Reviewerly/exHarmony huggingface dataset:
Pre-training on physical dynamics enhances indirect sensing in recurrent neural networks

A McKibben-type pneumatic artificial muscle (PAM), which can achieve a large contraction force, is a promising candidate for the actuation of soft robots. Currently, to control PAMs, many sensors are required for controlling factors such as the length and internal pressure of the PAM, and the load applied to it. To reduce the weight and cost of soft robots and keep their characteristics intact, it is important to reduce the number of sensors as much as possible. In this study, we propose a machine learning scheme that can accurately predict PAM length using a small number of sensors. This is realized through the pre-training on the physical property of PAM in recurrent neural networks under the reservoir computing framework. First, we removed the load and length sensors and predicted the length of the PAM using only the internal pressure and electrical resistance sensors. The load applied to the PAM greatly affected its length, so the prediction accuracy was low, and the normalized mean square error (NMSE), which is an index of error, was NMSE =0.220. However, when the load was predicted from the internal pressure and electrical resistance, the load could be predicted with a high accuracy, and when self-feedback from this emulated load was added into the reservoir, the prediction accuracy of the length was greatly improved (NMSE =0.0330). In the end, we removed the load, length, and internal pressure sensors, and we predicted the length using only the electrical resistance sensor. The load and internal pressure were predicted from the electrical resistance, and these predicted values were fed back to the reservoir and used to predict the length. When the length was finally predicted, the prediction accuracy was higher than when it was predicted from just the electrical resistance. The mechanisms that supported this improvement and the role of feedback in the reservoir are discussed in detail.
Comparison of Classification Models of Novel Quantum Machine Learning on QisKit Dataset

Classical computers utilize ones and zeroes, but quantum computers employ qubits. Quantum computers employ ones and zeros, but qubits have a third state called “superposition” that lets them represent both at once. In quantum computing, qubits can serve double duty. A quantum computer functions under different rules than a regular computer. It doesn't use bits and bytes like ordinary computers, but qubits. Quantum computers measure and retrieve data using qubits. Unlike standard computer bits, qubits can hold several values at once. They have a speed advantage over traditional computers and algorithms. Quantum machine learning uses qubits, quantum processes, or other specialized quantum systems to boost algorithm computation and storage speed. Data is computed using machine learning methods. Quantum machine learning adds quantum computers to the pool of machine learning gear. Quantum computing exploits quantum theory to handle information. Three quantum machine learning models were constructed for dataset categorization to test quantum computing. Comparing quantum and classical ML models. Quantum ML models showed the most accuracy after analysis. QSVM got 98%, QLR and QKNN 97%. Quantum deep learning can also be used for future investigations.
Machine Learning based Object Detection and Classification for Future Multi-Media Application

Object detection is a very important aspect of multimedia applications. The recent innovations that are being deployed in the current era with the latest trends in technological advancements have made researchers and scientists develop systems that are capable of identifying objects using various machine learning algorithms. The objects that are being identified are further classified for identifying the particular class of the object. This process seems to be simple but is quite complex and also has various complexities and complications that need to be analyzed before deploying any algorithm into the machine for automated detection and classification. This paper explores the recent research that is carried out, in particular in the area of object detection and classification using machine learning algorithms. The performance measures like accuracy, sensitivity, recall, and F-measures have been compared with the available model.
Blockchain and CES Based Optimal Pricing Decision for P2P Energy Transactions

The development of distributed renewable energy has promoted the emergence of peer-to-peer (P2P) energy transaction. In this paper, we consider the decentralization of blockchain and the equitable distribution of Cloud Energy Storage (CES), construct a distributed energy trading mechanism to balance the interests of buyers and sellers, and formulates P2P energy trading as a problem of P2P trading price determination and CES capacity allocation, which is finally addressed by converting the KKT condition based on the optimal problem for all participants into a mixed integer linear programming (MILP). The solution results show that P2P energy trading through blockchain and CES can effectively reduce the transaction costs and increase the revenues of market participants.
Sprayful Interaction: Perceptions of a Moss-based SLow-resolution Living Media Display

Sustainability in HCI is a growing area of research, and a driver of creativity for developing new and interesting interfaces. Low-power technology such as e-ink or flip-dot displays have provided some alternatives, and living media displays present a new opportunity to study displays that consume low amounts of energy. This pilot study explores the design space for a moss-based display installed in a design school, through think-aloud, observation and interview with 10 participants. The study found that users enjoyed the novelty, appearance and interaction with the moss display, showing promise for future applications. We contribute three design considerations and potential applications based on these findings.
An Efficient Association Rule Mining Using Multi-Objective Based Adaptive Horse Herd Optimization in Frequent Item Sets

