Reviewerly/exHarmony huggingface dataset:
Three-Degree-of-Freedom Voice Coil Actuator Driven by a Four-Phase Current

Camera attitude control systems for robots require a compact structure and high responsiveness. However, due to the combination structure of several actuators, the camera attitude control system is large. To address this issue, this study proposes a three-degree-of-freedom (3DOF) voice coil actuator. A single actuator is used to generate 3DOF motion, which is driven by a four-phase current. This study also describes the basic structure and operating principle of the actuator and clarifies the torque characteristics using a three-dimensional (3D) finite element method (FEM). Furthermore, the dynamic modeling and control methods are presented. The FEM and dynamic simulation results reveal that the proposed actuator can be arbitrarily driven in 3DOF.
Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis

Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers' raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users' shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework.
Managing the three-party entanglement challenge

We introduce the challenges of multi-party quantum entanglement and explain a recent success in learning to take its measure.Given the widely accepted reputation of entanglement as a counter-intuitive feature of quantum theory, we first describe pure-state entanglement itself.We restrict attention to multi-party qubit states.Then we introduce the features that have made it challenging for several decades to extend an entanglement measure beyond the 2-qubit case of Bell states.We finish with a description of the current understanding that solves the 3-qubit entanglement challenge.This necessarily takes into account the fundamental division of the 3-qubit state space into two completely independent sectors identified with the so-called GHZ and W states.
Action chunking as policy compression

Many skills in our everyday lives are learned by sequencing actions towards a desired goal. The action sequence can become a chunk when individual actions are grouped together and executed as one unit, making them more efficient to store and execute. While chunking has been studied extensively across various domains, a puzzle remains as to why and under what conditions action chunking occurs. To tackle these questions, we apply the concept of policy compression---the reduction in cognitive cost by making policies simpler---to action selection in order to explain the origin of chunking. We argue that chunking is a result of optimizing the trade-off between reward and policy complexity. Chunking compresses policies when there is structure in the mapping from environment states to optimal action sequences, reducing the amount of memory necessary to encode the policy. We experimentally confirm our model's predictions, showing that chunking reduces policy complexity and reaction time. Chunking also increases with working memory load, consistent with the hypothesis that the degree of policy compression scales with the scarcity of cognitive resources.
Data Management Architecture for Service-Oriented Maritime Testbeds

In recent years, numerous new approaches that rely on data-intensive methods have been developed for maritime assistance systems, leading to a compelling need for more elaborate verification and validation procedures.Modern testbeds that can meet these demands are often developed separately from the system itself and provided as generically usable services.However, the joint usage of such testbeds by multiple stakeholders from research and industry confronts them with various challenges in terms of data management: Data control and protection is required to preserve possible competitive advantages or comply with legal framework conditions.The resulting decentralization in data management complicates collaboration, especially in the joint processing and analysis of testbed data.In this paper, we present a decentralized software system, which can deal with these challenges by modelling interrelationships between the stakeholders in a data space, considering their various interests.With the help of a modular data management architecture, the organization of a testbed data basis, as well as the support of verification and validation processes and the evaluation of data streams is made possible.This is achieved with a workflow model for mapping complex and distributed data processing steps.We demonstrate the applicability of the system in an application scenario for the development of a maritime assistance system.
Geomembrane Filter Thread Technique’s Potential to Increase Salt Production in Jeneponto Regency

The problem of salt production by Jeneponto Regency is influenced by the traditional way of making salt. The introduction of GFTT (Geomembrane Filter Thread Technique) as a salt-making technology is an effort to increase salt production in the Jeneponto district, both in quality and quantity. This activity was carried out in Biring Parang Jaya Village, Jeneponto Regency. Activities are carried out by applying threaded techniques to salt ponds, making filter devices on salt ponds, and installing geomembranes as salt tablets. Based on the application of technology, the salt produced by GFTT has better quality and quantity than traditional salt. Salt produced by GFTT is classified as high grade with 99.96% NaCl and 0.04% moisture content.
A New Certificateless Signcryption Scheme for Securing Internet of Vehicles in the 5G Era

