Reviewerly/exHarmony huggingface dataset:
Analisis Perbandingan Algoritma Forecasting dalam Prediksi Harga Saham LQ45 PT Bank Mandiri Sekuritas (BMRI)

Economic development in Indonesia has slowed in recent years. This resulted in the movement of the index for several stocks listed on BEIm, especially LQ45 which also experienced increases and decreases. Therefore, it is necessary to analyze stock price movements so that the results of the analysis can be used by investors to make investment decisions. This study will apply several Forecasting algorithms such as Linear Regression and Neural Network to predict the stock price of LQ45 in the case study of Bank Mandiri Sekuritas (BMRI). By using four attributes, namely open, high, and low values as predictors and close as a class, this study focuses on determining the accuracy value, namely Root Mean Squared Error (RMSE) by optimizing parameter values. The test results obtained an RMSE value of 0.034 on the Neural Network method with the addition of a hidden layer and an RMSE value of 0.052 on the Linear Regression method with M5 Prime and Greedy Feature Selection with a min-tolerance value of 0.05.
Classification of Sentiment Analysis based on Adaptive Neuro Fuzzy Inference Model (ANFIS)

With the growing demand of Social media and its various networks and with the emergence of virtual nature of Online Shopping users are not easily able to determine whether the quality of the product or service is good or worse. For this work there is no such specific model existed that can find similar or dissimilar products from the people posting tweets in the form of post, messages or hashtags using similar sort of textual content. Also the companies are giving their Product reviews, which contain the direct / Indirect feedback from the customers. The companies are also getting feedback from the social media in the form of tweets / post / blogs from the twitter in the form of text for the same product. So, to find the sentiments of an individual based on the product recommendations the work had been proposed. The reviews from various intelligent algorithm for sentimental classification based on Adaptive Neuro-Fuzzy Inference System were studied. The research work is based on the comparison of the other traditional based Sentiment Analysis methodology with the ANFIS. Therefore, we are able to identify the best method to be used for the reviews identification in the best possible method.
SYNTACTIC COMPRESSION IN SINGLISH SMS (BASED ON THE CORPUS NUS SMS)

Traditionally, the compression of language is considered as a result of the linguistic economy law and attracts more and more attention of linguists as the opposite of the expansion of language phenomenon. The article provides an overview of the syntactic compression in Singlish SMS. SMS of 2004 and 2012 from the National University of Singapore corpus NUS SMS were taken as the basic material for the analysis. English plays a key role in Singapore, and therefore possible changes at the syntactic language level become worthy of study due to the impact of the economy principle in conditions of a limited communication channel. The methods of corpus and statistical data analysis were used in the study. The novelty of this study lies in the fact that for the first time an attempt to analyze the methods of syntactic compression is made on the examples of Singapore English through the SMS communication. As a result, various types of syntactic compression were identified, typical for messages of 2004 (omission of superscript non-letter characters) and messages of 2012 (omission of a space), as well as compression inherent for SMS of both years from the corpus NUS SMS corpus (lack of punctuation). Such methods of syntactic compression allow the addressees to save time and electronic space in terms of technical character limitations without distorting the semantic meaning of sentences or the entire message.
AI-based Cyber Threats Intrusion Detection With Hyper parameter Optimization

Many technologies have presented security frameworks to society of which IoT-based security is more demanded. Camera and Sensors act as the backbone of the system to get security. We are implementing a crime detection Surveillance system by using an IoT module and PIR Sensor. IoT plays an important role in the automatic process and helps people to work smarter. In this system sensor named PIR is used for motion detection when an unauthorized person enters the protected area. The PIR sensor and camera are combined in such a way that when any action happens in the area, automatically it switches on the camera. The authorized person can view the IoT data which is the action of an unauthorized person inside the protected area. A mail-services is included in this to notify them regarding the stranger's action to the owner. The proposed method provides a smart surveillance security system for Intruder detection.
Code of Ethics for Automatic Speech Recognition

Automatic Speech Recognition (ASR), which turns human speech into text, has had a long history of development and continues to improve and expand. Both academic and industry discussions on the technology present ethical issues regarding the privacy, security, and use of user data, as well the accuracy of ASR for different demographics. This paper reviews the literature on such ethical issues and proposes a code of ethics that addresses them. A case study on Google ASR products is conducted, evaluating that the industry still has improvements to make, especially in user data collection and use, and in accuracy.
Comparison of Static Signature Identification using Artificial Neural Networks Based on Haar, Daubechies and Symlets Wavelet Transformations

