Reviewerly/exHarmony huggingface dataset:
Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications

The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning paradigms, such as federated learning, offer a potential solution for privacy-preserving knowledge sharing without sending raw personal data. However, federated learning is vulnerable to harmful participants that can degrade the overall model quality by sharing low-quality data. Therefore, it is critical to select suitable participants to ensure the accuracy and efficiency of federated learning. In this article, a unique clustering-based approach is proposed to use social context data for participant selection. Different edge participant groups will be established, and group-specific federated learning will be performed. The models of various edge groups will be further aggregated to strengthen the robustness of the global model. The experimental results demonstrated that through participant selection, clustering-based hierarchical federated learning can achieve better results with less participants in two different IoMT applications for ECG and human motion monitoring. This shows the efficacy of the proposed method in improving federated learning performance and efficiency in various IoMT applications.
Partial Multi-Label Learning via Credible Label Elicitation

Partial multi-label learning (PML) deals with the problem where each training example is associated with an overcomplete set of candidate labels, among which only some candidate labels are valid. The task of PML naturally arises in learning scenarios with inaccurate supervision, and the goal is to induce a multi-label predictor which can assign a set of proper labels for unseen instance. The PML training procedure is prone to be misled by false positive labels concealed in the candidate label set, which serves as the major modeling difficulty for partial multi-label learning. In this paper, a novel two-stage PML approach is proposed which works by eliciting credible labels from the candidate label set for model induction. In the first stage, the labeling confidence of candidate label for each PML training example is estimated via iterative label propagation. In the second stage, by utilizing credible labels with high labeling confidence, multi-label predictor is induced via pairwise label ranking coupled with virtual label splitting or maximum a posteriori (MAP) reasoning. Experimental studies show that the proposed approach can achieve highly competitive generalization performance by excluding most false positive labels from the training procedure via credible label elicitation.
Data Oblivious ISA Extensions for Side Channel-Resistant and High Performance Computing

Blocking microarchitectural (digital) side channels is one of the most pressing challenges in hardware security today.Recently, there has been a surge of effort that attempts to block these leakages by writing programs data obliviously.In this model, programs are written to avoid placing sensitive data-dependent pressure on shared resources.Despite recent efforts, however, running data oblivious programs on modern machines today is insecure and low performance.First, writing programs obliviously assumes certain instructions in today's ISAs will not leak privacy, whereas today's ISAs and hardware provide no such guarantees.Second, writing programs to avoid data-dependent behavior is inherently high performance overhead.This paper tackles both the security and performance aspects of this problem by proposing a Data Oblivious ISA extension (OISA).On the security side, we present ISA design principles to block microarchitectural side channels, and embody these ideas in a concrete ISA capable of safely executing existing data oblivious programs.On the performance side, we design the OISA with support for efficient memory oblivious computation, and with safety features that allow modern hardware optimizations, e.g., out-of-order speculative execution, to remain enabled in the common case.We provide a complete hardware prototype of our ideas, built on top of the RISC-V out-of-order, speculative BOOM processor, and prove that the OISA can provide the advertised security through a formal analysis of an abstract BOOM-style machine.We evaluate area overhead of hardware mechanisms needed to support our prototype, and provide performance experiments showing how the OISA speeds up a variety of existing data oblivious codes (including "constant time" cryptography and memory oblivious data structures), in addition to improving their security and portability.
Feature Image-Based Automatic Modulation Classification Method Using CNN Algorithm

In this paper, we propose a feature image-based automatic modulation classification (AMC) method to classify modulation type. The proposed method uses a convolutional neural network (CNN) which is one of deep learning algorithms for image classification. In order to classify the modulation type, various features are transformed in a two-dimensional image and this image is used as the input of the CNN. From the simulation results, we show that the proposed method improves classification performance.
OBFUSCURO: A Commodity Obfuscation Engine on Intel SGX

