Reviewerly/exHarmony huggingface dataset:
Quantifying User Behaviour in Multisensory Immersive Experiences

With the advent of standalone virtual reality (VR) headsets, multisensory VR experiences are emerging with the hopes of better simulating real-world experiences. For example, innovations in haptic suits and scent diffusion devices are burgeoning. While stimulating multiple senses re-orientates the perceived quality of experience (QoE) by increasing factors such as realism, presence and immersion, the impact it has on user behaviour has yet to be fully quantified. Advances in wearables and instrumented VR headsets, however, have allowed for such user behaviours to be easily measured in real-time. In this paper, we describe a pilot experiment in which participants play a custom-developed VR game under two conditions: (1) conventional audio-visual and (2) multisensory, where haptic feedback is provided via a haptic sleeve and olfaction is enabled via a scent diffusion add-on for the VR headset. We describe a developed instrumented VR headset capable of measuring electroencephalography (EEG), electrocardiography (ECG), and electro-oculography (EOG) signals in real-time. From these signals, metrics of mental workload, engagement, and attention are extracted. We report changes seen in these behavioural measures between the two conditions, thus providing insights on factors driving the improved QoE seen with multisensory experiences.
Wellbeing Assessment of a Museum Experience in Virtual Reality through UCL Measurement Tool Kit and Heart Rate Measurement: a Pilot Study

A visit experience in art museums or galleries can bring significant benefits to people’s health, since it can act on the sense of identity, with a consequent impact on positive emotions and self-esteem. Information and Communication Technologies (ICT) can represent a useful means of accessibility and daily good practice, for instance for therapeutic purposes: in this scenario, Virtual Reality (VR) plays a crucial role, thanks to the sense of presence and the possibility of interactive and collaborative environments. In this pilot study, an experiment was conducted to measure and discuss the effects of a virtual visit to an archaeological museum, in terms of improvement of wellbeing. The experiment was conducted on two groups of participants: a first group experiencing the visit and the artifacts fruition in Virtual Reality (VR) and a second group performing a visit only on a screen. The Positive and Negative Wellbeing Umbrella, which is part of the UCL Museum Wellbeing measurement Tool Kit, and the measurement of Heart rate pre and post-session were used for the assessment. For the next step, these data will be compared with the data taken from a real visit to the archaeological Museum. The final goal of the whole study is the optimization and improvement of a VR application for remote fruition of an archaeological museum, acting on stress reduction of hospitalized patients.
Edge AI - Use Case on Google Coral Dev Board Mini

The Google Coral Dev Board Mini leads to a great opportunity in developing applications, using object detection or image classification on the edge by having an affordable price at the same time. The evaluation board provides easy testing of AI on the edge implementations using an Edge TPU coprocessor with overall 4 TOPS. By using performance benchmarks to compare the Dev Board with the Dev Board Mini, clear differences could be identified. The Dev Board Mini turns out to have a two to fifteen times slower inference times than the Dev Board compared on the same object detection and image classification models. When running a custom trained face mask detection model using the EfficientDet-liteO architecture the Dev Board Mini still achieves 6 fps, suitable for low cost Edge AI applications.
Automatic Alignment for Side-Channel Attack Against Cryptographic Modules

Data encryption is critical for information security. Previous studies show that power analysis is a powerful tool for side-channel attack (SCA) against cryptographic modules. Therefore, it is essential to carry out power analysis to assess the vulnerability of cryptographic modules. The success of power analysis relies on aligned power traces, which is not easy without an external trigger point. In this paper, we propose an automatic power trace alignment method so that operations in power traces and be located without external trigger points. Experimental results show that the proposed method can be applied to various ciphers, including AES and RSA.
Zero Payload Enhanced AES Cryptography Against Side-Channel Attacks

the development of side-channel attacks makes secrets visible due to the physical leakage of confidential operations. In order to prevent the crack of cryptography, various cost sensitive countermeasures of side channel attacks are also being developed. This paper proposes a zero payload deformative method, which can increase the resistant sidechannel attacks of Advanced Encryption Standard cryptography by 10 to 100 times. Whose payload is better than the currently known methods.
Design and Develop A Part of Speech Tagging for Ge’ez Language using Deep Learning Approach

