Reviewerly/exHarmony huggingface dataset:
Staining, magnification, and algorithmic conditions for highly accurate cell detection and cell classification by deep learning

Abstract Objectives Research into cytodiagnosis has seen an active exploration of cell detection and classification using deep learning models. We aimed to clarify the challenges of magnification, staining methods, and false positives in creating general purpose deep learning–based cytology models. Methods Using 11 types of human cancer cell lines, we prepared Papanicolaou- and May-Grünwald-Giemsa (MGG)–stained specimens. We created deep learning models with different cell types, staining, and magnifications from each cell image using the You Only Look Once, version 8 (YOLOv8) algorithm. Detection and classification rates were calculated to compare the models. Results The classification rates of all the created models were over 95.9%. The highest detection rates of the Papanicolaou and MGG models were 92.3% and 91.3%, respectively. The highest detection rates of the object detection and instance segmentation models, which were 11 cell types with Papanicolaou staining, were 94.6% and 91.7%, respectively. Conclusions We believe that the artificial intelligence technology of YOLOv8 has sufficient performance for applications in screening and cell classification in clinical settings. Conducting research to demonstrate the efficacy of YOLOv8 artificial intelligence technology on clinical specimens is crucial for overcoming the unique challenges associated with cytology.
Agnostic proper learning of monotone functions: beyond the black-box correction barrier

We give the first agnostic, efficient, proper learning algorithm for monotone Boolean functions. Given $2^{\tilde{O}(\sqrt{n} / \varepsilon)}$ uniformly random examples of an unknown function $f:\{ \pm 1\}^{n} \rightarrow\{ \pm 1\}$, our algorithm outputs a hypothesis $g:\{ \pm 1\}^{n} \rightarrow\{ \pm 1\}$ that is monotone and (opt $+\varepsilon$)-close to f, where opt is the distance from f to the closest monotone function. The running time of the algorithm (and consequently the size and evaluation time of the hypothesis) is also $2^{\tilde{O}(\sqrt{n} / \varepsilon)}$, nearly matching the lower bound of [13]. We also give an algorithm for estimating up to additive error $\varepsilon$ the distance of an unknown function f to monotone using a run-time of $2^{\tilde{O}(\sqrt{n} / \varepsilon)}$. Previously, for both of these problems, sample-efficient algorithms were known, but these algorithms were not run-time efficient. Our work thus closes this gap in our knowledge between the run-time and sample complexity.This work builds upon the improper learning algorithm of [17] and the proper semiagnostic learning algorithm of [40], which obtains a non-monotone Boolean-valued hypothesis, then “corrects” it to monotone using query-efficient local computation algorithms on graphs. This black-box correction approach can achieve no error better than 2 opt $+\varepsilon$ information-theoretically; we bypass this barrier bya)augmenting the improper learner with a convex optimization step, andb)learning and correcting a real-valued function before rounding its values to Boolean. Our real-valued correction algorithm solves the “poset sorting” problem of [40] for functions over general posets with non-Boolean labels.
NEURAL-UML: Intelligent Recognition System of Structural Elements in UML Class Diagram

Design models are essential for many tasks in software engineering, such as consistency checking, code generation and design-to-code tracing. Unfortunately, many UML class diagrams are stored as images, which limits their use and evolution. It is therefore important to identify the semantic elements of design models from images. Although a number of studies focus on the recognition of a UML class diagram, very few address semantic analysis, which is a relatively complex task. In this paper, we propose a framework for training a learning model to categorise and locate semantic elements in class diagram from an image. A large set of annotated design models is proposed and made available online. Qualitative and quantitative evaluations have been carried out on two subsets of data, giving accuracy scores of 92.59% and 94.11% respectively. Evaluations highlight the ability of the proposed learning model to generalise to a wide range of examples.
DAANet: Dual Attention Aggregating Network for Salient Object Detection*

When the Convolutional neural network (CNN) has been introduced for computer vision, many researches use CNN-based model to perform salient object detection (SOD). In recent years, The feature pyramid network (FPN) based structure is more popular for salient object detection tasks. In this paper, to improve the overall performance of salient object detection tasks, we propose a dual attention aggregating network (DAANet), which is an FPN-based deep convolutional neural network with a dual attention aggregation module (DAAM) and dilated refinement block (DRB). The DRB module uses convolutions with different dilation rates to expand the receptive field. The DAAM considers the salient map prediction from the low-level output as pseudo-attention which can efficiently aggregate multi-scale information. The convolution block attention module (CBAM) in DAAM can refine the aggregation of pseudo-attention which enables better performance. We evaluate DAANet on six benchmark datasets that prove the effectiveness of DAANet and its components. Our implementation can be found at: https://github.com/Att100/DAANet.
VGRISys: A Vision-Guided Robotic Intelligent System for Autonomous Instrument Calibration*

