Reviewerly/exHarmony huggingface dataset:
Splitting Vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation

In this paper we focus on the task of weakly-supervised semantic segmentation supervised with image-level labels. Since the pixel-level annotation is not available in the training process, we rely on region mining models to estimate the pseudo-masks from the image-level labels. Thus, in order to improve the final segmentation results, we aim to train a region-mining model which could accurately and completely highlight the target object regions for generating high-quality pseudo-masks. However, the region mining models are likely to only highlight the most discriminative regions instead of the entire objects. In this paper, we aim to tackle this problem from a novel perspective of optimization process. We propose a Splitting vs. Merging optimization strategy, which is mainly composed of the Discrepancy loss and the Intersection loss. The proposed Discrepancy loss aims at mining out regions of different spatial patterns instead of only the most discriminative region, which leads to the splitting effect. The Intersection loss aims at mining the common regions of the different maps, which leads to the merging effect. Our Splitting vs. Merging strategy helps to expand the output heatmap of the region mining model to the object scale. Finally, by training the segmentation model with the masks generated by our Splitting vs Merging strategy, we achieve the state-of-the-art weakly-supervised segmentation results on the Pascal VOC 2012 benchmark.
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking

Commit messages record code changes (e.g., feature modifications and bug repairs) in natural language, and are useful for program comprehension. Due to the frequent updates of software and time cost, developers are generally unmotivated to write commit messages for code changes. Therefore, automating the message writing process is necessitated. Previous studies on commit message generation have been benefited from generation models or retrieval models, but the code structure of changed code, i.e., AST, which can be important for capturing code semantics, has not been explicitly involved. Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models. In this paper, we propose a novel commit message generation model, named ATOM, which explicitly incorporates the abstract syntax tree for representing code changes and integrates both retrieved and generated messages through hybrid ranking. Specifically, the hybrid ranking module can prioritize the most accurate message from both retrieved and generated messages regarding one code change. We evaluate the proposed model ATOM on our dataset crawled from 56 popular Java repositories. Experimental results demonstrate that ATOM increases the state-of-the-art models by 30.72 percent in terms of BLEU-4 (an accuracy measure that is widely used to evaluate text generation systems). Qualitative analysis also demonstrates the effectiveness of ATOM in generating accurate code commit messages.
Personalized Review Recommendation based on Users’ Aspect Sentiment

Product reviews play an important role in guiding users’ purchase decision-making in e-commerce platforms. However, it is challenging for users to find helpful reviews that meet their preferences and experiences among an overwhelming amount of reviews. Some works have been done to recommend helpful reviews to users, either from personalized or non-personalized views. While some existing models recommend similar users’ reviews for a target user, they either neglect the target user’s aspect preferences or the user-product interactions for measuring user similarity. Moreover, those models predict review helpfulness at the review-level (a review is taken as a whole); few of them consider the aspect-level. To address the above issues, we propose an aspect sentiment similarity-based personalized review recommendation model ( A2SPR ), which quantifies review helpfulness and recommends reviews that are customized for each individual. We analyze users’ aspect preferences from reviews and improve user similarity with users’ fine-grained sentiment and product relevance. Furthermore, we redefine the review helpfulness score at the aspect level, which indicates the review’s reference value for users’ purchase decisions. Finally, we recommend the top k helpful reviews for individuals based on the review helpfulness score. To validate the performance of the proposed model, eight baselines are developed and compared. Experimental results show that our model performs better than those baselines in both the coverage and precision.
Chip-Integrated Voltage Sources for Control of Trapped Ions

Quantum computers based on trapped ions have approached the point of executing interesting near-term algorithms, but scaling beyond tens of ion qubits will require either increasingly large experiments or integration of control technology. This article presents the design and implementation of a cryogenically compatible dc voltage source incorporated into the substrate beneath the electrodes of an ion trap. An analog switch enables the integrated circuit to reach noise levels similar to those in much larger commercial electronics. These results herald the application of similar designs in future experiments focused on creating scalable or deployable quantum systems.
Research Paper Classification using Supervised Machine Learning Techniques

