Reviewerly/exHarmony huggingface dataset:
Large Scale Data Using K-Means

Because of the exponential growth of high-layered datasets, conventional database querying strategies are inadequate for extracting useful information, and analysts must now devise novel techniques to meet these demands. Such massive articulation data results in a plethora of new computational triggers as a result of both the rise in data protests and the increase of elements/ascribes. Preprocessing the data with a reliable dimensionality reduction method improves the efficacy and precision of mining operations on densely layered data. Therefore, we have compiled the opinions of numerous academics. Cluster analysis is a data analysis tool that has recently acquired prominence in a number of different disciplines. K-means, a common parceling-based clustering algorithm, looks for a fixed number of clusters that can be identified using only their centroids. However, the outcomes depend heavily on the starting points of the clusters' focuses. Again, there is a dramatic rise in the number of distance calculations with increasing data complexity. This is due to the fact that assembling a detailed model typically calls for a substantial and distributed amount of preliminary data. There may be a substantial time commitment involved in preparing a broad collection of ingredients. For huge data sets in particular, there is a cost/benefit analysis to consider when deciding how to create orders: speed vs. accuracy. The k-means method is commonly used to compress and sum vector data, as well as cluster it. For precautious k-means (ASB K-means), we present No Concurrent Specific Clumped K-means, a fast and memory-effective GPU-based method. Our method can be adjusted to use much less GPU RAM than the size of the full dataset, which is a significant improvement over earlier GPU-based k-means methods. Datasets that are too large to fit in RAM may be clustered. The approach uses a clustered architecture and applies the triangle disparity in each k-means focus to remove a data point if its enrollment task or cluster it belongs to remains unaltered, allowing it to efficiently handle big datasets. This reduces the number of data guides that must be transferred between the CPU's Slam and the GPU's global memory.
Long-Lived Particles Anomaly Detection with Parametrized Quantum Circuits

We investigate the possibility to apply quantum machine learning techniques for data analysis, with particular regard to an interesting use-case in high-energy physics. We propose an anomaly detection algorithm based on a parametrized quantum circuit. This algorithm has been trained on a classical computer and tested with simulations as well as on real quantum hardware. Tests on NISQ devices have been performed with IBM quantum computers. For the execution on quantum hardware specific hardware driven adaptations have been devised and implemented. The quantum anomaly detection algorithm is able to detect simple anomalies like different characters in handwritten digits as well as more complex structures like anomalous patterns in the particle detectors produced by the decay products of long-lived particles produced at a collider experiment. For the high-energy physics application, performance is estimated in simulation only, as the quantum circuit is not simple enough to be executed on the available quantum hardware. This work demonstrates that it is possible to perform anomaly detection with quantum algorithms, however, as amplitude encoding of classical data is required for the task, due to the noise level in the available quantum hardware, current implementation cannot outperform classic anomaly detection algorithms based on deep neural networks.
The Creation of a Holistic Interactive Dining Experience with Shape-Changing Food Materials at Restaurant Alchemist

Various studies demonstrated how to enrich edible materials with interactivity and shape-change using digital technologies such as 3D printing. We illustrate a collaborative and iterative process of developing a dish for two-Michelin starred restaurant ALCHEMIST in Copenhagen involving designers, material scientists, and chefs. They combined expertise in creative technologies, material behavior, and culinary arts to create novel dining experiences. Through a playful experience workshop, participants generated ideas to incorporate shape-changing food materials into their dishes. Digital prototyping technologies combined with material experiments resulted in an edible flower that through transforming petals becomes a tartelette when in contact with low or high pH solutions. We contribute a vision on how interactive dining experiences can be enriched with material-driven explorations, digital technologies, and experience design methods to create novel opportunities for human-food interaction. We reflect on the role of interaction designers when collaborating with chefs, and material scientists towards achieving this vision.
Episodic retrieval for model-based evaluation in sequential decision tasks

It has long been hypothesized that episodic memory supports adaptive decision making by enabling mental simulation of future events. Yet, memory research is often carried out in settings that are far removed from ecological contexts of decision making, and models of adaptive choice, conversely, only invoke episodic memory in highly stylized terms, if it all. To address these gaps, we propose a novel process-level model of choice that grounds model-based evaluation in empirically informed dynamics of episodic recall. In this model, the probability of retrieving each available memory sample is given by the Successor Representation (SR), a biologically plausible world model in reinforcement learning. The evolution of these probabilities based on past retrievals, in turn, is dictated by the Temporal Context Model (TCM), a prominent model of episodic retrieval. Through a series of simulations, we demonstrate that the patterns of episodic retrieval suggested by this model enables flexible computation of decision variables. On this basis, we argue that a number of previously described features of episodic memory serve an adaptive purpose in sequential decision making. For instance, we show that the classic retrieval bias known as contiguity effect, when viewed from a decision making perspective, leads to model-based rollouts for forward simulation. We also show that features of episodic memory such as emotional modulation enable generalization and efficient decisions given limited experience. By bridging theoretical models across these two domains, we make a set of theoretical predictions linking episodic memory properties to adaptive choice in sequential tasks that may guide future empirical endeavors.
Optical flow for autonomous driving: Applications, challenges and improvements