With the increasing sophistication of musical instruments and the importance of precise tuning, a novel and efficient approach to instrument calibration is of significant necessity. This paper presents VGRISys, an innovative system leveraging robotics and artificial intelligence to facilitate the intricate process of instrument tuning. VGRISys incorporates a multi-axis robotic arm, meticulously engineered for fine-tuned physical interaction with various musical instruments, alongside an intelligent system that guides the robotic arm using real-time audio feedback analysis. In addition, it employs advanced signal processing and machine learning algorithms, allowing the system to identify pitch discrepancies and respond with appropriate corrective action. VGRISys remarkably reduces the time required for instrument tuning, thus improving efficiency and democratizing access to high-quality tuning. Furthermore, the system’s machine learning capabilities allow it to refine its tuning process over repeated operations, providing a scalable solution to the ever-evolving complexities of instrument tuning. The exploration and development of VGRISys contribute significantly to the wider fields of robotics, artificial intelligence, and music technology.
Real-Time RGB-D Pedestrian Tracking for Mobile Robot

Pedestrian tracking is an important research direction in the field of mobile robotics. In order to complete tasks more efficiently and without hindering the original intention of pedestrians, mobile robots need to track pedestrians accurately in real time. In this paper, we propose a real-time RGB-D pedestrian tracking framework. First, we propose a pedestrian segmentation detection algorithm to detect pedestrians and obtain their two-dimensional positions. Second, due to limited computational resources and the rarity of missed detection for pedestrians, we use an nearest neighbor tracker for pedestrian tracking. To address the issue of inaccurate pedestrian localization, we use our detection algorithm to obtain the center of pedestrians from RGB images. By combining them with point clouds, the 2D coordinates of pedestrians are obtained. Our method enables accurate pedestrian tracking in the world coordinate, by adaptively fusing RGB images with their corresponding depth-based point clouds. Besides, our light-weight detection and tracking algorithm guarantee the real-time pedestrian tracking for realistic mobile robot applications. To validate the effectiveness and real-time performance of tracking algorithm, we conduct experiments using multiple pedestrian datasets of approximately half a minute in length, captured from two different perspectives. To validate the practicality and accuracy of the tracking algorithm in real-world scenarios, we extend our tracking algorithm to apply it to trajectory prediction.
Weight Coefficient Based Adaptive Federated Learning for Vehicular Data Transmission

With the ever-increasing amount of vehicle data being generated, the collection and transmission of this data-to-data processing centers is consuming significant amounts of communication resources. The traditional method of compressing and transmitting the vehicle data is not effective in addressing the issue of efficient utilization of this data. In order to overcome this challenge, we propose an adaptive federated learning approach that avoids the need for transmitting data per vehicle. Our approach leverages the vehicle as a distributed training device node and enables the training of vehicle data using the vehicle's own computing power, thereby eliminating the need to transmit the data over the network. To further enhance the efficiency of the federated learning aggregation calculation, we introduce the information entropy function and cosine similarity calculation. By computing the similarity between the model and the benchmark model, we are able to give a new round of model aggregation calculation weight. Finally, we validate the proposed algorithm using the actual MNIST dataset, demonstrating its high effectiveness.
QFuture: Learning Future Expectation Cognition in Multi-Agent Reinforcement Learning

In multi-agent reinforcement learning (MARL), agents must learn to cooperate by observing the environment and selecting actions that maximize their rewards. However, this learning process can be hampered by myopia, wherein agents’ strategies fail to consider the long-term consequences of their actions. A primary reason for this problem is the inaccurate estimation of the long-term value of each action. Socially, humans derive future expectation cognition from available information to anticipate potential future outcomes and adjust their actions accordingly to avoid myopia. Motivated by these insights, this paper proposes a novel framework called QFuture to address the myopia problem. Specifically, we first design a future expectation cognition module (FECM) in this framework to build future expectation cognition in the calculation of individual actionvalue (IAV) and joint action-value (JAV). We model future expectation cognition as random variables in FECM, which learn representation by maximizing mutual information with the future trajectory based on current information. Furthermore, a return-based regularizer is designed to reflect ”expectation” and ensure informativeness in the future expectation representation module (FERM) which encodes the future trajectory. Experiments on StarCraft II micromanagement tasks and Google Research Football show that QFuture achieves significant state-of-the-art performance. Demonstrative videos are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/qfuture</uri> .
The development of a digital NGO mapping platform in Tanzania, as a tool to strengthen civil society engagement, in pursuit of Universal Health Coverage

