Reviewerly/exHarmony huggingface dataset:
A novel sign detection method in residue number system based on Chinese remainder theorem with fractional values

Sign detection is a non-modular operation in the residue number system (RNS). It requires the calculation of the number positional characteristic represented in the RNS. This work proposes a new sign detection method based on the Chinese Remainder Theorem (CRT) with fractional values implemented using the Wallace tree and the modified Kogge-Stone adder. Hardware modelling on FPGA for the proposed method shows that it provides 1.3 – 36.3 times less hardware costs than the other state-of-the-art (SOTA) methods, and for ASIC modelling the proposed method provides 1.14 – 35.74 times less hardware costs than the other SOTA methods. The presented sign detection method can be helpful in RNS-based devices in implementing comparison and division operations, providing an extension of the RNS application in areas such as cryptography, machine learning, and digital signal processing.
Convergence analysis of deep residual networks

Various powerful deep neural network architectures have made great contributions to the exciting successes of deep learning in the past two decades. Among them, deep Residual Networks (ResNets) are of particular importance because they demonstrated great usefulness in computer vision by winning the first place in many deep learning competitions. Also, ResNets are the first class of neural networks in the development history of deep learning that are really deep. It is of mathematical interest and practical meaning to understand the convergence of deep ResNets. We aim at studying the convergence of deep ResNets as the depth tends to infinity in terms of the parameters of the networks. Toward this purpose, we first give a matrix–vector description of general deep neural networks with shortcut connections and formulate an explicit expression for the networks by using the notion of activation matrices. The convergence is then reduced to the convergence of two series involving infinite products of non-square matrices. By studying the two series, we establish a sufficient condition for pointwise convergence of ResNets. We also conduct experiments on benchmark machine learning data to illustrate the potential usefulness of the results.
Analyzing the Success of Adopting Metaverse in Construction Industry: Structural Equation Modelling

The application of metaverse technology in the field of civil engineering has the potential to improve project efficiency and accuracy. Nevertheless, the pervasive adoption and effective integration of metaverses are contingent on a number of crucial factors. This study investigates the critical factors underlying the successful implementation of metaverse technology within a business context. This research employs a comprehensive mixed-method approach comprised of exploratory factor analysis (EFA) and structural equation modelling (SEM) to examine survey responses gathered from seasoned professionals in the architecture, engineering, and construction (AEC) sectors in Bangladesh. In areas such as communication and collaboration, design visualization, and monitoring and maintenance, the construction industry has made extraordinary strides. As imperatives for managerial consideration strategic investment in resources, targeted training initiatives, heightened awareness campaigns, and prudent deployment of cost-effective and efficient metaverse-based solutions emerge, future investigations should include a larger sample size and an evaluation of the lasting effects over extended time periods. The key to unleashing the full potential of the metaverse within the architecture, engineering, and construction industries lies in addressing these identified success determinants, thereby ushering in enhanced project outcomes and enhanced efficiency within the constructed environment. The combination of these initiatives is expected to pave the way for a new era in the AEC landscape.
Integrating Artificial Intelligence Tools into the Formation of Professional Identity

My claim in this Article is that a lawyer’s personal use of artificial intelligence (AI) in the practice of law is now an essential component of a lawyer’s professional identity that must be intentionally developed as a law student before entering the practice of law. After demonstrating the strong connection between the use of AI tools in legal practice, the requirement of lawyer competence, and the formation of professional identity, the Article proposes four “best practices” principles for integrating AI tools with traditional lawyering skills exercises to assist students in the formation of professional identity. The Article concludes with an example that can be used in the first-year Property course.
EAMNet: an Alzheimer’s disease prediction model based on representation learning

