Reviewerly/exHarmony huggingface dataset:
Punctuation—Who Needs It?

Abstract Let’s face it: writing isn’t a natural act. Our ancestors may have started speaking between fifty thousand and two million years ago, but it was only five thousand years ago that people started to write. Three thousand more years passed before they began to use a system of little points that eventually evolved into our modern punctuation. Then it took centuries for those punctuation practices to develop. To put that in perspective, let’s say that humanity has existed for twenty-four hours. By traditional estimates, more than twenty-three of those hours passed before people invented writing. And punctuation didn’t come along until closer to midnight.1
Empowering Certificate Management with Blockchain Technology

The rise of online courses and certifications has created new opportunities for individuals to enhance their skills. However, this digital transformation has also given rise to coun- terfeit certificates. To address this multifaceted issue, we present a comprehensive certificate management system founded on blockchain technology and strengthened by smart contracts. Our innovative system comprises three pivotal components: certificate generation, authenticity verification, and a user-centric digital locker for certificate storage. Blockchain technology underpins the entire system, ensuring the immutability and integrity of each certificate. The inclusion of a cryptographic hash for each certificate is a fundamental aspect of our design. Any alteration in the certificate’s data will yield a distinct hash, a powerful indicator of potential tampering. Furthermore, our system includes a secure digital locker based on cloud storage that empowers users to efficiently manage and access all their certificates in one place. Moreover, our project is committed to providing features for certificate revocation and updating, thereby enhancing the system’s flexibility and security. Hence, the blockchain and smart contract-based certificate management system offers a robust and one-stop solution to the escalating problem of counterfeit certificates in the digital era
BEDOI: BENCHMARKS FOR DETERMINING OVERLAPPING IMAGES WITH PHOTOGRAMMETRIC INFORMATION

Abstract. For conventional SfM pipeline, image matching is enduring limitation when considering the time efficiency. In the last few years, to speed up image matching procedure, many image retrieval works were proposed to fast find overlapping image pairs, e.g., bag-of-word that clusters hand-crafted local features in a hierarchical way for efficient similar image retrieval, or learning-based global features (such as, VGG or ResNet) are used to represent image in a global compact manner. However, there are rarely benchmarks with referenced overlapping information to: first, evaluate the retrieval performance; second, fine tune deep-learning models along the direction that is more capable to deal with overlapping image pairs.In this work, based on traditional photogrammetric procedures, relevant photogrammetric information is obtained including image orientation parameters, 3D mesh model and etc., we then generate a benchmark for determining Overlapping Images - BeDOI, in which referenced pairwise overlapping relationships are estimated via rigorous photogrammetric geometry. To extend the generality, in total, BeDOI contains 13667 images which are basically UAV and close-range images of various scene categories, e.g., urban cities, campus, village, historical relics, green land, buildings and etc. Lastly, to demonstrate the efficacy of the proposed BeDOI, several image retrieval methods are tested and the experimental results are reported as a competition challenge.
TATune: A RocksDB Knob Tuning System Based on Transformer

RocksDB is a powerful database engine that offers a wide range of adjustable knobs, which greatly influence its performance. However, configuring RocksDB manually for optimal performance is challenging due to the large number of available knobs and their complex settings. To address this issue, we propose Transformer Adaptive Gentic Algorithm Tune(TATune), an auto-tuning system for RocksDB knobs. In TATune, knob configuration files for RocksDB are randomly generated and executed at different preset workloads first. Subsequently, the correlation between the knob and RocksDB performance is learned by the prediction model based on Transformer. Finally, an adaptive genetic algorithm that utilizes the prediction model as a fitness function to recommend the RocksDB knob setting. Additionally, a novel optimization metric is also proposed to evaluate the performance of the auto-tuning RocksDB knob system. TATune is compared with other approaches to configure RockDB knobs on six distinct workloads. The results indicate that TATune is effective and achieves significant performance improvement across various target workloads. The final average optimization performance is 26% better than K2vTune and 72% better than RTune.
Certifier Dapp - Decentralized and Secured Certification System Using Blockchain

Current certification systems suffer from various problems such as vulnerability to fraud, time-consuming verification processes, delays in obtaining certificates, limited access due to geographical limitations, high costs, outdated content, lack of flexibility, credibility concerns, quality control issues, technical difficulties, limited interaction, limited feedback, and barriers to access. To address these problems, we propose a solution in the form of Certifier Dapp, a decentralized application built on the Ethereum blockchain. The Certifier Dapp leverages the MetaMask wallet for authentication and communication with the blockchain and offers functionalities such as issuing new certificates, updating issued certificates, verifying certificates, and tracking change history. Utilizing blockchain technology, the Certifier Dapp ensures data integrity, immutability, transparency, and security. The proposed solution aims to provide a seamless and efficient certification process, overcoming the limitations of current certification systems and making certification more accessible and credible for users.
[Fraction Characteristic and Risk Assessment of Heavy Metals in Surface Sediments of the Yellow River Mainstream].