The role of civil society in economic development, improving livelihoods and in providing pathways towards achieving health for all has become increasingly evident. By mapping these organizations, the scope and scale as well as existing capacities, gaps, and opportunities are brought to light. This paper describes the implementation of a digital mapping platform for NGOs; an interactive site which collects, analyses, and visualizes data from a variety of sources about NGOs in Tanzania, through a series of interactive maps, graphs, and charts. We describe the approach and the technology used to develop the platform and its potential contribution towards improving health outcomes. A situation analysis and needs assessment exercise was conducted in February 2023. The developed system requirement specification document served as the guiding document in the design and development of new modules. Participatory techniques and agile iterative methodologies comprising regular stakeholder engagement were employed. A distributed revision control system was used to keep track of system revisions. The modules were deployed to the production server at the National Internet Data Center (NIDC) server room, followed by a system commissioning activity in October 2023. The NGO Information System, NGO Digital Mapping tool, NGO Analytic tool, NGO Search tool and NGO Opportunities module were designed, developed, and commissioned to support NGO operations in Tanzania. The platform was launched during the annual NGO Forum in Dodoma, Tanzania, on October 5, 2023. The modules are publicly accessible and are housed within the NGO Information System (NIS) platform. Investment in whole-of-society engagement to build health systems resilience for universal health coverage is crucial. Leveraging the unique positioning of NGOs draws us a step closer to the ambitious goal of achieving health for all. Through this one-stop web application system, information on the near real-time status, existing gaps, and opportunities for collaboration to serve communities is readily available for all stakeholders. Wide dissemination and enhancement of utilization of the platform across all sectors is now needed, for data to truly inform action.
Virtual Reading Room for Diagnostic Radiology

To assess the perceptions of radiology staff regarding the role of virtual reality technology in diagnostic radiology after using a virtual reality (VR) headset Participants completed a pre-study questionnaire assessing their familiarity with VR technology and its potential role in radiology. Using a VR headset, participants entered a simulated reading room (SieVRt, Luxsonic Technologies) with three large virtual monitors. They were able to view plain radiographs, ultrasound, CT, and MRI images and pull up and compare multiple images simultaneously. They then completed a post-study questionnaire to re-assess their perception about the role of VR technology for diagnostic radiology. Fifteen participants were enrolled, with 33.3% attendings, 40% fellows, and 26.7% residents. Pre-study, 60% reported they were “not familiar” with VR technology and 66.7% had never used it. On a 1 to 5 scale, the median perceived likelihood of VR having a role in radiology significantly increased from 3 (IQR 2-3) pre-study to 4 (IQR 4-4) post-study; p=0.014. Image contrast and resolution were adequate according to most participants, with 53.3% strongly agreeing and 33.3% agreeing. The headset was comfortable for 73.3% and did not induce nausea in any participant. Confidence in VR technology improved after using the headset for 80%. According to 80%, future VR technology could replace a PACS workstation. Radiologists’ perception regarding the role of virtual reality in diagnostic interpretation improves after a hands-on trial of the technology, and VR has the potential to replace a traditional workstation in certain situations.
Enhancing Code Completion with Implicit Feedback

Code completion has become an important feature of today’s integrated development environments (IDEs). This task involves predicting the next code token(s) based on its contextual information within the code. However, most existing code completion approaches do not consider users’ feedback during the completion process. In this paper, we propose a framework, EHOPE (Enhance Code Completion with Implicit Feedback)), which exploits LSTM(Long Short-Term Memory) and pre-trained model BERT(Bidirectional Encoder Representation from Transformers) to enhance the performance of token-level code completion. By leveraging users’ feedback information, we train an LSTM model to supplement the recommendation list. In addition, we re-rank the list of recommendations using the pre-trained model BERT, which is fine-tuned with feedback information. Existing token-level code completion tools can be plugged into EHOPE. We choose two representative code completion approaches from different categories: one based on statistical methods and the other based on deep learning. These approaches serve as baselines to showcase the performance improvements of EHOPE, evaluated using Hit@k (Top-k) and MRR(Mean Reciprocal Rank) metrics. Empirical experiments show that the recommendation performance steadily and substantially improves as the feedback data increases compared with the baselines.
Forward Chaining and Certainly Factor Method Optimization for Lung Disease Expert System