Optical flow estimation is a well-studied topic for automated driving applications.Many outstanding optical flow estimation methods have been proposed, but they become erroneous when tested in challenging scenarios that are commonly encountered.Despite the increasing use of fisheye cameras for near-field sensing in automated driving, there is very limited literature on optical flow estimation with strong lens distortion.Thus we propose and evaluate training strategies to improve a learning-based optical flow algorithm by leveraging the only existing fisheye dataset with optical flow ground truth.While trained with synthetic data, the model demonstrates strong capabilities to generalize to real world fisheye data.The other challenge neglected by existing state-ofthe-art algorithms is low light.We propose a novel, generic semisupervised framework that significantly boosts performances of existing methods in such conditions.To the best of our knowledge, this is the first approach that explicitly handles optical flow estimation in low light.
Intelligent Time Delay Control of Telepresence Robots Using Novel Deep Reinforcement Learning Algorithm to Interact with Patients

Telepresence robots are gaining more popularity as a means of remote communication and human–robot interaction, allowing users to control and operate a physical robot remotely. However, controlling these robots can be challenging due to the inherent delays and latency in the communication systems. In this research paper, we propose a novel hybrid algorithm exploiting deep reinforcement learning (DRL) with a dueling double-deep Q-network (DDQN) and a gated recurrent unit (GRU) to assist and maneuver the telepresence robot during the delayed operating signals from the operator. The DDQN is used to learn the optimal control policy for the telepresence robot in a remote healthcare environment during delayed communication signals. In contrast, the GRU is employed to model the control signals’ temporal dependencies and handle the variable time delays in the communication system. The proposed hybrid approach is evaluated analytically and experimentally. The results demonstrate the approach’s effectiveness in improving telepresence robots’ tracking accuracy and stability performance. Multiple experiments show that the proposed technique depicts improved controlling efficiency with no guidance from the teleoperator. It can control and manage the operations of the telepresence robot during the delayed communication of 15 seconds by itself, which is 2.4% better than the existing approaches. Overall, the proposed hybrid approach demonstrates the potential implementation of RL and deep learning techniques in improving the control and stability of the telepresence robot during delayed operating signals from the operator.
Dynamic conditioning of two particle discrete-time quantum walks

Abstract In real photonic quantum systems losses are an unavoidable factor limiting the scalability to many modes and particles, restraining their application in fields as quantum information and communication. For this reason, a considerable amount of engineering effort has been taken in order to improve the quality of particle sources and system components. At the same time, data analysis and collection methods based on post-selection have been used to mitigate the effect of particle losses. This has allowed for investigating experimentally multi-particle evolutions where the observer lacks knowledge about the system's intermediate propagation states. Nonetheless, the fundamental question how losses affect the behaviour of the surviving subset of a multi-particle system has not been investigated so far. For this reason, here we study the impact of particle losses in a quantum walk of two photons reconstructing the output probability distributions for one photon conditioned on the loss of the other in a known mode and temporal step of our evolution network. We present the underlying theoretical scheme that we have devised in order to model controlled particle losses, we describe an experimental platform capable of implementing our theory in a time multiplexing encoding. In the end we show how localized particle losses change the output distributions without altering their asymptotic spreading properties. Finally we devise a quantum civilization problem, a two walker generalisation of single particle recurrence processes.
Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems

