Reviewerly/exHarmony huggingface dataset:
Evolutionary algorithms for many-objective cloud service composition: Performance assessments and comparisons

Service composition and optimal selection (SCOS) concerns the building of optimal composite service by integrating existing services with the aim of performing complex task. Due to a plethora of affordable cloud services providing similar functionalities while differing in quality of service (QoS), how to determine suitable candidates to orchestrate the best composite service, also known as QoS-aware SCOS problem, becomes more complicated. A number of evolutionary optimizers have been developed to resolve SCOS. Unfortunately, a large majority of these optimizers carry out the optimization by aggregating many diverse QoS attributes into a single objective or simply considering two or three representative QoS attributes. SCOS, particularly, from the perspective of many-objective optimization, has not received an appropriate attention. As more factors come into play, SCOS is strictly a many-objective problem. This study explores the scalability of recently state-of-the-art evolutionary many-objective optimization (EMaO) algorithms in addressing SCOS. Comparative results reveal that these EMaO algorithms, never before applied to many-objective SCOS, exhibit distinct search abilities with respect to the objective space dimensionality and problem scale. Based on the empirical observation, useful suggestions and insights for choosing suitable EMaO algorithms pertaining to different SCOS problems are given.
Sashimi: Cutting up CSI-FiSh Secret Keys to Produce an Actively Secure Distributed Signing Protocol

We present the first actively secure variant of a distributed signature scheme based on isogenies. The protocol produces signatures from the recent CSI-FiSh signature scheme. Our scheme works for any access structure, as we use a replicated secret sharing scheme to define the underlying secret sharing; as such it is only practical when the number of maximally unqualified sets is relatively small. This, however, includes the important case of full threshold, and (n, t)-threshold schemes when n is small.
FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos

Recently, facial landmark detection algorithms have achieved remarkable performance on static images. However, these algorithms are neither accurate nor stable in motion-blurred videos. The missing of structure information makes it difficult for state-of-the-art facial landmark detection algorithms to yield good results. In this paper, we propose a framework named FAB that takes advantage of structure consistency in the temporal dimension for facial landmark detection in motion-blurred videos. A structure predictor is proposed to predict the missing face structural information temporally, which serves as a geometry prior. This allows our framework to work as a virtuous circle. On one hand, the geometry prior helps our structure-aware deblurring network generates high quality deblurred images which lead to better landmark detection results. On the other hand, better landmark detection results help structure predictor generate better geometry prior for the next frame. Moreover, it is a flexible video-based framework that can incorporate any static image-based methods to provide a performance boost on video datasets. Extensive experiments on Blurred-300VW, the proposed Realworld Motion Blur (RWMB) datasets and 300VW demonstrate the superior performance to the state-of-the-art methods. Datasets and models will be publicly available at https://keqiangsun.github.io/projects/FAB/FAB.html.
UrbanIxD: From Ethnography to Speculative Design Fictions for the Hybrid City

This paper presents research in the field of Urban Interaction Design which seeks to understand how people's personal, emotional relationships with urban places could potentially inform the design of new technological devices and services. A Speculative Design approach is taken by creating Speculative Design Fictions informed by the data gathered using the ethnographically-informed Walking & Talking method. However, creating the crucial link between themes in the data corpus and possible future scenarios for Speculative Design Fictions can be challenging. Building outwards from a sophisticated understanding of the approach, this paper presents a case study which focuses on the step-by-step process of designing and creating this crucial link for a Speculative Design Fiction in the form of a short film, using design techniques such as Tactics for Ambiguity, PACT-analysis and SCAMPER.
An integrated ship segmentation method based on discriminator and extractor

Ship segmentation is an important task in maritime surveillance systems. A great deal of research on image segmentation has been done in the past few years, but there appears to be some problems when directly utilizing them for ship segmentation under complex maritime background. The interference factors decreasing segmentation performance usually are from the peculiarity of complex maritime background, such as the existence of sea fog, large wakes and large waves. To deal with these interference factors, this paper presents an integrated ship segmentation method based on discriminator and extractor (ISDE). Different from traditional segmentation methods, our method consists of two components in light of the structure: Interference Factor Discriminator (IFD) and Ship Extractor (SE). SqueezeNet is employed for the implementation of IFD as the first step to make a judgment on what interference factors are contained in the input image. While DeepLabv3 + and improved DeepLabv3 + are employed for the implementation of SE as the second step to finally extract ships. We collect a ship segmentation dataset and conduct intensive experiments on it. The experimental results demonstrate that our method for ship segmentation outperforms state-of-the-art methods in terms of segmentation accuracy, especially for the images contain sea fog. Besides our method can run in real time as well.
Few-Shot Generalization for Single-Image 3D Reconstruction via Priors