Heavy metals(HMs) are highly toxic and do not easily degrade in the environment. They can accumulate in the human body through the food chain, with serious impacts on the ecological environment and human health. In this study, 14 sampling sites along the mainstream of the Yellow River were investigated, and the total content and chemical fractions of six heavy metals(As, Cd, Cr, Cu, Pb, and Zn) in sediments were analyzed. The geo-accumulation index, potential ecological risk index, and risk assessment coding were used to assess the contamination level and bioavailability of HMs in sediment. Principal component analysis and redundancy analysis were used to identify the main sources of HMs. The results showed that the average content of heavy metals was in the order of Cr>Zn>Cu>Pb>As>Cd. Cd had the highest excess rate relative to the background value of each reach, reaching 85.7%. In the sediments, As, Zn, Pb, and Cu were predominantly in the oxidizable fraction(F3:sulfide and organic matter-bound), Cd was mainly occupied by the acid-soluble(F1:exchangeable and carbonate-bound) and a residual fraction(F4:mineral matrix-bound), and Cr was predominantly in the residual fraction. As, Cr, Cu, Pb, and Zn showed the lowest bioavailability in the upstream, increased in the midstream, and finally decreased in the downstream. The risk assessment showed that Cd, the element with the highest pollution risk level in the Yellow River, was prone to pose a serious threat to the ecological environment and should be prevented and controlled first. The PCA and RDA analyses revealed that fine sediment and total organic matter controlled HMs contamination. Therefore, the prevention and control of soil erosion and sediment migration should be emphasized to control HMs pollution in the Yellow River Basin.
Manual Segmentation of the Human Choroid Plexus Using Brain MRI

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for brain maturation, immune/ inflammatory regulation, and behavioral/cognitive functioning. However, current automated neuroimaging segmentation tools are poor at accurately and reliably segmenting the lateral ventricle choroid plexus. Furthermore, there is no existing tool that segments the choroid plexus located in the third and fourth ventricles of the brain. Thus, a protocol delineating how to segment the choroid plexus in the lateral, third, and fourth ventricle is needed to increase the reliability and replicability of studies examining the choroid plexus in neurodevelopmental and brain disorders. This protocol provides detailed steps to create separately labeled files in 3D Slicer for the choroid plexus based on DICOM or NIFTI images. The choroid plexus will be manually segmented using the axial, sagittal, and coronal planes of T1w images making sure to exclude voxels from gray or white matter structures bordering the ventricles. Windowing will be adjusted to assist in the localization of the choroid plexus and its anatomical boundaries. Methods for assessing accuracy and reliability will be demonstrated as part of this protocol. Gold standard segmentation of the choroid plexus using manual delineations can be used to develop better and more reliable automated segmentation tools that can be openly shared to elucidate changes in the choroid plexus across the lifespan and within various brain disorders.
Development of Natural Language Dialogue Software Systems

This paper discusses problems of natural language dialogue systems development. Here are considered different types of such program systems and their peculiarities based on our development experience. The systems discussed here are aimed at using graph ontological databases. Five basic types of natural language systems are highlighted: reference system, reference system with clarifying and technical questions, questionnaire system, active initiative system, control system. The first four of them are considered in detail. The reference system type, which is the basic type, is shown and discussed. Problems considered here are: natural language phrase to a formal query transformation, semantic analysis and answer formation. The ways are shown for these processes program implementation. For all the types the problem of the current dialogue subject keeping is considered, especially for an active initiative system where it is the most crucial. The different approaches are shown and compared for the principles of graph database (ontology) construction, which could be used in the dialogue systems.
VetLLM: Large Language Model for Predicting Diagnosis from Veterinary Notes

Lack of diagnosis coding is a barrier to leveraging veterinary notes for medical and public health research. Previous work is limited to develop specialized rule-based or customized supervised learning models to predict diagnosis coding, which is tedious and not easily transferable. In this work, we show that open-source large language models (LLMs) pretrained on general corpus can achieve reasonable performance in a zero-shot setting. Alpaca-7B can achieve a zero-shot F1 of 0.538 on CSU test data and 0.389 on PP test data, two standard benchmarks for coding from veterinary notes. Furthermore, with appropriate fine-tuning, the performance of LLMs can be substantially boosted, exceeding those of strong state-of-the-art supervised models. VetLLM, which is fine-tuned on Alpaca-7B using just 5000 veterinary notes, can achieve a F1 of 0.747 on CSU test data and 0.637 on PP test data. It is of note that our fine-tuning is data-efficient: using 200 notes can outperform supervised models trained with more than 100,000 notes. The findings demonstrate the great potential of leveraging LLMs for language processing tasks in medicine, and we advocate this new paradigm for processing clinical text.
DGT-AR: Visualizing Code Dependencies in AR

