Reviewerly/exHarmony huggingface dataset:
Many-Objective Cloud Task Scheduling

Task scheduling problem refers to how to reasonably arrange many tasks provided by users in virtual machines, which is very important in the cloud computing. And the quality of the scheduling performance directly affects the customer satisfaction and the provider benefits. In order to describe the task scheduling problem of cloud computing more precisely and improve the scheduling performance. This paper establishes many-objective cloud model, including four objectives: minimizing time, minimizing costs, maximizing resource utilization, and balancing load. At the same time, a many-objective optimization algorithm based on hybrid angles (MaOEA-HA) is proposed to solve this model. Hybrid angle strategy is designed to optimize the algorithm better, which combines two angle strategies: individual-to-individual angle and individual-to-reference point angle. One by one elimination strategy was introduced to remain individuals with better performance. By comparing with five other advanced many-objective optimization algorithms, MaOEA-HA shows the best performance on the DTLZ test suite. Moreover, different algorithms are applied to solve the cloud task scheduling problem, and MaOEA-HA algorithm achieves best results.
Boosting Verifiable Computation on Encrypted Data

We consider the setting in which an untrusted server stores a collection of data and is asked to compute a function over it. In this scenario, we aim for solutions where the untrusted server does not learn information about the data and is prevented from cheating. This problem is addressed by verifiable and private delegation of computation, proposed by Gennaro, Gentry and Parno (CRYPTO'10), a notion that is close to both the active areas of homomorphic encryption and verifiable computation (VC). However, in spite of the efficiency advances in the respective areas, VC protocols that guarantee privacy of the inputs are still expensive. The only exception is a protocol by Fiore, Gennaro and Pastro (CCS'14) that supports arithmetic circuits of degree at most 2. In this paper we propose new efficient protocols for VC on encrypted data that improve over the state of the art solution of Fiore et al. in multiple aspects. First, we can support computations of degree higher than 2. Second, we achieve public delegatability and public verifiability whereas Fiore et al. need the same secret key to encode inputs and verify outputs. Third, we achieve a new property that guarantees that verifiers can be convinced about the correctness of the outputs without learning information on the inputs. The key tool to obtain our new protocols is a new SNARK that can efficiently handle computations over a quotient polynomial ring, such as the one used by Ring-LWE somewhat homomorphic encryption schemes. This SNARK in turn relies on a new commit-and-prove SNARK for proving evaluations on the same point of several committed polynomials. We propose a construction of this scheme under an extractability assumption over bilinear groups in the random oracle model.
A Temporal Sequence Dual-Branch Network for Classifying Hybrid Ultrasound Data of Breast Cancer

In clinical medicine, the contrast-enhanced ultrasound (CEUS) has been a commonly used imaging modality for diagnosis of breast tumor. However, most researchers in computer vision field only focus on B-mode ultrasound image which does not get good results. To improve the accuracy of classification, first, we propose a novel method, i.e., a Temporal Sequence Dual-Branch Network (TSDBN) which, for the first time, can use B-mode ultrasound data and CEUS data simultaneously. Second, we designed a new Gram matrix to model the temporal sequence, and then proposed a Temporal Sequence Regression Mechanism (TSRM), which is a novel method to extract the enhancement features from CEUS video based on the matrix. For B-mode ultrasound branch, we use the traditional ResNeXt network for feature extraction. While CEUS branch uses ResNeXt + R(2 + 1) D network as the backbone network. We propose a TSRM to learning temporal sequence relationship among frames, and design a Shuffle Temporal Sequence Mechanism (STSM) to shuffle temporal sequences, the purpose of which is to further enhance temporal information among frames. Experimental results show that the proposed TSRM could use temporal information effectively and the accuracy of TSDBN is higher than that of state-of-art approaches in breast cancer classification by nearly 4%.
Generative Text Summary Based on Enhanced Semantic Attention and Gain-Benefit Gate

Generative text summary is an important branch of natural language processing. Aiming at the problems of insufficient use of semantic information, insufficient summary precision and the problem of semantics-loss in the current generated text summary method, an enhanced semantic model is proposed based on dual-encoder, which can provide richer semantic information for sequence-to-sequence architecture through dual-encoder. The enhanced attention architecture with dual-channel semantics is optimized, and the empirical distribution and Gain-Benefit gate are built for decoding. In addition, the position embedding and word embedding are merged into the word embedding technology, and the TF-IDF(term frequency-inverse document frequency), part of speech, key score are added to word's feature. Meanwhile, the optimal dimension of word embedding is optimized. This paper aims to optimize the traditional sequence mapping and word feature representation, enhance the model's semantic understanding, and improve the quality of the summary. The LCSTS and SOGOU datasets are used to validate proposed method. The experimental results show that the proposed method can improve the performance of the ROUGE evaluation system by 10-13 percentage points compared with other listed algorithms. We can observe that the semantic understanding of the text summaries is more accurate and the generation effect is better, which has a better application prospect.
Secure Digital Certificate-Based Data Access Control Scheme in Blockchain

