Reviewerly/exHarmony huggingface dataset:
Quantum-enhanced noise radar

We propose a novel protocol for quantum illumination: a quantum-enhanced noise radar. A two-mode squeezed state, which exhibits continuous-variable entanglement between so-called signal and idler beams, is used as input to the radar system. Compared to existing proposals for quantum illumination, our protocol does not require joint measurement of the signal and idler beams. This greatly enhances the practicality of the system by, for instance, eliminating the need for a quantum memory to store the idler. We perform a proof-of-principle experiment in the microwave regime, directly comparing the performance of a two-mode squeezed source to an ideal classical noise source that saturates the classical bound for correlation. We find that, even in the presence of significant added noise and loss, the quantum source outperforms the classical source by as much as an order of magnitude.
R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery.
Regional analysis of the green development level differences in Chinese mineral resource-based cities

Mineral resource exploitation has a negative impact on the immediate environment. Green development has been promoted as a new method for economic growth and environmental protection because of increasingly serious resource and environmental issues. Evaluating the green development level (GDL) of mineral resource-based cities (MRBC) is beneficial to understanding the key drawbacks in the green development process in each city. In this study, a comprehensive GDL evaluation index system is constructed from societal, economic, and environmental perspectives. The entropy method and AHP are used to empower the index, and a GM (1, 1) model is then utilized to forecast the GDL of each city. A time series weighting method is also employed to determine the comprehensive GDL in each city during the 11th, 12th, and 13th Five-Year Plan. The GDL in the eastern, northeastern, central, and western regions is comprehensively analyzed, and the following observations are found: (1) the changes in GDL in most MRBC in China were good, and 25% of the cities were lagging behind; (2) the GDL in MRBC had significant regional differences and were progressively low from east to west; (3) from 2006 to 2020, the GDL in MRBC in the western region was rapidly promoted, whereas the GDL in the eastern region was deteriorating, and a polarizing trend emerged in the northeastern region. The proportion of the four categories, namely, A, B, C, and D, in the central region was gradually showing an equal value. Finally, countermeasures to improve the GDL in MRBC are proposed to provide a reference for sustainable development.
Cross-modality interactive attention network for multispectral pedestrian detection

Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To exploit the complementary nature and remedy contradictory appearance between modalities, in this paper, we propose a novel cross-modality interactive attention network that takes full advantage of the interactive properties of multispectral input sources. Specifically, we first utilize the color (RGB) and thermal streams to build up two detached feature hierarchy for each modality, then by taking the global features, correlations between two modalities are encoded in the attention module. Next, the channel responses of halfway feature maps are recalibrated adaptively for subsequent fusion operation. Our architecture is constructed in the multi-scale format to better deal with different scales of pedestrians, and the whole network is trained in an end-to-end way. The proposed method is extensively evaluated on the challenging KAIST multispectral pedestrian dataset and achieves state-of-the-art performance with high efficiency.
RowHammer: A Retrospective

This retrospective paper describes the RowHammer problem in dynamic random access memory (DRAM), which was initially introduced by Kim et al. at the ISCA 2014 Conference. RowHammer is a prime (and perhaps the first) example of how a circuit-level failure mechanism can cause a practical and widespread system security vulnerability. It is the phenomenon that repeatedly accessing a row in a modern DRAM chip causes bit flips in physically adjacent rows at consistently predictable bit locations. RowHammer is caused by a hardware failure mechanism called DRAM disturbance errors, which is a manifestation of circuit-level cell-to-cell interference in a scaled memory technology. Researchers from Google Project Zero demonstrated in 2015 that this hardware failure mechanism can be effectively exploited by user-level programs to gain kernel privileges on real systems. Many other follow-up works demonstrated other practical attacks exploiting RowHammer. In this paper, we comprehensively survey the scientific literature on RowHammer-based attacks as well as mitigation techniques to prevent RowHammer. We also discuss what other related vulnerabilities may be lurking in DRAM and other types of memories, e.g., NAND flash memory or phase change memory, that can potentially threaten the foundations of secure systems, as the memory technologies scale to higher densities. We conclude by describing and advocating a principled approach to memory reliability and security research that can enable us to better anticipate and prevent such vulnerabilities.
Bitcoin: Safe haven, hedge or diversifier? Perception of bitcoin in the context of a country’s economic situation — A stochastic volatility approach

