Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition from Knee Angle Using Machine Learning Techniques

Human Activity Recognition (HAR) is a crucial technology for many applications such as smart homes, surveillance, human assistance and health care. This technology utilises pattern recognition and can contribute to the development of human-in-the-loop control of different systems such as orthoses and exoskeletons. The majority of reported studies use a small dataset collected from an experiment for a specific purpose. The downsides of this approach include: 1) it is hard to generalise the outcome to different people with different biomechanical characteristics and health conditions, and 2) it cannot be implemented in applications other than the original experiment. To address these deficiencies, the current study investigates using a publicly available dataset collected for pathology diagnosis purposes to train Machine Learning (ML) algorithms. A dataset containing knee motion of participants performing different exercises has been used to classify human activity. The algorithms used in this study are Gaussian Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors Vote, Support Vector Machine and Gradient Boosting. Furthermore, two training approaches are compared to raw data (de-noised) and manually extracted features. The results show up to 0.94 performance of the Area Under the ROC Curve (AUC) metric for 11-fold cross-validation for Gradient Boosting algorithm using raw data. This outcome reflects the validity and potential use of the proposed approach for this type of dataset.
A comparative study and analysis of developer communications on Slack and Gitter﻿

Software developers are often using instant messaging platforms to communicate with each other and other stakeholders. Among these platforms, Gitter has emerged as a popular choice and the messages it contains can reveal important information to researchers studying open source software systems. Uncovering what developers are communicating about through Gitter is an essential first step towards successfully understanding and leveraging this information. In this paper, we first describe the largest manually labeled and curated dataset of Gitter developer messages, named GitterCom, obtained by manually analyzing and labeling 10,000 Gitter messages in 10 software projects. We then present a qualitative study to understand the extent to which the categories identified in previous work by Lin et al. (2016) found on Slack through surveys are applicable to developer messages exchanged on Gitter. Further, in an effort to automate the labeling process, we investigate the accuracy of 9 traditional machine learning and deep learning algorithms in predicting the intent of Gitter messages. We found that Decision Trees and Random Forest performed the best, achieving an accuracy of 88%, which is very promising for this multi-class classification task. Finally, we discuss the potential directions for future research enabled by labeled Gitter datasets such as GitterCom.
A Secured Patient’s Online Data Monitoring through Blockchain: An Intelligent way to Store Lifetime Medical Records

The most reliable all-together cryptosystem is Blockchain technology that gives a framework for safe transactions across networks expect to its immutability and unchanging features. Blockchain technology has attracted the attention of various startups, developers, and administrators as a decentralized infrastructure. This system protects all supplies from fraud and provides a tracking tool to track all previous network activities. The Online Medical Record (OMR) system allows patients to share information and control the health status of specific people. Unable to grant emergency personnel access to patient OMR in case of any emergency. In addition, the patient needs a history record management system of OMR, which displays a large amount of personal data. This paper proposes a framework for medical healthcare record management that engages Blockchain network to provide a defensive protection system and consider secure policies. These components involved the detection, protection, and monitoring of extensible access control in an urgent condition. The suggested model supports higher efficiency than related refined healthcare systems related to privacy, accessibility, data auditing in trials, and access control in an emergency situation.
A boosting resampling method for regression based on a conditional variational autoencoder

Resampling is the most commonly used method for dealing with imbalanced data, in addition to modifying the algorithm mechanism, it can, for example, generate new minority samples or reduce majority samples to adjust the data distribution. However, to date, related research has predominantly focused on solving the classification problem, while the issue of imbalanced regression data has rarely been discussed. In real-world applications, predicting regression data is a common and valuable issue in decision making, especially in regard to those rare samples with extremely high or low values, such as those encountered in the fields of signal processing, finance, or meteorology. This study therefore divided its regression data into rare samples and normal samples, with self-defined relevance functions and, in addition, proposed a boosting resampling method based on a conditional variational autoencoder. The experimental results showed that when using the proposed resampling method was employed, the prediction performance of the whole testing data set was slightly increased, while the performance for the rare samples was significantly improved.
Differentiable Subset Pruning of Transformer Heads