Part of Speech (POS) tagging is one of the basic and important applications of Natural Language Processing (NLP).Different POS tagging systems in various languages have been developed. In some languages, POS tagging works well with higher accuracy, but in the Ge’ez language, it is still an unsolved problem. Ge’ez is a morphological rich, free word order language, and very ambiguous where every word has many more variants based on its suffixes and prefixes. This paper proposed a deep learning-based POS tagging for Ge’ez language. The Gated Recurrent Unit(GRU), Bidirectional Gated Recurrent Unit(Bi-GRU), Long-Short Term Memory(LSTM), and Bidirectional Long-Short Term Memory(Bi-LSTM) models were developed by varying the number of training epochs and important hyperparameters. Ge’ez language has not standard POS corpus. Therefore, we have taken 2,552 sentences from Holy Bible then divided into 1,786 as a training data set and 766 as a test data set. The corpus has 26,607 total words and the experimental results show that the Bi-GRU model has achieved an accuracy value of 86.70% with 10 epochs,2 hidden layers, 128 neurons per hidden layer, and a learning rate of 0.01. The second best model is the GRU model with an accuracy value of 86.15% using the same hyperparameters with Bi-GRU model. The final result shown in the experiment shows that the GRU and Bi-GRU Ge’ez POS tagging has performed better according to tag-wise classification results shown in and the overall accuracy results of the training and testing data set. This implies that the GRU and Bi-GRU based POS tagger outperforms the LSTM and Bi-LSTM tagger when used separately. Overall, deep learning approach is better than other traditional approaches for developing POS tagger for low-resource language-Ge’ez.
Combining Augmented Reality and Multi-User Remote Collaboration to Improve Sustainable Agriculture and Economy

Agri-food education activity becomes more and more important as modern agriculture has caused damage to biological ecology. However, the activity is faced with a lack of expert human resources and motivation to participate. It results in ineffective cognition of healthy consumption. In this study, a novel model of multi-user remote collaboration using wearable augmented reality (AR) that aims to improve participants' motivation is proposed. At present, the system only proposes concepts and prototype designs, expert interviews and participant interviews will be used to improve the system in the future. The expected results of this study are as follows. Using the proposed model, which could increase participants’ motivation and reduce over-reliance on experts in the activity, to solve the lack in the above. Finally, through prototyping and interview comments, five key points about how the techniques improve problems in the activity are concluded at the end of this paper, providing relevant studies and departments as references.
Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models

Deep Neural Networks (DNNs) have had a significant impact on domains like autonomous vehicles and smart cities through low-latency inferencing on edge computing devices close to the data source. However, DNN training on the edge is poorly explored. Techniques like federated learning and the growing capacity of GPU-accelerated edge devices like NVIDIA Jetson motivate the need for a holistic characterization of DNN training on the edge. Training DNNs is resource-intensive and can stress an edge's GPU, CPU, memory and storage capacities. Edge devices also have different resources compared to workstations and servers, such as slower shared memory and diverse storage media. Here, we perform a principled study of DNN training on individual devices of three contemporary Jetson device types: AGX Xavier, Xavier NX and Nano for three diverse DNN model--dataset combinations. We vary device and training parameters such as I/O pipelining and parallelism, storage media, mini-batch sizes and power modes, and examine their effect on CPU and GPU utilization, fetch stalls, training time, energy usage, and variability. Our analysis exposes several resource inter-dependencies and counter-intuitive insights, while also helping quantify known wisdom. Our rigorous study can help tune the training performance on the edge, trade-off time and energy usage on constrained devices, and even select an ideal edge hardware for a DNN workload, and, in future, extend to federated learning too. As an illustration, we use these results to build a simple model to predict the training time and energy per epoch for any given DNN across different power modes, with minimal additional profiling.
Partitioning based multi-persistence model for multi-paradigm database

In the era of Internet of things and social media huge amount of data are generated day by day from numerous sources. The problem for each and every organization is to tackle with the volume, variety and velocity of data. This data is commonly referred to as “Big Data”. Most of the recent surveys provide the tools and techniques to deal with pace of data. In past decades traditional databases have shown tremendous growth in consistency, durability and isolation of data. But in current trends of data analytics, traditional database are fertile to deal with the volume, variety and velocity of database. In this paper, initially comparison is drawn to check the drawbacks of the existing system. To cope with the problems in existing system a partitioning-based technique is proposed and implemented using NoSQL Database. The final goal of this work is to identify the problems faced in traditional system and accordingly a partitioning based techniques are designed which will optimize the performance of query operation in terms of size and query time. To check the prediction accuracy around the regression line error estimation is used in the scenario. The measurement of speed difference between these proposed scenarios is depending on error estimation. Higher estimation is equal to higher performance. In this work the performance of MongoDB tuning technique is increased approximately by 65% as compare to the performance with default configuration of MongoDB. By using these techniques, the optimization of the system using MongoDB is increased compare to the Neo4j, Oracle 11g and traditional database. The final result shows that MongoDB performs best, followed by and Oracle 11g, Neo4j.
Towards Anomaly Detection for Monitoring Power Consumption in HPC Facilities

