Reviewerly/exHarmony huggingface dataset:
XVDPU: A High Performance CNN Accelerator on the Versal Platform Powered by the AI Engine

The convolution neural networks (CNNs) are widely used in computer vision applications nowadays. However, the trends of higher accuracy and higher resolution generate larger networks, indicating that computation and I/O bandwidth are key bottlenecks to reach performance. The Xilinx's latest 7nm Versal ACAP platform with AI-Engine (AIE) cores can deliver up-to 8x silicon compute density at 50% the power consumption compared with the traditional FPGA solutions. In this paper, we propose XVDPU: the AIE-based int8-precision CNN accelerator on Versal chips, scaling from 16-AIE-core (C16B1) to 320-AIE-core (C64B5, Peak:109.2 TOPs) to meet computation requirements. To resolve IO bottleneck, we adopt several techniques such as multi-batch (MB), shared-weights (SHRWGT), feature-map-stationary (FMS) and long-load-weights (LLW) to improve data-reuse and reduce I/O requirements. An Arithmetic Logic Unit (ALU) design is further proposed into the accelerator which mainly performs non-convolution layers such as Depthwise-Conv layer, Pooling layer and Non-linear function layers using the same logic resources, which can better balance resource utilization, new feature support and efficiency of the whole system. We have successfully deployed more than 100 CNN models with our accelerator. Our experimental results show that the 96-AIE-core (C32B3, Peak: 32.76 TOPs) implementation can achieve 1653 FPS for ResNet50 on VCK190, which is 9.8x faster than the design on ZCU102 running at 168.5 FPS with peak 3.6 TOPs. The 256-AIE-core (C32B8, Peak: 87.36 TOPs) implementation can further achieve 4050 FPS which better leverages the computing power of Versal AIE devices. The powerful XVDPU will help enable many applications on the embedded system, such as low-latency data center, high level ADAS and complex robotics.
Sign language recognition from digital videos using feature pyramid network with detection transformer

Abstract Sign language recognition is one of the fundamental ways to assist deaf people to communicate with others. An accurate vision-based sign language recognition system using deep learning is a fundamental goal for many researchers. Deep convolutional neural networks have been extensively considered in the last few years, and a slew of architectures have been proposed. Recently, Vision Transformer and other Transformers have shown apparent advantages in object recognition compared to traditional computer vision models such as Faster R-CNN, YOLO, SSD, and other deep learning models. In this paper, we propose a Vision Transformer-based sign language recognition method called DETR (Detection Transformer), aiming to improve the current state-of-the-art sign language recognition accuracy. The DETR method proposed in this paper is able to recognize sign language from digital videos with a high accuracy using a new deep learning model ResNet152 + FPN (i.e., Feature Pyramid Network), which is based on Detection Transformer. Our experiments show that the method has excellent potential for improving sign language recognition accuracy. For instance, our newly proposed net ResNet152 + FPN is able to enhance the detection accuracy up to 1.70% on the test dataset of sign language compared to the standard Detection Transformer models. Besides, an overall accuracy 96.45% was attained by using the proposed method.
Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation

Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model using unlabeled target data and the knowledge of a well-trained source domain model. Most previous SFUDA works focus on inferring semantics of target data based on the source knowledge. Without measuring the transferability of the source knowledge, these methods <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">insufficiently</i> exploit the source knowledge, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fail to</i> identify the reliability of the inferred target semantics. However, existing transferability measurements require either source data or target labels, which are infeasible in SFUDA. To this end, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">firstly</i> , we propose a novel Uncertainty-induced Transferability Representation (UTR), which leverages uncertainty as the tool to analyse the channel-wise transferability of the source encoder in the absence of the source data and target labels. The domain-level UTR unravels how transferable the encoder channels are to the target domain and the instance-level UTR characterizes the reliability of the inferred target semantics. Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the source knowledge calibration module</i> that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the target semantics calibration module</i> that calibrates the unreliable semantics. With the help of the calibrated source knowledge and the target semantics, the model adapts to the target domain safely and ultimately better. We verified the effectiveness of our method using experimental results and demonstrated that the proposed method achieves state-of-the-art performances on the three SFUDA benchmarks. Code is available at https://github.com/SPIresearch/UTR.
Barriers to blockchain technology adoption in supply chains: the case of India

Abstract In the era of digitalization, Blockchain is an evolving technology that has the potential to change the shape of numerous industries. Blockchain is considered the transforming technology that has the ability to change the conventional supply chain network by providing additional transparency of transactions in terms of information and physical goods. Additionally, the implementation of blockchain technology in the supply chain is required to accomplish the objectives of industry 4.0. However, there has to date been a scarcity of blockchain implementations due to the numerous barriers associated with it. Therefore, the primary aim of this research is to identify and investigate the major barriers to implementing blockchain technology in supply chains. We identified ten significant barriers to adopting blockchain technology through a literature review and expert opinions. Additionally, the finalized barriers were categorized into an influential and influenced group using the DEMATEL method. The findings of this study show that 'influential group' barriers require more attention from the supply chain partners to mitigate these barriers. The primary influential barriers are 'Lack of information sharing,' 'Trust management issues,' and 'Lack of upgraded technologies’, and these barriers require immediate attention from supply chain stakeholders wishing to use blockchain. These findings contribute to improving managerial decisions and digital strategies regarding blockchain within organisations, and how implementation can effectively be achieved.
Colon Cancer Tissue Classification Using ML

