Reviewerly/exHarmony huggingface dataset:
Application Based Categorization of Datasets for Implementing Data Mining Techniques

Clustering, Classification, Prediction, Association, outlier analysis and evolution and deviation analysis are data mining functionalities which are widely used for improving health care, customer segmentation, fraud detection, etc. Variety of datasets are available for implementing these techniques. In this paper, various verified datasets used for implementing data mining techniques, are discussed in detail. All the datasets are described and are categorized based upon their application area. Various dataset repositories such as promise, UCI, kaggle, etc. contain further application based datasets which can be used as per database functionality implemented in research. Experiments are conducted to discuss the usage of these datasets to implement data mining applications. Scopus and Web of science resources are also used in this work to conduct experiments using global analysis, citation count, source-wise and author wise number of documents available, etc. The focus of this work is to conduct application based categorization of datasets and analyzing research trends in data mining domain.
A Blockchain integration to support transactions of assets in multi-agent systems

Multi-Agent systems technology can offer valuable tools to develop applications in domains involving transactions of assets. However, they usually do not have a proper support for reliable and decentralized recording of the transactions that are common in this kind of system. This support can be provided by the Blockchain technology. Furthermore, it is important to have means to represent in the system concepts that are intangible, such as asset and ownership. This paper presents a model of integration between Multi-Agent Systems and Blockchain where an artificial institution connects the intangible concepts related to transactions of assets to the concrete elements composing the system. An application example illustrates an implementation following the proposed integration model, showing its advantages and limitations. In the example, agents contract each other to provide services upon the payment of a dealt value through a system based on cryptocurrencies and blockchain. It highlights the essential contributions of the proposed approach to systems where agents transact assets: regulation of the system, representation of the notion of asset that does not depend on agents, and reliable recording of transactions based on Blockchain.
ESS: An Efficient Storage Scheme for Improving the Scalability of Bitcoin Network

With the continuous development of blockchain technology, Bitcoin as the first cryptocurrency has drawn massive attention from various sectors. Bitcoin on-chain data storage is over 338GB as of September 1, 2021. According to the exponential growth trend of block data, the size of a Bitcoin full node will exceed 500GB in two years. The huge storage problem makes it difficult for general users to store complete Bitcoin data conveniently, which weakens the decentralization capability of Bitcoin network. In this work, we propose an efficient storage scheme (ESS) based on the distribution characteristics of the unspent transaction outputs in Bitcoin network. ESS sets a UTXO-weight for each block. According to UTXO-weight, it dynamically prunes the blocks which have lower query frequency, improving the scalability of the Bitcoin network. When a new block is generated, ESS enables joining nodes to verify most of the transaction inputs instantly. Only a small amount of transaction outputs of older blocks need to be retrieved from the full node for payment verification. Experimental results demonstrated that ESS can reduce the size of a normal node in current Bitcoin network by about 82.14% at a low communication cost.
RDF Query Path Optimization Using Hybrid Genetic Algorithms

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.
Towards a Contemplative Research Framework for Training Self-Observation in HCI: A Study of Compassion Cultivation

With the emergence in human–computer interaction (HCI) of researching contemplative practices, authentic descriptions of first-person lived experience informing design are few. Most researchers in HCI are not trained in observing the mind. We draw on learnings from neurophenomenology, inspired by well-established Buddhist techniques for mind-training. We present a self-observation of Tonglen , a Buddhist meditation technique for compassion, conducted over 12 weeks. We found that to keenly observe and document the practice, it is important to go through preparatory stages of stabilising attention and observing the mind. For the practitioner-cum-researcher, the technique should be embedded into a framework training self-observation and developing meta-awareness, supported by documentation of somatic snapshots and reflective journal writing. The first-person method of self-enquiry and account of self-evidence offer insight and directions for refining first-person approaches for future HCI research in body and mind cultivation, and design implications for interactive technologies supporting any practice with a contemplative component.
Eye-Tracking Monitoring Based on PMUT Arrays