The application of digital signature technology to the Internet of vehicles (IoV) is affected by its network and communication environment. In the 5G era, the influx of a large number of intelligent devices into the mobile Internet requires a low transmission delay and power consumption as well as high-security requirements. To the best of our knowledge, a well-designed solution in which signcryption technology is used has not been proposed in the IoV research area. Motivated by the fact, a certificateless signcryption scheme based on the elliptic curve digital signature algorithm, in which pseudonym and timestamp mechanism are also considered, has been designed in this paper. We prove that the scheme proposed by us can be reduced to solving the difficulty of the computational Diffie–Hellman problem with a standard model, showing that the scheme meets requirements on both security and efficiency, which provides a comparative analysis with the state-of-the-art schemes in terms of security analysis, computational cost, and communication cost, demonstrating that the scheme proposed by us is suitable to be deployed in the IoV environment, which is of the characteristics of high-speed vehicle movement.
The Arabic Citation Index - Toward a better understanding of Arab scientific literature

Abstract The Arabic Citation Index (ARCI) was launched in 2020. This article provides an overview of the scientific literature contained in this new database and explores its possible usage in research evaluation. As of May 2022, ARCI had indexed 138,283 scientific publications published between 2015 and 2020. ARCI's coverage is characterised by using the metadata available in scientific publications. First, I investigate the distributions of the indexed literature at various levels (research domains, countries, languages, open access). Articles make up nearly all the documents indexed with a share of 99% of ARCI. The Arts &amp; Humanities and Social Sciences fields have the highest concentration of publications. Most indexed journals are published in Egypt, Algeria, Iraq, Jordan, and Saudi Arabia. About 8% of publications in ARCI are published in languages other than Arabic. Second, I use an unsupervised machine learning model, LDA (Latent Dirichlet Allocation), and the text mining algorithm of VOSviewer to uncover the main topics in ARCI. These methods provide a better understanding of ARCI's thematic structure. Next, I discuss how ARCI can complement global standards in the context of a more inclusive research evaluation. Finally, I suggest a few research opportunities after discussing the findings of this study.
Parallel window decoding enables scalable fault tolerant quantum computation

Large-scale quantum computers have the potential to hold computational capabilities beyond conventional computers for certain problems. However, the physical qubits within a quantum computer are prone to noise and decoherence, which must be corrected in order to perform reliable, fault-tolerant quantum computations. Quantum Error Correction (QEC) provides the path for realizing such computations. QEC continuously generates a continuous stream of data that decoders must process at the rate it is received, which can be as fast as 1 MHz in superconducting quantum computers. A little known fact of QEC is that if the decoder infrastructure cannot keep up, a data backlog problem is encountered and the quantum computer runs exponentially slower. Today's leading approaches to quantum error correction are not scalable as existing decoders typically run slower as the problem size is increased, inevitably hitting the backlog problem. That is: the current leading proposal for fault-tolerant quantum computation is not scalable. Here, we show how to parallelize decoding to achieve almost arbitrary speed, removing this roadblock to scalability. Our parallelization requires some classical feed forward decisions to be delayed, leading to a slow-down of the logical clock speed. However, the slow-down is now only polynomial in code size, averting the exponential slowdown. We numerically demonstrate our parallel decoder for the surface code, showing no noticeable reduction in logical fidelity compared to previous decoders and demonstrating the parallelization speedup.
Realistic photon-number resolution in generalized Hong-Ou-Mandel experiment

Abstract We consider realistic photodetection in a generalization of the Hong-Ou-Mandel experiment to the multimode case. The basic layout of this experiment underlies boson sampling—a promising model of nonuniversal quantum computations. Peculiarities of photocounting probabilities in such an experiment witness important nonclassical properties of electromagnetic field related to indistinguishability of boson particles. In practice, these probabilities are changed from their theoretical values due to the imperfect ability of realistic detectors to distinguish numbers of bunched photons. We derive analytical expressions for photocounting distributions in the generalized Hong-Ou-Mandel experiment for the case of realistic photon-number resolving (PNR) detectors. It is shown that probabilities of properly postselected events are proportional to probabilities obtained for perfect PNR detectors. Our results are illustrated with examples of arrays of on/off detectors and detectors affected by a finite dead time.
Property as the law of virtual things

