Reviewerly/exHarmony huggingface dataset:
Variation mode-decomposition based surface recovery algorithm for white light interferometry

White light interferometry (WLI) provides noncontact, high-precision surface profiling and inspection for ultra-precision machining. This paper presented a signal time-domain mode-decomposition denoising based surface recovery algorithm for WLI. In this work, the captured correlogram is firstly decomposed into a series of modes with different central spectrums by means of the variation mode-decomposition (VMD), and the spectral component of each intrinsic mode can be derived through the Fourier transform. Afterwards, the noise existed in each spectral component is eliminated through windowed Fourier filtering (WFF), where the filtering threshold is decided by the ratio of spectral energy of intrinsic mode comparing with that of the correlogram. The denoised correlogram could then be extracted as the sum of filtered intrinsic modes. And the surface height isfinally retrieved through envelope peak location by applying Hilbert transform. The effectiveness of the proposed method on noise suppression is investigated under different levels of additive noises occurred on simulated correlograms. Moreover, a height step standard with calibrated values 1.762±0.010μm is further testified, where the measurement accuracy of the proposed method is totally verified.
Medical Anomaly Detection Using Human Action Recognition

Medical anomaly detection in public areas is a crucial application for ensuring improvements in public safety. During the times of the Covid-19 pandemic, in particular, it became crucial to monitor visual symptoms in public areas in order to take necessary precautions. Recent advancements in the field of computer vision, combined with the modern sequence classification models, made the action recognition and action classification which demand high computation power possible to deploy in real time. This chapter analyzes the necessity for the deployment of automated monitoring software to provide safety to the public. Most of the existing human activity monitoring tools use various types of human wearable sensors to obtain data and interpret action based on the collected data. This is not a very available source of data, making it a very non-scalable solution. Accordingly, we use video data as a source and thus we can scale our solution regardless of the circumstances that we face in public area. Firstly, we give an overview of the recent advancements done in this field by exploring the existing methods and we delve into all the scope of improvements. In this chapter we propose an automated approach which combines the power and versatility of the two state-of-art architectures (HR-Net [5] and Transformer [2], [17]) in order to achieve a highly accurate system.
Federated Learning Framework Based on Data Value Evaluation in Industrial IoT

With the continuous maturity and development of the big data technology system, deep learning has been widely used in the field of the Industrial Internet of Things. However, the traditional training model with centralized data is prone to the leakage of private information in the industry, such as facial information. In recent years, federated learning solves the problem of privacy leakage caused by data sharing by not sharing data and only contributing to local models. Federated learning does not share data, which also makes it impossible to evaluate the contribution of each client to the federated task. We propose a federated learning framework based on data value evaluation. In this method, under the premise of effectively completing the training task of federated learning and ensuring the privacy of client data, a data value evaluator is designed in the central server, and the model uploaded by the client is evaluated to obtain the corresponding selection probability as the model aggregation weight. Experimental results show that the proposed method improves the accuracy of the global model obtained by existing federated aggregation.
Efficient Dual Adversarial Cross Modal Retrieval By Advanced Triplet Loss

With the development of technology, the modality of multimedia information become diverse, such as pictures, short videos, text, and so on. However, there is a semantic gap between different media, for example, the image and text are independent of each other and do not interact with each other. How establish retrieval links between different modalities has become more and more important. In this paper, we proposed a modal consisting of dual adversarial neural networks, which obtain the high-order semantics of image and text respectively. Then, the triplet loss is used to widen the distance between different categories in the common space, to obtain a better cross-modal retrieval performance. We conduct experiments on three commonly used benchmark datasets (Wikipedia, NUS-WIDE, and Pascal Sentences), and the experimental results show that our method can effectively improve the performance of cross-modal retrieval.
Enabling trust and security between fog nodes using blockchain technology

Fog computing enables the data analysis done nearer to the place of data generated, which makes a very short response time. Trust is essential for the effective performance of the fog nodes to overcome uncertainty, vulnerability, and risk. To enhance the trusted connection in fog computing, blockchain technology is adopted as a solution. Inclusion of blockchain in fog environment ensures consistency and security among the fog nodes. Each fog node mines every transaction and stores them as block creating a chain of blocks. In this proposed work, the adaption of blockchain technology is designed as a suitable solution for establishing trusted security between fog nodes and for which a qualitative assessment is done.
BoFL

Federated learning (FL) is a machine learning paradigm that enables a cluster of decentralized edge devices to collaboratively train a shared machine learning model without exposing users' raw data. However, the intensive model training computation is energy-demanding and poses severe challenges to end devices' battery life. In this paper, we present BoFL, a training pace controller deployed on the edge devices that actuates the hardware operational frequencies over multiple configurations to achieve energy-efficient federated learning. BoFL operates in an explore-then-exploit manner within limited rounds of FL tasks. BoFL explores the large hardware frequency space strategically with a tailor-designed Bayesian optimization algorithm. BoFL first finds a set of good operational configurations within few task training rounds, and then exploits these configurations in the remaining rounds to achieve minimized energy consumption for model training. Experiments on multiple real-world edge devices with different FL tasks suggest that BoFL can reduce energy consumption of model training by around 26%, and achieve near-optimal energy efficiency.
Autonomous approach for moving object detection and classification in road applications

