Reviewerly/exHarmony huggingface dataset:
A Data Analytics Methodology for Benchmarking of Sentiment Scoring Algorithms in the Analysis of Customer Reviews

Due to the digitalization, there exists an increased amount of user-generated content on the Internet, where people express their opinions on various topics. Sentiment analysis is the statistical and analytical examination of human emotions and opinions regarding a certain subject. Our study extends the literature by developing a data analytics methodology for the benchmarking of sentiment scoring algorithms in the context of online customer reviews. We demonstrate the applicability of the methodology using Amazon product reviews as the source data. Analyzing text-based content such as Amazon customers’ reviews through text analytics and sentiment analysis can help Amazon and other online retailers to discover valuable actionable insights regarding their products. The contributions of this study are twofolds: to examine the predictive power of machine learning (ML) algorithms with respect to predicting sentiment scores and to analyze patterns in the differences between scores obtained from different sentiment scoring algorithms.
PERANCANGAN SISTEM INFORMASI PERJALANAN WISATA ALAM DAN KOMUNITAS BERBASIS WEBSITE

Indonesia is a country that has a tourist attraction and is a tourist destination. Open trips are a trending vacation style, open trips are often also called combined trips. Community natural tourism or community-based nature tourism is a group or association of people who travel. The number of open trip fraud cases makes us have to be careful in looking for open trip events. The application of an open trip event information system and a website-based natural tourism community is an effort to promote trusted open trip events to tourists. This website provides information about open trip events and also community-based nature tourism in Indonesia and educational articles when traveling on open trips. All of this is provided so that the names of tourist destinations are more widely known and encourage interest in domestic tourism. This application provides information on domestic community-based natural and natural tourism trips and payments that are still manual. In developing this website using HTML, CSS, Laravel framework, Bootstrap, and for the database using MySQL.
Computational Long Exposure Mobile Photography

Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers. More information and supplementary material can be found on our project webpage: https://motion-mode.github.io/
ANALYSIS OF STUDENT ABILITY OF USING SIMPLE PAST AND PAST PERFECT TENSE IN WRITING RECOUNT TEXT FOR 4TH SEMETER STUDENTS OF TBI STAI RAKHA AMUNTAI

This researcher focuses on analysis of student ability of using simple past and past perfect tense in writing recount text for 4th semester students of TBI STAI RAKHA Amuntai. The purpose of this study are 1) To find out the use of simple past and past perfect tense in writing recount text for 4th semester students of TBI STAI RAKHA Amuntai. 2) To find out the use of regular verb and irregular verb in writing recount text for 4th semester students of TBI STAI RAKHA Amuntai.&#x0D; The method used in this research is qualitative semester employed 4th TBI STAI RAKHA Amuntai students which was selected as the subject of this research consisting of 24 students. Then, the object of this research was the ability of students to use simple past and past perfect in writing recount text for 4th semester students at TBI STAI RAKHA Amuntai. Data collection technique used by researcher in this study were test, interview, and documentary. The test was distributed to semester 4th students and then to strengthen the answer from the test, the researcher conducted interviews to students and lecturers who teach structure and writing courses. The collected data was analyzed by reducing data, presenting data, drawing conclusion and verifying data.&#x0D; The result of the research shows that the students’ ability to use simple past and past perfect in writing recount text is 71 which is good category and the ability of students to use regular verb and irregular verb in simple past and past perfect the average score of students is 82,8 very good category.&#x0D; Moreover, based on the result of students’ interview, every student has difficulties in writing text. There are four difficulties that the researcher encounters when they wrote recount text, namely their lack of knowledge about making recount text, learning method of teacher in their previous schools were less interesting, lack of vocabulary, and lack of interest in learning English.&#x0D; It is important for students and lecturer concerned to know this. The hope is that students are able to improve their abilities because good skill will encourage motivation in learning English, especially writing skill. Lecturer are expected to pay attention to student habits in the learning process, especially in writing skill.
Virtual Bot Powered by Machine Learning and NLP Technologies: Emulating Human-Like Conversations through Speech-to-Text Conversions