Property law in the twentieth century moved from the law of things to the law of rights in things. This was a process of fragmentation: Under Hohfeldian property, we conceive of property as a bundle of sticks, and those sticks can be moved to different holders; the right to possess can be separated from the record ownership right, for example. The downside of Hohfeld's model is that physical objects—things—become informationally complicated. Thing-ness constrains the extravagances of Hohfeldian property: although we can split off the right to possess from the right to exclude, use, destroy, copy, manage, repair, and so on, there is a gravitational pull to tie these sticks back into a useful bundle centered on the asset, the thing. Correspondingly, there has been an “informational turn” to property law, looking at the ways in which property law serves to limit property forms to reduce search costs, and to identify and celebrate the informational characteristics of thing-ness. The question of thing-ness came to a head in the context of digital and smart assets with the formation of non-fungible tokens. NFTs were attempts to generate and sell “things,” a conceptually coherent something that can contain a loose bundle of rights. The project was an attempt to re-create thing-ness by an amalgam of cryptography, game theory, and intellectual property. This essay discusses thing-ness in the context of digital assets, how simulated thing-ness differs from physical thing-ness, and the problems that arise from attempts to reify digital assets.
A Hybrid Supervised Learning Approach for Detection and Mitigation of Job Failure with Virtual Machines in Distributed Environments

Distributed data processing techniques are very popular nowadays due to high data generation from various resources. To increase work learning outcomes and to reduce consumption, modern massive computational systems divide jobs into several smaller tasks that perform in parallel. Nevertheless, responding with straggler processes, which are sluggish running processes that rise the total response time, is a typical performance issue in such platforms. In this paper, we proposed the detection of struggler nodes in a large distributed environment using a hybrid machine learning technique. Initially, the data has been collected from numerous virtual machine network logs. The entire data set has various fields such as Virtual Machine ID, CPU load, memory load, bandwidth utilization, etc. Memory utilization an input to the proposed system is collected from the garbage collection log files where the memory consumption on each VM and its timestamp is recorded. This is the most efficient way to get the memory consumption in web/desktop applications. Similarly, the CPU, I/O and bandwidth utilization is grabbed from the process monitoring functionality and SAR (System Activity Report) utility from the respective VM boxes. This data set is useful to identify weather-specific virtual machine is heated up or not. In this approach, we proposed three conventional machine learning algorithms and a hybrid machine learning algorithm for the identification of node status. Main purpose of the proposed system is to identify the slow performing node in an efficient way to prevent the other nodes from failures. This can provide effective load balancing and low response time for task execution from available VM’s in distributed cloud environments. To create its training program, several extractions of features approaches were used. TF-IDF, correlational co-occurrence, and density-based features have been mined from the whole data set. With extensive experimental analysis, we evaluate our system with our proposed classification algorithm. As a result, the system produces higher classification accuracy of 94.5% over the traditional machine learning classifiers. If the proposed system is tested against the data set fields, memory load and CPU load on the homogenous machine configurations, we see more efficiency while detecting the underperforming node than the heterogenous machine configurations.
Modified Vigenère cipher algorithm based on new key generation method

Nowadays, as communication and network technologies evolve in modern life, ensuring the confidentiality of a cryptography system has become a critical requirement. The Vigenère cipher is attracting the attention of cryptography specialists, although the Vigenère cipher algorithm has a problem. The problem is due to a repeating encryption key. As a result of the multiple cryptographic approaches described in the literature, this paper proposes a novel encryption strategy for safe and secure data exchange by utilizing a new key generation process. The proposed encryption approach avoids the issue of repeating keys. Additionally, the classic Vigenère cipher encrypts the plaintext using a 26x26 Vigenère table, the researcher modified the original Vigenère table to 95x95, which adds more potential letters, mathematical symbols, numerals, and punctuation to a standard QWERTY keyboard layout. Additionally, the researcher added case sensitivity. To observe the performance of the proposed method, the index of coincidence and entropy have been calculated. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper. The primary goal of this paper is to make cryptanalysis extremely complex and to promote data security.
Experimental Solution for Estimating Pedestrian Locations from UAV Imagery

