Reviewerly/exHarmony huggingface dataset:
High-efficiency dynamic three-dimensional shape measurement based on misaligned Gray-code light

The combination of Gray-code method with phase-shifting algorithm has been widely used in three-dimensional (3D) shape measurement. However, the non-uniform surface reflectance, the defocus of the projector and the object motion will cause order jump error at the boundary of adjacent Gray-code. This paper proposes a 3D shape measurement method based on misaligned Gray-code (MGC) light to avoid this kind of error. In this method, all the traditional Gray-code patterns are pre-moved by half fringe period before projection so that the edges of the decoding order and the wrapped phase are staggered exactly, and then each period of decoding order is divided into four sub-regions to construct correct phase order and unwrap the wrapped phase. In addition, the fringe period number can be further expanded by introducing the virtual plane to improve the measurement efficiency and accuracy simultaneously. Two sets of comparative experiments on static scenes confirm that our method can successfully avoid the order jump error without projecting additional Gray-code pattern. And the dynamic experimental results demonstrate that proposed method can especially realize high-efficiency and high-speed 3D shape measurement at a rate of 2381 fps by labeling 32-period fringe patterns with only 4 misaligned Gray-code patterns.
Radar signal recognition based on triplet convolutional neural network

Abstract Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.
Zero-Knowledge IOPs with Linear-Time Prover and Polylogarithmic-Time Verifier

Interactive oracle proofs (IOPs) are a multi-round generalization of probabilistically checkable proofs that play a fundamental role in the construction of efficient cryptographic proofs. We present an IOP that simultaneously achieves the properties of zero knowledge, linear-time proving, and polylogarithmic-time verification. We construct a zero-knowledge IOP where, for the satisfiability of an N-gate arithmetic circuit over any field of size $$\varOmega (N)$$ , the prover uses O(N) field operations and the verifier uses $${\mathsf {polylog}}(N)$$ field operations (with proof length O(N) and query complexity $${\mathsf {polylog}}(N)$$ ). Polylogarithmic verification is achieved in the holographic setting for every circuit (the verifier has oracle access to a linear-time-computable encoding of the circuit whose satisfiability is being proved). Our result implies progress on a basic goal in the area of efficient zero knowledge. Via a known transformation, we obtain a zero knowledge argument system where the prover runs in linear time and the verifier runs in polylogarithmic time; the construction is plausibly post-quantum and only makes a black-box use of lightweight cryptography (collision-resistant hash functions).
Smart Age-Friendly Environments

A growing number of frail and disabled older adults suffer from a decline in functional capacities. This decline can lead to difficulties in performing the necessary activities of daily living and exposes older adults to the risk of accidents because the build environment is not adapted to the functional capacities of frail older residents with sarcopenia. Smart, age-friendly environments with embedded ambient-assisted living technologies will be an essential part of how ageing societies will maintain and improve the quality of life for a growing number of older adults with declining functional capacities. A review of relevant literature revealed the central gap, which includes the lack of models for locating the assistance centres, lack of common standards for measurement and recording patterns of functional decline, and lack of identification of common risk factors in the events that lead to ill health and disability.
Digital localisation in an illicit market space: interactional creation of a psychedelic assemblage in a darknet community of exchange

Sociology of drugs and digital sociology-albeit for different reasons-need the analysis of interactions, an approach underdeveloped in current scholarship. We address this gap by providing a specific analytical framework for the analysis of digital interactions which enables an ethnomethodological account of micro-interactional dynamics within a cryptomarket: an anonymous darknet market of illicit drugs. As a case study we chose the 'PsychForumMarket' which is unusual in that it operates as a forum based market space and explicitly rejects centralised technical market solutions such as escrow and encryption systems. Instead, it emphasises personal relationships between buyers and vendors as the basis of trust. Hence it forms a community of exchange, both material and cultural. The data were collected through a process of manual scraping from the forum from 2017 to 2020. The data was purposefully sampled to construct a 'thick data' set, and analysed thematically to examine the micro interactional turn taking, sanctioning and norming processes by which the market culture is normalized and embedded. This market is a laboratory to investigate the constitutive nature of digital group interactions. Due to the very nature of this market the disciplining process cannot lie with external authorities. Interactions between community members are permeated with mutual monitoring and policing. We find that in and through digital communication a particular culture emerges to which individuals who wish to join this community have to ascribe. We refer to this particular culture as a 'psychedelic assemblage,' i.e., a local constellation of cultural constructs which frames the experience of drug using and trading. Our investigation reveals the constitutive methods which enable the norming of members' practices and underpin the emergence of a shared lifeworld which in turn ensures the operability of this cryptomarket.
Collaborative Unsupervised Multi-View Representation Learning