As <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> common and natural activity of human beings, eye movement has been regarded as an indicator of human physiological and psychological health; eye movement tracking has been applied to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> wide range of domains, such as disease diagnostics, drug therapy testing and human-computer interaction. Conventionally, eye movement is captured by three approaches, i.e., recording video using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> camera, measuring time-varying Electrooculography (EOG) signals using electrodes, and detecting reflected infrared light using infrared sensor. However, all these techniques incur certain limitations that impede their adoption for practical eye tracking, such as privacy violation, high computation resources, discomfort and strong light reliability. This paper presents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> portable and robust eye tracking technique based on ultrasound. Specifically, piezoelectric MEMS ultrasonic transducer arrays integrated on glasses are used to transmit and receive bio-safe pulse echo ultrasound individually. With experiment on volunteers, the developed devices can accurately track the rotation and direction of eyeball movement in real-time, based on the time of flight (TOF) principle. The tracking device is lightweight (< 25 mg) and compact (millimeters in size), showing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> great potential for wearable eye tracking application like virtual reality. [2021-0193]
Chunk-Level Password Guessing: Towards Modeling Refined Password Composition Representations

Textual password security hinges on the guessing models adopted by attackers, in which a suitable password composition representation is an influential factor. Unfortunately, the conventional models roughly regard a password as a sequence of characters, or natural-language-based words, which are password-irrelevant. Experience shows that passwords exhibit internal and refined patterns, e.g., "4ever, ing or 2015", varying significantly among periods and regions. However, the refined representations and their security impacts could not be automatically understood by state-of-the-art guessing models (e.g., Markov).
Zero-Bias Deep-Learning-Enabled Quickest Abnormal Event Detection in IoT

Abnormal event detection with the lowest latency is an indispensable function for safety-critical systems, such as cyber defense systems. However, as systems become increasingly complicated, conventional sequential event detection methods become less effective, especially when we need to define indicator metrics from complicated data manually. Although deep neural networks (DNNs) have been used to handle heterogeneous data, the theoretic assurability and explainability are still insufficient. This article provides a holistic framework for the quickest and sequential detection of abnormalities and time-dependent abnormal events. We explore the latent space characteristics of zero-bias neural networks considering the classification boundaries and abnormalities. We then provide a novel method to convert zero-bias DNN classifiers into performance-assured binary abnormality detectors. Finally, we provide a sequential quickest detection (QD) scheme that provides the theoretically assured lowest abnormal event detection delay under false alarm constraints using the converted abnormality detector. We verify the effectiveness of the framework using real massive signal records in aviation communication systems and simulation. Codes and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pcwhy/AbnormalityDetectionInZbDNN</uri> .
Assessing cyber threats for storyless systems

A proper assessment of potential cyber threats is vital for security decision-making. This becomes an even more challenging task when dealing with new system designs and industry sectors where there is little or no historical data about past security incidents. We have developed a threat likelihood estimation approach that supports risk management under such circumstances. Quantifiable conditions are determined from the environment in which the system will reside and operate, that is the availability of potential threat actors, their opportunities of performing attacks, the required means that are needed for the attack to succeed, and motivation factors. Our research method follows the principles of practice research where both researchers and practitioners have played central roles in a real-life development project for a maritime communication system. We used a qualitative case study for feature-based evaluation of the approach and associated tool template, and to gather evidence on practical aspects such as suitability for purpose, efficiency and drawbacks from five user groups. The results show that representative participants from the cyber security and maritime community gave positive and consistent scores on the features, and regarded time usage, traceability of the threat assessment and the ability to indicate underlying uncertainty to be very appropriate. The approach has been proven useful for this domain and should be applicable to others as well, but the template requires up-front investments in gathering knowledge that is relevant and reusable in additional context situations.
The Exact Security of BIP32 Wallets

