Reviewerly/exHarmony huggingface dataset:
Methods to estimate subpixel level small motion from video of vibrating cutting tools

Cutting tools can vibrate with frequencies of up to a few kHz with amplitudes ranging from a few µm to hundreds of µm. To characterize these vibrations using vision-based modal analysis methods, video of vibrating tools must be acquired at high speed and resolution. High speed acquisition will ensure modes are temporally resolved without aliasing, whereas high resolution acquisition ensures that the pixel size is small enough to resolve small motion. However, since most digital cameras trade speed for resolution, estimating small motion of high frequency modes becomes difficult. To address this issue, this paper presents new methods to estimate small motion from video of vibrating cutting tools. Two methods are illustrated on a video of a slender end mill. One is hardware-based, and the other is software-based. In the hardware-based method, we use combinations of extension tubes and a reverse ring fitted with a standard lens to achieve a pixel size of as less as 1.3 µm. In the software-based method, we leverage the capability of intensity- and phase-based motion registration methods to detect small subpixel level motion, even when the pixel size is 83 µm. In both methods we use the object’s own features to detect and register motion, thus overcoming the limitation of digital image correlation schemes that need markers for target tracking. Modal parameters evaluated from motion estimated with both methods are found to agree with those extracted from twice integrated accelerations. Since methods proposed herein proffer new ways to register small oscillatory motion, we believe that our findings will further help leverage the adoption of vision-based modal analysis methods in machine tool systems as an alternative to the more traditional experimental modal analysis procedures.
Uniformitarianism Re-Examined, or the Present is the Key to the Past, Except When It Isn’t (And Even Then It Kind of Is)

Abstract Perhaps no term in the geological lexicon excites more passions than uniformitarianism, whose motto is “the present is the key to the past.” The term is controversial in part because it contains several meanings, which have been implicated in creating a situation of “semantic chaos” in the geological literature. Yet I argue that debates about uniformitarianism do not arise from a simple chaos of meanings. Instead, they arise from legitimate disagreements about substantive questions. This paper examines these questions and relates them to several “forms of understanding” pursued by researchers in geohistory.
Attention-based multiple-instance learning for Pediatric bone age assessment with efficient and interpretable

Pediatric bone age assessment (BAA) is a common clinical technique for evaluating children's endocrine, genetic, and growth disorders. However, the deep learning BAA method based on global images neglects fine-grained concerns, and regions of interest (ROIs) need additional annotation and complex processing. To overcome these shortcomings, we proposed an interpretable deep-learning architecture based on multiple-instance learning to address BAA efficiently without additional annotations. We cropped the entire image into small patches and got patch features by feature extraction network. Then, an attention backbone ranked feature vectors of the entire image and aggregates its information according to its relative importance. Finally, each image's features and gender were aggregated to predict bone age. The proposed method can identify ROIs by attention-based multi-instance aggregation without additional labels and produce interpretable heatmaps. Moreover, by cropping the complete image into patches and reducing the dimensionality, the proposed model can notice the fine-grained information of the image and improve the model training speed. We validated the proposed method in the Radiological Society of North America 2017 dataset. The results showed that the proposed model achieved an advanced performance of MAE 4.17 months. Furthermore, the visualization results indicated that the proposed model was highly interpretable, which can localize the ROIs without spatial labeling. In conclusion, a novel method for high performance and interpretable bone age prediction without additional manual annotations has been developed, which can be used to effectively assess the pediatric's bone age.
Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGB-colored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., <8:8>. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems.
Intrinsic image decomposition using physics-based cues and CNNs

Intrinsic image decomposition is the decomposition of an image into its reflectance and shading components. The intrinsic image decomposition problem is inherently ill-posed, since there can be multiple solutions to compute the intrinsic components forming the same image. In this paper, we explore the use of physics-based priors. We also propose a new architecture that separates the learning components in a stacked manner. We explore various ways of integrating such priors into a deep learning system. Our method is trained and tested on a large synthetic garden dataset to assess its performance. It is evaluated and compared to state-of-the-art methods using two standard intrinsic datasets. Finally, the pre-trained network is tested on real world images to show the generalisation capabilities of the network.
A Bitstring Semantics for Calculus CL