Previous contract protocols in blockchains ensure their fairness and traceability by utilizing centralized credible nodes. If credible nodes are dishonest or conspire with the signatory, then other nodes are compromised. Meanwhile, the leakage of sensitive information of participant nodes poses a serious threat to the privacy security of data access in blockchains. To address this issue, this study proposes a secure control method of digital certificate-based data access in blockchains. The proposed method combines blockchain and digital certificate technologies and designs a secure authentication protocol for privacy data in blockchains without verifying the encrypted identity signature of the third-party participant. The high-efficiency network forwarding protocol proposed in this work can support the fair contract signing of multiple signers via blockchain. This protocol can protect the privacy of contracts and identities of participants. Experimental results show that the proposed scheme is superior in terms of communication overhead, storage overhead, and detection rate.
Deep Network-Based Frame Extrapolation With Reference Frame Alignment

Frame extrapolation is to predict future frames from the past (reference) frames, which has been studied intensively in the computer vision research and has great potential in video coding. Recently, a number of studies have been devoted to the use of deep networks for frame extrapolation, which achieves certain success. However, due to the complex and diverse motion patterns in natural video, it is still difficult to extrapolate frames with high fidelity directly from reference frames. To address this problem, we introduce reference frame alignment as a key technique for deep network-based frame extrapolation. We propose to align the reference frames, e.g. using block-based motion estimation and motion compensation, and then to extrapolate from the aligned frames by a trained deep network. Since the alignment, a preprocessing step, effectively reduces the diversity of network input, we observe that the network is easier to train and the extrapolated frames are of higher quality. We verify the proposed technique in video coding, using the extrapolated frame for inter prediction in High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC). We investigate different schemes, including whether to align between the target frame and the reference frames, and whether to perform motion estimation on the extrapolated frame. We conduct a comprehensive set of experiments to study the efficiency of the proposed method and to compare different schemes. Experimental results show that our proposal achieves on average 5.3% and 2.8% BD-rate reduction in Y component compared to HEVC, under low-delay P and low-delay B configurations, respectively. Our proposal performs much better than the frame extrapolation without reference frame alignment.
CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

Although a variety of techniques to detect malicious websites have been proposed, it becomes more and more difficult for those methods to provide a satisfying result nowadays. Many malicious websites can still escape detection with various Web spam techniques. In this paper, we first summarize three types of Web spam techniques used by malicious websites, such as redirection spam, hidden IFrame spam, and content hiding spam. We then present a new detection method that adopts the perspective of users and takes screenshots of malicious webpages to invalidate Web spams. The proposed detection method uses a Convolutional Neural Network, which is a class of deep neural networks, as a classification algorithm. In order to verify the effectiveness of the method, two different experiments have been conducted. First, the proposed method was tested based on a constructed complex dataset. We present comparison results between the proposed method and representative machine learning-based detection algorithms. Second, the proposed method was tested to detect malicious websites in a real-world Web environment for three months. These experimental results illustrate that the proposed method has a better performance and is applicable to a practical Web environment.
Parameter Sharing is Surprisingly Useful for Multi-Agent Deep Reinforcement Learning.

Nonstationarity is a fundamental problem in cooperative multi-agent reinforcement learning (MARL)--each agent must relearn information about the other agent's policies due to the other agents learning, causing information to ring between agents and convergence to be slow. The MAILP model, introduced by Terry and Grammel (2020), is a novel model of information transfer during multi-agent learning. We use the MAILP model to show that increasing training centralization arbitrarily mitigates the slowing of convergence due to nonstationarity. The most centralized case of learning is parameter sharing, an uncommonly used MARL method, specific to environments with homogeneous agents, that bootstraps a single-agent reinforcement learning (RL) methods and learns an identical policy for each agent. We experimentally replicate the result of increased learning centralization leading to better performance on the MARL benchmark set from Gupta et al. (2017). We further apply parameter sharing to 8 modern single-agent deep RL (DRL) methods for the first time in the literature. With this, we achieved the best documented performance on a set of MARL benchmarks and achieved upto 44 times more average reward in as little as 16% as many episodes compared to documented parameter sharing arrangement. We finally offer a formal proof of a set of methods that allow parameter sharing to serve in environments with heterogeneous agents.
Adaptively secure certificate-based broadcast encryption and its application to cloud storage service