-The rapid advancements in machine learning and natural language processing (NLP) technologies have paved the way for the development of virtual bots capable of emulating human-like conversations. These virtual bots, also known as chatbots or conversational agents, utilize speech- to-text conversions to understand user input and generate appropriate responses. This research paper explores the key components and techniques involved in creating a virtual bot that leverages machine learning and NLP to facilitate human-like interactions. We discuss the challenges and opportunities associated with developing such virtual bots, including data collection, model training, and real-time speech recognition. Furthermore, we highlight the potential applications and future directions of virtual bots in various domains, such as customer service, personal assistants, and education. Key Words: Speech to text, NLP, Audio Transcribe.
Evolving Fuzzy Prediction Intervals in Nonstationary Environments

This work aims at designing a novel evolving fuzzy prediction interval able to model nonlinear time-variant systems. To achieve this goal, we integrate a passive mechanism meant to update the model when new data are available with an active mechanism able to trigger ad-hoc adaptation mechanisms when changes are detected in the data-generating process. The base model considered for this proposal was the prediction interval based on fuzzy numbers, which allow handling the characterization of the uncertainty of a system through additional parameters focused on the computation of the interval width. The performance of the proposed solution has been tested on both synthetic and real data. In this work, the synthetic data was generated from a generic nonlinear time-variant system, while the real data correspond to measurements of solar power generation obtained from photovoltaic panels. The simulation results confirm the effectiveness of the proposed evolving fuzzy prediction interval for modeling systems that present changes in their dynamics over time.
Integrated Photonic Sigmoid Activation Function at 10 Gbaud/s for Photonic Neural Networks

We experimentally demonstrate an optoelectronic assembly of a monolithic SOA/UTC-PD chip interconnected to a TIA and realizing a 10 Gbaud/s photonic sigmoid activation function with adjustable steepness slope.
An improvement in the safety of big data using blockchaintechnology

The development of big data in the information technology sector has made data management and analysis farmore challenging. It is necessary to take into account everything, including volume, variety, speed, value, andcomplexity. Clustering makes the processing of vast volumes of data simpler. This is especially helpful whenworking with unstructured data. By the usage of cloud computing, which makes use of the internet as its deliverychannel, it is possible to provide a variety of computer services, including servers, storage, databases, andnetworking, in addition to analytics and intelligence, at a lower cost. The main problem is the security of suchbig amounts of data. One way to enforce strong security is blockchain technology which is also the backbone ofcryptocurrency. The distributed, incontrovertible, and publicly verifiable record of every transaction activity that maybe provided by blockchain technology has the potential to revolutionize security in a big way for different industries.
Privacy protection and data coordination mechanism of supply chain based on blockchain

With the rapid development of science and knowledge economy, traditional supply chain management has been unable to adapt to the ever-changing competitive environment. When a large amount of data is exchanged, it is easy to cause various privacy and security issues, and data between enterprises cannot be accurately shared. At the same time, there are still a lot of manual operations in each link, with long process delays and low efficiency. This paper proposes a blockchain-based supply chain privacy protection and data collaboration scheme. By using blockchain, data privacy and collaboration can be guaranteed based on a more accurate assessment of supplier capabilities. At the same time, this solution improves the efficiency of cooperation between enterprises more safely and reasonably, and makes the supply chain system circulate more smoothly.
Attitudes towards Fraudulent Behavior and the Decision to File First-Party and Third-Party Bodily Injury Auto Insurance Claims

There is evidence that attitudes towards fraudulent behavior are representative of a psychic cost barrier to filing fraudulent insurance claims. This research further explores the connection between these attitudes and the insurance claims filing decision. Previous studies demonstrate this relationship as it relates only to the decision to file third-party, or liability, auto insurance claims for bodily injuries. This research seeks to determine how these attitudes influence the propensity to file first-party bodily injury auto insurance claims as well. Consistent with earlier conclusions, the findings from this study suggest a strong negative relationship exists between the percentage of people believing insurance fraud to be unacceptable and the relative likelihood of filing first-party bodily injury auto insurance claims, either alone or in addition to filing third-party claims.
Lax Liftings and Lax Distributive Laws