In many cryptocurrencies, the problem of key management has become one of the most fundamental security challenges. Typically, keys are kept in designated schemes called wallets, whose main purpose is to store these keys securely. One such system is the BIP32 wallet (Bitcoin Improvement Proposal 32), which since its introduction in 2012 has been adopted by countless Bitcoin users and is one of the most frequently used wallet system today. Surprisingly, very little is known about the concrete security properties offered by this system. In this work, we propose the first formal analysis of the BIP32 system in its entirety and without any modification. Building on the recent work of Das et al. (CCS '19), we put forth a formal model for hierarchical deterministic wallet systems (such as BIP32) and give a security reduction in this model from the existential unforgeability of the ECDSA signature algorithm that is used in BIP32. We conclude by giving concrete security parameter estimates achieved by the BIP32 standard, and show that by moving to an alternative key derivation method we can achieve a tighter reduction offering an additional 20 bits of security (111 vs. 91 bits of security) at no additional costs.
A Secure EMR Sharing System With Tamper Resistance and Expressive Access Control

To reduce the cost of human and material resources and improve the collaborations among medical systems, research laboratories and insurance companies for healthcare researches and commercial activities, electronic medical records (EMRs) have been proposed to shift from paperwork to friendly shareable electronic records. To take advantage of EMRs efficiently and reduce the cost of local storage, EMRs are usually outsourced to the remote cloud for sharing medical data with authorized users. However, cloud service providers are untrustworthy. In this paper, we propose an efficient, secure, and flexible EMR sharing system by introducing a novel cryptosystem called dual-policy revocable attribute-based encryption and tamper resistance blockchain technology. Our proposed system enables EMRs to be shared at a fine-grained level and allows data users to detect any unauthorized manipulation. Moreover, the key generation center can revoke malicious users without affecting the honest users. We provide the formal security model as well as the concrete scheme with security analysis. The experimental simulation and experimental analysis of our proposed scheme demonstrate that our proposed system has superior performances to the most relevant solutions.
Quantifying Auditory Presence Using Electroencephalography

Presence is used to assess the subjective experience of being in one place when physically situated in another. Recently, the research on presence has gained increasing attention due to the wide use of immersive audio technologies. Currently, the most widely-used measurement of presence is based on post-experiment self-report questionnaires. It is reliable but imperfect due to the psychological changes caused by the act of answering the questionnaire when immersed in the virtual environment. Therefore, the present work aims to find an objective way to measure presence, and electroencephalography (EEG) was investigated as a possible tool for this objective measurement. In this study, two listening tests were conducted, where eight loudspeakers were used to reproduce urban soundscapes to stimulate auditory presence. Presence was measured by both questionnaires and EEG. Results showed a significant correlation between T/B (Theta/Beta Ratio) extracted from EEG and subjective presence levels assessed by questionnaires, suggesting the possible use of EEG to measure presence objectively. This study could bring some insight for the research of presence, and related technologies, such as VR, video games and immersive audio production.
Optimizing Cloud Function Configuration via Local Simulations

Function as a Service (FaaS) - the reason why so many practitioners and researchers talk about Serverless Computing - claims to hide all operational concerns. The promise when using FaaS is that users only have to focus on the core business functionality in form of cloud functions. However, a few configuration options remain within the developer's responsibility. Most of the currently available cloud function offerings force the user to choose a memory or other resource setting and a timeout value. CPU is scaled based on the chosen options. At a first glance, this seems like an easy task, but the tradeoff between performance and cost has implications on the quality of service of a cloud function. Therefore, in this paper we present a local simulation approach for cloud functions and support developers in choosing a suitable configuration. The methodology we propose simulates the execution behavior of cloud functions locally, makes the cloud and local environment comparable and maps the local profiling data to a cloud platform. This reduces time during the development and enables developers to work with their familiar tools. This is especially helpful when implementing multi-threaded cloud functions.
Yoga Pose Detection and Validation

Yoga is an excellent medium of physical exercise, it is very beneficial for health if done properly. But if yoga is practiced in the wrong manner it can have an adverse effect on one’s health. So, for practicing yoga a trainer is important who can guide and monitor the perfectness of different yoga poses. In this paper, a novel system is proposed which aim to assist yoga enthusiast for different yoga poses and validate it for correctness. Integrating computer vision techniques, the proposed system analyses the user’s human pose then based on the domain knowledge of yoga, the user is guided to correct the pose. Precise recognition of yoga pose is a difficult task because of high computation and lack of availability of dataset. For the accurate detection of the yoga pose, different feature extraction and preprocessing methods are applied to the dataset which results in 97.4% accuracy just by using machine learning algorithms. To make the system efficient and low-cost, Human Pose Estimation technique based on computer vision is used.
Synthesis of novel demulsifier nano-materials and their application in the oil industry