The aim of the article is to verify whether bitcoin can act as a hedge, diversifier or safe haven on various stock markets, depending on the economic situation in the countries. To diversify the sample, we include five very different countries in our study: Japan, Venezuela, China, Estonia, and Sweden. Using daily data over the period 2014–2017, we estimate the dynamic conditional correlation model between main stock indices and bitcoin price in local currencies (Bitflyer — in the case of the yen, Kraken — in the case of the euro, Huobi in the case of yuan and LocalBitcoins in all the remaining cases), as well as between main stock indices and the bitcoin price in the US dollar (Bitfinex exchange). We apply the Stochastic Volatility Model with the Dynamic Conditional Correlation. We add binary variables into the dynamic correlation equation, indicating the occurrence of extreme return on the stock-exchange index in the lower 1%, 5% and 10% quantile. The conclusions vary, depending whether we consider trade on the local bitcoin exchanges or in the global one. We conclude that bitcoin was treated as a safe haven asset only in the case of Venezuela and investments in bolivars. In the case of local investments in Japan and China bitcoin behaved as a diversifier. In the bitcoin-friendly economies of Sweden and Estonia it acted as a weak hedge. In the case of the USD trade, the results suggest that bitcoin is a weak hedge with respect to all of the analyzed markets.
Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems

Non-negativity is vital for a latent factor (LF)-based model to preserve the important feature of a high-dimensional and sparse (HiDS) matrix in recommender systems, i.e., none of its entries is negative. Current non-negative models rely on constraints-combined training schemes. However, they lack flexibility, scalability, or compatibility with general training schemes. This work aims to perform unconstrained non-negative latent factor analysis (UNLFA) on HiDS matrices. To do so, we innovatively transfer the non-negativity constraints from the decision parameters to the output LFs, and connect them through a single-element-dependent mapping function. Then we theoretically prove that by making a mapping function fulfill specific conditions, the resultant model is able to represent the original one precisely. We subsequently design highly efficient UNLFA algorithms for recommender systems. Experimental results on four industrial-size HiDS matrices demonstrate that compared with four state-of-the-art non-negative models, a UNLFA-based model obtains advantage in prediction accuracy for missing data and computational efficiency. Moreover, such high performance is achieved through its unconstrained training process which is compatible with various general training schemes, on the premise of fulfilling non-negativity constraints. Hence, UNLFA algorithms are highly valuable for industrial applications with the need of performing non-negative latent factor analysis on HiDS matrices.
Feature Space Perturbations Yield More Transferable Adversarial Examples

Many recent works have shown that deep learning models are vulnerable to quasi-imperceptible input perturbations, yet practitioners cannot fully explain this behavior. This work describes a transfer-based blackbox targeted adversarial attack of deep feature space representations that also provides insights into cross-model class representations of deep CNNs. The attack is explicitly designed for transferability and drives feature space representation of a source image at layer L towards the representation of a target image at L. The attack yields highly transferable targeted examples, which outperform competition winning methods by over 30\% in targeted attack metrics. We also show the choice of L to generate examples from is important, transferability characteristics are blackbox model agnostic, and indicate that well trained deep models have similar highly-abstract representations.
Interactive Image Segmentation via Backpropagating Refinement Scheme