In the new era, the trend of using wearable devices and smart accessories gained considerable popularity and become a necessity utility for human life due to their major role to keep monitoring health conditions and providing healthcare services. The combination of IoT networks with edge computing paradigms develops an intelligent e-health system that aims to monitor different real-time scenarios. The deployment of an e-health system exposes several challenges regarding the security and privacy aspects, particularly in the case of dealing with an enormous quantity of medical data and the risk presented by exchanging operations with external entities. In this paper a comprehensive presentation covered the basic topics of e-health system layers thus the advantages and limitations in terms of existing challenges has been mentioned, subsequently, adapted to the exposed cyber risk through the traditional systems in exchanging medical data, a discussion of the blockchain technology come over for new application opportunities, where this approach efficiently ensure the security of data transactions over the network, in addition, an overview outlined the main research works related to this technology. Therefore, a presentation study of diverse works reveals different security framework solutions related to e-health system’s layers, furthermore, uncovering the benefits of integrating intelligent technologies such as Machine Learning (supervised, and unsupervised types), Deep Learning, and Reinforcement Learning as well as introducing a comparison analysis of multiple AI algorithm models based on their efficiency for future deployment related security purposes to provide a smart healthcare monitoring system that meets patient needs. The end of this review highlighted further research directions and the actual open challenges regarding the e-Health system’s limitations.
Efficient Equality Test on Identity-Based Ciphertexts Supporting Flexible Authorization

In the cloud, uploading encrypted data is the most effective way to ensure that the data are not leaked. However, data access control is still an open problem in cloud storage systems. To provide an authorization mechanism to limit the comparison of a user's ciphertexts with those of another, public key encryption supporting the equality test with four flexible authorizations (PKEET-FA) is presented. Subsequently, more functional identity-based encryption supporting the equality test (IBEET-FA) further combines identity-based encryption with flexible authorization. The bilinear pairing has always been intended to be replaced due to the high computational cost. Hence, in this paper, we use general trapdoor discrete log groups to construct a new and secure IBEET-FA scheme, which is more efficient. The computational cost for the encryption algorithm in our scheme was reduced to 43% of that of the scheme of Li et al. In Type 2 and 3 authorization algorithms, the computational cost of both was reduced to 40% of that of the scheme of Li et al. Furthermore, we give proof that our scheme is secure against one-wayness under the chosen identity and chosen ciphertext attacks (OW-ID-CCA), and indistinguishable against chosen identity and chosen ciphertext attacks (IND-ID-CCA).
Data sharing attitudes and practices of researchers in Korean government research institutes: a survey-based descriptive study

Purpose: This study explored to what extent and how researchers in five Korean government research institutes that implement research data management practices share their research data and investigated the challenges they perceive regarding data sharing.Methods: The study collected survey data from 224 respondents by posting a link to a SurveyMonkey questionnaire on the homepage of each of the five research institutes from June 15 to 29, 2022. Descriptive statistical analyses were conducted.Results: Among 148 respondents with data sharing experience, the majority had shared some or most of their data. Restricted data sharing within a project was more common than sharing data with outside researchers on request or making data publicly available. Sharing data directly with researchers who asked was the most common method of data sharing, while sharing data via institutional repositories was the second most common method. The most frequently cited factors impeding data sharing included the time and effort required to organize data, concerns about copyright or ownership of data, lack of recognition and reward, and concerns about data containing sensitive information.Conclusion: Researchers need ongoing training and support on making decisions about access to data, which are nuanced rather than binary. Research institutes’ commitment to developing and maintaining institutional data repositories is also important to facilitate data sharing. To address barriers to data sharing, it is necessary to implement research data management services that help reduce effort and mitigate concerns about legal issues. Possible incentives for researchers who share data should also continue to be explored.
DDSG-GAN: Generative Adversarial Network with Dual Discriminators and Single Generator for Black-Box Attacks

As one of the top ten security threats faced by artificial intelligence, the adversarial attack has caused scholars to think deeply from theory to practice. However, in the black-box attack scenario, how to raise the visual quality of an adversarial example (AE) and perform a more efficient query should be further explored. This study aims to use the architecture of GAN combined with the model-stealing attack to train surrogate models and generate high-quality AE. This study proposes an image AE generation method based on the generative adversarial networks with dual discriminators and a single generator (DDSG-GAN) and designs the corresponding loss function for each model. The generator can generate adversarial perturbation, and two discriminators constrain the perturbation, respectively, to ensure the visual quality and attack effect of the generated AE. We extensively experiment on MNIST, CIFAR10, and Tiny-ImageNet datasets. The experimental results illustrate that our method can effectively use query feedback to generate an AE, which significantly reduces the number of queries on the target model and can implement effective attacks.
Market Basket Analysis Using FP-Growth Algorithm to Design Marketing Strategy by Determining Consumer Purchasing Patterns