Recent work on single-view 3D reconstruction shows impressive results, but has been restricted to a few fixed categories where extensive training data is available. The problem of generalizing these models to new classes with limited training data is largely open. To address this problem, we present a new model architecture that reframes single-view 3D reconstruction as learnt, category agnostic refinement of a provided, category-specific prior. The provided prior shape for a novel class can be obtained from as few as one 3D shape from this class. Our model can start reconstructing objects from the novel class using this prior without seeing any training image for this class and without any retraining. Our model outperforms category-agnostic baselines and remains competitive with more sophisticated baselines that finetune on the novel categories. Additionally, our network is capable of improving the reconstruction given multiple views despite not being trained on task of multi-view reconstruction.
Ripping the Fabric: Attacks and Mitigations on Hyperledger Fabric

In this paper, we take a closer look at the attack surface of permissioned blockchains by focusing on Hyperledger Fabric and present scenarios where the assumptions of trust could lead to possible attacks on an organization's ledger. We systematically examine each participant and treating it as a malicious entity look at the attacks possible on the system as a whole. With the global shift in adopting blockchains as vehicles of trust, a collection of such scenarios would be useful to organizations using Fabric to implement their own Distributed Ledger Technologies (DLT), as it would highlight the possible misuse cases of the platform. It would also help fellow researchers by presenting a primitive idea of the possible attacks and promote further research in preventing them.
MPC-based Unified Trajectory Planning and Tracking Control Approach for Automated Guided Vehicles

Autonomous navigation of Automated Guided Vehicles (AGVs) in manufacturing environment is an important part of industrial automation. This paper presents an MPC-based unified trajectory planning and tracking control approach for AGVs. Based on the model of the AGV, improved path planning and reference velocity planning techniques are developed. Then a model predictive controller is designed to track the generated trajectory. In addition, obstacle avoidance capability is also incorporated in the navigation framework. To evaluate the proposed method, simulations and experiments are conducted. The results show that the AGV can achieve high trajectory tracking accuracy with smooth movement.
RAMPARTS

Homomorphic Encryption (HE) is an emerging technology that enables computing on data while the data is encrypted. A major challenge with homomorphic encryption is that it takes extensive expert knowledge to design meaningful and useful programs that are constructed from atomic HE operations.
MBSE for Satellite Communication System Architecting

The risk of failure for aerospace missions can be reduced if architects model the product in a systematic way and make decisions for physical implementation based on stakeholder needs. Satellite communication system architecting should take one of its dominating elements, the communication satellite, into account for consistent modeling during the whole product lifecycle. Model-based system engineering (MBSE) serves as a useful tool for system modeling activities and connections with manufacturing. In this paper, satellite communication system architecting is investigated in the preliminary design stage via MBSE methodologies and the system modeling language (SysML). Application scenarios and use cases are built up aiming at satisfying stakeholder needs. System black-box analysis and white-box logical decomposition are further realized. The logical architecture is then partitioned for physical implementation and system optimization is carried out to give architecting suggestions. Requirement traceability is examined to finish the current design stage. The models realized by the MBSE method are reusable and easily extendible to detailed system design and implementation in the whole product lifecycle.
Regularization of Building Boundaries in Satellite Images Using Adversarial and Regularized Losses

In this paper we present a method for building boundary refinement and regularization in satellite images using a fully convolutional neural network trained with a combination of adversarial and regularized losses. Compared to a pure Mask R-CNN model, the overall algorithm can achieve equivalent performance in terms of accuracy and completeness. However, unlike Mask R-CNN that produces irregular footprints, our framework generates regularized and visually pleasing building boundaries which are beneficial in many applications.
Effect of User Involvement in Supply Chain Cloud Innovation

Cloud innovation has become increasingly important to supply chain innovation and performance. User involvement is a crucial part of cloud innovation. However, the effect of user involvement in supply chain cloud innovation has not been thoroughly studied, particularly its effect on product cost and optimal price. In this paper, the authors attempted to bridge this major gap in the literature. The authors reviewed the relevant literature to define cloud innovation and user involvement in supply chain cloud innovation. Then the authors developed a game model based on the Bertrand model. Analysis of the model showed that user involvement affects product cost and optimal pricing in an interesting way. The authors also presented a real-life example of how user innovation takes place at Tailg electric vehicle company.
A Novel Multitier Blockchain Architecture to Protect Data in Smart Metering Systems