Abstract Multi-head attention, a collection of several attention mechanisms that independently attend to different parts of the input, is the key ingredient in the Transformer. Recent work has shown, however, that a large proportion of the heads in a Transformer’s multi-head attention mechanism can be safely pruned away without significantly harming the performance of the model; such pruning leads to models that are noticeably smaller and faster in practice. Our work introduces a new head pruning technique that we term differentiable subset pruning. ntuitively, our method learns per- head importance variables and then enforces a user-specified hard constraint on the number of unpruned heads. he importance variables are learned via stochastic gradient descent. e conduct experiments on natural language inference and machine translation; we show that differentiable subset pruning performs comparably or better than previous works while offering precise control of the sparsity level.1
A DQN-Based Consensus Mechanism for Blockchain in IoT Networks

The integration of the blockchain and Internet of Things (IoT) systems can effectively guarantee data security in IoT applications. To facilitate the use of blockchain on resource-constrained IoT end devices, we propose RAFT+ with a new leader selection scheme in this article, which is based on the distributed consensus algorithm RAFT. The design of RAFT+ aims at mitigating the imparities between different types of IoT end devices and enabling these devices to allow different types of IoT end devices to participate in block consensus, thus maintaining strong consistency of the blockchain network. The leader selection scheme is generated by a deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -Network (DQN), which can make the optimal selection of the leader under various conditions by leveraging the limited system resources as well as balancing the load of the consensus mechanism on multiple IoT end devices. Simulation results show that RAFT+ can enhance the system performance while maintaining the security of the system under high load conditions.
Full-Custom Design of Multiplicative Inverse for AES S-BOX using 4-Transistor XOR Gate

The Multiplicative Inverse is the most intricate step involved in computation of Substitution-Box values for Private Key Encryption algorithms. It is the shared common step for encryption as well as decryption. Computing the Multiplicative Inverse of a value in Galois Field GF (p) for small numbers is an easy task which is straight away obtained from its computation tables. However, it is not convenient for higher values of p. Recent implementation of Multiplicative Inverse focuses on composite field arithmetic based approaches which uses lower order fields in its implementation. This paper presents Multiplicative Inverse design in composite field arithmetic using 4-transistor XOR gate to obtain a compact Substitution-Box module in 90 nm technology. The design requires a total of 442 transistors with average power dissipation of 3.75 watts at 0.6 supply voltage. The design shows a significant drop in transistor count by 43 % compared to previously published work.
A Comparative Evaluation of Ensemble Classifiers for Malicious Webpage Detection

Malicious webpage is developed or manipulated to be used as attack tool where it is considered as one of the main reasons of Internet criminal activities. Thus, it is essential to detect such webpages and prevent end users form accessing it. The conventional malicious webpages detection techniques are based on searching through a blacklist that contains a list of webpages classified as malicious from the perspective of users. However, these techniques have high false-negative rates especially with aforesaid sophisticated attacks due to technical and computational limitations. Hence, machine learning techniques have been employed to classify webpages by systemically analyzing set of features that reflect the characteristics of a malicious webpage. This paper compares the prediction accuracy of several machine learning classification algorithms and ensemble techniques. A data set of 5000 instances of URLs, with 189 different features are used in the comparative study. The results show that the most accurate classification technique in MultiBoost and Adaboost is Support Vector Machine (SVM), while K-Nearest Neighbor (k-NN) technique in bagging and random subspace.
A context-based meta-reinforcement learning approach to efficient hyperparameter optimization

In this paper, we present a context-based meta-reinforcement learning approach to tackle the challenging data-inefficiency problem of Hyperparameter Optimization (HPO). Specifically, we design an agent which sequentially selects hyperparameters to maximize the expected accuracy of the machine learning algorithm on the validation set. First, we design a context variable that learns the latent embedding of prior experience, and the agent can solve the new tasks efficiently conditioned on it. Second, we employ a multi-task objective method that aims to maximize the average reward across all the meta-training tasks to meta-train the agent. Third, in the adaptation phase, we introduce a quadratic penalty technique to achieve better performance of the agent. Finally, to further improve the efficiency in the adaptation phase, we use a predictive model to evaluate the accuracy of machine learning algorithm instead of training it. We evaluate our approach on 18 real-world datasets and the results demonstrate that our approach outperforms other state-of-the-art optimization methods in terms of test set accuracy and runtime performance.
Attention-based skill translation models for expert finding

