Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Movie Reviews Using Twitter

Machine learning can help people to perform complex tasks and solve problems as it uses historical data to learn its pattern and make predictions based on the past data. This research addresses the problem about movie reviews on social media specifically Twitter; where it will gather the tweets on movie reviews and display a rating based on the sentiment of the tweet. Twitter is an online social media website where people from all walks of life communicate by tweeting short updates without exceeding the character limit which is 240 characters. Twitter is continuously growing as a business and became one of the biggest platform for communication and instant messaging. Due to the large number of users, there are voluminous amounts of data available that can be used for more in depth information and insights and to get the sentiments from analysing the tweets. In today’s world, there are many applications that are using sentiment analysis in various fields such as to gets insights about a particular brand or product. To do sentiment analysis using the traditional ways can be time consuming and becomes very complex. The aim of this research is to investigate about the domain of sentiment analysis and incorporate a machine learning algorithm to create a system that is able to get and display the ratings of a particular movie. The machine learning algorithms used are Naïve Bayes Classifier and SVM. The algorithm with better accuracy will be chosen for the implementation phase.
Development of a Gait Recognition Visualization System Using Augmented Reality

The gait rehabilitation is generally done through the traditional way, the diagnostics are only made according to the eye of the person who is doing the treatment for identifying the places where the people feel any kind of pain. This project wants to develop a system that uses augmented reality for visualizing the cinematics of the human gait to help experts to give diagnostics. The Opencv library was used as a tool to implement the augmented reality, providing functions such as the calibration of the camera, the detection and the tracking of the objects that will be the markers located between the joint segments of the legs (hip, knee and ankle), through this, the tracking of the displacement of the person and the angle formed can be visualized. This system, that uses augmented reality, gives support to the diagnostic of the gait through technological tools which will help the experts to get a highest precision for making this kind of analysis.
Demodulation of fringe patterns corrupted by non uniform intensity variations in dynamics studies

Optical interferometric techniques have come to forefront in precision metrology applications such as surface profilometry, deformation analysis and defect testing. Reliable phase measurement from a recorded fringe pat- tern is the primary goal in most of these interferometric techniques. A primary constraint for accurate phase measurement is non uniform intensity fluctuations in fringe patterns caused by irregular illumination, uneven reflectivities and pixel defects. This problem is further aggravated in dynamics studies with multiple image capture where the intensity fluctuations can vary with time and thus lead to several fringe patterns getting affected. In this paper, this problem is investigated using the second order optimization framework along with the total variation regularization on a graphic processing unit (GPU). In our study, a series of fringe patterns are simulated with additive white Gaussian noise at signal to noise ratio of 20 dB. To induce fringe corruption due to the non uniform intensity conditions, the object wave amplitude is varied using an ellipse shaped filter which enables brighter conditions or stronger amplitudes inside the elliptical boundary and relatively darker conditions or weaker amplitudes outside of it. For time varying studies, we varied the size of the filter sequentially to gener- ate a series of interferograms with temporally varying non-uniform intensities. Further, orientations of elliptical illuminations are changed to verify the robustness of the algorithm. For processing a series of interferograms each with size of 512 by 512 pixels with dynamic range of 10 (ratio of brightest to darkest amplitude), we obtained root mean square error less than 0.25 radians for every interferogram using the proposed method. The results show that the proposed method is robust for handling non-uniform intensity corruptions in fringe patterns for dynamics based investigations.
Robustness Detection Method of Chinese Spam Based on the Features of Joint Characters-Words

Since the current Chinese spam messages often contain artificial adversarial perturbation and can bypass the traditional detection system, this paper proposed an integrated model based on “ALBERT-Capsule Network.”On the one hand, the model accepts both raw text and word segmentation text as input data, uses ALBERT and capsule networks to extract character-level features and word-level features respectively, and uses a multi-head output structure to ensure that both the word feature stream and the word feature stream can be meaningfully trained to prevent the neural network from depending on one of its branches. Experiments on the SMS spam dataset and trec06c datasets prove that the model is better than the traditional single-feature models such as Bi-LSTM and TextCNN; On the other hand, the traditional character replacement, pinyin replacement and word splitting and other adversarial methods are used to augment the original training dataset, which further enhances the robustness of the model, so that the model can effectively detect spam messages containing adversarial perturbation.
Ontological Model to Represent Information About Songs

The purpose of this article is to carry out the design of an ontological model for the domain of songs, that is, its purpose is to cover the relevant information corresponding to the characteristic data currently sought to be known in the songs from different music genres, such as title, performer, album which it belongs, duration, release date, among many others. It is wanted for this model to answer queries made by users when they are facing the need to know a specific data. For this work realization, the Grüninger and Fox’s methodology design phases, proper for the ontologies design, are followed, along with this, this document includes the proposal of a scenario, competition questions, definition of classes, properties, formalization and evaluation through the responses obtained through the SPARQL query language. Furthermore, the implementation of the presented ontological model was carried out using the software known as Protégé.
Health Information Retrieval