In this paper, we delve into the challenging problem in multi-view learning, namely unsupervised multi-view representation learning, the goal of which is to effectively integrate information from multiple views and learn the unified feature representation with comprehensive information in an unsupervised manner. Despite the progress attained in recent years, it is still a challenging issue since the correlations across multiple views are complex and difficult to model during the learning process, especially in the absence of label information. To address this problem, we introduce a novel method, termed Collaborative Unsupervised Multi-view Representation Learning (CUMRL), which benefits from the high-order view correlations of multi-view data by introducing a collaborative learning strategy. Specifically, the low-rank tensor constraint is employed and plays the role of a bridge, which links the view-specific compact learning and unified representation learning in CUMRL. Experiments demonstrate the effectiveness and competitiveness of the multi-view representation achieved by the proposed method for different learning tasks, compared to several state-of-the-art methods.
Updatable Public Key Encryption in the Standard Model

Forward security (FS) ensures that corrupting the current secret key in the system preserves the privacy or integrity of the prior usages of the system. Achieving forward security is especially hard in the setting of public-key encryption (PKE), where time is divided into periods, and in each period the receiver derives the next-period secret key from their current secret key, while the public key stays constant. Indeed, all current constructions of FS-PKE are built from hierarchical identity-based encryption (HIBE) and are rather complicated.
Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach

In digital agriculture, agronomists are required to make timely, profitable and more actionable precise decisions based on knowledge and experience. The input can be cultivated and related agricultural data, and one of them is text data, including news articles, business news, policy documents, or farming notes. To process this kind of data, identifying agricultural entities in the text is necessary to update news with agricultural orientation. This task is called Agriculture Entity Recognition (AGER - a kind of Named Entity Recognition task, NER, in the agriculture domain). However, there are very few approaches on AGER because of a lack of the consistent tagset and resources. In this study, we developed a new tagset for AGER to cover popular concepts in agriculture and we also propose a process for this task that consists of two stages: in the first stage, we use semantic-based approaches for detecting agricultural entities and semi-automatically build an annotated corpus of agricultural entities, while in the second stage, we identify the agricultural entities from the plain text using a deep learning approach, train on the annotated corpus. For the evaluation and validation, we build an annotated agriculture corpus and demonstrated the efficiency and robustness of our approach.
Collaborative Model-Driven Software Engineering: A Systematic Update

Current software engineering practices rely on highly heterogeneous and distributed teams working in a collaborative setting. Between 2013–2020, the publication output in the field of collaborative Model-Driven Software Engineering (MDSE) has significantly increased. However, the only systematic mapping study available is limited to studies published until 2015. In this paper, we provide an update on that study for the complementing 2016–2020 period, and report the latest results, challenges, and trends. Our analysis led to selecting 29 clusters of 54 new peer-reviewed publications on collaborative MDSE. Based on the novel developments in the field, we have extended and improved the original classification framework, making it applicable to recent and future research contributions on collaborative MDSE. The insights in this paper relate to the changing trends in the field and present new relevant information.
CodeCity: On-Screen or in Virtual Reality?

Over the past decades, researchers proposed numerous approaches to visualize source code. A prominent one is CodeCity, an interactive 3D software visualization that leverages the "city metaphor" to represent software system as cities: buildings represent classes (or files) and districts represent packages (or folders). Building dimensions represent values of software metrics, such as the number of methods or the lines of code. There are many implementations of CodeCity, the vast majority of them running on-screen. Recently, some implementations visualizing CodeCity in virtual reality (VR) have appeared. While exciting as a technology, VR’s usefulness remains to be proven.The question we pose is: Is VR well suited to visualize CodeCity, compared to the traditional on-screen implementation?We performed an experiment in our interactive web-based application to visualize CodeCity. Users can fetch data from any git repository and visualize its source code. Our application enables users to navigate CodeCity both on-screen and in an immersive VR environment, using consumer-grade VR headsets like Oculus Quest. Our controlled experiment involved 24 participants from academia and industry. Results show that people using the VR version performed the assigned tasks in much less time, while still maintaining a comparable level of correctness.Therefore, our results show that VR is at least equally well-suited as on-screen for visualizing CodeCity, and likely better.
Demons in the Shared Kernel