Given the increasing complexity and the heterogeneity of today's computing system infrastructure, power efficiency and fault tolerance remain the top challenges of an High Performance Computing (HPC) facility operation. Recently, many research efforts are focusing on monitoring solutions for collecting, correlating, and analyzing computing infrastructures health events and metrics data to not only identify the normal events but also the anomalous, thus aiding to reduce downtime and power consumption in the face of a computational center's and users' critical needs. In this preliminary work, we present an anomaly detection methodology integrated with the Operations Monitoring and Notification Infrastructure (OMNI) data warehouse at Lawrence Berkeley National Laboratory's (LBNL) National Energy Scientific Computing Center (NERSC) that has implemented anomaly detection algorithms for identifying abnormal power patterns. We evaluated our methodology using five million unlabeled power datasets from the Cori computation system at NERSC and reported on the accuracy of the anomaly detection algorithms in detecting different anomalous behavior pertaining to the amount of power consumed. The methodology is employed to aid in monitoring and automating power alerting to achieve power efficiency and reliability in future systems to be deployed at NERSC or other HPC facilities.
Business Insights Using Knowledge Graphs by Text Analytics in Dynamic Environments

Business Intelligence (BI) requires the collection and organization of important pieces of information (i.e. entities) from multiple sources to provide valuable insights (e.g. business trends) as events (i.e. a specific happening linked with a specific location and time) to users. Online news articles are one of the important information sources that present business news offered by various companies in the market every day all around the world. These news articles often cover the same events and report redundant information. Existing news platforms aim at collecting the key entities from news articles and providing a mechanism to view the latest and relevant business events based on user interest. However, they do not provide a method to model business events and understand them temporally, spatially, and contextually (i.e. changes in the event). For instance, it is crucial to know for how long a business event has been active? How important is its evolution locally, or worldwide? Or how did different companies come up with this event as competitors in the market? The contribution of this research is the exploration of the possibilities of modeling spatial, temporal, and contextual information evolution related to business events through the application of knowledge graphs and text analytics, more specifically, Natural Language Processing (NLP) methods. The constructed knowledge graphs through Named-Entity Recognition (NER), i.e., an NLP technique, present a compact news representation that tells the key entities of the business event at one glance using linked open data concepts. It enables the assessment of other related news events as well as provides the means for analysis of the influence and evolution of business events.
Information Theoretically Private and Secure Distributed Voting Without a Trusted Authority

In this paper, we present a private voting system that consists of N voters who may vote to one of the K candidates or vote abstain. Each voter wants to compute the final tally, while staying private and robust against malicious voters, who try to gain information about the vote of the other voters beyond the final result, or send incorrect information to affect the final tally. We design an information-theoretic voting system that uses verifiable secret sharing and multi-party computation, which is secure and private as long as there are up to $\left\lfloor {\frac{{N - 1}}{3}} \right\rfloor $ malicious voters.
Seeded Database Matching Under Noisy Column Repetitions

The re-identification or de-anonymization of users from anonymized data through matching with publicly-available correlated user data has raised privacy concerns, leading to the complementary measure of obfuscation in addition to anonymization. Recent research provides a fundamental understanding of the conditions under which privacy attacks are successful, either in the presence of obfuscation or synchronization errors stemming from the sampling of time-indexed databases. This paper presents a unified framework considering both obfuscation and synchronization errors and investigates the matching of databases under noisy column repetitions. By devising replica detection and seeded deletion detection algorithms, and using information-theoretic tools, sufficient conditions for successful matching are derived. It is shown that a seed size logarithmic in the row size is enough to guarantee the detection of all deleted columns. It is also proved that this sufficient condition is necessary, thus characterizing the database matching capacity of database matching under noisy column repetitions and providing insights on privacy-preserving publication of anonymized and obfuscated time-indexed data.
Root of Unity for Secure Distributed Matrix Multiplication: Grid Partition Case

