Reviewerly/exHarmony huggingface dataset:
A New Bounding Box based Pseudo Annotation Generation Method for Semantic Segmentation

This paper proposes a fusion-based method to generate pseudo-annotations from bounding boxes for semantic segmentation. The idea is to first generate diverse foreground masks by multiple bounding box segmentation methods, and then combine these masks to generate pseudo-annotations. Existing methods generate foreground masks from bounding boxes by classical segmentation methods driving by low-level features and own local information, which is hard to generate accurate and diverse results for the fusion. Different from the traditional methods, multiple class-agnostic models are modeled to learn the objectiveness cues by using existing labeled pixel-level annotations and then to fuse. Firstly, the classical Fully Convolutional Network (FCN) that densely predicts the pixels' labels is used. Then, two new sparse prediction based class-agnostic models are proposed, which simplify the segmentation task as sparsely predicting the boundary points through predicting the distance from the bounding box border to the object boundary in Cartesian Coordinate System and the Polar Coordinate System, respectively. Finally, a voting-based strategy is proposed to combine these segmentation results to form better pseudo-annotations. We conduct experiments on PASCAL VOC 2012 dataset. The mIoU of the proposed method is 68.7%, which outperforms the state-of-the-art method by 1.9%.
A modular architecture for high resolution image dehazing

Haze is an atmospheric phenomenon which diminishes visibility in outdoor images. Algorithms based on dark channel prior (DCP) and haze line prior are found to be effective for dehazing images. These two methods make use of the Laplacian matrix, which is computationally complex, memory intensive and slow, thus making it impossible to use them on high-resolution (large) images. Multiple strategies have been suggested in the literature to speed up dehazing process by avoiding the Laplacian matrix, but these methods compromise on the quality of dehazing. We propose an effective modular method which divides the input image into blocks and processes each block independently. This makes it possible to use our method for dehazing large images retaining Laplacian matting and thus ensuring the output image quality. This division results in the possibility of assuming local values of atmospheric light. We show that this approach results in better dehazing in the local regions. The effectiveness of the proposed modular architecture is tested also on a learning based method. The output of the modular method is compared with those of different state-of-the-art dehazing methods for multiple quality parameters. Toward this, we have created a dataset of hazy natural outdoor images of large size.
Tennantite–Tetrahedrite-Series Minerals and Related Pyrite in the Nibao Carlin-Type Gold Deposit, Guizhou, SW China

A number of sediment-hosted, Carlin-type/-like gold deposits are distributed in the Youjiang basin of SW China. The gold ores are characterized by high As, Hg, and Sb contents but with low base metal contents (Cu+Pb+Zn &lt; 500–1000 ppm). The Nibao deposit is unique among these gold deposits by having tennantite–tetrahedrite-series minerals in its ores. The deposit is also unique in being primarily hosted in the relatively unreactive siliceous pyroclastic rocks, unlike classic Carlin-type gold deposits that are hosted in carbonates or calcareous clastic rocks. In this study, we have identified tennantite-(Zn), tennantite-(Hg), and tetrahedrite-(Zn) from the tennantite–tetrahedrite-series mineral assemblage. The tennantite-(Zn) can be further divided into two sub-types of Tn-(Zn)-I; and Tn-(Zn)-II;. Tn-(Zn)-I; usually occurs in the core of a Tennantite–tetrahedrite composite and appears the darkest under the SEM image, whereas Tn-(Zn)-II overgrows on Tn-(Zn)-I and is overgrown by tetrahedrite-(Zn). Tennantite-(Hg) occasionally occurs as inclusions near the uneven boundary between Tn-(Zn)-I and Tn-(Zn)-II. An appreciable amount of Au (up to 3540 ppm) resides in the tennantite–tetrahedrite-series minerals, indicating that the latter is a major Au host at Nibao. The coexistence of tennantite–tetrahedrite-series minerals and Au-bearing pyrite indicates the Nibao ore fluids were more oxidized than the Carlin-type ore fluids. The tennantite–tetrahedrite series at Nibao evolved from Tn-(Zn)-I through Tn-(Zn)-II to tetrahedrite-(Zn), which is likely caused by Sb accumulation in the ore fluids. This indicates that the Nibao ore fluids may have become more reduced and less acidic during Au precipitation.
EdgeNAS: Discovering Efficient Neural Architectures for Edge Systems