Due to its faster start-up speed and better resource utilization efficiency, OS-level virtualization has been widely adopted and has become a fundamental technology in cloud computing. Compared to hardware virtualization, OS-level virtualization leverages the shared-kernel design to achieve high efficiency and runs multiple user-space instances (a.k.a., containers) on the shared kernel. However, in this paper, we reveal a new attack surface that is intrinsic to OS-level virtualization, affecting Linux, FreeBSD, and Fuchsia. The root cause is that the shared-kernel design in OS-level virtualization results containers in sharing thousands of kernel variables and data structures directly and indirectly. Without exploiting any kernel vulnerabilities, a non-privileged container can easily exhaust the shared kernel variables and data structure instances to cause DoS attacks against other containers. Compared with the physical resources, these kernel variables or data structure instances (termed abstract resources) are more prevalent but under-protected. To show the importance of confining abstract resources, we conduct abstract resource attacks that target different aspects of the OS kernel. The results show that attacking abstract resources is highly practical and critical. We further conduct a systematic analysis to identify vulnerable abstract resources in the Linux kernel, which successfully detects 1,010 abstract resources and 501 of them can be repeatedly consumed dynamically. We also conduct the attacking experiments in the self-deployed shared-kernel container environments on the top 4 cloud vendors. The results show that all environments are vulnerable to abstract resource attacks. We conclude that containing abstract resources is hard and give out multiple strategies for mitigating the risks.
Human Activity Recognition Machine With an Anchor-Based Loss Function

More recently, Human Activity Recognition (HAR) based on sensors has become a hot topic due to its wide application. Researchers significantly reduce the cost of feature extraction and improve the accuracy of recognition by introducing deep learning networks. However, human activity data are greatly affected by the inter-personal variability which brings the interclass similarity and the intraclass diversity. They not only increase the difficulty of the closed-set classification, but also affect the performance on the open-set problem. To solve the problem, we design a framework using a loss function of Euclidean distance and a high-dimensional embedding layer to enhance the ability of deep learning networks to mine discriminative features. Furthermore, we define two kinds of open-set problems in HAR: the pseudo open-set problem and the completely open-set problem. We propose a new clustering method based on the Euclidean distance for the pseudo open-set problem, which reduces the computation cost and improves the accuracy. For the completely open-set problem ignored by other researches, we introduce the rejection score to evaluate the distance score between samples and all known classes, and realize the completely open-set classification. We conduct experiments using four common deep learning networks on three public datasets: OPPORTUNITY, PAMAP2 and UniMiB-SHAR. The results show that the performances of the model modified by our method are much better than those of the original model.
Email Classification and Forensics Analysis using Machine Learning

Emails are being used as a reliable, secure, and formal mode of communication for a long time. With fast and secure communication technologies, reliance on Email has increased as well. The massive increase in email data has led to a big challenge in managing emails. Emails so far can be classified and grouped based on sender, size, and date. However, there is a need to detect and classify emails based on the contents contained therein. Several approaches have been used in the past for content-based classification of emails as Spam or Non-Spam Email. In this paper, we propose a multi-label email classification approach to organize emails. An efficient classification method has been proposed for forensic investigations of massive email data (e.g., a disk image of an email server). This method would help the investigator in Email related crimes investigations. A comparative study of machine learning algorithms identified Logistic Regression as a method that achieves the highest accuracy compared to Naive Bayes, Stochastic Gradient Descent, Random Forest, and Support Vector Machine. Experiments conducted on benchmark data sets depicted that logistic Regression performs best, with an accuracy of 91.9% with bi-gram features.
Few-shot Learning with Online Self-Distillation

