Reviewerly/exHarmony huggingface dataset:
Multilingual Automatic Extraction of Linguistic Data from Grammars

One of the goals of field linguistics is compilation of descriptive grammars for relatively little-studied languages. Until recently, extracting linguistic characteristics from grammatical descriptions and creating databases based on them was done manually. The aim of this paper is to apply methods of multilingual automatic information extraction to grammatical descriptions written in different languages of the world: we present a search engine for grammars, which would accelerate the tedious and time-consuming process of searching for information about linguistic features and facilitate research in the field of linguistic typology.
Identification of Alzheimer Detection Using Hybrid Machine Learning Approach

Abstract: Early detection of Alzheimer's is very important. The early and precise detection of Alzheimer’s disease-associated side effects and fundamental malady pathology by clinicians is essential for the screening, determination, and ensuing administration of Alzheimer’s illness patients. It moreover empowers patients and their caregivers to arrange for a long-standing time and make fitting way-of-life changes that might offer assistance to keep up their quality of life for longer. Tragically, recognizing early-stage Alzheimer’s illness in clinical hone can be challenging and is hindered by a few boundaries counting imperatives on clinicians’ time, trouble precisely diagnosing Alzheimer’s pathology, which patients and healthcare suppliers frequently expel indications as portion of the typical maturing prepare. As the predominance of this infection proceeds to develop, the current show for Alzheimer’s infection conclusion and persistent administration will be got to advance to coordinated care over clinical disciplines and the infection continuum, starting with essential care
Triple Entry Accounting with Blockchain Technology

Blockchain technology has led the evolution of double entry accounting system to triple entry accounting system. Triple entry accounting is an innovative, promising and potential accounting method when implemented properly would be a game changer for dissemination of accounting information. It is an efficient way to address fundamental concerns of accounting information. This chapter discusses the triple entry accounting system, how it is different from double entry accounting and what are the concerns in implementing triple entry accounting. Triple entry accounting holds the potential to fundamentally evolve accounting practices, can enhance the effective utilisation and sustainable management of resources, and can contribute in development of financial markets.
The effect of mixed orientation on the accuracy of a forecast model for building integrated photovoltaic systems

BIPVs (Building Integrated Photovoltaics) are expected to dominate the solar industry in urban areas, especially in connection with zero-emission buildings. They allow the use of mixed orientations which result in multiple generation peaks throughout the day that can precisely match the consumption profile of the building. Multiple generation peaks however make the design of an accurate PV (Photovoltaic) output power forecasting tool a complex task. This paper, therefore, aims to quantify the effect of these mixed orientations on the accuracy of such prediction models for a 181.15 kWp BIPV located in Trondheim, Norway. The results show that a forecast model that has a complete perception of all the mixed orientations reduced the RMSE (Root Mean Squared Error) forecast error by 34%. The findings in this work have important implications for developing a practical energy management system that is common in the operation of BIPVs.
Central Bank Digital Currency Can Lead to the Collapse of Cryptocurrency*

The chapter evaluates how the demise of cryptocurrencies as a medium of exchange may result from the issue of digital money by central banks. To evaluate the likelihood that central bank digital money would cause the demise of cryptocurrencies, the research employs discourse analysis and literature review. In this chapter, I demonstrate how the issuing of a digital currency by a central bank might result in the demise of private digital currencies like bitcoin. I contend that central banks will make use of their monetary authority and the confidence that people have in currency guaranteed by the government. This might provide considerable motivation for central banks to launch their own digital money. The creation of a digital currency by a central bank has the potential to reduce confidence in cryptocurrencies, which might eventually cause them to collapse. The chapter is the first to argue that fiat digital money should prevail over private digital currency.
OpenCCO: An Implementation of Constrained Constructive Optimization for Generating 2D and 3D Vascular Trees

In this article, we focus on the algorithm called CCO (Constrained Constructive Optimization), initially proposed by Schreiner and Buxbaum [17] and further extended in [7].This algorithm can be considered as one of the gold standards for vascular tree structure generation.Modeling and/or simulating the morphology of vascular networks is a challenging but crucial task that can have a strong impact on different applications such as fluid simulation or learning processes related to image segmentation.Various implementations of CCO were proposed over the last years.However, to the best of our knowledge, there does not exist any open-source version that faithfully follows the native CCO algorithm.Our purpose is to propose such an implementation both in 2D and 3D.
Confidence-guided mask learning for semi-supervised medical image segmentation