The aim of this chapter is to develop a semantics for Calculus CL. CL is a diagrammatic calculus based on a logic machine presented by Johann Christian Lange in 1714, which combines features of Euler-, Venn-type, tree diagrams, squares of oppositions etc. In this chapter, it is argued that a Boolean account of formal ontology in CL helps to deal with logical oppositions and inferences of extended syllogistics. The result is a combination of Lange’s diagrams with an algebraic semantics of terms: Bit-CL, in which any ordered objects are identified by characteristic bitstrings. Then, a number of objections to Bit-CL are answered to, and the process of inference is explained in this new logical framework.
Digital technologies as a factor in increasing the competitiveness of educational services in the context of the spread of online learning

Ensuring the high quality of online learning is one of the most significant factors in the competitiveness of universities in the educational market. The purpose of the article is to analyze the specifics of online learning for students in the context of the epidemiological crisis, to study the role of digital technologies, digital competence of teachers in improving the competitiveness of educational services. The authors used a set of methods, which included: a questionnaire survey of students of Russian universities conducted in May 2020 (N = 146 people), the focus group method (12 full-time students). A drop in the level of self-organization and self-control of students has been established, students reduce the requirements for themselves as active participants in the educational process. The conclusion is confirmed that the interest of students in online learning is connected with the teacher’s charisma, his ability to teach, while entertaining, as well as with the teacher’s active use of digital innovative technologies. The conducted research allowed us to conclude that there are a number of restrictions that limit the preservation of the quality of educational services in the context of online learning: digital inequality, insufficient development of the digital infrastructure of universities and the use of digital systems for organizing educational activities. Certain risks for maintaining the competitiveness of the university in the long term are: the inability of universities to develop high-end, comprehensive, scalable digital solutions, the request for simplification of educational work by young people due to the general availability of information materials on the Internet, high costs for updating information systems, maintaining a developed digital educational environment.
A Granular Approach to Secure the Privacy of Electronic Health Records Through Blockchain Technology

The method of storing data in traditional Electronic Health Records (EHRs) becomes even more obsolete when it has to undertake data from more than one institution. This problem is bigger than it seems as, most of the times the patients' medical data is scattered among various Healthcare Centers. Also, these multiple and inconsistent health records are almost never reviewed which further makes them unreliable. All this can be achieved by implementing the Blockchain Technology into the traditional Electronic Health Records. We propose a blockchain technology based Electronic Health Record portal for patients, doctors, and other users to provide temper proof, secure, and efficient access to health records while maintaining the privacy of patient’s health records. The aim of this paper is to deal with the various needs of patients, doctors, and third parties, as well as to see how the system could resolve privacy and security issues in the healthcare industry.
Vulnerability prediction for secure healthcare supply chain service delivery

Healthcare organisations are constantly facing sophisticated cyberattacks due to the sensitivity and criticality of patient health care information and wide connectivity of medical devices. Such attacks can pose potential disruptions to critical services delivery. There are number of existing works that focus on using Machine Learning (ML) models for predicting vulnerability and exploitation but most of these works focused on parameterized values to predict severity and exploitability. This paper proposes a novel method that uses ontology axioms to define essential concepts related to the overall healthcare ecosystem and to ensure semantic consistency checking among such concepts. The application of ontology enables the formal specification and description of healthcare ecosystem and the key elements used in vulnerability assessment as a set of concepts. Such specification also strengthens the relationships that exist between healthcare-based and vulnerability assessment concepts, in addition to semantic definition and reasoning of the concepts. Our work also makes use of Machine Learning techniques to predict possible security vulnerabilities in health care supply chain services. The paper demonstrates the applicability of our work by using vulnerability datasets to predict the exploitation. The results show that the conceptualization of healthcare sector cybersecurity using an ontological approach provides mechanisms to better understand the correlation between the healthcare sector and the security domain, while the ML algorithms increase the accuracy of the vulnerability exploitability prediction. Our result shows that using Linear Regression, Decision Tree and Random Forest provided a reasonable result for predicting vulnerability exploitability.
Quantum Cramér-Rao bound for quantum statistical models with parameter-dependent rank

