Reviewerly/exHarmony huggingface dataset:
CNN Implementation and Analysis on Xilinx Versal ACAP at European XFEL

Developers have proposed various hardware accelerators to improve the CNN inference performance on embedded platforms. Recently, Xilinx announced its first 7-nm FPGA accelerator, the Versal ACAP, delivering a high-performance, heterogeneous computing platform adaptable to the application requirements. However, as early studies were concerned with the most common deep learning architectures for CNN, e.g. VGG, Resnet, Inception, etc., under full support of the Xilinx Vitis-AI, the implementation and analysis of the Versal ACAP performance with customized CNN architectures is yet to be explored. In this study, we implement one of the CNN architectures considered at the European XFEL and compare its performance to a state-of-the-art GPU and other FPGA generation. In addition, this study evaluates the validity of using the quantization methods for critical regression applications and presents a complete analysis of the results built upon the device time traces, providing recommendations for configuring the runtime parameters. The experimental results confirm a superior performance of the Versal ACAP in terms of latency and throughput. When the neural network layers were all supported by the ACAP processing unit, it achieved 17x and 18x better throughput and latency compared to GPU. In addition, when quantized using the fine-tuning method, the CNN model shows an improved accuracy compared to the floating-point model, with a reduction of 6% in loss.
AI-enabled IoT penetration testing: state-of-the-art and research challenges

Internet of Things (IoT) is gaining importance as its applications are found in many critical infrastructure sectors (e.g., Industry 4.0, healthcare, transportation, and commercial facilities). This reinforces the importance of investigating the security risks associated with IoT deployment. Hence, in this paper, we perform a comprehensive review of the literature on penetration testing of IoT devices and systems. Specifically, a total of 99 articles published between 2015 and 2021 was reviewed to identify existing and potential IoT penetration testing applications and proposed approaches. We finally provide recent advances of AI-enabled penetration testing methods that can notably be performed at the network edge.
Intelligent Digital Signal Modulation Recognition using Machine Learning

In recent years, modulation type recognition has received a lot of attention across the board.There are several methods to detect modulation types, but there are only a few effective methods to handle signals with a higher level of noise.This study introduces an approach to verify the ability of different machine learning algorithms to automatically manage noise in detecting digital modulations.This research examines two of the most common digital modulations, Phase Shift Keying and Quadrature Phase Shift Keying.A signal noise rate ranging from-10 to +25 dB is used to identify these modulations and use PCA to reduce the number of features and the data complexity.We used machine learning algorithms like Decision Tree, Random Forest, Support Vectors Machine and k-nearest neighbors to identify the modulation type.Our proposed method considers signals' features after proper shifting of the IQ and RF signals samples.The findings show that the proposed method successfully recognizes the signals with higher noise levels.The random forest algorithm presents better results with low noise levels and SVM with higher noise levels.
Romou

Mobile GPU, as a ubiquitous and powerful accelerator, plays an important role in accelerating on-device DNN (Deep Neural Network) inference. The frequent-upgrade and diversity of mobile GPUs require automatic kernel generation to empower fast DNN deployment. However, current generated kernels have poor performance.
End-to-end metastasis detection of breast cancer from histopathology whole slide images

Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection.
A Multi-granularity Network for Emotion-Cause Pair Extraction via Matrix Capsule

The task of Emotion-Cause Pair Extraction (ECPE) aims at extracting the clause pairs with the corresponding causality from the text.Existing approaches emphasize their multi-task settings. We argue that the clause-level encoders are ill-suited to the ECPE task where text information has many granularity features. In this paper, we design a Matrix Capsule-based multi-granularity framework (MaCa) for this task. Specifically, we first introduce a word-level encoder to obtain the token-aware representations. Then, two sentence-level extractors are used to generate emotion prediction and cause prediction. Finally, to obtain more fine-grained features of clause pairs, the matrix capsule is introduced, which can cluster the relationship of each clause pair. The empirical results on the widely used ECPE dataset show that our framework significantly outperforms most current methodsin the Emotion-Cause Extraction (ECE) and the challenging ECPE task.
SoK: Benchmarking the Performance of a Quantum Computer

The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubits in a quantum computer and consider it as a standard to evaluate the performance of the quantum computer intuitively. However, it is quite misleading in most times, especially for investors or governments. This is because the quantum computer works in a quite different way than classical computers. Thus, quantum benchmarking is of great importance. Currently, many quantum benchmarks are proposed from different aspects. In this paper, we review the existing performance benchmarking protocols, models, and metrics. We classify the benchmarking techniques into three categories: physical benchmarking, aggregative benchmarking, and application-level benchmarking. We also discuss the future trend for quantum computer's benchmarking and propose setting up the QTOP100.
It’s Elemental: Technology Enhanced Learning (TEL) as scalable and sustainable student-centered practice in context