Searching health-related information on the Web has been increasing in the recent past. The methodologies and techniques to access and/or retrieve information related to health using the process of information retrieval are referred to Health Information Retrieval (HIR). In other words, a health information retrieval system produces a ranking of relevant documents that covers users’ information needs underlying health-related query. In this chapter, we present the noticeable challenges in health IR, the most prominent existing methods to solve the problems and future directions. The methods and techniques for health information retrieval are generally categorized into (a) general classical retrieval models and (b) advanced re-ranking techniques. This chapter also presents the details of the datasets, evaluation metrics and procedures to validate the performance of any HIR methods.
Potential utility of reflectance spectroscopy in understanding the paleoecology and depositional history of different fossils

Abstract The potential of reflectance spectroscopy to infer the paleoecological and depositional evolution of different micro and macro invertebrate fossils has been evaluated by analyzing their reflectance spectra within the spectral domain of 350–2500 nm using the FIELDSPEC3 spectroradiometer. Mineralogical information derived from the rapid and non-destructive spectral analysis has been substantiated using concurrent mineralogical data from conventional geochemical analyses. The diagnostic Fe-crystal field effect induced spectral features are identified on the representative spectra of different benthic foraminifera. These spectral features are resulted due to the incorporation of Fe during the biomineralization process. These features are absent in planktic foraminifera. The encrustation of Fe-oxides is inferred to be responsible for imprinting the Fe-crystal field feature in the spectra of micro and macrofossils at 900–1200 nm. Vibrational spectral features of the Al–OH bond are also identified. Both of these features are an indicator of post-depositional diagenetic history. The presence of Al and Fe in macrofossil shells is also believed to be related to ecological conditions as these elements are biogenically incorporated during shell formation. This study reveals the value of reflectance spectroscopy to infer ecological behavior and post-depositional environment of different organisms.
Identifikasi Komentar Toksik Dengan BERT

Abstrak Dalam penelitian ini, penulis menggunakan metode BERT untuk mengidentifikasi komentar toksik. Penulis membandingkan model yang dihasilkan dari dataset User Generated Content(UGC) dan dataset UGC yang telah dinormalisasikan. Arsitektur BERT yang telah dipre-train ditambah dengan output layer untuk klasifikasi teks. Hasil akhir dari penelitian tugas akhir ini menyatakan bahwa menormalisasikan dataset UGC terlebih dahulu tidak diperlukan d alam pelatihan model BERT. Kata kunci: klasifikasi teks, deep learning, bert, transformers, attention, klasifikasi Abstract In this research paper, the author used BERT method to identify toxic comments. In this paper, the author compares BERT models which are trained with a raw User Generated Content(UGC) dataset and a normalized UGC dataset. The pre-trained BERT model is added with an output layer to classify the texts. The end result from this research paper revealed that UGC dataset normalization is not required in BERT model training. Keywords: text classification, deep learning, bert, transformers, attention, classification
Global Context Enhanced Multi-modal Fusion for Referring Image Segmentation

The referring image segmentation is a challenging task which aims to segment the object of interest in an image according to a natural language expression. Most existing works directly concatenate the global language representation with local visual features, and follow by a convolutional operation to fuse two modalities. These works ignore that the global contextual information from vision is essential for vision-language fusing and inferring the referred objects. The global context can establish a perception of the full image, thus it's fusion with global language representation is beneficial to reduce mislabeled pixels of similar objects in an image. To address aforementioned issue, we propose a global fusion network (GFNet), which is composed of visual guided global fusion module and language guided global fusion module. By modeling the expression-region interactions, two modules can aggregate the expression-related visual contextual information and fuse it with global representation of language expression. Moreover, to alleviate the distribution differences between two modalities, we introduce a channel-wise self-gate on visual-language concatenated features. We validate the proposed network on four standard datasets, the experimental results show that our approach outperforms state-of-the-art methods.
The Implementation of Artificial Intelligence in Charity Box at Mosque and Musholla as RFID Based Security System

The high crime rate in Indonesia has had a bad impact and loss on society, so that various efforts have been made to increase awareness and security in society. Charity box theft is a target of crime for criminals. For this reason, it is necessary to take strict steps in terms of vigilance and security so that the crime of theft of charity boxes can be avoided. One of the steps to increase awareness and safety is to apply the concept of security to the charity box. By utilizing several supporting sensors and supporting components in the charity box, the security system will work automatically, so that if there is a charity box theft, the system will provide an SMS notification to the mosque management. This research is focused on the problem of security facilities and supervision of charity boxes in mosques or mushalla. Using this system will reduce the risk of theft of charity boxes in mosques and mushalla, because apart from being equipped with an alarm and SMS gateway, this system is also equipped with RFID so that access to open charity boxes can be safer. This form of system works if the charity box is lifted or dismantled by force, the system will provide notification in the form of an alarm and SMS, so that the crime of theft of the charity box can be more aware of.
Developing Reduced Gradient Approach for Solving Multi-Stage Stochastic Nonlinear Programs

