Reviewerly/exHarmony huggingface dataset:
Automatic Table Union Search with Tabular Representation Learning

Given a data lake of tabular data as well as a query table, how can we retrieve all the tables in the data lake that can be unioned with the query table?Table union search constitutes an essential task in data discovery and preparation as it enables data scientists to navigate massive open data repositories.Existing methods identify uniability based on column representations (word surface forms or token embeddings) and column relation represented by column representation similarity.However, the semantic similarity obtained between column representations is often insufficient to reveal latent relational features to describe the column relation between pair of columns and not robust to the table noise.To address these issues, in this paper, we propose a multi-stage self-supervised table union search framework called AUTOTUS, which represents column relation as a vector-column relational representation and learn column relational representation in a multi-stage manner that can better describe column relation for table unionability prediction.In particular, the large language model powered contextualized column relation encoder is updated by adaptive clustering and pseudo label classification iteratively so that the better column relational representation can be learned.Moreover, to improve the robustness of the model against table noises, we propose table noise generator to add table noise to the training table data.Experiments on real-world datasets and synthetic test set augmented with table noise show that AUTOTUS achieves 5.2% performance gain over the SOTA baseline.
itk-elastix: Medical image registration in Python

Image registration plays a vital role in understanding changes that occur in 2D and 3D scientific imaging datasets.Registration involves finding a spatial transformation that aligns one image to another by optimizing relevant image similarity metrics.In this paper, we introduce itk-elastix, a userfriendly Python wrapping of the mature elastix registration toolbox.The open-source tool supports rigid, affine, and B-spline deformable registration, making it versatile for various imaging datasets.By utilizing the modular design of itk-elastix, users can efficiently configure and compare different registration methods, and embed these in image analysis workflows.
Exploration of Digital Intelligence for Personal Credit Sharing Platform Based on Blockchain Technology

The decentralized, untrusted, timestamp, asymmetric encryption and other features of blockchain support limited and controllable credit data sharing and verification while effectively collecting and protecting data privacy. The smart contract of blockchain has created conditions for the digital cons
Research on Scientific Data Curation Model of Institutional Knowledge Repository Integrated with Block Chain Technology

Building a data curation platform that can be used and saved for a long time is the core link and key of scientific research data curation service． Aiming at scientific data curation platform of institutional repository data standardiza- tion，dependability，security and privacy problem，using the blo
An Evaluation of New Enrollment Strategies to Boost PharmD Applicant Retention and Deposit Yield

Increase enrollment and retain more applicants throughout the enrollment funnel with the implementation of new recruitment initiatives that demonstrate an investment in students and the profession.
An Interactive Learning Network That Maintains Sentiment Consistency in End-to-End Aspect-Based Sentiment Analysis

Most of the aspect-based sentiment analysis research completes the two subtasks (aspect terms extraction and aspect sentiment classification) separately, and it cannot see the full picture and actual effect of the complete aspect-based sentiment analysis. The purpose of end-to-end aspect-based sentiment analysis is to complete the two subtasks of aspect terms extraction and aspect sentiment classification at the same time, and the current research in this area focuses on the connection between the two subtasks and uses the connection between them to construct the model. However, they rarely pay attention to the connection between different aspects and ignore the sentiment inconsistency within the aspects caused by the end-to-end model. Therefore, we propose an interactive learning network to maintain sentiment consistency, first using the multi-head attention mechanism to achieve the interaction between aspects and subtasks and then using the gate mechanism to design an auxiliary module to maintain sentiment consistency within aspect items. The experimental results on the datasets Laptop14, Restaurant14, and Twitter showed that, compared with the optimal benchmark method, the F1 values of the proposed method increased by 0.4%, 1.21%, and 5.22%, respectively. This indicates that the proposed method can effectively consider the relationships between aspect items and maintain emotional consistency within the aspect items.
A statistical examination of utilization trends in decentralized applications

Decentralized applications (dApp) have proliferated in recent years, but their long-term viability is a topic of debate. However, for dApps to be sustainable, and suitable for integration into a larger service networks, they need to attract users and promise reliable availability. Therefore, assessing their longevity is crucial. Analyzing the utilization trajectory of a service is, however, challenging due to several factors, such as demand spikes, noise, autocorrelation, and non-stationarity. In this study, we employ robust statistical techniques to identify trends in currently popular dApps. Our findings demonstrate that a significant proportion of dApps, across a range of categories, exhibit statistically significant positive overall trends, indicating that success in decentralized computing can be sustainable and transcends specific fields. However, there is also a substantial number of dApps showing negative trends, with a disproportionately high number from the decentralized finance (DeFi) category. Furthermore, a more detailed inspection of time series segments shows a clearly diminishing proportion of positive trends from mid-2021 to the present. In summary, we conclude that the dApp economy might have lost some momentum, and that there is a strong element of uncertainty regarding its future significance.
Effect of Dust Accumulation on Efficiency of Solar Panels in Clement Town Region (Dehradun) India: An Empirical Study