Signature is a biometric attribute that is quite important for each individual that can be used as self-identity. Until now, the signature is still used as a sign of legal approval and is agreed upon by everyone. This makes the signature worthy of attention from a security aspect. Various approaches have been proposed in the development of signature identification to minimize signature forgery. This study will discuss the identification of signatures by using the image of the signature on paper. This identification consists of two processes, namely training and testing by utilizing Artificial Neural Networks Backpropagation and Wavelet Transform. Optimal results are obtained by using ANN which has learning rate 0,09, two hidden layers, each 20 and 10 nodes with the most superior Wavelet Haar reaching 94.44%.
Construction of Garden Landscape Design System Based on Multimodal Intelligent Computing and Deep Neural Network

The problem of module discrimination and identification in the field of landscape design is the focus of researchers. Based on multimodal intelligent computing, this paper constructs a landscape design system based on deep neural network. The article first uses a deep neural network to train multimodal garden landscape images, and then performs pooling and convolution operations on garden landscape images on the multimodal training model of convergence speed on the edge and solve the problem of low model accuracy. In the simulation process, the neural network module of MATLAB software is used to extract the spatiotemporal features of the dynamic garden landscape image from the three directions of the bottom block of the garden to achieve feature complementarity. This method only uses 15% of the features of the original feature set. The complexity of the recognition system also reduces the recognition error rate. The experimental results show that by adopting the design of feature series fusion, maximum value fusion, and multiplicative fusion in the score layer, the feature series fusion achieves a high accuracy rate under the multiplicative fusion of the three modalities, reaching 77.1%, and the test error is within 0.118, which effectively improves the multimodal characteristics of the integrated landscape and makes the modeling results more accurate.
Pretraining on Interactions for Learning Grounded Affordance Representations

Lexical semantics and cognitive science point to affordances (i.e. the actions that objects support) as critical for understanding and representing nouns and verbs. However, study of these semantic features has not yet been integrated with the ?foundation? models that currently dominate language representation research. We hypothesize that predictive modeling of object state over time will result in representations that encode object affordance information ?for free?. We train a neural network to predict objects? trajectories in a simulated interaction and show that our network?s latent representations differentiate between both observed and unobserved affordances. We find that models trained using 3D simulations outperform conventional 2D computer vision models trained on a similar task, and, on initial inspection, that differences between concepts correspond to expected features (e.g., roll entails rotation) . Our results suggest a way in which modern deep learning approaches to grounded language learning can be integrated with traditional formal semantic notions of lexical representations.
Classification of Hotspots Causing Forest and Land Fires Using the Naive Bayes Algorithm

Forest and land fires that occurred in Indonesia have caused many losses for the community. Forest fires generally occur in August and September, coinciding with the dry season in most parts of Indonesia. One indicator of the occurrence of forest fires is hotspots. This study uses one of the data mining techniques, namely classifying hotspots in Riau Province. This study used a dataset of forest fires in Pelalawan Regency from 2015 to 2019 using the Naïve Bayes algorithm. The hot spots to be analyzed consist of temperature, humidity, rainfall, wind speed, and class. The highest accuracy of the dataset of forest and land fires in 2019 is 96.95%. The classification method using the Naïve Bayes algorithm can be used to predict the emergence of hotspots in the future so that they can take preventive measures before forest and land fires occur.
Inequality-constrained 3D Morphable Face Model Fitting

&lt;p&gt;A new 3D morphable model (fitting) framework, based on inequality-constrained optimization and gradient correlation. An optimization-based method that does not require any training, and works robustly. Moreover, the method is capable of simultaneously fitting a 3DMM to multiple frames of a person. Finally, camera matrix can be injected.&lt;/p&gt;
Local Relighting of Real Scenes

We introduce the task of local relighting, which changes a photograph of a scene by switching on and off the light sources that are visible within the image. This new task differs from the traditional image relighting problem, as it introduces the challenge of detecting light sources and inferring the pattern of light that emanates from them. We propose an approach for local relighting that trains a model without supervision of any novel image dataset by using synthetically generated image pairs from another model. Concretely, we collect paired training images from a stylespace-manipulated GAN; then we use these images to train a conditional image-to-image model. To benchmark local relighting, we introduce Lonoff, a collection of 306 precisely aligned images taken in indoor spaces with different combinations of lights switched on. We show that our method significantly outperforms baseline methods based on GAN inversion. Finally, we demonstrate extensions of our method that control different light sources separately. We invite the community to tackle this new task of local relighting.
ECCNet: Efficient chained centre network for real‐time multi‐category vehicle tracking and vehicle speed estimation