&#x0D; &#x0D; &#x0D; Many educational institutions are facing the challenge of how to implement Technology Enhanced Learning (TEL) in a meaningful, scalable and sustainable manner. This implementation is not a transition from change project to business as usual, but a fluid and continual narrative embraced by all that must be personalized for the diverse cohort it impacts. RMITs ongoing and evolving solution involves a combination of approaches that touch on strategic initiatives that operationalize standards (Elements) for consistency of student- centered teaching, capability building and training, resource development, technology implementations and organizational structures. This solution allows meaningful collaboration between professional and academic staff that values the interplay between learning design and discipline specific teaching practice. To operationalize this TEL solution with a focus on student-centred teaching, agile approaches have been used at RMIT. These approaches assist in placing value on people and the holistic relationship and interdependencies between the context of teaching and learning activities, and the methodologies used to enable change in practice.&#x0D; &#x0D; &#x0D;
The Variety of Mathematics Learning Media for Early Childhood in Improving Basic Mathematics Ability

Introduction to mathematics in early childhood is a very important cognitive aspect to be given. One of the things that can be done is the development of innovative mathematics learning media for early childhood. This study aims to conduct a literature review related to the various uses of mathematics learning media in early childhood. Data collection was carried out by reviewing all related articles published in the 2019-2022 period. After conducting a thorough review, there were 30 reputable articles from accredited journals used in this study. The results showed that there were 27 effective learning media that could be used by teachers at the time of learning to improve early childhood math skills. The researchers found that the teachers changed the normal learning strategy to a limited face-to-face learning strategy. For example in schools, teachers as educators had their own short-time learning strategies, so the learning process can be conducted effectively.&#x0D; Keywords: Online Learning, Application E-Learning, Web-Based E-Learning
Designing Visualisations for Bayesian Problems According to Multimedia Principles

Questions involving Bayesian Reasoning often arise in events of everyday life, such as assessing the results of a breathalyser test or a medical diagnostic test. Bayesian Reasoning is perceived to be difficult, but visualisations are known to support it. However, prior research on visualisations for Bayesian Reasoning has only rarely addressed the issue on how to design such visualisations in the most effective way according to research on multimedia learning. In this article, we present a concise overview on subject-didactical considerations, together with the most fundamental research of both Bayesian Reasoning and multimedia learning. Building on these aspects, we provide a step-by-step development of the design of visualisations which support Bayesian problems, particularly for so-called double-trees and unit squares.
Controllable single-photon routing between two waveguides by two giant two-level atoms

We investigate the single-photon quantum routing composed of two infinite waveguides coupled to two giant two-level atoms. The exact expressions of the single-photon transmission and reflection amplitudes are derived with the real-space approach. It is found that the single photon scattering behavior is strongly dependent on the phase difference between the two adjacent atom-waveguide coupling points, the frequency detuning, the coupling strength between the two giant atoms, and the interaction strengths between the giant atoms and the waveguides. Our studies show that an ideal single photon router with unit efficiency can be realised by designing the size of the giant atom, and the frequency detuning or adjusting the interaction strengths between the atoms and the waveguides. The results suggest the potential to effectively control the single-photon quantum routing based on the giant-atom setup.
Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction

We propose a pedestrian trajectory prediction algorithm based on the bimodal extended Kalman filter. With this filter, we are able to make full use of the dual-state nature of the pedestrian movement, i.e., the pedestrian is either moving or remains stationary. We apply the dual-mode probability model to describe the state of the pedestrian. Based on this model, we construct the proposed bimodal extended Kalman filter to estimate pedestrian state distribution. The filter obtains the state distribution for each pedestrian in the scene, respectively, and use that state distribution to predict the future trajectories of all the people in the scene. This prediction method estimates the prior probability of each parameter of the model through the dataset and updates the individual posterior probability of the pedestrian state through the bimodal extended Kalman filter. Our model can predict the trajectory of every individual, by taking the social interaction of pedestrians as well as the surrounding physical obstacles into account, with less than fifty model parameters being used, while with the limited parameter, our model could be nearly accurate as other deep learning models and still be comprehensible for model users.
Cerberus