Objective. Brain18F-FDG PET images indicate brain lesions' metabolic status and offer the predictive potential for Alzheimer's disease (AD). However, the complexity of extracting relevant lesion features and dealing with extraneous information in PET images poses challenges for accurate prediction.Approach. To address these issues, we propose an innovative solution called the efficient adaptive multiscale network (EAMNet) for predicting potential patient populations using positron emission tomography (PET) image slices, enabling effective intervention and treatment. Firstly, we introduce an efficient convolutional strategy to enhance the receptive field of PET images during the feature learning process, avoiding excessive extraction of fine tissue features by deep-level networks while reducing the model's computational complexity. Secondly, we construct a channel attention module that enables the prediction model to adaptively allocate weights between different channels, compensating for the spatial noise in PET images' impact on classification. Finally, we use skip connections to merge features from different-scale lesion information. Through visual analysis, the network constructed in this article aligns with the regions of interest of clinical doctors.Main results. Through visualization analysis, our network aligns with regions of interest identified by clinical doctors. Experimental evaluations conducted on the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset demonstrate the outstanding classification performance of our proposed method. The accuracy rates for AD versus NC (Normal Controls), AD versus MCI (Mild Cognitive Impairment), MCI versus NC, and AD versus MCI versus NC classifications achieve 97.66%, 96.32%, 95.23%, and 95.68%, respectively.Significance. The proposed method surpasses advanced algorithms in the field, providing a hopeful advancement in accurately predicting and classifying Alzheimer's Disease using18F-FDG PET images. The source code has been uploaded tohttps://github.com/Haoliang-D-AHU/EAMNet/tree/master.
Pixel-based data preparation for digital scanner

Nikon has been developing the Digital Scanner, an optical maskless exposure tool with a DUV light source. The Digital Scanner uses a spatial light modulator and rasterized pattern data, instead of glass photomasks, to project an optical image. The modulator is a micromirror array and each micromirror takes one of two possible states, so the pattern data are essentially equivalent to a one bit per pixel bitmap image. In spite of the one-bit depth input similar to a black-and-white bitmap, the Digital Scanner can control projected patterns in subpixel resolution because the pixel size is chosen to be smaller than the resolution of the projection optics. Besides the projection hardware, we have also developed special pattern data preparation system for the Digital Scanner in order to realize the subpixel controllability. Polygons from GDSII or OASIS files are rasterized by dedicated pixel-based algorithms so that the optical image of the resulting pixel data becomes equivalent to that of input polygons. Another pattern data converter with optical proximity correction (OPC) capability is also being developed and available for large area conversion. We explain the exposure system of the Digital Scanner and report the progress of the pixel-based data preparation system including recent demonstration printing results of exposure data generated by the new converter that has OPC capability.
A post-quantum associative memory

Abstract Associative memories are devices storing information that can be fully retrieved given partial disclosure of it. We examine a toy model of associative memory and the ultimate limitations to which it is subjected within the framework of general probabilistic theories (GPTs), which represent the most general class of physical theories satisfying some basic operational axioms. We ask ourselves how large the dimension of a GPT should be so that it can accommodate 2 m states with the property that any N of them are perfectly distinguishable. Call <?CDATA $d(N,m)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>N</mml:mi> <mml:mo>,</mml:mo> <mml:mi>m</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> the minimal such dimension. Invoking an old result by Danzer and Grünbaum, we prove that <?CDATA $d(2,m) = m+1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mi>m</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mi>m</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:math> , to be compared with <?CDATA $O(2^m)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>m</mml:mi> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:math> when the GPT is required to be either classical or quantum. This yields an example of a task where GPTs outperform both classical and quantum theory exponentially. More generally, we resolve the case of fixed N and asymptotically large m , proving that <?CDATA $d(N,m) \unicode{x2A7D} m^{1+o_N(1)}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>N</mml:mi> <mml:mo>,</mml:mo> <mml:mi>m</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mtext>⩽</mml:mtext> <mml:msup> <mml:mi>m</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:msub> <mml:mi>o</mml:mi> <mml:mi>N</mml:mi> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msup> </mml:math> (as <?CDATA $m\to\infty$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>m</mml:mi> <mml:mo stretchy="false">→</mml:mo> <mml:mi mathvariant="normal">∞</mml:mi> </mml:math> ) for every <?CDATA $N\unicode{x2A7E} 2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mtext>⩾</mml:mtext> <mml:mn>2</mml:mn> </mml:math> , which yields again an exponential improvement over classical and quantum theories. Finally, we develop a numerical approach to the general problem of finding the largest N -wise mutually distinguishable set for a given GPT, which can be seen as an instance of the maximum clique problem on N -regular hypergraphs.
Fourth Industrial Revolution: Evaluating the Engineering Education Challenge from the Perspective of Chemical Engineering