To achieve the intelligent perception of traffic video and overcome the dispersion of computing modules and the separation processing of multiple tasks, efficient chained centre network (ECCNet) is proposed as a unified framework, accomplishing detection, vehicle classification, tracking, and vehicle speed estimation simultaneously. First, for the speed-accuracy trade-off, CA-CenterNet is presented, which can detect vehicles and classify vehicle types to serve cross-frame tasks more accurately by embedding coordinate attention. Second, 3D convolution is employed to construct a self-adaptive branch for data association and speed estimation, respectively. This self-adaptive approach leverages the power of deep learning to enhance tracking performance and avoid camera calibration via capturing motion information across frames. Moreover, a chained structure is adopted to reuse the backbone feature map. The spatio-temporal information of adjacent frames can be extracted at almost no additional cost. Finally, the above single-frame and cross-frame tasks are integrated into a unified multi-task collaborative optimization model. The effectiveness of ECCNet is verified with experiments on the UA-DETRAC dataset. ECCNet achieves 55.5% MOTA, 0.76 F1 value, and 3.10 MAE with an inference speed of 32.5 Hz in the tracking, classification, and speed estimation tasks, respectively.
An Experimental Study of Image Thresholding Based on Refined Histogram using Distinction Neighborhood Metrics

In this study, we aimed to illustrate that the thresholding method gives different results when tested on the original and the refined histograms.We use the global thresholding method, the well-known image segmentation method for separating objects and background from the image, and the refined histogram is created by the neighborhood distinction metric.If the original histogram of an image has some large bins which occupy the most density of whole intensity distribution, it is a problem for global methods such as segmentation and contrast enhancement.We refined the histogram to overcome the big bin problem in which sub-bins are created from big bins based on distinction metric.We suggest the refined histogram for preprocessing of thresholding in order to reduce the big bin problem.In the test, we use Otsu and medianbased thresholding techniques and experimental results prove that their results on the refined histograms are more effective compared with the original ones.
Identification of Malaria Parasite Patterns With Gray Level Co-Occurance Matrix Algorithm (GLCM)

The results of the test using 5 data of malaria parasite test imagery found that image 1 has an average accuracy value of the energy of 0.55627, homogeneity average of 0.8371, PSNR of 6.1336db, and MSE of 0.24358. Image 2 has an average energy accuracy value of 0.22274, an average Homonegity of 0.98532, a PSNR of 6.1336db, and an MSE of 0.24358. Image 3 has an energy average accuracy value of 0.28735, a Homonegity average accuracy value of 0.9793, a PSNR of 6.133db, and an MSE of 0.24358. Image 4 has an energy average accuracy value of 0.32907 and an average homogeneity accuracy value of 0.97073, PSNR 6.133db, and MSE 0.24358. Image 5 has an average accuracy value of 0.74102, Homonegity average of 0.99844, PSNR of 6.133db, and MSE of 0.4358. Image 6 has an accuracy value of 0.34758 energy, an average accuracy value of homogeneity of 0.99129, a PNSR of 6.133db, and an MSE of 0.24358. Obtained the rule if the average value of energy &gt; = 0.50 then the pattern of malaria parasites is very clear, namely Image 1 and image 5 with a pattern of malaria parasites is very clear.
The Relationship of Nutrition Extension Through Monopoly Game Media With Knowledge And Attitudes About Balanced Nutrition in Elementary School 03 Children in Pasir Village, Tripe Jaya District. Gayo Lues Regency, 2022

The purpose of the study was to determine the relationship of nutrition counseling through the media of monopoly game to knowledge and attitudes about balanced nutrition in SDN 03 students in Pasir Village, Tripe Jaya District, Gayo Lues Regency in 2022. This type of research is a quantitative research (analytic survey) with a cross sectional approach. Location This research was conducted at SDN 03 Pasir Village, Tripe Jaya District, Gayo Lues Regency. The population in this study were taken from 2 classes at SDN 03, 15 students were sampled from class IV, and 15 students were taken from class V. The population technique in this study was non-probability sampling. The results showed that from 30 respondents that the majority of good knowledge as many as 15 respondents (50%). And the majority of children's nutritional attitudes in the very high category were 15 respondents (50%). This shows that nutrition counseling is closely related to knowledge of children's nutritional attitudes in understanding the importance of healthy food. Based on research conducted at SDN 03 Gayo Lues on the Relationship of Nutrition Extension Through Monopoly Game Media with Knowledge and Attitudes About Balanced Nutrition in Children at SDN 03 in Pasir Village, Tripe Jaya District, Gayo Lues Regency in 2022, the following conclusions can be drawn : The majority of children's knowledge about balanced nutrition at SDN 03 Gayo Lues, Tripe Jaya District, Gayo Lues Regency in 2022 is in the good category
Online resources for the syntactic-semantic classification of verbs

