Reviewerly/exHarmony huggingface dataset:
Optimal Image-Based Task-Sequence/Path Planning and Robust Hybrid Vision/Force Control of Industrial Robots

In this paper, a novel image-based task-sequence/path planning scheme coupled with a robust vision/force control method is suggested for solving the multi-task operation problem of an eye-in-hand (EIH) industrial robot interacting with a workpiece. The proposed method suggests an optimal task sequence planning scheme to perform all the tasks and an optimal path planning method to generate a collision-free path between the tasks when the robot performs free-motion. To this end, a new method is presented which solves both problems simultaneously. A novel deadlock-free modified artificial potential field (MAPF) based on rotational potential force is developed for generating the collision-free path betweentasks in the three-dimensional (3D) environment. The parameters of the MAPF and the sequence of the tasks are found by an optimizer simultaneously. This problem can be considered as a MAPF-constrained-generalized-traveling-salesman-problem (MAPF-CGTSP), which is a mix-integer optimization problem. The mix-integer version of multi-tracker optimization algorithm (MTOA) is developed to solve the problem. However, since image-based visual servoing (IBVS) is used for motion control, the planning is conducted in the image space. Integrated with the proposed planning method, a novel chattering-free filtered quasi sliding mode controller (FQSMC) is specially designed for robust vision/force control of the robot. FQSMC exploits a novel variable-gain orthogonal-sliding-manifold (VGOSM)which enables the robot to switch between free-motion mode and interaction mode. FQSMC overcomes large uncertainties and filters out the existing noises by exploiting an intrinsic filter within its control law. Experimental results show the superiority of the proposed approach to other state-of-the-art methods.
A hybrid with distributed pooling blockchain protocol for image storage

As a distributed storage scheme, the blockchain network lacks storage space has been a long-term concern in this field. At present, there are relatively few research on algorithms and protocols to reduce the storage requirement of blockchain, and the existing research has limitations such as sacrificing fault tolerance performance and raising time cost, which need to be further improved. Facing the above problems, this paper proposes a protocol based on Distributed Image Storage Protocol (DISP), which can effectively improve blockchain storage space and reduces computational costs in the help of InterPlanetary File System (IPFS). In order to prove the feasibility of the protocol, we make full use of IPFS and distributed database to design a simulation experiment for blockchain. Through distributed pooling (DP) algorithm in this protocol, we can divide image evidence into recognizable several small files and stored in several nodes. And these files can be restored to lossless original documents again by inverse distributed pooling (IDP) algorithm after authorization. These advantages in performance create conditions for large scale industrial and commercial applications.
A Comprehensive Study of the Trends and Analysis of Distributed Ledger Technology and Blockchain Technology in the Healthcare Industry

In today’s scenario, it is essential for the healthcare sector to focus on balancing patient care records with information relevant to completeness, accessibility, and privacy concerns. Advancements in information technology and health infrastructure exponentially bolster transformative changes in the healthcare industry. Incorporation of blockchain along with distributed ledger technology (DLT) owes the potentiality to cater to the interoperability restraints in health IT systems and enables medical researchers, healthcare entities, and healthcare providers to share electronic health data in a secured and well-mannered system. In addition to these, such technologies also propose and offer latest models for health statistics exchange by making the records more secure and efficient. However, successful implementation of blockchain technology and DLT necessitates efficient infrastructure, connectivity, and other factors. Hence, there poses to be several challenges restraining the mainstream usage of blockchain technology in the healthcare sector. The article illustrates different generations of blockchain, issues in healthcare data, and network structures as well as the solutions offered by the sector to cater to such problems. In addition to these, the article also emphasizes on the different application areas of blockchain and DLT in healthcare infrastructure. This article further discusses latest trends and factors driving the need for the incorporation of blockchain and distributed ledger technology in the healthcare sector and the future scenario for the same.
IVP: An Intelligent Video Processing Architecture for Video Streaming

Recently, video processing tasks, such as video enhancement and analysis, have received increasing attention from both academics and industries. However, the current video processing procedure on edge decouples the decoding phase and the subsequent video processing tasks, missing the opportunity to accelerate the procedure by orchestrating video decoding and enhancement stages. Thus, we propose an intelligent video processing workflow and architecture(IVP) for cloud-edge video streaming. For edge devices that receive compressed videos, IVP can perform direct DNN-based video enhancement, e.g., super-resolution and frame-interpolation. By leveraging the metadata motion vectors and residuals extracted from the encoded video, our architecture will significantly eliminate unnecessary frame pixels being processed by the DNNs and improve execution efficiency. The proposed IVP and workflow are proved to reduce up to 90% of the processing latency while producing accurate and high-quality videos. Furthermore, we observe a significant portion of similar optical flow in time domain of continuous videos, which can be used to reduce the computation overhead. Thus, to utilize such temporal similarity of optical flow, the proposed IVP is upgraded to be capable of reusing previous computation results, which further improves energy efficiency of the whole system.
Affine Extractors for Almost Logarithmic Entropy