This research describes an experimental solution used for estimating the positions of pedestrians from video recordings. Additionally, clustering algorithms were utilized to interpret the data. The system employs the You Only Look Once (YOLO) algorithm for object detection. The detection algorithm is applied to video recordings provided by an unmanned aerial vehicle (UAV). An experimental method for calculating the pedestrian’s geolocation is proposed. The output of the calculation, i.e., the data file, can be visualized on a map and analyzed using cluster analyses, including K-means, DBSCAN, and OPTICS algorithms. The experimental software solution can be deployed on a UAV or other computing devices. Further testing was performed to evaluate the suitability of the selected algorithms and to identify optimal use cases. This solution can successfully detect groups of pedestrians from video recordings and it provides tools for subsequent cluster analyses.
Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images

A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity of neural network parameters by using separable convolutional layers. In the proposed structure of the dual autoencoder, the first autoencoder aims to denoise the image, while the second one aims to enhance the quality of the denoised image. The research includes Gaussian noise (Gaussian blur), Poisson noise, speckle noise, and random impulse noise. The advantages of the proposed neural network are the number reduction in the trainable parameters and the increase in the similarity between the denoised or deblurred image and the original one. The similarity is increased by decreasing the main square error and increasing the structural similarity index. The advantages of a dual autoencoder network with separable convolutional layers are demonstrated by a comparison of the proposed network with a convolutional autoencoder and dual convolutional autoencoder.
A Systematic Review of Studies on Augmented Reality Based Applications in Primary Education

This research aims to examine the researches based on augmented reality-based applications conducted for primary school students between 2015-2021 and to determine the positive and negative effects of this applications on students. The document analysis method was used in the research. In this context, articles containing AR-based applications prepared for primary school students using the keywords “augmented reality”, “primary education”, “elementary education”, “teachers ‘and parents’ perceptions” were examined in ScienceDirect, ERIC, Sagepub and SpringerLink databases. As a result of the research, 48 articles were determined in accordance with the criteria. As a result of the content analysis, these studies were divided into seven different categories under the augmented reality theme: teacher and parent perceptions, reading and literacy skills, science education, special education, language education, mathematics education, and history/social sciences education. It has been observed that these practices improve students’ motivation, academic achievement and remembering skills, increase their vocabulary, help them develop a positive attitude towards lessons, and improve their spatial thinking and creativity skills. In addition, there are also studies that have negative results stating that it limits the imagination, requires a lot of time for the application, it prevents reading because it is distracting, there is no available amount of free AR-based applications, and there are inadequate professional support to teachers for the use of these applications.
Complex Human Activity Recognition Using a Local Weighted Approach

Due to the life expectancy increase, there will be a workforce shortage in elderly care sector in forthcoming years. Ambient Assisted Living (AAL) systems can cope with this issue. A subset of AAL, Human activity recognition (HAR) provides an efficient way to tackle this issue. It can help with evaluating general health and welfare of elderly by automatically tracking their activities. Lifelogging and home diary applications will reduce the load on physicians and caregivers. On the other hand, complex activities play a vital role as they have high level semantic characteristics that truly represent daily life of the user. The main objective is to track these high-level semantic motions with low-cost single sensor systems with efficient machine learning frameworks. To achieve this objective, a framework is proposed to predict complex human activities from a single sensor using a machine learning approach. Time and frequency features are extracted from PAAL ADL Accelerometry Dataset and fed to Locally Weighted Random Forest (LWRF) machine learning algorithm. This algorithm is a hybrid structure that utilizes local weighting by introducing neighboring samples on Random Forest tree building phases. Proposed approach achieved 91% accuracy for HAR and 91.3% for gender recognition, outperforming other machine learning algorithms and previous study on the same dataset. This is the first study that utilize a local weighted approach for accelerometer signal domain. For prospective application, proposed framework can be embedded in lifelogging and home diary applications in home environments to track mental status of elderlies.