Analyzing source code dependencies between components within a program is an essential activity in software development. While various software visualization tools have been proposed to aid in this activity, most are limited to desktop applications. As a result, the potential impact of augmented reality (AR) on improving dependency analysis remains largely unexplored. In this paper, we present DGT-AR, a node-link visualization tool for code dependencies in immersive augmented reality. DG T-AR extends the physical screen space of IDEs to the infinite virtual space. That is, developers neither have to sacrifice screen space nor leave the IDE and use third-party applications. We present the preliminary results of a pilot user study along with four key lessons learned. Additionally, we have made DGT-AR publicly available.
Detection and Measurement of Object Distances Using the Combination Function of 2D LiDAR Sensor and Camera

Automation systems in transport vehicles such as forklifts can provide solutions in terms of time, quantity, and accommodation costs during operation, as conventional forklift operation requires drivers with specific certifications and qualifications. Automation in forklifts requires various control functions that utilize various types of data. Some functions needed in forklift automation include object detection and object distance measurement. Object distance measurement will utilize LiDAR (Light Detection and Ranging) technology, which offers high accuracy with a wide detection range and can be used at various scanning angles. Using 2D LiDAR for scanning can only provide output in the form of detection along a single horizontal line, so for three-dimensional object recognition, the system requires additional supporting sensors such as cameras. Object detection or recognition using cameras can be performed using deep learning applications with specific datasets of objects to be detected. Object detection is implemented using YOLOv5, while object distance measurement utilizes data fusion and projection from 2D LiDAR with camera data. In this system, object detection data is provided with a precision of 0.97945 using YOLOv5 model with type-m epoch 100, while the best distance measurement is achieved using the projection method from 2D LiDAR data to the camera with an error rate of 0.15%.
3D Face Reconstruction as Complementary Data to Enhance Face Recognition

Thanks to advancements in deep neural networks and the availability of large datasets, face recognition (FR) has made remarkable progress in recent years, achieving a level of accuracy comparable to that of humans. Nonetheless, the current state-of-the-art methods primarily depend on training with 2D facial images (RGB), which may not be able to capture certain discriminant geometric facial features. Unfortunately, there are only small datasets containing 3D point cloud faces acquired using specific sensors such as laser or infrared, which poses challenges in training large 3D FR models. In this paper, we introduce a novel approach to enhance state-of-the-art 2D facial recognition by adapting 3D object recognition models trained using 3D reconstructed faces from single RGB images. Through our experiments involving training the ResNet, PointNet++, and PointNeXt models using 1k, 2k, 5k, and 10k subjects with the ArcFace loss, we have observed that the combined model achieves higher accuracy compared to each individual model. This improvement can be attributed to the diverse and complementary facial features extracted by different models. Leveraging 3D reconstructed faces presents a viable solution for addressing the scarcity of available 3D scanned faces. However, the extent of improvement will rely on the quality of the reconstruction process.
Automated Formal Register Scoring of Student Narrative Essays Written in Portuguese

Automated essay scoring (AES) is the task of automatically assigning scores (i.e., grades) to written texts. Although AES has been widely studied in the literature (e.g., informational and argumentative essays), specific types of texts still need more attention. Narrative essays are characterized by texts describing personal experiences and stories, either real or fictional. In this work, we describe a study on scoring student essays written in Portuguese under the aspect of Formal Register, which evaluates aspects related to the use of Brazilian Portuguese formal grammar and proficiency. The dataset created in this study provides a rich corpus of narrative essays produced in the context of a motivational situation, with a diverse set of language proficiency levels annotated by two professional graders. Different machine learning algorithms were evaluated using a diverse set of handcrafted linguistic features, and their results were compared against manual scores by the two human annotators. The results of the proposed analysis demonstrated that the AES model proposed achieved an equivalent agreement to that of the two human annotators.
Foreign Language Anxiety of College English Teachers and Their Countermeasures