Verb classes are defined in lexical-semantics literature as syntactically coherent groups of verbs that also share meaning components. Thus, the investigation of verb classes leads to important insights into the syntax-semantics interface. In this perspective, starting from Levin’s (1993) foundational work for English, many authors have developed analyses for the syntactic-semantic classification of verbs in different languages. The aim of this paper is to provide an overview of such studies, focusing on those that constitute online open access resources for the syntactic-semantic classification of verbs: VerbNet for English, BVI for Basque, AnCoraVerb for Spanish and Catalan, VerboWeb for Brazilian Portuguese, CROVALLEX for Croatian, and VALLEX for Czech. Thus, this paper reviews the general theoretical assumptions on verb classification shared by all the works presented, and shows the specific characteristics of each one, considering the body of data, the specific theoretical perspective, analyses and methodology, and the search engines they provide. Finally, it offers a comparative analysis of two specific verb classes (namely, image creation verbs and verbs of creation and transformation), drawing on data from the studies mentioned above. Although these resources assume a specific lexical-semantic approach for verb classification, they provide valuable data and analyses that can be useful for research in different theoretical perspectives.
Proposal of a Model for the Analysis of the State of the Use of ICT in Education Applied to Technological Institutes of Higher Education

The inclusion of information and communication technologies in education has become a priority for all educational models, particularly for higher education institutes that have observed the need to integrate these technologies in the classroom. However, to guarantee educational quality and learning, establishing a process that allows the identification of the response of the students towards its use is necessary. For this purpose, there are several works that address the issue and have determined the functionality of these technologies, but each environment is different, and this is recognized by the higher education institutes of Ecuador that have limited economic, technological, and academic resources. This work seeks to create a method that allows the needs and doubts of students about the use of educational technologies in the classroom to be established without affecting their academic performance. To perform this, a process has been designed that identifies learning needs through the validation of data obtained from surveys and a comparison of two groups of students, in which one group makes use of technologies in the classroom and the other group uses a model of traditional education. By obtaining the results of the analysis, the method determines the impact of technology on learning.
Metaphorical Description and Model Description of Complex Systems

From metaphorical description to model description is the symbol of the maturity of complexity science. This paper focuses on two model description methods of complex systems: formal system model and relevance theory model, and compares their advantages and disadvantages. These two models are descriptions of complex systems from different perspectives and have important and irreplaceable significance in the theoretical research and practical application of complex systems. The future research direction of complex systems is not the “integration” of the two models but the alternation of the two models, which scientifically reveals the essence of complex systems from multiple perspectives. In view of the complexity of education and the scientificity of educational evaluation, a fuzzy evaluation method is proposed. Fuzzy evaluation is a mathematical thinking mode based on fuzzy theory and mathematical methods, which studies fuzzy and complex phenomena, and provides quantitative relationship and description for educational evaluation.
MIA 2022 Shared Task Submission: Leveraging Entity Representations, Dense-Sparse Hybrids, and Fusion-in-Decoder for Cross-Lingual Question Answering

We describe our two-stage system for the Multilingual Information Access (MIA) 2022 Shared Task on Cross-Lingual Open-Retrieval Question Answering. The first stage consists of multilingual passage retrieval with a hybrid dense and sparse retrieval strategy. The second stage consists of a reader which outputs the answer from the top passages returned by the first stage. We show the efficacy of using entity representations, sparse retrieval signals to help dense retrieval, and Fusion-in-Decoder. On the development set, we obtain 43.46 F1 on XOR-TyDi QA and 21.99 F1 on MKQA, for an average F1 score of 32.73. On the test set, we obtain 40.93 F1 on XOR-TyDi QA and 22.29 F1 on MKQA, for an average F1 score of 31.61. We improve over the official baseline by over 4 F1 points on both the development and test sets.
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning

Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large repository of unlabeled data to improve performance while relying on a small set of labeled data. One common assumption in most SSL methods is that the labeled and unlabeled data are from the same data distribution. However, this is hardly the case in many real-world scenarios, which limits their applicability. In this work, instead, we attempt to solve the challenging open-world SSL problem that does not make such an assumption. In the open-world SSL problem, the objective is to recognize samples of known classes, and simultaneously detect and cluster samples belonging to novel classes present in unlabeled data. This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes. Using a bi-level optimization rule this pairwise similarity loss exploits the information available in the labeled set to implicitly cluster novel class samples, while simultaneously recognizing samples from known classes. After discovering novel classes, OpenLDN transforms the open-world SSL problem into a standard SSL problem to achieve additional performance gains using existing SSL methods. Our extensive experiments demonstrate that OpenLDN outperforms the current state-of-the-art methods on multiple popular classification benchmarks while providing a better accuracy/training time trade-off.