Smart metering is among the most important current and future trends in smart grid development. Utility companies spearheading the smart meter revolution claim many benefits for the end-user. One of the key information technology solutions applied to smart meters is ensuring data protection and cybersecurity. Nowadays, the data tampering in smart meters is a big issue for the utilities and end-users. In particular, the advent of secure monetary transactions has inspired the diversification of blockchains as means to provide cybersecurity and encourage end-user trust. The blockchain technology has some issues such as storage and processing, which are necessary to improve for getting better performance in smart metering systems. Here, the authors propose a novel cybersecurity architecture for smart metering systems, based on blockchains to significantly enhance data security. The proposed architecture is divided into multiple tiers directed toward improving performance based on a scheme of edge, fog, and cloud computing. Besides, the authors present an optimized consensus algorithm for smart meters called proof-of-efficiency. The results show that the proposed architecture implemented can effectively improve cybersecurity and data protection for smart metering applications.
Mobile Five Senses Augmented Reality System: Technology Acceptance Study

The application of the most recent technologies is fundamental to add value to tourism experiences, as well as in other economic sectors. Mobile Five Senses Augmented Reality (M5SAR) system is a mobile guide instrument for cultural, historical, and museum events. In order to realize the proclaimed five senses, the system has two main modules: a (i) mobile application which deals mainly with the senses of sight and hearing, using for that the mobile device camera to recognize and track on-the-fly (museum's) objects and give related information about them; and a (ii) portable device capable of enhancing the augmented reality (AR) experience to the full five senses through the stimulus of touch, taste, and smell, by associating itself to the users' smartphone or tablet. This paper briefly presents the system's architecture but, the main focus is on the analysis of the users' acceptance for this technology, namely the AR (software) application, and its integration with the (hardware) device to achieve the five senses AR. Results show that social influence, effort expectancy, and facilitating conditions are the key constructs that drive the users to accept and M5SAR's technology.
Prediction of Rockburst Based on Multidimensional Connection Cloud Model and Set Pair Analysis

Prediction of rockburst involves numerous random and fuzzy indicators asymmetrically distributed in finite intervals. Herein, a novel multidimensional connection cloud model was introduced to depict uncertainties and distribution characteristics of indicators, and the fuzziness of the classification boundary. In the model, numerical characteristics of the connection cloud model were first determined on the basis of the set pair analysis (SPA) of measured indicators relative to the classification standard. Then a multidimensional connection cloud model was presented to express the interval-valued classification standard. Next, based on the combination weight specified by a distance function, the integrated connection degree for a grade was identified for the sample. Finally, a case study and comparison of the proposed model with the normal cloud model and extensible evaluation method were performed to confirm the validity and reliability of the proposed model. The results show that the proposed model, with a quicker and simpler calculation process than a normal cloud model, can describe the multiple types of uncertainties of interval-valued indicators and overcome the subjectivity when determining numerical characteristics of the cloud model.
Efficient Zero-Knowledge Arguments in the Discrete Log Setting, Revisited

Zero-knowledge arguments have become practical, and widely used, especially in the world of Blockchain, for example in Zcash. This work revisits zero-knowledge proofs in the discrete logarithm setting. First, we identify and carve out basic techniques (partly being used implicitly before) to optimise proofs in this setting. In particular, the linear combination of protocols is a useful tool to obtain zero-knowledge and/or reduce communication. With these techniques, we are able to devise zero-knowledge variants of the logarithmic communication arguments by Bootle et al. (EUROCRYPT '16) and Bünz et al. (S&P '18) thereby introducing almost no overhead. We then construct a conceptually simple commit-and-prove argument for satisfiability of a set of quadratic equations. Unlike previous work, we are not restricted to rank 1 constraint systems (R1CS). This is, to the best of our knowledge, the first work demonstrating that general quadratic constraints, not just R1CS, are a natural relation in the dlog (or ideal linear commitment) setting. This enables new possibilities for optimisation, as, eg., any degree n2 polynomial f(X) can now be "evaluated" with at most 2n quadratic constraints. Our protocols are modular. We easily construct an efficient, logarithmic size shuffle proof, which can be used in electronic voting. Additionally, we take a closer look at quantitative security measures, eg. the efficiency of an extractor. We formalise short-circuit extraction, which allows us to give tighter bounds on the efficiency of an extractor.
Multichannel Residual Conditional GAN-Leveraged Abdominal Pseudo-CT Generation via Dixon MR Images