The fourth industrial revolution (IR4.0) pushes the automation of machineries which revolutionises the manufacturing industry through the integration of emerging technologies. As a result, the demand for chemical engineering graduates with the skills to utilise new technologies has been proliferating. To evaluate the current chemical engineering curriculum's sufficiency in preparing students for the current industry from the student's perspective, a questionnaire-based study was conducted. The questionnaire consisted of three main sections, each formulated to achieve different research objectives. The main research objectives were to determine the awareness of chemical engineering students on IR4.0, their opinions on the importance of IR4.0 and the potential benefits of obtaining more knowledge towards IR4.0 within the education curriculum. The targeted participants were current chemical engineering students from universities situated in Malaysia. Responses from 30 students were collected and analysed with RStudio. The results suggested that the participants were all aware of IR4.0 and considered it an integral part of chemical engineering. Additionally, the study revealed a lack of familiarity with programming and the need to incorporate more programming into the current curriculum.
Research on the Construction of Multimodal Datasets for Digital Libraries

In order to apply cross-modal retrieval to digital library retrieval services, this paper firstly collects image and text data in digital libraries by crawlers and filters out the unqualified data; then adds text descriptions for images without text descriptions; and finally labels the images using annotation tools. A multimodal dataset containing 4400 image-text pairs is constructed and experimentally validated on several cross-modal retrieval methods. The experiments show that the dataset constructed in this paper is suitable for cross-modal retrieval research.
Performance Evaluation of Recurrent Neural Networks-LSTM and GRU for Automatic Speech Recognition

The work presented in this paper evaluates and compares the performance of the conventional Recurrent neural network(RNN), with elegant variations in the RNN architecture namely the LSTM and Gated Recurrent Units(GRU) for speech recognition. The conventional RNNs inept handling of the long-term dependencies due to the vanishing/exploding gradient problem led to the LSTMs and GRUs which specialises in handling long-term dependencies and does well in speech recognition tasks. The LSTM and GRU networks provide much greater frame precision than the RNNs while converging much more quickly. Further in the paper, the Unidirectional LSTMs and GRUs are extended to their bidirectional counterparts, which train input data in both the forward and reverse directions. The work presented in this paper clearly indicates a superior performance of the bidirectional models compared to their unidirectional counterparts. The bidirectional LSTM shows an accuracy of about 90% with a nominal loss of 0.0388 for the same training iterations and learning rate and being tested on a well established english speech commands data-set.
Enhancing Wave Function Collapse Algorithm using Bitwise Operations

The wave function collapse algorithm is a method for generating procedural content in the field of computer graphics and video games introduced in 2016. In this algorithm, a large number of potential outcomes are represented as a wave function, and as the algorithm progresses, the wave function collapses into a single, definite state that represents the final output. Over the years there have been advances in improving design constraints, automatic rule writing and applying Wave Function Collapse in other domains like navigation etc. This paper aims to improve the runtime and size of the Wave Function Collapse algorithm by modifying its implementation using a Bitwise Grid and bitwise operations. A terrain is generated using 4 states. The constraints or rules are defined using a new constraint matrix according to which many random terrains are generated quickly. After various tests and iterations, we can conclude that Bitwise Grid implementation can produce 8000 terrains in approximately 1 second whereas the original implementation took 8 seconds.
Financial Services 4.0 - Future Perspective based on Mixed Reality

With the development of futuristic technology, augmented reality (AR) and virtual reality (VR) have come to the light as significant communal media and are being employed more frequently in the retail context for clothing. AR and VR are examples of “consumer-facing” mechanism, which refers to goods and/or activities that users may engage with and immediately experience when making purchases online or offline. This research focuses on both AR and VR which has increased the consumer interest in the academic life as well as of the fashion and retail sector. Using secondary research that suggests the shifting tendency towards emerging technologies, this study explored several aspects and prospects of AR and VR. This article looked at the undeveloped future of AR and VR, especially towards the retail industry. This article concludes about the expanding sector of the economy, which is the consumer sector, which is very crucial today. It is high time to capitalize on how retail is changing and integrate contemporary technology to aid consumers in their purchasing choices. This article has concluded with suggestions that how further AR and VR are used to defy the current trend in the retail sector.
To Explore the Effects of Virtual Reality in Vocational Education and Training

The features of Virtual Reality (VR) present a learner-centric platform, make leaning more vivid, and solve the problems that cannot complete in physical classes. It can encourage the students' motivation and improve the learning experience. However, results pertaining to the effects of VR in vocational education and training (VET) have not been explored deeply. As for the design skills, whether VR can improve the design outcomes, it is still a big question. In this paper, we chose the VR to support interior designers for designing the office space. After analyzing this instructional experiment, we investigate the effects of VR in designing office space compared with the traditional method on proportion, composition, and innovation from design outcomes. The results show that VR can enhance the design outcomes compare with paper sketching except innovation. On the other hand, the design outcomes can be impacted by different orders between paper sketch first after VR or VR first after paper sketch. As for quantitative analysis, we find the first combination is better than the second one. As for the discussion section, we explain several factors why VR can improve the design skill in vocational education.
Research on the Text Clustering of Electricity Production Safety Hazards Based on K-Means Algorithm

