Reviewerly/exHarmony huggingface dataset:
Toward effective mobile encrypted traffic classification through deep learning

Traffic Classification (TC), consisting in how to infer applications generating network traffic, is currently the enabler for valuable profiling information, other than being the workhorse for service differentiation/blocking. Further, TC is fostered by the blooming of mobile (mostly encrypted) traffic volumes, fueled by the huge adoption of hand-held devices. While researchers and network operators still rely on machine learning to pursue accurate inference, we envision Deep Learning (DL) paradigm as the stepping stone toward the design of practical (and effective) mobile traffic classifiers based on automatically-extracted features, able to operate with encrypted traffic, and reflecting complex traffic patterns. In this context, the paper contribution is fourfold. First, it provides a taxonomy of the key network traffic analysis subjects where DL is foreseen as attractive. Secondly, it delves into the non-trivial adoption of DL to mobile TC, surfacing potential gains. Thirdly, to capitalize such gains, it proposes and validates a general framework for DL-based encrypted TC. Two concrete instances originating from our framework are then experimentally evaluated on three mobile datasets of human users’ activity. Lastly, our framework is leveraged to point to future research perspectives.
Learning to Faithfully Rationalize by Construction

In many settings it is important for one to be able to understand why a model made a particular prediction. In NLP this often entails extracting snippets of an input text ‘responsible for’ corresponding model output; when such a snippet comprises tokens that indeed informed the model’s prediction, it is a faithful explanation. In some settings, faithfulness may be critical to ensure transparency. Lei et al. (2016) proposed a model to produce faithful rationales for neural text classification by defining independent snippet extraction and prediction modules. However, the discrete selection over input tokens performed by this method complicates training, leading to high variance and requiring careful hyperparameter tuning. We propose a simpler variant of this approach that provides faithful explanations by construction. In our scheme, named FRESH, arbitrary feature importance scores (e.g., gradients from a trained model) are used to induce binary labels over token inputs, which an extractor can be trained to predict. An independent classifier module is then trained exclusively on snippets provided by the extractor; these snippets thus constitute faithful explanations, even if the classifier is arbitrarily complex. In both automatic and manual evaluations we find that variants of this simple framework yield predictive performance superior to ‘end-to-end’ approaches, while being more general and easier to train. Code is available at https://github.com/successar/FRESH.
Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network

Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have been proposed based on various FHE schemes and approaches. Although FHE schemes are suitable as tools for implementing PPML models, previous PPML models based on FHE, such as CryptoNet, SEALion, and CryptoDL, are limited to simple and nonstandard types of machine learning models; they have not proven to be efficient and accurate with more practical and advanced datasets. Previous PPML schemes replaced non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and did not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could neither use standard activation functions nor employ large numbers of layers. In this work, we first implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and plaintext model parameters. Instead of replacing the non-arithmetic functions with simple arithmetic functions, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as ReLU and Softmax, with sufficient precision. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate an arbitrary deep learning model on encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.43&#x0025; identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 92.43&#x0025;&#x00B1;2.65&#x0025;, which is quite close to that of the original ResNet-20 CNN model (91.89&#x0025;). It takes approximately 3 h for inference on a dual Intel Xeon Platinum 8280 CPU (112 cores) with 172 GB of memory. We believe that this opens the possibility of applying FHE to an advanced deep PPML model.
Influence of Blockchain Technology in Manufacturing Supply Chain and Logistics

Background: Blockchain is a digitally managed, distributed, and decentralized ledger used to record transactions in an immutable format. Its characteristics in providing trust, transparency, and traceability make it attractive for applications where transactions are involved. Originally intended to support financial transactions, the technology has gained attention even in non-financial sectors such as health care, manufacturing, retail, and government services. Methods: For centuries, the various functions of manufacturing industries have worked based on the relationship and trust that they have with their upstream and downstream stakeholders. In addition, as the conventional factories are growing into giant gigafactories, the participation of several intermediaries further complicates the supply chain and logistics operations. Hence, the article aims to provide a comprehensive overview of the role of blockchain technology in addressing supply chain and logistics-related challenges by analyzing, organizing, and reviewing the literature. Results: The study shows that blockchain technology can transform the supply chain and logistics into secure, agile, trusted, and transparent functions. A conceptualized application scenario demonstrates the benefits of blockchain technology in providing provenance and traceability to critical products. Conclusions: In particular, a private or permissioned blockchain is suitable for multi-organizational businesses such as supply chain and logistics. In addition, IoT-blockchain integration, smart contracts, and asset tracking has immense benefits in the future.
Domain Space Transfer Extreme Learning Machine for Domain Adaptation

