Reviewerly/exHarmony huggingface dataset:
On the Capacity of Secure <i>K</i>-User Product Computation Over a Quantum MAC

Inspired by recent work by Christensen and Popovski on secure 2-user product computation for finite-fields of prime-order over a quantum multiple access channel, the generalization to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users and arbitrary finite fields is explored. Asymptotically optimal (capacity-achieving for large alphabet) schemes are proposed. Additionally, the capacity of modulo- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d\geq 2$ </tex-math></inline-formula> ) secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -sum computation is shown to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2/K$ </tex-math></inline-formula> computations/qudit, generalizing a result of Nishimura and Kawachi beyond binary, and improving upon it for odd <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> .
Semantic Segmentation for Pet Detection Using Convolutional Neural Network

This research project encompasses an in-depth exploration of semantic segmentation methods for pet detection using the Oxford Pets Dataset. The primary objective involves the developmentof a convolutional neural network model rooted in deep-learning principles, designed to achieve precise segmentation and detection of pets within images. The approach integrates advanced image processing techniques, leveraging deep learning methodologies, and dataset augmentation strategies to enhance pet detection accuracy substantially. The outcomes underscore the considerable potential of semantic segmentation in elevating the effectiveness of pet detection applications. This study offers promising avenues for practical integration in real-world contexts such as pet care and surveillance systems. The achieved advancements underscore the proposed technique's viability and contribute to the broader discourse on enhancing object detection through sophisticated segmentation strategies.
Considerations for Community Engagement in Design Education

The Future of Design Education working group on Community Engagement describes the active involvement of students and faculty with community groups as important co-design and participatory research opportunities that shift the designer’s role from expert to facilitator. The working group offers overarching principles for designer behavior when working in communities and recommendations for the management of community-based student projects. The goal of engagement is to share knowledge and power, building toward autonomous decision-making about the community’s future. A matrix "A Spectrum of Community Engagement in Design Education" illustrates a continuum of participation to explain the types of work in which students, faculty, and researchers might engage. A discussion of assessment both acknowledges challenges that are different from the typical individual studio projects and offers insights from other service sectors for a broader review of engagement program success. Short descriptions of cases illustrate key concepts.
Security-Aware Efficient Multi-tenant Cloud Environment

Multi-tenancy is a significant aspect of the service-oriented computing paradigm, including infrastructure, platform, and application as a service. The idea of sharing virtual cloud infrastructure with system servers supports access control and resource availability. Multi-tenancy provides various benefits to cloud users, including developers and end users. The Cloud has several multi-tenancy issues as well. Security is the topmost issue among all available multi-tenancy issues. To address the issue of security in Multi-tenancy, SLA Aware Multi-Tenant Secure Cloud Environment has been built using CloudSim 4.0. Simulation results show enhancement of security in a Multi-tenant Cloud Environment.
A Hash Based Feature Extraction Strategy for Vector Size Reduction in Text Sentiment Analysis

In the past two decades, lots of algorithms have been developed for analysing human sentiment. Publicly generated content about goods, services, laws, and politics can be found in sentiments or opinions. People are frequently curious to learn about the likes and dislikes that people have for specific characteristics of a product or service. A machine learning or deep learning based algorithm can be used to analysis human sentiment. However, due to noisy and irrelevant features, the above mentioned techniques suffer from high dimensionality problem. To overcome this dimensionality reduction technique is required. Dimensionality reduction works well as a preprocessing step before machine learning or deep learning to get rid of useless and redundant data, boost learning accuracy, make results easier to understand and get better accuracy in less time. In this paper, we proposed a hash based features extraction technique for sentiment analysis to overcome the existing challenges in this area. By contrasting the suggested feature extraction method with other feature extraction techniques like TF-IDF, we assessed its effectiveness. The performances are assessed using classification accuracy from Naive Bayes and Support Vector Machine. We carried out considerable experiments by choosing several feature sets in order to show the efficacy of the suggested feature extraction strategy.
AAL with Deep Learning to Classify the Diseases Remotely from the Image Data

