Reviewerly/exHarmony huggingface dataset:
Dual-Core Adaptive NLM Image Denoising Algorithm Based on Variable-Size Window and Neighborhood Multifeatures

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian–Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect.
SMART DOOR LOCK SYSTEM USING BLOCKCHAIN BASED ON SOLANA TECHNOLOGY

Smart door lock system is based on blockchain technology which plays an important role in securing homes and properties. Blockchain technology improves security, trust, and privacy in smart door lock systems. This research paper presents an ideal approach to enhancing the security and efficiency of smart door lock systems by incorporating blockchain technology based on the Solana platform. It enhances security by using a decentralized consensus method to necessitate centralized authority. The system is resistant to single points of failure and unauthorized access attempts. In this study, we propose a blockchain-based smart door lock, specifically designed with the help of the Solana platform. The goal is to enhance the security and performance of traditional door lock systems by utilizing the decentralized, tamperproof, and unchangeable properties offered by blockchain technology. Solana blockchain technology makes smart door lock systems safer and more efficient.
A global optimization generation method of stitching dental panorama with anti-perspective transformation

&lt;abstract&gt;&lt;p&gt;To address the limitation of narrow field-of-view in local oral cavity images that fail to capture large-area targets at once, this paper designs a method for generating natural dental panoramas based on oral endoscopic imaging that consists of two main stages: the anti-perspective transformation feature extraction and the coarse-to-fine global optimization matching. In the first stage, we increase the number of matched pairs and improve the robustness of the algorithm to viewpoint transformation by normalizing the anti-affine transformation region extracted from the Gaussian scale space and using log-polar coordinates to compute the gradient histogram of the octagonal region to obtain the set of perspective transformation resistant feature points. In the second stage, we design a coarse-to-fine global optimization matching strategy. Initially, we incorporate motion smoothing constraints and improve the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm by utilizing neighborhood information for coarse matching. Then, we eliminate mismatches via homography-guided Random Sample Consensus (RANSAC) and further refine the matching using the Levenberg-Marquardt (L-M) algorithm to reduce cumulative errors and achieve global optimization. Finally, multi-band blending is used to eliminate the ghosting due to unalignment and make the image transition more natural. Experiments show that the visual effect of dental panoramas generated by the proposed method is significantly better than that of other methods, addressing the problems of sparse splicing discontinuities caused by sparse keypoints, ghosting due to parallax, and distortion caused by the accumulation of errors in multi-image splicing in oral endoscopic image stitching.&lt;/p&gt;&lt;/abstract&gt;
The relationship between teacher candidates' lifelong learning tendencies and their digital competencies

Digital competencies and lifelong learning tendencies are among the features that every teacher candidate should have. Therefore, carrying out research on both lifelong learning trends and digital competencies can contribute to the development and updating of teacher education programs. In this context, the aim of this research is to analyses the relationship between lifelong learning tendencies and digital competencies of teacher candidates. In this research, scanning model from descriptive research models and explanatory model from relational research methods were used. The sample of this research consists of 287 teacher candidates selected by simple random sampling method from among teacher candidates studying at this state university. In this study, two scales were used to determine the relationship between the lifelong learning tendencies of teacher candidates and their digital competencies. The first of these scales is the “Lifelong Learning Tendency Scale”. The second scale used in the research is the "Digital Competency Scale". There is a significant and moderate relationship between the lifelong learning tendencies scale in general and the competencies of using basic internet tools and the competencies of acquiring and using information in digital environment. Since there is a moderately significant relationship between lifelong learning tendencies and digital competence, these two concepts should be reflected in teacher education programs in a way that supports each other, without considering each other separately.
How do decoding algorithms distribute information in dialogue responses?

Humans tend to follow the Uniform Information Density (UID) principle by distributing information evenly in utterances. We study if decoding algorithms implicitly follow this UID principle, and under what conditions adherence to UID might be desirable for dialogue generation. We generate responses using different decoding algorithms with GPT-2 on the Persona-Chat dataset and collect human judgments on their quality using Amazon Mechanical Turk. We find that (i) surprisingly, model-generated responses follow the UID principle to a greater extent than human responses, and (ii) decoding algorithms that promote UID do not generate higher-quality responses. Instead, when we control for surprisal, non-uniformity of information density correlates with the quality of responses with very low/high surprisal. Our findings indicate that encouraging non-uniform responses is a potential solution to the “likelihood trap” problem (quality degradation in very high-likelihood text). Our dataset containing multiple candidate responses per dialog history along with human-annotated quality ratings is available at: https://huggingface.co/datasets/saranya132/dialog_uid_gpt2.
Causal Reasoning of Entities and Events in Procedural Texts

