Reviewerly/exHarmony huggingface dataset:
An Identity-Based and Revocable Data-Sharing Scheme in VANETs

Ensuring data confidentiality in a vehicular ad hoc network (VANET) is an increasingly important issue. Message confidentiality, user privacy and access control are the most important problems that affect services provided by VANETs. However, access control that addresses data downloads while preserving users' privacy remains an open problem. Based on a set of attributes, the ciphertext-policy attribute-based encryption (CP-ABE) algorithm proposes a party data encryption/decryption mechanism for shared data; consequently, the algorithm has become a popular solution for data-sharing access control. However, the current CP-ABE schemes are still infeasible for VANETs because these schemes use a single authority and inefficient encryption/decryption and ignore revocation mechanisms. Here, over CP-ABE with revocation, we introduce an identity-based scheme that achieves secure data sharing in VANETs. To reduce the computation load for in-vehicle on-board units (OBUs), we outsource computationally intensive encryption and decryption operations to cloud compute nodes. Attributes are decentralized and managed by application service providers that provide services to vehicles based on subscriptions. Comprehensive experimental results and security analysis show that our scheme achieves fine-grained access control while preserving user privacy. Through implementation, performance analysis demonstrates that our scheme is suitable for VANETs.
Active protection of a superconducting qubit with an interferometric Josephson isolator

Nonreciprocal microwave devices play critical roles in high-fidelity, quantum-nondemolition (QND) measurement schemes. They impose unidirectional routing of readout signals and protect the quantum systems from unwanted noise originated by the output chain. However, cryogenic circulators and isolators are disadvantageous in scalable superconducting architectures because they use magnetic materials and strong magnetic fields. Here, we realize an active isolator formed by coupling two nondegenerate Josephson mixers in an interferometric scheme and driving them with phase-shifted, same-frequency pumps. By incorporating our Josephson-based isolator into a superconducting qubit setup, we demonstrate fast, high-fidelity, QND measurements of the qubit while providing 20 dB of protection within a bandwidth of 10 MHz against amplified noise reflected off the Josephson amplifier in the output chain. A moderate reduction of 35% is observed in T2E when the Josephson-based isolator is turned on. Such a moderate degradation can be mitigated by minimizing heat dissipation in the pump lines.
Collective Decision for Open Set Recognition

In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or categorize them to some known class using empirically-set threshold. Thus the decision threshold plays a key role. However, the selection for it usually depends on the knowledge of known classes, inevitably incurring risks due to lacking available information from unknown classes. On the other hand, a more realistic OSR system should NOT just rest on a reject decision but should go further, especially for discovering the hidden unknown classes among the reject instances, whereas existing OSR methods do not pay special attention. In this paper, we introduce a novel collective/batch decision strategy with an aim to extend existing OSR for new class discovery while considering correlations among the testing instances. Specifically, a collective decision-based OSR framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet process (HDP). Thanks to HDP, our CD-OSR does not need to define the decision threshold and can implement the open set recognition and new class discovery simultaneously. Finally, extensive experiments on benchmark datasets indicate the validity of CD-OSR.
QPSO algorithm based on Lévy flight and its application in fuzzy portfolio

To solve constrained portfolio selection model effectively, an improved quantum-behaved particle swarm optimization algorithm(LQPSO) is presented. Firstly, considering its practicality in real dealing process, a class of fuzzy portfolio models with transaction costs and background risk is established. Then in the design of improved algorithm, Lévy flight strategy and contraction–expansion coefficient with nonlinear structure are taken into account for enhancing particle’s exploration ability, and premature prevention mechanism is used to increase population diversity. According to the following performance test, LQPSO demonstrates better convergence and robustness than PSO with inertia weight, QPSO and QPSO with a hybrid probability distribution in 12 benchmark functions. Furthermore, experimental results indicate that LQPSO outperforms several metaheuristics when seeking optimal solution for the fuzzy portfolio model with constraints.
Deep Multi-View Learning Using Neuron-Wise Correlation-Maximizing Regularizers