Our paper presents robust approaches for all moving object detection processes. First of all, we propose an automatic and non-parametric method in the segmentation phase based on Delaunay triangulation applied to the image histogram. For the feature extraction phase, we proceed by the GLCM technique for textural feature extraction and the HSV histogram method for the colour feature extraction. Those features will be used as input of the support vector machine (SVM) algorithm to design a robust classification model that will be used to differentiate between moving and static objects. Thus, static objects will be considered as a part of background, and in the other hand moving objects are surrounded by a bounding box in furtherance of careful tracking.
Variational meta reinforcement learning for social robotics

With the increasing presence of robots in our everyday environments, improving their social skills is of utmost importance. Nonetheless, social robotics still faces many challenges. One bottleneck is that robotic behaviors often need to be adapted, as social norms depend strongly on the environment. For example, a robot should navigate more carefully around patients in a hospital than around workers in an office. In this work, we investigate meta-reinforcement learning (meta-RL) as a potential solution. Here, robot behaviors are learned via reinforcement learning, where a reward function needs to be chosen so that the robot learns an appropriate behavior for a given environment. We propose to use a variational meta-RL procedure that quickly adapts the robots’ behavior to new reward functions. As a result, in a new environment different reward functions can be quickly evaluated and an appropriate one selected. The procedure learns a vectorized representation for reward functions and a meta-policy that can be conditioned to such a representation. Given observations from a new reward function, the procedure identifies its representation and conditions the meta-policy to it. While investigating the procedure’s capabilities, we realized that it suffers from posterior collapse where only a subset of the dimensions in the representation encode useful information, resulting in reduced performance. Our second contribution, a radial basis function (RBF) layer, partially mitigates this negative effect. The RBF layer lifts the representation to a higher dimensional space, which is more easily exploitable for the meta-policy. We demonstrate the interest of the RBF layer and the usage of meta-RL for social robotics in four robotic simulation tasks.
GALLERY WALK TECHNIQUE IN IMPROVING STUDENTS SPEAKING SKILL AT SMP UNISMUH MAKASSAR

This research aimed at finding out whether or not the implementation gallery walk technique in improve the students speaking skill term of accuracy and fluency at the SMP UNISMUH Makassar. This research was conducted through pre-experimental method. The populasi of this research consisted of one class of the second grade and the sample of the research, the researcher took VIII A 1 class which consisted of 23 students at High School SMP UNISMUH Makassar, year 2019/2020.In collecting data, the researcher gave a speaking test, the speaking test help into two steps, there were pre-test and post-test, while to analyzed data , the researcher used (t)test. The findings of this research showed that the mean score in pre-test in term of pronouncation was (53,30) and post-test was (76,56). The mean score in pre-test in term smoothness was (51,86) and post-test was (80,04). And the total result of pronouncation and smoothness in pre-test or X1 was (53,39) and the post-test or X2 was (76,56). So, this is showed students got improvement in thier speaking after being taught through gallery walk technique, the students ability in speaking english became better than befofe. Keywords: Speaking, Gallery walk , Accuracy (pronouncation), Fluency (Smoothness), students
Virtual Reality For Training: A Computer Assembly Application

Virtual reality applications aimed at worker training to train professionals are more common with the virtual reality advancements observed in this day and age. More companies search for ways to improve the efficiency and efficacy of their training programs, whilst also reducing training costs. There are several training applications found in the literature, but not many focus on the theme of computer assembly, and only a few have options like an observer’s menu or a scoring system. With that in mind, a training application for assembling computer towers was designed. This article will focus on the application’s functionalities, the results of questionnaires made to evaluate its quality and usability and potential future work. The study realized had good results and a good, varied sample of volunteers, with a score of 93.4% in the custom-made questionnaire, a cybersickness (SSQ) score of 26.53%, a usability score (SUS) of 90% and a satisfaction (ASQ) score of 17.67%, being that a higher score is better in custom made and SUS questionnaires, and a lower score is better in the SSQ and ASQ questionnaires. Although this project is just a proof of concept, it focuses on a theme that will certainly be explored soon, with the rise of demand for training applications, the ever-growing “gamer” market, and workstations for the design of virtual reality applications, like the one described on this paper.
Mammography Quality Evaluation and Model Interpretation Based on CNN-based Inframammary Fold Classification

The Inframammary fold is the lower breast boundary connected to the underlying chest wall. The presence of an inframammary fold is one of the positioning criteria for mammography quality evaluation. As the inframammary fold appeared on a MedioLateral Oblique- view mammogram means that posterior breast tissue is well-presented. This paper introduces an inframammary fold classification method based on a convolutional neural network (CNN) for mammography quality evaluation. The classifier utilizes three CNN models separately, including, AlexNet, GoogLeNet, and Vgg-16. In addition, we implement the two-category and the three-category classifications. Our method extracts a class activation map and then analyzes it to interpret what features the classifier observes. The experiment results demonstrate that the proposed method outperforms the previous work. The CAM depicts why the classifier efficiently determines the presence of an inframammary fold.
Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.
Analyzing and Improving Resilience and Robustness of Autonomous Systems

