Reviewerly/exHarmony huggingface dataset:
Defining a Use Case for ADMS Testbed: Data Quality Requirements for ADMS Deployment

Advanced distribution management systems (ADMS) provide a suite of tools to meet the needs of a modern grid: increased reliability and power quality, improved resiliency and security, reduced costs, and enhanced customer participation. A critical challenge that utilities face with adoption of ADMS is the quality of models and data that the ADMS uses for making control decisions. Data quality has a two-fold impact on ADMS adoption: 1. Data quality improvement might constitute up to 25% of ADMS deployment costs. 2. The accuracy of data and models used by the ADMS affects the utility's ability to meet its operational objectives. Thus, quantifying the data quality requirements and its impact on performance is critical to reducing the overall cost of deployment, enabling increased adoption and ensuring that the ADMS performs as specified. This paper offers the motivation, methodology, and evaluation strategy to fill this critical gap.
Query Expansion Using DBpedia and WordNet

This paper presents a semantic approach based on DBpedia and WordNet to extract the candidate terms for query expansion. We divided DBpedia features into: general features and specific features that are available depending on the domain of the resource. We evaluated our approach using 44 queries from TREC AP88-90, and discovered that the results are improved when using the hybrid approach that combines WordNet and DBpedia, versus using only WordNet for short and long queries. The comparison of our approach with literature technique that used DBpedia for query expansion showed an improvement in [email protected] for both short and long queries.
Intelligent Mobile-Based Recommender System Framework for Smart Freight Transport

This paper1 presents a smart freight transporting system that uses machine learning in order to optimize resources and a mobile apps to display results to potential users. In the proposed framework, we developed a clustering based recommender system for customers, strategic objectives are statements that indicate what is critical to the final decision, and the proposed smart system has as an objective to reduce CO2 emissions, fuel consumption, and congestion by reducing the waiting time of the customers. The framework has the potential to identify more efficient and affordable solutions for traffic management and customer satisfactions.
Visual Mesh: Real-Time Object Detection Using Constant Sample Density

This paper proposes an enhancement of convolutional neural networks for object detection in resource-constrained robotics through a geometric input transformation called Visual Mesh. It uses object geometry to create a graph in vision space, reducing computational complexity by normalizing the pixel and feature density of objects. The experiments compare the Visual Mesh with several other fast convolutional neural networks. The results demonstrate execution times sixteen times quicker than the fastest competitor tested, while achieving outstanding accuracy.
What Object Should I Use? - Task Driven Object Detection

When humans have to solve everyday tasks, they simply pick the objects that are most suitable. While the question which object should one use for a specific task sounds trivial for humans, it is very difficult to answer for robots or other autonomous systems. This issue, however, is not addressed by current benchmarks for object detection that focus on detecting object categories. We therefore introduce the COCO-Tasks dataset which comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated. We furthermore propose an approach that detects the most suitable objects for a given task. The approach builds on a Gated Graph Neural Network to exploit the appearance of each object as well as the global context of all present objects in the scene. In our experiments, we show that the proposed approach outperforms other approaches that are evaluated on the dataset like classification or ranking approaches.
Entity Disambiguation Leveraging Multi-Perspective Attention

Entity disambiguation aims to map mentions in text to the corresponding entities in a knowledge base, which is a basic task in natural language processing (NLP). The major challenge in entity disambiguation is how to extract key information in mention context and entity description that is discriminative for disambiguation. State-of-the-art entity disambiguation systems apply attention mechanism to identify the informative components, but most of the methods only focus on mention context, and neglect entity side. Besides, attention mechanism is employed in a single aspect, which may not be effective in difficult circumstances. In this work, we propose a neural network with multi-perspective attention to enrich the representation of mentions and entities in different perspectives. Specifically, we utilize intra-attention to aggregate internal pivotal information in mention context and entity description separately, and utilize inter-attention to interact their latent semantics in multiple directions and highlight the interrelated information, so as to capture more informative features and improve the disambiguation performance. The experimental results show that our proposed model outperforms other state-of-the-art entity disambiguation models and attain more improvements on hard datasets, which validates the effectiveness and superiority of our model, especially in complex situations.
An Automated Approach to Management of a Collection of Autonomic Systems