Program obfuscation is a popular cryptographic construct with a wide range of uses such as IP theft prevention.Although cryptographic solutions for program obfuscation impose impractically high overheads, a recent breakthrough leveraging trusted hardware has shown promise.However, the existing solution is based on special-purpose trusted hardware, restricting its use-cases to a limited few.In this paper, we first study if such obfuscation is feasible based on commodity trusted hardware, Intel SGX, and we observe that certain important security considerations are not afforded by commodity hardware.In particular, we found that existing obfuscation/obliviousness schemes are insecure if directly applied to Intel SGX primarily due to side-channel limitations.To this end, we present OBFUSCURO, the first system providing program obfuscation using commodity trusted hardware, Intel SGX.The key idea is to leverage ORAM operations to perform secure code execution and data access.Initially, OBFUSCURO transforms the regular program layout into a side-channelsecure and ORAM-compatible layout.Then, OBFUSCURO ensures that its ORAM controller performs data oblivious accesses in order to protect itself from all memory-based side-channels.Furthermore, OBFUSCURO ensures that the program is secure from timing attacks by ensuring that the program always runs for a pre-configured time interval.Along the way, OBFUSCURO also introduces a systematic optimization such as register-based ORAM stash.We provide a thorough security analysis of OBFUSCURO along with empirical attack evaluations showing that OBFUSCURO can protect the SGX program execution from being leaked by access pattern-based and timing-based channels.We also provide a detailed performance benchmark results in order to show the practical aspects of OBFUSCURO.
Conditional Structure Generation through Graph Variational Generative Adversarial Nets

Graph embedding has been intensively studied recently, due to the advance of various neural network models. Theoretical analyses and empirical studies have pushed forward the translation of discrete graph structures into distributed representation vectors, but seldom considered the reverse direction, i.e., generation of graphs from given related context spaces. Particularly, since graphs often become more meaningful when associated with semantic contexts (e.g., social networks of certain communities, gene networks of certain diseases), the ability to infer graph structures according to given semantic conditions could be of great value. While existing graph generative models only consider graph structures without semantic contexts, we formulate the novel problem of conditional structure generation, and propose a novel unified model of graph variational generative adversarial nets (CondGen) to handle the intrinsic challenges of flexible context-structure conditioning and permutation-invariant generation. Extensive experiments on two deliberately created benchmark datasets of real-world context-enriched networks demonstrate the supreme effectiveness and generalizability of CondGen.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog

In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to underlying semantic meanings and uses robotic sensors to create multi-modal models of perceptual concepts like red and heavy. The agent can be used for showing navigation routes, delivering objects to people, and relocating objects from one location to another. We use dialog clari_cation questions both to understand commands and to generate additional parsing training data. The agent employs opportunistic active learning to select questions about how words relate to objects, improving its understanding of perceptual concepts. We evaluated this agent on Amazon Mechanical Turk. After training on data induced from conversations, the agent reduced the number of dialog questions it asked while receiving higher usability ratings. Additionally, we demonstrated the agent on a robotic platform, where it learned new perceptual concepts on the y while completing a real-world task.
Exploring Deep Learning Approaches to Recognize Handwritten Arabic Texts

Recognition of cursive handwritten Arabic text is a difficult problem because of contextsensitive character shapes, the non-uniform spacing between words and within a word, diverse placements of dots, and diacritics, and very low inter-class variation among individual classes.In this paper, we review and investigate different deep learning architectures and modeling choices for Arabic handwriting recognition.Further, we address the problem that imbalanced data sets present to deep learning systems.In order to address this issue, we are presenting a novel adaptive data-augmentation algorithm to promote class diversity.This algorithm assigns a weight to each word in the database lexicon.This weight is calculated based on the average probability of each class in a word.Experimental results on the IFN/ENIT and AHDB databases have shown that our presented approach yields state-of-the-art results.INDEX TERMS Arabic handwriting recognition (AHR), deep learning neural network (DLNN), convolutional neural networks (CNN), connectionist temporal classification (CTC), recurrent neural network (RNN), IFN/ENIT database, long short-term memory (LSTM), bi-directional long short-term memory (BLSTM), word beam search (WBS).
Active Preference-Based Gaussian Process Regression for Reward Learning

