Reviewerly/exHarmony huggingface dataset:
Sales, Purchase, and Inventory Information System Design at SMEs

The rapid development of technology and fierce business competition, organization need technology that is able to support operational activities quickly and precisely so that business processes can run smoothly. SMEs is an organization that buys, sells and supplies spice products. At this time, SMEs is still doing paper-based records which hinder the running of business processes. In this study, analysis and design of sales, purchasing and inventory information systems will be carried out which aims to support operational activities. The methodology used in this analysis and design is the SDLC methodology. The information system will be designed on a desktop basis using Visual Studio and SQL Server. The design will be modeled with UML which consists of Use Case Diagrams, Activity Diagrams, Sequence Diagrams, Class Diagrams and DFD. The system will be tested with Black Box Testing and evaluated whether the system has met its objectives. The result of this research is an information system that can manage the entire inventory management process, which comes from inputted purchase and sales data. The system also generates reports that can meet the needs of SMEs.
CyberSignature: A user authentication tool based on behavioural biometrics

<h2>Abstract</h2> Behavioural biometrics, such as the way people type on computer keyboard and/or move the cursor are almost impossible to steal. This paper presents CyberSignature<sup>1</sup>1LinkedIn and Twitter: @cybersignature., a tool that uses behavioural biometrics to create unique digital identities that can be used during online card transactions to distinguish legitimate users from fraudsters. The tool is implemented in Python, with a machine learning algorithm at its core. It receives user input data entries from a graphical user interface, similar to an online payment form, and transforms them into unique digital identities. The tool is freely available on Github and is entitled ‘CyberSignature'.
Retracted: White blood cell differentials performance of a new automated digital cell morphology analyzer: Mindray <scp>MC</scp>‐80

White blood cell differentials performance of a new automated digital cell morphology analyzer: Mindray MC-80, K. Paisooksantivatana; N. Khongjaroensakun; P. Chinudomwong; N. Chaothai; L. Chamchomdao; K. Suriyachand, International Journal of Laboratory Hematology, 10.1111/ijlh.13995 The above article, published online on 18 November 2022, in Wiley Online Library (wileyonlinelibrary.com), had been retracted by agreement between the authors, the journal's Editors-in-Chief, Giuseppe D'Onofrio and Ian Mackie, and John Wiley & Sons. The authors contacted the journal after publication to propose extensive changes to the data presented in the accepted article such that it would no longer reflect the version that was peer reviewed. As a result, this retraction has been undertaken.
Efficient Transmission and Reconstruction of Dependent Data Streams via Edge Sampling

Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for real-time analytics. Geo-distributed streaming systems, where cloud-based queries utilize data streams from multiple distributed devices, face challenges since wide-area network (WAN) bandwidth is often scarce or expensive. Edge computing allows us to address these bandwidth costs by utilizing resources close to the devices, e.g. to perform sampling over the incoming data streams, which trades downstream query accuracy to reduce the overall transmission cost. In this paper, we leverage the fact that correlations between data streams may exist across devices located in the same geographical region. Using this insight, we develop a hybrid edge-cloud system which systematically trades off between sampling at the edge and estimation of missing values in the cloud to reduce traffic over the WAN. We present an optimization framework which computes sample sizes at the edge and systematically bounds the number of samples we can estimate in the cloud given the strength of the correlation between streams. Our evaluation with three real-world datasets shows that compared to existing sampling techniques, our system could provide comparable error rates over multiple aggregate queries while reducing WAN traffic by 27-42%.
Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics

Federated learning is becoming increasingly more popular as the concern of privacy breaches rises across disciplines including the biological and biomedical fields. The main idea is to train models locally on each server using data that are only available to that server and aggregate the model (not data) information at the global level. While federated learning has made significant advancements for machine learning methods such as deep neural networks, to the best of our knowledge, its development in sparse Bayesian models is still lacking. Sparse Bayesian models are highly interpretable with natural uncertain quantification, a desirable property for many scientific problems. However, without a federated learning algorithm, their applicability to sensitive biological/biomedical data from multiple sources is limited. Therefore, to fill this gap in the literature, we propose a new Bayesian federated learning framework that is capable of pooling information from different data sources without breaching privacy. The proposed method is conceptually simple to understand and implement, accommodates sampling heterogeneity (i.e., non-iid observations) across data sources, and allows for principled uncertainty quantification. We illustrate the proposed framework with three concrete sparse Bayesian models, namely, sparse regression, Markov random field, and directed graphical models. The application of these three models is demonstrated through three real data examples including a multi-hospital COVID-19 study, breast cancer protein-protein interaction networks, and gene regulatory networks.
Programmable time-multiplexed squeezed light source

