Reviewerly/exHarmony huggingface dataset:
Influence of vestibular signals on bodily self-consciousness: Different sensory weighting strategies based on visual dependency

Previous studies showed that the vestibular system is crucial for multisensory integration, however, its contribution to bodily self-consciousness more specifically on full-body illusions is not well understood. Thus, the current study examined the role of visuo-vestibular conflict on a full-body illusion (FBI) experiment that was induced during a supine body position. In a mixed design experiment, 56 participants underwent through a full-body illusion protocol. During the experiment, half of the participants received synchronous visuo-tactile stimulation, and the other half received asynchronous visuo-tactile stimulation, while their physical body was lying in a supine position, but the virtual body was standing. Additionally, the contribution of individual sensory weighting strategies was investigated via the Rod and Frame task (RFT), which was applied both before (pre-FBI standing and pre-FBI supine) and after the full-body illusion (post-FBI supine) protocol. Subjective reports of the participants confirmed previous findings suggesting that there was a significant increase in ownership over a virtual body during synchronous visuo-tactile stimulation. Additionally, further categorization of participants based on their visual dependency (by RFT) showed that those participants who rely more on visual information (visual field dependents) perceived the full-body illusion more strongly than non-visual field dependents during the synchronous visuo-tactile stimulation condition. Further analysis provided not only a quantitative demonstration of full-body illusion but also revealed changes in perceived self-orientation based on their field dependency. Altogether, findings of the current study make further contributions to our understanding of the vestibular system and brought new insight for individual sensory weighting strategies during a full-body illusion.
Plan Optimization to Bilingual Dictionary Induction for Low-resource Language Families

Creating bilingual dictionary is the first crucial step in enriching low-resource languages. Especially for the closely related ones, it has been shown that the constraint-based approach is useful for inducing bilingual lexicons from two bilingual dictionaries via the pivot language. However, if there are no available machine-readable dictionaries as input, we need to consider manual creation by bilingual native speakers. To reach a goal of comprehensively create multiple bilingual dictionaries, even if we already have several existing machine-readable bilingual dictionaries, it is still difficult to determine the execution order of the constraint-based approach to reducing the total cost. Plan optimization is crucial in composing the order of bilingual dictionaries creation with the consideration of the methods and their costs. We formalize the plan optimization for creating bilingual dictionaries by utilizing Markov Decision Process (MDP) with the goal to get a more accurate estimation of the most feasible optimal plan with the least total cost before fully implementing the constraint-based bilingual lexicon induction. We model a prior beta distribution of bilingual lexicon induction precision with language similarity and polysemy of the topology as <?TeX $\alpha$?> and <?TeX $\beta$?> parameters. It is further used to model cost function and state transition probability. We estimated the cost of all investment plans as a baseline for evaluating the proposed MDP-based approach with total cost as an evaluation metric. After utilizing the posterior beta distribution in the first batch of experiments to construct the prior beta distribution in the second batch of experiments, the result shows 61.5% of cost reduction compared to the estimated all investment plans and 39.4% of cost reduction compared to the estimated MDP optimal plan. The MDP-based proposal outperformed the baseline on the total cost.
Supporting unknown number of users in keystroke dynamics models

In recent years, keystroke dynamics has gained popularity as a reliable means of verifying user identity in remote systems. Due to its high performance in verification and the fact that it does not require additional effort from the user, keystroke dynamics has become one of the most preferred second factor of authentication. Despite its prominence, it has one major limitation: keystroke dynamics algorithms are good at fitting a model to one user and one user only. When such algorithms try to fit a model to more than one user, the verification accuracy decreases dramatically. However, in real-world applications it is common practice for two or more users to use the same credentials, such as in shared bank accounts, shared social media profiles, and shared streaming licenses which allow multiple users in one account. In these cases, keystroke dynamics solutions become unreliable. To address this limitation, we propose a method that can leverage existing keystroke dynamics algorithms to automatically determine the number of users sharing the account and accurately support accounts that are shared with multiple users. We evaluate our method using eight state-of-the-art keystroke dynamics algorithms and three public datasets, with up to five different users in one model, achieving an average improvement in verification of 9.2% for the AUC and 8.6% for the EER in the multi-user cases, with just a negligible reduction of 0.2% for the AUC and 0.3% for the EER in the one-user cases.
Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks

Recommender systems have become an essential instrument in a wide range of industries to personalize the user experience. A significant issue that has captured both researchers' and industry experts' attention is the cold start problem for new items. This work presents a graph neural network recommender system using item hierarchy graphs and a bespoke architecture to handle the cold start case for items. The experimental study on multiple datasets and millions of users and interactions indicates that our method achieves better forecasting quality than the state-of-the-art with a comparable computational time.
Keyword spotting techniques to improve the recognition accuracy of user-defined keywords