Designing reward functions is a challenging problem in AI and robotics.Humans usually have a difficult time directly specifying all the desirable behaviors that a robot needs to optimize.One common approach is to learn reward functions from collected expert demonstrations.However, learning reward functions from demonstrations introduces many challenges: some methods require highly structured models, e.g.reward functions that are linear in some predefined set of features, while others adopt less structured reward functions that on the other hand require tremendous amount of data.In addition, humans tend to have a difficult time providing demonstrations on robots with high degrees of freedom, or even quantifying reward values for given demonstrations.To address these challenges, we present a preference-based learning approach, where as an alternative, the human feedback is only in the form of comparisons between trajectories.Furthermore, we do not assume highly constrained structures on the reward function.Instead, we model the reward function using a Gaussian Process (GP) and propose a mathematical formulation to actively find a GP using only human preferences.Our approach enables us to tackle both inflexibility and data-inefficiency problems within a preferencebased learning framework.Our results in simulations and a user study suggest that our approach can efficiently learn expressive reward functions for robotics tasks.
Creation of a Mixed‐Mode Fracture Network at Mesoscale Through Hydraulic Fracturing and Shear Stimulation

Abstract Enhanced Geothermal Systems could provide a substantial contribution to the global energy demand if their implementation could overcome inherent challenges. Examples are insufficient created permeability, early thermal breakthrough, and unacceptable induced seismicity. Here we report on the seismic response of a mesoscale hydraulic fracturing experiment performed at 1.5‐km depth at the Sanford Underground Research Facility. We have measured the seismic activity by utilizing a 100‐kHz, continuous seismic monitoring system deployed in six 60‐m length monitoring boreholes surrounding the experimental domain in 3‐D. The achieved location uncertainty was on the order of 1 m and limited by the signal‐to‐noise ratio of detected events. These uncertainties were corroborated by detections of fracture intersections at the monitoring boreholes. Three intervals of the dedicated injection borehole were hydraulically stimulated by water injection at pressures up to 33 MPa and flow rates up to 5 L/min. We located 1,933 seismic events during several injection periods. The recorded seismicity delineates a complex fracture network comprised of multistrand hydraulic fractures and shear‐reactivated, preexisting planes of weakness that grew unilaterally from the point of initiation. We find that heterogeneity of stress dictates the seismic outcome of hydraulic stimulations, even when relying on theoretically well‐behaved hydraulic fractures. Once hydraulic fractures intersected boreholes, the boreholes acted as a pressure relief and fracture propagation ceased. In order to create an efficient subsurface heat exchanger, production boreholes should not be drilled before the end of hydraulic stimulations.
Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

The epidemic increase in online reviews' growth made the sentiment classification a fascinating domain in academic and industrial research. The reviews assist several domains, which is complicated to gather annotated training data. Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed. In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN). In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user's seeking time. Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews. The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments. To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure. With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 97.7%, precision of 95.5%, recall of 94.6%, and F1-score 96.7%, respectively.
Interacting with virtual objects via embodied avatar hands reduces pain intensity and diverts attention