Nanotechnology is a scientific discipline that has a wide range of applications in practically every aspect of modern life, particularly in the fabrication of nanomaterials and nanocomposites. Due to the importance of this sector economically and in terms of energy supply, one of the most important applications of nanomaterials is their large scale use to remove or minimize the hurdles that the oil and gas industry faces. The hydrothermal method was used to prepare novel Rhodium borate nanoparticles, which were made by first obtaining Rhodium oxide from Rhodium acetate, then reacting with boric acid and other cofactors. The efficiency of the process for generating Rhodium borate nanoparticles was extremely high. The efficiency of the process for generating Rhodium borate nanoparticles was extremely high. The validity of the obtained results was confirmed by the diagnostic and estimation procedures utilized in this work (XRD, FT-IR, SEM, and TEM). One of the most critical processes in petroleum processing is demulsification. To avoid issues during oil transportation and handling, oil emulsions must be broken down. Because of the water content and dissolved salts in the aqueous phase of the emulsion, the presence of water in the crude oil is undesirable and can lead to pipeline corrosion and increased transportation costs. The physical and chemical features of crude oil from the Halfaya oil field in southern Iraq, which is known to contain a certain percentage of water, have been investigated. Rhodium borate (Rh2O3. B2O3·H2O) (DF1) nanoparticles were utilized in this work to separate water from crude oil emulsions. Effective aggregates exist in the synthesized Rhodium borate nanoparticles, allowing them to operate as demulsifying agents. The separation of water from the emulsion by the produced rhodium borate (DF1) and the standard demulsifier (RP96BQ) used in the same oil field at constant temperature (60 °C) and with varying concentrations was compared in this study using crude oil samples. A high separation efficiency was obtained for the prepared nano demulsifier with a percentage of up to (84.2%) compared with the standard demulsifier (RP96BQ).
Cross-Device Profiled Side-Channel Attacks using Meta-Transfer Learning

Deep learning (DL) based profiling side channel analysis (SCA) pose a great threat to embedded devices. An adversary can break the target encryption engine through physical leakage of power or electromagnetic (EM) emanations collected from a profiling device. However, creating a successful DL based SCA model relies on a large amount of data. This presents a large barrier to those interested in applying DL for SCA. In this paper, we propose a novel attack mechanism that adopts meta-transfer learning to transfer DL networks among target devices by judiciously extracting information from a profiling device even using different side-channel sources. Supported by our method, a cross-device and/or cross-domain SCA attack becomes possible among different designs. In comparison to previous attack methodologies, we significantly reduce training costs and the number of traces $(\lt 3$ for power and $\lt 8$ for EM) required for SCA attacks on both unprotected or masked Advanced Encryption Standard (AES) implementations.
Where Are They Going? Predicting Human Behaviors in Crowded Scenes

In this article, we propose a framework for crowd behavior prediction in complicated scenarios. The fundamental framework is designed using the standard encoder-decoder scheme, which is built upon the long short-term memory module to capture the temporal evolution of crowd behaviors. To model interactions among humans and environments, we embed both the social and the physical attention mechanisms into the long short-term memory. The social attention component can model the interactions among different pedestrians, whereas the physical attention component helps to understand the spatial configurations of the scene. Since pedestrians’ behaviors demonstrate multi-modal properties, we use the generative model to produce multiple acceptable future paths. The proposed framework not only predicts an individual’s trajectory accurately but also forecasts the ongoing group behaviors by leveraging on the coherent filtering approach. Experiments are carried out on the standard crowd benchmarks (namely, the ETH, the UCY, the CUHK crowd, and the CrowdFlow datasets), which demonstrate that the proposed framework is effective in forecasting crowd behaviors in complex scenarios.