In this work, different Machine Learning (ML) techniques are used and evaluated based on their performance of classifying peer reviewed published content. The ultimate objective is to extract meaningful information from published abstracts. In pursuing this objective, the ML techniques are utilized to classify different publications into three fields: Science, Business, and Social Science. The ML techniques applied in this work are Support Vector Machines, Naïve Bayes, K-Nearest Neighbor, and Decision Tree. In addition to the description of the utilized ML algorithms, the methodology and algorithms for text recognition using the aforementioned ML techniques are provided. The comparative study based on four different performance measures suggests that - with the exception of Decision Tree algorithm - the proposed ML techniques with the detailed pre-processing algorithms work well for classifying publications into categories based on the text provided in the abstract.
On the order problem in construction of unitary operators for the variational quantum eigensolver

We propose an approach based on the Lie algebra – Lie group connection that reduces the order dependence in unitary transformations used in quantum computing.
Resource theory of asymmetric distinguishability

This paper systematically develops the resource theory of asymmetric distinguishability, as initiated roughly a decade ago [K. Matsumoto, arXiv:1010.1030 (2010)]. The key constituents of this resource theory are quantum boxes, consisting of a pair of quantum states, which can be manipulated for free by means of an arbitrary quantum channel. We introduce bits of asymmetric distinguishability as the basic currency in this resource theory, and we prove that it is a reversible resource theory in the asymptotic limit, with the quantum relative entropy being the fundamental rate of resource interconversion. The distillable distinguishability is the optimal rate at which a quantum box consisting of independent and identically distributed (i.i.d.) states can be converted to bits of asymmetric distinguishability, and the distinguishability cost is the optimal rate for the reverse transformation. Both of these quantities are equal to the quantum relative entropy. The exact one-shot distillable distinguishability is equal to the min-relative entropy, and the exact one-shot distinguishability cost is equal to the max-relative entropy. Generalizing these results, the approximate one-shot distillable distinguishability is equal to the smooth min-relative entropy, and the approximate one-shot distinguishability cost is equal to the smooth max-relative entropy. As a notable application of the former results, we prove that the optimal rate of asymptotic conversion from a pair of i.i.d. quantum states to another pair of i.i.d. quantum states is fully characterized by the ratio of their quantum relative entropies.
BINet: Multi-perspective business process anomaly classification

In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level but also on event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets.
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice

In this paper, we study the formalism of unsupervised multi-class domain adaptation (multi-class UDA), which underlies a few recent algorithms whose learning objectives are only motivated empirically. Multi-Class Scoring Disagreement (MCSD) divergence is presented by aggregating the absolute margin violations in multi-class classification, and this proposed MCSD is able to fully characterize the relations between any pair of multi-class scoring hypotheses. By using MCSD as a measure of domain distance, we develop a new domain adaptation bound for multi-class UDA; its data-dependent, probably approximately correct bound is also developed that naturally suggests adversarial learning objectives to align conditional feature distributions across source and target domains. Consequently, an algorithmic framework of Multi-class Domain-adversarial learning Networks (McDalNets) is developed, and its different instantiations via surrogate learning objectives either coincide with or resemble a few recently popular methods, thus (partially) underscoring their practical effectiveness. Based on our identical theory for multi-class UDA, we also introduce a new algorithm of Domain-Symmetric Networks (SymmNets), which is featured by a novel adversarial strategy of domain confusion and discrimination. SymmNets affords simple extensions that work equally well under the problem settings of either closed set, partial, or open set UDA. We conduct careful empirical studies to compare different algorithms of McDalNets and our newly introduced SymmNets. Experiments verify our theoretical analysis and show the efficacy of our proposed SymmNets. In addition, we have made our implementation code publicly available.
An improved hybrid modeling method based on extreme learning machine for gas turbine engine