The existing keyword spotting (KWS) techniques can recognize pre-defined keywords well but have a poor recognition accuracy for user-defined keywords. In real use cases, there is a high demand for users to define their keywords for various reasons. To address the problem, in this work, three techniques have been proposed, including incremental training with revised loss function, data augmentation, and fine-grained training, to improve the accuracy for the user-defined keywords while maintaining high accuracy for pre-defined keywords. The proposed techniques are applied to a classical KWS model (cnn-trad-fpool3) and a state-of-the-art KWS model (res15) respectively. The experimental results show that the proposed techniques have better recognition accuracy than several existing methods for the recognition of use-defined keywords. With the proposed techniques, the recognition accuracy of user-defined keywords on cnn-trad-fpool3 and res15 are significantly improved by 21.78% and 24.42%, respectively.
HIVE-4-MAT: Advancing the Ontology Infrastructure for Materials Science

This paper introduces Helping Interdisciplinary Vocabulary Engineering for Materials Science (HIVE-4-MAT), an automatic linked data ontology application. The paper provides contextual background for materials science, shared ontology infrastructures, and knowledge extraction applications. HIVE-4-MAT’s three key features are reviewed: 1) Vocabulary browsing, 2) Term search and selection, and 3) Knowledge Extraction/Indexing, as well as the basics of named entity recognition (NER). The discussion elaborates on the importance of ontology infrastructures and steps taken to enhance knowledge extraction. The conclusion highlights next steps surveying the ontology landscape, including NER work as a step toward relation extraction (RE), and support for better ontologies.
Automatic Tuning of Hyperparameters for Neural Networks in Serverless Cloud

Deep Neural Networks are used to solve the most challenging world problems. In spite of the numerous advancements in the field, most of the models are being tuned manually. Experienced Data Scientists have to manually optimize hyperparameters, such as dropout rate, learning rate or number of neurons for Big Data applications. We have implemented a flexible automatic real-time hyperparameter tuning methodology. It works for arbitrary models written in Python and Keras. We also utilized state of the art Cloud services such as trigger based serverless computing (Lambda), and advanced GPU instances to implement automation, reliability and scalability.The existing tuning libraries, such as hyperopt, Scikit-Optimize or SageMaker, require developers to provide a list of hyperparameters and the range of their values manually. Our novel approach detects potential hyperparameters automatically from the source code, updates the original model to tune the parameters, runs the evaluation in the Cloud on spot instances, finds the optimal hyperparameters, and saves the results in the No-SQL database. The methodology can be applied to numerous Big Data Machine Learning systems.
Analysis of Human Activity Recognition using Deep Learning

Nowadays the deluge of data is increasing with new technologies coming up daily. These advancements in recent times have also led to an increased growth in fields like Robotics and Internet of Things (IoT). This paper helps us draw a comparison between the usage and accuracy of different Human Activity Recognition models. There will be discussion on mainly two models-2-D Convolutional Neural Network and Long-Short term Memory. In order to maintain the consistency and credibility of the survey, both models are trained using the same dataset containing information collected using wearable sensors which was acquired from a public website. They are compared using their accuracy and confusion matrix to check the true and false positives and later the various aspects and fields, where the two models can separately and together be used in the wider field of Human Activity Recognition using image data have been explained. The experimental results signified that both Convolutional Neural Networks and Long-Short term memory model are equally equipped for different situations, yet Long-Short Term memory model mostly appears to be more consistent than Convolutional Neural Networks.
Cellular estimation of distribution algorithm designed to solve the energy resource management problem under uncertainty

The Energy Resource Management (ERM) can be modeled as a Mixed-Integer Non-Linear Problem whose aim is to maximize profits generally using smart grid capabilities more than importing energy from external markets. Due to this, many resources and customers are involved in optimization, making ERM a complex problem. Moreover, when the inherent uncertainty of weather conditions, load forecast, electric vehicles planned trips, or market prices is considered, deterministic approaches might fail in obtaining optimal solutions to the problem. In this context, evolutionary algorithms are a useful tool to find effective near-optimal solutions. In fact, to design and test evolutionary algorithms to solve the ERM problem under uncertainty, the research community has developed a simulation framework. In this paper, we propose the Cellular Univariate Marginal Distribution Algorithm with Normal-Cauchy distribution (CUMDANCauchy) to address the ERM problem in uncertain environments. CUMDANCauchy uses a univariate estimation of the product of Normal and Cauchy distributions over each feature, and produces new individuals not only by the sampling of the learned distributions but also using neighborhoods of individuals from a ring cellular structure. The experiments performed over two case studies show that: CUMDANCauchy is as competitive as the previous dominant class of algorithms in terms of the global fitness achieved; its convergence behavior is among the best in comparison with the other tested algorithms; its running time is similar to the algorithm with the best global fitness achieved in the first case study, and it is the fastest algorithm in the second one.
Differentially private data release via statistical election to partition sequentially

Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques produce and release synthetic individual-level data in the DP framework. One key challenge to develop DIPS methods is the preservation of the statistical utility of synthetic data, especially in high-dimensional settings. We propose a new DIPS approach, STatistical Election to Partition Sequentially (STEPS) that partitions data by attributes according to their importance ranks according to either a practical or statistical importance measure. STEPS aims to achieve better original information preservation for the attributes with higher importance ranks and produce thus more useful synthetic data overall. We present an algorithm to implement the STEPS procedure and employ the privacy budget composability to ensure the overall privacy cost is controlled at the pre-specified value. We apply the STEPS procedure to both simulated data and the 2000–2012 Current Population Survey youth voter data. The results suggest STEPS can better preserve the population-level information and the original information for some analyses compared to PrivBayes, a modified Uniform histogram approach, and the flat Laplace sanitizer.
A Novel Medical Image Encryption using Cyclic Coding in Covid-19 Pandemic Situation

Abstract During this Covid - 19 pandemic situation, encryption of medical images takes a major role in medical information systems as well as in telemedicine. However according to government rule, it is essential to hide the information of the patients. Recent development in computer network enhances a lot of facilities in communication area. Unfortunately, hackers are misusing this facility and always try to attack on the transmitted information in insecure network. For secure transmission of medical images, it is essential to encrypt the information before transmitting. In this communication, we are going to present a novel approach of medical image encryption using cyclic coding. We have proved that it is quite difficult to decrypt the original information from encoded data in one common mode of attacking-chosen ciphertext attack. Moreover, we have proved the effectiveness of the encryption using correlation coefficients. Our proposed scheme is suitable for efficient encoding of multiple medical images.
Early Prediction of Sepsis using Machine Learning

Sepsis is a fatal disease caused by infection. It has a significantly high mortality rate, particularly for patients in the ICU. The early and accurate detection of Sepsis is crucial as delayed treatment causes a sharp increase in the mortality rate. The proposed research aims to develop a classifier that accurately predicts Sepsis up to six hours before the clinical diagnosis of the disease. This is achieved using the patient's EMR, vital signs and demographics. The research shows several imputation techniques and proposes a new filling algorithm known as Mixed Filling. The main features contributing to the classifier's predictions have been described, thereby making the model more interpretable for medical personnel. Six models namely Random Forest, Logistic Regression, Light Gradient Boosting Machine, eXtreme Gradient Boosting, Neural Network and Long Short-Term Memory have been investigated for the classification of patients. The evaluation metrics that have been obtained are unprecedented and can be extremely useful for the timely and accurate prediction of Sepsis.
EVAL Cane: An IoT based Smart Cane for the Evaluation of Walking Gait and Environment

The usage of Internet of Things (IoTs) in healthcare has increased sharply recently. The IoT technology is identified as a potential solution to alleviate the pressures on healthcare systems. This paper proposed a low-cost, non-intrusive and multi-function IoT based smart cane that can be used for the evaluation of human walking and evironment, i.e., EVAL Cane, for the elderly people or fragile people. Several sensors such as accelerometer/gyroscope, ultrasonic sensor, force sensitive resistor (FSR), and temperature sensor were instrumented on the cane to collect the data of user's walking gait and walking environment, which will be connected to the Internet through communication protocols like LoRa or WI-FI. Although these sensors were not installed on the human body directly, the user's gait feature can be described by certain of them such as accelerometer and FSR, that means the user's gait can be analyzed and evaluated during the walking with the cane. Besides, based on the data analysis of accelerometer, we can also detect unwanted events such as falls during the walking, which is helpful for the monitoring of user's health status. In addition, the cane has an ultrasonic sensor used to detect an obstacle in front of the cane and this sensor will work together with a buzzer in order to give a sound signal to the user when an obstruction is detected in front of him/her. The buzzer will also make a sound when a fall is detected. Moreover, the environmental temperature of the cane can be measured and be displayed on a LCD screen to the user along with the number of steps taken and the battery percentage level of the device. In a word, EVAL Cane is capable to monitor user's walking, detect the surrounding environment, give output back to the user, and collect data used for further analysis.
Evaluating MASHAP as a faster alternative to LIME for model-agnostic machine learning interpretability