Semi-supervised learning aims to train a high-performance model with a minority of labeled data and a majority of unlabeled data. Existing methods mostly adopt the mechanism of prediction task to obtain precise segmentation maps with the constraints of consistency or pseudo-labels, whereas the mechanism usually fails to overcome confirmation bias. To address this issue, in this paper, we propose a novel Confidence-Guided Mask Learning (CGML) for semi-supervised medical image segmentation. Specifically, on the basis of the prediction task, we further introduce an auxiliary generation task with mask learning, which intends to reconstruct the masked images for extremely facilitating the model capability of learning feature representations. Moreover, a confidence-guided masking strategy is developed to enhance model discrimination in uncertain regions. Besides, we introduce a triple-consistency loss to enforce a consistent prediction of the masked unlabeled image, original unlabeled image and reconstructed unlabeled image for generating more reliable results. Extensive experiments on two datasets demonstrate that our proposed method achieves remarkable performance.
NAIVE BAYES ALGORITHM IN HS CODE CLASSIFICATION FOR OPTIMIZING CUSTOMS REVENUE AND MITIGATION OF POTENTIAL RESTITUTION

The Directorate General of Customs and Excise, as a government revenue collector, must maximise import duty receipts each year. One common issue is the return of unpaid import duty and/or administrative punishments in the form of fines based on the objection judgement document. The Tax Court could help you minimise your gross receipts at the Customs Office. Data mining techniques are intended to provide valuable information regarding the HS Code classification technique, which can assist customs agents in determining duties and/or customs values. This study makes use of data from the Notification of Import of Goods at Customs Regional Office XYZ from 2018 to 2020. The Cross-industry Standard Process for Data Mining (CRISP-DM) model is used in this study, and the Naive Bayes Algorithm in Rapidminer 9.10 is used for data classification. According to the model, the calculation accuracy is 99.97 percent, the classification error value is 0.03 percent, and the Kappa coefficient is 0.999..
The general method of the tanker car mouth pose measurement

Purpose The coupling process between the loading mechanism and the tank car mouth is a crucial step in the tank car loading process. The purpose of this paper is to design a method to accurately measure the pose of the tanker car. Design/methodology/approach The collected image is first subjected to a gray enhancement operation, and the black parts of the image are extracted using Otsu’s threshold segmentation and morphological processing. The edge pixels are then filtered to remove outliers and noise, and the remaining effective points are used to fit the contour information of the tank car mouth. Using the successfully extracted contour information, the pose information of the tank car mouth in the camera coordinate system is obtained by establishing a binocular projection elliptical cone model, and the pixel position of the real circle center is obtained through the projection section. Finally, the binocular triangulation method is used to determine the position information of the tank car mouth in space. Findings Experimental results have shown that this method for measuring the position and orientation of the tank car mouth is highly accurate and can meet the requirements for industrial loading accuracy. Originality/value A method for extracting the contours of various types of complex tanker mouth is proposed. This method can accurately extract the contour of the tanker mouth when the contour is occluded or disturbed. Based on the binocular elliptic conical model and perspective projection theory, an innovative method for measuring the pose of the tanker mouth is proposed, and according to the space characteristics of the tanker mouth itself, the ambiguity of understanding is removed. This provides a new idea for the automatic loading of ash tank cars.
Ten Simple Rules for Crafting Effective Prompts for Large Language Models

As large language models (LLMs) become increasingly integrated into research settings, the ability to interact effectively with these models becomes crucial. This article sets forth ten rules—from understanding LLM capabilities to the art of asking well-structured questions—that serve as a foundational guide for crafting prompts that maximize LLM utility.
Murreviikko - A Dialectologically Annotated and Normalized Dataset of Finnish Tweets

This paper presents Murreviikko, a dataset of dialectal Finnish tweets which have been dialectologically annotated and manually normalized to a standard form. The dataset can be used as a test set for dialect identification and dialect-to-standard normalization, for instance. We evaluate the dataset on the normalization task, comparing an existing normalization model built on a spoken dialect corpus and three newly trained models with different architectures. We find that there are significant differences in normalization difficulty between the dialects, and that a character-level statistical machine translation model performs best on the Murreviikko tweet dataset.
RegularSearch, a fast performance algorithm for typical testors computation

In pattern recognition tasks, selecting the appropriate features for a supervised classification problem is a critical task. One theoretical tool used for this purpose is the Test Theory, which identifies feature relevance and reduces the complexity of the training and classification processes. However, searching for irreducible subsets of features, called typical testors, that can differentiate objects belonging to different classes, is an exponential problem in terms of the number of features. To address this problem, various algorithms have been proposed in the literature that use different strategies to avoid unnecessary comparisons. In this paper, is proposed the RegularSearch algorithm to find all typical testors in a dataset associated with a supervised classification problem. The algorithm incrementally searches for candidate subsets of features that are most likely to be associated with a typical testor, reducing the number of comparisons as new features are added to the candidate subset. Our comparisons with the best-performing algorithms reported in the literature demonstrate that RegularSearch is more efficient for processing synthetic and real problem datasets in certain scenarios.
Two-stage Pipeline for Multilingual Dialect Detection