Engine model plays a critical role in many applications of gas turbine engine, especially for fault tolerant control and gas-path prognostic and health management. However, traditional pure physics-based modeling method has difficulty in dealing with engine-to-model mismatch, sensor measurement uncertainty, etc., leading to a big limitation in real applications. In this paper, a novel improved hybrid modeling (IHM) method is proposed to accurately model a dual spool turbofan gas turbine engine. The proposed IHM method is innovatively formulated by combining a nonlinear physics-based model (PBM), a series of residual learning models (RLMs) and an input selection module for each RLM. The PBM is constructed based on component level method while the RLMs are constructed based on extreme learning machine (ELM). Particularly, the parameters applied to train the RLMs are selected independently in a customized way based on the input selection module. The performance of the proposed method is illustrated through a series of simulations considering engine-to-model mismatch, sensor measurement uncertainty and verified through a group of ground test data. The results demonstrate that the proposed IHM method can improve modeling accuracy effectively compared with PBM. In addition, through incorporating input selection module, the RLMs in IHM have better performance than using traditional input selection way. Meanwhile, when compared to other networks, the ELM-based RLM guarantees faster training speed while achieving better residual learning effect, and shows more effectiveness and superiority.
From distributed machine learning to federated learning: In the view of data privacy and security

Summary Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micromanaging the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a submodel locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multiparty computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.
GATCluster: Self-supervised Gaussian-Attention Network for Image Clustering

We propose a self-supervised Gaussian ATtention network for image Clustering (GATCluster). Rather than extracting intermediate features first and then performing traditional clustering algorithms, GATCluster directly outputs semantic cluster labels without further post-processing. We give a Label Feature Theorem to guarantee that the learned features are one-hot encoded vectors and the trivial solutions are avoided. Based on this theorem, we design four self-learning tasks with the constraints of transformation invariance, separability maximization, entropy analysis, and attention mapping. Specifically, the transformation invariance and separability maximization tasks learn the relations between samples. The entropy analysis task aims to avoid trivial solutions. To capture the object-oriented semantics, we design a self-supervised attention mechanism that includes a Gaussian attention module and a soft-attention loss. Moreover, we design a two-step learning algorithm that is memory-efficient for clustering large-size images. Extensive experiments demonstrate the superiority of our proposed method in comparison with the state-of-the-art image clustering benchmarks.
When Wireless Communication Responds to COVID-19: Combating the Pandemic and Saving the Economy

The year 2020 is witnessing a global health and economic crisis due to the COVID-19 pandemic. Countries across the world are using digital technologies to fight this global crisis. These digital technologies strongly rely, in one way or another, on the availability of wireless communication systems. This paper aims to outline the role of wireless communications in the COVID-19 pandemic from multiple perspectives. First, we show how wireless communication technologies are helping to combat this pandemic by monitoring the spread of the virus, enabling healthcare automation, and enabling virtual education and conferencing. We emphasize the importance of digital inclusiveness in the pandemic and possible solutions to connect the unconnected. Next, we discuss the challenges posed by the use of wireless technologies, including concerns about privacy, security, and misinformation. Later, we highlight the importance of wireless technologies in the survival of the global economy, such as automation of industries and supply chain, e-commerce, and supporting occupations that are at risk. Finally, we outline that the rapid development of wireless technologies during the pandemic is likely to be useful in the post-pandemic era.
TrafficSliver: Fighting Website Fingerprinting Attacks with Traffic Splitting

Website fingerprinting (WFP) aims to infer information about the content of encrypted and anonymized connections by observing patterns of data flows based on the size and direction of packets. By collecting traffic traces at a malicious Tor entry node --- one of the weakest adversaries in the attacker model of Tor --- a passive eavesdropper can leverage the captured meta-data to reveal the websites visited by a Tor user. As recently shown, WFP is significantly more effective and realistic than assumed. Concurrently, former WFP defenses are either infeasible for deployment in real-world settings or defend against specific WFP attacks only.
Towards Security Threats of Deep Learning Systems: A Survey