Edge systems integrated with deep neural networks (DNNs) are deemed to pave the way for future artificial intelligence (AI). However, designing accurate and efficient DNNs for resource-limited edge systems is challenging as well as requires a huge amount of engineering efforts from human experts since the design space is highly complex and diverse. Also, previous works mostly focus on designing DNNs with less floating-point operations (FLOPs), but indirect FLOPs count does not necessarily reflect the complexity of DNNs. To tackle these, we, in this paper, propose a novel neural architecture search (NAS) approach, namely EdgeNAS, to automatically discover efficient DNNs for less capable edge systems. To this end, we propose an end-to-end learning-based latency estimator, which is able to directly approximate the architecture latency on edge systems while incurring negligible computational overheads. Further, we effectively incorporate the latency estimator into EdgeNAS with a uniform sampling strategy, which guides the architecture search towards an edge-efficient direction. Moreover, a search space regularization approach is introduced to balance the trade-off between efficiency and accuracy. We evaluate EdgeNAS on the edge platform, Nvidia Jetson Xavier, with three popular datasets. Experimental results demonstrate the superiority of EdgeNAS over state-of-the-art approaches in terms of latency, accuracy, number of parameters, and the search cost.
Asynchronous Forensic Investigative Approach to Recover Deleted Data from Instant Messaging Applications

Proliferation of digital platforms specifically Instant Messaging Applications (IMAs), have introduced new challenges to digital forensic investigations. With the rapidly increased use of WhatsApp application, it is plausible to speculate that WhatsApp became a potential source of threat and/or cybercrime. Some newly added features on WhatsApp, such as `delete for everyone', giving the users the ability to delete messages from both ends (sender and receiver), have resulted in complicating the cybercrime investigation process. Therefore, there is a need to revisit the investigation process and the structure of such updated features to be able to create a comprehensive digital forensic technique. This paper examines the forensic artifacts of the WhatsApp's `delete for everyone' feature. This feature is a great addition to the overall usability of IMAs, however, it is also crucial to update the digital forensic investigation techniques and test the capability of commercial forensic tools in recovering forensic evidence when a new technology has been introduced. During the course of this research, we tested and validated the digital forensic methodology and compared the investigation results with forensically sound commercial tools. During the data acquisition process, we conducted physical and logical forensic acquisitions of an Android device which led to a breakthrough discovery of a SQLite file called Write-Ahead-Log (WAL) which contains the application's latest messages, including deleted (allegedly) messages.
Packing Planning and Execution Considering Arrangement Rules

To send a consumer the ordered products in e-commerce, it is necessary to pack the products into a container. When packing, we need to consider the arrangement of the products to avoid damaging them during transportation by using the know-how of packing. In this paper, we propose a packing robot that plans the arrangement and then executes it. To develop this robot, we employ two ideas: one idea is to split the whole process into the arrangement of the objects and robot motion generation. The arrangement is regarded as the 3D bin-packing problem. The other idea is to use the heuristics of the packing so that objects are stacked from the bottom. Since the space above the target position is free from collisions, the strategy to approach from the top reduces the possibility of the collision. In the experiments, we succeeded in arranging eleven objects satisfying the assigned rules and packing ten of the eleven objects using the robot.
Near-Infrared Image Colorization by Convolutional Neural Network with Perceptual Loss

Near-infrared (NIR) imaging is widely used in various fields, such as surveillance and autonomous driving, because the NIR frequency band is invisible to the human eye. NIR images are usually represented in grayscale, which may disagree with human cognition. Thus, an RGB-color-model representation is preferable for human perception. We proposed a deep neural network for NIR image colorization that employs an encoder-decoder-based convolutional neural network for the colorization. For a realistic colorization, we used perceptual loss and pixelwise loss functions. Experimental results reveal that the proposed network can be suitably generalized to unseen data owing to perceptual loss.
A deep machine learning algorithm for construction of the Kolmogorov–Arnold representation

The Kolmogorov–Arnold representation is a proven adequate replacement of a continuous multivariate function by a hierarchical structure of multiple functions of one variable. The proven existence of such representation inspired many researchers to search for a practical way of its construction, since such model answers the needs of machine learning. This article shows that the Kolmogorov–Arnold representation is not only a composition of functions but also a particular case of a tree of the discrete Urysohn operators. The article introduces new, quick and computationally stable algorithm for constructing of such Urysohn trees. Besides continuous multivariate functions, the suggested algorithm covers the cases with quantised inputs and combination of quantised and continuous inputs. The article also contains multiple results of testing of the suggested algorithm on publicly available datasets, used also by other researchers for benchmarking.
Path Planning Using Artificial Potential Field Method And A-star Fusion Algorithm

