Reviewerly/exHarmony huggingface dataset:
A Modified DBSCAN Algorithm for Anomaly Detection in Time-series Data with Seasonality

Anomaly detection concerns identifying anomalous observations or patterns that are a deviation from the dataset's expected behaviour. The detection of anomalies has significant and practical applications in several industrial domains such as public health, finance, Information Technology (IT), security, medical, energy, and climate studies. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm is a density-based clustering algorithm with the capability of identifying anomalous data. In this paper, a modified DBSCAN algorithm is proposed for anomaly detection in time-series data with seasonality. For experimental evaluation, a monthly temperature dataset was employed and the analysis set forth the advantages of the modified DBSCAN over the standard DBSCAN algorithm for the seasonal datasets. From the result analysis, we may conclude that DBSCAN is used for finding the anomalies in a dataset but fails to find local anomalies in seasonal data. The proposed Modified DBSCAN approach helps to find both the global and local anomalies from the seasonal data. Using normal DBSCAN we are able to get 19 (2.16%) anomaly points. While using the modified approach for DBSCAN we are able to get 42 (4.79%) anomaly points. In comparison we can say that we are able to get 2.11% more anomalies using the modified DBSCAN approach. Hence, the proposed Modified DBSCAN algorithm outperforms in comparison with the DBSCAN algorithm to find local anomalies.
Towards collaborative appearance and semantic adaptation for medical image segmentation

This paper proposes a new unsupervised domain adaptation framework, named as Collaborative Appearance and Semantic Adaptation (CASA), for addressing the medical domain mismatch problem. Domain adaptation techniques have become one of the hot topics, especially when applying the established deep neural network into new domains in the medical analysis, i.e., semantic segmentation of medical lesions. To achieve unsupervised domain adaptation, our designed CASA framework could preserve synergistic fusion of adaptation knowledge from the perspectives of appearance and semantic. To be specific, we transform the appearance of medical lesions across domains via a Characterization Transfer Module (CTM), which can mitigate the appearance divergence of medical lesions across domains. Meanwhile, a Representation Transfer Module (RTM) is proposed via incorporating with a conditional generative adversarial network, which could transform features of source lesions to target-like feature, and further narrow the domain-wise distribution gap of underlying semantic knowledge. To the end, a challenging application of medical image segmentation is used to extensively validate the effectiveness of our proposed CASA framework. Various experiment results show its superior performance by a significant margin when comparing to the state-of-the-art domain adaptation methods.
A Mixed-Initiative Approach to Reusing Infographic Charts

Infographic bar charts have been widely adopted for communicating numerical information because of their attractiveness and memorability. However, these infographics are often created manually with general tools, such as PowerPoint and Adobe Illustrator, and merely composed of primitive visual elements, such as text blocks and shapes. With the absence of chart models, updating or reusing these infographics requires tedious and error-prone manual edits. In this paper, we propose a mixed-initiative approach to mitigate this pain point. On one hand, machines are adopted to perform precise and trivial operations, such as mapping numerical values to shape attributes and aligning shapes. On the other hand, we rely on humans to perform subjective and creative tasks, such as changing embellishments or approving the edits made by machines. We encapsulate our technique in a PowerPoint add-in prototype and demonstrate the effectiveness by applying our technique on a diverse set of infographic bar chart examples.
Real-Time Automatic Investigation of Indian Roadway Animals by 3D Reconstruction Detection Using Deep Learning for R-3D-YOLOv3 Image Classification and Filtering

Statistical reports say that, from 2011 to 2021, more than 11,915 stray animals, such as cats, dogs, goats, cows, etc., and wild animals were wounded in road accidents. Most of the accidents occurred due to negligence and doziness of drivers. These issues can be handled brilliantly using stray and wild animals-vehicle interaction and the pedestrians’ awareness. This paper briefs a detailed forum on GPU-based embedded systems and ODT real-time applications. ML trains machines to recognize images more accurately than humans. This provides a unique and real-time solution using deep-learning real 3D motion-based YOLOv3 (DL-R-3D-YOLOv3) ODT of images on mobility. Besides, it discovers methods for multiple views of flexible objects using 3D reconstruction, especially for stray and wild animals. Computer vision-based IoT devices are also besieged by this DL-R-3D-YOLOv3 model. It seeks solutions by forecasting image filters to find object properties and semantics for object recognition methods leading to closed-loop ODT.
Supply chain equilibrium on a game theory-incentivized blockchain network

