Reviewerly/exHarmony huggingface dataset:
Phase demodulation from a spatial carrier fringe pattern using extended complex Kalman filter

A spatial carrier fringe pattern analysis technique is proposed for the phase demodulation using the extended complex Kalman filter. A state space model is defined based on the spatial evolution model of the fringe signal in a given row or column of the fringe pattern depending on the direction of carrier fringes. Since the state space model incorporates one of the state associated with the carrier fringe frequency, its value need not be known apriori for the phase demodulation. Furthermore, the proposed method is capable of removing carrier phase contribution from the estimated phase using the carrier fringe frequency estimate. Simulation and experimental results substantiates the applicability of the proposed method.
Vision-Based Building Seismic Displacement Measurement by Stratification of Projective Rectification Using Lines

We propose a new flexible technique for accurate vision-based seismic displacement measurement of building structures via a single non-stationary camera with any perspective view. No a priori information about the camera's parameters or only partial knowledge of the internal camera parameters is required, and geometric constraints in the world coordinate system are employed for projective rectification in this research. Whereas most projective rectifications are conducted by specifying the positions of four or more fixed reference points, our method adopts a stratified approach to partially determine the projective transformation from line-based geometric relationships on the world plane. Since line features are natural and plentiful in a man-made architectural building environment, robust estimation techniques for automatic projective/affine distortion removal can be applied in a more practical way. Both simulations and real-recorded data were used to verify the effectiveness and robustness of the proposed method. We hope that the proposed method could advance the consumer-grade camera system for vision-based structural measurement one more step, from laboratory environments to real-world structural health monitoring systems.
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models

In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple hypotheses. The framework defines a controlled configuration for testing a number of hypotheses as to whether and how some extra information about a “concept” or “feature” may benefit or hinder an ML model. Because reasoning multiple hypotheses is not always straightforward, we provide HypoML as a visual analysis tool, with which, the multi-thread testing results are first transformed to analytical results using statistical and logical inferences, and then to a visual representation for rapid observation of the conclusions and the logical flow between the testing results and hypotheses. We have applied HypoML to a number of hypothesized concepts, demonstrating the intuitive and explainable nature of the visual analysis.
Inspecting TLS Anytime Anywhere: A New Approach to TLS Interception

Transport Layer Security (TLS) is one of the most widely-used security protocols for the modern internet. However, TLS does not differentiate regular users from threat actors who want to evade detection through the privacy provided by TLS. For this reason, organizations have been increasingly interested in middlebox technology whereby encrypted TLS traffic can be filtered and inspected.
Lessons Learned from Teaching Cybersecurity Courses During Covid-19

The coronavirus pandemic brings tremendous challenges to teaching cybersecurity courses online, from learning how to use different online teaching platforms to innovative instruction methodologies within two weeks.
An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration

How do analysts think about grouping and spatial operations? This overarching research question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship between grouping and spatial structures, the decisions analysts make when exploring individual observations, and the role of external information. This work contributes the design and results of such a study, in which a group of participants are asked to organize the data contained within an unfamiliar quantitative dataset. We identify several overarching approaches taken by participants to design their organizational space, discuss the interactions performed by the participants, and propose design recommendations to improve the usability of future high-dimensional data exploration tools that make use of grouping (clustering) and spatial (dimension reduction) operations.
IoTSpy: Uncovering Human Privacy Leakage in IoT Networks via Mining Wireless Context