Recently, a widely-used computation expression for quantum Fisher information was shown to be discontinuous at the parameter points where the rank of the parametric density operator changes. The quantum Cram\'er-Rao bound can be violated on such singular parameter points if one uses this computation expression for quantum Fisher information. We point out that the discontinuity of the computation expression of quantum Fisher information is accompanied with the unboundedness of the symmetric logarithmic derivation operators, based on which the quantum Fisher information is formally defined and the quantum Cram\'er-Rao bound is originally proved. We argue that the limiting version of quantum Cram\'er-Rao bound still holds when the parametric density operator changes its rank by closing the potential loophole of involving an unbounded SLD operator in the proof of the bound. Moreover, we analyze a typical example of the quantum statistical models with parameter-dependent rank.
Sentiment Analysis of Chinese Reviews Based on BiTCN-Attention Model

It is of great significance for individuals, enterprises, and government departments to analyze and excavate the sentiment in the comments. Many deep learning models are used for text sentiment analysis, and the BiTCN model has good efficacy on sentiment analysis. However, in the actual semantic expression, the contribution of each word to the sentiment tendency is different, BiTCN treats it fairly and does not pay more attention to the key sentiment words. For this problem, a sentiment analysis model based on the BiTCN-Attention is proposed in this paper. The Self-Attention mechanism and Multi-Head Self-Attention mechanism are added to BiTCN respectively to form BiTCN-SA and BiTCN-MHSA, which improve the weight of sentiment words and the accuracy of feature extraction, to increase the effect of sentiment analysis. The experimental results show that the model accuracies of BiTCN-SA and BiTCN-MHSA in the JingDong commodity review data set are 3.96% and 2.41% higher than that of BiTCN, respectively. In the comment data set of DianPing, the accuracy of BiTCN-SA and BiTCN-MHSA improved by 4.62% and 3.49%, respectively, compared with that of BiTCN.
A Technology Exploration towards Trustable and Safe Use of Social Media for Vulnerable Women based on Islam and Arab Culture

We live in an era characterized by unprecedented communication possibilities; yet significant parts of the world's population are left behind and at increased vulnerability of harm due to low digital literacy, poverty, war, surveillance, gender and cultural bias, etc. Social media platforms are not perceived as trustable and safe (e.g., privacy-preserving) enough by many users, especially by the most vulnerable ones. In this context, we aim at creating a bridge between vulnerable users and computer science to let researchers know where they should address their efforts to improve these tools. In particular, utilizing the results from ongoing fieldwork with conservative Sunni-Muslims, mostly Syrian war refugees, in Jordan, we identify some crucial features that messaging systems should include, discuss how they are indeed important for any user, and propose some possible technological approaches to implement them. Finally, we also discuss how we have added one these highly ranked features to a custom messaging system, proving its feasibility.
Leveraging alternative data sources for socio-economic nowcasting

This article uses the French case to argue that alternative datasets can be used to improve the ability of policymakers to monitor socio-economic phenomena in real-time and at specific locations. In the first part of the article, we present an inflation nowcast based on internet data, both at the French level and for the city of Provins, the birthplace of the Yellow Vest movement. We then present a job-openings nowcast based on job postings online. Finally, we present a machine learning model used to predict soft wheat harvests in France in real-time. This indicator can anticipate wheat production levels and, therefore, food prices. In the sequence, we present an analysis of the French presidential election based on social media analytics. First, we employ NLP techniques to extract the hottest political topics from a pool of social media posts. Second, we use a transformers-based deep learning model to create a sentiment analysis of Emmanuel Macron and Marine Le Pen in the run-up to the ballot in order to estimate their vote intentions in real-time.
Waste Classification Using ANN, CNN And Transfer Learning