The existing public key broadcast encryption schemes are mainly constructed in identity-based cryptosystem, which bears the inherent problems of key escrow and key distribution. The certificate-based encryption mechanism can effectively address the problems in identity-based cryptosystem. Meanwhile, it simplifies the certificate revocation issue for traditional public key cryptosystem. Inspired by the idea of certificate-based encryption, we put forward the new primitive certificate-based broadcast encryption as well as its formal definition and security model. In virtue of prime order bilinear groups, we present an instantiation scheme of certificate-based broadcast encryption. To our best knowledge, the proposed scheme is the first adaptively secure scheme for certificate-based broadcast encryption in the standard model against chosen-ciphertext attack. Compared with the previous work, our scheme has advantages in the respects of computation cost as well as security properties. Furthermore, we present an application scenario of the proposed scheme for data access control in cloud storage service.
Remote Entanglement via Adiabatic Passage Using a Tunably Dissipative Quantum Communication System

Effective quantum communication between remote quantum nodes requires high fidelity quantum state transfer and remote entanglement generation. Recent experiments have demonstrated that microwave photons, as well as phonons, can be used to couple superconducting qubits, with a fidelity limited primarily by loss in the communication channel [P. Kurpiers et al., Nature (London) 558, 264 (2018)NATUAS0028-083610.1038/s41586-018-0195-y; C. J. Axline et al., Nat. Phys. 14, 705 (2018)NPAHAX1745-247310.1038/s41567-018-0115-y; P. Campagne-Ibarcq et al., Phys. Rev. Lett. 120, 200501 (2018)PRLTAO0031-900710.1103/PhysRevLett.120.200501; N. Leung et al., npj Quantum Inf. 5, 18 (2019)2056-638710.1038/s41534-019-0128-0; Y. P. Zhong et al., Nat. Phys. 15, 741 (2019)NPAHAX1745-247310.1038/s41567-019-0507-7; A. Bienfait et al., Science 364, 368 (2019)SCIEAS0036-807510.1126/science.aaw8415]. Adiabatic protocols can overcome channel loss by transferring quantum states without populating the lossy communication channel. Here, we present a unique superconducting quantum communication system, comprising two superconducting qubits connected by a 0.73 m-long communication channel. Significantly, we can introduce large tunable loss to the channel, allowing exploration of different entanglement protocols in the presence of dissipation. When set for minimum loss in the channel, we demonstrate an adiabatic quantum state transfer protocol that achieves 99% transfer efficiency as well as the deterministic generation of entangled Bell states with a fidelity of 96%, all without populating the intervening communication channel, and competitive with a qubit-resonant mode-qubit relay method. We also explore the performance of the adiabatic protocol in the presence of significant channel loss, and show that the adiabatic protocol protects against loss in the channel, achieving higher state transfer and entanglement fidelities than the relay method.
Multisized Object Detection Using Spaceborne Optical Imagery

This article addresses the highly challenging problem of vehicle detection from high-resolution remote sensing imagery by introducing a novel medium size annotated dataset named satellite imagery multivehicles dataset (SIMD) along with an adapted single pass deep multiscale object detection framework with the aim to detect multisized/type objects for catering above-ground perspective of vehicles. The dataset images are acquired from multiple locations in the EU/US regions available in the public Google Earth satellite imagery. Specifically, it comprises 5000 images of resolution 1024 × 768 and collectively contains 45 096 objects in 15 different classes of vehicles including cars, trucks, buses, long vehicles, various types of aircrafts, and boats. In the proposed architecture, we demonstrate the relevant modifications needed to translate the state-of-the-art object detection frameworks to solve the object detection problem from remote sensing imagery. The proposed architecture has been evaluated on SIMD and a public dataset VEDAI. The comparative analysis has been performed with existing off-the-shelf single-shot object detection models including YOLO and YOLT yielding superior performance measured with standard evaluation strategies. To ignite further research in this domain, the introduced SIMD dataset and the corresponding architecture is publicly available at this link: http://vision.seecs.edu.pk/simd.
bifacial_radiance: a python package for modeling bifacial solar photovoltaic systems

bifacial_radiance is a national-laboratory-developed, community-supported, open-source toolkit that provides a set of functions and classes for simulating the performance of bifacial photovoltaic (PV) systems.(Bifacial PV modules collect light on the front as well as the rear side.)bifacial_radiance automates calculations of PV system layout and performance to use along with the popular ray-tracing software tool RADIANCE (Ward, 1994).Specific algorithms include design and layout of PV modules, reflective ground surfaces, shading obstructions, and irradiance calculations throughout the system, among others.bifacial_radiance is an important component of a growing ecosystem of open-source tools for solar energy (William F Holmgren et al., 2018).Figure 1: Visualization of a bifacial photovoltaic array generated through bifacial_radiance.Courtesy of J. Alderman.
Content-aware rate control scheme for HEVC based on static and dynamic saliency detection