Internet of Things (IoT) has been emerging as one of the most significant technologies that may change the world. The development and deployment of IoT systems significantly improve the efficiency of work, advance the development of productive force, and affect the human society's modes of production, working, and life deeply. The number of deployed IoT devices and applications has had explosive growth in the past years. While people enjoy the benefits brought by IoT, the addressing of its security and privacy issues does not keep pace with the development of IoT technologies. We find that encrypted wireless IoT traffic can still leak information about user privacy. We can infer what the user is doing at home by deploying a wireless sniffer outside the user's house. In this paper, we propose a method to eavesdrop different kinds of user privacy via analyzing the wireless context. First, we extract the packet sequence features to fingerprint and detect the IoT events. Then we analyze the detected IoT events and infer the user's activities and even the user's moods. Furthermore, by analyzing the wireless context, which is discovered by mining the IoT event dependencies, we can infer the user's living habits, routines, and even installed IoT applications. The leakage of such information not only exposes the user's privacy but also extends the attack surface that an attacker can utilize to control the smart home. We implement our eavesdropping approach and conduct extensive experiments on the SmartThings platform. The evaluation demonstrates the feasibility and effectiveness of our eavesdropping approach.
Active contours driven by modified LoG energy term and optimised penalty term for image segmentation

An active contour model to segment the images is proposed by combining local binary fitting (LBF) energy function and modified Laplacian of Gaussian (MLoG) energy function. A MLoG energy function based on a new boundary indicator function or edge stop function (ESF) is introduced to smoothen the homogeneous regions and enhance the edge information of objects. Also, MLoG energy term with LBF energy term is incorporated to drive the initial contour towards the object boundary. Finally, the penalty term is replaced with a new optimized potential function, which can improve the corresponding speed function. By adding the optimized area energy term, contour position is accelerated towards the object boundary. Further, the addition of MLoG based on new ESF, makes the proposed model insensitive to the initial contour. Experiments are performed on various real images, MS-COCO 2014 train data set images and Segmentation Evaluation Database images shared in Weizmann Institute of Science website. The proposed model provides better segmentation results compared to the other state of the art models in terms of segmentation accuracy, F-score and CPU execution time. Further, experimental results also prove the robustness of the proposed model in terms of contour initialization, intensity inhomogeneity and noise.
Collaborative Image Relevance Learning for Visual Re-Ranking

In content-based image retrieval, the initial retrieval result may be unsatisfactory, which can be refined with visual re-ranking techniques, such as query expansion, geometric verification, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . In this work, we approach visual re-ranking from a novel perspective. Observing that the contextual similarity of images from a retrieval result list exhibits strong visual relevance, we propose to collaboratively learn the semantic relevance among images for visual re-ranking. In our approach, we represent the image set of a fixed-length retrieval list into a correlation matrix, and learn the relevance of all image pairs simultaneously with a lightweight CNN model. To optimize the CNN model, a weighted MSE loss is defined, which takes into account the sparsity of labels. To find the optimal length of retrieval result list for different queries, we present a query sensitive selection method. We conduct comprehensive experiments on five benchmark datasets, and demonstrate the generality, and effectiveness of the proposed visual re-ranking method.
VQA-CPC: a novel visual quality assessment metric of color point clouds

Color point clouds can provide users more realistic visual information and better immersive experience than traditional imaging techniques. How to evaluate the visual quality of color point clouds accurately is an important issue to be solved urgently. In this work, we propose a novel full reference metric, called as Visual Quality Assessment of Color Point Clouds (VQA-CPC). Starting from the geometry and texture of color point cloud, the proposed metric calculates the distances from color point cloud’s points to their geometric centroid and the distances from the texture coordinates of the points to texture centroid. Then, a measuring distortion strategy based on distortion measurement is designed and used to extract the features of color point cloud. Finally, the extracted geometric features and texture features are used to construct the feature vector and predict quality of the distorted color point cloud. Moreover, we construct a color point cloud database, called as NBU-PCD1.0, for verifying the effectiveness of the proposed metric. Experimental results show that the proposed VQA-CPC metric is better than the existing point cloud metrics.
Target Tracking with Multi-rotor Aerial Vehicles based on a Robust Visual Servo Controller with Prescribed Performance

In this paper, we present an image-based visual servo control scheme for tracking a moving target using a multirotor aerial vehicle. The proposed scheme is based on the prescribed performance control notion and it is able to impose appropriately selected performance specifications on the image feature errors to satisfy visibility constraints arising from the camera limited field of view, while exhibiting robustness against calibration and depth estimation errors. The proposed controller is of low complexity and computational cost and thus can be easily implemented on the embedded control unit of an autonomous aerial vehicle. The resulting scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are verified via a realistic simulation scenario, where a quadrotor successfully tracks a visual target located on top of a moving mobile robot.
Elastic net regularized kernel non-negative matrix factorization algorithm for clustering guided image representation

