Reviewerly/exHarmony huggingface dataset:
Video2mesh: 3D human pose and shape recovery by a temporal convolutional transformer network

Abstract From a 2D video of a person in action, human mesh recovery aims to infer the 3D human pose and shape frame by frame. Despite progress on video‐based human pose and shape estimation, it is still challenging to guarantee high accuracy and smoothness simultaneously. To tackle this problem, we propose a Video2mesh, a temporal convolutional transformer (TConvTransformer) based temporal network which is able to recover accurate and smooth human mesh from 2D video. The temporal convolution block achieves the sequence‐level smoothness by aggregating image features from adjacent frames. The subsequent multi‐attention transformer improves the accuracy due to its multi‐subspace for better middle‐frame feature representation. Meanwhile, we add a TConvTransformer discriminator which is trained together with our 3D human mesh temporal encoder. This TConvTransformer discriminator further improves the accuracy and smoothness by restricting the pose and shape in a more reliable space based on the AMASS dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed Video2mesh outperforms other state‐of‐the‐art methods in both accuracy and smoothness.
Spatiotemporal directional coherence-based scrolling-text detection for frame rate up-conversion

This paper proposes a novel scrolling-text detection method that uses spatiotemporal directional coherence for frame rate up-conversion. Spatiotemporal directional coherence is defined as the coherence level of the spatiotemporal gradient distribution in consecutive frames. Existing scrolling-text detection methods usually detect text using the motion vectors of an image or the distributions of gradient components. The number of motion vectors to determine the scrolling-text decreases at the start and end points of the frame boundary. Therefore, they have difficulty accurately detecting the scrolling text from the start or end points of a frame boundary. The distributions of gradient components can be generated by non-scrolling-text components and they cannot consider the temporal information of consecutive frames, so they may erroneously detect non-scrolling-text regions as the scrolling-text. Unlike the previous methods, which are vulnerable to the presence of texture and noise, the core idea of the proposed method is to use the spatiotemporal directional coherence of the gradient components in the consecutive frames to detect the regions with a dominant edge orientation component for generation of the scrolling-text map and use bit codes from the luminance values to analyze the diversity of luminance patterns for refinement process. With these, the proposed method can further improve the detection accuracy compared to the benchmark methods. The experimental results showed that the proposed method enhanced the average F1 score by up to 0.5195 (a 137.69 % improvement) compared to the benchmark methods. The average computation time per pixel of the proposed method was also reduced by up to 16.205 μs (a 70.57 % reduction) compared to the benchmark methods.
IMPLEMENTASI FORECASTING PADA PERENCANAAN SISTEM PEMESANAN BUKU LKS (LEMBAR KERJA SISWA) MENGGUNAKAN ALGORITMA REGRESI LINEAR. (STUDI KASUS: TOKO BUKU DARUL ULUM, PUNGGUR, LAMPUNG TENGAH

Darul Ulum Bookstore is engaged in distributing LKS books to be sent to schools. The need for worksheets that support learning is one of the most important aspects of availability in the store. so it takes sufficient stock in the order at the beginning of the semester. In this case, the shop owner has difficulty in estimating the number of books to be ordered, so a calculation model is needed to estimate how many books will be ordered at the beginning of the semester. The Multiple Linear Regression method is one of the methods used to predict how many books will be ordered. This method uses the dependent variable and the independent variable as the basis by taking into account the initial stock of books for 2018 and 2019 as the independent variable (x) and the initial stock of 2020 as the dependent variable (y). The results of this study obtained a predictive accuracy value from each printing, namely for CV. Hasan Pratama with MAPE testing of 6.42% with very good indicators. CV. Pratama Mitra Aksara with MAPE testing of 23.52% the results of the indicators are feasible, and CV. Pilar Pustaka with MAPE testing of 6.75% the indicator results are very good. And visualization of predictive data using R-Markdown. Keywords: Linear Regression, Predicting, Interactive Website, R-Markdown
Analisa Penjualan Menggunakan Algoritma K-Means

Garbage is leftover material that is no longer used from daily human activities. The Garbage Bank system is currently still manual in nature such as buying and selling waste from the Bengkalis community, waste price lists, types of waste and grouping waste categories. As a solution to these problems, the author builds a Software Design to digitize the previous system which was done manually. In this study the research methodology used is the Kanban method. In making this application the programming language used is PHP Framework Codeigniter, and MYSQL as a data storage area. With this application, it can help the Waste Bank to buy waste easily, safely and more efficiently.
Application of Machine Learning in Rheumatoid Arthritis Diseases Research: Review and Future Directions