One of the leading approaches to large-scale quantum information processing (QIP) is the continuous-variable (CV) scheme based on time multiplexing (TM). As a fundamental building block for this approach, quantum light sources to sequentially produce time-multiplexed squeezed-light pulses are required; however, conventional CV TM experiments have used fixed light sources that can only output the squeezed pulses with the same squeezing levels and phases. We here demonstrate a programmable time-multiplexed squeezed light source that can generate sequential squeezed pulses with various squeezing levels and phases at a time interval below 100 ns. The generation pattern can be arbitrarily chosen by software without changing its hardware configuration. This is enabled by using a waveguide optical parametric amplifier and modulating its continuous pump light. Our light source will implement various large-scale CV QIP tasks.
An analysis of weight initialization methods in connection with different activation functions for feedforward neural networks

Abstract The selection of weight initialization in an artificial neural network is one of the key aspects and affects the learning speed, convergence rate and correctness of classification by an artificial neural network. In this paper, we investigate the effects of weight initialization in an artificial neural network. Nguyen-Widrow weight initialization, random initialization, and Xavier initialization method are paired with five different activation functions. This paper deals with a feedforward neural network, consisting of an input layer, a hidden layer, and an output layer. The paired combination of weight initialization methods with activation functions are examined and tested and compared based on their best achieved loss rate in training. This work aims to better understand how weight initialization methods in neural networks, in combination with activation functions, affect the learning speed in comparison after a fixed number of training epochs.
PERSONAL DATA PROTECTION APPLICABLE TO AN E-LEARNING SYSTEM USING AN INDIVIDUAL PROFILE

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 2952-2957ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0732Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Answering Count Questions with Structured Answers from Text

In this work we address the challenging case of answering count queries in web search, such as number of songs by John Lennon. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries, including existing benchmark show the benefits of our method, and the influence of specific parameter settings. Our code, data and an interactive system demonstration are publicly available at https://github.com/ghoshs/CoQEx and https://nlcounqer.mpi-inf.mpg.de/.
Deep Learning-Based Algorithm for Recognizing Tennis Balls

In this paper, we adjust the hyperparameters of the training model based on the gradient estimation theory and optimize the structure of the model based on the loss function theory of Mask R-CNN convolutional network and propose a scheme to help a tennis picking robot to perform target recognition and improve the ability of the tennis picking robot to acquire and analyze image information. By collecting suitable image samples of tennis balls and training the image samples using Mask R-CNN convolutional network an algorithmic model dedicated to recognizing tennis balls is output; the final data of various loss functions after gradient descent are recorded, the iterative graph of the model is drawn, and the iterative process of the neural network at different iteration levels is observed; finally, this improved and optimized algorithm for recognizing tennis balls is compared with other algorithms for recognizing tennis balls and a comparison is made. The experimental results show that the improved algorithm based on Mask R-CNN recognizes tennis balls with 92% accuracy between iteration levels 30 and 35, which has higher accuracy and recognition distance compared with other tennis ball recognition algorithms, confirming the feasibility and applicability of the optimized algorithm in this paper.
Machine learning in renal pathology

Introduction When assessing kidney biopsies, pathologists use light microscopy, immunofluorescence, and electron microscopy to describe and diagnose glomerular lesions and diseases. These methods can be laborious, costly, fraught with inter-observer variability, and can have delays in turn-around time. Thus, computational approaches can be designed as screening and/or diagnostic tools, potentially relieving pathologist time, healthcare resources, while also having the ability to identify novel biomarkers, including subvisual features. Methods Here, we implement our recently published biomarker feature extraction (BFE) model along with 3 pre-trained deep learning models (VGG16, VGG19, and InceptionV3) to diagnose 3 glomerular diseases using PAS-stained digital pathology images alone. The BFE model extracts a panel of 233 explainable features related to underlying pathology, which are subsequently narrowed down to 10 morphological and microstructural texture features for classification with a linear discriminant analysis machine learning classifier. 45 patient renal biopsies (371 glomeruli) from minimal change disease (MCD), membranous nephropathy (MN), and thin-basement membrane nephropathy (TBMN) were split into training/validation and held out sets. For the 3 deep learningmodels, data augmentation and Grad-CAM were used for better performance and interpretability. Results The BFE model showed glomerular validation accuracy of 67.6% and testing accuracy of 76.8%. All deep learning approaches had higher validation accuracies (most for VGG16 at 78.5%) but lower testing accuracies. The highest testing accuracy at the glomerular level was VGG16 at 71.9%, while at the patient-level was InceptionV3 at 73.3%. Discussion The results highlight the potential of both traditional machine learning and deep learning-based approaches for kidney biopsy evaluation.
An Exploratory Study of Social Presence in a Collaborative Desktop Virtual Reality (VR) Land Surveying Task