The solar energy is considered to be the ultimate source of energy and primary source of renewable energy on the Earth.From the average solar energy of 342 W/m 2 available in the upper atmosphere only 239 W/m 2 reaches earth surface and rest is radiated back to space.Harvesting solar energy through semiconductor based solar cells is affected by several factors and dust accumulation is one such factor.Dust accumulation creates a coating over the panel that limits the reception of sunlight and increases the temperature inside the solar panels.As a result of dust coating the number of incident photons reaching the solar cells decreased substantially, resulting in diminished current output and lower efficiency.Further, due to insulating effect of the dust layer, heat from the panel does not radiate effectively and induces a random movement in the charge carriers inside solar cells, depreciating overall power output of the solar cell.With increasing pollution, the amount of dust particles in the atmosphere is increasing exponentially.Consequently, the efficiency of solar cells is being adversely affected universally, offsetting the positive effects of technological improvements in designs and manufacturing of solar cells.Over a period of 45 days an average drop of 22.5% is observed in the efficiency of polysilicon solar panels in clement town region in Dehradun.Further it has been found in the study that accumulation of dust particles offsets the advantages of higher altitude, greater irradiation, and lower temperature which are considered favorable for harnessing solar energy.
The role of sense of presence in expressing cognitive abilities in a virtual reality task: an initial validation study

Abstract There is a raised interest in literature to use Virtual Reality (VR) technology as an assessment tool for cognitive domains. One of the essential advantages of transforming tests in an immersive virtual environment is the possibility of automatically calculating the test’s score, a time-consuming process under natural conditions. Although the characteristics of VR can deliver different degrees of immersion in a virtual environment, the sense of presence could jeopardize the evolution of these practices. The sense of presence results from a complex interaction between human, contextual factors, and the VR environment. The present study has two aims: firstly, it contributes to the validation of a virtual version of the naturalistic action test (i.e., virtual reality action test); second, it aims to evaluate the role of sense of presence as a critical booster of the expression of cognitive abilities during virtual reality tasks. The study relies on healthy adults tested in virtual and real conditions in a cross-over research design. The study’s results support the validity of the virtual reality action test. Furthermore, two structural equation models are tested to comprehend the role of sense of presence as a moderator in the relationship between cognitive abilities and virtual task performance.
Enhancing 21st Century Teachers Through Eco-Digital Pedagogy

Pedagogy plays an essential role in the learning process.Ecopedagogy and digital pedagogy are two of its derivatives.This study focuses on the combination of the two pedagogical models, namely Eco-Digital Pedagogy (EDY).It proposes to reveal the effectiveness of the implementation of the EDY model on the readiness of prospective 21st century teachers.This study's population was Geography students at Semarang State University (UNNES) in Indonesia.The data obtained were tested by paired t-test and strengthened by the N-Gain test.The results show that the implementation of the EDY model has proven effective in increasing the ability of prospective teachers, namely in 1) showing the leadership skills, 2) creating an environment that respects the diversity of students, 3) understanding every content they deliver, 4) facilitating learning to their students, and 5) reflecting on their teaching practices.This positive result indicates that the EDY model can be used to further develop the abilities of prospective teachers to generate better outputs in the learning process.
Hand Gestures Controlled Intelligent Wheelchair with Device Switch

The main aim of this paper is to use accelerometer sensor (Micro Electro-Mechanical Systems) technology to operate a wheelchair.MEMS (Micro Electro Mechanical Sensors) are a type of accelerometer sensor.This sensor is extremely sensitive and capable of detecting movements.The movement is detected by this micro electro mechanical sensor.The accelerometer is used to modify the direction of the wheelchair based on tilt.As an example, if the tilt is to leave, the wheelchair will go in that direction; if the tilt is to the right, the wheelchair will move in the opposite way.The wheelchair then goes to the left.Wheelchair movement may be regulated in three ways: forward, backward and reverse, Left, and Right directions are all available.This system may also operate devices such as a DC fan and a DC light to assist physically handicapped persons.
Form follows function: Applying photographic content analysis to forensic firearm identification