Aiming at the deficiencies in A-star algorithm and artificial potential field method, this paper proposes a fusion algorithm based on artificial potential field method and A-star algorithm. Although the A-star algorithm can obtain a relatively short path, it cannot handle dynamic obstacles, the artificial potential field method can handle dynamic obstacles but the generated path is much longer than the A-star algorithm. Artificial potential field method and A-star fusion algorithm is proposed to avoid dynamic obstacles and find a shorter path simultaneously. Then we evaluated our proposed method by comparing it with the A-star algorithm, artificial potential field method and two other reference methods. The simulation results show that our proposed method can generate a shorter path and effectively avoid dynamic obstacles.
Privacy Illusion: Beware of Unpadded DoH

DNS over HTTPS (DoH) has been created with ambitions to improve the privacy of users on the internet. Domain names that are being resolved by DoH are transferred via an encrypted channel, ensures nobody should be able to read the content. However, even though the communication is encrypted, we show that it still leaks some private information, which can be misused. Therefore, this paper studies the behavior of the DoH protocol implementation in Firefox and Chrome web-browsers, and the level of detail that can be revealed by observing and analyzing packet-level information. The aim of this paper is to evaluate and highlight discovered privacy weaknesses hidden in DoH. By the trained machine learning classifier, it is possible to infer individual domain names only from the captured encrypted DoH connection. The resulting trained classifier can infer domain name from encrypted DNS traffic with surprisingly high accuracy up to 90% on HTTP 1.1, and up to 70% on HTTP 2 protocol.
Location Determination of Impact on the Wind Turbine Blade Surface Based on the FBG and the Time Difference

This paper proposes an approach to the determination of the precise location of an impact on the surface of a wind turbine blade (WTB) based on a fiber Bragg grating (FBG) and the time difference, and its effectiveness is verified by experiments. First, the strain on the WTB surface is detected with an FBG. Then, the signal is decomposed into a series of components via variational mode decomposition (VMD), and some signals with impact characteristics are chosen for reconstruction. The instant energy of the reconstructed signal is then amplified through the Teager energy operator (TEO) to identify the time difference between FBGs. Finally, the coordinate of the impact point is obtained by solving the hyperbolic mode with the time difference. The results of experiments demonstrate that the proposed approach exhibits good performance with high accuracy (97%) and low error (12.3 mm).
Equitable Multiparty Quantum Communication Without a Trusted Third Party

Multiparty quantum communication provides delightful applications, including quantum cryptographic communication and quantum secret sharing. Quantum communication based on the Greenberg-Horne-Zeilinger (GHZ) state measurement provides a practical way to implement multiparty quantum communication. With the standard spatially localized GHZ state measurement, however, information can be imbalanced among the communication parties that can cause significant problems in some applications of multiparty cryptographic communication, e.g., secret sharing. Here, we propose an equitable multiparty quantum communication where information balance among the communication parties is achieved without a trusted third party. Our scheme is based on the GHZ state measurement that is not spatially localized but implemented in a way that all the distant communication parties symmetrically participate. We also verify the feasibility of our scheme by presenting the proof-of-principle experimental demonstration of informationally balanced three-party quantum communication using weak coherent pulses.
Learning Matching Behavior Differences for Compressing Vehicle Re-identification Models

Vehicle re-identification matching vehicles captured by different cameras has great potential in the field of public security. However, recent vehicle re-identification approaches exploit complex networks, causing large computations in their testing phases. In this paper, we propose a matching behavior difference learning (MBDL) method to compress vehicle re-identification models for saving testing computations. In order to represent the matching behavior evolution across two different layers of a deep network, a matching behavior difference (MBD) matrix is designed. Then, our MBDL method minimizes the L1 loss function among MBD matrixes from a small student network and a complex teacher network, ensuring the student network use less computations to simulate the teacher network's matching behaviors. During the testing phase, only the small student network is utilized so that testing computations can be significantly reduced. Experiments on VeRi776 and VehicleID datasets show that MBDL outperforms many state-of-the-art approaches in terms of accuracy and testing time performance.
Engagement Analysis of ADHD Students using Visual Cues from Eye Tracker

In this paper, we focus on finding the correlation between visual attention and engagement of ADHD students in one-on-one sessions with specialized educators using visual cues and eye-tracking data. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used to model the cognitive state of subjects and to explore the integration of multiple sensor modalities to improve the reliability of detection of human displays of awareness and emotion in the context of ADHD affected children. This is a novel problem since no previous studies have aimed to identify markers of attentiveness in the context of students affected with ADHD. The experiment has been designed to collect data in a controlled environment and later on can be used to generate Machine Learning models to assist real-world educators. Additionally, we propose a novel approach for AOI (Area of Interest) detection for eye-tracking analysis in dynamic scenarios using existing deep learning-based saliency prediction and fixation prediction models. We aim to use the processed data to extract the features from a subject's eye-movement patterns and use Machine Learning models to classify the attention levels.
A stigmergic approach to teaching-learning-based optimization for continuous domains