This work-in-progress research paper investigates the role of virtual reality (VR) in engineering education lab courses. While there are ongoing debates about the feasibility and practicality of deploying VR on a large scale for engineering instruction, there has been growing evidence in support of the efficacy of VR to promote certain types of engineering instruction[1]. Most importantly, it is essential that we understand students’ social interactions in VR-based learning environments. This work-in-progress explores patterns of social presence indicators during a collaborative desktop virtual reality (VR) Land surveying task. Participants were asked to think-aloud and video record their interactions and conversations while completing learning tasks using a desktop VR environment. The desktop program was a computer program that simulates land surveying. The qualitative data analysis was based on the framework of social presence by Rourke et al. [2]. The study reported on indicators, frequencies and patterns or themes of social presence observed during student’s interactions with one another in the learning environment. This study also highlighted potential implications of this observation for future research on students’ social experiences in VR-based engineering education.
Real‐Time Video Deblurring via Lightweight Motion Compensation

Abstract While motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper proposes a real‐time video deblurring framework consisting of a lightweight multi‐task unit that supports both video deblurring and motion compensation in an efficient way. The multi‐task unit is specifically designed to handle large portions of the two tasks using a single shared network and consists of a multi‐task detail network and simple networks for deblurring and motion compensation. The multi‐task unit minimizes the cost of incorporating motion compensation into video deblurring and enables real‐time deblurring. Moreover, by stacking multiple multi‐task units, our framework provides flexible control between the cost and deblurring quality. We experimentally validate the state‐of‐the‐art deblurring quality of our approach, which runs at a much faster speed compared to previous methods and show practical real‐time performance (30.99dB@30fps measured on the DVD dataset).
Quantum algorithm for time-dependent differential equations using Dyson series

Time-dependent linear differential equations are a common type of problem that needs to be solved in classical physics. Here we provide a quantum algorithm for solving time-dependent linear differential equations with logarithmic dependence of the complexity on the error and derivative. As usual, there is an exponential improvement over classical approaches in the scaling of the complexity with the dimension, with the caveat that the solution is encoded in the amplitudes of a quantum state. Our method is to encode the Dyson series in a system of linear equations, then solve via the optimal quantum linear equation solver. Our method also provides a simplified approach in the case of time-independent differential equations.
Multi-target tracking algorithm based on YOLO+DeepSORT

Abstract After several years of development, the multi-target tracking algorithm has significantly transitioned from being researched to being put into practical production and life. The application field of human detection and tracking technology is closely related to our daily life. In order to solve the problems of the background complexity, the diversity of object shapes in the application of multi-target algorithms, and the mutual occlusion between multiple tracking targets and the lost target, this paper improves the DeepSORT target tracking algorithm, uses the improved YOLO network to detect pedestrians, inputs the detection frame to the Kalman filter for prediction output, and then uses the Hungarian algorithm to realize a tracking frame and detection frame of the predicted output. The experimental results show that target tracking accuracy is increased by 4.3%, the running time is the shortest, and the number of successfully tracked targets is relatively high.
Real-time Detection of Anthropic Events by 10G Channels in Metro Network Segments

We present an experimental proof-of-concept on detecting SOP variations including information on mechanical stresses on the network footprint by tapping out a small amount of power from 10G channels. Data can be gathered and classified locally, in each node, delivering alerts through streaming telemetry © 2022 The Author(s)
Are goats chèvres, chévres, chēvres, and chevres? Unveiling the orthographic code of diacritical vowels.