Abstract: Rheumatoid arthritis (RA) is a chronic, destructive condition that affects and destroys the joints of the hand, fingers, and legs. Patients may forfeit the ability to conduct a normal lifestyle if neglected. The requirement for implementing data science to improve medical care and disease monitoring is emerging rapidly as a consequence of advancements in computational technologies. Machine learning (ML) is one of these approaches that has emerged to resolve complicated issues across various scientific disciplines. Based on enormous amounts of data, ML enables the formulation of standards and drafting of the assessment process for complex diseases. ML can be expected to be very beneficial in assessing the underlying interdependencies in the disease progression and development of RA. This could perhaps improve our comprehension of the disease, promote health stratification, optimize treatment interventions, and speculate prognosis and outcomes.
How do technology use patterns influence phishing susceptibility? A two-wave study of the role of reformulated locus of control

Phishing attacks continue to be a concern for academia and practice. Practitioners ranked phishing attacks second to data breaches in a recent industry survey. For scholars, interest in understanding the factors that influence phishing susceptibility, defined as user vulnerability to phishing attacks, continues to grow. While prior research has identified either state (situational cues) or trait (technology use) factors that influence users’ response to phishing attacks, little previous research has investigated simultaneously user control of both state and trait factors on susceptibility to phishing. Additionally, the influence of users’ automatic or routine technology use, user traits, on phishing susceptibility has not been examined. We investigate the effects of users’ control of both state and trait factors on phishing susceptibility. Our results offer several interesting insights. Specifically, while routine technology use trait decreases phishing susceptibility, automatic technology use trait increases phishing susceptibility. Furthermore, while situational cues are related to phishing susceptibility, only users’ automatic technology use is related to susceptibility to phishing under message sender situational cues. Our findings provide practical insights for developing countermeasures that incorporate the level of control into training programs that target trainees with customised training aimed at preventing successful phishing attacks.
Linear Temporal Public Announcement Logic: A New Perspective for Reasoning About the Knowledge of Multi-classifiers

In this paper, a formal transition system model is presented called Linear Temporal Public Announcement Logic (LTPAL) to extract knowledge in a classification process. The model combines Public Announcement Logic (PAL) and Linear Temporal Logic (LTL). For this purpose, first, an epistemic logic model is created to capture information gathered by classifiers in single-framed data input. Next, using LTL, classifiers are considered for data stream inputs. Then, a verification method is proposed for such data streams. Finally, we formalize natural language properties in LTPAL with a video-stream object detection sample.
Predicting the next word using the Markov chain model according to profiling personality

Understanding human data has been the focus of philosophers and scientists. Social media platforms encourage people to be creative and share their personal information. By analyzing data, we will be able to identify people’s personalities and information that is also important to specific profiles. The aim of this paper is to propose an approach that predicts the next word during writing a sentence based on the user’s personality. To achieve this goal, our approach is illustrated by two points: (1) An approximate extraction of the big five personality model for a specific user from his tweets. (2) Predicting the next word while a user is writing a new tweet depends on his personality using the Markov chain model. On the basis of these two notions, our approach makes writing posts easier by predicting and suggesting next words based on the user’s personality. Experience represents the ease of predicting the next word during the writing of a new post related to individual potential.
Early Prediction of Hand Position in Work Operation Considering Object Placement

In this paper, we propose an early prediction method for work operations to estimate the destination of the hand from the initial motion of the worker. A feature of the work operation is that it consists of grasping actions of parts and tools placed around the worker. Therefore, the positions of these objects are considered to provide effective information for early prediction of actions. In this study, we propose a spatial graph, Human-Object Relational Graph (HORG), which describes the positional relationship between human joints and objects, and apply it to Graph Convolutional Networks. Experimental results using an original dataset consisting of 6,900 grasping actions (about 26k frames) by 23 subjects showed that the proposed method achieved a recognition rate of 66.8% at 0.36 seconds before the end of the motion, which is 8.5% higher than that of the conventional method (ST-GCN). The advantage of our method was also confirmed in terms of the positional accuracy of the hand position prediction.
A hunger-based scheduling strategy for distributed crawler

Distributed crawling is one of the mainstream text data collection technologies, which is essential for mining boundless data available on the Internet for users. Internet information is clustered by the correlation of keywords, and users employ search engines to retrieve relevant keywords to get the information they care about. Distributed crawlers are used to mine the Internet information by simulating users’ behavior, the more important keywords that users care about, the higher the correlation of data to keywords. In order to preferentially collect information that users care about with minimal resource consumption, in this paper, we design a scheduling framework and propose a novel scheduling strategy based on hunger for distributed crawler. We first define the load capacity of distributed crawler as hunger which reflects the ability to complete tasks and divide keywords queues into sub-queues based on the hunger of distributed crawlers. Then, we use a vector space model and cosine similarity algorithm to learn the correlation of keywords to text data and apply the optimized logistic algorithm to measure the importance of keywords. Meanwhile, we design a comprehensive evaluation algorithm to quantify the contribution of keywords, so that updating sub-queues order. Finally, new sub-queues are used in the deeper scheduling to preferentially get data that users desire and sacrifice the least number of resources. Experimental results demonstrate that our method optimizes the scheduling procedures and makes crawling more efficient with less run time.
Approaches to blur reduction in cervical images for automated visual evaluation