Modern enterprise IT systems are increasingly becoming compositions of many subsystems each of which is an autonomic system. These individual autonomic systems act independently to maintain their locally defined SLAs but can take actions which are inconsistent with and potentially detrimental to the global system objective. Currently, human administrators intervene to resolve these conflicts but are challenged by complexity in the prediction of current and future states of the constituent systems and their managers, multiple conflicting quality dimensions which may change over time, combinatorially large configuration space across the set of constituent systems, and the time critical nature of the decisions to be made to prevent further degradation. To address these challenges, this paper proposes an approach that enables the creation of a higher level autonomic system, referred to as a meta-manager, that does not subsume the control functions nor does it directly orchestrate the actions of the sub-autonomic managers. Instead, we encapsulate and abstract the behavior of each subsystem as a parameterized adaptation policy which can be adjusted by the meta-manager to tune the adaptive behavior of the subsystem adaptation. We can effectively instantiate this idea by considering each of the subsystems as a player in a stochastic multi-player game against it's local environment, and synthesize an adaptation.
A Context-Aware Technical Information Manager for Presentation in Augmented Reality

Technical information presentation is evolving from static contents presented on paper or via digital publishing to real-time context-aware contents displayed via virtual and augmented reality devices. We present a Context-Aware Technical Information Management system (CATIM), that dynamically manages (1) what information as well as (2) how information is presented in an augmented reality interface. CATIM acquires context data about activity, operator, and environment, and then based on these data, proposes a dynamic augmented reality output tailored to the current context. The system was successfully implemented and preliminarily evaluated in a case study regarding the maintenance of a hydraulic valve.
A Collaborative Virtual Reality Escape Room with Passive Haptics

Escape rooms have recently become a popular way to socialize and problem solve in an immersive environment. However, it can be difficult and expensive to create elaborate escape rooms with realistic props. Virtual reality (VR) technology allows developers to create and customize escape rooms more easily. However, to truly make the VR escape room immersive, physical and cooperative interactions are necessary. In this paper, the authors propose and demonstrate a two-player VR escape room developed for the HTC Vive. Physical interactions were made possible by using passive haptics in the form of simple props tracked using HTC Vive trackers and controllers. Additionally, the HTC Vives were networked, and players hands were tracked using the Leap Motion to provide head and hand position cues to teammates.
Improve Theoretical Upper Bound of Jumpk Function by Evolutionary Multitasking

Recently, the concept of evolutionary multitasking has emerged in the field of evolutionary computation as a promising approach to exploit the latent synergies among distinct optimization problems automatically. Many experimental studies have shown multifactorial evolutionary algorithm (MFEA), an implemented algorithm of evolutionary multitasking, can outperform the traditional optimization approaches of solving each task independently on handling synthetic and real-world multi-task optimization (MTO) problems in terms of solution quality and computation resource. However, as far as we know, there exists no study demonstrating the superiority of evolutionary multitasking from the aspect of theoretical analysis. In this paper, we propose a simple (4+2) MFEA to optimize the benchmarks Jumpk and LeadingOnes functions simultaneously. Our theoretical analysis shows that the upper bound of expected running time for the proposed algorithm on the Jumpk function can be improved to O(n2 + 2k) while the best upper bound for single-task optimization on this problem is O(nk-1). Moreover, the upper bound of expected running time to optimize LeadingOnes function is not increased. This result indicates that evolutionary multitasking is probably a promising approach to deal with some problems which traditional optimization methods can't well tackle. This paper provides an evidence of the effectiveness of the evolutionary multitasking from the aspect of theoretical analysis.
Multimodal Policy Search using Overlapping Mixtures of Sparse Gaussian Process Prior

In this paper, we present a novel policy search reinforcement learning algorithm that can deal with multimodality in control policies based on Gaussian processes. Our approach employs Overlapping Mixtures of Gaussian Processes (OMGPs) for a control policy, in which all the GPs in the mixture are global and overlapped in the input space. We first extend the OMGPs by combing sparse pseudo-input GPs as OMSGPs to reduce its computational cost of learning and prediction suitable for policy search. Then, we derive a novel multimodal policy search algorithm based on variational Bayesian inference by placing the OMSGPs as the prior of the multimodal control policy. To validate the effectiveness of our algorithm, we applied it to two typical robotic tasks in simulation: 1) object grasping and 2) table-sweep tasks since they both require the multimodality in the optimal policies. Simulation results demonstrate that our algorithm can efficiently learn multimodal policies even with high dimensional observations.
Efficient Exact Collision Detection between Ellipsoids and Superquadrics via Closed-form Minkowski Sums