High efficiency video coding (HEVC) greatly outperforms previous standards H.264/AVC in terms of coding bit rate and video quality. However, it does not take into account the human visual system (HVS), that people pay more attention to specific areas and moving objects. In this paper, we present a content-aware rate control scheme for HEVC based on static and dynamic saliency detection. The proposed strategy mainly consists of three techniques, static saliency detection, dynamic saliency detection, and adaptive bit rate allocation. Firstly, we train a deep convolution network (DCN) model to extract the static saliency map by highlighting semantically salient regions. Compared to traditional texture-based or color-based region of interest (ROI) extraction techniques, our models are more in line with the HVS. Secondly, we develop a moving object segmentation technique to automatically extract the dynamic salient regions for each frame. Furthermore, according to the fusion saliency map, a coding tree unit (CTU) level bit control technique is exploited to realize flexible and adaptive bit rate allocation. As a result, the quality of salient regions is improved by allocating more bits, while allocating fewer bits to the non-salient regions. We verified the proposed method on both the JCT-VC recommended data set and eye-tracking data set. Experiment results show that the PSNR of salient regions can improve by an average of 1.85 dB without adding bit rate burden, which significantly improves the visual experience.
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings

Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in few-shot segmentation settings, using pixel-level, scribbles and bounding box supervision. This paper takes another approach, i.e., only requiring image-level label for few-shot object segmentation. We propose a novel multi-modal interaction module for few-shot object segmentation that utilizes a co-attention mechanism using both visual and word embedding. Our model using image-level labels achieves 4.8% improvement over previously proposed image-level few-shot object segmentation. It also outperforms state-of-the-art methods that use weak bounding box supervision on PASCAL-5^i. Our results show that few-shot segmentation benefits from utilizing word embeddings, and that we are able to perform few-shot segmentation using stacked joint visual semantic processing with weak image-level labels. We further propose a novel setup, Temporal Object Segmentation for Few-shot Learning (TOSFL) for videos. TOSFL can be used on a variety of public video data such as Youtube-VOS, as demonstrated in both instance-level and category-level TOSFL experiments.
Multi-Label and Multilingual News Framing Analysis

News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation.In NLP, although recent works have studied framing in English news, few have studied how the analysis can be extended to other languages and in a multi-label setting.In this work, we explore multilingual transfer learning to detect multiple frames from just the news headline in a genuinely low-resource context where there are few/no frame annotations in the target language.We propose a novel method that can leverage elementary resources consisting of a dictionary and few annotations to detect frames in the target language.Our method performs comparably or better than translating the entire target language headline to the source language for which we have annotated data.This work opens up an exciting new capability of scaling up frame analysis to many languages, even those without existing translation technologies.Lastly, we apply our method to detect frames on the issue of U.S. gun violence in multiple languages and obtain exciting insights on the relationship between different frames of the same problem across different countries with different languages.
TopicKA: Generating Commonsense Knowledge-Aware Dialogue Responses Towards the Recommended Topic Fact

Insufficient semantic understanding of dialogue always leads to the appearance of generic responses, in generative dialogue systems. Recently, high-quality knowledge bases have been introduced to enhance dialogue understanding, as well as to reduce the prevalence of boring responses. Although such knowledge-aware approaches have shown tremendous potential, they always utilize the knowledge in a black-box fashion. As a result, the generation process is somewhat uncontrollable, and it is also not interpretable. In this paper, we introduce a topic fact-based commonsense knowledge-aware approach, TopicKA. Different from previous works, TopicKA generates responses conditioned not only on the query message but also on a topic fact with an explicit semantic meaning, which also controls the direction of generation. Topic facts are recommended by a recommendation network trained under the Teacher-Student framework. To integrate the recommendation network and the generation network, this paper designs four schemes, which include two non-sampling schemes and two sampling methods. We collected and constructed a large-scale Chinese commonsense knowledge graph. Experimental results on an open Chinese benchmark dataset indicate that our model outperforms baselines in terms of both the objective and the subjective metrics.