Extreme learning machine (ELM) has been applied in a wide range of classification and regression problems due to its high accuracy and efficiency. However, ELM can only deal with cases where training and testing data are from identical distribution, while in real world situations, this assumption is often violated. As a result, ELM performs poorly in domain adaptation problems, in which the training data (source domain) and testing data (target domain) are differently distributed but somehow related. In this paper, an ELM-based space learning algorithm, domain space transfer ELM (DST-ELM), is developed to deal with unsupervised domain adaptation problems. To be specific, through DST-ELM, the source and target data are reconstructed in a domain invariant space with target data labels unavailable. Two goals are achieved simultaneously. One is that, the target data are input into an ELM-based feature space learning network, and the output is supposed to approximate the input such that the target domain structural knowledge and the intrinsic discriminative information can be preserved as much as possible. The other one is that, the source data are projected into the same space as the target data and the distribution distance between the two domains is minimized in the space. This unsupervised feature transformation network is followed by an adaptive ELM classifier which is trained from the transferred labeled source samples, and is used for target data label prediction. Moreover, the ELMs in the proposed method, including both the space learning ELM and the classifier, require just a small number of hidden nodes, thus maintaining low computation complexity. Extensive experiments on real-world image and text datasets are conducted and verify that our approach outperforms several existing domain adaptation methods in terms of accuracy while maintaining high efficiency.
Pyramid Constrained Self-Attention Network for Fast Video Salient Object Detection

Spatiotemporal information is essential for video salient object detection (VSOD) due to the highly attractive object motion for human's attention. Previous VSOD methods usually use Long Short-Term Memory (LSTM) or 3D ConvNet (C3D), which can only encode motion information through step-by-step propagation in the temporal domain. Recently, the non-local mechanism is proposed to capture long-range dependencies directly. However, it is not straightforward to apply the non-local mechanism into VSOD, because i) it fails to capture motion cues and tends to learn motion-independent global contexts; ii) its computation and memory costs are prohibitive for video dense prediction tasks such as VSOD. To address the above problems, we design a Constrained Self-Attention (CSA) operation to capture motion cues, based on the prior that objects always move in a continuous trajectory. We group a set of CSA operations in Pyramid structures (PCSA) to capture objects at various scales and speeds. Extensive experimental results demonstrate that our method outperforms previous state-of-the-art methods in both accuracy and speed (110 FPS on a single Titan Xp) on five challenge datasets. Our code is available at https://github.com/guyuchao/PyramidCSA.
A conceptual framework of the service delivery system design for hospitality firms in the (post-)viral world: The role of service robots

This study aims to develop a conceptual framework of the service delivery system design for hospitality firms in the (post-)viral world. Several theoretical approaches such as resource-based view, value chain analysis, stakeholder theory, PESTEL analysis, positioning strategy, and service delivery system design were adopted. The paper identified three service delivery system designs (robotic, human-based, and mixed) and analyses their requirements, advantages, disadvantages, and potential target markets. According to the suggested model, hospitality firms need first to explore the expectations of tourists. Then comes the analysis phase (based on a holistic perspective, and consisting of RBV, Value chain, Stakeholder, and PESTEL analyses), which helps hospitality firms to identify how they should differentiate and position themselves in the market. Following, companies decide on what kind of service delivery system they should offer to their target customers, and position themselves in the market according to the chosen system.
Emergence and control of complex behaviors in driven systems of interacting qubits with dissipation