The growing popularity of community question answering websites can be seen by the growing number of users. Many methods are proposed to identify talented users in these communities, but many of them suffer from vocabulary mismatches. The solution to this problem can be found in translation approaches. The present paper proposes two translation methods for extracting more relevant translations. The proposed methods rely on the attention mechanism. The methods use multi-label classifiers that take each question as input and predict the skills related to the question. Using the attention mechanism, the model is able to focus on specific parts of the given input and predict the correct labels. The ultimate goal of these networks is to predict skills related to questions. Using word attention scores, we can find out how relevant a single word is to a particular skill. As a result of these attention scores, we obtain more relevant translations for each skill. We then use these translations to bridge the lexical gap and improve expert retrieval results. Extensive experiments on two large sub-collections of the StackOverflow dataset demonstrate that the proposed methods outperform the best baseline method by up to 14.11/% MAP improvement.
EM Side Channels in Hardware Security: Attacks and Defenses

Side channels are present in all systems, including digital ones. This article analyzes electromagnetic side channels from both the attack and defense perspectives.
Arbitrage, contract design, and market structure in Bitcoin futures markets

Abstract Perpetual futures, first proposed by Shiller (1993), have only seen wide use in cryptocurrency markets. We examine the contract design and market microstructure differences for the behavior of Bitcoin quarterly and perpetual futures prices and assess the implications for market participants and policymakers. We find perpetual futures exhibit multiple “u‐shaped” curves, seasonal effects, and opening effects despite lacking opening and closing hours. There is suggestive evidence of spillover effects between perpetual and quarterly futures contracts. We find quarterly futures offer cash‐and‐carry arbitrage opportunities, but similar to Hattori and Ishida (2021) these opportunities primarily exist during market dislocations.
Short-term Solar Power Forecasting Using XGBoost with Numerical Weather Prediction

In recent years, solar photovoltaic (PV) generation becomes one of the most relevant energies. However, the intermittent characteristics of solar generation create significant problems to power system operations. To overcome this problem, many solar power forecasting techniques have been developed, and different forecasting horizons require different methodologies. For a short-term prediction, forecasting horizons generally require numerical weather prediction models (NWP) that provide an important estimation of weather variables such as solar irradiance, temperature, wind speed, rainfall, air pressure, etc. This research proposes a machine learning model based on Kernel Principal Component Analysis (PCA)- XGBoost to improve the accuracy of one-hour-ahead solar power forecasts. The model considered the deterministic Weather Research and Forecasting (WRFD) provided by Taiwan Central Weather Bureau (CWB). Furthermore, a XGBoost model was built on an ensemble of decision trees, providing important information and appropriate results in the forecasting process.
Nonparametric regression on low-dimensional manifolds using deep ReLU networks: function approximation and statistical recovery

Abstract Real-world data often exhibit low-dimensional geometric structures and can be viewed as samples near a low-dimensional manifold. This paper studies nonparametric regression of Hölder functions on low-dimensional manifolds using deep Rectified Linear Unit (ReLU) networks. Suppose $n$ training data are sampled from a Hölder function in $\mathcal{H}^{\ s,\alpha }$ supported on a $d$-dimensional Riemannian manifold isometrically embedded in $\mathbb{R}^D$. A deep ReLU network architecture is designed to estimate the underlying function from the training data. The mean squared error of the empirical estimator is proved to converge in the order of $n^{-\frac{2(s+\alpha )}{2(s+\alpha ) + d}}\log ^3 n$. This result shows that deep ReLU networks give rise to a fast convergence rate depending on the data intrinsic dimension $d$, which is usually much smaller than the ambient dimension $D$. It therefore demonstrates the adaptivity of deep ReLU networks to low-dimensional geometric structures in data and partially explains the power of deep ReLU networks in tackling high-dimensional data with low-dimensional geometric structures.
MARS: Learning Modality-Agnostic Representation for Scalable Cross-Media Retrieval

Cross-media retrieval (CMR) offers a flexible retrieval experience across multiple modalities. Existing CMR approaches are constrained by the assumption that the paired modalities are available in training, and they leverage the data of all modalities to obtain a common representation. However, as dealing with the data from new modality, the previous all modalities need to be re-trained, compromising the flexibility and practicality of CMR. In this paper, we propose an approach termed learning Modality-Agnostic Representation for Scalable cross-media retrieval (MARS), which allows each modality to be trained independently. To be specific, MARS treats the label information as a distinct modality, and introduces a label parsing module LabNet to generate semantic representation for correlating different modalities. Meanwhile, MARS constructs the modality-specific representation module DataNet to obtain the modality-shared representation and modality-exclusive representation equipped with unbiased semantic classification. Technically, for the first modality, we jointly train the LabNet and its DataNet to preserve the semantic similarity between the Label-derived representation and the modality-shared representation. For new modalities, MARS employs the well-learned LabNet to extract the representation in labels, and then such representation is served as the privilege to guide the associated DataNet training via the same objective. Furthermore, we assign the same classifier to the representation module of all modalities for better semantic alignment. With the above schema, the obtained modality-shared representation is considered to be modality-agnostic. Extensive experiments on several benchmark multi-modality datasets demonstrate that the proposed MARS achieves better results than existing methods.
Big Data Handling Approach for Unauthorized Cloud Computing Access

