Reviewerly/exHarmony huggingface dataset:
More on Multidimensional Scaling and Unfolding in <i>R</i>: <b>smacof</b> Version 2

The smacof package offers a comprehensive implementation of multidimensional scaling (MDS) techniques in R. Since its first publication (De Leeuw and Mair 2009b) the functionality of the package has been enhanced, and several additional methods, features and utilities were added. Major updates include a complete re-implementation of multidimensional unfolding allowing for monotone dissimilarity transformations, including row-conditional, circular, and external unfolding. Additionally, the constrained MDS implementation was extended in terms of optimal scaling of the external variables. Further package additions include various tools and functions for goodness-of-fit assessment, unidimensional scaling, gravity MDS, asymmetric MDS, Procrustes, and MDS biplots. All these new package functionalities are illustrated using a variety of real-life applications.
Sign Language Recognition for Arabic Alphabets Using Transfer Learning Technique

Sign language is essential for deaf and mute people to communicate with normal people and themselves. As ordinary people tend to ignore the importance of sign language, which is the mere source of communication for the deaf and the mute communities. These people are facing significant downfalls in their lives because of these disabilities or impairments leading to unemployment, severe depression, and several other symptoms. One of the services they are using for communication is the sign language interpreters. But hiring these interpreters is very costly, and therefore, a cheap solution is required for resolving this issue. Therefore, a system has been developed that will use the visual hand dataset based on an Arabic Sign Language and interpret this visual data in textual information. The dataset used consists of 54049 images of Arabic sign language alphabets consisting of 1500\ images per class, and each class represents a different meaning by its hand gesture or sign. Various preprocessing and data augmentation techniques have been applied to the images. The experiments have been performed using various pretrained models on the given dataset. Most of them performed pretty normally and in the final stage, the EfficientNetB4 model has been considered the best fit for the case. Considering the complexity of the dataset, models other than EfficientNetB4 do not perform well due to their lightweight architecture. EfficientNetB4 is a heavy-weight architecture that possesses more complexities comparatively. The best model is exposed with a training accuracy of 98 percent and a testing accuracy of 95 percent.
Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches

Abstract As a new type of currency introduced in the new millennium, cryptocurrency has established its ecosystems and attracts many people to use and invest in it. However, cryptocurrencies are highly dynamic and volatile, making it challenging to predict their future values. In this research, we use a multivariate prediction approach and three different recurrent neural networks (RNNs), namely the long short-term memory (LSTM), the bidirectional LSTM (Bi-LSTM), and the gated recurrent unit (GRU). We also propose simple three layers deep networks architecture for the regression task in this study. From the experimental results on five major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Tether (USDT), and Binance Coin (BNB), we find that both Bi-LSTM and GRU have similar performance results in terms of accuracy. However, in terms of the execution time, both LSTM and GRU have similar results, where GRU is slightly better and has lower variation results on average.
Analysis of Heat Potential in Solar Panels for Thermoelectric Generators using ANSYS Software

The growing demand for energy has an impact on the development of environmentally friendly renewable energy. The sun is energy that has the potential to be used as electrical energy through light energy and heat energy. Recently, research interest related to photovoltaic performance has increased. Several studies have investigated the effect of panel cooling on photovoltaic performance. In this study, the use of exergy solar panels is considered to improve performance by adding a thermoelectric system. Research work related to photovoltaic testing with thermoelectrics at low temperatures has not been carried out. Therefore, experimental methods to obtain temperature profiles and simulation methods to see the power potential generated from thermoelectrics have been carried out. The experimental method is carried out using mono-crystalline panels with type K sensors to retrieve temperature data and data acquisition as deviations from the current, voltage, and temperature results of the panel. The simulation model was carried out using the ANSYS software. Tests are carried out, taking into account the effect of back panel temperature on system performance. The results showed that the photovoltaic temperature fluctuated due to the influence of cloud cover, the highest photovoltaic temperature was 57°C, and the lowest temperature was 30°C. The maximum power produced by photovoltaic is 39.8W. It is then applied to the thermoelectric simulation based on the highest temperature, and the maximum power value is 1673.4 mW. This photovoltaic-thermoelectric generator system produces a 4.2% increase in power value over conventional photovoltaic systems. The findings in this study can be used as a reference for all types of low-temperature photovoltaic-thermoelectric systems. Doi: 10.28991/CEJ-2022-08-07-02 Full Text: PDF
SCImago Graphica: a new tool for exploring and visually communicating data