Dialect Identification is a crucial task for localizing various Large Language Models. This paper outlines our approach to the VarDial 2023 shared task. Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively. Our proposed approach consists of a two-stage system and outperforms other participants’ systems and previous works in this domain. We achieve a score of 58.54% for Track-1 and 85.61% for Track-2. Our codebase is available publicly (https://github.com/ankit-vaidya19/EACL_VarDial2023).
Motion information supplement for joint detection and embedding tracking

Most existing multi-object tracking models only focus on the spatial information generated by image-level input while ignoring the necessary temporal information. The temporal motion information between consecutive frames can effectively reflect the target’s motion status, which is essential to improving the performance in dealing with occlusion and motion blur of the model. We propose MISTracker to realize the motion information supplement to the original tracking model. Specifically, we divide multi-scale feature maps into two categories from the perspective of space and channel information. Meanwhile, the spatial-level frame differences processing (SFDP) module and the channel-level frame differences processing (CFDP) module are proposed to deal with these differences between continuous frames, respectively. The SFDP processes the differences from the perspective of spatial information and supplements the motion information through the perception of pixel-level information changes in the feature maps. The CFDP processes the differences from the perspective of channels and enhances the information of motion-sensitive channels through the overall pixel differences of different channels. Eventually, temporal and motion information are complementary to each other after upsampling fusion. The whole process is realized by simple convolution, which reduces the computational force as much as possible and enhances the tracking performance of the model.
Safe-LBP: A visually meaningful image encryption scheme based on LBP and compressive sensing

Recently, some visually meaningful image encryption schemes have been proposed to protect the visual security of images. However, much of the existing literature on visual image encryption ignores the features of cover images. To address the above issues, this paper proposes a content-adaptive visual image encryption scheme based on piecewise linear chaotic map (PWLCM), compressive sensing (CS), and safe local binary pattern (Safe-LBP). First of all, the plain image are sparse by applying discrete wavelet transform (DWT). Secondly, the cipher data is obtained by encrypting and compressing the sparse matrix using Arnold scrambling and CS. Next, we design a new LBP operator to discover appropriate embedding place in the cover image, which tremendously improves the imperceptibility of the cipher image. Finally, the visually meaningful steganographic image is obtained by embedded all cipher data through the least significant bit (LSB) method. Simulations and comparisons show that the proposed algorithm has excellent visual security and decryption quality and proves its robustness against various types of noises and data loss.
Peningkatan Kemampuan Literas Numerasi Siswa Melalui Pendekatan Saintifik di SMP Swasta PAB 2 Helvetia

This research is classroom action research which aims to improve students' numeracy literacy skills through a scientific approach carried out in class VIII PAB 2 Helvetia in the even semester of the 2022/2023 academic year with a total of 32 students. This research was conducted in three cycles. Data collection was carried out using the results of students' numeracy ability tests. The data obtained was collected and analyzed using descriptive statistical analysis, where the indicator of the success of this research was an increase in the N-Gain pretest posttest value of students' numeracy abilities during each learning cycle. The research results showed that in the first cycle, the N-Gain value of students' numeracy abilities was 0.15 in the low category, in the second cycle it increased to 0.84 in the high category and in the third cycle it increased to 0.87 in the high category. Thus it can be concluded that the application of a scientific approach is able to improve students' numeracy skills
Optimal Synthesis of Robust IDK Classifier Cascades

An IDK classifier is a computing component that categorizes inputs into one of a number of classes, if it is able to do so with the required level of confidence, otherwise it returns “I Don’t Know” (IDK). IDK classifier cascades have been proposed as a way of balancing the needs for fast response and high accuracy in classification-based machine perception. Efficient algorithms for the synthesis of IDK classifier cascades have been derived; however, the responsiveness of these cascades is highly dependent on the accuracy of predictions regarding the run-time behavior of the classifiers from which they are built. Accurate predictions of such run-time behavior is difficult to obtain for many of the classifiers used for perception. By applying the algorithms using predictions framework, we propose efficient algorithms for the synthesis of IDK classifier cascades that are robust to inaccurate predictions in the following sense: the IDK classifier cascades synthesized by our algorithms have short expected execution durations when the predictions are accurate, and these expected durations increase only within specified bounds when the predictions are inaccurate.
Parametric design for custom-fit eyewear frames