Background: Cervical cancer is a significant burden in many health systems in low and middle income countries (LMICs). Recently, automated visual evaluation (AVE) – using artificial intelligence to analyze cervical images at the point of care (PoC) – has been gaining interest as a new diagnostic test in LMICs. Multiple studies showed that blur (defocus) is the most common challenge to capturing cervical images that are adequate for evaluation by AVE. Methods to reduce blur in cervical images are critical, yet auto-focus functionality degrades when placing an auxiliary lens on a phone. Methods: A cervical image quality analysis algorithm that included blur assessment was developed into an Android application. This algorithm includes an auto-focus module, and secondary blur assessment using deep learning (DL). The auto-focus module was evaluated by bench testing on static cervix images. Two DL approaches (supervised and self-supervised models) were compared against an external dataset. Results and Discussion: A frame by frame analysis on the Samsung J530 and A52, each imaging 3 static images, verified the frame with the least blurry image was selected. The average time for one auto-focus sweep was 8367 ± 630 ms and 7555 ± 146 ms for the J530 and A52, respectively. Within the obstructions detector, the self-supervised model performed better under high blur, with area under the receiver operating characteristic (ROC) curve (AUC) as high as 0.888, while the supervised model performed better with less blur, with ROC AUC values reaching 0.735. To our knowledge, this is the first working targeted auto-focus for cervical imaging.
Comparison of performance of different automated visual evaluation quality assessment modules

Background: Cervical cancer disproportionally harms women in low and middle income countries (LMICs). There is increasing interest in automated visual evaluation (AVE) – using artificial intelligence to analyze cervical images at the point of care (PoC) – for managing patients in LMICs. AVE has a diagnostic component (for pathology) and a quality component (to ensure image adequacy). The quality component must run on the imaging device at the PoC, and is limited by its processors for computation. Methods: A novel, multiple-module algorithm for assessing cervical image quality was developed in an Android application. One module located the cervix and another sought objects obstructing the transformation zone. The cervix locator module is an object detection model that determined the bounding box of the cervix. Models trained on multiple architectures (YOLOv5 and EfficientDet-Lite2) with the same data were compared. For obstructions classification, a multi-task model was trained to detect 5 common obstructions (blood, SCJ inside of os, loose vaginal walls, mucus, blur/glare) and obstruction-free cervix. Performance of the model’s tasks were compared for 2 different imaging devices. Results and Discussion: The cervix locator performed better and was faster for YOLOv5, although differences were minimal. In the obstructions classifier, 4 different tasks (loose vaginal walls, blood, SCJ inside of os, and obstruction-free) performed satisfactorily. For all modules, the full computation time was &lt;10 sec. Both modules met the desired performance thresholds for image adequacy assessment. The algorithm shown here is to our knowledge, the first AVE quality classifier running on a mobile device.
Evaluation of AR image tracking for AR-guided surgical applications

Augmented reality (AR) image tracking may be used in AR-guided surgical applications for real-time guidance and quantitative feedback. With AR-guided applications allowing for broader accessibility compared to specialized systems used in traditional surgical image-guidance, we evaluated the measurement errors of monocular AR image tracking against current gold standard infrared optical and electromagnetic (EM) tracking. A measurement stylus was designed and 3D printed, allowing for monocular AR image tracking using a Logitech C920 camera, infrared optical tracking with Northern Digital Inc. (NDI) Vicra, and EM tracking with NDI Aurora through corresponding sensor attachments. A measurement phantom was also designed and 3D printed, consisting of 3 measurement planes with 81 measurement points in each plane, totaling 243 measurement points across a 16 cm x 16 cm x 18 cm measurement volume. Pivot calibration was performed using random sample consensus (RANSAC) sphere fitting to calculate the offsets between sensor attachments to stylus tip across each tracking system. Measurements of the stylus tip were collected across the measurement phantom for each tracking system. Each system’s fiducial registration error was quantified using the collected tip positions through rigid registration between the tracking system and the designed phantom points from CAD. Fiducial registration errors were 1.19 mm, 0.59 mm, and 0.51 mm for monocular AR, infrared optical, and EM tracking. Monocular AR image tracking presents a cost effective and accessible solution for surgical guidance applications. Errors close to 1 mm may be suitable for scenarios such as surgical simulators in competency-based education and AR-based planning.
Automated Visual Evaluation (AVE) of cervical precancerous lesions with integrated patient meta-information: an initial study