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Waste Classification Using ANN, CNN And Transfer Learning 8 Pages Posted: 24 Jun 2022 Last revised: 22 Aug 2022 See all articles by Manoj PatilManoj PatilDr. A.P.J. Abdul Kalam University University , Indore, Madhya PradeshNaumanurrahman ShaikhSSBT's College of Engineering and Technology Bambhori, Jalgaon Date Written: June 10, 2022 Abstract Waste management is one of the biggest issues of the current era. We mainly have two types of garbage one "Organic" which is self-degradable and another one is "Recyclable" which is not degradable and can be harmful to the environment but can be reused in this paper we have applied three methods of Classification of this material into two types using Machine Learning and Deep Learning from the images. We have used three methods which are Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Transfer Learning. We have performed data analysis and data preprocessing, after building and compiling the model we trained the models and recorded all the data in the forms of plots and graphs which gave insight into the performance of each model. Suggested Citation: Suggested Citation Patil, Manoj and Shaikh, Naumanurrahman, Waste Classification Using ANN, CNN And Transfer Learning (June 10, 2022). Available at SSRN: https://ssrn.com/abstract=4133206 Manoj Patil (Contact Author) Dr. A.P.J. Abdul Kalam University University , Indore, Madhya Pradesh ( email ) Village, Post, Indore Bypass Rd, Arandia,JhalariyaMadhya PradeshIndore, 452016India HOME PAGE: http://https://www.aku.ac.in/website Naumanurrahman Shaikh SSBT's College of Engineering and Technology Bambhori, Jalgaon ( email ) Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 12 Abstract Views 68 PlumX Metrics Related eJournals Waste eJournal Follow Waste eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 549 PAPERS 2,653 Electrical Engineering eJournal Follow Electrical Engineering eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 164 PAPERS 9,472 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo4 in 1.811 seconds
Less is More: Proxy Datasets in NAS approaches

Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections between them. In this work, we extensively analyze the role of the dataset size based on several sampling approaches for reducing the dataset size (unsupervised and supervised cases) as an agnostic approach to reduce search time. We compared these techniques with four common NAS approaches in NAS-Bench201 in roughly 1,400 experiments on CIFAR-100. One of our surprising findings is that in most cases we can reduce the amount of training data to 25%, consequently also reducing search time to 25%, while at the same time maintaining the same accuracy as if training on the full dataset. In addition, some designs derived from subsets out-perform designs derived from the full dataset by up to 22 p.p. accuracy.
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems

Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (robustness bias), where certain subgroups in a dataset (e.g. based on class, gender, etc.) are less robust than others. This bias not only persists even after adversarial training, but often results in severe performance discrepancies across these subgroups. Existing works characterize the subgroup’s robustness bias by only checking individual sample’s proximity to the decision boundary. In this work, we argue that this measure alone is not sufficient and validate our argument via extensive experimental analysis. It has been observed that adversarial attacks often corrupt the high-frequency components of the input image. We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i.e., degree of model’s reliance on high-frequency features and the (conventional) sample-distance to the decision boundary. We demonstrate that by reliably estimating adversarial vulnerability at the sample level using the proposed holistic metric, it is possible to develop a trustworthy system where humans can be alerted about the incoming samples that are highly likely to be misclassified at test time. This is achieved with better precision when our holistic metric is used over individual measures. To further corroborate the utility of the proposed holistic approach, we perform knowledge distillation in a limited-sample setting. We observe that the student network trained with the subset of samples selected using our combined metric performs better than both the competing baselines, viz., where samples are selected randomly or based on their distances to the decision boundary.
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles

The development of autonomous vehicles provides an opportunity to have a complete set of camera sensors capturing the environment around the car. Thus, it is important for object detection and tracking to address new challenges, such as achieving consistent results across views of cameras. To address these challenges, this work presents a new Global Association Graph Model with Link Prediction approach to predict existing tracklets location and link detections with tracklets via cross-attention motion modeling and appearance re-identification. This approach aims at solving issues caused by inconsistent 3D object detection. Moreover, our model exploits to improve the detection ac-curacy of a standard 3D object detector in the nuScenes detection challenge. The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method to produce SOTA performance on the existing vision-based tracking dataset.