Data mining is a knowledge discovery process that uses multiple techniques to categorize, examine, and organize an enormous amount of data using a variety of techniques. The main goals of this research are the efficient management of the space and the quick identification of the frequently used goods from the online transaction data. In order to generate the frequent item set, each customerâ€™s shopping habits are studied to the products purchased. The customer searches for the product and purchases it to set up the frequently occurring item. The paper presents a Multi-Objective Adaptive Horse Herd Optimization (MO-AHHO) to find common item sets from many items. Here Support, Confidence, Lift, and conviction is considered multi-objective functions. This method is useful for finding the transmission user estimate based on rules related to high quality mapping. Effective association rule mining using multi-objective-based adaptive horse herd optimization in frequent item sets called MO-AHHO. The performance evaluation results showed that the proposed methodology performed better in execution time, trust, interestingness, support, coverage, and memory usage.
Dynamic Inference From IoT Traffic Flows Under Concept Drifts in Residential ISP Networks

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to mitigate risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands of home networks is a nontrivial task. This article focuses on the challenges of developing and applying data-driven inference models when labeled data of device behaviors is limited and the distribution of data changes across time and space domains (concept drifts). Our contributions are fourfold: 1) we collect and analyze more than six million network traffic flows of 24 types of consumer IoT devices from 12 real homes over six weeks to highlight the challenge of temporal and spatial concept drifts in network behaviors of IoT devices—we publicly release our training and testing instances data; 2) we analyze the performance of two inference strategies, namely global inference (a model trained on a combined set of all labeled data from training homes) and contextualized inference (several models each trained on the labeled data from a training home) in the presence of concept drifts; 3) to manage concept drifts, we develop a method that dynamically applies the “best” model (from a set) to network traffic of unseen homes during the testing phase, yielding better performance in a fifth of scenarios when the labels are available for the testing data (ideal but unrealistic settings); and 4) we develop a method to automatically select the best model without needing labels of unseen data (a realistic inference) and show that it can achieve 94% of the ideal model’s accuracy.
A Multiscale Nonlocal Feature Extraction Network for Breast Lesion Segmentation in Ultrasound Images

Breast lesion segmentation in ultrasound images is of great importance since it can help us to characterize and localize lesion regions. However, low-quality imaging, blurred boundary, and variable lesion shapes bring challenges to accurate segmentation. In recent years, many U-Net variants have been proposed and successfully applied to breast lesion segmentation. However, these methods suffer from two limitations: (1) Ignoring the ability to capture rich global context information, and (2) Introducing extra complex operations. To alleviate these challenges, we propose a multiscale nonlocal feature extraction network (MNFE-Net) for accurately segmenting breast lesions. The core idea includes three points: (1) Parallel Encoder models long-range dependencies, (2) Multiscale Feature Module refines local features without introducing extra complex operations, and (3) Global Feature Guidance Module extracts global semantic information. MNFE-Net mainly has the following advantages: (1) The method has excellent performance for segmentation of malignant breast lesions, (2) The Parallel Encoder increases network parameters without significantly decreasing inference speed, and (3) The method is easy to understand and execute. Extensive experiment results with six state-of-the-art (SOTA) methods on three public breast ultrasound datasets demonstrate the superior segmentation performance of our proposed MNFE-Net.
Gaf-Net: Graph Attention Fusion Network for Multi-View Semi-Supervised Classification

Multi-view semi-supervised classification is a typical task to classify data using a small amount of supervised information, which has attracted a lot of attention from researchers in past years. In practice, existing methods focus more on extracting spatial or spectral features using graph convolutional neural networks without considering the diversity and variability of graph structures and the contributions of different views. To address this challenge, a network framework termed graph attention fusion network is proposed, which is comprised of two phases: view-specific feature embedding and graph embedding fusion. In the former feature extraction stage, view-specific feature embedding module can flexibly focus on the neighborhood calculation operation to learn a weight for each neighboring node. In the latter feature fusion stage, the graph embedding fusion module is performed by complementarity and consistency to fuse these embeddings for semi-supervised classification tasks. We conduct extensive experiments in terms of semi-supervised classification on real-world datasets to validate the superiority of the proposed method over various existing state-ofthe-art methods.
Dt2f-Tlnet: A Novel Text-Independent Writer Identification Model Using a Combination of Deep Type-2 Fuzzy Architecture and Transfer Learning Networks Based on Handwriting Data