The development and competition that exists in the business world today leads every manager or company to be more dexterous in making marketing strategies to increase sales. Various things are done to keep up with existing market competition, such as analyzing customer purchase transaction data to serve as a policy determination and decision-making system in making marketing strategies. In determining marketing strategies, it can be done by taking transaction data to see existing purchase or transaction patterns. Market Basket Analysis is part of a data mining method that uses the FP-Growth algorithm technique to find out associated products. This research uses data taken from sales transaction data archives as much as 150 sales transaction data and 26 product data. In this study, it is determined that the minimum support value is 50% and the minimum confidence is ≥ 0.75 From the test results, 9 products have superior support values and meet the minimum value. From the test results, a rule with a confidence value of 0.870 was obtained: D → W (if consumers buy Wardah Lightening Gentle Wash, then buy Azarine Sunscreen SPF50), and 0.808: A → E → O (if consumers buy Emina Face Wash, then buy Azarine Night Moisturizer and Himalaya Neem Mask).
An Empirical Study of Segmented Linear Regression Search in LevelDB

The purpose of this paper is proposing a novel search mechanism, called SLR (Segmented Linear Regression) search, based on the concept of learned index. It is motivated by our observation that a lot of big data, collected and used by previous studies, have a linearity property, meaning that keys and their stored locations show a strong linear correlation. This observation leads us to design SLR search where we apply segmentation into the well-known machine learning algorithm, linear regression, for identifying a location from a given key. We devise two segmentation techniques, equal-size and error-aware, with the consideration of both prediction accuracy and segmentation overhead. We implement our proposal in LevelDB, Google’s key-value store, and verify that it can improve search performance by up to 12.7%. In addition, we find that the equal-size technique provides efficiency in training while the error-aware one is tolerable to noisy data.
Outlier Detection Technique for Wireless Sensor Network Using GAN with Autoencoder to Increase the Network Lifetime

In wireless sensor networks (WSN), sensor nodes are expected to operate autonomously in a human inaccessible and the hostile environment for which the sensor nodes and communication links are therefore, prone to faults and potential malicious attacks. Sensor readings that differ significantly from the usual pattern of sensed data due to faults in sensor nodes, unreliable communication links, and physical and logical malicious attacks are considered as outliers. This paper presents an outlier detection technique based on deep learning namely, generative adversarial networks (GANs) with autoencoder neural network. The two-level architecture proposed for WSN makes the proposed technique robust. The simulation result indicates improvement in detection accuracy compared to existing state-of-the-art techniques applied for WSNs and increase of the network lifetime. Robustness of outlier detection algorithm with respect to channel fault and robustness concerning different types of distribution of faulty communication channel is analyzed.
Threat Modelling and Detection Using Semantic Network for Improving Social Media Safety

Social media provides a free space to users to post their information, opinions, feelings, etc. Also, it allows users to easily and simultaneously communicate with each other. As a result, threat detection in social media is critical for ensuring the user’s safety and preventing suspicious activities such as criminal behavior, hate speech, ethnic conflicts and terrorist plots. These suspicious activities have a negative impact on the community’s life and cause tension and social unrest among individuals in both inside and outside of cyberspace. Furthermore, with the recent popularity of social networking sites, the number of discussions containing threats is increasing, causing fear in various parties, whether at the individual or state level. Moreover, these social networking service providers do not have complete control over the content that users post. In this paper, we propose to design a threat detection model on Twitter using a semantic network. To achieve this aim, we designed a threat semantic network, named, ThrNet that will be integrated in our proposed threat detection model called, DetThr. We compared the performance of our model (DetThr) with a set of well-known Machine Learning algorithms. Results show that the DetThr model achieves an accuracy of 76% better than Machine Learning algorithms. It works well with an error rate of forecasting threatening tweet messages as non-threatening (false negatives) is about 29%, while the error rate of forecasting non-threatening tweet messages as threatening (false positives) is about 19%.
Exploring the Legal and Policy Implications of Non-Fungible Tokens

Legal and policy issues surround NFTs. This study examines NFT legal and policy issues and their effects on the digital economy. This study showed that NFTs could revolutionize digital asset ownership, but they also raise questions about intellectual property, privacy, and taxes. As NFT ownership evolves, buyers and sellers must understand their legal rights and obligations and avoid infringing on others' intellectual property rights. NFTs with photos or other personal data raise privacy concerns. To avoid privacy violations, buyers and sellers should consider NFT privacy implications. NFTs may be used for money laundering or terrorism. Regulators and law enforcement closely monitor NFT transactions to prevent misuse. Policymakers, regulators, and industry participants trying to establish a clear and effective legal framework that protects all parties should consider this study. These legal and policy issues must be addressed as NFTs gain popularity to maximize their potential. NFTs are legally and politically complex. This study emphasizes the need for ongoing research and stakeholder collaboration to responsibly and legally use NFTs and realize the digital economy's transformative potential.