Few-shot learning has been a long-standing problem in learning to learn. This problem typically involves training a model on an extremely small amount of data and testing the model on the out-of-distribution data. The focus of recent few-shot learning research has been on the development of good representation models that can quickly adapt to test tasks. To that end, we come up with a model that learns representation through online self-distillation. Our model combines supervised training with knowledge distillation via a continuously updated teacher. We also identify that data augmentation plays an important role in producing robust features. Our final model is trained with CutMix augmentation and online self-distillation. On the commonly used benchmark miniImageNet, our model achieves 67.07% and 83.03% under the 5-way 1-shot setting and the 5-way 5-shot setting, respectively. It outperforms counterparts of its kind by 2.25% and 0.89%.
Design of Exponentially Weighted Median Filter Cascaded With Adaptive Median Filter

Abstract The objective of this paper is to design an II phase algorithm employing median filters for enlightening the performance in removing impulse noise during the processing of the image. The cascaded filter section employs an Adaptive median filter in the first phase followed by a Recursive weighted median filter (RWM) in the second phase. The RWM filter weight is selected with the Median Controlled Algorithm. As a design parameter, the exponential weights of RWM filters are used in the feedback path. The projected algorithm can achieve suggestively improved quality of image when compared to fixed weight or the Center Weighted Median filters.
Multi-scale residual attention network for single image dehazing

Single image dehazing is a fundamental but difficult task in image processing. Although various deep learning-based methods have achieved good dehazing performance, it is still a challenge to recover a haze-free image without color distortion from a hazy image. The color performance of nowadays dehazing methods needs further improvement. In this paper, the effect of haze on the luminance and chrominance of CIELAB color space is firstly studied. Based on CIELAB color space, a novel multi-scale residual attention network (MSRA-Net) is proposed for single image dehazing. The proposed network consists of two subnetworks: one is designed to remove haze from the luminance channel; another focuses on enhancing the chrominance components and makes the color more realistic. To improve the performance of MSRA-Net, the multi-scale residual attention block (MSRA-block) and feature aggregation building block (FAB-block) are presented. Both the subjective and objective performance show that the proposed method outperforms the existing dehazing algorithms, especially in terms of color performance.
Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher

In this paper, we aim to improve the performance of in-the-wild Facial Expression Recognition (FER) by exploiting semi-supervised learning. Large-scale labeled data and deep learning methods have greatly improved the performance of image recognition. However, the performance of FER is still not ideal due to the lack of training data and incorrect annotations (e.g., label noises). Among existing in-the-wild FER datasets, reliable ones contain insufficient data to train robust deep models while large-scale ones are annotated in lower quality. To address this problem, we propose a semi-supervised learning algorithm named Progressive Teacher (PT) to utilize reliable FER datasets as well as large-scale unlabeled expression images for effective training. On the one hand, PT introduces semi-supervised learning method to relieve the shortage of data in FER. On the other hand, it selects useful labeled training samples automatically and progressively to alleviate label noise. PT uses selected clean labeled data for computing the supervised classification loss and unlabeled data for unsupervised consistency loss. Experiments on widely-used databases RAF-DB and FERPlus validate the effectiveness of our method, which achieves state-of-the-art performance with accuracy of 89.57% on RAF-DB. Additionally, when the synthetic noise rate reaches even 30%, the performance of our PT algorithm only degrades by 4.37%.
SoK: Practical Foundations for Software Spectre Defenses

Spectre vulnerabilities violate our fundamental assumptions about architectural abstractions, allowing attackers to steal sensitive data despite previously state-of-the-art countermeasures. To defend against Spectre, developers of verification tools and compiler-based mitigations are forced to reason about microarchitectural details such as speculative execution. In order to aid developers with these attacks in a principled way, the research community has sought formal foundations for speculative execution upon which to rebuild provable security guarantees.This paper systematizes the community’s current knowledge about software verification and mitigation for Spectre. We study state-of-the-art software defenses, both with and without associated formal models, and use a cohesive framework to compare the security properties each defense provides. We explore a wide variety of tradeoffs in the expressiveness of formal frameworks, the complexity of defense tools, and the resulting security guarantees. As a result of our analysis, we suggest practical choices for developers of analysis and mitigation tools, and we identify several open problems in this area to guide future work on grounded software defenses.