Identifying and verifying the identity of people based on scanned images of handwritten documents is an applicable biometric modality with applications in forensic and historic document investigation, and it is an important study area within the research field of behavioral biometrics. Despite this, there are few studies in this field. Furthermore, there are very few standard datasets for identifying and verify handwritten documents. Also, handwritten documents lose their character during time because of ink spread and drying. Therefore, it is necessary to provide a method that can identify and verify handwritten documents under various uncertainties. In this study, a text-independent writer identification and verification model in offline state under different experimental conditions is developed using a combination of Deep Type-2 Fuzzy architecture and Transfer Learning networks (DT2F-TLNet). So, a right-to-left dataset has been collected. The proposed DT2F-TLNet model is validated using both the designed dataset and other benchmark datasets. The proposed model is distinguished by the fact that it is developed to be independent of the textual content of the handwritten cases and can be used for various languages. The study's findings show that the developed DT2F-TLNet model can learn properties from heterogeneous handwriting data and results in higher accuracy than other comparable approaches.
Display and imaging system sharpness modeling and requirement in high-resolution VR and AR

Pixels per degree (PPD) alone is not a reliable predictor for high-resolution experience in VR and AR. This is because "high-resolution experience" not only depends on PPD but also display fill factor, pixel arrangement, graphics rendering, and other factors. This complicates architecture decisions and design comparisons. Is there a simple way to capture all the contributors and match user experience? In this paper, we present a system level model, system MTF, to predict perceptual quality considering all the key VR/AR dimensions: pixel shape (display), pixel per degree (display), fill factor (display), optical blur (Optics), and image processing (graphics pipeline). The metric can be defined in much the same way of traditional MTF for imaging systems by examining image formation of a point source and then performing Fourier transform over the response function, with special mathematical treatment. One application is presented on perceived text quality, where two weight functions depending on text orientation and frequency incorporated into the above model. A perceptual study about text quality was performed to validate the system MTF model.
AN INSIGHT INTO CRYPTO-CURRENCY ACCOUNTING UNDER INDIAN ACCOUNTING FRAMEWORK VIS-À-VIS INTERNATIONAL ACCOUNTING FRAMEWORK AND US-GAAP

In the last decade, the market size of cryptocurrencies multi-folds, as more and more individuals and businesses including investment entities shown interest in this asset class. Despite ambiguity due to the dicey stand of regulators towards cryptocurrency, across the world; there are around 21,581 Cryptos traded over 521 exchanges, with a market value above $1 trillion as on 31st October 2022. This also makes accounting for cryptocurrency a much debatable concern among accounting professionals in addition to holders (as well as miners) of crypto-currency units, especially in absence of specific standards and guidelines on recognition, measure, and presentation of crypto assets in books of accounts. This article evaluates the existing Indian Accounting Frameworks vis-à-vis International Accounting Framework and US-GAAP to suggest the accounting for cryptocurrencies in the books of the holder from three perspectives, the Trader, Investor, and Miners’ perspective.
Towards real-time formula driven dataset feed for large scale deep learning training

Recently, a new deep learning architecture, the Vision Transformer, has emerged as the new standard for classification tasks, overtaking the conventional Convolutional Neural Network (CNN) models. However, these state-of-the-art models require large amounts of data, typically over 100 million images, to achieve optimal performance through transfer learning. This requirement is met by using proprietary datasets like JFT-300M or 3B, which are not publicly available. To overcome these challenges and address privacy concerns, Formula-Driven Supervised Learning (FDSL) has been introduced. FDSL trains deep learning models using synthetic images generated from mathematical formulas, such as Fractals and Radial Contour images. The main objective of this approach is to reduce the I/O bottleneck that occurs during training with large datasets. Our implementation of FDSL generates instances in real-time during training, and uses a custom data loader based on EGL (Native Platform Graphics Interface) for fast rendering via shaders. The evaluation of our custom data loader on the FractalDB-100k dataset comprising 100 million images revealed a loading time that is three times faster compared to the PyTorch Vision loader.
Fundamentals of Natural Language Processing

The goal of natural language processing is to create machines that comprehend text or voice data and respond in a manner similar to how humans do by responding with their own text or speech. In this chapter, you will learn what natural language processing is, how it works, and its various stages. After that, you’ll learn about core NLP tasks like text analysis and entity recognition, sentiment analysis, speech recognition and synthesis, machine translation, and semantic language modeling. Additionally, we’ll cover AI for conversational interaction and its benefits, as well as Microsoft Azure NLP offerings. Here, you’ll learn about core Azure NLP workloads like language, speech, and translator. Finally, under the section “Microsoft Azure Platform for Conversational AI,” you will learn about Power Virtual Agents and the Azure Bot Service.
Secure Data Transfer Using Cloud