In the supply chain ecosystem, trust among transacting parties is a critical component to ensure profitability. However, participating parties act selfishly in their interest resulting in a sub-optimal output for transactions. This makes it difficult for trust to be established and an effective equilibrium to be reached among parties. Therefore, it is essential to ensure coordination among all parties and for all parties to collaborate effectively to avoid this problem. This helps to create a favourable environment and ensures profitability on the supply chain. Previous works have attempted to achieve this by making use of contracting mechanisms to regulate IoT devices and nodes on the network. In these systems, parties are enticed to act in a way so that profit achieved in the supply chain is maximized. However, these contracting mechanisms utilized are centralized and can easily be compromised. Also, these contracts are not enforced by a framework that ensures the effective establishment of trust among parties on the supply chain. Also, equilibrium remains in-established due to the complexity and lack of an effective system to regulate behaviour effectively. In this work, we design a blockchain-adaptive framework that incentivizes participants to work together on the supply chain to establish an equilibrium. We show through a proof of Concept, that the blockchain possesses the necessary attributes needed in establishing a Nash Equilibrium and can be used to coordinate the supply chain in complex scenarios to ensure security, trust, and profit. Using game theory models, we develop novel approaches and prove mathematically that the blockchain is more efficient in establishing a supply chain equilibrium among parties, ensuring fairness among various parties. Additionally, we utilize smart contracts to calculate each participating party’s expected payoff and profit. We also attempt to reduce the level of uncertainties across the supply chain by presenting profit-maximizing parties. Here the transparency provided by the blockchain provides a means to collate information relevant for making effective decisions on the blockchain which proves relevant in multi-complex supply chain networks such as aircraft, and vehicle manufacturing and assembling networks. Here, parties are thus incentivized to make the right decisions. We observe through our test that the blockchain provides all parties with the best payoff that satisfies all parties by incorporating each individual’s decisions on the supply chain, ensuring trust and equilibrium are established. The implementation of our work was achieved using standard blockchain networks. Our results show the cost-efficiency and feasibility of our proposed idea.
Generalization Analysis of CNNs for Classification on Spheres

Deep learning based on deep convolutional neural networks (CNNs) is extremely efficient in solving classification problems in speech recognition, computer vision, and many other fields. But there is no enough theoretical understanding about this topic, especially the generalization ability of the induced CNN algorithms. In this article, we develop some generalization analysis of a deep CNN algorithm for binary classification with data on spheres. An essential property of the classification problem is the lack of continuity or high smoothness of the target function associated with a convex loss function such as the hinge loss. This motivates us to consider the approximation of functions in the Lp space with 1 ≤ p ≤ ∞ . We provide rates of Lp -approximation when the approximated function lies in a Sobolev space and then present generalization bounds and learning rates for the excess misclassification error of the deep CNN classification algorithm. Our novel analysis is based on efficient cubature formulae on spheres and other tools from spherical analysis and approximation theory.
An effective watermarking technique using BTC and SVD for image authentication and quality recovery

Digital image protection has become a major concern in digital communication. Watermarking digital images has long been thought to be a good way to ensure the image quality. This paper proposes a watermarking scheme for image authentication and recovery of tampered images based on block truncation coding (BTC) and singular value decomposition (SVD). Here the image is divided into 4x4 non-overlapping blocks. Then the watermark information for authentication and recovery is embedded into it. The authentication watermark for each block will be generated using SVD and XOR operation, then it gets embedded into it. Simultaneously, the recovery watermark of each block will be generated using BTC and gets embedded in a mapped block. The watermark information gets embedded in two least significant bits (LSBs) of each pixel in a block. The proposed scheme got a higher degree of watermarking with better recovery against various kinds of attacks.
Data, Machine Learning, and Human Domain Experts: None Is Better than Their Collaboration

