. Due to constant technical progress, online spamming activities have been more and more concealed. This brings much fuzziness to spammer detection scenarios, yielding the issue of fuzzy detection of spammers. Although existing detection techniques for spammers utilized idea of deep learning, they still ignore to release power of label spaces. As real nature about a user may be usually fuzzy, but the label annotated for a user is always certain. To remedy such gap, this article proposes a label smoothing-based fuzzy detection method for spammers (Fuz-Spam). First of all, deep representation is still utilized to deeply fuse features, which acts as the foundation of neural computing. On this basis, generative adversarial learning is introduced to transform previous label spaces into distributed forms. In addition, two groups of experiments are carried out on two real-world datasets for evaluation. The results demonstrate that the Fuz-Spam improves identification efficiency about 10% to 20% than previous ones, and that the Fuz-Spam is endowed with proper stability.
Polarization-based exploration for clear underwater vision in natural illumination

Underwater imaging provides human vision system friendly images; however, it often suffers from severe image degradation. This research developed an underwater polarization imaging model, which considers the water scattering effect, as well as absorption effect. It fully explored the polarization information of the target scene that backscattered light is partially polarized and target light is unpolarized. Then backscattered light is first estimated and removed. The target scene's distance information is derived based upon the polarization information, and then applied to develop a distance-based Lambertian model. This model enables estimation of the intensity loss caused by water absorption and accurate target radiance recovery. Furthermore, real-world experiments show that the developed model handled the underwater image degradation well. In particular, it enables effective color cast correction resulting from water absorption, which traditional imaging methods always ignore.
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds

Deep neural networks are vulnerable to adversarial attacks, in which imperceptible perturbations to their input lead to erroneous network predictions. This phenomenon has been extensively studied in the image domain, and has only recently been extended to 3D point clouds. In this work, we present novel data-driven adversarial attacks against 3D point cloud networks. We aim to address the following problems in current 3D point cloud adversarial attacks: they do not transfer well between different networks, and they are easy to defend against via simple statistical methods. To this extent, we develop a new point cloud attack (dubbed AdvPC) that exploits the input data distribution by adding an adversarial loss, after Auto-Encoder reconstruction, to the objective it optimizes. AdvPC leads to perturbations that are resilient against current defenses, while remaining highly transferable compared to state-of-the-art attacks. We test AdvPC using four popular point cloud networks: PointNet, PointNet++ (MSG and SSG), and DGCNN. Our proposed attack increases the attack success rate by up to 40% for those transferred to unseen networks (transferability), while maintaining a high success rate on the attacked network. AdvPC also increases the ability to break defenses by up to 38% as compared to other baselines on the ModelNet40 dataset.
Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey

In e-commerce websites and related micro-blogs, users supply online reviews expressing their preferences regarding various items. Such reviews are typically in the textual comments form, and account for a valuable information source about user interests. Recently, several works have used review texts and their related rich information like review words, review topics and review sentiments, for improving the rating-based collaborative filtering recommender systems. These works vary from one another on how they exploit the review texts for deriving user interests. This paper provides a detailed survey of recent works that integrate review texts and also discusses how these review texts are exploited for addressing some main issues of standard collaborative filtering algorithms.
Hardware Acceleration of Graph Neural Networks

Graph neural networks (GNNs) have been shown to extend the power of machine learning to problems with graph-structured inputs. Recent research has shown that these algorithms can exceed state-of-the-art performance on applications ranging from molecular inference to community detection. We observe that existing execution platforms (including existing machine learning accelerators) are a poor fit for GNNs due to their unique memory access and data movement requirements. We propose, to the best of our knowledge, the first accelerator architecture targeting GNNs. The architecture includes dedicated hardware units to efficiently execute the irregular data movement required for graph computation in GNNs, while also providing high compute throughput required by GNN models. We show that our architecture outperforms existing execution platforms in terms of inference latency on several key GNN benchmarks (e.g., 7.5x higher performance than GPUs and 18x higher performance than CPUs at iso-bandwidth).
A Proxy Re-Encryption Approach to Secure Data Sharing in the Internet of Things Based on Blockchain

The evolution of the Internet of Things has seen data sharing as one of its most useful applications in cloud computing. As eye-catching as this technology has been, data security remains one of the obstacles it faces since the wrongful use of data leads to several damages. In this article, we propose a proxy re-encryption approach to secure data sharing in cloud environments. Data owners can outsource their encrypted data to the cloud using identity-based encryption, while proxy re-encryption construction will grant legitimate users access to the data. With the Internet of Things devices being resource-constrained, an edge device acts as a proxy server to handle intensive computations. Also, we make use of the features of information-centric networking to deliver cached content in the proxy effectively, thus improving the quality of service and making good use of the network bandwidth. Further, our system model is based on blockchain, a disruptive technology that enables decentralization in data sharing. It mitigates the bottlenecks in centralized systems and achieves fine-grained access control to data. The security analysis and evaluation of our scheme show the promise of our approach in ensuring data confidentiality, integrity, and security.
High-quality Image Restoration Using Low-Rank Patch Regularization and Global Structure Sparsity

In recent years, approaches based on nonlocal self similarity and global structure regularization have led to significant improvements in image restoration. Nonlocal self similarity exploits the repetitiveness of small image patches as a powerful prior in the reconstruction process. Likewise, global structure regularization is based on the principle that the structure of objects in the image is represented by a relatively small portion of pixels. Enforcing this structural information to be sparse can thus reduce the occurrence of reconstruction artifacts. So far, most image restoration approaches have considered one of these two strategies, but not both. This paper presents a novel image restoration method that combines nonlocal self similarity and global structure sparsity in a single efficient model. Group of similar patches are reconstructed simultaneously, via an adaptive regularization technique based on the weighted nuclear norm. Moreover, global structure is preserved using an innovative strategy, which decomposes the image into a smooth component and a sparse residual, the latter regularized using l1 norm. An optimization technique, based on the Alternating Direction Method of Multipliers (ADMM) algorithm, is used to recover corrupted images efficiently. The performance of the proposed method is evaluated on two important image restoration tasks: image completion and super-resolution. Experimental results show our method to outperform state-of-the-art approaches for these tasks, for various types and levels of image corruption.
A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection

Classification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of research in machine learning community. Meta-learning has demonstrated substantial success in solving ASP, especially in the domain of classification. Considerable progress has been made in classification algorithm recommendation and researchers have proposed various methods in literature that tackles ASP in many different ways in meta-learning setup. Yet there is a lack of survey and comparative study that critically analyze, summarize and assess the performance of existing methods. To fill these gaps, in this paper we first present a literature survey of classification algorithm recommendation methods. The survey shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifier selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we critically analyzed and summarized the existing studies from the literature in three important dimensions i.e., meta-features, meta-learner and meta-target. In the second part of this paper, we present extensive comparative evaluation of all the prominent methods for classifier selection based on 17 classification algorithms and 84 benchmark datasets. The comparative study quantitatively assesses the performance of classifier selection methods and highlight the limitations and strengths of meta-features, meta-learners and meta-target in classification algorithm recommendation system. Finally, we conclude this paper by identifying current challenges and suggesting future work directions. We expect that this work will provide baseline and a solid overview of state of the art works in this domain to new researchers, and will steer future research in this direction.