Cervical precancer – a major threat in low- and middle-income countries (LMICs) – is often screened through Visual Inspection with acetic Acid (VIA) but suffers from limited reproducibility and inaccuracy. Automatic Visual Evaluation (AVE) utilizes deep learning algorithms based on cervigram or digital images to detect cervical precancer accurately and efficiently. However, important patient data and test results that can provide additional context to the images have not been integrated into the AVE workflow. The NCI ALTS dataset provides over 30,000 cervigram images with hundreds of histopathology-verified positive cases, paired with HPV, cytology, colposcopy, and histology results. To take advantage of both the images and other data, we designed a deep learning image classification algorithm, then modified its architecture to integrate age, HPV status, and cytology result with two methods – concatenation, and multiplication via an added “MetaNet”. We then proposed a two-stage training process that maximized the predictive power of both the images and meta-information while allowing flexibility for missing data. This enabled us to run concurrent inference on a patient's images, test results, and age, and return a binary cervical precancer prediction. The experiments demonstrated that in certain scenarios, the MetaNet model produced a synergistic effect between the images and meta-information by outperformingmodels that only use each component individually, increasing specificity and maintaining high sensitivity. This work provides a direction for the integration of patient meta-information into an end-to-end AVE prediction model, and generally for other medical imaging data, to potentially increase prediction power andmore precisely calibrate patient risk.
Continuous Sign Language Recognition Via Temporal Super-Resolution Network

Aiming the problem that the spatial-temporal hierarchical continuous sign language recognition (CSLR) model with video as input is computationally intensive, thus limiting the real-time application, this paper proposes a temporal super-resolution network (TSRNet) to reduce the model computation while keeping the loss of accuracy to a minimum, achieving the best compromise between the real-time performance and accuracy. The TSRNet-based CSLR constructed in this paper consists of three main parts: frame-level feature extraction, temporal feature extraction and the proposed TSRNet, where the TSRNet is located between them, and consists of two branches: detail and coarse descriptors. The extracted frame-level features are first sparse, after which they are passed through the two branches designed for feature reconstruction; the fused dense sequence is subjected to temporal feature extraction. In order to better recover the semantic-level information, this paper also proposes a self-generating adversarial network training method, which treats the TSRNet as the generator and the frame-level and temporal processing parts as the discriminator. In addition, to unify the criteria for judging the loss of model accuracy under different benchmarks, this paper proposes word error rate deviation (WERD), where the error rate between estimated WER and reference WER obtained by reconstructed frame-level feature sequence and complete original frame-level feature sequence, respectively. Experiments on two large-scale sign language datasets demonstrate the effectiveness of the model. The method proposed in this paper is not only applicable to CSLR, but is general to spatial-temporal hierarchical models where the input is video data. Code is available at https://github.com/woshisad159/CSLR.git .
MFPD-LSTM: A steganalysis method based on multiple features of pitch delay using RNN-LSTM

In recent years, adaptive multi-rate (AMR) steganalysis based on pitch delay has become a hot research area for researchers. The main reason is that the steganalysis algorithm based on AMR can sensitively detect the change in the statistical characteristics of the pitch delay before and after the secret information is embedded, and achieve high accuracy. At present, many excellent AMR steganalysis algorithms based on pitch delay have been proposed. However, when these algorithms are aimed at short-term, low-embedding-rate speech samples, there is room for improvement in accuracy and performance. In this paper, a Recurrent Neural Network-Long-Short Term Memory (RNN-LSTM) is used to design a steganalysis method through multi-feature fusion based on pitch delay (MF-PD). This method extracts the pitch delay sequence in the speech information stream, mines four features that characterize the statistical feature of the intra-frame and the inter-frame, and establishes the feature matrix. Then construct the RNN-LSTM model, the dropout layer is introduced to avoid over-fitting and the model is pruned to improve the efficiency. Experimental results show that when the speech sample length is 0.1s, this method can achieve a detection accuracy of more than 87%, which is significantly higher than other steganalysis algorithms. For samples with low embedding rate, the method proposed in this paper can also achieve better performance, which satisfies the need for short time and low embedding rate sample detection.
Online Hate Speech as a Moral Issue: Exploring Moral Reasoning of Young Italian Users on Social Network Sites

Taking a neo-Kohlbergian approach, we explore the moral reasoning of 486 young Italian users of social network sites exposed to moral dilemmas concerning online hate speech. The aims are to understand what moral reasoning schemas they use as they face homophobic, racist, or sexist online hate speech, and what influence personal values and moral disengagement might have on their moral reasoning process. The results reveal the prevalence of Maintaining Norms reasoning (conformity to rules and authority) in making moral decisions concerning online hate speech and confirms the mediating role of Hate Speech Moral Disengagement in the relationship between personal values and the moral reasoning process.