Entities and events are crucial to natural language reasoning and common in procedural texts. Existing work has focused either exclusively on entity state tracking (e.g., whether a pan is hot) or on event reasoning (e.g., whether one would burn themselves by touching the pan), while these two tasks are often causally related. We propose CREPE, the first benchmark on causal reasoning of event plausibility and entity states. We show that most language models, including GPT-3, perform close to chance at .35 F1, lagging far behind human at .87 F1. We boost model performance to .59 F1 by creatively representing events as programming languages while prompting language models pretrained on code. By injecting the causal relations between entities and events as intermediate reasoning steps in our representation, we further boost the performance to .67 F1. Our findings indicate not only the challenge that CREPE brings for language models, but also the efficacy of code-like prompting combined with chain-of-thought prompting for multihop event reasoning.
Zero-Shot On-the-Fly Event Schema Induction

What are the events involved in a pandemic outbreak? What steps should be taken when planning a wedding? The answers to these questions can be found by collecting many documents on the complex event of interest, extracting relevant information, and analyzing it. We present a new approach in which large language models are utilized to generate source documents that allow predicting, given a high-level event definition, the specific events, arguments, and relations between them to construct a schema that describes the complex event in its entirety.Using our model, complete schemas on any topic can be generated on-the-fly without any manual data collection, i.e., in a zero-shot manner. Moreover, we develop efficient methods to extract pertinent information from texts and demonstrate in a series of experiments that these schemas are considered to be more complete than human-curated ones in the majority of examined scenarios. Finally, we show that this framework is comparable in performance with previous supervised schema induction methods that rely on collecting real texts and even reaching the best score in the prediction task.
Adapting Information Security Policy Messaging Styles to the Targeted Audience

Current research aims to investigate whether it would be possible to identify all information security policy (ISP) writing styles and how these would influence ISP compliance. Almost all businesses use ISPs to establish boundaries and require secure behaviour from their employees. Unfortunately, professional surveys and academic research demonstrate a high level of non-compliance with the ISP. While the justification for the employee’s behaviour has been discussed, very few research papers have investigated whether the ISP writing style impacted the intent to comply with ISP. The research methodology incorporates content analysis and a quantitative descriptive review of published papers on ISP and non-IS policy compliance. The theoretical research allowed the identification of five major ISP writing styles: belonging, deterrence, goal, motivation, and specialist, as well as writing style influencers such as timeliness and readability. To achieve a higher level of compliance with the ISP, it was suggested that the writing styles of belonging, goal, and motivation be used primarily. Deterrence is generally discouraged. The study enabled us to determine when ISP writing styles were mentioned and the type of influence on the intent to comply with ISP. It also allowed for comparison and possible differences in ISPs versus standard workplace policies. There are proposals on which writing styles to put forward, along with recommendations on creating an ISP.
A blockchain based private framework for facilitating digital forensics using IoT

Introduction: Images play an essential role as evidence in any forensic analysis of a police investigation. They are a crucial part of the analysis and store important and nuanced information required to guide the analysis in the right direction and then finally lead the investigation to a just conclusion. However, these images are often subject to tampering. Malicious elements of society external or internal entities involved in conducting the investigation, try to tamper with, mend, and destroy pieces of evidence. This change can mislead the direction of the evidence and can benefit the accused. Research Problem: Through the scope of this paper, we will try to tackle this problem and find a solution for it. Methods: The approach proposed to tackle this problem is to implement a blockchain-based, distributed virtual private network (VPN) system. The network consists of devices spread across the internet, connected via a VPN. Only authorized devices are a part of the network and each device has a particular role and key. The image is captured from the IoT device at the crime scene, it is then encrypted using a Mixed Alphabet Cipher substitution algorithm that converts the RGB values to a string at the camera node and then transferred to the main server. It is further decrypted and again encrypted using the key of the main server. Upon encryption, the string is converted to a hash key which is then added to the blockchain. Results: The performance of the proposed network is tested on two different parameters- operating system and image size. It was found that the encryption time of images varies linearly with image size, operating system and image size. The different operating systems are Ubuntu and two variants of MAC OS. The images being tested vary from 200x200 px to 3840x2160px. The encryption and decryption time of different images is recorded, and it was found that the encryption time of images varies linearly with image size. Conclusion: The proposed solution was successfully developed and tested on different sets of images and operating systems. As our solution uses an in-house developed blockchain network, it has a better working cost than using an Ethereum-based blockchain.
The Effectiveness of Childcare Teachers' Play Support and the Creativity of Professors Effect of the Play-oriented Childcare Course on Practice Competency