In the preceding decennium, Deployment of ambient assistive living technology to promote self-dependent life is keep on intensifying [1]. The populace and the divergence of inherent features on the way to an aged population results in incorporating unfamiliar provocations to the current habitants from each of two a remunerative and communal perspective. Ambient Assistive Living technology can be able to proffer a bunch of clarifications for refining the fineness of survival of mankind, permitting personnel to stay finer and unaccompanied for long time, to assist the people possess disorders, and the subsisting mechanization proffers enormous assistance for caregivers, the proposed technology offers immense support for caretakers and medical subordinates. An extensive investigation is demonstrated to label the prime fashion towards the blossoming of Ambient Assistive Living technology and its requirement for self-dependent living [2]. The ambient technology incorporates deep learning techniques [3–8] to scrutinize the information gathered by the system and to eliminate the requirement of superior’s suggestions.
E-LEARNING BERBASIS SCHOOLOGY PADA PEMBELAJARAN MATEMATIKA SD

The purpose of this study is to develop digital class learning models using social learning network Schoology in elementary mathematics learning that is practical, valid and effective so that it can improve the quality of learning being carried out. This study focuses on designing learning tools used in digital classes in elementary schools, learning frameworks are expected to be valid and practical and effective. This research development uses the ADDIE model. Targeted outputs in research are digital class learning models using social learning network workogy and learning tools in the form of lesson plans, student activity sheets, student books, teacher books, and learning assessments. Digital class learning models using social learning netrwork schoology are targeted to be valid, practical, and effective in increasing the competency of elementary school students. In addition to learning models and tools, research output targets are JME (Journal on Mathematics Education / Scopus) international journals, accredited national journals namely International of Trends in Mathematics Education Research (IJTMER), proceedings of international seminars Journal of Physics and Astronomy, and proceedings of national seminars . The proposed level of technological readiness (TKT) of the proposed research is 3. The target of the TKT to be achieved in this study is 4. The results of the study can add references about models and learning tools for Digital Class.
Wavefronts reconstruction of transparent flats by redividing the detectable folded interference harmonics

The difficulty in multi-surface measurements comes from that each measured surface contributes harmonics to the fringe patterns, and the wavelength-shifting technique is an efficient technique that allows the harmonics to have different frequencies. When under-sampling in multi-surface measurements occurs, harmonic frequencies can not be detected by the spectrum directly. As a typical case in under-sampling, the problem of harmonic frequencies folded in the obtained spectrum as false frequencies are addressed in this paper. This contribution demonstrates an efficient algorithm enabling the detection of real and accurate harmonic frequencies, whereby the wavefronts can also be correctly reconstructed. Then the frequency detection accuracy is improved by redividing a smaller spectral interval of the selected frequency bands. Comparative studies based on the Zernike polynomials verify the effectiveness of the proposed method. Experiments for a transparent flat were performed, and the maximum PVQ, PV, and maximum RMS errors for repeatable measurements verified the effectiveness of the proposed algorithm in practical application.
Empirical evaluation of language modeling to ascertain cancer outcomes from clinical text reports

Longitudinal data on key cancer outcomes for clinical research, such as response to treatment and disease progression, are not captured in standard cancer registry reporting. Manual extraction of such outcomes from unstructured electronic health records is a slow, resource-intensive process. Natural language processing (NLP) methods can accelerate outcome annotation, but they require substantial labeled data. Transfer learning based on language modeling, particularly using the Transformer architecture, has achieved improvements in NLP performance. However, there has been no systematic evaluation of NLP model training strategies on the extraction of cancer outcomes from unstructured text.We evaluated the performance of nine NLP models at the two tasks of identifying cancer response and cancer progression within imaging reports at a single academic center among patients with non-small cell lung cancer. We trained the classification models under different conditions, including training sample size, classification architecture, and language model pre-training. The training involved a labeled dataset of 14,218 imaging reports for 1112 patients with lung cancer. A subset of models was based on a pre-trained language model, DFCI-ImagingBERT, created by further pre-training a BERT-based model using an unlabeled dataset of 662,579 reports from 27,483 patients with cancer from our center. A classifier based on our DFCI-ImagingBERT, trained on more than 200 patients, achieved the best results in most experiments; however, these results were marginally better than simpler "bag of words" or convolutional neural network models.When developing AI models to extract outcomes from imaging reports for clinical cancer research, if computational resources are plentiful but labeled training data are limited, large language models can be used for zero- or few-shot learning to achieve reasonable performance. When computational resources are more limited but labeled training data are readily available, even simple machine learning architectures can achieve good performance for such tasks.
Learning to Segment From Object Thickness Annotations