Advent of Cloud Computing has been a phenomenal phase in the history of computer wisdom. It handed capabilities to break numerous problems that were before supposed insolvable to be reckoned by a machine. It removed the pressure from those responsible for manufacturing better machines to keep up with the adding complexity of the problems that the machines are intended to break. Cloud Computing handed platform for better application of the resource across the world. Being a incipient field, it's crowded with numerous different problems that the masterminds and scientists are working assiduously to exclude. One of the main downsides with pall is security. So, this design proposes a medium for secure train storehouse pall using encryption and Diffie Hellman. The algorithm involves cracking the train stored on the pall and using Diffie Hellman for authenticating the stoner to decipher the needed train.
Measurement of basic parameters

In this chapter, we present some basic background information on human motion basic parameters, and on common statistical methods used to evaluate the feasibility of a new sensor-based measurement instrument (such as an IMU or Kinect-based system) to measure human motion, which would offer much lower cost and provide much better accessibility to patients, and ultimately facilitate practicing rehabilitation exercises at home.
Gradual changes in temperature, humidity, rainfall, and solar irradiation as indicators of city climate change and increasing hydrometeorological disaster: a case study in Yogyakarta, Indonesia

Climate change began in the 1980s when global warming became a global concern, and it has continued in several cities in Indonesia. This paper measures climate change in Yogyakarta, Indonesia based on gradual changes in the air temperature, air humidity, rainfall depth, and duration of the sun's irradiation. The data were obtained for a period of 43 years (1976-2018) from the meteorological recording data at the climatology station in Yogyakarta. Data processing is done using the average statistical method, as outlined in the relationship graph between the years and the parameters studied. The results show that climate change in Yogyakarta has been clearly occurring. It is indicated by the increase of the average air temperature, the decrease of the average air humidity, the rising rainfall in the rainy season, and the changing of the sun's irradiation duration in the rainy season and in the dry season.
Challenges and future work directions in e-healthcare using blockchain technology

Electronic healthcare (e-healthcare) emerges as a trend for enhancing traditional healthcare systems that are costly and ineffective. Besides, blockchain technology provides a distributed and secure environment for sensitive applications. This chapter discusses challenges and future work directions for digital healthcare using blockchain technology. First, the chapter introduces how blockchain technology applies to healthcare applications. Then, it discusses the main requirements for developing blockchain-based healthcare applications. In addition, the chapter presents the key challenges for applying blockchain in this sensitive-data context, emphasizing critical technological and social aspects. Next, the chapter highlights the advantages and disadvantages of applying blockchain in healthcare. Finally, it concludes with future work directions and opportunities for research on the topic.
A modified un-realisation approach for effective data perturbation

In recent times, data has been evolving from multiple sources like social media, Facebook, Twitter, etc. in large volumes and acquiring in multiple forms. These data have multi-dimensional sensitive features from different resources entail that privacy preserving is a significant research issue. In this context, un-realisation algorithms have evolved to hide the collected data with the addition of noise to them to generate a distorted dataset while attempting privacy preservation. In this paper, a novel modified un-realisation algorithm has been proposed to generate a distorted dataset by removing duplicate elements in the dataset decreasing computational time of decision tree construction process. These techniques add noise to the original data and generate a distorted dataset by using a un-realisation algorithm. This novel approach converts the original sample datasets into different perturbed datasets by inducing the noise through set theory. It experimentally produces better results than un-realisation algorithm in terms of CPU execution time and space complexity.
Understanding GasToken Adoption in the Ethereum Blockchain

GasTokens are created by smart contracts executing in the Ethereum blockchain to support a form of transaction fee pre-payment. Even though GasTokens had a period of significant activity on Ethereum (regularly surpassing half of the overall weekly smart contract creation and destruction activities), few systematic studies have been conducted on GasToken usage and its effect on the blockchain network. This paper focuses on understanding the adoption of GasTokens on Ethereum since May of 2020. We first analyze the usage patterns, including the creation, destruction, and holding of GasTokens, then report several price findings related to GasTokens, and compare the fees paid by users submitting transactions that create and destroy GasTokens to the fees paid by all the transactions in the network over different time periods. We characterize the benefits obtained by GasToken transaction senders and report the impact of GasToken creations and destructions on the major mining pools.