Reviewerly/exHarmony huggingface dataset:
Perceptual loss function for generating high-resolution climate data

&lt;abstract&gt; &lt;p&gt;When planning the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally significant systems, policy makers require accurate and high-resolution data reflecting different climate scenarios. There is widely documented evidence that perceptual loss can be used to generate perceptually realistic results when mapping low-resolution inputs to high-resolution outputs, but its application is limited to images at present. In this paper, we study the perceptual loss when increasing the resolution of raw precipitation data by ×4 and ×8 under training modes of CNN and GAN. We examine the difference in the perceptual loss calculated by using different layers of feature maps and demonstrate how low- and mid-level feature maps can yield comparable results to pixel-wise loss. In particular, from both qualitative and quantitative points of view, Conv2_1 and Conv3_1 are the best compromises between obtaining detailed information and maintaining the overall error in our case. We propose a new approach to benefit from perceptual loss while considering the characteristics of climate data. We show that in comparison to calculating perceptual loss directly for the entire sample, our proposed approach can obtain detailed information of extreme events regions while reducing error.&lt;/p&gt; &lt;/abstract&gt;
CSL-YOLO: A Cross-Stage Lightweight Object Detector with Low FLOPs

The development of lightweight object detectors is essential due to the limited computation resources. To reduce the computation cost, how to generate features plays a significant role. This paper proposes a new lightweight convolution method Cross-Stage Lightweight Module (CSL-M). It combines the Inverted Residual Block (IRB) and Cross-Stage Partial (CSP) concept. Experiments conducted at CIFAR-10 show that the proposed CSL-Net based on CSL-M performs better with fewer FLOPs than the other lightweight backbones. Finally, we use CSL-Net as the backbone to construct a lightweight detector CSL-YOLO, achieving better detection performance with only 43% FLOPs and 52% parameters than Tiny-YOLOv4.
Polynomial Time Near-Time-Optimal Multi-Robot Path Planning in Three Dimensions with Applications to Large-Scale UAV Coordination

For enabling efficient, large-scale coordination of unmanned aerial vehicles (UAV s) under the labeled setting, in this work, we develop the first polynomial time algorithm for the reconfiguration of many moving bodies in three-dimensional spaces, with provable 1. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> asymptotic makespan optimality guarantee under high robot density. More precisely, on an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m_{1} \times m_{2} \times m_{3}$</tex> grid, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m_{1}\geq m_{2}\geq m_{3}$</tex> , our method computes solutions for routing up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\displaystyle \frac{m_{1}m_{2}m_{3}}{3}$</tex> uniquely labeled robots with uniformly randomly distributed start and goal configurations within a makespan of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m_{1}+2m_{2}+2m_{3}+o(m_{1})$</tex> , with high probability. Because the makespan lower bound for such instances is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m_{1}+m_{2}+m_{3}-o(m_{1})$</tex> , also with high probability, as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m_{1}\displaystyle \rightarrow\infty, \frac{m_{1}+2m_{2}+2m_{3}}{m_{1}+m_{2}+m_{3}}$</tex> optimality guarantee is achieved. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\displaystyle \frac{m_{1}+2 m_{2}+2m_{3}}{m_{1}+m_{2}+m_{3}}\in\left(1, \displaystyle \frac{5}{3}\right]$</tex> , yielding 1. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> optimality. In contrast, it is well-known that multi-robot path planning is NP-hard to optimally solve. In numerical evaluations, our method readily scales to support the motion planning of over 100, 000 robots in 3D while simultaneously achieving 1. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> optimality. We demonstrate the application of our method in coordinating many quadcopters in both simulation and hardware experiments.
Blockchain-Powered Tensor Meta-Learning-Driven Intelligent Healthcare System With IoT Assistance

The rapid development and gradual integration of artificial intelligence and the Internet of Things have brought unprecedented opportunities for radically changing healthcare and treatments. However, the burgeoning in intelligent healthcare systems is severely bounded by data privacy and the security of AI models. Meanwhile, the limited local data forces conventional AI models to face the predicament in achieving personalized healthcare. Hence, we propose a blockchain-powered tensor meta-learning-driven intelligent healthcare system with IoT assistance. IoT devices as light nodes upload the local shareable data to the edge server(full node) for model training and perform the local private data by non-tampered model downloaded via smart contract. The system can not only use blockchain technology to ensure the strong consistency of the healthcare model but also protect private data from being leaked. Especially, we develop a tensor meta-learning model named tensor-prototype graph network to achieve efficient modeling of heterogeneous healthcare data. Building on the tensors and graph network, the model is conducive to capturing the data distribution when there are few labeled data. To evaluate our proposed approach, we have conducted experiments on three classic databases. The results demonstrate that our approach is capable of effectively promoting the performance of intelligent healthcare.
CADW: CGAN-Based Attack on Deep Robust Image Watermarking