An interactive image segmentation algorithm, which accepts user-annotations about a target object and the background, is proposed in this work. We convert user-annotations into interaction maps by measuring distances of each pixel to the annotated locations. Then, we perform the forward pass in a convolutional neural network, which outputs an initial segmentation map. However, the user-annotated locations can be mislabeled in the initial result. Therefore, we develop the backpropagating refinement scheme (BRS), which corrects the mislabeled pixels. Experimental results demonstrate that the proposed algorithm outperforms the conventional algorithms on four challenging datasets. Furthermore, we demonstrate the generality and applicability of BRS in other computer vision tasks, by transforming existing convolutional neural networks into user-interactive ones.
A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors

The academic evidence for examining the educational influences of immersive virtual reality (VR) with head-mounted displays (HMD) has been relatively limited until now, in particular for virtual field trips which allow teachers to guide students to explore learning elements in virtual environments. This study therefore invited 24 elementary school students to engage in an immersive virtual field trip which was part of a 2-week summer camp on the learning subject of social studies. The students' learning experiences (i.e., perceived presence, motivational beliefs change, and attitudes) were investigated and the teacher-student interaction behaviors in the learning activity were explored. The results showed that the students' motivation was generally enhanced, particularly for the diminishment of test anxiety. The important role of the perceptions of spatial presence and experienced realism in the students’ motivational beliefs was also addressed. Moreover, different behavioral patterns of teacher-student interactions during the process of the virtual field trips were identified by lag sequential analysis. This work started a pedagogical research to probe how HMD-based VR technology was applied in classrooms for teachers to lead their students on virtual field trips. The proposed instructional strategies for appropriately guiding students to learn during the process of immersive virtual field trips were also the contribution of this study.
The Seven Sins: Security Smells in Infrastructure as Code Scripts

Practitioners use infrastructure as code (IaC) scripts to provision servers and development environments. While developing IaC scripts, practitioners may inadvertently introduce security smells. Security smells are recurring coding patterns that are indicative of security weakness and can potentially lead to security breaches. The goal of this paper is to help practitioners avoid insecure coding practices while developing infrastructure as code (IaC) scripts through an empirical study of security smells in IaC scripts. We apply qualitative analysis on 1,726 IaC scripts to identify seven security smells. Next, we implement and validate a static analysis tool called Security Linter for Infrastructure as Code scripts (SLIC) to identify the occurrence of each smell in 15,232 IaC scripts collected from 293 open source repositories. We identify 21,201 occurrences of security smells that include 1,326 occurrences of hard-coded passwords. We submitted bug reports for 1,000 randomly-selected security smell occurrences. We obtain 212 responses to these bug reports, of which 148 occurrences were accepted by the development teams to be fixed. We observe security smells can have a long lifetime, e.g., a hard-coded secret can persist for as long as 98 months, with a median lifetime of 20 months.
Cryptocurrencies: market analysis and perspectives

The papers in this special issue focus on the emerging phenomenon of cryptocurrencies. Cryptocurrencies are digital financial assets, for which ownership and transfers of ownership are guaranteed by a cryptographic decentralized technology. The rise of cryptocurrencies’ value on the market and the growing popularity around the world open a number of challenges and concerns for business and industrial economics. Using the lenses of both neoclassical and behavioral theories, this introductory article discusses the main trends in the academic research related to cryptocurrencies and highlights the contributions of the selected works to the literature. A particular emphasis is on socio-economic, misconduct and sustainability issues. We posit that cryptocurrencies may perform some useful functions and add economic value, but there are reasons to favor the regulation of the market. While this would go against the original libertarian rationale behind cryptocurrencies, it appears a necessary step to improve social welfare.
Architectural Implications of Function-as-a-Service Computing