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today’s world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world’s most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC–VAL–HE–7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.
Observations of the delayed-choice quantum eraser using coherent photons

Quantum superposition is the cornerstone of quantum mechanics, where interference fringes originate in the self-interference of a single photon via indistinguishable photon characteristics. Wheeler's delayed-choice experiments have been extensively studied for the wave-particle duality over the last several decades to understand the complementarity theory of quantum mechanics. The heart of the delayed-choice quantum eraser is in the mutually exclusive quantum feature violating the cause-effect relation. Here, we experimentally demonstrate the quantum eraser using coherent photon pairs by the delayed choice of a polarizer placed out of the interferometer. Coherence solutions of the observed quantum eraser are derived from a typical Mach-Zehnder interferometer, where the violation of the cause-effect relation is due to selective measurements of basis choice.
Blueprint for a Molecular-Spin Quantum Processor

The physical implementation of any quantum computer still faces important issues related to scalability and error correction. This study sets up the blueprint for a quantum processor based on molecular spins coupled to superconducting resonators, which control different qudits, read out their final states, and establish coherent communication channels between them. The feasibility of this route is demonstrated by an accurate design of the resonator to reach the strong-coupling regime between a single photon and a single molecule. Very good results for proof-of-principle quantum algorithms and quantum simulations are obtained by numerical simulations of a realistic experimental scenario.
The impact of augmented reality on student attitudes, motivation, and learning achievements—a meta-analysis (2016–2023)

Abstract In light of the COVID-19 pandemic, a significant number of students have been compelled to remain at home while receiving education supported by augmented reality (AR) technologies. To determine the impact of AR technologies on educational outcomes, the present study undertook a meta-analysis utilizing Stata/MP 14.0. The study found that the attitudes of learners towards AR-assisted education were more positive, and their learning achievements were significantly higher compared to those who did not use AR technologies. However, there was no significant difference in motivation levels between the AR-assisted and non-AR-assisted educational models. The researchers explored several reasons for this result, but they could not identify any clear explanation. Future studies could take into account other factors that might affect education outcomes such as learning styles and learner personality. Doing so could shed more light on the impact of AR technologies on education.
ProgPrompt: program generation for situated robot task planning using large language models

Abstract Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to score potential next actions during task planning, and even generate action sequences directly, given an instruction in natural language with no additional domain information. However, such methods either require enumerating all possible next steps for scoring, or generate free-form text that may contain actions not possible on a given robot in its current context. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments, robot capabilities, and tasks. Our key insight is to prompt the LLM with program-like specifications of the available actions and objects in an environment, as well as with example that can be executed. We make concrete recommendations about prompt structure and generation constraints through ablation experiments, demonstrate state of the art success rates in VirtualHome household tasks, and deploy our method on a physical robot arm for tabletop tasks. Website and code at progprompt.github.io
Integrating Story-Centric and Character-Centric Processes for Authoring Interactive Drama

Computer aided interactive drama has been widely applied for entertainment and pedagogy. Most existing approaches for authoring interactive drama use either story-centric or character-centric processes. In this work, we present a new framework that integrates both types of processes to support authoring. This framework uses a multi-agent system to control virtual characters in a story. The characters&amp;rsquo; motivations are encoded as the agents&amp;rsquo; goals, and are configured based on well-structured story paths generated using a partial order planner. This framework allows the use of a planner that has an equivalent model of the story as that in the multi-agent system or models the story at a more abstract level, e.g. only the plot points. We explore the use of this new framework for authoring interactive dramas. Preliminary examples of application are presented.
Deep neural networks for record counting in historical handwritten documents

In this paper, we investigate the use of Convolutional Neural Networks for counting the number of records in historical handwritten documents. With this work we demonstrate that training the networks only with synthetic images allows us to perform a near perfect evaluation of the number of records printed on historical documents. The experiments have been performed on a benchmark dataset composed by marriage records and outperform previous results on this dataset.
The Musical Metacreation Weekend: Challenges Arising from the Live Presentation of Musically Metacreative Systems

This paper discusses the approach to curation of the MuMe Weekend (held in Sydney, Australia, 2013) and the experience the authors gained from hosting the event. We identify open challenges arising from this applied demonstration of musically metacreative systems. Identified challenges are not technical, but rather methodological, concerning pragmatic aspects of presenting and collaboratively innovating musically metacreative work. The paper presents our approach to curating the event, the categories of performances offered — and responses to these — issues to do with the selection and presentation of the work, and issues to do with the evaluation by audience and performers.
Introduction to Electromagnetic Information Security

With the rising importance of information security, the necessity of implementing better security measures in the physical layer as well as the upper layers is becoming increasing apparent. Given the development of more accurate and less expensive measurement devices, high-performance computers, and larger storage devices, the threat of advanced attacks at the physical level has expanded from the military and governmental spheres to commercial products. In this paper, we review the issue of information security degradation through electromagnetic (EM)-based compromising of security measures in the physical layer (i.e., EM information security). Owing to the invisibility of EM radiation, such attacks can be serious threats. We first introduce the mechanism of information leakage through EM radiation and interference and then present possible countermeasures. Finally, we explain the latest research and standardization trends related to EM information security.
Rejoinder: Probabilistic Integration: A Role in Statistical Computation?