Due to the capacity of data representation, non-negative matrix factorization has been investigated widely by introducing a variety of constraints. The general processing of non-negative matrix factorization for image clustering consists of two steps: (i) achieving the r-dimensional non-negative image representations, where the rank r is set to the expected number of clusters; (ii) adopting the traditional clustering techniques to accomplish the clustering task. Nevertheless, the previous non-negative matrix factorization variants derive image representations from the original space which cannot handle the nonlinear structure of images. This paper focuses on the existing issues and proposes an elastic net regularized kernel non-negative matrix factorization algorithm for clustering guided image representation. In order to explore the nonlinear relations of images, this paper uses the kernel trick to extend original non-negative matrix factorization. A self-organized graph and elastic net regularization are incorporated into the proposed objective of kernel non-negative matrix factorization, besides, the rank is allowed to be larger than the expected number of clusters. By doing so, the graph defined in the feature space is more qualified to represent the intrinsic structure of images. As an accompanying advantage, the clusters of images can be determined using the graph directly without using the two-step trick. According to the proposed alternating update algorithm for solving the optimization problem, the image representation and clustering result can be obtained simultaneously. Extensive experiments on challenging data sets demonstrate the effectiveness of the proposed algorithm compared with the prominent non-negative matrix factorization variants for image clustering.
FSRF:An Improved Random Forest for Classification

Random forest algorithm is a flexible and easy-to-use machine learning algorithm, which is widely used in classification problems. However, the traditional random forest has some limitations. Because the randomness added by random forest to decision trees almost only occurs in the feature selection when the decision trees are generated, the fixity of decision trees generation rules will lead to relatively serious over fitting. At the same time, in the face of data with high and unbalanced feature dimensions, the performance of algorithm is seriously weakened because high-dimensional data usually contains many irrelevant and redundant features. To solve these problems, we propose an improved random forest algorithm FSRF. Based on the traditional random forest algorithm, we use the feature selection methods to preprocess the data and get the feature subset with the best classification performance to construct the random forest. At the same time, we introduce sparse matrix projection to improve the generation of the random forest. Experiments show that our method reduces the influence of redundant features on classification and improves the accuracy.
Google Classroom as a Blended Learning and M-learning Strategy for Training Representatives of the Student Federation of the Salesian Polytechnic University (Guayaquil, Ecuador)

This paper presents a new way of training youth aspiring to participate in the Salesian Polytechnic University (Guayaquil, Ecuador) (UPS-Guayaquil) Student Federation (FEUPS), proposing specific content for each federation position using blended learning and M-learning strategies with the help of Google Classroom. The methodology uses technologies that combine both classroom and distance learning activities, supported by the infrastructure of Google’s online platform, and allows the Salesian Polytechnic University’s current Student Federation to participate in efficient, accessible, timely and responsible learning; the use of technologies increases participants comfort and mobility as well as improving their time management. Collaborative applications permit real time access to information. Course planning is conducted according to the objectives of the FEUPS and the effectiveness of course implementation is documented through the verification of obtained knowledge. As the study shows, participants are highly satisfied with the innovative methodology used to implement the course. This study opens the possibility of uniting learning strategies for training through a combination of methodologies suitable for each intended purpose.
APISonar: Mining API usage examples