본 연구의 목적은 보육교사의 놀이 지원 효능감과 교수 창의성이 놀이 중심 보육 과정 실천역량에 미치는 영향을 알아보고자 진행되었다. 연구대상은 충청남도에 거주하는 보육교사 300명을 대상으로 설문조사를 실시하였고 설문 300부 중 응답이 불성실하게 작성된 것을 제외하고 최종적으로 283부를 연구 자료로 사용하였다. 자료 분석은 SPSS 28.0을 사용하여 빈도와 백분율 산출, Cronbach’s α 산출, 상관관계분석, 다중회귀분석을 실시하였다. 본 연구 결과를 요약하면 첫째, 보육교사의 놀이 지원 효능감과 교수 창의성, 놀이 중심 보육 과정 실천역량 모두 평균이 중간 이상의 높은 수준을 보였다. 둘째, 보육교사의 놀이 지원 효능감과 교수 창의성이 모두 유의미한 정적 상관이 나타났다. 셋째, 보육교사의 놀이 지원 효능감과 교수 창의성이 보육교사의 놀이 중심 보육 과정 실천역량에 미치는 요인으로는 보육교사의 놀이 환경 조성과 놀이 지원 성찰 관련 효능감이 높을수록 놀이 중심 보육 과정 실천역량은 높아지고, 확산적 사고와 행위, 집중하여 과제 수행하기, 영유아에 대한 개방성과 모호함의 허용 수준이 높을수록 놀이 중심 보육 과정 실천역량은 높아지는 것을 의미하므로, 요인들이 놀이 중심 보육 과정 실천역량에 긍정적인 영향을 미치는 주요 예측 요인임을 알 수 있다. 이를 통해. 보육교사의 놀이 지원 효능감과 교수 창의성이 놀이 중심 보육 과정 실천역량에 증진을 위한 다양한 노력이 필요하며 보육교사의 놀이 중심 보육 과정 실천향상을 위한 기초자료를 제공하고자 한다.The purpose of this study is to investigate the effect of childcare teachers' play support efficacy and teaching creativity on the play-oriented childcare process practice competency. The subjects of the study were a survey of 300 childcare teachers living in Chungcheongnam-do, and 283 out of 300 surveys were finally used as research data, except where responses were faithfully written. For data analysis, frequency and percentage calculation, Cronbach's α calculation, correlation analysis, and multiple regression analysis were performed using SPSS 28.0. The results of this study are summarized as follows. First, the average of childcare teachers' play support efficacy, teaching creativity, and play-oriented childcare process practice skills were higher than moderate. Second, there was a significant positive correlation between both play support efficacy and teaching creativity of childcare teachers. Third, the higher the effectiveness of nursery teachers on the play-centered childcare process, the higher the play-centered childcare process, and the higher the level of openness and ambiguity, the more positive the play-centered childcare process is. Through this, various efforts are needed to improve the play support efficacy and teaching creativity of childcare teachers, and it is meaningful to provide basic data for improving the play-oriented childcare process.
A double-cycle echo state network topology for time series prediction

Echo state network (ESN) has gained wide acceptance in the field of time series prediction, relying on sufficiently complex reservoir connections to remember the historical features of the data and using these features to obtain the outputs by a simple linear readout. However, the randomness of its input and reservoir connections pose negative impacts on the prediction performance and performance stability of the models, the complexity of reservoir connections brings high time consumption during network computing, and the presence of randomness and complexity makes the hardware implementation of the ESN difficult. In response, we propose a double-cycle ESN (DCESN) based on the Li-ESN model, which has fixed weights to improve prediction performance and performance stability and simpler reservoir connections compared to the classical ESN to reduce the time consumption. The existence of both greatly reduces the difficulty of hardware implementation of the ESN and provides many conveniences for the future application of the ESN. Experimental results on many widely used time series datasets show that the DCESN has comparable or even better prediction performance than the ESN and good robustness against noise and parameter fluctuations.
Local-Aware Intra Template Matching Prediction