An often overlooked but fundamental issue for any comprehensive model of visual-word recognition is the representation of diacritical vowels: Do diacritical and nondiacritical vowels share their abstract letter representations? Recent research suggests that the answer is "yes" in languages where diacritics indicate suprasegmental information (e.g., lexical stress, as in cámara ['ka.ma.ɾa] camera; Spanish), but "no" in languages where diacritics indicate segmental information such as a different phoneme (e.g., the German vowels ä /ɛ/ and a /a/). Here we examined this issue in French, a language that contains a complex set of diacritical vowels (e.g., for the letter e: é, è, ê, and ë). In Experiment 1, using a semantic categorization task, we compared the word identification times to intact diacritical words (e.g., chèvre, goat in English) with a condition with omitted diacritics (chevre). Results showed that the two conditions behaved similarly. In Experiments 2-4, we compared the intact diacritical words with a condition containing a mismatching diacritic, either existing in French (e.g., chévre, chêvre) or not (the macron sign, as in chēvre). We only found a reading cost when replacing the diacritic with an existing one. In Experiments 5-6, we compared the semantic categorization times to intact nondiacritical words (e.g., cheval, horse in English) versus a condition with an added diacritic, either existing (chèval) or not (chēval). We found a reading cost for the words with the added diacritical mark in both cases. We discuss how models of visual-word recognition can be modified to represent diacritical vowels. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Mastering Agile Practice Adoption through a Model-Driven Approach for the Combination of Development Methods

Many software companies are adapting their traditional development processes to incorporate agile practices. In this context, it is necessary to count on expert knowledge to evaluate different agile practices and configure them according to project needs. However, this expert knowledge is scarce, difficult to validate, and time-consuming, since it is applied manually. As a solution, the paper presents a model-driven approach, called SIAM, which automatically generates guidelines for the adoption of agile practices through the combination of different development methods. SIAM is supported by a meta-model architecture to implement a knowledge repository that characterizes method configuration decisions, which can be reused in different development projects. SIAM has been implemented in a tool suite that facilitates the specification of models and the identification of issues during the definition of the development processes. The approach has been successfully applied to reconfigure an industrial development process with agile methods, showing that the effort required for tailoring agile practices according to organizational standards is considerably reduced.
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo

In this paper, we study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. Due to the difficulty of obtaining ground-truth disparity labels in diverse real-world systems, we start from an unsupervised learning perspective. However, resolution asymmetry caused by unknown degradations between two views hinders the effectiveness of the generally assumed photometric consistency. To overcome this challenge, we propose to impose the consistency between two views in a feature space instead of the image space, named feature-metric consistency. Interestingly, we find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features. These features can then be utilized to formulate a feature-metric loss to avoid the photometric inconsistency. Moreover, we introduce a self-boosting strategy to optimize the feature extractor progressively, which further strengthens the feature-metric consistency. Experiments on both simulated datasets with various degradations and a self-collected real-world dataset validate the superior performance of the proposed method over existing solutions.
Detection of Breast Cancer from Histopathological Images using Image Processing and Deep-Learning

Breast cancer is the most commonly occurring cancer in women. Cancer patients frequently develop metastasis, which is responsible for more than 90% of their deaths. The mortality rate will be significantly reduced if it is identified and treated in an early phase. The categorization of cancer cells is critical for medical diagnosis, tailored therapy, and disease prevention. Classifying various types of these cells with great precision has remained a difficult issue. Deep learning has emerged as a significant tool for such challenging tasks in the fields of biology and medicine. In this research, we propose a novel model that throws light on image processing and deep learning for breast cancer classification from histopathological images. The proposed Vision transformer model outperforms the state-of-the-art convolution neural networks in classifying the breast cancer cell with an accuracy of 96%.
Certificate Transparency With Enhanced Privacy

Digital certificates play an important role in the authentication of communicating parties for transport layer security. Recently, however, frequent incidents such as the illegal issuance of fake certificates by a compromised certificate authority have raised concerns about the legacy certificate system. Certificate Transparency (CT) mitigates such issues by employing a log server to audit issued certificates publicly, making the certificate issuance and verification processes transparent. Unfortunately, the legacy CT ecosystem suffers from log server compromises and user browsing information leakage. Furthermore, the data structure for the certificate management in the legacy CT system incurs computation overhead linear to the number of registered certificates in the log. In this paper, we propose a secure CT scheme by leveraging a shared value tree (SVT), a novel log structure specifically designed to address the log server compromise and browsing information leakage problems. The verification time of SVT remains constant regardless of the number of registered certificates in the log. We analyze our scheme on the legacy CT system to demonstrate its incremental deployability, guaranteeing a smooth transition toward a more secure web ecosystem.