Robust watermarking plays an essential role in copyright protection for digital images. Meanwhile, the studies of robust image watermarking and corresponding attack methods promote and complement each other. Because the traditional attack methods are weak to attack the deep watermarking, in this work, we thus design an effective watermarking attack method based on the conditional generative adversarial nets (CGAN). Also, according to the network structure of CGAN, a targeted and combined loss function is constructed, which can guarantee an acceptable visual quality of attacked image and make the watermark extraction fail simultaneously. Experimental results demonstrate that our method is effective for some state-of-the-art deep robust image watermarking methods with the transferability. In addition, as an attack method, it can be regarded as an evaluation standard to measure the robustness of watermarking.
Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes

Network telescopes or “Darknets” received unsolicited Internet-wide traffic, thus providing a unique window into macroscopic Internet activities associated with malware propagation, denial of service attacks, network reconnaissance, misconfigurations and network outages. Analysis of the resulting data can provide actionable insights to security analysts that can be used to prevent or mitigate cyber-threats. Large network telescopes, however, observe millions of nefarious scanning activities on a daily basis which makes the transformation of the captured information into meaningful threat intelligence challenging. To address this challenge, we present a novel framework for characterizing the structure and temporal evolution of scanning behaviors observed in network telescopes. The proposed framework includes four components. It (i) extracts a rich, high-dimensional representation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scanning profiles</i> composed of features distilled from network telescope data; (ii) learns, in an unsupervised fashion, information-preserving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">succinct representations</i> of these scanning behaviors using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep representation learning</i> that is amenable to clustering; (iii) performs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clustering</i> of the scanner profiles in the resulting latent representation space on daily Darknet data, and (iv) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">detects temporal changes</i> in scanning behavior using techniques from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal mass transport</i> . We robustly evaluate the proposed system using both synthetic data and real-world Darknet data. We demonstrate its ability to detect real-world, high-impact cybersecurity incidents such as the onset of the Mirai botnet in late 2016 and several interesting cluster formations in early 2022 (e.g., heavy scanners, evolved Mirai variants, Darknet “backscatter” activities, etc.). Comparisons with state-of-the-art methods showcase that the integration of the proposed features with the deep representation learning scheme leads to better classification performance of Darknet scanners.
A Real World Dataset for Multi-view 3D Reconstruction

We present a dataset of 998 3D models of everyday tabletop objects along with their 847,000 real world RGB and depth images. Accurate annotation of camera pose and object pose for each image is performed in a semi-automated fashion to facilitate the use of the dataset in a myriad 3D applications like shape reconstruction, object pose estimation, shape retrieval etc. We primarily focus on learned multi-view 3D reconstruction due to the lack of appropriate real world benchmark for the task and demonstrate that our dataset can fill that gap. The entire annotated dataset along with the source code for the annotation tools and evaluation baselines is available at http://www.ocrtoc.org/3d-reconstruction.html .
POP: Mining POtential Performance of New Fashion Products via Webly Cross-modal Query Expansion

We propose a data-centric pipeline able to generate exogenous observation data for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e., predicting the performance of a brand-new clothing probe with no available past observations. Our pipeline manufactures the missing past starting from a single, available image of the clothing probe. It starts by expanding textual tags associated with the image, querying related fashionable or unfashionable images uploaded on the web at a specific time in the past. A binary classifier is robustly trained on these web images by confident learning, to learn what was fashionable in the past and how much the probe image conforms to this notion of fashionability. This compliance produces the POtential Performance (POP) time series, indicating how performing the probe could have been if it were available earlier. POP proves to be highly predictive for the probe's future performance, ameliorating the sales forecasts of all state-of-the-art models on the recent VISUELLE fast-fashion dataset. We also show that POP reflects the ground-truth popularity of new styles (ensembles of clothing items) on the Fashion Forward benchmark, demonstrating that our webly-learned signal is a truthful expression of popularity, accessible by everyone and generalizable to any time of analysis. Forecasting code, data and the POP time series are available at: https://github.com/HumaticsLAB/POP-Mining-POtential-Performance .
Research Directions in Process Modeling and Mining Using Knowledge Graphs and Machine Learning

