Reviewerly/exHarmony huggingface dataset:
BCGeo: Blockchain-Assisted Geospatial Web Service for Smart Healthcare System

Most recent research on healthcare systems has focused on integrating the Internet of Things (IoT), Blockchain technology, and cloud computing to enhance the performance of IoT devices with limited resource availability, create smart healthcare platforms, and offer patients the best possible healthcare service. Modern healthcare systems use large-scale sensor devices to address many challenges brought on by the conventional delivery of healthcare services. Most studies have lately identified data collection, massive data processing, geolocating, access management, device prioritization, and storing as primary issues in most IoT healthcare systems. Decentralization, privacy, security, scalability, trust, anonymity, and building geospatial-based intelligent healthcare systems for patient care are significant difficulties that most healthcare systems today must overcome. Blockchain technology in healthcare platforms is noteworthy and innovative since it opens platforms for data privacy, anonymity, and validity through the consensus process. In this work, we proposed a novel decentralized Blockchain-enabled geospatial service architecture for smart healthcare systems called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>BCGeo</i></b> . The proposed framework offers an online geospatial healthcare service for residents of Bhubaneswar, a city in India, who are newcomers to the city and are less familiar with its local healthcare organizations. An analytical queueing method prioritizes serving Critical patients more than other patients. In contrast to previously proposed frameworks, the proposed framework includes immutability, scalability, geospatial mapping, patient prioritizing, and decentralized privacy protection policies for addressing the technical challenges in most of the current healthcare systems. Additionally, it explains the performance analysis of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>BCGeo</i></b> . It includes graphs showing the various possible outcomes of arithmetic operations, performance measurement, and experimental results on the proposed architecture.
SecDedup: Secure data deduplication with dynamic auditing in the cloud

In the era of big data, data explosion has brought challenges to cloud storage management. To improve cloud storage efficiency and save network communication bandwidth, cloud data deduplication has emerged as a research hotspot, especially in the field of encrypted cloud data storage. How to enhance the security of encrypted data deduplication by resisting various attacks on deduplication has become an important research issue. However, existing solutions suffer from security flaws and are vulnerable to a series of attacks, e.g., duplicate faking attacks, file ownership spoofing attacks, and file tampering attacks. Besides, dynamic data operation is rarely considered or audited. To solve the above problems, we propose a novel scheme, named SecDedup, to enhance the security of encrypted cloud data deduplication with dynamic auditing. SecDedup applies a homomorphic authenticator and designs a multi-functional data tag with optimized storage to support deduplication and auditing at the same time with security guarantee against various attacks as mentioned above. In particular, We embed multi-set hash functions into data tags to achieve dynamic data auditing. In addition, SecDedup supports batch auditing with optimized computational cost for multiple deduplication auditing tasks. We formally prove the correctness and security of SecDedup, showing that it can successfully achieve our design goals for resisting the above listed attacks. We also analyze and evaluate the performance of SecDedup in terms of computation, communication, and tag storage overheads by comparing them with existing works. The results show its effectiveness and scalability.
Graph Convolutional Neural Network for Multimodal Movie Recommendation

The Recommendation System (RS) development and recommending customers' preferred products to the customer are highly desirable motives in today's digital market. Most of the RSs are mainly based on textual information of the engaged entities in the platform and the ratings provided by the users to the products. This paper develops a movie recommendation system where the cold-start problem relating to rating information dependency has been dealt with and the multi-modality approach is introduced. The proposed method differs from existing approaches in three main aspects: (a) implementation of knowledge graph for text embedding, (b) besides textual information, other modalities of movies like video, and audio are employed rather than rating information for generating movie/user representation and this approach deals with the cold-start problem effectively, (c) utilization of graph convolutional network (GCN) for generating some further hidden features and also for developing regression system.
Efficient Resource Augmentation of Resource Constrained UAVs Through EdgeCPS