Liftings of endofunctors on sets to endofunctors on relations are commonly used to capture bisimulation of coalgebras. Lax versions have been used in those cases where strict lifting fails to capture bisimilarity, as well as in modeling other notions of simulation. This paper provides tools for defining and manipulating lax liftings. As a central result, we define a notion of a lax distributive law of a functor over the powerset monad, and show that there is an isomorphism between the lattice of lax liftings and the lattice of lax distributive laws. We also study two functors in detail: (i) we show that the lifting for monotone bisimilarity is the minimal lifting for the monotone neighbourhood functor, and (ii) we show that the lattice of liftings for the (ordinary) neighbourhood functor is isomorphic to P(4), the powerset of a 4-element set.
PENGARUH METODE OUTDOOR LEARNING TERHADAP KETERAMPILAN MENULIS PUISI SISWA DI KELAS IV UPT SD NEGERI 30 GRESIK

The purpose of this study was to determine the effect of the Outdoor Learning method on Students Poetry Writing Skills in Grade IV UPT SD Negeri 30 Gresik in the 2020/2021 academic year. The research method used in collecting research data is an experimental method with a Pretest - Posttest Control Group Design. The subjects in this study were students in class IV UPT SD Negeri 30 Gresik with a total of 50 students consisting of 25 students in class IVA and 25 students in class 1VB. Data collection techniques and instruments used were validation sheets, observation sheets, validity tests, reliability tests, normality tests, homogeneity tests, T tests and poetry writing skill tests. Based on the analysis of research data, it was found that there was an effect of the Outdoor Learning method on students' poetry writing skills in Class IV UPT SD Negeri 30 Gresik. It is proven by the significant Paired Sample T test in SPSS. Based on the statistical output table, the results obtained are sig (2-tailed) or a probability of 0.034 while the significance level is 0.05 (5%). This shows the probability value is smaller than the significance level (0.034 &lt;0.05) then H0 is rejected and H1 is accepted. This shows that there is an influence of the Outdoor Learning method on students' poetry writing skills
Joint Masked Autoencoding with Global Reconstruction for Point Cloud Learning

As a new pre-training paradigm, masked autoencoding has significantly advanced self-supervised learning in NLP and computer vision. However, it remains under exploration whether masked autoencoding can be generalized to feature learning in point clouds. In this paper, we present a novel self-supervised learning framework based on Joint Masked Autoencoding with global reconstruction (JMA). The key idea is to randomly mask some patches in the point clouds and use the visible patches to reconstruct the masked ones. In contrast with previous methods based on masked autoencoding, our JMA splits the point clouds into multiple partitions and uses every single partition to predict all other partitions, which involves simultaneous learning of multiple masked autoencoding tasks. Moreover, each partition is supervised to learn the global shape of the point clouds, which enables the model to capture global pictures of the original point clouds. Extensive results demonstrate the advantages of the proposed method on various downstream tasks. Specifically, on the widely used ModelNet40 and the more challenging ScanObjectNN datasets, the pre-trained model achieves consistently improved performances.
Brain Tumor Image Segmentation Network Based on Dual Attention Mechanism

Due to the different shape, location and size of brain tumors, and their appearance is highly heterogeneous, which makes it difficult to establish effective brain tumor image segmentation rules. In view of the excessive depth and the lack of connection between global and local feature information of current medical image segmentation network, which leads to the reduction of image segmentation accuracy, we propose an improve brain tumor image segmentation network based on depth residuals and dual attention mechanisms. Firstly, inspired by the residual network, we replaced the traditional convolutional block with deep residual block, which not only can more feature information be extracted, but also network degradation can be suppressed and convergence can be accelerated. Secondly, the introduction of dual attention mechanism in each skip connection can fuse richer context information, making the model more focused on the features that need to be segmented in the tumor region, while suppressing the irrelevant regions. Then, the loss function of our model is the combination of cross entropy function and dice similarity coefficient function. Finally, MRI images from the BraTS2019 dataset are used to train and test, and the Dice coefficient is used to evaluate the segmentation accuracy. The average Dice coefficient of the whole tumor area WT, the core tumor area TC and the enhanced tumor area ET are 0.8214, 0.8408 and 0.7448, respectively. The experiment results show that our model can effectively improve the segmentation accuracy of brain tumor images compared with other deep segmentation models.
Exploiting Query Knowledge Embedding and Trilinear Joint Embedding for Visual Question Answering