Collision detection has attracted attention of researchers for decades in the field of computer graphics, robot motion planning, computer aided design, etc. A large number of successful algorithms have been proposed and applied, which make use of convex polytopes and bounding volumes as primitives. However, algorithms for those shapes rely significantly on the complexity of the meshes. This paper deals with collision detection for shapes with simple and exact mathematical descriptions, such as ellipsoids and superquadrics. These primitives have a wide range of applications in representing complex objects and have much fewer parameters than meshes. The foundation of the proposed collision detection scheme relies on the closed-form Minkowski sums between ellipsoids and superquadrics in n-dimensional Euclidean space. The basic idea here is to shrink the ellipsoid into a point and expand each superquadric into a new offset surface with closed-form parametric expression. The solutions for detecting relative positions between a point and a general convex differentiable parametric surface in both 2D and 3D are derived, leading to an algorithm for exact collision detection. To compare between exact and inexact algorithms, an accuracy metric is introduced based on the Principal Kinematic Formula (PKF). The proposed algorithm is then compared with existing wellknown algorithms: Gilbert-Johnson-Keerthi (GJK) and Algebraic Separation Conditions (ASC). The results show that the proposed algorithm performs competitively with these efficient checkers.
Artificial Intelligence and Blockchain Technology Adaptation for Human Resources Democratic Ergonomization on Team Management

The integration of advanced technology in management and decision making is a continuous effort for organizational optimization. This paper focuses on developing a teaming requirements elicitation process using AI and Blockchain technologies under the democratic knowledge management perspective. AI will be primarily addressed via Machine Learning, Deep Learning, Expert Systems and Fuzzy Logic, but also through the integration of cognitive sciences that can contribute effectively on the selection of team members in team building. Furthermore, Blockchain maximizes the company's potential through personnel growth and diversity by supplementing AI on understanding deeper the capacity and competence teams have on responding to changes through continuous learning from each experience. This integration of AI and Blockchain in corporate teaming will be driven through the Company Democracy Model as the base framework for co-evolutionary processes development allowing an essential mutation from autocratic to liberal democratic leadership, and from skill-based to knowledge-based human capitalization.
A Demonstration of Image Obfuscation with Provable Privacy

Widely sharing image data captured by personal or surveillance cameras can enable a variety of research studies, e.g., computer vision, intelligent systems, and social sciences, to name a few. However, image data may contain a range of sensitive information, such as identity, location, and health, which creates significant challenges for sharing image data with untrusted parties, e.g., researchers. Standard obfuscation methods obscure regions-of-interest (ROIs) in image data with pixelization and blurring, which do not offer formal privacy guarantees. Moreover, such obfuscated image data can be re-identified by convolutional neural networks. In this demonstration, we will showcase a novel method for sanitizing sensitive image ROIs with quantifiable privacy guarantees. The audience will observe the obfuscation in a camera live stream, which demonstrates the feasibility of our method for real-time applications. Furthermore, the audience can interact with our method by selecting photos from publicly available datasets and choosing different privacy levels.
Fast image stitching of unmanned aerial vehicle remote sensing image based on SURF algorithm

Fast acquisition and processing of effective data sources are a heated topic in remote sensing image processing research. Unmanned aerial vehicle (UAV) remote sensing system has the advantages of maneuverability, rapidity and economical, it has become a hot topic in the world. The study analyzes the characteristics of remote sensing image and the characteristics of UAV remote sensing system and refers a variety of images fast processing algorithms to explore the rapid remote sensing images stitching and rapid information extraction methods. Based on the analysis of the relevant research at home and abroad, this paper draws lessons from some image processing ideas of modern photogrammetry and proposes a fast image stitching method of UAV remote sensing images based on SURF (Speed Up Robust Features) feature description. This method is applied to UAV remote sensing fast image stitching to achieve high-quality UAV remote sensing images for fast and automatic splicing. The stitching speed of this method is much faster than that of SIFT (Scale-invariant feature transform) algorithm. And the splicing effect of this method is satisfactory.
Prediction of Breast Cancer Recurrence: A Machine Learning Approach

Breast Cancer is a major health problem and is one of the significant causes of death among women. Recurrence occurs when cancer returns after few years of treatment. To aid in the medical treatment and for clinical management, the early cancer diagnosis and prognosis have become the necessity in breast cancer recurrence. As the medical data is increasing with the advancement in medical technology, data mining facilitates to manage the data and provide useful medical progression and treatment of cancerous conditions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others. These findings will help the physicians in identifying the best features which can lead to breast recurrence.
Systolic hand gesture recognition/detection system based on FPGA with multi-port BRAMs