We propose an efficient Resource Augmentation Framework (RAF) for resource-constrained UAVs through EdgeCPS. Typical UAVs with small form factors have limited computation power which hinders their ability to perform critical or computation-intensive missions. By exploiting EdgeCPS, UAVs can get computational support from the EdgeCPS and diversity its missions. Existing solutions allow exploiting the EdgeCPS; however, the network overhead is too great that it cannot be adopted in resource-constrained UAVs. The proposed framework, RAF, provides Task Management Module (TMM) and Offloading Inference Module (OIM) to solve the issue. Using Raspberry Pi 4 as the mission computer for the UAV, RAF shows an inference performance of 11.9 FPS in the ResNet-18 model, whereas the existing work shows about 6 FPS.
Quality Diversity Evolutionary Learning of Decision Trees

Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI). While the current dominant paradigm in the field is based on black-box models, typically in the form of (deep) neural networks, these models lack direct interpretability for human users, i.e., their outcomes (and, even more so, their inner working) are opaque and hard to understand. This is hindering the adoption of AI in safety-critical applications, where high interests are at stake. In these applications, explainable by design models, such as decision trees, may be more suitable, as they provide interpretability. Recent works have proposed the hybridization of decision trees and Reinforcement Learning, to combine the advantages of the two approaches. So far, however, these works have focused on the optimization of those hybrid models. Here, we apply MAP-Elites for diversifying hybrid models over a feature space that captures both the model complexity and its behavioral variability. We apply our method on two well-known control problems from the OpenAI Gym library, on which we discuss the "illumination" patterns projected by MAP-Elites, comparing its results against existing similar approaches.
SBvote: Scalable Self-Tallying Blockchain-Based Voting

Decentralized electronic voting solutions represent a promising advancement in electronic voting. One of the e-voting paradigms, the self-tallying scheme, offers strong protection of the voters' privacy while making the whole voting process verifiable. Decentralized smart contract platforms became interesting practical instantiation of the immutable bulletin board that this scheme requires to preserve its properties. Existing smart contract-based approaches employing the self-tallying scheme (such as OVN or BBB-Voting) are only suitable for a boardroom voting due to their scalability limitation. The goal of our work is to build on existing solutions to achieve scalability without losing privacy guarantees and verifiability. We present SBvote, a blockchain-based self-tallying voting protocol that is scalable in the number of voters, and therefore suitable for large-scale elections. The evaluation of our proof-of-concept implementation shows that the protocol's scalability is limited only by the underlying blockchain platform. We evaluated the scalability of SBvote on two public smart contract platforms - Gnosis and Harmony. Despite the limitations imposed by the throughput of the blockchain platforms, SBvote can accommodate elections with millions of voters.
Fractal dimension used as a proxy to understand the spatial distribution for carlin-type gold deposits

The spatial distribution of ore-forming element grades typically exhibits strong nonlinear characteristics as a result of continuous geological processes. The trend of change in ore grade sequences is a crucial indicator of the degree of enrichment of an ore deposit. Therefore, quantitative evaluation of the spatial distribution of ore-forming elements holds great significance for understanding ore-forming processes as well as for ore exploration. In this work, based on the principles of fractal theory, a MATLAB-based program to calculate fractal dimensions was developed, and fractal analysis was conducted on the gold concentration values (g/t) of 25 drill holes in the Lannigou Gold Mining Area, Guizhou, China. Rescaled range analysis and the fractal correlation integral were utilized to obtain the spatial distribution of gold concentrations in the drill holes. Rescaled range analysis revealed that the Hurst exponent (HR/S) values vary in the range of 0.58–0.97, which reflected the persistent behavior of vertical variation in gold tonnage at the studied wells. Furthermore, the relationship between gold grade distribution and associated fluctuations in the fractal dimension derived from the correlation dimension (DC) was discussed. The DC value was found to vary between 1.01 and 1.59, indicating that high-grade gold deposits were distributed along steep fractal gradients and that fractal dimension values could be used as a reliable proxy to efficiently understand the migration pathways of auriferous fluid. Studies have shown that the Hurst exponent of the Au grade sequence exhibited a strong dependence on the grade of mineralization, with gold mineralized veins persisting throughout the studied drill holes. Thus, fractal dimension gradients can serve as an important proxy for identifying the locations of potential economic gold deposits in unexplored areas. Moreover, this approach can be employed to map the distribution patterns of other types of ore deposits.
Enhancing molecular visualization: Perceptual evaluation of line variables with application to uncertainty visualization

Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the user’s trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Thus, we conducted two perceptual studies to determine the suitability of the line variables blur, dashing, grayscale, sketchiness, and width for distinguishing several values in molecular visualizations. While our work was motivated by uncertainty visualization, our techniques and study results also apply to other types of scalar data.
HA-Transformer: Harmonious aggregation from local to global for object detection

Recently, the Vision Transformer (ViT) with global modeling capability has shown its excellent performance in classification task, which innovates the development direction for a series of vision tasks. However, due to the enormous cost of multi-head self-attention, reducing computational cost while holding the capability of global interaction remains a big challenge. In this paper, we propose a new architecture by establishing an end-to-end connection from local to global via bridge tokens, so that the global interaction is completed at the window level, effectively solving the quadratic complexity problem of transformer. Besides, we consider a hierarchy of information from short-distance to long-distance, which adds a transition module from local to global to make a more harmonious aggregation of information. Our proposed method is named HA-Transformer. The experimental results on COCO dataset show excellent performance of HA-Transformer for object detection, outperforming several state-of-the-art methods.
Validation of wastewater data using artificial intelligence tools and the evaluation of their performance regarding annotator agreement

To prevent the pollution of water resources, the measurement and the limitation of wastewater discharges are required. Despite the progress in the field of data acquisition systems, sensors are subject to malfunctions that can bias the evaluation of the pollution flow. It is therefore essential to identify potential anomalies in the data before any use. The objective of this work is to deploy artificial intelligence tools to automate the data validation and to assess the added value of this approach in assisting the validation performed by an operator. To do so, we compare two state-of-the-art anomaly detection algorithms on turbidity data in a sewer network. On the one hand, we conclude that the One-class SVM model is not adapted to the nature of the studied data which is heterogeneous and noisy. The Matrix Profile model, on the other hand, provides promising results with a majority of anomalies detected and a relatively limited number of false positives. By comparing these results to the expert validation, it turns out that the use of the Matrix Profile model objectifies and accelerates the validation task while maintaining the same level of performance compared to the annotator agreement rate between two experts.
Security-Preserving Multi-Robot Path Planning for Boolean Specification Tasks Using Labeled Petri Nets

This letter investigates the path planning of multi-robot systems for high-level tasks described by Boolean specifications and security constraints. We assume that the behavior of each robot can be identified and partially monitored by a passive intruder. The problem aims to plan an optimal path for each robot such that the tasks expressed in conjunction, disjunction, and negation for trajectories and final states are coordinately completed. The security constraints require that the intruder should never infer the final locations of a set of robots called secret robots. In order to solve this problem, labeled Petri nets are adopted to model the mobile capability of the multi-robot systems. Then an integer linear programming problem is proposed to find an optimal solution (if it exists) such that the Boolean specification is fulfilled, while the securities of secret robots are preserved. Finally, the effectiveness of the proposed method is illustrated through several simulation studies.
Revolutionizing Retail Stores with Computer Vision and Edge AI: A Novel Shelf Management System

Retail stores often exhibit disorganized features, such as suboptimal store layout, inadequate inventory manage-ment, disarrayed items, and irregularly stocked shelves. These issues can lead to a loss of customers, as well as stock-outs or overstocking, resulting in revenue loss and wastage. Stores find it challenging to attract and retain customers, leading to declining revenues and eventually closures. To improve the competitiveness of stores and enhance the shopping experience for customers, it is imperative to adopt technology and use it to mitigate these issues. To this extent, this research study proposes a novel shelf management system that utilizes computer vision and edge AI (Artificial Intelligence) to help retail staff identify and restock items, as well as to detect and remove misplaced shelf items. Here, the state-of-the-art object detection, image classification, and OCR models are implemented to identify shelf objects and voids. Then, the product-barcode matching algorithm is used to identify misplaced items and alert employees if required. The proposed approach also includes incorporating task scheduling to ensure seamless performance, as well as optimizing our models for edge deployment.
A method of knowledge distillation based on feature fusion and attention mechanism for complex traffic scenes