We consider the problem of secure distributed matrix multiplication (SDMM), where a user has two matrices and wishes to compute their product with the help of N honest but curious servers under the security constraint that any information about either A or B is not leaked to any server. This paper presents a new scheme that considers a grid product partition for matrices A and B, which achieves an upload cost significantly lower than the existing results in the literature. Also, it significantly reduces the recovery threshold compared to the PolyDot codes presented for grid partition when T > 0. Since the grid partition is a general partition that incorporates the inner and outer ones, it turns out that the communication load of the proposed scheme matches the best-known protocols for those extreme cases.
Multi-User Linearly Separable Computation: A Coding Theoretic Approach

In this work, we investigate the problem of multi-user linearly separable function computation, where N servers help compute the desired functions (jobs) of K users. In this setting each desired function can be written as a linear combination of up to L (generally non-linear) sub-functions. Each server computes some of the sub-tasks, and communicates a linear combination of its computed outputs (files) in a single-shot to some of the users, then each user linearly combines its received data in order to recover its desired function. We explore the range of the optimal computation cost via establishing a novel relationship between our problem, syndrome decoding and covering codes. The work reveals that in the limit of large N, the optimal computation cost — in the form of the maximum fraction of all servers that must compute any subfunction — is lower bounded as $\gamma \geq H_q^{ - 1}\left( {\frac{{{{\log }_q}(L)}}{N}} \right)$, for any fixed log <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> (L)/N. The result reveals the role of the computational rate log <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> (L)/N, which cannot exceed what one might call the computational capacity H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> (γ) of the system.
General Framework for Linear Secure Distributed Matrix Multiplication with Byzantine Servers

In this paper, a general framework for linear secure distributed matrix multiplication (SDMM) is introduced. The model allows for a neat treatment of straggling and Byzantine servers via a star product interpretation as well as simplified security proofs. Known properties of star products also immediately yield a lower bound for the recovery threshold, as well as an upper bound for the number of colluding workers the system can tolerate. It produces many of the known SDMM schemes as special cases, hence providing unification for the previous literature on the topic. Furthermore, error behavior specific to SDMM is discussed and interleaved codes are proposed as a suitable means for efficient error correction in the proposed model. Analysis of the error correction capability is also provided, largely based on well-known results on interleaved codes.
An Algebra of Alignment for Relational Verification

Relational verification encompasses information flow security, regression verification, translation validation for compilers, and more. Effective alignment of the programs and computations to be related facilitates use of simpler relational invariants and relational procedure specs, which in turn enables automation and modular reasoning. Alignment has been explored in terms of trace pairs, deductive rules of relational Hoare logics (RHL), and several forms of product automata. This article shows how a simple extension of Kleene Algebra with Tests (KAT), called BiKAT, subsumes prior formulations, including alignment witnesses for forall-exists properties, which brings to light new RHL-style rules for such properties. Alignments can be discovered algorithmically or devised manually but, in either case, their adequacy with respect to the original programs must be proved; an explicit algebra enables constructive proof by equational reasoning. Furthermore our approach inherits algorithmic benefits from existing KAT-based techniques and tools, which are applicable to a range of semantic models.
AI-Based Assistant for Determining the Required Performance Level for a Safety Function

Standards such as ISO 13849 and ISO 12100 enable users to model safety related control elements with safety functions, according to a specified architecture and required performance level. In this direction, a novel Artificial Intelligence (AI)-based assistant is introduced in this paper to aid in determining the required performance level parameter by indulging the user in a dialog-based conversation regarding hazard scenarios. This will help inexperienced machinery safety personnel (e.g. mechanical engineer) to get an overview of the safety engineering aspects, before consulting with safety experts for risk assessment.
Tokenization of sustainable real estate in Smart Cities : Monetization as basis for construction, authorization and carbon neutralization in CPS

Tokenization is a new means to monetize assets in new decentralized economies. New types of tokens allow for the tokenization of physical assets and real-world projects. They can also be used to monetize digital assets in Cyber-Physical Systems, as well as provide a decentralized means to authorize access to such devices and systems. This paper describes one such effort, where this new type of tokens acts as the basis for financing the construction of sustainable real estate (smart buildings), as well as provide a means for private investors (owners) and other interested third parties to gain access to revenue, as well as cyber-physical systems and digital twins within the constructed smart buildings, in an interoperable and transparent manner. Inter-operability with underlying systems and equipment is performed using IEEE P1451.99 IoT Harmonization interfaces. Monetization of underlying resources allows for advanced optimization of the process, in accordance with principles defined within Industry 4.0, smart cities, sustainability and quality of life for citizens.