Software designers are coming up with tools having machine learning (ML) capabilities integrated into them. Even naive users can build ML-based models for their respective problem domains using these tools. However, a majority of the advanced ML models behave like a black box in the sense that their behavior is not easily interpretable by humans. This lack of human interpretability acts as a hurdle toward acceptance and subsequent deployment of ML-based solutions. This research work is based on an intuitive idea that in an ideal ML-based solution, the provided dataset, the ML model learned using this dataset, and human domain experts must agree in terms of their perception regarding the problem domain. The objective of this work is to propose a framework that first listens to the dataset, the ML model, and human domain experts individually, and then measure the degree of agreement between them. Listening to the dataset refers identifying important characteristics by computing information gain using entropy and Gini index. Listening to the ML model refers interpreting its decision-making behavior by computing variable importance measures. Listening to human experts refers taking feedback in terms of important features as per their perception regarding the problem domain. The task of measuring agreement between dataset, model, and human experts has been modeled as a "two-judges n-participants" rank correlation problem. The proposed approach has been evaluated on a novel problem domain of predicting joining behavior of freshmen students. A positive degree of agreement was observed between the dataset, the ML model, and human experts in terms of their perception regarding the problem domain. The agreement between the provided dataset, the learned model, and human domain experts helps verify learning acquired by the ML model against the prevailing domain knowledge. This work has the potential to form the basis for developing formal quantitative metrics for evaluating ML models in terms of reliable learning and capability to facilitate trust of human users.
Comparative analysis of blockchain technology to support digital transformation in ports and shipping

Abstract Blockchain is one of the technologies that can support digital transformation in industries in many aspects. This sophisticated technology can provide a decentralized, transparent, and secure environment for organizations and businesses. This review article discusses the adoption of blockchain in the ports and shipping industry to support digital transformation. It also explores the integration of this technology into the current ports and shipping ecosystem. Besides, the study highlighted the situation of the supply chains management in ports and shipping domain as a case study in this field. The investigated studies show that blockchain can be integrated into processes such as financial and document workflow. This review contributes to research by focusing on the adoption of blockchain in the ports and shipping industry to support digital transformation. It also aims to understand the existing port practice and map it with current tendencies based on blockchain. This study gives insight analysis to incorporate blockchain technology into ports and shipping processes globally.
Blockchain-Based Smart Contracts to Provide Crop Insurance for Smallholder Farmers in Developing Countries

Agricultural insurance can help smallholder farmers in developing countries deal with risks that are too large for them to manage on their own. Thus, significant welfare gains can be achieved by increasing smallholder farmers’ access to such insurance. Estimates also suggest that by removing farming risks through crop insurance, it is possible to increase smallholder farmers’ investments and income by 20%–30%.1
Scale-aware heatmap representation for human pose estimation

The performance of multi-person pose estimation is seriously affected by scale variation. Extensive works have been devoted to reducing the effect by modifying convolutional network structure or loss function, but little attention has been paid to the problem in the construction of heatmaps. In this paper, we focus on the scale variation of keypoints within heatmap generation and propose a novel method called scale-aware heatmap generator, which constructs a customized heatmap for each type of keypoints based on their relative scales. In addition, we design a weight-redistributed loss function to facilitate the detection of keypoints that are hard to identify. Our approach outperforms the baseline by nearly 2.5% in average precision and performs on par with the state-of-the-art result in bottom-up pose estimation with multi-scale testing (69.4% AP) on the COCO test-dev dataset.
Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation

We propose a novel benchmark environment for Safe Reinforcement Learning focusing on aquatic navigation. Aquatic navigation is an extremely challenging task due to the non-stationary environment and the uncertainties of the robotic platform, hence it is crucial to consider the safety aspect of the problem, by analyzing the behavior of the trained network to avoid dangerous situations (e.g., collisions). To this end, we consider a value-based and policy-gradient Deep Reinforcement Learning (DRL) and we propose a crossover-based strategy that combines gradient-based and gradient-free DRL to improve sample-efficiency. Moreover, we propose a verification strategy based on interval analysis that checks the behavior of the trained models over a set of desired properties. Our results show that the crossover-based training outperforms prior DRL approaches, while our verification allows us to quantify the number of configurations that violate the behaviors that are described by the properties. Crucially, this will serve as a benchmark for future research in this domain of applications.
Class-Ordered LPA*: An Incremental-Search Algorithm for Weighted Colored Graphs

