Reviewerly/exHarmony huggingface dataset:
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the key source of user-item interaction data; 2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field.
Cybersecurity awareness in the context of the Industrial Internet of Things: A systematic literature review

Cybersecurity is one of the main challenges faced by companies in the context of the Industrial Internet of Things (IIoT), in which a number of smart devices associated with machines, computers and people are networked and communicate with each other. In this connected industrial scenario, personnel need to be aware of cybersecurity issues in order to prevent or minimise the occurrence of cybersecurity incidents and corporate data breaches, and thus to make companies resilient to cyber-attacks. In addition, the recent increase in smart working due to the COVID-19 pandemic means that the need for cybersecurity awareness is more relevant than ever. In this study, we carry out a systematic literature review in order to analyse how the existing state of the art deals with cybersecurity awareness in the context of IIoT, and to provide a comprehensive overview of this topic. Four areas of analysis are considered: (i) definitions of the concepts of cybersecurity awareness and information security awareness, with keyword extrapolation (e.g. cybersecurity control level, information and responsibility); (ii) the industrial context of the analysed studies (e.g. manufacturing, critical infrastructure); (iii) the techniques adopted to raise company awareness of cybersecurity (e.g. serious games, online questionnaires); and (iv) the main benefits of a large-scale campaign of cybersecurity awareness (e.g. the effectiveness of employees in terms of managing cybersecurity issues, identification of cyber-attacks). Practitioners and researchers can benefit from our analysis of the features of each area in their future research and applications.
Invisible Backdoor Attack with Sample-Specific Triggers

Recently, backdoor attacks pose a new security threat to the training process of deep neural networks (DNNs). Attackers intend to inject hidden backdoors into DNNs, such that the attacked model performs well on benign samples, whereas its prediction will be maliciously changed if hidden backdoors are activated by the attacker-defined trigger. Existing backdoor attacks usually adopt the setting that triggers are sample-agnostic, i.e., different poisoned samples contain the same trigger, resulting in that the attacks could be easily mitigated by current backdoor defenses. In this work, we explore a novel attack paradigm, where backdoor triggers are sample-specific. In our attack, we only need to modify certain training samples with invisible perturbation, while not need to manipulate other training components (e.g., training loss, and model structure) as required in many existing attacks. Specifically, inspired by the recent advance in DNN-based image steganography, we generate sample-specific invisible additive noises as backdoor triggers by encoding an attacker-specified string into benign images through an encoder-decoder network. The mapping from the string to the target label will be generated when DNNs are trained on the poisoned dataset. Extensive experiments on benchmark datasets verify the effectiveness of our method in attacking models with or without defenses. The code will be available at https://github.com/yuezunli/ISSBA.
Parametric Contrastive Learning

In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our PaCo loss under a balanced setting. Our analysis demonstrates that PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist 2018 manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models trained with PaCo loss surpass supervised contrastive learning across various ResNet backbones, e.g., our ResNet-200 achieves 81.8% top-1 accuracy. Our code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.
Understanding Cryptocurrencies*

Abstract Cryptocurrency refers to a type of digital asset that uses distributed ledger, or blockchain, technology to enable a secure transaction. Although the technology is widely misunderstood, many central banks are considering launching their own national cryptocurrency. In contrast to most data in financial economics, detailed data on the history of every transaction in the cryptocurrency complex are freely available. Furthermore, empirically oriented research is only now beginning, presenting an extraordinary research opportunity for academia. We provide some insights into the mechanics of cryptocurrencies, describing summary statistics and focusing on potential future research avenues in financial economics.
CTNet: Context-Based Tandem Network for Semantic Segmentation

Contextual information has been shown to be powerful for semantic segmentation. This work proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the spatial contextual information and the channel contextual information, which can discover the semantic context for semantic segmentation. Specifically, the Spatial Contextual Module (SCM) is leveraged to uncover the spatial contextual dependency between pixels by exploring the correlation between pixels and categories. Meanwhile, the Channel Contextual Module (CCM) is introduced to learn the semantic features including the semantic feature maps and class-specific features by modeling the long-term semantic dependence between channels. The learned semantic features are utilized as the prior knowledge to guide the learning of SCM, which can make SCM obtain more accurate long-range spatial dependency. Finally, to further improve the performance of the learned representations for semantic segmentation, the results of the two context modules are adaptively integrated to achieve better results. Extensive experiments are conducted on four widely-used datasets, i.e., PASCAL-Context, Cityscapes, ADE20K and PASCAL VOC2012. The results demonstrate the superior performance of the proposed CTNet by comparison with several state-of-the-art methods. The source code and models are available at https://github.com/syp2ysy/CTNet.
Classifying With Adaptive Hyper-Spheres: An Incremental Classifier Based on Competitive Learning