A new systolic approach of real-time vision-based hand gesture recognition is proposed. Hand gesture recognition is classified into two type, static and dynamic. The proposed system can be employed in both types. The system can detect and classify either a static hand shape in one image or a static hand shape at the start, within, and the end of gesture in a dynamic sequence in video stream. An efficient template matching (TM) based similarity measure is used to recognize gesture within the inter frame sequence of video frames. The template can be used to store the static class of different hand gesture shapes, while fast scanning approach of window over an image frame dynamically detects or recognizes the hand gesture. A suitable hardware systolic model of TM is designed. The systolic architecture is expanded to be used with multiple windows (k-templates) simultaneously. The required templates are equal to the number (k) of gestures that is to be recognized. They are stored in FPGA RAM blocks (BRAMs). To increase the bandwidth of templates BRAMs, the BRAM is multi-ported. A considerable reduction of memory access operations is achieved for the parallel systolic model in comparison to sequential/parallel software models. Consequently, the hand gesture is detected, tracked and recognized within the inter frame space of video. An acceptable recognition accuracy of up to 95% is achieved under variant observation conditions of three hand gesture classes dataset. A high frame rate video can be processed in real time.
Variation Based Extended Kalman Filter on $S^{2}$

In this paper, we propose a variation-based extended Kalman filter (V-EKF) on the two-sphere manifold. We consider the spherical pendulum dynamical system whose nonlinear geometric dynamics evolve on the two-sphere. These dynamics are linearized about the current state using a variation-based linearization resulting in a time-varying linear system with state constraints that describe the dynamics of the variation states. The Kalman filter is applied on the resulting variation states with the pendulum position as measurements for measurement updates. The V-EKF also has a constraint update where the estimated state and covariance are updated to ensure they satisfy the constraints. Desirable properties of V-EKF, such as preserving the geometric structure of the estimated state are thus achieved. The proposed method is illustrated through numerical simulations and also validated through experiments.
Construction of non-convex fuzzy sets and its application

Although non-convex fuzzy set (FS) has the high potential of great performance in data modeling and controlling, it is seldom used and discussed because the lack of linguistic explanation and normative construction way. To address this problem, we propose a method named “parametric qualitative fuzzy set (PQ FS) plus choice strategy” for the construction and linguistic explanation of non-convex FS, in which PQ FS is a collection of convex FSs with special structure, and choice strategy is an approach to choose convex FSs from PQ FS. Based on this method, a non-convex FS is obtained as the trajectory of a collection of convex FS by choosing specific convex FS under specific situation. Thus, the linguistic explanation of non-convex FS is obtained: using non-convex FSs to represent linguistic variables does not violate the routine of using convex FSs, because it shows that the linguistic variable is just represented by different convex FS at different situation. Theorems are shown to demonstrate that the “PQ FS plus choice strategy” can effectively construct a non-convex FS. Furthermore, “Why a fuzzy logic system (FLS) adopting non-convex FSs may have a higher approximation capability” is discussed by introducing a parametric qualitative FLS (PQ FLS) that is compared with a typical Mamdani FLS as a function approximator. This indicates that non-convex FSs can approximate more extrema in a given universe with smaller partition numbers or fewer rules than convex FSs. Finally, the experimental results verify that a PQ FLS designed with the proposed non-convex FS construction method can outperform traditional convex fuzzy logic controllers (FLCs). Meanwhile, using parallel computing in the model training phase of PQ FLSs can reduce the calculation time compared to single-thread mode.
Recursive Bayesian Inference and Learning of Gaussian-Process State-Space Models

Gaussian processes in combination with sequential Monte-Carlo methods have emerged as promising tools for offline nonlinear system identification. However, sometimes the dynamical system evolves in such a way that online learning is preferable. This paper addresses the online joint state estimation and learning problem for nonlinear dynamical systems. We leverage a recently developed reduced-rank formulation of Gaussian-process state-space models (GP-SSMs), and develop a recursive formulation for updating the sufficient statistics associated with the GP-SSM by exploiting marginalization and conjugate priors. The results indicate that our method efficiently learns the system jointly with estimating the state, and that the approach for certain scenarios gives similar performance as more computation-heavy offline approaches.
KS-FALL: Indoor Human Fall Detection Method Under 5GHz Wireless Signals

Abstract In modern society, it has become the main threat to the elderly fall’s health or even death in the elderly. The real-time and reliable fall detection system can save the fall and hurt the elderly in time. In this paper, a human fall detection method KS-FALL based on Channel State Information and in 5G environment is proposed. KS-FALL uses Atheros commercial NIC equipment to map the amplitude information in the wireless signal to the human body’s fall action, and does not require the user to wear any equipment. Compared with the traditional 2.4 GHz signal, the 5 GHz signal provides richer sub-carrier frequency domain information, which better reflects the relationship between human motion and wireless signals, thereby more effectively distinguishing and recognizing walking, squatting, falling, etc. action, filter the environmental interference through powerful denoising method, use K-means to cluster different action data, combine SVM classifier to construct fine-grained offline fingerprint database, and use SoftMax regression model to correct SVM classification in real-time detection stage. And real-time test in two different scenarios, and the detection accuracy of the fall reached 92.3%, realizing the device-free, non-invasive, high-precision human fall detection.