Reviewerly/exHarmony huggingface dataset:
Fast Tree Variants of Gromov-Wasserstein

Gromov-Wasserstein (GW) is a powerful tool to compare probability measureswhose supports are in different metric spaces. GW suffers however from acomputational drawback since it requires to solve a complex non-convexquadratic program. We consider in this work a specific family of groundmetrics, namely \textit{tree metrics} for a space of supports of eachprobability measure in GW. By leveraging a tree structure, we propose to use\textit{flows} from a root to each support to represent a probability measurewhose supports are in a tree metric space. We consequently propose a novel treevariant of GW, namely flow-based tree GW (\FlowTGW), by matching the flows ofthe probability measures. We then show that \FlowTGW~shares a similar structureas a univariate optimal transport distance. Therefore, \FlowTGW~is fast forcomputation and can scale up for large-scale applications. In order to furtherexplore tree structures, we propose another tree variant of GW, namelydepth-based tree GW (\DepthTGW), by aligning the flows of the probabilitymeasures hierarchically along each depth level of the tree structures.Theoretically, we prove that both \FlowTGW~and \DepthTGW~are pseudo-distances.Moreover, we also derive tree-sliced variants, computed by averaging thecorresponding tree variants of GW using random tree metrics, built adaptivelyin spaces of supports. Finally, we test our proposed discrepancies againstother baselines on some benchmark tasks.
Effective Penetration Testing Report Writing

This paper studies the methods used to effectively report the outcome of penetration testing. It involves all the necessary components essential in writing up methodologies for any report on penetration testing. Conducting penetration tests to identify security vulnerabilities is critical, but it has become complex and time-consuming leading to poor reporting. The findings indicated that proper test reporting should contain an executive summary, testing objective, penetration testing, and other components. The last aspect in the report should be the remedial options for the identified vulnerabilities. From the evaluated reports, the penetration testers had adhered to the test report methodology. Most reports contained all the needed sections. The penetration report is addressed to the executive teams, managers, and IT experts in companies. From the evaluated reports, the companies endured low, medium, and high-security vulnerabilities. The general remedial options suggested for the companies through different reports included risk assessment, establishing a monitoring process and providing specific solutions to the identified vulnerabilities.
Privacy Guarantees of Bluetooth Low Energy Contact Tracing: A Case Study on COVIDWISE

Google and Apple jointly introduced a digital contact tracing technology and an API called "exposure notification," to help health organizations and governments with contact tracing. The technology and its interplay with security and privacy constraints require investigation. In this study, we examine and analyze the security, privacy, and reliability of the technology with actual and typical scenarios (and expected typical adversary in mind), and quite realistic use cases. We do it in the context of Virginia's COVIDWISE app. This experimental analysis validates the properties of the system under the above conditions, a result that seems crucial for the peace of mind of the exposure notification technology adopting authorities, and may also help with the system's transparency and overall user trust.
Efficient cellular annotation of histopathology slides with real-time AI augmentation

In recent years, a steady swell of biological image data has driven rapid progress in healthcare applications of computer vision and machine learning. To make sense of this data, scientists often rely on detailed annotations from domain experts for training artificial intelligence (AI) algorithms. The time-consuming and costly process of collecting annotations presents a sizable bottleneck for AI research and development. HALS (Human-Augmenting Labeling System) is a collaborative human-AI labeling workflow that uses an iterative “review-and-revise” model to improve the efficiency of this critical process in computational pathology.
Large-capacity reversible image watermarking based on improved DE

Aiming at solving the problems of bad imperceptibility and low embedding rate of existing algorithms, a novel large-capacity reversible image watermarking based on improved difference expansion (DE) is proposed. Firstly, the smoothness calculation algorithm is used to calculate and sort the smoothness values of the divided image sub-blocks; then, the scrambled watermark is embedded into the sub-blocks with less smoothness after removing the abrupt point by using the generalized difference expansion (GDE); finally, the absolute difference operation is applied to the generated overflow pixels to make their pixel values within a reasonable range for embedding watermark information. Under the premise of ensuring a certain visual quality, multiple watermark embedding can effectively improve the embedding rate. The simulation results show that this algorithm not only realizes blind extraction, but also recovers the original images without loss. At the same time, this algorithm achieves a high embedding rate (the average embedding rate is as high as 77.91 dB) without decreasing the visual quality.
Object Detection in Real-World Hazy Scene