It is necessary for English teachers to grasp the causes of students' language anxiety and explore ways to avoid, reduce, and eliminate students' anxiety. This paper discusses the foreign language anxiety of college English teachers in classroom teaching, its possible causes, teachers' awareness of anxiety, and countermeasures. This paper introduces the composition of student affairs analysis system from data layer, analysis layer, application layer, and display layer and combines data warehouse and data mining technology to improve the functions of student information, teacher information, achievement information, course selection information, and course evaluation. On the premise of data mining and data information management, it realizes the construction and application of teaching management data analysis system, using classification model. Apriori algorithm improves the algorithm, uses big data technology to analyze data and design courses, and analyzes the inherent relationship between mental health problems and attributes.
Indian Sign Language Recognition Using Scale-Invariant Feature Transform by Depth Sensor

Deaf and hard of hearing people in the Indian community now have a way to communicate with society because to Indian Sign Language (ISL). The hand is accorded significant importance in the framework of the ISL. ISL is composed of both static and dynamic sign hand gestures. Both static and dynamic signs are made up of three components: hand movement, hand orientation, and hand position. The proposed method can recognize static ISL signs. The technique that was suggested makes use of the Microsoft Kinect sensor to locate and separate a hand from its background in a complex scene. SIFT, which stands for “scale-invariant feature transform,” was used to extract the feature. The SIFT algorithm is invariant under changes in perspective, orientation, and lighting. Recognize the 10 ISL digit movements with an accuracy of 94.9% using a multi support vector machine (MSVM).
Empowering Secure and Cost-Efficient Blockchain Electronic Voting by Optimized ZK-SNARK Algorithm

Electronic voting systems are crucial for maintaining democratic principles and ensuring transparency in modern democracies. Initially designed for secure and decentralized record-keeping, blockchain technology holds significant potential for revolutionizing the voting process by providing tamper-resistant and auditable ledgers. To this end, our research proposes a robust blockchain platform to enhance the security and affordability of electronic voting. The platform's architecture leverages the innovative Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) algorithm to enhance privacy and security while minimizing computational overhead. By integrating ZK-SNARKs, we enable voters to prove their eligibility and vote correctness without disclosing their votes. Our proposed platform also incorporates circuit design optimizations and pairing-friendly elliptic curves, using efficient cryptographic curves like the alt_bn128 or similar options suitable for ZK-SNARKs. This approach ensures the integrity and authenticity of the voting process. This paper offers a comprehensive understanding of the challenges posed by traditional voting systems, outlines the architecture of the proposed blockchain platform, and details the optimized implementation of the ZK-SNARK algorithm. Our research aims to advance secure, cost-effective electronic voting systems that enable inclusive and trustworthy elections.
Automatic Early Detection System of Feverish Infectious Diseases in Indoor Public Areas

This paper introduces a 3-layer IoT architecture designed to automatically detect and identify individuals with feverish infectious diseases in indoor public areas. The architecture comprises the sensing layer, edge computing, and cloud computing components. Sensor modules capture facial images and body temperature, which are wirelessly transmitted to an edge device through MQTT for identification using machine learning techniques. Subsequently, the data is forwarded to a cloud server through HTTPS for storage, feedback generation, and presentation to authorized users. Each subsystem is meticulously designed to meet its specifications. We conclude the prototype's design and implementation are successful within a controlled condition. The proposed system is also viable for future developments such as attendance management, airport security, and others. However, the design and implementation process also exhibits certain limitations, notably concerning the reliability and uptime of the sensory module.
Active Learning Method for Pairwise Comparison Data

Companies need to create a product position map and correctly grasp the current situation. In the recent increase in the number of e-commerce sites, the influence of product images on customers' willingness to purchase is huge. In other words, if we can construct a product map based on customers' impressions of product images, we can expect to make the product lineup on e-commerce sites more efficient and attractive. Semantic Differential (SD) method and Multi-dimensional scaling (MDS) have been conventionally used to create product maps. However, these methods are unsuitable for analyzing a target with a large number of products, such as EC sites, because the number of product evaluation data required for the analysis increases depending on the number of products. Therefore, the authors proposed a method to construct a product image map of product images with high accuracy from a relatively small number of pairwise comparison data. Specifically, a model that estimates the pairwise comparison data by subjects is machine-learned. The learned model is used to supplement the missing pairwise comparison data to estimate the evaluation values of product images for a specific axis. The values are used to construct a map. The method of selecting a small number of data used to train the model can quickly identify the overall trend. Therefore, this study proposes an adequate data selection criterion to improve the accuracy of the map based on the concept of active learning. Experiments show that, in addition to uncertainty considerations, it is adequate to have a uniform amount of pairwise comparison data for each subject in the problem of complementing pairwise comparison data between subjects, as in the case of this study.