Abstract Progress in the creation of large-scale, artificial quantum coherent structures demands the investigation of their nonequilibrium dynamics when strong interactions, even between remote parts, are non-perturbative. Analysis of multiparticle quantum correlations in a large system in the presence of decoherence and external driving is especially topical. Still, the scaling behavior of dynamics and related emergent phenomena are not yet well understood. We investigate how the dynamics of a driven system of several quantum elements (e.g., qubits or Rydberg atoms) changes with increasing number of elements. Surprisingly, a two-element system exhibits chaotic behaviors. For larger system sizes, a highly stochastic, far from equilibrium, hyperchaotic regime emerges. Its complexity systematically scales with the size of the system, proportionally to the number of elements. Finally, we demonstrate that these chaotic dynamics can be efficiently controlled by a periodic driving field. The insights provided by our results indicate the possibility of a reduced description for the behavior of a large quantum system in terms of the transitions between its qualitatively different dynamical regimes. These transitions are controlled by a relatively small number of parameters, which may prove useful in the design, characterization, and control of large artificial quantum structures.
Susceptibility to Spear-Phishing Emails

Phishing is fundamental to cyber attacks. This research determined the effect of Internet user age and email content such as weapons of influence (persuasive techniques that attackers can use to lure individuals to fall for an attack) and life domains (a specific topic or aspect of an individual's life that attackers can focus an email on) on spear-phishing (targeted phishing) susceptibility. In total, 100 young and 58 older users received, without their knowledge, daily simulated phishing emails over 21 days. A browser plugin recorded their clicking on links in the emails as an indicator of their susceptibility. Forty-three percent of users fell for the simulated phishing emails, with older women showing the highest susceptibility. While susceptibility in young users declined across the study, susceptibility in older users remained stable. The relative effectiveness of the attacks differed by weapons of influence and life domains with age-group variability. In addition, older compared to young users reported lower susceptibility awareness. These findings support effects of Internet user demographics and email content on susceptibility to phishing and emphasize the need for personalization of the next generation of security solutions.
Self-Attention for Speech Emotion Recognition

Speech Emotion Recognition (SER) has been shown to benefit from many of the recent advances in deep learning, including recurrent based and attention based neural network architectures as well.Nevertheless, performance still falls short of that of humans.In this work, we investigate whether SER could benefit from the self-attention and global windowing of the transformer model.We show on the IEMOCAP database that this is indeed the case.Finally, we investigate whether using the distribution of, possibly conflicting, annotations in the training data, as soft targets could outperform a majority voting.We prove that this performance increases with the agreement level of the annotators.
Dynamic Digital Twin and Federated Learning With Incentives for Air-Ground Networks

The air-ground network provides users with seamless connections and real-time services, while its resource constraint triggers a paradigm shift from machine learning to federated learning. Federated learning enables clients to collaboratively train models without sharing data. Digital twins provide virtual representation of the air-ground networks to reflect the time-varying status, which in combination with federated learning reconcile the conflict between privacy protection and data training in air-ground networks. In this paper, we consider dynamic digital twin and federated learning for air-ground networks where a drone works as the aggregator and the ground clients collaboratively train the model based on the network dynamics captured by digital twins. We design incentives for federated learning based on Stackelberg game, in which the digital twin of the drone acts as the leader to set preferences for clients, and clients as follower choose the global training rounds. Furthermore, considering the varying digital twin deviations and network dynamics, we design a dynamic incentive scheme to adaptively adjust the selection of the optimal clients and their participation level. Numerical results show that the proposed schemes can significantly improve accuracy and energy efficiency.
Early Perceptions of COVID-19 Contact Tracing Apps in German-Speaking Countries: Comparative Mixed Methods Study