Accurate object detection in the real-world hazy scene is very important to some potential visual task, such as video surveillance, smart city, autonomous driving and so on.This paper focuses on two research problems, which are to build a synthetic dataset of object detection in hazy scene and to analyze the effect of prior knowledge and joint learning of model on object detection in real-world hazy scene.Two frameworks are proposed which are the knowledge-guided object detection (KODNet) and the joint learning in dehazing and object detection (DONet).In KODNet, statistical prior knowledge will be used to guide the general object detection network to learn object features in the hazy scene during the training, makes the general object detector better adapt to the special object detection scenario.DONet can effectively solve the problem of structural detail missing and color distortion caused by image dehazing, thereby realizing the improvement of the objects 734 计算机辅助设计与图形学学报 第 33 卷 detection accuracy in real-world scene.The experimental results on RTTS show that KODNet and DONet are effective to the object detection in the real-world hazy scene and they achieve the mAP of 70.5 % and 66.6 %.
THE INTEGRATION OF IOT PROJECTS IN UNDERGRADUATE EDUCATION

The Internet of Things (IoT) is defined by the interconnection between networks of smart devices (objects) through specific communication standards and protocols. Data retrieved from these objects is loaded on cloud servers where it is analyzed, processed and then accessed by users through simple, intuitive, easy-to-use interfaces. The rapid development of IoT applications will steadily increase the demand for skilled personnel in this field. Under these circumstances, the study of IoT basic notions in classrooms becomes a necessity. Using the Arduino and Raspberry Pi development platforms, a wide range of IoT projects can be developed, according to the students' level. These platforms capture environmental data through an extended array of sensors (temperature, light, pressure, humidity, force, acceleration, rotation, sound, IR, magnetic field, etc.), making it perfectly suited to projects development in the educational STEM field (Science, Technology, Engineering and Matemathics). In this study my aim is to show, based on examples, how several IoT projects can be developed in class using the two above mentioned platforms and I emphasize the didactical aspects associated with this study. For the first project I used the Arduino Uno development board, the DHT11 temperature and humidity sensor, and ESP8266 WiFi module, and showed how the ambient temperature and humidity can be monitored via Internet. The data collected by the sensor was processed by Arduino, sent to the WiFi module and further on a cloud server on the IoT ThingSpeak platform where it was stored, analyzed and graphically represented in real time. In the second project I used a Raspberry Pi development board, a mobile robotic platform powered by two electric motors and equipped with a webcam in order to video monitor, via the Internet, a particular perimeter under observation. The robotic platform on which the surveillance camera is located can be positional controlled using a web browser that can access an HTML page built for this purpose. The final part of this study is dedicated to the assessment of the quality of STEM training results by classes of students who have worked on IoT projects compared with classes that have undergone traditional training courses.
Digital image correlation at long working distances: The influence of diffraction limits

Digital Image Correlation (DIC) is an optical measurement technique that can easily be adapted for high magnification applications. These high magnifications involve competing phenomena which must be balanced to produce the highest quality measurements. When out-of-plane displacements cause the specimen under investigation to move out of the depth of field, poor focus negatively affects the measurement. As a result, it is often recommended to reduce aperture size to improve the depth of field. However, smaller aperture sizes can also cause poor focus as the diffraction limit of light causes larger Airy disks, particularly at longer working distances. This work investigates the competing effects of both depth of field and Airy disk size in three test cases: higher magnification and shorter working distance, lower magnification and shorter working distance, and lower magnification and longer working distance. Different aperture sizes are found to change which effect dominates, and a recommendation for selecting the aperture setting to minimize measurement error from both phenomena is made.
Infrastructure eXpression for Codified Cyber Attack Surfaces and Automated Applicability

The internal laboratory directed research and development (LDRD) project Infrastructure eXpression (IX) at the Idaho National Laboratory (INL), is based on codifying infrastructure to support automatic applicability to emerging cyber issues, enabling automated cyber responses, codifying attack surfaces, and analysis of cyber impacts to our nation's most critical infrastructure. IX uses the Structured Threat Information eXpression (STIX) open international standard version 2.1 which supports STIX Cyber Observable (SCO) to codify infrastructure characteristics and exposures. Using these codified infrastructures, STIX Relationship Objects (SRO) connect to STIX Domain Objects (SDO) used for modeling cyber threat used to create attack surfaces integrated with specific infrastructure. This IX model creates a shareable, actionable and implementable attack surface that is updateable with emerging threat or infrastructure modifications. Enrichment of cyber threat information includes attack patterns, indicators, courses of action, malware and threat actors. Codifying infrastructure in IX enables creation of software and hardware bill of materials (SBoM/HBoM) information, analysis of emerging cyber vulnerabilities including supply chain threat to infrastructure.
DLVGen: A Dual Latent Variable Approach to Personalized Dialogue Generation