Drawing forensic conclusions from an image or a video is known as "photographic content analysis." It involves the analysis of an image, as well as objects, actions, and events depicted in images or video. In recent years, photographic depictions of objects suspected as illegal firearms have substantially increased, appearing on CCTV surveillance footage, captured by mobile phones and shared on social media. However, the law in Israel states that a person can be charged with illegally possessing a firearm only if it can be proven that the object is capable of shooting with lethal bullet energy. This becomes more challenging in cases where the firearm was not physically seized, and the evidence exclusively consists of images and video. In this study, photographic content analysis was applied to images and video where objects suspected as commercial or improvised firearms had been depicted. An image and event sequence reconstruction video databases of both firearms and replicas were created in order to better define firearm-specific functional morphological features. We demonstrate that it is possible to classify an object as a firearm by analyzing the functional, and not only the esthetic, morphology in images and video. It is also shown that event sequence reconstruction in video may be used to infer that an object suspected as a firearm has the capacity to shoot by confirming the occurrence of a shooting act or shooting process. Thus, photographic content analysis may be used to forensically establish that an object depicted in an image or a video is a firearm by ruling out other known scenarios, and without physically seizing it.
Is Prompt the Future?

A vast amount of unstructured data is being generated in the age of big data. Relation extraction (RE) is the critical way to improve the utility of the data by extracting structured data, which has seen a great evolution in recent years. This paper first introduces five paradigms of RE, namely the rule-based paradigm, the machine learning paradigm, the deep learning model-based paradigm, and the two types of current mainstream methods with pretrained language models. Based on the RE scenario, a comprehensive introduction is made for the currently popular paradigm with prompt learning, which is investigated regarding four aspects. The main contributions of this paper are as follows. Since big models are too large to be easily trained, prompt learning has become a promising research direction for RE, our work is, therefore, a systematic introduction to this paradigm for RE and compared with traditional paradigms. In addition, this paper summarizes the current problems faced by RE tasks and proposes valuable research directions with prompt learning.
In-situ enhanced anchor-free deep CNN framework for a high-speed human-machine interaction

Human-Robot Interaction (HRI) constitutes a demanding research field that integrates artificial intelligence, informatics, robotics, engineering, and human-machine interaction. In the present era, there is an increased focus on natural user interfaces, with particular attention to gestural modalities. This leads to the coordination of modern robotic systems, where real-time integration and interpretation of manual gestures play a vital role in facilitating effective manipulation. Hand gesture recognition holds a crucial role in the field of machine vision, perhaps greatly exacerbated with varying lighting conditions and backgrounds. In this paper, first an Enhanced Anchor-free Network (EAF-Net) is proposed to perform hand gesture recognition in real-time. The EAF-Net is deep single-stage Convolutional Neural Network (CNN) based architecture. The Custom Precise Prediction Function (CPPF) is utilized for a continuous recognition of a specific gesture from a video feed. Then, the 6-axis collaborative Robot is manipulated using the predicted gesture. The EAF-Net model utilizes the Enriched Hourglass as a backbone feature extraction network. The proposed EAF-Net model undergoes training and assessment using the MITI HD-II dataset. The performance of the model is also analyzed for the standard benchmark datasets (NUSHP-II and Senz-3D). The evaluation of the EAF-Net model’s performance spans a range of IoU values from 0.5 to 0.95. The EAF-Net model led to higher precision values (AP0.5) as 99.22%, recall values (AR0.5) as 98.20%, and F1-Score0.5 as 98.64% on the MITI HD-II dataset. The EAF-Net model consumed a prediction time of 259 ms on NVIDIA Jetson Nano Processor and 14 ms on NVIDIA Titan X GPU. It aided in implementing a portable high-speed 6-axis Robot control framework to pick and place operation in 3D space. The robot would be able to imitate human hand activities such as hand rotation, hand tilting, picking and arranging items, and so on in the near future. Further reduction in prediction time of the model on the portable processors (NVIDIA Jetson Nano, Raspberry-Pi) could enhance the speed of the Human Machine Interaction process.
Development of a table-top surface inspection system based on phase-measuring deflectometry

In this research, we developed a surface inspection system based on Phase-Measuring Deflectometry for specular surfaces. PMD is a non-contact, full-field optical measuring technique that measures the phase shifts caused by surface deformations. While PMD has seen significant advancements and increasing use in various industries due to its simple configuration and moderate precision, uncertainties in alignment and calibration can limit the practical performance of the system for industrial applications. To address this, we propose a system that measures freeform optical surfaces smaller than 50x50 mm2 using stereoscopic redundancy to eliminate height ambiguity. We apply self-calibration methods to maintain measurement precision below sub-micrometer level. Our measurement results of specular surfaces demonstrate a measurement precision of less than 0.1 um, with excellent convenience and less throughput time.
Variational Disentangle Zero-Shot Learning