We give an explicit construction of an affine extractor (over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2}$</tex> ) that works for affine sources on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> bits with min-entropy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k\geq\log n\cdot(\log\log n)^{1+o(1)}$</tex> . This improves prior work of Li (FOCS'16) that requires min-entropy at least <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{poly} (\log n)$</tex> . Our construction is based on the framework of using correlation breakers and resilient functions, a paradigm that was also used by Li. On a high level, the key sources of our improvement are based on the following new ingredients: (i) A new construction of an affine somewhere random extractor, that we use in a crucial step instead of a linear seeded extractor (for which optimal constructions are not known) that was used by Li. (ii) A near optimal construction of a correlation breaker for linearly correlated sources. The construction of our correlation breaker takes inspiration from an exciting line of recent work that constructs two-source extractors for near logarithmic min-entropy.
An Unsupervised Software Fault Prediction Approach Using Threshold Derivation

Software fault prediction models help the software quality assurance team to manage the resources optimally during software maintenance. Most of the recently proposed fault prediction approaches are helpful on labeled datasets only. Recently, several threshold-based software fault prediction approaches have been proposed. However, these approaches do not incorporate the distribution of software metrics for metric threshold derivation; hence, they demonstrate poor performance. To fill this gap, we develop an automated fault prediction approach, namely threshold clustering labeling/threshold clustering labeling plus (TCLP), which does not need a labeled dataset. It can identify the faulty and nonfaulty artifacts on unlabeled datasets by self-learning. Our proposed approach is an extension of the state-of-the-art technique known as CLAMI. Unlike CLAMI, we derive the metrics threshold using logarithmic transformation. Thereafter, we label the instances into binary classes (faulty/nonfaulty) using the metric threshold values. TCLP extends this approach one step further by performing fault prediction using a random forest algorithm. The empirical evaluation of the proposed approach on 28 datasets (with the different number of metrics and granularity) collected from five software groups shows that the proposed unsupervised method obtains significantly better results than those of the state-of-the-art methods. The proposed approach impressively enhances the performance of CLAMI in terms of accuracy, F-measure, and Mathew’s correlation coefficient.
Aerial Supervision of Drones and Other Flying Objects Using Convolutional Neural Networks

Accurate detection of distant drones in clustered environment amidst other flying objects such as birds is of critical importance in anti-drone system design. This study proposed a novel object detection model that efficiently detect and differentiate drones from other flying objects under different weather conditions. The custom dataset consists of manually generated drone images and bird samples under sunny, cloudy and evening conditions. The simulation result shows that KITYOLO outperformed YOLOv5 both in precision (sunny 96.2% vs 85%; cloudy 73.7% vs 26.3%; evening 58.5% vs 26.1%) and recall (evening 42.4% vs 15%) in all aspects with an overall F1-score of 98% as against 91.9% while maintaining timeliness and memory usage.
Guaranteed cost coherent control for quantum systems with non-quadratic perturbations in the system Hamiltonian

This paper considers a class of uncertain quantum systems subject to non-quadratic perturbations in the system Hamiltonian . Results on performance analysis for this class of systems are presented using a linear matrix inequality method. Guaranteed cost coherent controllers are designed through adding a controller Hamiltonian to the quantum systems. A numerical procedure is proposed for the controller synthesis to deal with a non-convex problem in the design process. An example of a quantum system comprised of a Josephson junction coupled to an electromagnetic resonant cavity is presented to illustrate the control method.
“In the shoes of another”: immersive technology for social and emotional learning