Serverless computing is a rapidly growing cloud application model, popularized by Amazon's Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user demand. Function-as-a-Service (FaaS) applications follow this serverless model, with the developer providing their application as a set of functions which are executed in response to a user- or system-generated event. Functions are designed to be short-lived and execute inside containers or virtual machines, introducing a range of system-level overheads. This paper studies the architectural implications of this emerging paradigm. Using the commercial-grade Apache OpenWhisk FaaS platform on real servers, this work investigates and identifies the architectural implications of FaaS serverless computing. The workloads, along with the way that FaaS inherently interleaves short functions from many tenants frustrates many of the locality-preserving architectural structures common in modern processors. In particular, we find that: FaaS containerization brings up to 20x slowdown compared to native execution, cold-start can be over 10x a short function's execution time, branch mispredictions per kilo-instruction are 20x higher for short functions, memory bandwidth increases by 6x due to the invocation pattern, and IPC decreases by as much as 35% due to inter-function interference. We open-source FaaSProfiler, the FaaS testing and profiling platform that we developed for this work.
End-to-End Wireframe Parsing

We present a conceptually simple yet effective algorithm to detect wireframes in a given image. Compared to the previous methods which first predict an intermediate heat map and then extract straight lines with heuristic algorithms, our method is end-to-end trainable and can directly output a vectorized wireframe that contains semantically meaningful and geometrically salient junctions and lines. To better understand the quality of the outputs, we propose a new metric for wireframe evaluation that penalizes overlapped line segments and incorrect line connectivities. We conduct extensive experiments and show that our method significantly outperforms the previous state-of-the-art wireframe and line extraction algorithms. We hope our simple approach can be served as a baseline for future wireframe parsing studies. Code has been made publicly available at https://github.com/zhou13/lcnn.
Inherent Disagreements in Human Textual Inferences

We analyze human’s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation “noise”, but rather persist as we collect more ratings and as we vary the amount of context provided to raters. We further show that the type of uncertainty captured by current state-of-the-art models for natural language inference is not reflective of the type of uncertainty present in human disagreements. We discuss implications of our results in relation to the recognizing textual entailment (RTE)/natural language inference (NLI) task. We argue for a refined evaluation objective that requires models to explicitly capture the full distribution of plausible human judgments.
Task2Vec: Task Embedding for Meta-Learning

We introduce a method to generate vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations. Given a dataset with ground-truth labels and a loss function, we process images through a "probe network" and compute an embedding based on estimates of the Fisher information matrix associated with the probe network parameters. This provides a fixed-dimensional embedding of the task that is independent of details such as the number of classes and requires no understanding of the class label semantics. We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks. We demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a novel task. We present a simple meta-learning framework for learning a metric on embeddings that is capable of predicting which feature extractors will perform well on which task. Selecting a feature extractor with task embedding yields performance close to the best available feature extractor, with substantially less computational effort than exhaustively training and evaluating all available models.
A Survey of Transfer Learning for Convolutional Neural Networks

Transfer learning is an emerging topic that may drive the success of machine learning in research and industry. The lack of data on specific tasks is one of the main reasons to use it, since collecting and labeling data can be very expensive and can take time, and recent concerns with privacy make difficult to use real data from users. The use of transfer learning helps to fast prototype new machine learning models using pre-trained models from a source task since training on millions of images can take time and requires expensive GPUs. In this survey, we review the concepts and definitions related to transfer learning and we list the different terms used in the literature. We bring the point of view from different authors of prior surveys, adding some more recent findings in order to give a clear vision of directions for future work in this field of research.
Material Based Object Tracking in Hyperspectral Videos

Traditional color images only depict color intensities in red, green and blue channels, often making object trackers fail in challenging scenarios, e.g., background clutter and rapid changes of target appearance. Alternatively, material information of targets contained in large amount of bands of hyperspectral images (HSI) is more robust to these difficult conditions. In this paper, we conduct a comprehensive study on how material information can be utilized to boost object tracking from three aspects: dataset, material feature representation and material based tracking. In terms of dataset, we construct a dataset of fully-annotated videos, which contain both hyperspectral and color sequences of the same scene. Material information is represented by spectral-spatial histogram of multidimensional gradients, which describes the 3D local spectral-spatial structure in an HSI, and fractional abundances of constituted material components which encode the underlying material distribution. These two types of features are embedded into correlation filters, yielding material based tracking. Experimental results on the collected dataset show the potentials and advantages of material based object tracking.