Object detectors based on deep learning can run smoothly on a terminal device in complex traffic scenes, and the model compression method has become a research hotspot. Considering student network single learning in the knowledge distillation algorithm, the dependence on loss function design leads to parameter sensitivity and other problems, we propose a new knowledge distillation method with second-order term attention mechanisms and feature fusion of adjacent layers. First, we build a knowledge distillation framework based on YOLOv5 and propose a new attention mechanism in the teacher network backbone to extract the hot map. Then, we combine the hot map features with the next level features through the fusion module. By fusing the useful information of the low convolution layer and the feature map of the high convolution layer to help the student network obtain the final prediction map. Finally, to improve the accuracy of small objects, we add a 160 × 160 detection head and use a transformer encoder block module to replace the convolution network of the head. Sufficient experimental results show that our method achieves state-of-the-art performance. The speed and number of parameters remain unchanged, but the average detection accuracy is 97.4% on the KITTI test set. On the Cityscapes test set, the average detection accuracy reaches 92.7%.
From their point of view: the article category as a hierarchically structured referent tracking system

Abstract Full-fledged grammatical article systems as attested in Germanic and Romance languages are rather uncommon from a typological perspective. The frequency with which articles occur in these languages, together with the difficulty encountered in detecting them and the lack of a water-tight account of article use, make article errors one of the most frequent errors in language produced by L2 learners whose L1 does not feature an article system of similar complexity, all the while appearing unproblematic for L1 users. We present a conceptually and methodologically interdisciplinary approach to the grammatical category of articles in English and combine a usage-based, cognitive linguistic account of the function and use of articles that respects its discourse-based nature with a computational exploration of the challenges the system poses from the perspective of learning. Running a statistical classifier on a large sample of spoken and written discourse chunks extracted from the BNC and annotated for the five main determinants of article use reveals that Hearer Knowledge is the driver of a hierarchical system. Once Hearer Knowledge is acknowledged as the motivating principle of the category, article use becomes eminently predictable and restrictions are in line with the forms from which the articles have developed historically, with the and a acting as category defaults and zero acting as default override. Simulations with a computational model anchored in the psychology of learning shed light on whether and how human cognition would handle the proposed relations detected in the data. We find that different articles have different learnability profiles that, again, are in line with their historical development: while the can be learned from one strong indicator, the relationships for the zero article are less exclusive. On the basis of these findings, we argue that the article category appears as a referent tracking system that grammaticalizes the principles of “audience design”: it forces a speaker to track and mark reference from the vantage point of the memory of the hearer, thereby reducing the processing effort required from the hearer. This particular mindset inverses the typologically dominant situation in which this information is not explicitly marked by the speaker but implicitly retrieved from context by the hearer.
The Task Decomposition and Dedicated Reward-System-Based Reinforcement Learning Algorithm for Pick-and-Place

This paper proposes a task decomposition and dedicated reward-system-based reinforcement learning algorithm for the Pick-and-Place task, which is one of the high-level tasks of robot manipulators. The proposed method decomposes the Pick-and-Place task into three subtasks: two reaching tasks and one grasping task. One of the two reaching tasks is approaching the object, and the other is reaching the place position. These two reaching tasks are carried out using each optimal policy of the agents which are trained using Soft Actor-Critic (SAC). Different from the two reaching tasks, the grasping is implemented via simple logic which is easily designable but may result in improper gripping. To assist the grasping task properly, a dedicated reward system for approaching the object is designed through using individual axis-based weights. To verify the validity of the proposed method, wecarry out various experiments in the MuJoCo physics engine with the Robosuite framework. According to the simulation results of four trials, the robot manipulator picked up and released the object in the goal position with an average success rate of 93.2%.
Graph Signal Processing: History, development, impact, and outlook

Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able to model irregular interactions, easy to interpret, and endowed with a corpus of mathematical results, rendering them natural candidates to serve as the basis for a theory of processing signals in more irregular domains. In this article, we provide an overview of the evolution of GSP, from its origins to the challenges ahead. The first half is devoted to reviewing the history of GSP and explaining how it gave rise to an encompassing framework that shares multiple similarities with SP. A key message is that GSP has been critical to develop novel and technically sound tools, theory, and algorithms that, by leveraging analogies with and the insights of digital SP, provide new ways to analyze, process, and learn from graph signals. In the second half, we shift focus to review the impact of GSP on other disciplines. First, we look at the use of GSP in data science problems, including graph learning and graph-based deep learning. Second, we discuss the impact of GSP on applications, including neuroscience and image and video processing. We conclude with a brief discussion of the emerging and future directions of GSP.