Deep learning has gained tremendous success and great popularity in the past few years. However, deep learning systems are suffering several inherent weaknesses, which can threaten the security of learning models. Deep learning’s wide use further magnifies the impact and consequences. To this end, lots of research has been conducted with the purpose of exhaustively identifying intrinsic weaknesses and subsequently proposing feasible mitigation. Yet few are clear about how these weaknesses are incurred and how effective these attack approaches are in assaulting deep learning. In order to unveil the security weaknesses and aid in the development of a robust deep learning system, we undertake an investigation on attacks towards deep learning, and analyze these attacks to conclude some findings in multiple views. In particular, we focus on four types of attacks associated with security threats of deep learning: model extraction attack, model inversion attack, poisoning attack and adversarial attack. For each type of attack, we construct its essential workflow as well as adversary capabilities and attack goals. Pivot metrics are devised for comparing the attack approaches, by which we perform quantitative and qualitative analyses. From the analysis, we have identified significant and indispensable factors in an attack vector, e.g., how to reduce queries to target models, what distance should be used for measuring perturbation. We shed light on 18 findings covering these approaches’ merits and demerits, success probability, deployment complexity and prospects. Moreover, we discuss other potential security weaknesses and possible mitigation which can inspire relevant research in this area.
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation

In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task. To capture the inherent correlation among video frames, we learn discriminative features (D-features) from the input images that reveal feature distribution from a global perspective. The D-features are then used to establish correspondence with all features of test image under conditional random field (CRF) formulation, which is leveraged to enforce consistency between pixels. The experiments verify that DFNet outperforms state-of-the-art methods by a large margin with a mean IoU score of 83.4% and ranks first on the DAVIS-2016 leaderboard while using much fewer parameters and achieving much more efficient performance in the inference phase. We further evaluate DFNet on the FBMS dataset and the video saliency dataset ViSal, reaching a new state-of-the-art. To further demonstrate the generalizability of our framework, DFNet is also applied to the image object co-segmentation task. We perform experiments on a challenging dataset PASCAL-VOC and observe the superiority of DFNet. The thorough experiments verify that DFNet is able to capture and mine the underlying relations of images and discover the common foreground objects.
An Incentive Mechanism for Building a Secure Blockchain-Based Internet of Things

The world-changing blockchain technique provides a novel method to establish a secure, trusted and decentralized system for solving the security and personal privacy problems in Industrial Internet of Things (IIoT) applications. The mining process in blockchain requires miners to solve a proof-of-work puzzle, which requires high computational power. However, the lightweight IIoT devices cannot directly participate in the mining process due to the limitation of power and computational resources. The edge computing service makes it possible for IIoT applications to build a blockchain network, in which IIoT devices purchase computational resources from edge servers and thus can offload their computational tasks. The amount of computational resource purchased by IIoT devices depends on how many profits they can get in the mining process, and will directly affect the security of the blockchain network. In this paper, we investigate the incentive mechanism for the blockchain platform to attract IIoT devices to purchase more computational power from edge servers to participate in the mining process, thereby building a more secure blockchain network. We model the interaction between the blockchain platform and IIoT devices as a two-stage Stackelberg game, where the blockchain platform act as the leader, and IIoT devices act as followers. We analyze the existence and uniqueness of the Stackelberg equilibrium, and propose an efficient algorithm to compute the Stackelberg equilibrium point. Furthermore, we evaluate the performance of our algorithm through extensive simulations, and analyze the strategies of blockchain platform and IIoT devices under different situations.
Initial coin offerings for blockchain based product provenance authentication platforms

Blockchain is a distributed technology that supports financial operations and also helps establish a secure and trustworthy system for product provenance authentication. In the real world, blockchain based product provenance authentication (BPPA) platforms are developed to help certify products such as diamond. In this paper, we theoretically examine the initial coin offerings (ICO) project of such a BPPA platform. In the main models, we analytically build models to explore the cases without blockchain (Model NPF) and with blockchain (Model PF). We study the optimal pricing and optimal effort (for collecting product authentication information) decisions under each model. We examine the value of having the BPPA platform for both the firm and consumers. We reveal the analytical conditions governing when it is beneficial to use the BPPA platform. Then, we uncover how ICO can be used to finance the project with the launch of the BPPA platform. Finally, in the extended models and analyses, we consider the scenarios in which (i) the product's selling price is exogenous and remains the same for the cases without and with the BPPA platform, and (ii) the fixed setup cost for operating the BPPA platform and the fixed service fee charged (to the firm) are present. We find that all the qualitative insights remain valid and hence the findings derived from the main models are robust.