Abstract Developers spend a significant part of their time searching for code examples on the web. Often, they look for Application Programming Interface (API) usage examples, that is, how to use APIs provided by libraries and frameworks. For this purpose, several programming websites are available. Some programming websites provide manually created examples: unfortunately, as millions of APIs are available nowadays, they do not cover the majority of the APIs. To alleviate this limitation, other programming websites focus on automatically mining API usage examples from code repositories. To the best of our knowledge, however, these solutions are still very limited: they often present poor, duplicated, and similar API usage examples. In this article, we propose an approach, APISonar, to automatically mine API usage examples from code repositories. Our approach aims to overcome the limitations of current solutions: we focus on presenting readable and reusable API usage examples. We analyze millions of source files provided by 4486 software projects hosted on GitHub. Based on this data, we extract 11 million API usage examples about 1.5 million distinct APIs. We evaluate APISonar by assessing its quality and usage. We show that APISonar is a competitive solution, providing the best API examples in terms of readability and reusability, as compared with popular programming websites. Moreover, despite being a novel website, APISonar attracted a significant amount of users in a short period (3.7K users from 119 countries during 5 months). APISonar is available at www.apisonar.com.
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data parallelism and model parallelism are two well-known approaches for parallel training. However, data parallelism does not help reduce memory footprint per device. In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input’s and deep 3D ConvNets’ size on segmentation accuracy. Through automated model parallelism, it is feasible to train large deep 3D ConvNets with a large input patch, even the whole image. Extensive experiments demonstrate that, facilitated by the automated model parallelism, the segmentation accuracy can be improved through increasing model size and input context size, and large input yields significant inference speedup compared with sliding window of small patches in the inference. Code is available (https://monai.io/research/lamp-automated-model-parallelism).
Uncontrolled Randomness in Blockchains: Covert Bulletin Board for Illicit Activity

Public blockchains can be abused to covertly store and disseminate potentially harmful digital content which poses a serious regulatory issue. In this work, we show the severity of the problem by demonstrating that blockchains can be exploited to surreptitiously distribute arbitrary content. More specifically, all major blockchain systems use randomized cryptographic primitives, such as digital signatures and non-interactive zero-knowledge proofs; we illustrate how the uncontrolled randomness in such primitives can be maliciously manipulated to enable covert communication and hidden persistent storage. To clarify the potential risk, we design, implement and evaluate our technique against the widely-used ECDSA signature scheme, the CryptoNote's ring signature scheme, and Monero's ring confidential transactions. Importantly, the significance of the demonstrated attacks stems from their undetectability, their adverse effect on the future of decentralized blockchains, and their serious repercussions on users' privacy and crypto funds. Finally, we present a generic framework to immunize blockchains against these attacks.
Automatic Students Attendance Marking System Using Image Processing And Machine Learning

To track student attendance, colleges and schools use traditional methods involving manual marking on sheets. These methods are time-consuming and tedious for teachers. The proposed system automatically records the student's attendance during lecture hours using facial recognition technology with image processing instead of the traditional manual methods. The proposed system works in a controlled environment, in which students, face images are captured and then upon recognition, their attendance is automatically marked in an Excel sheet. Student's face is detected using the Viola-Jones technique, whereas Linear Discriminate Analysis (LDA) along with KNN and SVM is used for face recognition. Experimentation shows that it provides better accuracy than the existing PCA and other techniques.
Be the Phisher -- Understanding Users' Perception of Malicious Domains

Attackers use various domain squatting techniques to convince users that their services are legitimate. Previous work has shown that methods liketyposquatting, where single characters are removed or duplicated, can successfully deceive users.
Applicability of Security Standards for Operational Technology by SMEs and Large Enterprises

Establishing adequate cybersecurity for their operational technology (OT) is an existential challenge for manufacturing enterprises. Domain-specific security standards should provide essential support in this challenge. However, they cannot be implemented equally for enterprises of all sizes.We investigate to what extent domain-specific security standards for operational technology are applicable by small and medium-sized as well as large manufacturing enterprises, and how their individual need for action can be identified and addressed. We support our investigation with the results of two independent surveys among manufacturers about their needs for cybersecurity support.In the course of this investigation, we learned that most domain-specific security standards are well applicable to large enterprises. In contrast, small and medium-sized enterprises (SME) seek the support of security experts, who, for their part, are often struggling with a lack of experience in operational technology. To facilitate this cooperation, we provide an introduction for OT-and cybersecurity-experts to the respective basic concepts of their collaborators.