Measuring object size is fast and a standard part of many radiological evaluation procedures. We describe a deep learning segmentation method that can be trained on a small number of pixel-wise reference segmentation and then fine-tuned from the weak annotations of the object thickness. The difficulty is in the non-differentiability of the thickness function defined using the pixel-wise distance transform. We overcome it by optimizing the expected value of the loss function after the injection of a virtual random noise. Further speedup is possible using the properties of the distance transform. We demonstrate the benefit of the proposed method on ultrasound images of the carotid artery. The fine-tuning improves the performance by about 10% IoU.
Exploring Turkish Speech Recognition via Hybrid CTC/Attention Architecture and Multi-feature Fusion Network

In recent years, there has been rapid development in deep learning-based End-to-End speech recognition technology. However, the performance of Turkish speech recognition systems has been hindered by the lack of Turkish speech data. To address this, this paper focuses on several speech recognition tuning techniques. Experimental results demonstrate that the best performance is achieved when utilizing a combination of speed perturbation and noise addition for data augmentation, along with the beam search width set to 16. Furthermore, to maximize the utilization of effective feature information and enhance feature extraction accuracy, a novel feature extractor called LSPC is proposed. By combining LSPC with the LiGRU network, a shared encoder structure is formed, and model compression is achieved. The results indicate that when using only Fbank features, the performance of LSPC surpasses that of MSPC and VGGnet, resulting in a respective improvement of 1.01% and 2.53% in the word error rate (WER). Finally, building upon the aforementioned advancements, a new multifeature fusion network is proposed as the primary encoder structure. The results demonstrate that the WER of the proposed feature fusion network, based on LSPC, further improves by 0.82% and 1.94% when compared to single-feature extraction using LSPC, specifically utilizing Fbank features and spectrogram features. As a result, our model achieves comparable performance to advanced End-to-End models.
Self-Supervised Learning to Improve Topology-Optimized Axon Segmentation and Centerline Detection

Large-scale brain mapping requires preserving the topology of axonal structures to understand how neurons connect throughout the brain and intersect different brain regions. The ability to leverage unannotated data for algorithm development would mitigate laborious annotations by subject matter experts. In this work, we applied self-supervised training to a Residual 3D U-Net with an auxiliary classifier to predict the correct order of sliced and shuffled voxels samples of mouse brain data. Pretrained encoder weights from the auxiliary classifier were subsequently used to train a Residual 3D U-Net for axon segmentation and centerline detection with a topology-preserving loss function, soft centerline-Dice. We report improved feature space representation of axonal structures as well as improved evaluation metrics over prior methods. We observed that both the α and k hyperparameters of the topologically-aware loss function show sensitivity to the target task of axon segmentation and centerline detection.
Research and implementation of garbage classification recognition algorithm

The prosperous development of the national economy, while continuously improving people’s living standards, has also given rise to a series of environmental problems, such as the effective disposal of garbage. Traditional garbage disposal is mainly done manually, which is time-consuming and inefficient. With the advent of the era of artificial intelligence, the classification of garbage based on deep learning and image recognition technology has become practical and feasible, which can greatly improve the efficiency of garbage disposal. In this paper, we take the garbage image data set as the sample, build a training model to classify the garbage data, improve the classification accuracy through model optimization, realize the intelligent requirements of daily life garbage classification, reduce environmental pollution, and thus protect the environment
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-Based Multiple Instance Learning

In many histopathology tasks, sample classification depends on morphological details in tissue or single cells that are only visible at the highest magnification. For a pathologist, this implies tedious zooming in and out, while for a computational decision support algorithm, it leads to the analysis of a huge number of small image patches per whole slide image (WSI). Attention-based multiple instance learning (MIL), where attention estimation is learned in a weakly supervised manner, has been successfully applied in computational histopathology, but it is challenged by large numbers of irrelevant patches, reducing its accuracy. Here, we present an active learning approach to the problem. Querying the expert to annotate regions of interest in a WSI guides the formation of high-attention regions for MIL. We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. We test our approach on the CAMELYON17 dataset classifying metastatic lymph node sections in breast cancer. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. Active learning thus improves WSIs classification accuracy, leads to faster and more robust convergence, and speeds up the annotation process. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology.
Decentralized Federated Learning With Asynchronous Parameter Sharing

Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Whatever parameter sharing is applied, the learning model shall adapt to distinct network architectures because an improper learning model will deteriorate learning performance and, even worse, lead to model divergence, especially for the asynchronous transmission in resource-limited distributed networks. To address this issue, this paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed Internet of Things (IoT) networks. It can improve learning efficiency and realize efficient communication. By jointly accounting for the convergence bound of federated learning and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to improve the learning performance. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low transmission delay.
Discrete Text Summarization (DeTS): A Novel Method Grounded in Natural Language Inference and Grammatical Sequence Scoring

Extracting information and insights from textual data is a challenge and there are many gaps in the natural language processing field. In this paper, we proposed a new unsupervised method for discrete text summarization (DeTS) that summarizes a whole text corpus using a novel combination of natural language inference and grammatical sequence scoring. Instead of creating one summary from the text corpus, we capture the main themes of the text by extracting a series of concise and independent key points. DeTS can be used for theme extraction and text summarization purposes across any type of textual data, and it is a bottom-up approach free from the data bias from which most unsupervised and supervised methods suffer. It is a fully automated method without a need for any human annotations, but with optional guardrails and configurations, including a new concept that we introduce called “startwords” which accelerates the computation speed by 50%. Notably, DeTS outperforms existing methods due to its detailed nature, paired with high levels of accuracy and coverage. Furthermore, it is an ensemble of open-source libraries that are available to the public. We analyzed employees’ comments from a large multinational organization to evaluate the performance of the new method. The results have shown that the new method can be used to extract meaningful and valuable insights at scale from large textual data.
UCITransNet: A Deep Neural Model with FC-CRFs and Object-Level Data Augmentation for Cell Instance Segmentation

U-Net and its variants have been the mainstream model paradigm for a wide spectrum of semantic segmentation tasks. A recent proposal was to replace the skip connections of U-Net with a multi-scale Channel Cross fusion with Transformer (CCT) module, forming a so-called UCTransNet architecture with SOTA performance on several tasks. The characteristics of the multi-head attention module adopted in CCT lead to the fusion of multi-channel information, but it also loses some valuable feature information in the fusion calculation process, putting much pressure on the backbone network. We propose Channel-wise Isomerism fusion Transformer (CIT) module to replace the CCT module, yielding the UCITransNet model. We adopt an FC-CRF module as a single channel enhancement module to work in coordination with the CCT. The motivation has been retaining the function of channel integration as much as possible while taking into account the significance of conveying multi-scale features. In addition, we propose an object-level data augmentation method, named cell instance augmentation (CIA), to enhance the quantity and diversity of the training data, coupled with the enhanced model architecture. The proposed method was validated on two datasets, including the Gland Segmentation in Colon Histology Images Challenge (GlaS) dataset and the Multi-Organ Nucleus Segmentation Challenge (MoNuSeg) dataset using the Dice and IoU performance metrics. On both datasets, our method presents superior performance compared to the SOTA and other benchmark methods.
Improving quantum state transfer: correcting non-Markovian and distortion effects

Abstract Quantum state transfer is a key operation for quantum information processing. The original pitch-and-catch protocols rely on flying qubits or single photons with engineered wavepacket shapes to achieve a deterministic, fast and high-fidelity transfer. Yet, these protocols overlook two important factors, namely, the distortion of the wavepacket during the propagation and non-Markovian effects during the emission and reabsorption processes due to time-dependent controls. Here we address both difficulties in a general quantum-optical model and propose a correction strategy to improve quantum state transfer protocols. Including non-Markovian effects in our theoretical description, we show how to derive control pulses that imprint phases on the wavepacket that compensate the distortion caused by propagation. Our theoretical results are supported by detailed numerical simulations showing that a suitable correction strategy can improve state transfer fidelities up to three orders of magnitude.