This article is the rejoinder for the paper "Probabilistic Integration: A Role in Statistical Computation?" (Statist.Sci.34 (2019) 1-22).We would first like to thank the reviewers and many of our colleagues who helped shape this paper, the Editor for selecting our paper for discussion, and of course all of the discussants for their thoughtful, insightful and constructive comments.In this rejoinder, we respond to some of the points raised by the discussants and comment further on the fundamental questions underlying the paper: (i) Should Bayesian ideas be used in numerical analysis?and (ii) If so, what role should such approaches have in statistical computation?
Techno-Optimism &amp; Access to the Legal System

For legal technologists, apps raise the prospect of putting the law in the hands of disadvantaged people who feel powerless to deal with their legal problems. These aspirations are heartening, but they rest on unrealistic assumptions about how people living in poverty deal with legal problems. People who are poor very rarely resort to the law to solve their problems. In the situations when they do seek solutions, they confront educational and material impediments to finding, understanding, and using online legal tools effectively. Literacy is a significant barrier. More than 15 percent of all adults living in the United States are functionally illiterate, meaning that, at best, they read at the fourth-grade level. Inadequate access to the Internet and limited research skills compound the challenges. To reach people from marginalized groups, access-to-justice technologies need to be integrated with human assistance.
An enhanced hybridized artificial bee colony algorithm for optimization problems

Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.
Quantum Multiple Q-Learning

In this paper, a collection of value-based quantum reinforcement learning algorithms are introduced which use Grover's algorithm to update the policy, which is stored as a superposition of qubits associated with each possible action, and their parameters are explored.These algorithms may be grouped in two classes, one class which uses value functions ( ( ) V s ) and new class which uses action value functions ( ( ) , Q s a ).The new ( ) , Q s a -based quantum al- gorithms are found to converge faster than ( ) V s -based algorithms, and in general the quantum algorithms are found to converge in fewer iterations than their classical counterparts, netting larger returns during training.This is due to fact that the ( ) , Q s a algorithms are more precise than those based on ( )V s , meaning that updates are incorporated into the value function more efficiently.This effect is also enhanced by the observation that the ( ) , Q s a -based algorithms may be trained with higher learning rates.These algorithms are then extended by adding multiple value functions, which are observed to allow larger learning rates and have improved convergence properties in environments with stochastic rewards, the latter of which is further improved by the probabilistic nature of the quantum algorithms.Finally, the quantum algorithms were found to use less CPU time than their classical counterparts overall, meaning that their benefits may be realized even without a full quantum computer.
Pair of primitive elements with prescribed traces over finite fields

In this article, we establish a sufficient condition for the existence of a primitive element α∈Fqn such that for any matrix (abc0de)∈M2×3(Fqn) of rank 2, the element (aα2+bα+c)/(dα+e) is a primitive element of Fqn, and TrFqn|Fq(α)=μ, TrFqn|Fq(α−1)=ν for any prescribed μ,ν∈Fq, where q=2k for some positive integer k. We establish existence of such elements for all pairs (q,n) (n≥7) except for finitely many pairs.
Password Guessing Based on Semantic Analysis and Neural Networks

Passwords remain the dominant method in data encryption and identity authentication, but they are vulnerable to guessing attack. Most users incline to choose meaningful words to make up passwords. Lots of these words are human-memorable. In this paper, we propose a hierarchical semantic model that combines LSTM with semantic analysis to implement password guessing. With our model, the potential probability relationship between words can be mined. After training the model with 4.5 million passwords from leaked Chinese passwords, we generate lots of passwords guesses ordered by probability. 0.5 million passwords are reserved for model testing. In addition, we also pick up CSDN passwords, the Rockyou passwords, and Facebook passwords as model-testing sets. Each dataset contains 0.5 million passwords. LSTM-based model, PCFG, and Markov-based model are selected for comparison. Experiments show that our model has a higher coverage rate than the other models of the reserved dataset and CSDN dataset. Besides, our model can hit more passwords for the Rockyou dataset and Facebook dataset than PCFG.
Semantically Aware Text Categorisation for Metadata Annotation

In this paper we illustrate a system aimed at solving a long-standing and challenging problem: acquiring a classifier to automatically annotate bibliographic records by starting from a huge set of unbalanced and unlabelled data. We illustrate the main features of the dataset, the learning algorithm adopted, and how it was used to discriminate philosophical documents from documents of other disciplines. One strength of our approach lies in the novel combination of a standard learning approach with a semantic one: the results of the acquired classifier are improved by accessing a semantic network containing conceptual information. We illustrate the experimentation by describing the construction rationale of training and test set, we report and discuss the obtained results and conclude by drawing future work.