Many machine learning problems concern with discovering or associating common patterns in data of multiple views or modalities. Multi-view learning is of the methods to achieve such goals. Recent methods propose deep multi-view networks via adaptation of generic Deep Neural Networks (DNNs), which concatenate features of individual views at intermediate network layers (i.e., fusion layers). In this work, we study the problem of multi-view learning in such end-to-end networks. We take a regularization approach via multi-view learning criteria, and propose a novel, effective, and efficient neuron-wise correlation-maximizing regularizer. We implement our proposed regularizers collectively as a correlation-regularized network layer (CorrReg). CorrReg can be applied to either fully-connected or convolutional fusion layers, simply by replacing them with their CorrReg counterparts. By partitioning neurons of a hidden layer in generic DNNs into multiple subsets, we also consider a multi-view feature learning perspective of generic DNNs. Such a perspective enables us to study deep multi-view learning in the context of regularized network training, for which we present control experiments of benchmark image classification to show the efficacy of our proposed CorrReg. To investigate how CorrReg is useful for practical multi-view learning problems, we conduct experiments of RGB-D object/scene recognition and multi-view based 3D object recognition, using networks with fusion layers that concatenate intermediate features of individual modalities or views for subsequent classification. Applying CorrReg to fusion layers of these networks consistently improves classification performance. In particular, we achieve the new state of the art on the benchmark RGB-D object and RGB-D scene datasets. We make the implementation of CorrReg publicly available.
Simple method for asymmetric twin-field quantum key distribution

Twin-Field quantum key distribution (TF-QKD) can beat the linear bound of repeaterless QKD systems. After the proposal of the original protocol, multiple papers have extended the protocol to prove its security. However, these works are limited to the case where the two channels have equal amount of loss (i.e. are symmetric). In a practical network setting, it is very likely that the channels are asymmetric due to e.g. geographical locations. In this paper we extend the "simple TF-QKD" protocol to the scenario with asymmetric channels. We show that by simply adjusting the two signal states of the two users (and not the decoy states) they can effectively compensate for channel asymmetry and consistently obtain an order of magnitude higher key rate than previous symmetric protocol. It also can provide 2-3 times higher key rate than the strategy of deliberately adding fibre to the shorter channel until channels have equal loss (and is more convenient as users only need to optimize their laser intensities and do not need to physically modify the channels). We also perform simulation for a practical case with three decoy states and finite data size, and show that our method works well and has a clear advantage over prior art methods with realistic parameters.
Optimal Verification of Greenberger-Horne-Zeilinger States

We construct optimal protocols for verifying qubit and qudit GHZ states using local projective measurements. When the local dimension is a prime, an optimal protocol is constructed from Pauli measurements only. Our protocols provide a highly efficient way for estimating the fidelity and certifying genuine multipartite entanglement. In particular, they enable the certification of genuine multipartite entanglement using only one test when the local dimension is sufficiently large. By virtue of adaptive local projective measurements, we then construct protocols for verifying GHZ-like states that are optimal over all protocols based on one-way communication. The efficiency can be improved further if additional communications are allowed. Finally, we construct optimal protocols for verifying GHZ states and nearly optimal protocols for GHZ-like states in the adversarial scenario.
GraphPulse: An Event-Driven Hardware Accelerator for Asynchronous Graph Processing

Graph processing workloads are memory intensive with irregular access patterns and large memory footprint resulting in low data locality. Their popular software implementations typically employ either Push or Pull style propagation of changes through the graph over multiple iterations that follow the Bulk Synchronous Model. The performance of these algorithms on traditional computing systems is limited by random reads/writes of vertex values, synchronization overheads, and additional overheads for tracking active sets of vertices or edges across iterations. In this paper, we present GraphPulse, a hardware framework for asynchronous graph processing with event-driven scheduling that overcomes the performance limitations of software frameworks. Event-driven computation model enables a parallel dataflow-style execution where atomic updates and active sets tracking are inherent to the model; thus, scheduling complexity is reduced and scalability is enhanced. The dataflow nature of the architecture also reduces random reads of vertex values by carrying the values in the events themselves. We capitalize on the update properties commonly present in graph algorithms to coalesce in-flight events and substantially reduce the event storage requirement and the processing overheads incurred. GraphPulse event-model naturally supports asynchronous graph processing, enabling substantially faster convergence by exploiting available parallelism, reducing work, and eliminating synchronization at iteration boundaries. The framework provides easy to use programming interface for faster development of hardware graph accelerators. A single GraphPulse accelerator achieves up to 74x speedup (28x on average) over Ligra, a state of the art software framework, running on a 12 core CPU. It also achieves an average of 6.2x speedup over Graphicionado, a state of the art graph processing accelerator.
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning

Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results.However, this comes with the cost of having a single, large model for each task, which is not ideal in low-memory/power scenarios (e.g., mobile).In this paper, we propose an effective way to fine-tune multiple down-stream generation tasks simultaneously using a single, large pretrained model.The experiments on five diverse language generation tasks show that by just using an additional 2-3% parameters for each task, our model can maintain or even improve the performance of fine-tuning the whole model 1 .
Visual Quality of 3D Meshes With Diffuse Colors in Virtual Reality: Subjective and Objective Evaluation

Surface meshes associated with diffuse texture or color attributes are becoming popular multimedia contents.They provide a high degree of realism and allow six degrees of freedom (6DoF) interactions in immersive virtual reality environments.Just like other types of multimedia, 3D meshes are subject to a wide range of processing, e.g., simplification and compression, which result in a loss of quality of the final rendered scene.Thus, both subjective studies and objective metrics are needed to understand and predict this visual loss.In this work, we introduce a large dataset of 480 animated meshes with diffuse color information, and associated with perceived quality judgments.The stimuli were generated from 5 source models subjected to geometry and color distortions.Each stimulus was associated with 6 hypothetical rendering trajectories (HRTs): combinations of 3 viewpoints and 2 animations.A total of 11520 quality judgments (24 per stimulus) were acquired in a subjective experiment conducted in virtual reality.The results allowed us to explore the influence of source models, animations and viewpoints on both the quality scores and their confidence intervals.Based on these findings, we propose the first metric for quality assessment of 3D meshes with diffuse colors, which works entirely on the mesh domain.This metric incorporates perceptually-relevant curvature-based and color-based features.We evaluate its performance, as well as a number of Image Quality Metrics (IQMs), on two datasets: ours and a dataset of distorted textured meshes.Our metric demonstrates good results and a better stability than IQMs.Finally, we investigated how the knowledge of the viewpoint (i.e., the visible parts of the 3D model) may improve the results of objective metrics.
Customizable Reliable Privacy-Preserving Data Sharing in Cyber-Physical Social Networks

Privacy leakage becomes increasingly serious because massive volumes of data are constantly shared in diverse booming cyber-physical social networks (CPSN). Differential privacy is one of the dominating privacy-preserving methods, but most of its extensions assume all data users share the same privacy requirement, which fails to satisfy various privacy expectations in practice. To address this issue, customizable privacy preservation based on differential privacy is a potentially promising countermeasure. However, we found that customizable protection will trigger the composition mechanism of differential privacy and leads to unexpected correlations among injected noises that weakens privacy protection and reveal more sensitive inforamtion. As a result, customizable privacy protection is vulnerable to two primary attacks: background knowledge attack and collusion attack. To optimize the tradeoff between customizable privacy preservation and data utility, we propose a customizable reliable differential privacy model (CRDP), which provides customizable protection on each individual while being attack-proof. We define social distance as the shortest path between two nodes, which is used as an index to customize the privacy protection levels, followed by quantitatively modeling the attacks under the framework of differential privacy. We develop a modified Laplacian mechanism in which the noise generation complies with a Markov stochastic process.Consequently, the correlations of noises are properly de-coupled so that CRDP can simultaneously minimize background knowledge attacks and eliminate collusion attacks in this particular scenario. The evaluation results from real-world datasets show the feasibility and superiority of CRDP in terms of customizable privacy preservation and reliable attack resistance.
A Hybrid Quantum-Classical Approach to Mitigating Measurement Errors in Quantum Algorithms

When noisy intermediate scalable quantum (NISQ) devices are applied in information processing, all of the stages through preparation, manipulation, and measurement of multipartite qubit states contain various types of noise that are generally hard to be verified in practice. In this work, we present a scheme to deal with unknown quantum noise and show that it can be used to mitigate errors in measurement readout with NISQ devices. Quantum detector tomography that identifies a type of noise in a measurement can be circumvented. The scheme applies single-qubit operations only, that are with relatively higher precision than measurement readout or two-qubit gates. A classical post-processing is then performed with measurement outcomes. The scheme is implemented in quantum algorithms with NISQ devices: the Bernstein-Vazirani algorithm and a quantum amplitude estimation algorithm in IBMQ yorktown and IBMQ essex. The enhancement in the statistics of the measurement outcomes is presented for both of the algorithms with NISQ devices.
Understanding Managers’ Reactions to Blockchain Technologies in the Supply Chain: The Reliable and Unbiased Software Agent