Autonomous systems have reached a tipping point, with a myriad of self-driving cars, unmanned aerial vehicles (UAVs), and robots being widely applied and revolutionizing new applications. The continuous deployment of autonomous systems reveals the need for designs that facilitate increased resiliency and safety. The ability of an autonomous system to tolerate, or mitigate against errors, such as environmental conditions, sensor, hardware and software faults, and adversarial attacks, is essential to ensure its functional safety. Application-aware resilience metrics, holistic fault analysis frameworks, and lightweight fault mitigation techniques are being proposed for accurate and effective resilience and robustness assessment and improvement. This paper explores the origination of fault sources across the computing stack of autonomous systems, discusses the various fault impacts and fault mitigation techniques of different scales of autonomous systems, and concludes with challenges and opportunities for assessing and building next-generation resilient and robust autonomous systems.
An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array

Deep convolutional neural networks (CNNs) have shown strong abilities in the application of artificial intelligence. However, due to their extensive amount of computation, traditional processors have low energy efficiency when executing CNN algorithms, which is unacceptable for portable devices with limited hardware cost and battery capacity, so designing a CNN-specific processor is necessary. In this paper, we propose an energy-efficient CNN processor architecture for lightweight devices with a processing elements (PEs) array consisting of 384 PEs. Using the systolic array-based PE array, it realizes parallel operations between filter rows and between channels of output feature maps, supporting the acceleration of 3D convolution and fully connected computation with various parameters by configuring internal instruction registers. The computing strategy based on the proposed systolic dataflow achieves less hardware overhead compared with other strategies, and the reuse of image values and weight values, which effectively reduce the power of memory access. A memory system with a multi-level storage structure combined with register file (RF) and SRAM is used in the proposed CNN processor, which further reduces the energy overhead of computing. The proposed CNN processor architecture has been verified on a ZC706 FPGA platform using VGG-16 based on the proposed image segmentation method, the evaluation results indicate that the peak throughput achieves 115.2 GOP/s consuming 3.801 W at 150 MHz, energy efficiency and DSP efficiency reaches 30.32 GOP/s/W and 0.26 GOP/s/DSP, respectively.
Prediction and Visualisation of SICONV Project Profiles Using Machine Learning

Background: Inefficient use of public funds can have a negative impact on the lives of citizens. The development of machine learning-based technologies for data visualisation and prediction has opened the possibility of evaluating the accountability of publicly funded projects. Methods: This study describes the conception and evaluation of the architecture of a system that can be utilised for project profile definition and prediction. The system was used to analyse data from 20,942 System of Management of Agreements and Transfer Contracts (SICONV) projects in Brazil, which are government-funded projects. SICONV is a Brazilian Government initiative that records the entire life cycle of agreements, transfer contracts, and partnership terms, from proposal formalisation to final accountability. The projects were represented by seven variables, all of which were related to the timeline and budget of the project. Data statistics and clustering in a lower-dimensional space calculated using t-SNE were used to generate project profiles. Performance measures were used to test and compare several project-profile prediction models based on classifiers. Results: Data clustering was achieved, and ten project profiles were defined as a result. Among 25 prediction models, k-Nearest-Neighbor (kknn) was the one that yielded the highest accuracy (0.991±0.002). Conclusions: The system predicted SICONV project profiles accurately. This system can help auditors and citizens evaluate new and ongoing project profiles, identifying inappropriate public funding.
A Light Field Full-Focus Image Feature Point Matching Method with an Improved ORB Algorithm

Most of the traditional image feature point extraction and matching methods are based on a series of light properties of images. These light properties easily conflict with the distinguishability of the image features. The traditional light imaging methods focus only on a fixed depth of the target scene, and subjects at other depths are often easily blurred. This makes the traditional image feature point extraction and matching methods suffer from a low accuracy and a poor robustness. Therefore, in this paper, a light field camera is used as a sensor to acquire image data and to generate a full-focus image with the help of the rich depth information inherent in the original image of the light field. The traditional ORB feature point extraction and matching algorithm is enhanced with the goal of improving the number and accuracy of the feature point extraction for the light field full-focus images. The results show that the improved ORB algorithm extracts not only most of the features in the target scene but also covers the edge part of the image to a greater extent and produces extracted feature points which are evenly distributed for the light field full-focus image. Moreover, the extracted feature points are not repeated in a large number in a certain part of the image, eliminating the aggregation phenomenon that exists in traditional ORB algorithms.
Community-driven governance of FAIRness assessment: an open issue, an open discussion

<ns3:p>Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral. A range of FAIR assessment metrics and tools have been designed that measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected behaviours. This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners - and provides examples outlining specific stakeholders' (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable. We invite engagement in this conversation in an open Google Group <ns3:ext-link xmlns:ns4="http://www.w3.org/1999/xlink" ext-link-type="uri" ns4:href="mailto:fair-assessment-governance@googlegroups.com">fair-assessment-governance@googlegroups.com</ns3:ext-link></ns3:p>