Existing zero-shot learning (ZSL) methods typically focus on mapping from the feature space (e.g., visual space) to class-level attributes, often leading to a non-injective projection. Such a mapping may cause a significant loss of instance-level information. While an ideal projection to instance-level attributes would be desirable, it can also be prohibitively expensive and thus impractical in many scenarios. In this work, we propose a variational disentangle zero-shot learning (VDZSL) framework that addresses this problem by constructing variational instance-specific attributes from a class-specific semantic latent distribution. Specifically, our approach disentangles each instance into class-specific attributes and the corresponding variant features. Unlike transductive ZSL, which assumes that unseen classes’ attributions are known beforehand, our VDZSL method does not rely on this strong assumption, making it more applicable in real-world scenarios. Extensive experiments conducted on three popular ZSL benchmark datasets (i.e., AwA2, CUB, and FLO) validate the effectiveness of our approach. In the conventional ZSL setting, our method demonstrates an improvement of 12∼15% relative to the advanced approaches and achieves a classification accuracy of 70% on the AwA2 dataset. Furthermore, under the more challenging generalized ZSL setting, our approach can gain an improvement of 5∼15% compared with the advanced methods.
Simulasi Pertolongan Pertama Manajemen Fraktur di SMP Islam Terpadu Al-Ghozali Jember

This community service aims to increase students' knowledge and skills in first aid for fractures at Al-Ghozali Integrated Islamic Middle School Jember. The method used in carrying out community service activities regarding first aid to fracture victims involved lectures, discussions, and simulations related to handling fracture victims. The fracture Knowledge Questionnaire evaluated students' knowledge about fracture first aid. The results of the Wilcoxon test from the pre-test and post-test had a significance value of &lt; 0.05, meaning there are differences in students' knowledge before and after health education about first aid trauma management fractures. Fracture education and simulation improved students' ability to identify and perform appropriate fracture first aid.
ICDAR 2023 Competition on Detecting Tampered Text in Images

Document analysis and recognition techniques are evolving quickly and have been widely used in real-world applications. However, detecting tampered text in images is rarely studied. Existing image forensics research mainly focuses on detecting tampered objects in natural images. Text manipulation in images exhibits different characteristics, e.g.,, text consistency, imperceptibility, etc., which bring new challenges for image forensics. Therefore, We organized the ICDAR 2023 Competition on Detecting Tampered Text in Images (DTTI) and established a new dataset named TII, which consists of 11,385 images. 5,500 images are tampered using various manipulation techniques and annotated by pixel-level masks. Two tasks are set up: text manipulation classification and text manipulation detection. The contest started on 15th February, 2023 and ended on 20th March, 2023. Over a thousand teams were registered for the competition, with 277 and 176 valid submissions in each task. In this competition report, we describe the details of the proposed dataset, tasks, evaluation protocols and the results summaries. We hope that this competition could promote the research of text manipulation detection in images.
Global visibility of publications through Digital Object Identifiers

This brief research report analyzes the availability of Digital Object Identifiers (DOIs) worldwide, highlighting the dominance of large publishing houses and the need for unique persistent identifiers to increase the visibility of publications from developing countries. The study reveals that a considerable amount of publications from developing countries are excluded from the global flow of scientific information due to the absence of DOIs, emphasizing the need for alternative publishing models. The authors suggest that the availability of DOIs should receive more attention in scholarly communication and scientometrics, contributing to a necessary debate on DOIs relevant for librarians, publishers, and scientometricians.
Burden of Regulation on the performance and growth of SMEs: Empirical analysis in Silte Zone, Southern, Ethiopia

The main purpose of this study was to investigate the perception to wards the burden of regulation on the performance and growth of SMEs in Silate Zone, SNNPRS, Ethiopia . To attain the intended objective the study used quantitative research of descriptive statistics and multiple regression for pooled least squares estimations method used. Bassed on the accessed data, there were 1718 formally registered SMEs in the form of private limited companies and sole proprietor types of legal status in Silte Zone, SNNRS from 2016 to 2021. The result of this mixed approach explores that SMEs face challenges from different factors but, they perceived challenges from regulatory burden outweigh other factors. Pooled multiple regression for all independent and control variables simultaneous indicated that, except for inapproprate enforcement of regulatory fream works (0.157) and compatibility in the globa market of SMEs rate (0.213) all variables had negative coefficient. At the same time, apart from debt ratio p-value (0.258) all variables are significant at 1 percent degree of freedom. The value of adjusted R2 is 75.2 percent; while Durbin-Watson and F-statistic are, 1.47 and 25.81 respectively. The result also identifies mechanisms mostly used by SMEs to mitigate problems from regulations and how much the regulators have smooth contact with them and to what extent the regulators made inspection, consultation and communication. Moreover, the result identifies the relation between regulations and SMEs performance and growth based on their view i.e. they have positively related each other. As a whole, the study suggests a series of measures which could be taken by the government policy makers/regulators and for SMEs. This measure includes to adopt government impact assessment task force and review the existing regulations how much they enable and constrain businesses and at the same manner suggest for SMEs to incorporate different mechanisms to comply with regulation rather than rely more on specific mechanisms.