The generation of personalized dialogue is vital to natural and human-like conversation. Typically, personalized dialogue generation models involve conditioning the generated response on the dialogue history and a representation of the persona/personality of the interlocutor. As it is impractical to obtain the persona/personality representations for every interlocutor, recent works have explored the possibility of generating personalized dialogue by finetuning the model with dialogue examples corresponding to a given persona instead. However, in real-world implementations, a sufficient number of corresponding dialogue examples are also rarely available. Hence, in this paper, we propose a Dual Latent Variable Generator (DLVGen) capable of generating personalized dialogue in the absence of any persona/personality information or any corresponding dialogue examples. Unlike prior work, DLVGen models the latent distribution over potential responses as well as the latent distribution over the agent's potential persona. During inference, latent variables are sampled from both distributions and fed into the decoder. Empirical results show that DLVGen is capable of generating diverse responses which accurately incorporate the agent's persona.
Mastering the technology of electronic portfolio by future tutors in the conditions of distance learning

The article describes the processes of organizing and implementing distance learning at the Institute of Pedagogy, Psychology and Sociology of the Siberian Federal University in the context of the COVID-19 pandemic. The process of mastering the technology of electronic portfolio by bachelor students of the training direction “Pedagogical education”, the profile “Tutor” in the implementation of the discipline “E-portfolio in personal and professional development” is considered. The possibilities of using the means of distance educational technologies in the process of studying by students of the electronic training course “E-portfolio in personal and professional development” are presented. The methods of organizing classroom (practical) and independent work of students by the teacher in the implementation of this course using distance learning technologies are described. Examples of classes with a specific description of the stages, the distance technologies used, the actions of participants in the educational process — students and teachers; the results of students’ activities to perform part of the classroom and independent tasks of the electronic training course under consideration are presented. The results of students’ reflection on the course which was organized using the forum are presented in the article.
Cryptanalysis of a New Chaotic Image Encryption Technique Based on Multiple Discrete Dynamical Maps

Recently, a new chaotic image encryption technique was proposed based on multiple discrete dynamic maps. The authors claim that the scheme can provide excellent privacy for traditional digital images. However, in order to minimize the computational cost, the encryption scheme adopts one-round encryption and a traditional permutation-diffusion structure. Through cryptanalysis, there is no strong correlation between the key and the plain image, which leads to the collapse of cryptosystem. Based on this, two methods of chosen-plaintext attacks are proposed in this paper. The two methods require 3 pairs and 258 pairs of plain and cipher images, respectively, to break the original encryption system. The simulation results show the effectiveness of the two schemes.
An Analysis of Face Recognition under Face Mask Occlusions

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.
Comprehensive Methodology to Evaluate Parasitic Energy Consumption for Different Types of Dual-Axis Sun Tracking Systems

A dual-axis sun tracking system is an essential strategy to maximize the optical efficiency of harnessing solar energy. However, there is no significant study yet to optimize the net performance of the photovoltaic (PV) or concentrator photovoltaic (CPV) system equipped with a dual-axis sun tracking system. Parasitic energy loss associated with the power consumption of the sun tracking system is one of the major concerns for the solar industrial players. To address this issue, a comprehensive methodology has been developed to evaluate the yearly cumulative range of motion for dual-axis sun tracking systems in the cases of with and without fixed parking positions across the latitudes ranging from 45°N to 45°S. The parasitic energy consumptions have been investigated for three selected types of dual-axis sun tracking systems, i.e., the azimuth-elevation sun tracking system (AE-STS), polar dual-axis sun tracking system (PD-STS), and horizontal dual-axis sun tracking system (HD-STS). The simulated results indicate that the dual-axis sun tracking system with the nonfixed parking (or stow) position has lower yearly cumulative parasitic energy consumption with respect to the sun tracking system with a fixed parking position. Lastly, our simulation result has shown that the parasitic energy consumption of the sun tracking is relatively smaller to that of the electrical energy generated by the concentrator photovoltaic system with the ratio between 0.15% and 0.29% for AE-STS, between 0.15% and 0.30% for PD-STS, and between 0.17% and 0.35% for HD-STS.
Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement

The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i.e.style feature, and the feature representing the invariant semantic content, i.e. content feature. Previous methods separate content feature apart by utilizing it to classify haze image during the training process. However, in this paper we recognize the incompleteness of the content-style disentanglement in such technical routine. The flawed style feature entangled with content information inevitably leads the ill-rendering of the haze images. To address, we propose a self-supervised style regression via stochastic linear interpolation to reduce the content information in style feature. The ablative experiments demonstrate the disentangling completeness and its superiority in level-aware haze image synthesis. Moreover, the generated haze data are applied in the testing generalization of vehicle detectors. Further study between haze-level and detection performance shows that haze has obvious impact on the generalization of the vehicle detectors and such performance degrading level is linearly correlated to the haze-level, which, in turn, validates the effectiveness of the proposed method.
Blockchain-Enabled Network Slicing

To the beginning of the next decade, the world community will become a more digitalized, information-guided, and intelligently managed ecosystem with needs for ubiquitous connectivity for a plethora of various traditional and emerging services. Next-generation mobile communications play a steering role in this evolution. Network slicing is a perspective resource provisioning technique in the future design of mobile systems with the purpose to meet heterogeneous service requirements. The integration of blockchain technology in mobile networks will allow more efficient and transparent resource management and utilization while reducing its administration costs and complexity of negotiation. Moreover, security aspects are of great importance, too. Motivated by these facts, this paper seeks to provide a comprehensive survey on the fusion of blockchain technology with 5G and beyond systems in the network slicing domain. An efficient and realistic network slicing framework based on blockchain technology is proposed. Lastly, the survey is completed by shedding new light on open research directions in this emerging field.
One-shot fringe pattern analysis based on deep learning image denoiser

Extracting phase from a single-frame fringe pattern is a key and challenging problem in fringe projection 3D measurement, especially for dynamic 3D measurement. We propose a single-shot phase extraction approach based on a low-pass filter with a well-trained image denoiser. Various comparative experiments have verified the effectiveness of the proposed method. More importantly, we associate the single-shot phase extraction problem with image denoising using deep learning. Thus more existing well-designed deep neural network models can be reused in the proposed method, without having to design a new model.
Investigating the Affective State of VR HMD User When Watching Videos Displayed in Different Formats

In recent years, many studies have discussed whether virtual reality (VR) can enhance the sensory experience of the audience via a controllable virtual environment. Most of the researches focus on comparing the difference of perceptual immersion between LCD screen and VR, or the impact on the narrative of the two media. The goal of this study is to investigate the affective state in VR head-mounted display (HMD) when the user watches videos displayed in different formats, i.e., 2D video, panoramic video and 360 video. In order to conduct this study, we designed an experiment procedure as well as an emotion annotation and monitoring system, which can not only obtain affective states of users but also collect physiological signals and behavior movements when the user watches videos in different formats. Furthermore, we used post-questionnaire to investigate the sense of presence, emotional induction and dominance of users. Some findings in the current study are summarized in the experimental results to give an insight for VR application developers so that they can use videos in suitable formats to evoke emotions in accordance with their requirements.
Vehicle localisation and deep model for automatic calibration of monocular camera in expressway scenes

The authors present a fully automatic method for camera calibration in expressway scenes and vehicle localisation on curved roads. Our approach does not depend on specific targets or a priori information and adapts to a wide range of variations in road appearances and camera views. We make three main contributions to automatic camera calibration. Firstly, we propose a Deep Calibration Network to estimate the vanishing point and camera extrinsic parameters from an RGB image in an end-to-end manner. The vanishing point and camera rotation angles are then used to calculate the camera focal length according to the perspective projection geometry, thus constructing the complete camera matrix. Secondly, we introduce a Multi-View Camera Calibration Dataset, which contains a total of 32,712 images for 17 expressway scenes. The dataset covers a wide range of camera views and expressway types, which is challenging. Thirdly, we present a novel Curved Reference Line System adapted to the expressway shape to locate vehicles on curved roads accurately. This coordinate system can effectively transform the vehicle coordinates from a Cartesian system to the vehicle's mileage and horizontal distance relative to the lane line. A comprehensive evaluation on Multi-View Camera Calibration Dataset shows that our technique can automatically calibrate a single camera at about 40fps on an NVIDIA GTX1060 processor and accurately locate vehicles on curved roads, making it suitable for deployment in expressway surveillance systems.