Abstract Organizations investing in supply chain information systems struggle to ensure successful adoption and implementation. Projects fail because of technical caveats, inability to meet business needs, and poor management of implementation. Implementation of blockchain technologies across a network of supply chain partners is more complex than internally focused technologies. It is necessary for partner firms to implement, contribute, and share information, and employees to actively use the capabilities of the technology to realize potential. Blockchain technologies can substitute for traditional interfirm intermediaries acting as an unbiased software agent embedded in the supply chain network. Understanding managers’ perceptions of and willingness to use blockchain technologies is crucial for successful implementation. Integrating design theory with classic diffusion processes, we conducted a scenario‐based role‐playing experiment with industry professionals to examine managers’ perceptions of blockchain technologies and willingness to use. We find that trustworthiness with regard to competence and perceived distributive justice is the focal drivers of managers’ willingness to use the technology. Additionally, both risk and interactional justice are not drivers of willingness to use blockchain technology despite significant claims to that effect. We provide implications for how managers can leverage these drivers to influence supply chain partners’ willingness to use the technology.
Deep Learning for Hindi Text Classification: A Comparison

Natural Language Processing (NLP) and especially natural language text analysis have seen great advances in recent times. Usage of deep learning in text processing has revolutionized the techniques for text processing and achieved remarkable results. Different deep learning architectures like CNN, LSTM, and very recent Transformer have been used to achieve state of the art results variety on NLP tasks. In this work, we survey a host of deep learning architectures for text classification tasks. The work is specifically concerned with the classification of Hindi text. The research in the classification of morphologically rich and low resource Hindi language written in Devanagari script has been limited due to the absence of large labeled corpus. In this work, we used translated versions of English data-sets to evaluate models based on CNN, LSTM and Attention. Multilingual pre-trained sentence embeddings based on BERT and LASER are also compared to evaluate their effectiveness for the Hindi language. The paper also serves as a tutorial for popular text classification techniques.
Fast and Efficient Facial Expression Recognition Using a Gabor Convolutional Network

Automatic facial expression recognition (FER) is a fundamental topic in computer vision. Many studies have indicated that facial emotion changes are strongly related to certain regions of interest (ROIs), such as the mouth, eyes, eyebrows, and nose; therefore, the features of these facial ROIs are very important for identifying expressions. Since Gabor filters are very efficient in extracting visual content, Gabor orientation filters (GoFs) modulated by Gabor kernels and traditional convolutional filters can capture such ROI information better than conventional convolutional filters. Consequently, this letter presents a light Gabor convolutional network (GCN) consisting of only four Gabor convolutional layers and two linear layers for FER tasks. Extensive experiments on the FER2013, FERPlus and Real-world Affective Faces (RAF) databases demonstrate that the proposed method achieves good recognition accuracy and requires very low computational costs. The source code can be found at https://github.com/general515/Facial_Expression_Recognition_Using _GCN.
Security in agile software development: A practitioner survey

Context: Software security engineering provides the means to define, implement and verify security in software products. Software security engineering is performed by following a software security development life cycle model or a security capability maturity model. However, agile software development methods and processes, dominant in the software industry, are viewed to be in conflict with these security practices and the security requirements. Objective: Empirically verify the use and impact of software security engineering activities in the context of agile software development, as practiced by software developer professionals. Method: A survey (N=61) was performed among software practitioners in Finland regarding their use of 40 common security engineering practices and their perceived security impact, in conjunction with the use of 16 agile software development items and activities. Results: The use of agile items and activities had a measurable effect on the selection of security engineering practices. Perceived impact of the security practices was lower than the rate of use would imply: This was taken to indicate a selection bias, caused by e.g. developers’ awareness of only certain security engineering practices, or by difficulties in applying the security engineering practices into an iterative software development workflow. Security practices deemed to have most impact were proactive and took place in the early phases of software development. Conclusion: Systematic use of agile practices conformed, and was observed to take place in conjunction with the use of security practices. Security activities were most common in the requirement and implementation phases. In general, the activities taking place early in the life cycle were also considered most impactful. A discrepancy between the level of use and the perceived security impact of many security activities was observed. This prompts research and methodological development for better integration of security engineering activities into software development processes, methods, and tools.