Visual Question Answering (VQA) aims to answer natural language questions about a given image. Researchers generally believe that incorporating external knowledge can improve VQA task’s performance. However, existing methods face limitations in acquiring and utilizing such knowledge, preventing them from effectively enhancing a model’s question-answering capability. In this paper, we propose a novel VQA approach based on question-query for Knowledge Embedding. In our approach, we design question query rules to obtain critical external knowledge and then embed this knowledge by integrating it with the question as input features for text modalities. Traditional multimodal feature fusion techniques rely solely on local features, which may result in the loss of global information. To address this issue, we introduce a feature fusion method based on Trilinear Joint Embedding. Utilizing an attention mechanism, we generate a feature matrix composed of question, knowledge, and image components. This matrix is then trilinearly joint embedded to form a novel global feature vector. Due to the computational challenges associated with high-dimensional vectors produced during the trilinear joint embedding process, we employ Tensor Decomposition to break down this vector into a sum of several low-rank tensors. Subsequently, we input the global feature vector into a classifier to obtain the answer in a multi-category classification fashion. Experimental results on the VQAv2, OKVQA, and VizWiz public datasets demonstrate that our approach can achieve accuracy improvements of 1.78%, 3.95%, and 1.16%. Our code are available at https://github.com/yxNoth/KB-VLT .
Exploring Fourier Transform and Fourier Series Approximation Numerically

This chapter contains practical demonstrations on numerically obtaining Fourier transform of given numerical data. In particular, we demonstrate how to obtain the frequencies that are present in the data numerically using what is called discrete Fourier transform. We also demonstrate how to numerically obtain Fourier series approximation to any function. Programs were written in Mathematica in this regard.
Improvement of Extraction Accuracy of Ambiguous Expressions in Requirement Specifications by Using a Dictionary Focusing on Compound Sentences

The purpose of this study is to propose a method for detecting ambiguous expressions from requirement specifications using text mining in order to improve the efficiency of checking the contents of the documents. It is assumed that questions and opinions about requirement specifications include details about ambiguities in the descriptions. Therefore, by text mining these questions and opinions, we construct a dictionary. We were able to detect ambiguous expressions in the requirement specification by using the dictionary. We believe that using our proposed method to detect ambiguous expressions with a high degree of accuracy will improve the efficiency of checking the contents of requirement specifications, reduce the difficulty of specification clarification work, delay delivery dates, and increase costs.
Analysis of Single and Double Faults Direction and Magnitude in Measurement and State Models of Tight GPS/INS System

Improving the quality of positioning for safe navigation has been investigated over the last two decades by multi-sensor integration techniques. Although considerable improvements have been obtained, occurring of faults in measurement or dynamic models could degrade the performance of such integrated systems. These faults are un-modeled and may occur with different magnitudes and directions throughout the navigation time. In this study, the magnitude and direction under the presence of single and double faults in tight GPS/INS measurement and dynamic model were analyzed using the detection, identification, and adaptation method (DIA). Furthermore, the influence of the correlation between fault tests when single and double faults occur has also been investigated. The results show that under the presence of single faults, the fault test correctly identifies the faulty measurement/state. However, since there is a correlation between the fault tests, the faulty measurement/state pulls other measurements/states in different directions. When multiple faults test is implemented, several wrong identifications occur. This results from the correlation between the fault test for measurements/states pair and causing fault separability impossible when elements intersect between two measurements/state pairs.
Chinese-clinical-record Named Entity Recognition using IDCNN-BiLSTM-Highway Network

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text.Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult.At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve.To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network.In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words.Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation.Finally, the global optimum tag result is obtained by introducing conditional random field (CRF).The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.
Application of Deep Learning in Algorithm Design

Deep learning has recently subverted algorithm design ideas in one and one fields, such as speech recognition, image classification, and text notification, by forming a model that begins with training data, goes through an end-to-end model, and then a new mode for direct output to obtain the final result. This not only makes everything easier, but because each layer in deep learning can tune itself for the final task, eventually cooperating between the layers, it can greatly improve task accuracy. This paper primarily introduces what deep learning is, the deep learning principle, and the application of deep learning in people's lives. This paper analyzes and clarifies the future development direction of deep learning and its practical application in many different fields, so as to offer some references for future studies.