Nowadays, datasets are always dynamic and patterns in them are changing. Instances with different labels are intertwined and often linearly inseparable, which bring new challenges to traditional learning algorithms. This paper proposes adaptive hyper-sphere (AdaHS), an adaptive incremental classifier, and its kernelized version: Nys-AdaHS. The classifier incorporates competitive training with a border zone. With adaptive hidden layer and tunable radii of hyper-spheres, AdaHS has strong capability of local learning like instance-based algorithms, but free from slow searching speed and excessive memory consumption. The experiments showed that AdaHS is robust, adaptive, and highly accurate. It is especially suitable for dynamic data in which patterns are changing, decision borders are complicated, and instances with the same label can be spherically clustered.
Importance-Aware Semantic Segmentation for Autonomous Vehicles

Semantic segmentation (SS) partitions an image into several coherent semantically meaningful parts and classifies each part into one of the pre-determined classes. In this paper, we argue that the existing SS methods cannot be reliably applied to autonomous driving system as they ignore the different importance levels of distinct classes for safe driving. For example, pedestrian, car, and bicyclist in the scene are much more important than sky and building when driving a car, so their segmentations should be as accurate as possible. To incorporate the importance information possessed by various object classes, this paper designs an “importance-aware loss” (IAL) that specifically emphasizes the critical objects for autonomous driving. The IAL operates under a hierarchical structure and the classes with different importance are located in different levels so that they are assigned distinct weights. Furthermore, we derive the forward and backward propagation rules for IAL and apply them to four typical deep neural networks for realizing SS in an intelligent driving system. The experiments on CamVid and Cityscapes data sets reveal that, by employing the proposed loss function, the existing deep learning models, including FCN, SegNet, ENet, and ERFNet, are able to consistently obtain the improved segmentation results on the pre-defined important classes for safe driving.
A ranking-based feature selection approach for handwritten character recognition

Feature selection is generally considered a very important step in any pattern recognition process. Its aim is that of reducing the computational cost of the classification task, in an attempt to increase, or not to reduce, the classification performance. In the framework of handwriting recognition, the large variability of the handwriting of different writers makes the selection of appropriate feature sets even more complex and have been widely investigated. Although promising, the results achieved so far present several limitations, that include, among others, the computational complexity, the dependence on the adopted classifiers and the difficulty in evaluating the interactions among features. In this study, we tried to overcome some of the above drawbacks by adopting a feature-ranking-based technique: we considered different univariate measures to produce a feature ranking and we proposed a greedy search approach for choosing the feature subset able to maximize the classification results. In the experiments, we considered one of the most effective and widely used set of features in handwriting recognition to verify whether our approach allows us to obtain good classification results by selecting a reduced set of features. The experimental results, obtained by using standard real word databases of handwritten characters, confirmed the effectiveness of our proposal.
Cyber-Dependent Crimes: An Interdisciplinary Review

Online crime has increased in severity and frequency over the past two decades. However, although several scientific disciplines have commonly employed criminological theories to explain this phenomenon, mainstream criminology has devoted relatively scant attention to the investigation of cyber-criminals and their victims. Drawing on this assumption that more criminological attention should be given to this important type of crime, this article presents an interdisciplinary review of the current state of research on cyber-dependent crimes (i.e., crimes that require the use of computer technology to exist, such as hacking). We begin with a brief discussion of the ecosystem of cyber-dependent crimes and the key actors who operate within it, including the online offenders and enablers, targets and victims, and guardians. Next, we review empirical scholarship that pertains to each actor while distinguishing between nontheoretical research and theoretically driven studies. We then detail methodological and theoretical avenues that should be pursued by future research and discuss why criminological research should lead policy initiatives and guide the design of technical tools that improve the scientific community's ability to generate a safer and more secure cyber-environment. We conclude by discussing potential ways in which cyber-dependent crime research could pave the way for the advancement of mainstream criminological theory and research.
Learning Fuzzy Cognitive Maps with modified asexual reproduction optimisation algorithm

This paper present a comparison between Fuzzy Cognitive Map (FCM) learning approaches and algorithms. FCMs are fuzzy digraphs with weights and feedback loops, consisting of nodes interconnected through directed arcs mostly used for knowledge representation and system modelling. One of the most important characteristics of FCMs is their learning capabilities. FCMs are normally constructed through experts' opinions, thus they maybe subjective. Learning algorithms are introduced to overcome this inconvenient. One of the main problem of the new proposed algorithms is their validation. Using theoretical and experimental analysis, this research aims to (1) compare FCM learning algorithms proposed in the literature, (2) provide a validation tool for new FCM learning algorithms (3) present a new algorithm called Asexual Reproduction Optimisation (ARO) with one of its extensions – Modified ARO (MARO) – as a novel FCM learning algorithm to use the validation tool proposed. According to the findings from the literature, it seems that among FCM learning approaches, population-based algorithms perform better compared to other algorithms. Also, the testing was done in five benchmark datasets and a synthetic dataset with different node sizes using two criteria of in-sample and out-of-sample errors. The results show that MARO outperforms other algorithms in both error functions in terms accuracy and robustness.
Port Contention for Fun and Profit