Despite the increasing number of data visualization authoring systems in recent years, it remains a challenge to simultaneously achieve high expressive power and ease of use in a single tool. In this paper we present SCImago Graphica, a no-code tool which allows the creation of complex visualizations by simple drag-and-drop interactions. Users bind the data variables to the different encoding channels, and specify the settings of each binding, from which the tool generates the interactive graphical display. Due to its efficiency of use, SCImago Graphica is not only suitable for visually communicating data, but also for exploratory data analysis. We evaluate the expressiveness and ease of use of SCImago Graphica through various examples of chart construction and a catalog of visualizations. The results show that SCImago Graphica makes it possible to create a wide variety of data visualizations quickly and easily.
Perceived Quality of Reference Service with WhatsApp

Academic libraries are experiencing significant changes and making efforts to deliver their service in the digital environment. Libraries are transforming from being places for reading to extensions of the classroom and learning spaces. Due to the globalized digital environment and intense competition, libraries are trying to improve their service quality through various evaluations. As reference service is crucial to users, this study explores user satisfaction towards the reference service through WhatsApp, a social media instant messenger, at a major university in Hong Kong and discusses the correlation between the satisfaction rating and three variables. Suggestions and recommendations are raised for future improvements. The study also sheds light on the usage of reference services through instant messaging in other academic libraries.
The Arrival of the Metaverse in Neurorehabilitation: Fact, Fake or Vision?

The metaverse is a new technology thought to provide a deeper, persistent, immersive 3D experience combining multiple different virtual approaches in a full continuum of physical-digital interaction spaces. Different from virtual reality (VR) and augmented reality (AR), the metaverse has a service-oriented solid model with an emphasis on social and content dimensions. It has widely been demonstrated that motor or cognitive deficits can be more effectively treated using VR/AR tools, but there are several issues that limit the real potential of immersive technologies applied to neurological patients. In this scoping review, we propose future research directions for applying technologies extracted from the metaverse in clinical neurorehabilitation. The multisensorial properties of the metaverse will boost the embodied cognition experience, thus influencing the internal body representations as well as learning strategies. Moreover, the immersive social environment shared with other patients will contribute to recovering social and psychoemotional abilities. In addition to the many potential pros, we will also discuss the cons, providing readers with the available information to better understand the complexity and limitations of the metaverse, which could be considered the future of neurorehabilitation.
Stokes meta-hologram toward optical cryptography

Optical cryptography manifests itself a powerful platform for information security, which involves encrypting secret images into visual patterns. Recently, encryption schemes demonstrated on metasurface platform have revolutionized optical cryptography, as the versatile design concept allows for unrestrained creativity. Despite rapid progresses, most efforts focus on the functionalities of cryptography rather than addressing performance issues, such as deep security, information capacity, and reconstruction quality. Here, we develop an optical encryption scheme by integrating visual cryptography with metasurface-assisted pattern masking, referred to as Stokes meta-hologram. Based on spatially structured polarization pattern masking, Stokes meta-hologram allows multichannel vectorial encryption to mask multiple secret images into unrecognizable visual patterns, and retrieve them following Stokes vector analysis. Further, an asymmetric encryption scheme based on Stokes vector rotation transformation is proposed to settle the inherent problem of the need to share the key in symmetric encryption. Our results show that Stokes meta-hologram can achieve optical cryptography with effectively improved security, and thereby paves a promising pathway toward optical and quantum security, optical communications, and anticounterfeiting.
GRU-INC: An inception-attention based approach using GRU for human activity recognition

Human Activity Recognition (HAR) is very useful for the clinical applications, and many machine learning algorithms have been successfully implemented to achieve high-performance results. Although handcrafted feature extraction techniques were used in the past, Artificial Neural Network (ANN) is now more popular. In this work, a model has been proposed called Gated Recurrent Unit-Inception (GRU-INC) model has been proposed, which is an Inception-Attention based approach using Gated Recurrent Unit (GRU) that effectively makes use of the temporal and spatial information of the time-series data. The proposed model achieved an F1-score of 96.27%, 90.05%, 90.30%, 99.12%, and 95.99% on the publicly available datasets such as, UCI-HAR, OPPORTUNITY, PAMAP2, WISDM, and Daphnet, respectively. GRU along with Attention Mechanism (AM) was utilized for the temporal part, and Inception module along with Convolutional Block Attention Module (CBAM) was exploited for the spatial part of the model. The proposed architecture was evaluated against state-of-the-art models and similar works. It has been proved that the GRU-INC model has a higher recognition rate as well as lower computational cost. Thus our framework could be applicable in activity associated clinical and rehabilitation applications.
SemAttNet: Toward Attention-Based Semantic Aware Guided Depth Completion

Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide the necessary semantic understanding of the scene. Consequently, the depth completion task suffers from sudden illumination changes in RGB images (e.g., shadows). In this paper, we propose a novel three-branch backbone comprising color-guided, semantic-guided, and depth-guided branches. Specifically, the color-guided branch takes a sparse depth map and RGB image as an input and generates color depth which includes color cues (e.g., object boundaries) of the scene. The predicted dense depth map of color-guided branch along-with semantic image and sparse depth map is passed as input to semantic-guided branch for estimating semantic depth. The depth-guided branch takes sparse, color, and semantic depths to generate the dense depth map. The color depth, semantic depth, and guided depth are adaptively fused to produce the output of our proposed three-branch backbone. In addition, we also propose to apply semantic-aware multi-modal attention-based fusion block (SAMMAFB) to fuse features between all three branches. We further use CSPN++ with Atrous convolutions to refine the dense depth map produced by our three-branch backbone. Extensive experiments show that our model achieves state-of-the-art performance in the KITTI depth completion benchmark at the time of submission.
The transaction behavior of cryptocurrency and electricity consumption

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production, increasing network hashrates and electricity consumption. Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity. These changes prompt cryptocurrency miners to become new investors, leading to cryptocurrency price increases. The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified. Hence, this research thus utilizes July 31 2015-July 12 2019 data from 13 cryptocurrencies to investigate the short- and long-run causal effects between cryptocurrency transaction and electricity consumption. Particularly, we consider structural breaks induced by external shocks through stationary analysis and comovement relationships. Over the examined time period, we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks, with prices trending together with hashrates. Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption. Therefore, owing to the computational power deployed to wherever high profit is found, transactions are vital determinants of electricity consumption.
Assessment of chemistry knowledge in large language models that generate code

In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes.
A Direct Key Recovery Attack on SIDH

We present an attack on SIDH utilising isogenies between polarized products of two supersingular elliptic curves. In the case of arbitrary starting curve, our attack (discovered independently from [8]) has subexponential complexity, thus significantly reducing the security of SIDH and SIKE. When the endomorphism ring of the starting curve is known, our attack (here derived from [8]) has polynomial-time complexity assuming the generalised Riemann hypothesis. Our attack applies to any isogeny-based cryptosystem that publishes the images of points under the secret isogeny, for example Séta [13] and B-SIDH [11]. It does not apply to CSIDH [9], CSI-FiSh [3], or SQISign [14].
Let Us Create An IoT Inside the AWS Cloud

Purpose: The Internet of Things (IoT) has become essential to modern technological advancements, with various industries leveraging its capabilities. One of the significant challenges of IoT implementation is the storage, processing, and analysis of the vast amounts of data generated. AWS is a popular platform for IoT operation. This paper proposes using Amazon Web Services (AWS) as a cloud computing platform for IoT applications. By leveraging AWS's robust infrastructure, scalability, and cost-effectiveness, IoT developers can focus on creating innovative applications that transform businesses and improve customer experiences. The interface of the various AWS service has changed. Using the New AWS IoT console, we demonstrate how to create IoT device shadows. Design/Methodology/Approach: IoT Thing is the backbone of any IoT interaction. First, we create Our AWS account. Because without an account, it cannot operate IoT devices. After a successful account setup, we create our things. After that, we create a device shadow, which is used to update or fetch the device data among the devices interconnected through the Internet. There are several ways to interact with device shadows. Here we talk about the few most straightforward and efficient ways, the hardware devices like ESP8266/ESP32 wifi modules, using MQTT and IoT device clients. Another efficient way is communicating through the Lambda function, which can create an Alexa-operated IoT device. Findings/Result: There are several ways to create an internet of things. Probably the best and most reliable platform is AWS. Its security and interface are user-friendly. Here, through a simple illustration, we discussed how to create Things inside the AWS cloud, the backbone of transferring or fetching data from one end to another over the Internet. Originality/Value: The several documents available over the web for understanding and creating the AWS IoT. Here, through simple illustration, we demonstrate how we can easily use AWS's new interface. Our researchers working or going to experiment with AWS's new IoT console can get valuable reference information for their research. Paper Type: Experimental-based Research.
An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse

While the Metaverse is becoming a popular trend and drawing much attention from academia, society, and businesses, processing cores used in its infrastructures need to be improved, particularly in terms of signal processing and pattern recognition. Accordingly, the speech emotion recognition (SER) method plays a crucial role in creating the Metaverse platforms more usable​ and enjoyable for its users. However, existing SER methods continue to be plagued by two significant problems in the online environment. The shortage of adequate engagement and customization between avatars and users is recognized as the first issue and the second problem is related to the complexity of SER problems in the Metaverse as we face people and their digital twins or avatars. This is why developing efficient machine learning (ML) techniques specified for hypercomplex signal processing is essential to enhance the impressiveness and tangibility of the Metaverse platforms. As a solution, echo state networks (ESNs), which are an ML powerful tool for SER, can be an appropriate technique to enhance the Metaverse’s foundations in this area. Nevertheless, ESNs have some technical issues restricting them from a precise and reliable analysis, especially in the aspect of high-dimensional data. The most significant limitation of these networks is the high memory consumption caused by their reservoir structure in face of high-dimensional signals. To solve all problems associated with ESNs and their application in the Metaverse, we have come up with a novel structure for ESNs empowered by octonion algebra called NO2GESNet. Octonion numbers have eight dimensions, compactly display high-dimensional data, and improve the network precision and performance in comparison to conventional ESNs. The proposed network also solves the weaknesses of the ESNs in the presentation of the higher-order statistics to the output layer by equipping it with a multidimensional bilinear filter. Three comprehensive scenarios to use the proposed network in the Metaverse have been designed and analyzed, not only do they show the accuracy and performance of the proposed approach, but also the ways how SER can be employed in the Metaverse platforms.
High-rate entanglement between a semiconductor spin and indistinguishable photons

Photonic graph states, quantum light states where multiple photons are mutually entangled, are key resources for optical quantum technologies. They are notably at the core of error-corrected measurement-based optical quantum computing and all-optical quantum networks. In the discrete variable framework, these applications require high efficiency generation of cluster-states whose nodes are indistinguishable photons. Such photonic cluster states can be generated with heralded single photon sources and probabilistic quantum gates, yet with challenging efficiency and scalability. Spin-photon entanglement has been proposed to deterministically generate linear cluster states. First demonstrations have been obtained with semiconductor spins achieving high photon indistinguishablity, and most recently with atomic systems at high collection efficiency and record length. Here we report on the efficient generation of three partite cluster states made of one semiconductor spin and two indistinguishable photons. We harness a semiconductor quantum dot inserted in an optical cavity for efficient photon collection and electrically controlled for high indistinguishability. We demonstrate two and three particle entanglement with fidelities of 80 % and 63 % respectively, with photon indistinguishability of 88%. The spin-photon and spin-photon-photon entanglement rates exceed by three and two orders of magnitude respectively the previous state of the art. Our system and experimental scheme, a monolithic solid-state device controlled with a resource efficient simple experimental configuration, are very promising for future scalable applications.
Quantum computing for finance

Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modelling, optimization and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle. Quantum computers are expected to surpass classical computers and transform industries. This Review focuses on quantum computing for financial applications and provides a summary for physicists on potential advantages and limitations of quantum techniques, as well as challenges that physicists could help tackle.
The Scanalyzer Domain: Greenhouse Logistics as a Planning Problem

We introduce the Scanalyzer planning domain, a domain for classical planning which models the problem of automatic greenhouse logistic management. At its mathematical core, the Scanalyzer domain is a permutation problem with striking similarities to common search benchmarks such as Rubik's Cube or TopSpin. At the same time, it is also a real application domain, and efficient algorithms for the problem are of considerable practical interest. The Scanalyzer domain was used as a benchmark for sequential planners at the last International Planning Competition. The competition results show that domain-independent automated planners can find solutions of comparable quality to those generated by specialized algorithms developed by domain experts, while being considerably more flexible.