Services Computing has seen a dramatic rise in the last twenty years. The foundation for services provided by enterprises is business processes, so progress in the development of effective and efficient processes is of utmost importance. The design or modeling of business processes is a challenging task. Over the years many research and development efforts have paid dividends, including languages and notations like the Business Process Executing Language and the Business Process Modeling Notation, along with supporting methodologies and tools. Research in Semantic Web Services and Processes showed promise for the automation of services discovery and composition (orchestration/choreography). The current large-scale deployment of enterprise knowledge graphs by many organizations coupled with huge advancements in machine learning (particularly deep learning) provides new opportunities for advancing this automation forward.
JSH-Net: joint semantic segmentation and height estimation using deep convolutional networks from single high-resolution remote sensing imagery

ABSTRACTABSTRACTSemantic segmentation for high-resolution remote sensing imagery is a pivotal component of land use and land cover categorization, and height estimation is essential for rebuilding the 3D information of an image. Because of the higher intra-class variation and smaller inter-class dissimilarity, these two challenging tasks are generally treated separately. This paper proposes a fully convolutional network that can tackle these problems simultaneously by estimating the land-cover categories and height values of pixels from a single aerial image. To handle these tasks, we develop a multi-task learning architecture (JSH-Net) that employs a shared feature representation and exploits their potential consistency across tasks, resulting in robust features and better prediction accuracy. Specifically, we propose a novel skip connection module that aggregates the contexts from the encoder part to the decoder part, bridging the semantic gap between them. In addition, we propose a progressive refinement strategy to recover detailed information about the objects. Moreover, we also proposed a height estimation branch on the head of the model to utilize shared features. The experiments we conducted on ISPRS 2D Labelling dataset verified that our network provided precise results of semantic segmentation and height estimation from two output branches and outperformed other state-of-the-art approaches.KEYWORDS: remote sensingsemantic segmentationheight estimationdeep learningmulti-task learningconvolutional neural network Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported in part by the National Natural Science Foundation of China under Grant 42030102, Grant 42192583, Grant 42001406, and Grant 62102268; in part by the Fund for InnovativeResearch Groups of the Hubei Natural Science Foundation under Grant 2020CFA003; and in part by the China Postdoctoral Science Foundation under Grant 2020M672416; and in part by the Major special projects of Guizhou[2022]001.
Do visual issue reports help developers fix bugs?

Issue reports are a pivotal interface between developers and users for receiving information about bugs in their products. In practice, issue reports often have incorrect information or insufficient information to enable bugs to be reproduced, and this has the effect of delaying the entire bug-fixing process. To facilitate their bug-reproduction work, GitHub has provided a new feature that allows users to share videos (e.g., mp4 files.) Using such videos, reports can be made to developers about the details of bugs by recording the symptoms, reproduction steps, and other important aspects of bug information.
Fake Profile Detection Using Machine Learning Techniques

Our lives are significantly impacted by social media platforms such as Facebook, Twitter, Instagram, LinkedIn, and others.People are actively participating in it the world over.However, it also has to deal with the issue of bogus profiles.False accounts are frequently created by humans, bots, or computers.They are used to disseminate rumors and engage in illicit activities like identity theft and phishing.So, in this project, the author'll talk about a detection model that uses a variety of machine learning techniques to distinguish between fake and real Twitter profiles based on attributes like follower and friend counts, status updates, and more.The author used the dataset of Twitter profiles, separating real accounts into TFP and E13 and false accounts into INT, TWT, and FSF.Here, the author discusses LSTM, XG Boost, Random Forest, and Neural Networks.The key characteristics are chosen to assess a social media profile's authenticity.Hyperparameters and the architecture are also covered.Finally, results are produced after training the models.The output is therefore 0 for genuine profiles and 1 for false profiles.When a phony profile is discovered, it can be disabled or destroyed so that cyber security problems can be prevented.Python and the necessary libraries, such as Sklearn, Numpy, and Pandas, are used for implementation.At the end of this study, the author will come to the conclusion that XG Boost is the best machine learning technique for finding fake profiles.
Scalable Collaborative Software Visualization as a Service: Short Industry and Experience Paper