Simultaneous Multithreading (SMT) architectures are attractive targets for side-channel enabled attackers, with their inherently broader attack surface that exposes more per physical core microarchitecture components than cross-core attacks. In this work, we explore SMT execution engine sharing as a side-channel leakage source. We target ports to stacks of execution units to create a high-resolution timing side-channel due to port contention, inherently stealthy since it does not depend on the memory subsystem like other cache or TLB based attacks. Implementing our channel on Intel Skylake and Kaby Lake architectures featuring Hyper-Threading, we mount an end-to-end attack that recovers a P-384 private key from an OpenSSL-powered TLS server using a small number of repeated TLS handshake attempts. Furthermore, we show that traces targeting shared libraries, static builds, and SGX enclaves are essentially identical, hence our channel has wide target application.
Improving Visual Behavior Research in Communication Science: An Overview, Review, and Reporting Recommendations for Using Eye-Tracking Methods

Eye tracking offers researchers an opportunity to collect an objective assessment of visual behavior. Visual behavior—referring broadly to metrics and measures of gaze positioning and movement—has been used to assess variables including exposure time, cognitive processing, prominence, and (visual) attention. Over the past decade, communication science researchers have increased their use of eye-tracking methods in published articles. During that same period, technological innovations have made eye-tracking units more affordable and accessible to interested researchers increasing the likelihood that eye-tracking research will continue to increase in the field. In this article, we provide information on eye tracking in hopes of improving the quality and reporting of eye-tracking research in communication. The article first provides an overview of basic eye-tracking information followed by a systematic review on the reporting of eye-tracking methods in communication-relevant research. We then provide eye-tracking research reporting recommendations and some ideas about how eye tracking might be integrated into scholarly work moving forward with the aim of improving the transparency and replicability of eye-tracking research in communication science.
Data preprocessing in predictive data mining

Abstract A large variety of issues influence the success of data mining on a given problem. Two primary and important issues are the representation and the quality of the dataset. Specifically, if much redundant and unrelated or noisy and unreliable information is presented, then knowledge discovery becomes a very difficult problem. It is well-known that data preparation steps require significant processing time in machine learning tasks. It would be very helpful and quite useful if there were various preprocessing algorithms with the same reliable and effective performance across all datasets, but this is impossible. To this end, we present the most well-known and widely used up-to-date algorithms for each step of data preprocessing in the framework of predictive data mining.
Trajectory Planning and Tracking for Autonomous Vehicle Based on State Lattice and Model Predictive Control

Trajectory planning and tracking control are two keys of collision avoidance for autonomous vehicles in critical traffic scenarios. It requires not only the system functionality, but also strong real-time. In this paper, we integrated trajectory planner and tracking controller for autonomous vehicle to implement trace planning and tracking for obstacle avoidance. The trajectory planner is based on the state lattice approach and the tracking controller is designed based on the model predictive control using the vehicle kinematics model. The simulation shows that the planner can generate smooth trajectories which could be selected as references for the controller. The maximum tracking error is less than 0.2 m when the vehicle speed is below 50 km/h. Additionally, the on field test shows that the test vehicle with this method is capable of following the reference path accurately, even at sharp corners.
SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

Automatic text summarization attempts to provide an effective solution to today’s unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL is used to achieve sentence semantic parsing whose word tokens are represented as a vector of weighted Wikipedia concepts using ESA method. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC 2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. On the other hand, the testing also demonstrates the scalability of the system, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectorial semantic representation when combined with the crowd-sourced knowledge base in Wikipedia.
A GIS-based high spatial resolution assessment of large-scale PV generation potential in China

The achievement of temperature control target requires a low carbon transition of global energy structure. While China is actively promoting the implementation of large-scale PV generation technology, there is still a lack of scientific knowledge of the generation potential in China. To address this deficiency, this study builds a GIS-based model with 600 land conversion factors incorporated to accurately estimate the large-scale PV power generation potential in China. The results show a potential installed capacity of 1.41 × 105 GW, corresponding to an annual power generation of 1.38874 × 1014 kWh or 21.4 times national electricity consumption in China 2016. If this potential were fully realized as a replacement for current fossil fuel-based power generation in China 2030, a reduction in China’s carbon intensity of 63–68% compared to 2005 would result, which is sufficient to meet China’s CO2 emission reduction commitment. On a provincial level, while generation potential in Northwest and Inner Mongolia together account for 86% of the total, China's eight economically developed coastal provinces only account for 1%. To achieve a maximum large-scale PV scenario in China 2030, the capacity of inter-regional transmission grids from Northwest region and Inner Mongolia to the regions with insufficient potential should reach an approximate 300 GW. Our study could provide decision-makers with the precise information on large-scale PV power generation map of China, and optimizing low carbon strategies and inter-regional power transmission for achieving sustainable development.