The current study introduces a new paradigm for exploring cognitive factors in pain. Interacting with virtual objects via embodied avatar hands increased the illusion of "being there" in the virtual world, increased VR analgesia for acute pain, and reduced accuracy on an attention demanding task. Twenty-four healthy volunteer college students participated in this within-subject randomized crossover design study. During Phase 1, each participant received brief thermal pain stimuli during interactive embodied avatar VR vs. passive VR (no avatar and no interactivity), VR treatment order randomized. After each pain stimulus, participants provided subjective 0-10 ratings of pain. Compared to the passive VR condition, during the interactive avatar VR, participants reported significant reductions in (1) worst pain, (2) pain unpleasantness, (3) time thinking about pain and (4). they had significantly more fun during the pain stimulus (p = .000 for each). During Phase 2, participants performed a divided attention task in each of the two VR conditions. Participants made significantly more errors on the divided attention task during the interactive avatar VR condition, compared to passive VR, implicating an attention mechanism for how virtual reality reduces pain and helping understand how VR influences pain perception.Trial registration: NCT04245475. Date of registration: 29/01/2020.
Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Over the past couple of decades, many telecommunication industries have passed through the different facets of the digital revolution by integrating artificial intelligence (AI) techniques into the way they run and define their processes. Relevant data acquisition, analysis, harnessing, and mining are now fully considered vital drivers for business growth in these industries. Machine learning, a subset of artificial intelligence (AI), can assist, particularly in learning patterns in big data chunks, intelligent extrapolative extraction of data and automatic decision-making in predictive learning. Firstly, in this paper, a detailed performance benchmarking of adaptive learning capacities of different key machine-learning-based regression models is provided for extrapolative analysis of throughput data acquired at the different user communication distances to the gNodeB transmitter in 5G new radio networks. Secondly, a random forest (RF)-based machine learning model combined with a least-squares boosting algorithm and Bayesian hyperparameter tuning method for further extrapolative analysis of the acquired throughput data is proposed. The proposed model is herein referred to as the RF-LS-BPT method. While the least-squares boosting algorithm is engaged to turn the possible RF weak learners to form stronger ones, resulting in a single strong prediction model, the Bayesian hyperparameter tuning automatically determines the best RF hyperparameter values, thereby enabling the proposed RF-LS-BPT model to obtain desired optimal prediction performance. The application of the proposed RF-LS-BPT method showed superior prediction accuracy over the ordinary random forest model and six other machine-learning-based regression models on the acquired throughput data. The coefficient of determination (Rsq) and mean absolute error (MAE) values obtained for the throughput prediction at different user locations using the proposed RF-LS-BPT method range from 0.9800 to 0.9999 and 0.42 to 4.24, respectively. The standard RF models attained 0.9644 to 0.9944 Rsq and 5.47 to 12.56 MAE values. The improved throughput prediction accuracy of the proposed RF-LS-BPT method demonstrates the significance of hyperparameter tuning/optimization in developing precise and reliable machine-learning-based regression models. The projected model would find valuable applications in throughput estimation and modeling in 5G and beyond 5G wireless communication systems.
PYSKL: Towards Good Practices for Skeleton Action Recognition

We present PYSKL: an open-source toolbox for skeleton-based action recognition based on PyTorch. The toolbox supports a wide variety of skeleton action recognition algorithms, including approaches based on GCN and CNN. In contrast to existing open-source skeleton action recognition projects that include only one or two algorithms, PYSKL implements six different algorithms under a unified framework with both the latest and original good practices to ease the comparison of efficacy and efficiency. We also provide an original GCN-based skeleton action recognition model named ST-GCN++, which achieves competitive recognition performance without any complicated attention schemes, serving as a strong baseline. Meanwhile, PYSKL supports the training and testing of nine skeleton-based action recognition benchmarks and achieves state-of-the-art recognition performance on eight of them. To facilitate future research on skeleton action recognition, we also provide a large number of trained models and detailed benchmark results to give some insights. PYSKL is released at https://github.com/kennymckormick/pyskl and is actively maintained.
High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology

This study examines the factors influencing blockchain’s adoption intention as a whole, relying on an organisational perspective and the Technology-Organisation-Environment Framework (TOE). To organise and investigate the factors and to study blockchain technology adoption intention, this research employs a mixed-methodology. After an extended literature review, first, a qualitative approach is used to discover the factors from primary data collected from 25 interviews. Second, a quantitative survey is employed directly relating the factors to blockchain adoption intention and empirically testing them with 146 employees from 71 North American organisations. A total of 15 factors are discovered, seven are tested, and six are significant. In particular, the technology factors perceived interoperability and perceived data quality have a positive impact upon blockchain adoption intention, while the effect is negative for perceived technological volatility, regulatory uncertainty, standardisation uncertainty and the perceived lack of technological knowledge.
Open-environment machine learning

Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks, particularly those involving open-environment scenarios where important factors are subject to change, called open-environment machine learning in this article, are present to the community. Evidently, it is a grand challenge for machine learning turning from close environment to open environment. It becomes even more challenging since, in various big data tasks, data are usually accumulated with time, like streams, while it is hard to train the machine learning model after collecting all data as in conventional studies. This article briefly introduces some advances in this line of research, focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions and varied learning objectives, and discusses some theoretical issues.