The purpose of this research is to build a system which is capable of diagnosing lung disease, to provide information about lung disease along with treatment solutions so that it can help the public, and to determine the best method to use in the system to be built. From the results of the analysis by comparing several methods that can be used in Expert Systems for diagnosing lung disease, the result is that by implementing a combination of the Forward Chaining method and the Certain Factor, the highest level of accuracy of results is obtained among other methods. After several tests using software testing, user questionnaires and expert validation, it is proved that this Expert System can assist patients in diagnosing lung disease with a diagnostic accuracy rate of 93 percent, and ready used by the public.
Web Login Authentication System Using QR-Code Scanner with Sha-1 Encryption Method

When entering a system, the validation method performs sensitive data transactions owned by other people. The interaction between the system and the user for authentication has traditionally only used one username and password authentication stage. It is hazardous for hacking attacks, for example, by brute force or phishing, so the system security is made layered to improve authentication validation. There are many ways to implement layered authentication, one of which is using the QR-code application library as a layered authentication tool to increase the security of transaction log validation to log into an archive management system. Where the mobile application is the key to creating a token replacement for the password used in the desktop application, previously on the desktop, it will generate a unique code converted into an image from the QR code, which the application on the mobile phone will then scan. Validation is relatively safer because it involves three keys, a username, password, and single-use token, generated from a random unique code. Then, every data exchange between the application on the cellphone and the server key is encrypted using Sha-1.
Analysis of Supporting Information Systems for Seminar Activities Using the Ruby on Rails Framework

Technological developments are increasingly advanced and help many activities. Information Systems Technology is not limited to the use of computers, especially hardware and software, but includes data collection and processing, and system control records. Currently, many marketing communities are holding business education seminars to prepare entrepreneurs to acquire or improve their skills, one of which is the Top-Quality Group Indonesia (TQG-IDN) community. In the process of organizing the seminar, the use of Information System Technology can be a tool to make the seminar more effective. In this research, we will conduct an analysis of the design and development of an information system that aims to assist the seminar process at TQG-IDN which is expected to facilitate the management of user data, event data, and data on seminar venues, seminar ticket stock data, ticket purchase transactions, process attendance by scanning a QR Code, printing seminar certificates, and preparing reports. This system is website-based and designed using the Ruby programming language and the Ruby on Rails framework, while QR code scanning uses the REST API and will be tested using Postman.
Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source’s position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased and further consistent estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves a 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case.
PEEP with Cloud Encryption: A Dual-Layered Privacy Solution for Emotion Recognition Systems

Recent advancements in image-based Facial emotion recognition systems offer deep insights into human psychological states, holding promise for valuable applications such as measuring customer satisfaction in public service areas or student engagement in classrooms. However, the potential impacts on the privacy of individual users of these systems cannot be ignored. In our study, we present PEEP (Privacy using EigEnface Perturbation) integrated with cloud storage encryption as a dual-layered approach to address privacy concerns in such systems. PEEP processes facial data by extracting eigenfaces and adding specific noise, ensuring the anonymity of identities while retaining the capability to recognize emotions. In tandem, cloud storage encryption guarantees that data, if intercepted during transmission or storage, stays encrypted and secure. This combined strategy offers an enhanced privacy solution for emotion recognition systems on remote servers. This study aligns with international initiatives to promote the responsible development and application of artificial intelligence, while emphasizing the importance of upholding human ethical standards and security.
Influences of Mental Stress Level on Individual Identification using Wearable Biosensors

As wearable biosensors become increasingly popular, personalized services are applied in various scenarios to identify individuals based on biometric signals with unique inherent characteristics of each individual. However, the varying levels of mental stress in different scenarios (e.g., work and relaxation) in individuals would obscure whether such stress could influence the identification process. To address this, we built CNN-based models to identify 12 subjects under four levels of mental stress by using three common biometric signals: R-R interval, galvanic skin reaction, and respiration. Our analysis revealed that individuals can be more easily identified when they are at certain levels of mental stress. The examination of the effect of mental stress on the identification of different subjects found that the inherent rhythm differences in biometric signals among individuals could affect the accuracy of identification. We further validated how stress levels impact individual identification over various time periods.