OAuth protocols have been widely adopted to simplify user authentication and service authorization for third-party applications. However, little effort has been devoted to automatically checking the security of the libraries that service providers widely use. In this paper, we formalize the OAuth specifications and security best practices, and design Cerberus, an automated static analyzer, to find logical flaws and identify vulnerabilities in the implementation of OAuth service provider libraries. To efficiently detect security violations in a large codebase of service provider implementation, Cerberus employs a query-driven algorithm for answering queries about OAuth specifications. We demonstrate the effectiveness of Cerberus by evaluating it on datasets of popular OAuth libraries with millions of downloads. Among these high-profile libraries, Cerberus has identified 47 vulnerabilities from ten classes of logical flaws, 24 of which were previously unknown. We got acknowledged by the developers of eight libraries and had three accepted CVEs.
We are not ready yet: limitations of state-of-the-art disease named entity recognizers

Abstract Background Intense research has been done in the area of biomedical natural language processing. Since the breakthrough of transfer learning-based methods, BERT models are used in a variety of biomedical and clinical applications. For the available data sets, these models show excellent results - partly exceeding the inter-annotator agreements. However, biomedical named entity recognition applied on COVID-19 preprints shows a performance drop compared to the results on test data. The question arises how well trained models are able to predict on completely new data, i.e. to generalize. Results Based on the example of disease named entity recognition, we investigate the robustness of different machine learning-based methods - thereof transfer learning - and show that current state-of-the-art methods work well for a given training and the corresponding test set but experience a significant lack of generalization when applying to new data. Conclusions We argue that there is a need for larger annotated data sets for training and testing. Therefore, we foresee the curation of further data sets and, moreover, the investigation of continual learning processes for machine learning-based models.
Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features

Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraep- ithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morpho- metric features. These algorithms have been tested on a public dataset. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The method we propose in the study achieved an accuracy performance of 78 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</i> 69% in a data set where morphology-based methods were 69 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</i> 07% and Convo- lutional Neural Network (CNN) patch-based algorithms were 75 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</i> 77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.
Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time.Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences.On the other hand, the most important part in traditional translation tasks is the translation of key information.In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results' quality can still be guaranteed.In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer.The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder.After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder.With incorporating keyword information from the source language sentence, the model's performance in the task of translating long sentences is very reliable.In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set.The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset.The experimental results show the advantages of the model proposed in this paper.
The uniform measure for quantum walk on hypercube: A quantum Bernoulli noises approach

In this paper, we present a quantum Bernoulli noises approach to quantum walks on hypercubes. We first obtain an alternative description of a general hypercube and then, based on the alternative description, we find that the operators $\partial_k^* + \partial_k$ behave actually as the shift operators, where $\partial_k$ and $\partial_k^*$ are the annihilation and creation operators acting on Bernoulli functionals, respectively. With the above operators as the shift operators on the position space, we introduce a discrete-time quantum walk model on a general hypercube and obtain an explicit formula for calculating its probability distribution at any time. We also establish two limit theorems showing that the averaged probability distribution of the walk even converges to the uniform probability distribution. Finally, we show that the walk produces the uniform measure as its stationary measure on the hypercube provided its initial state satisfies some mild conditions. Some other results are also proven.
No One Acts like You: AI based Behavioral Biometric Identification

There has already been a significant increase in Mobile Broadband (MBB) subscribers over the past few years, which is expected to increase further as a result of the current trend towards the Sixth Generation (6G) wireless systems. This is accompanied by a growing need for confidentiality, integrity and undoubtedly authentication of the communication units involved (human and machine). In particular, authentication is challenging with respect to human participants, as often only possession of a token or knowledge of a code/password is requested, which validates the underlying claim: who is the person. Furthermore, as knowledge-based passwords and object-based keys can be lost or stolen, biometric-based approaches rely on traits, which are intrinsically connected to one person. Therefore this work demonstrates the feasibility of Long Short-Term Memories (LSTMs) - a type of Neural Network (NN)-, to identify and authenticate people by their characteristic arm movements during specific everyday tasks. For measurements of the arm movement a wrist-band wearable has been developed. The focus of the evaluation with Deep Neural Networks (DNN) is on different high level structures of NNs and data. Multiple NNs are allocated to either different test subjects and user-activities. In a first task 1 out of n people had to be identified with a maximum average precision of 92.0 %. In a second evaluation the Networks performed in a n out of m authentication with an maximum average precision of 81.4 %. With those results the potential of NNs for biometric authentication with behavioral sensors is demonstrated. Additionally in view of the necessary personal biometric data, ethical and legal aspects are highlighted.