There has been increasing use of interactive technologies in the classroom today and a rising popularity of employing virtual environments as a means to engage students in sensorially rich contexts for more embodied forms of experiential learning. In particular, virtual reality (VR) or immersive virtual environments (IVEs) facilitated by head-mounted displays (HMDs) have been used in the teaching of subject content such as history, geography and science. This article presents the findings of an exploratory study of immersive technology, specifically immersive virtual environments (IVES), for the purpose of social and emotional learning (SEL), in the context of Character and Citizenship lessons in the Singapore classroom. The social and emotional competencies (SECs) examined in this project were specifically empathy and perspective-taking, and responsible decision-making. The study involved a sample of n = 75 students from a cohort of students in a Singapore school, averaged at 15 years of age. Students were randomly divided into three treatment conditions: IVEs, pen-and-paper mental simulation and video-viewing. Each treatment contained a problem scenario, told from a first-person perspective, involving a social and ethical dilemma young people today face. A quasi-experimental, pre-test post-test, non-equivalent group design was employed, and the study adopted a mixed-method approach to data collection. The findings reveal that IVEs are not necessarily more effective than the "pen-and-paper" and video viewing approaches to teaching SECs but they can better facilitate perspective-taking and empathy for a higher percentage of students.
MROS: runtime adaptation for robot control architectures

Known attempts to build autonomous robots rely on complex control architectures, usually implemented with the Robot Operating System (ROS). Runtime adaptation is needed in these systems, to cope with component failures and with contingencies arising from dynamic environments – otherwise these affect the reliability and quality of the mission execution. Existing proposals on how to build self-adaptive systems in robotics usually require a major re-design of the control architecture and rely on complex tools unfamiliar to the robotics community. Moreover, they are hard to reuse across applications. This paper presents MROS: a model-based framework for runtime adaptation of robot control architectures based on ROS. MROS uses a combination of domain-specific languages to model architectural variants and capture mission quality concerns, and an ontology-based implementation of the MAPE-K and meta-control visions for runtime adaptation. The experiment results obtained applying MROS in two realistic ROS-based robotic demonstrators show the benefits of our approach in terms of the quality of the mission execution, and MROS's extensibility and reusability across robotic applications.
Application of Machine Learning Models to the Detection of Breast Cancer

This work aims to build a binary breast cancer classifier algorithm based on the blood test and anthropometric data (age, body mass index, glucose, insulin, homeostasis model assessment, leptin, adiponectin, resistin, and monocyte chemotactic protein-1) of 116 subjects. For this study, a performance comparison of the following machine learning models was performed: decision tree, random forest, K-nearest neighbors, artificial neural networks, vector machines of support, and logistical regression. The methodologies used in the data were as follows: k‐fold cross‐validation (k = 10); splitting data into 80% training and 20% testing. For the first, the mean of accuracy and sensitivity were evaluated in the second, values of accuracy, sensitivity, specificity, and area under some tests. In addition, most mammograms are performed on benign tumors. With this, it is clear that these exams can use other tools to assist in decision-making, and machine learning can offer great utility and good cost/benefit in the diagnostic process of breast cancer. Many research papers for breast cancer biomarkers have been reported over the years. The present work will analyze the potential quantitative variables: age, receiver operating characteristic curve. Furthermore, the p value, Pearson correlation coefficient, and, depending on the input variable, the test only with variables with a significance threshold of 5% are computed from the normal distribution assessment (calculated from Kolmogorov–Smirnov test (KS test)) which were as follows: glucose, insulin, resistin, and homeostasis assessment model. As the best final classifier, the random forest was used in the training/test method and with nine variables, with 83.3% accuracy, 100% sensitivity, 64% specificity, and 0.881 of area under the curve.
Spatio-temporal characterization of long-term solar resource using spatial functional data analysis: Understanding the variability and complementarity of global horizontal irradiance in Ecuador

Understanding the spatio-temporal variability of the solar resource is crucial to effectively support solar power utilization. Unfortunately, long-term and high-resolved measurements of solar irradiance are generally scarce, challenging the characterization for larger areas. In this paper, we propose a methodology to characterize the spatio-temporal variability of global horizontal irradiance (GHI) at a regional scale using long-term satellite-derived data. Spatial functional data analysis (sFDA) is used to identify areas with similar intra-annual variability patterns. The methodology is applied to a 21-year period data on Ecuador retrieved from the National Solar Radiation Database. Being the first time that sFDA is used for this purpose, the results indicate that it provides an appropriate basis for the interannual variability and complementarity analyses. In Ecuador's mainland, twenty-two subregions with four seasonal patterns are identified. The highest GHI potential (5.4 kWhm−2d−1) with the lowest variability (3.4%) is found in the Inter-Andean valleys. Further, seasonal complementarities between the coast and western Andes are identified. In Galapagos, high values are found over all islands (≥4.8 kWhm−2d−1), characterized by three subregions with one seasonal pattern. Our findings provide the first comprehensive spatio-temporal characterization of GHI in Ecuador, which aims at supporting a sustainable energy transition in the country.
Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms

Automatic detection of very small and nonmass abnormalities from mammogram images has remained challenging. In clinical practice for each patient, radiologists commonly not only screen the mammogram images obtained during the examination, but also compare them with previous mammogram images to make a clinical decision. To design an artificial intelligence (AI) system to mimic radiologists for better cancer detection, in this work we proposed an end-to-end enhanced Siamese convolutional neural network to detect breast cancer using previous year and current year mammogram images.The proposed Siamese-based network uses high-resolution mammogram images and fuses features of pairs of previous year and current year mammogram images to predict cancer probabilities. The proposed approach is developed based on the concept of one-shot learning that learns the abnormal differences between current and prior images instead of abnormal objects, and as a result can perform better with small sample size data sets. We developed two variants of the proposed network. In the first model, to fuse the features of current and previous images, we designed an enhanced distance learning network that considers not only the overall distance, but also the pixel-wise distances between the features. In the other model, we concatenated the features of current and previous images to fuse them.We compared the performance of the proposed models with those of some baseline models that use current images only (ResNet and VGG) and also use current and prior images (long short-term memory [LSTM] and vanilla Siamese) in terms of accuracy, sensitivity, precision, F1 score, and area under the curve (AUC). Results show that the proposed models outperform the baseline models and the proposed model with the distance learning network performs the best (accuracy: 0.92, sensitivity: 0.93, precision: 0.91, specificity: 0.91, F1: 0.92 and AUC: 0.95).Integrating prior mammogram images improves automatic cancer classification, specially for very small and nonmass abnormalities. For classification models that integrate current and prior mammogram images, using an enhanced and effective distance learning network can advance the performance of the models.
CovaDel: a blockchain-enabled secure and QoS-aware drone delivery framework for COVID-like pandemics

Abstract With increase in the use of contactless deliveries during the times such as COVID-19 pandemic, the emphasis was to minimize the human presence to reduce the spread of virus. In this regard, drones are one promising alternative to be used as delivery agents. However, security and Quality of Service (QoS) are major concerns while making use of drones for deliveries. In order to secure the drone communication system, we propose, CovaDel: a blockchain-based scheme to secure data transactions for the drone delivery use case that works in a phased manner. The proposed scheme make use of decoupled blockchain architecture to overcome the limited resources capabilities of drones to perform blockchain-based computations. Further, to ensure the QoS adherence, we also propose a QoS-aware communication approach that handles collisions and congestion on the basis of firefly algorithm’s attractiveness parameter (light intensity) received by the drones. Results obtained using a simulated environment verify the efficacy of the proposed scheme on the basis of gas consumed, transaction time, average network throughput, and delay.
Next Word Prediction Using Deep Learning: A Comparative Study

Deep learning is a subclass of machine learning, it mimics the functionality of the humanbrain the way it processes and creates pattern in the facts for choice making. It is basically an AI function that has networks capable of learning unsupervised data that is shapeless. The succeeding word forecast is performed on dataset consisting of texts. Next Word Prediction is an application of NLP (Natural LanguageProcessing). It is also known as Language Modelling. Basically it is the process of predicting the next word in a sentence. It has many applications which are usedby most of us such as auto-correct which is mostly usedin Emails / Messages; it has also it&#x2019;s usage in MS Wordor google search where forecasts the next word based on our search history or the search we did for globe. In this work we have studied NLP, different deep learning techniques such as LSTM, BiLSTM and performed a comparative study. We found satisfactory results in BiLSTM and LSTM [1]The accuracy received using BiLSTM and LSTM are: 66.1&#x0025; and 58.27&#x0025; respectively.
Identification of the geometric design parameters of propeller blades from 3D scanning

Abstract This paper presents a new method for the identification of geometric design parameters of ISO 484 class propeller blades from scanned point cloud data. The method can be used for tolerance inspection and in-line measurement of manufactured blades, and for wear assessment and reverse engineering of propellers in service. The geometry of the propeller blades is specified by a set of blade sections stacked radially on concentric cylindrical surfaces. For each of these sections, the chord line and mean camber line is determined, and geometric design parameters such as the pitch, skew, chord length, camber, and thickness distributions are identified. The proposed method is a complete procedure for identifying the design parameters of marine propeller blades from point cloud data, and includes a novel method for the precise identification of the mean camber line based on Voronoi diagrams and Delaunay triangulation. The paper includes validation of the proposed method in experiments where reverse engineering is applied to a propeller blade of the KVLCC2 propeller, and in 3D scanning of a large, high-skew thruster blade where the results were compared to CMM measurements.