Nowadays, cloud computing is one of the important and rapidly growing services; its capabilities and applications have been extended to various areas of life. Cloud computing systems face many security issues, such as scalability, integrity, confidentiality, unauthorized access, etc. An illegitimate intruder may gain access to a sensitive cloud computing system and use the data for inappropriate purposes, which may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for big data in cloud computing. The HUDH scheme aims to restrict illegitimate users from accessing the cloud and to provide data security provisions. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. The HUDH scheme involves three algorithms: advanced encryption standards (AES) for encryption, attribute-based access control (ABAC) for data access control, and hybrid intrusion detection (HID) for unauthorized access detection. The proposed scheme is implemented using the Python and Java languages. The testing results demonstrated that the HUDH scheme can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% accuracy.
Performance of Forced-Alignment Algorithms on Children's Speech

Purpose Acoustic measurement of speech sounds requires first segmenting the speech signal into relevant units (words, phones, etc.). Manual segmentation is cumbersome and time consuming. Forced-alignment algorithms automate this process by aligning a transcript and a speech sample. We compared the phoneme-level alignment performance of five available forced-alignment algorithms on a corpus of child speech. Our goal was to document aligner performance for child speech researchers. Method The child speech sample included 42 children between 3 and 6 years of age. The corpus was force-aligned using the Montreal Forced Aligner with and without speaker adaptive training, triphone alignment from the Kaldi speech recognition engine, the Prosodylab-Aligner, and the Penn Phonetics Lab Forced Aligner. The sample was also manually aligned to create gold-standard alignments. We evaluated alignment algorithms in terms of accuracy (whether the interval covers the midpoint of the manual alignment) and difference in phone-onset times between the automatic and manual intervals. Results The Montreal Forced Aligner with speaker adaptive training showed the highest accuracy and smallest timing differences. Vowels were consistently the most accurately aligned class of sounds across all the aligners, and alignment accuracy increased with age for fricative sounds across the aligners too. Conclusion The best-performing aligner fell just short of human-level reliability for forced alignment. Researchers can use forced alignment with child speech for certain classes of sounds (vowels, fricatives for older children), especially as part of a semi-automated workflow where alignments are later inspected for gross errors. Supplemental Material https://doi.org/10.23641/asha.14167058.
Augmented reality‐guided positioning system for radiotherapy patients

In modern radiotherapy, error reduction in the patients' daily setup error is important for achieving accuracy. In our study, we proposed a new approach for the development of an assist system for the radiotherapy position setup by using augmented reality (AR). We aimed to improve the accuracy of the position setup of patients undergoing radiotherapy and to evaluate the error of the position setup of patients who were diagnosed with head and neck cancer, and that of patients diagnosed with chest and abdomen cancer. We acquired the patient's simulation CT data for the three-dimensional (3D) reconstruction of the external surface and organs. The AR tracking software detected the calibration module and loaded the 3D virtual model. The calibration module was aligned with the Linac isocenter by using room lasers. And then aligned the virtual cube with the calibration module to complete the calibration of the 3D virtual model and Linac isocenter. Then, the patient position setup was carried out, and point cloud registration was performed between the patient and the 3D virtual model, such the patient's posture was consistent with the 3D virtual model. Twenty patients diagnosed with head and neck cancer and 20 patients diagnosed with chest and abdomen cancer in the supine position setup were analyzed for the residual errors of the conventional laser and AR-guided position setup. Results show that for patients diagnosed with head and neck cancer, the difference between the two positioning methods was not statistically significant (P > 0.05). For patients diagnosed with chest and abdomen cancer, the residual errors of the two positioning methods in the superior and inferior direction and anterior and posterior direction were statistically significant (t = -5.80, -4.98, P < 0.05). The residual errors in the three rotation directions were statistically significant (t = -2.29 to -3.22, P < 0.05). The experimental results showed that the AR technology can effectively assist in the position setup of patients undergoing radiotherapy, significantly reduce the position setup errors in patients diagnosed with chest and abdomen cancer, and improve the accuracy of radiotherapy.