Explaining machine learning models without any knowledge of their inner workings is an ambitious and often a necessary challenge to be solved. Local interpretable model-agnostic explanations (LIME) is undoubtedly one of the most well-known methods to deal with this issue, however its slow performance might render LIME unsuitable for industry level tasks. In this paper, we evaluate the Model-Agnostic SHAPley value explanations (MASHAP) method as a faster alternative for explaining black-box models and we compare it with LIME across a series of evaluation metrics. Our experiments show valid reasons why one should choose MASHAP over LIME, since it delivers roughly the same consistency in a significantly faster way.
Exploring Parallelism to Improve the Performance of FrodoKEM in Hardware

Abstract FrodoKEM is a lattice-based key encapsulation mechanism, currently a semi-finalist in NIST’s post-quantum standardisation effort. A condition for these candidates is to use NIST standards for sources of randomness (i.e. seed-expanding), and as such most candidates utilise SHAKE, an XOF defined in the SHA-3 standard. However, for many of the candidates, this module is a significant implementation bottleneck. Trivium is a lightweight, ISO standard stream cipher which performs well in hardware and has been used in previous hardware designs for lattice-based cryptography. This research proposes optimised designs for FrodoKEM, concentrating on high throughput by parallelising the matrix multiplication operations within the cryptographic scheme. This process is eased by the use of Trivium due to its higher throughput and lower area consumption. The parallelisations proposed also complement the addition of first-order masking to the decapsulation module. Overall, we significantly increase the throughput of FrodoKEM; for encapsulation we see a $$16\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>16</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> speed-up, achieving 825 operations per second, and for decapsulation we see a $$14\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>14</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> speed-up, achieving 763 operations per second, compared to the previous state of the art, whilst also maintaining a similar FPGA area footprint of less than 2000 slices.
Digital Skills of Mobile Journalists: Exploring Learning Needs and Learner Experiences of Just-in-Time Learning With Smartphones

Through a semi-structured survey of mobile journalists ( N = 433), the study investigated mobile journalists’ learning needs and learner experiences related to their digital skills development with smartphones. Results indicate that not only novice journalism students but also experienced educators, professionals, or mass communication and media workers had a high level of demands on developing specific digital skills, especially skills with smartphones, such as writing better headlines and stories for mobile audiences, shooting and editing 360° videos, and programming skills such as HTML. Recommendations are made for future research that mobile journalists indicated mobile microlearning is a promising approach to support their just-in-time learning.
Delegated Key-Policy Attribute-Based Set Intersection over Outsourced Encrypted Data Sets for CloudIoT

Private set intersection (PSI) is a fundamental cryptographic primitive, allowing two parties to calculate the intersection of their data sets without exposing additional private information. In cloud-based IoT system, IoT-enabled devices would like to outsource their data sets in their encrypted form to the cloud. In this scenario, how to delegate the set intersection computation over outsourced encrypted data sets to the cloud and how to achieve the fine-grained access control for PSI without divulging any additional information to the cloud are still open problems. With that in mind, in this work, we combine key-policy attribute-based encryption (KP-ABE) and PSI to introduce such a novel concept, called delegated key-policy attribute-based set intersection over outsourced encrypted data sets (KP-ABSI), to solve this problem. Then we propose a first concrete KP-ABSI scheme and analyze its efficiency.
Heterogeneous Excitation-and-Squeeze Network for visual dialog

Visual dialog is a challenging vision-language task, which requires an agent to answer a series of questions about an image. To answer correctly, the agent should explore question-relevant clues from heterogeneous information, such as resolve visual coreference using dialog history, or locate the referred object in the image. Previous methods usually capture question-relevant factors from heterogeneous information in a sequential manner, that is, first enrich the question context with relevant dialog history, and then use the enriched question to perform visual grounding. However, this sequential way may bring unexpected information mix, leading to wrong answers. In this paper, we propose Heterogeneous Excitation-and-Squeeze Network (HESNet), which can handle with heterogeneous information in a parallel and adaptive way. HESNet contains two key modules: Heterogeneous Excitation Module (HE-Module) and Heterogeneous Squeeze Module (HS-Module). HE-Module excavates question-related clues in heterogeneous information, namely image, dialog history, and candidate answer parallelly by conducting bi-directional excitation operations. Then, the HS-Module squeezes the enriched question representation and multiple enhanced heterogeneous representations adaptively for final answer prediction. Experimental results on VisDial v0.9 and v1.0 datasets show the superiority of the proposed approach over the state-of-the-art methods. The visualization analysis further shows that the proposed HESNet alleviates the information mix by collecting the heterogeneous clues parallelly and adaptively.