Intra template matching prediction (IntraTMP) has been adopted in enhanced compression model (ECM) to code both screen and camera-captured content. In IntraTMP, a prediction block is copied from a reconstructed region of the current picture, wherein the location is derived using template matching. However, template matching may not find a perfect prediction block when the correlation between the template and the current block is limited. In this paper, a method of local-aware intra template matching prediction (LA-IntraTMP) is proposed, in which two prediction blocks are generated by IntraTMP and angular intra prediction, respectively, which are then fused together to obtain the final prediction block. Specifically, the template cost calculation is modified accordingly to construct an IntraTMP candidate list. Furthermore, the IntraTMP candidate list can be reordered based on template costs. Finally, the best IntraTMP candidate determined by the encoder can obtain a prediction block, fused with the angular intra prediction. Experimental results demonstrate that LA-IntraTMP can achieve 0.19% Bjontegaard Delta rate (BD-rate) saving compared to ECM-7.0 under all intra (AI) configuration.
Document Binarization with Multi-Branch Gated Convolutional Generative Adversarial Networks

Existing document binarization methods can not extract stroke edges finely, mainly due to the fair-treatment nature of vanilla convolutions and the extraction of stroke edges without adequate supervision by boundary-related information. In this paper, we formulate text extraction as the learning of gating values and propose a novel end-to-end gated convolutions-based network (GDB) to solve the problem of imprecise stroke edge extraction. The gated convolutions are applied to selectively extract the features of strokes with different attention. Firstly, a coarse sub-network with an extra edge branch is trained to get more precise feature maps by feeding a priori mask and edge. Secondly, a refinement sub-network is cascaded to refine the output of the first stage by gated convolutions based on the sharp edge. For global information, GDB also contains a multi-scale operation to combine local and global features. Experimental results show that our proposed methods outperform the SOTA methods in terms of all metrics on average over all DIBCO datasets from 2009 to 2019 and achieve top ranking on six benchmark datasets. Available codes: https://github.com/Royalvice/GDB.
Enhancing Low-Light Images Using Infrared Encoded Images

Low-light image enhancement task is essential yet challenging as it is ill-posed intrinsically. Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss, which limits the capacity of recovering the brightness, contrast, and texture details due to the small number of income photons. In this work, we propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter, which allows for the capture of more photons and results in improved signal-to-noise ratio due to the inclusion of information from the IR spectrum. To verify the proposed strategy, we collect a paired dataset of low-light images captured without the IR cut-off filter, with corresponding long-exposure reference images with an external filter. The experimental results on the proposed dataset demonstrate the effectiveness of the proposed method, showing better performance quantitatively and qualitatively. The dataset and code are publicly available at https://wyf0912.github.io/ELIEI/
Error Action Recognition on Playing The Erhu Musical Instrument Using Hybrid Classification Method with 3D-CNN and LSTM

Erhu is a stringed instrument originating from China. In playing this instrument, there are rules on how to position the player's body and hold the instrument correctly. Therefore, a system is needed that can detect every movement of the Erhu player. This study will discuss action recognition on video using the 3DCNN and LSTM methods. The 3D Convolutional Neural Network method is a method that has a CNN base. To improve the ability to capture every information stored in every movement, combining an LSTM layer in the 3D-CNN model is necessary. LSTM is capable of handling the vanishing gradient problem faced by RNN. This research uses RGB video as a dataset, and there are three main parts in preprocessing and feature extraction. The three main parts are the body, erhu pole, and bow. To perform preprocessing and feature extraction, this study uses a body landmark to perform preprocessing and feature extraction on the body segment. In contrast, the erhu and bow segments use the Hough Lines algorithm. Furthermore, for the classification process, we propose two algorithms, namely, traditional algorithm and deep learning algorithm. These two-classification algorithms will produce an error message output from every movement of the erhu player.
PREFAB-GEN : AD HOC Image Generation for Pre-Manufacturing of Tires Using Image-To-Image Translation

In the pneumatic industry, quality control is an essential step in assessing tire compliance. Artificial neural networks are increasingly used to accomplish this task. Their training requires a large number of images of the controlled products. However, at the launch production of a new tire, the lack of images causes a performance loss for the network. To solve this problem, we propose to translate perfect tires computer-based images into ad hoc manufacturing context-realistic ones as pre-manufacturing step to improve robustness and ensure production quality. The challenging work is to extract features in real images and apply them to computer-based images while maintaining the original geometry. In the paper, we propose Prefab-GEN, a novel architecture based on Cycle-GAN. In the generator part, an Inception U-Net architecture is developed to enforce geometrical structure conversion and extract more detailed features. The qualitative and quantitative evaluation on tire dataset shows improvements compared with state-of-art.