Magnetic resonance (MR) images have distinctive advantages in radiation treatment (RT) planning due to their superior, anatomic and functional information compared with computed tomography (CT). For the RT dose calculation, MR images cannot be directly used because of the lack of electron density information. To address this issue, we propose to generate pseudo-CT (pCT) in terms of multiple matching Dixon MR images to support MR-only RT, particularly in the challenging body section of the abdomen. To this end, we design the dedicated multichannel residual conditional generative adversarial network (MCRCGAN). The significance of our efforts is three-fold: 1) The MCRCGAN organically incorporates multiple theories and techniques, such as multichannel residual network (ResNet) and conditional generative adversarial network (cGAN), which facilitate its more authentic pCT generation than many existing methods. 2) The usage of residual modules effectively deepens the network without performance degradation, and the multichannel ResNet helps to simultaneously capture the substance of images, as extensively as possible, which is implicitly contained in the multiple different MR images of the same subject. 3) Due to the designed dedicated network structure, the MCRCGAN is capable of generating satisfactory pCTs under the condition of limited training data as well as prompt prediction response. Experimental studies on ten patients' paired MR-CT images demonstrate the effectiveness of our proposed MCRCGAN model on both the pCT generation quality and the performance stability.
Geneva

Researchers and censoring regimes have long engaged in a cat-and-mouse game, leading to increasingly sophisticated Internet-scale censorship techniques and methods to evade them. In this paper, we take a drastic departure from the previously manual evade-detect cycle by developing techniques to automate the discovery of censorship evasion strategies. We present Geneva, a novel genetic algorithm that evolves packet-manipulation-based censorship evasion strategies against nation-state level censors. Geneva composes, mutates, and evolves sophisticated strategies out of four basic packet manipulation primitives (drop, tamper headers, duplicate, and fragment). With experiments performed both in-lab and against several real censors (in China, India, and Kazakhstan), we demonstrate that Geneva is able to quickly and independently re-derive most strategies from prior work, and derive novel subspecies and altogether new species of packet manipulation strategies. Moreover, Geneva discovers successful strategies that prior work posited were not effective, and evolves extinct strategies into newly working variants. We analyze the novel strategies Geneva creates to infer previously unknown behavior in censors. Geneva is a first step towards automating censorship evasion; to this end, we have made our code and data publicly available.
Binary Control-Flow Trimming

A new method of automatically reducing the attack surfaces of binary software is introduced, affording code consumers the power to remove features that are unwanted or unused in a particular deployment context. The approach targets stripped binary native code with no source-derived metadata or symbols, can remove semantic features irrespective of whether they were intended and/or known to code developers, and anticipates consumers who can demonstrate desired features (e.g., via unit testing), but who may not know the existence of specific unwanted features, and who lack any formal specifications of the code's semantics.
Performance Characterization of DNN Training using TensorFlow and PyTorch on Modern Clusters

The recent surge of Deep Learning (DL) models and applications can be attributed to the rise in computational resources, availability of large-scale datasets, and accessible DL frameworks such as TensorFlow and PyTorch. Because these frameworks have been heavily optimized for NVIDIA GPUs, several performance characterization studies exist for GPU-based Deep Neural Network (DNN) training. However, there exist very few research studies that focus on CPU-based DNN training. In this paper, we provide an in-depth performance characterization of state-of-the-art DNNs such as ResNet(s) and Inception-v3/v4 on multiple CPU architectures including Intel Xeon Broadwell, three variants of the Intel Xeon Skylake, AMD EPYC, and NVIDIA GPUs like K80, P100, and V100. We provide three key insights: 1) Multi-process (MP) training should be used even for a single-node, because the single-process (SP) approach cannot fully exploit all the cores, 2) Performance of both SP and MP depend on various features such as the number of cores, the processes per node (ppn), and DNN architecture, and 3) There is a non-linear and complex relationship between CPU/system characteristics (core-count, ppn, hyper-threading, etc) and DNN specifications such as inherent parallelism between layers. We further provide a comparative analysis for CPU and GPU-based training and profiling analysis for Horovod. The fastest Skylake we had access to is up to 2.35× better than a K80 GPU but up to 3.32× slower than a V100 GPU. For ResNet-152 training, we observed that MP is up to 1.47× faster than SP and achieves 125× speedup on 128 Skylake nodes.
Two-party Private Set Intersection with an Untrusted Third Party

We construct new protocols for two parties to securely compute on the items in their intersection. Our protocols make use of an untrusted third party that has no input. The use of this party allows us to construct highly efficient protocols that are secure against a single malicious corruption.
Unsupervised Remote Sensing Image Super-Resolution Using Cycle CNN

Single image super-resolution (SISR) is a useful procedure for many remote sensing applications. However, paired high-resolution and low-resolution remote sensing images are actually hard to acquire for supervised learning SR methods. In this paper, we propose an unsupervised network named Cycle-CNN to handle this problem. Our network consists of two generative CNNs for down-sampling and super-resolution separately, and can be trained with unpaired data. Experiments on panchromatic and multi-spectral images of GaoFen-2 satellite indicate that our method achieves state-of-the-art SR results and is robust against noise and blur in the remote sensing images.