At present, the text classification of power production safety hazards mainly relies on the traditional manual and empirical judgment. It is inefficient and cannot guarantee its correctness. To address these problems, a text feature extraction method based on Word2vec and convolutional neural networks (CNN) is proposed and combined with K-Means clustering algorithm to automatically classify power production safety hazard text. The method is based on the pre-trained Word2vec vectors and retrained based on its own text library of power production safety hazards. It combines the formal representation of low-dimensional vectors and the deep semantic features of the text, and finally clusters the text using a clustering algorithm. The experimental results show that the method can effectively improve the efficiency and accuracy of text classification.
Teachers' First Contact with an Immersive Web Environment: Evaluating Social Presence and Possible Effects on Learning Outcomes

This study investigates the potential of immersive web environments (IWE) in education, focusing on primary and secondary school teachers' initial perceptions of using framevr.io. A questionnaire-based survey assessed overall impression, co-presence, attention, and various aspects of understanding and interdependence. Results showed positive IWE evaluations and a correlation between proficiency and satisfaction. Presence impacted communication, attitude, and behaviour, while message understanding was essential. Findings indicate that IWEs can support meaningful learning experiences.
Interactive Design of Service Robot Based on Big Data Algorithm and Computer Vision

Virtual robots are widely used in education and training, industrial production, simulation design and remote control. Simulating real equipment in virtual environment can reduce the cost and provide users with good display effect. At present, virtual robots are rarely used in service and user interaction. Natural user interaction system requires that computers can interact with people in a natural way, and carry out multi-channel and multi-scale human-computer interaction according to the information expressed in human gestures, sounds, expressions, movements and other natural behaviors, with the aim of realizing rapid and convenient human-computer interaction. This paper designs a natural language understanding algorithm and a task planning algorithm for the service robot, in which the dependency grammar and valence theory are used as the natural language understanding algorithm, and the improved evolutionary algorithm is used as the task planning algorithm. The effectiveness of the algorithm is verified through the implementation of the home service robot simulation competition platform; And through the comparison with the algorithm effect of other competition teams, its superiority is verified.
Research on Improved Faster R-CNN in Stacked Artifact Recognition

In order to improve the sorting efficiency of workpieces, improve the sorting quality, reduce the labor intensity of workers and improve the working environment, and at the same time meet the needs of small batch, multi-variety and mixed production, this paper studies the improvement of Faster R-CNN for the field of robotic sorting. Application in stacked workpiece recognition. This paper improves the Faster R-CNN and proposes the Bin-Soft-NMS algorithm based on the Soft-NMS algorithm. The dynamic threshold is used to improve the low recall rate of the model and the low score of the adjacent stacked workpiece bounding box caused by the inaccurate threshold. the problem of inhibition. A stacked workpiece dataset was made by using four kinds of workpieces, and the model was trained on the dataset to complete the experiment. The experimental results show that the average detection accuracy of the improved model in the stacked workpiece scene is 86.26%, and the improvement in mild, moderate and severe scenarios is improved respectively. increased by 1.0%, 3.2% and 4.825%, with an average increase of 3%.
Neighbour Discovery in Cognitive Radio Networks using Blockchain: Issues &amp; Challenges

Cognitive radio networks (CRNs) is an advanced networking paradigm that solves the spectrum scarcity problem of traditional wireless networks. CRN is recognised as the natural extension of traditional wireless networks. The major components of CRNs include primary users (PU), secondary users (SU), and spectrum holes. In CRNs, the SU nodes are making connections among each other in order to form the networks topology. Therefore, the neighbour discovery is a fundamental problem of CRNs. The neighbour discovery protocol has several challenges that can impact network performance, security, and scalability. The present works conducts a study of blockchain based neighbour discovery for cognitive radio network. The neighbour discovery is deals with to allow devices to discover and communicate with nearby devices. There are various uses of neighbour discovery in cognitive radio networks such as network formation, sharing spectrum sensing information, resource allocation etc. The blockchain techniques can make the neighbour discovery protocol reliable and secure. The paper includes the issues and challenges while performing neighbour discovery. Also, we highlight key advantages of using blockchain technology.