Background The main German-speaking countries (Germany, Austria, and Switzerland) have implemented digital contact tracing apps to assist the authorities with COVID-19 containment strategies. Low user rates for these apps can affect contact tracing and, thus, its usefulness in controlling the spread of the novel coronavirus. Objective This study aimed to assess the early perceptions of people living in the German-speaking countries and compare them with the frames portrayed in the newspapers during the first wave of the COVID-19 pandemic. Methods We conducted qualitative interviews with 159 participants of the SolPan project. Of those, 110 participants discussed contact tracing apps and were included in this study. We analyzed articles regarding contact tracing apps from 12 newspapers in the German-speaking countries. Results Study participants perceived and newspaper coverage in all German-speaking countries framed contact tracing apps as governmental surveillance tools and embedded them in a broader context of technological surveillance. Participants identified trust in authorities, respect of individual privacy, voluntariness, and temporary use of contact tracing apps as prerequisites for democratic compatibility. Newspapers commonly referenced the use of such apps in Asian countries, emphasizing the differences in privacy regulation among these countries. Conclusions The uptake of digital contact tracing apps in German-speaking countries may be undermined due to privacy risks that are not compensated by potential benefits and are rooted in a deeper skepticism towards digital tools. When authorities plan to implement new digital tools and practices in the future, they should be very transparent and proactive in communicating their objectives and the role of the technology—and how it differs from other, possibly similar, tools. It is also important to publicly address ethical, legal, and social issues related to such technologies prior to their launch.
Uncertainty Inspired RGB-D Saliency Detection

We propose the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection models treat this task as a point estimation problem by predicting a single saliency map following a deterministic learning pipeline. We argue that, however, the deterministic solution is relatively ill-posed. Inspired by the saliency data labeling process, we propose a generative architecture to achieve probabilistic RGB-D saliency detection which utilizes a latent variable to model the labeling variations. Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution. The generator model is an encoder-decoder saliency network. To infer the latent variable, we introduce two different solutions: i) a Conditional Variational Auto-encoder with an extra encoder to approximate the posterior distribution of the latent variable; and ii) an Alternating Back-Propagation technique, which directly samples the latent variable from the true posterior distribution. Qualitative and quantitative results on six challenging RGB-D benchmark datasets show our approach's superior performance in learning the distribution of saliency maps.
Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition

Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity. In this paper, we proposes a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives: the latent Distribution Mining and the pairwise Uncertainty Estimation. For the former, an auxiliary multi-branch learning framework is introduced to better mine and describe the latent distribution in the label space. For the latter, the pairwise relationship of semantic feature between instances are fully exploited to estimate the ambiguity extent in the instance space. The proposed method is independent to the backbone architectures, and brings no extra burden for inference. The experiments are conducted on the popular real-world benchmarks and the synthetic noisy datasets. Either way, the proposed DMUE stably achieves leading performance.
Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation

Advances in learning and representations have reinvigorated work that connects language to other modalities.A particularly exciting direction is Vision-and-Language Navigation (VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals.Despite recent progress, current research leaves unclear how much of a role language understanding plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions.Here, we highlight shortcomings of current metrics for the Room-to-Room dataset (Anderson et al., 2018b) and propose a new metric, Coverage weighted by Length Score (CLS).We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths.We join existing short paths to form more challenging extended paths to create a new data set, Room-for-Room (R4R).Using R4R and CLS, we show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion.
Smart Contract Security: A Software Lifecycle Perspective

Smart contract security is an emerging research area that deals with security issues arising from the execution of smart contracts in a blockchain system. Generally, a smart contract is a piece of executable code that automatically runs on the blockchain to enforce an agreement preset between parties involved in the transaction. As an innovative technology, smart contracts have been applied in various business areas, such as digital asset exchange, supply chains, crowdfunding, and intellectual property. Unfortunately, many security issues in smart contracts have been reported in the media, often leading to substantial financial losses. These security issues pose new challenges to security research because the execution environment of smart contracts is based on blockchain computing and its decentralized nature of execution. Thus far, many partial solutions have been proposed to address specific aspects of these security issues, and the trend is to develop new methods and tools to automatically detect common security vulnerabilities. However, smart contract security is systematic engineering that should be explored from a global perspective, and a comprehensive study of issues in smart contract security is urgently needed. To this end, we conduct a literature review of smart contract security from a software lifecycle perspective. We first analyze the key features of blockchain that can cause security issues in smart contracts and then summarize the common security vulnerabilities of smart contracts. To address these vulnerabilities, we examine recent advances in smart contract security spanning four development phases: 1) security design; 2) security implementation; 3) testing before deployment; and 4) monitoring and analysis. Finally, we outline emerging challenges and opportunities in smart contract security for blockchain engineers and researchers.