Replanning is an essential problem for robots operating in a dynamic and complex environment for responsive and robust autonomy. Previous incremental-search algorithms efficiently reuse existing search results to facilitate a new plan when the environment changes. Yet, they rely solely on geometric information of the environment encoded in an edge-weighted graph. However, semantic information often provides valuable insights that cannot easily be captured quantitatively. We encode both semantic and geometric information of the environment in a weighted colored graph, in which the edges are partitioned into a finite set of ordered semantic classes (e.g., colors), and then we incrementally search for the shortest path among the set of paths with minimal inclusion of inferior classes, using information from the previous search using ideas similar to LPA*. The proposed Class-Ordered LPA* (COLPA*) algorithm inherits the strong theoretical properties of LPA*, namely, optimality and efficiency, but optimality now is with respect to the total path order. Numerical examples show that semantic information helps reduce the relevant search space in a dynamic environment.
Trust-region filtered sequential convex programming for multi-UAV trajectory planning and collision avoidance

This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems for decreasing the complexity. The inactive constraints are detected based on the intersection relations between trust regions and collision-avoidance constraints. The trust-region based filter for different types of obstacles are tailored to address complex scenarios. An adaptive trust-region updating mechanism is also developed to mitigate infeasible iteration in TRF-SCP. The sizes of the trust regions are automatically adjusted according to the constraint violation of the optimized trajectory during the SCP iterations. TRF-SCP is then tested on several numerical multi-UAV formation scenarios involving cylindrical, spherical, conical, and polygon obstacles, respectively. Comparative studies demonstrate that TRF-SCP eliminates a large number of collision-avoidance constraints in the entire iterative process and outperforms SCP and Guaranteed Sequential Trajectory Optimization in terms of computational efficiency. The indoor flight experiments are presented to further evaluate the practicability of TRF-SCP.
A CBIR system based on saliency driven local image features and multi orientation texture features

In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially.
A unified weighted variational model for simultaneously haze removal and noise suppression of hazy images

Visibility degradation of outdoor hazy images caused by scattering medium (e.g. haze, fog or smoke) is a challenging issue due to its ill-posed nature. Moreover, the noises are inevitably introduced to the hazy images because of various factors in real-world haze imaging. However, traditional atmospheric scattering model (ASM) fails to consider the noise term, which may lead to noise amplification after restoration. In this paper, traditional ASM is first modified by adding the noise term to make it suitable for real-world hazy images. Based on the modified model, a novel unified weighted variational model is proposed to address the problems of both haze removal and noise suppression simultaneously. Specifically, we use ℓ1 norm to enforce the piece-wise continuous on the scene radiance, adopt weighted ℓ2 norm based on exponentiated mean local variance to constrain the transmission, and apply the overall intensity constraint on the noise. An alternating optimization method is adopted to solve the proposed model. Compared with various dehazing methods, the proposed method can provide comparable or better results both qualitative and quantitative assessments on real-world and synthetic hazy images.
Computational Design of Knit Templates

We present an interactive design system for knitting that allows users to create template patterns that can be fabricated using an industrial knitting machine. Our interactive design tool is novel in that it allows direct control of key knitting design axes we have identified in our formative study and does so consistently across the variations of an input parametric template geometry. This is achieved with two key technical advances. First, we present an interactive meshing tool that lets users build a coarse quadrilateral mesh that adheres to their knit design guidelines. This solution ensures consistency across the parameter space for further customization over shape variations and avoids helices, promoting knittability. Second, we lift and formalize low-level machine knitting constraints to the level of this coarse quad mesh. This enables us to not only guarantee hand- and machine-knittability, but also provides automatic design assistance through auto-completion and suggestions. We show the capabilities through a set of fabricated examples that illustrate the effectiveness of our approach in creating a wide variety of objects and interactively exploring the space of design variations.