More than half of the people on the planet use eyewear to correct or protect their vision. Eyewear products that fit poorly cause discomfort, dizziness, or blurred vision. One method to improve fit is to create custom-fit eyewear frames on an individual basis. In this paper we propose a new parametric design method to customize the eyewear frames based on individual 3D scanned data of head-and-face measurements. We take the eyeglasses frame as the case study to establish the landmark-product relationship and develop the parametric algorithm in Rhino/Grasshopper software. The results of the case study can generate custom-fitted eyeglass frame models for the two selected subjects, one 33% percentile Asian female and one 90% percentile Caucasian male. The future study will continue validating the eyewear frame fit and optimizing the parametric design method.
DaCapo: An On-Device Learning Scheme for Memory-Constrained Embedded Systems

The use of deep neural network (DNN) applications in microcontroller unit (MCU) embedded systems is getting popular. However, the DNN models in such systems frequently suffer from accuracy loss due to the dataset shift problem. On-device learning resolves this problem by updating the model parameters on-site with the real-world data, thus localizing the model to its surroundings. However, the backpropagation step during on-device learning requires the output of every layer computed during the forward pass to be stored in memory. This is usually infeasible in MCU devices as they are equipped only with a few KBs of SRAM. Given their energy limitation and the timeliness requirements, using flash memory to store the output of every layer is not practical either. Although there have been proposed a few research results to enable on-device learning under stringent memory conditions, they require the modification of the target models or the use of non-conventional gradient computation strategies. This paper proposes DaCapo, a backpropagation scheme that enables on-device learning in memory-constrained embedded systems. DaCapo stores only the output of certain layers, known as checkpoints, in SRAM, and discards the others. The discarded outputs are recomputed during backpropagation from the nearest checkpoint in front of them. In order to minimize the recomputation occurrences, DaCapo optimally plans the checkpoints to be stored in the SRAM area at a particular phase of the backpropagation and thus replaces the checkpoints stored in memory as the backpropagation progresses. We implemented the proposed scheme in an STM32F429ZI board and evaluated it with five representative DNN models. Our evaluation showed that DaCapo improved backpropagation time by up to 22% and saved energy consumption by up to 28% in comparison to AIfES, a machine learning platform optimized for MCU devices. In addition, our proposed approach enabled the training of MobileNet, which the MCU device had been previously unable to train.
An Improved CycleGAN for Data Augmentation in Person Re-Identification

Person re-identification (ReID) has attracted more and more attention, which is to retrieve interested persons across multiple non-overlapping cameras. Matching the same person between different camera styles has always been an enormous challenge. In the existing work, cross-camera styles images generated by the cycle-consistent generative adversarial network (CycleGAN) only transfer the camera resolution and ambient lighting. The generated images produce considerable redundancy and inappropriate pictures at the same time. Although the data is added to prevent over-fitting, it also makes significant noise, so the accuracy is not significantly improved. In this paper, an improved CycleGAN is proposed to generate images for achieving improved data augmentation. The transfer of pedestrian posture is added at the same time as transferring the image style. It not only increases the diversity of pedestrian posture but also reduces the domain gap caused by the style change between cameras. Besides, through the multi-pseudo regularized label (MpRL), the generated images are assigned virtual tags dynamically in training. Through many experimental evaluations, we have achieved a very high identification accuracy on Market-1501, DukeMTMC-reID, and CUHK03-NP datasets. On the three datasets, the quantitative results of mAP are 96.20%, 93.72%, and 86.65%, and the quantitative results of rank-1 are 98.27%, 95.37%, and 90.71%, respectively. The experimental results fully show the superiority of our proposed method.
Leveraging artificial intelligence and mutual authentication to optimize content caching in edge data centers

Available online Edge data centers are designed to meet the stringent QoE requirements of delay-sensitive and computationally intensive services in Content Delivery Network (CDN) and 5G networks. The primary purpose of this paper was to formulate and solve the problem of optimizing many control variables jointly: (i) what contents to store by taking into consideration edge capacity, and (ii) what contents to recommend to each Internet of Everything (IoE) item, based on identity and access management (IAM). In reactive caching policy, we proposed a new Two-Factor Authentication (2FA) scheme founded upon the Elliptic Curve Cryptography (ECC) and one-way hash function for access control. More interestingly, we use Non-negative Matrix Factorization (NMF), Fuzzy C-Means (FCM), Random Forest (RF) and Pearson Correlation (PC) to improve the accuracy and latency of traditional data filtering models. The intelligent recommendation engine we propose is designed to be implemented by cloud for caching and prefetching contents at the edge. The experimental results validate the theoretical guarantees of the proposed solution and its ability to achieve significant performance gains compared to common baseline models.