Teaching-Learning-Based Optimization (TLBO) has shown considerable success in solving complex optimization problems by imitating the higher cognitive leader-follower interactions among students and teachers in a classroom. Here, we propose to solve function optimization with a stigmergic probabilistic memory structure based on the indirect cooperation and distributed information exchange among students of a class along with a diversity preserving operator. The stigmergic approach integrates the historical experience, while the probabilistic representation and the diversity preserving operators avoid premature convergence. The resulting Stigmergic Real-domain TLBO (SRTLBO) is applied to optimization problems with continuous variables. The proposed SRTLBO is evaluated on 40 standard benchmark functions, as well as in modeling the earthquake source and wave propagation in the East of Iran from actual data. Results indicate overall improved solutions of the SRTLBO algorithm when compared to several competing algorithms. In several cases, particularly those functions with a simpler structure, considerable improvement is also observed in the rate of convergence to the optimum solution.
Phase estimation of time-bin qudits by time-resolved single-photon counting

We present a comprehensive framework for quantum state tomography (QST) of time-bin qudits sent through a fiber. Starting from basic assumptions, we define a positive-operator valued measure (POVM) which is then applied to the quantum state reconstruction problem. A realistic scenario is considered where the time uncertainty of the detector is treated as a source of experimental noise. The performance of the quantum tomography framework is examined through a series of numerical simulations conducted for different parameters describing the apparatus. The quality of state recovery, quantified by the notion of minimum fidelity, is depicted on graphs for a range of fiber lengths. Special attention is paid to relative phase reconstruction for qubits and qutrits. The results present relevant interdependence between the fiber length and the detector jitter.
Semi-Supervised Transformation and Deep Embedding-Based Anomaly Identification for Agricultural Internet of Things

This study aims at resolving the problem of anomaly identification for network flows in Internet of Things (IoT), and propose a novel netflow detection approach. Accurate recognition of anomaly is of fundamental importance to numerous network activities including cyberattack monitoring and intrusion prevention. In the past decades, a plethora of techniques have been presented to address this issue. Furthermore, the difference between the anomalous and normal networking activities is trivial in common. Therefore, the machine learning-based algorithms might be incapable of dealing with it. Bearing this in mind, a transformation-based pipeline is introduced to reveal the inconspicuous anomalies. The main idea behind our scheme is to apply various transformations upon data samples would be beneficial to accurate anomaly detection. The comparison experimental results indicate that our work could outperform the accuracy of state-of-the-art techniques.
Blockchain-Based Image Copyright Protection System using JPEG Resistant Digital Signature

A notable increase in the distribution of digital images was fueled by the social media platforms. In the fight for attention, the posts containing images have more chances to make users stop scrolling through the crowded news feed than the posts containing only text. The effect is enhanced if the photos that are used in the posts are professional grade. In the context of the COVID-19 pandemic, more and more businesses have moved to online. Every business makes efforts to catch the attention of potential customers with high quality images. These situations could lead to intended or accidental image copyright infringement. This paper proposes a system that can be used to detect and avoid copyright infringement. Photographers can use it to register their photos and businesses can use it to check if the image they want to use is copyright protected or not. If it is, the system also allows the purchase of the right to use the photo. The solution is based on blockchain because of its immutability property. Businesses can look for images on many sites, thus it is very probable that recompressed versions of the same photo would be stored in different places. Therefore, it is important to develop an algorithm that can extract a signature that is resistant to JPEG compression from the image. The signature should be stored on the blockchain along the identification data of the copyright owner.
FACER: A Universal Framework for Detecting Anomalous Operation of Deep Neural Networks

The detection of anomalies during the operation of deep neural networks (DNNs) is of essential importance in safety-critical applications, such as autonomous vehicles. In the field, classifiers may face rare environmental conditions, unknown objects, hardware failures, and other types of anomalies. Nevertheless, DNNs still predict arbitrarily high class probabilities in these cases and are unable to recognize out of-distribution operation modes. In this paper, we introduce FACER, an efficient and versatile framework that is trainable to detect various types of anomalies in pre-trained DNNs. FACER operates on compressed intermediate feature representations of the supervised network that can be easily obtained. We evaluate the detection of different input corruptions as well as outliers drawn from out-of-distribution datasets with CIFAR10, CIFAR-100 and SVHN classification models. The detection performance of our method is on par with other state-of-the art methods, while our method can be easier implemented and integrated into resource-constrained hardware systems.