Software visualizations are used by software developers, for instance, for program comprehension. In this context, a less researched aspect is the collaborative use of online visual-ization services. This paper presents the conceptual design and a prototype implementation of our approach for a collaborative software visualization service for program comprehension. The central idea is an online available software-as-a-service application that analyzes, persists, and visualizes software applications which users intend to explore and comprehend via software visualization. The resulting implementation is a redevelopment of our live trace visualization tool Explor Viz. In comparison to other approaches, ExplorViz utilizes WebGL and other browser technologies instead of a game engine to render its visualizations. As a result, we achieve a platform-independent interoperability that is crucial for (remote) collaboration. Here, users can explore the same visualization via our collaboratively usable desktop, virtual reality, and also augmented reality modes. Our prototype follows the Twelve-Factor App methodology to build a cloud-native application that comprises multiple scalable microservices. Thanks to horizontal scaling, our implementation is capable to analyze a large amount of visualization data; thus, allowing multiple users to simultaneously use the software visualization service. We conducted a set of preliminary performance exper-iments to benchmark our prototype's scalability. Results show that the evaluated service scales linearly with increasing load.
Security Issues in Blockchain Integrated WSN: Challenges and Concerns

In cutting edge era the foremost valuable information is ‘Data’, and WSN system has become one in all the integrated elements of our life. From smart phones to shrewd watches, there’s tons of data creation in each and every moment and wireless sensors are one of the primary components of WSN framework. Wireless sensors are utilized in all the WSN systems in corporated gadgets like advanced mobile phones, watches, fitness tracker and so on., these sensors are easy to use and have numerous distinctive characteristics. WSNs are used for real time trailing and surveillance purposes, where the nodes collaborate with each other to pass and retrieve the information. However, there are chances of compromise of data, so security turns into a major issue in WSN. Blockchain is a secure database i.e., a ledger that monitors all transactions occurring. In this paper, an in-depth survey on blockchain integrated WSN is done and the security parameters are also dealt thoroughly.
Digital and Computational Pathology: A Specialty Reimagined

Abstract The field of pathology, which provides tissue diagnoses for clinical and research purposes, is at the heart of medical decision-making. The current move to digital pathology (DP) is a fundamental change in how primary diagnostic work, consultations, education, and multidisciplinary conferences are performed. DP is the prerequisite for computational pathology (CPATH), the big-data approach to pathology that extracts information from images, typically using artificial intelligence (AI) methods. While CPATH offers significant new capabilities and workflows, it also brings new challenges. There will be knock-on effects on other specialties and in teaching and research. The emerging next-generation pathology will be more quantitative, will have more diagnostic consistency, and will be more connected to its medical peers.
Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition

Sensor-based human activity recognition (HAR) is a paramount technology in the Internet of Things services. HAR using representation learning, which automatically learns a feature representation from raw data, is the mainstream method because it is difficult to interpret relevant information from raw sensor data to design meaningful features. Ensemble learning is a robust approach to improve generalization performance; however, deep ensemble learning requires various procedures, such as data partitioning and training multiple models, which are time-consuming and computationally expensive. In this study, we propose an easy ensemble (EE) for HAR, which enables the easy implementation of deep ensemble learning in a single model. In addition, we propose various techniques (input variationer, stepwise ensemble, and channel shuffle) for the EE. Experiments on a benchmark data set for HAR demonstrated the effectiveness of EE and various techniques and their characteristics compared with conventional ensemble learning methods.
Ground Reaction Force Estimation in a Quadruped Robot via Liquid State Networks

This paper aims to investigate the Liquid State Machines (LSMs) learning capability and robustness of a complex robot-environment interaction. The goal is to design an efficient robot state estimation method based on reservoir computing. The method maps local proprioceptive information acquired at the level of the leg joints of a simulated quadruped robot. The robot taken into account is the simulated version of Lilibot, a small-sized and reconfigurable bio-inspired robot with multiple real-time sensory feedback. Global information was provided from the ground reaction forces acquired on the tips of each leg. Simulation results are reported and compared, also in presence of faulty conditions in the sensory system.
<i>PhisherCop</i>: Developing an NLP-Based Automated Tool for Phishing Detection

Phishing poses a major security risk to organizations and individuals leading to loss of billions of dollars yearly. While risk communication serves as a tool to mitigate phishing attempts, it is imperative to create automated phishing detection tools. Numerous Natural Language Processing (NLP) approaches have been deployed to tackle phishing. However, phishing attempts continue to increase exponentially, which reiterates the need for more effective approach. To address this, we have developed an anti-phishing tool called; PhisherCop. PhisherCop is built upon Stochastic Gradient Descent classifier (SGD) and Support Vector Classifier (SVC) which showed an average accuracy of 96%, performing better than six other popular classifiers including: Decision Tree, Logistics Regression, Random Forest, Gradient Boosting Classifier, K-Nearest Neighbors and Multinomial Naive Bayes. Our tool is significant in distinguishing between phishing and legitimate content both over emails and text messages.