An increasing number of practical scenarios continue to experience problems associated with multistage stochastic programming. This study proposes a decomposition method through which they can be a successful solution to multi-stage stochastic nonlinear programs. The proposed method entails the scenario analysis method. The proposed method also performs its role via search direction generation in such a way that sets of quadratic programming sub-issues are solved in a parallel way, especially when the size is significant lower, compared to the case involving original problems at the respective iterations. Relative to the dual multiplier derivation, which focuses on non-anticipativity constraints, the proposed system advocates for the introduction of generalized reduced gradient approaches.
Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming

Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create suboptimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This article presents Grammar-Based Genetic Programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic Programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.
SPD launches new community response team, reports immediate success and faster response times to 911 calls

We’re getting our first look at the Seattle Police Department’s efforts to restructure resources, and what they call their “first step” to reimagining policing, with the launch of their community response team.
Inducing the LCP Array

The LCP array is a fundamental data structure that provides the length of the longest common prefix between consecutive pairs of suffixes in the lexicographic order. The LCP array is commonly combined with the suffix array to speed up algorithms. In this chapter we present a variant of the suffix sorting algorithm SACA-K (Nong, 2013) that also computes the LCP array. This augmented algorithm, called SACA-K+LCP (Louza et al., 2017b), runs in O(nσ) time using 4σ + O(1) words of workspace, which is optimal for strings from constant alphabets.
Semantics-Preserving RDB2RDF Data Transformation Using Hierarchical Direct Mapping

Direct mapping is an automatic transformation method used to generate resource description framework (RDF) data from relational data. In the field of direct mapping, semantics preservation is critical to ensure that the mapping method outputs RDF data without information loss or incorrect semantic data generation. However, existing direct-mapping methods have problems that prevent semantics preservation in specific cases. For this reason, a mapping method is developed to perform a semantics-preserving transformation of relational databases (RDB) into RDF data without semantic information loss and to reduce the volume of incorrect RDF data. This research reviews cases that do not generate semantics-preserving results, and the corresponding problems into categories are arranged. This paper defines lemmas that represent the features of RDF data transformation to resolve those problems. Based on the lemmas, this work develops a hierarchical direct-mapping method to strictly abide by the definition of semantics preservation and to prevent semantic information loss, reducing the volume of incorrect RDF data generated. Experiments demonstrate the capability of the proposed method to perform semantics-preserving RDB2RDF data transformation, generating semantically accurate results. This work impacts future studies, which should involve the development of synchronization methods to achieve RDF data consistency when original RDB data are modified.
Onion Rotation: An Information-reserved Rotation Method to Augment Data in Image Recognition

With the amount of data dramatically increasing, models with countless parameters are naturally proposed to fit those massive data. As a basic form of data augmentation, rotation is widely used to generate new training samples. Effective as the standard rotation is, it commonly loses image information thus harmful to model performance sometimes. In this work, we propose a new image rotating technique which is called Onion Rotation. We consider the image as an "onion" which is composed of concentric circles and each circle is rotated separately. On the one hand, this rotation policy combines advantages of several standard data augmentation methods without information lost; on the other hand, Onion Rotation applies different augmentation policies to different pixels in the image. Therefore, our method creates great variations globally and locally, performing as a stronger regularization than standard augmentation methods. Moreover, Onion Rotation is easy and efficient to implement. We evaluate our method with ResNet and ResNet with the Squeeze and Excitation module on CIFAR-10, CIFAR-100 and SVHN datasets, comparing with several related data augmentation approaches. The experiment results show that our technique can fairly promote model performance solidly.
The Research of Intelligent Bandwidth Adjustment System

An automatic intelligent implementation scheme is proposed based on the analysis of the characteristics of the information network transmission services of surveying ship by corresponding service strategies designation, which can give high quality services. The key of this scheme is an intelligent bandwidth allocation system that is designed for this scheme. This bandwidth allocation method effectively accomplish the intelligent redistribution of the limited bandwidth and improve the efficiency and reliability of the whole public information network of the surveying ship through the mirror data grabbing and analyzing and accomplishing the remote controlling of the encoder and the switch by remote bandwidth adjustment instructions.
File Fragment Classification with Focus on OLE and OOXML classes

Classification of file fragments is a crucial step in digital forensics and determining file types based on available data fragments. Currently explored file fragment classification methods other than forensic hand-examination rely on machine learning techniques. Those methods most commonly use features based on byte frequency distribution as inputs in artificial neural networks. In this paper, some new approaches to file fragment classification are explored. Older MS Office file format files (doc, ppt, and xls), and the new MS Office format (docx, pptx, and xlsx), which were previously shown to be difficult to differentiate between, were joined into two separate higher-level classes due to similarities in the included files' structure. Different approaches to specifically differentiating between subtypes in each of those two higher-level classes are further explored in the paper. The results suggest small increases in classification accuracy can be achieved using the proposed approach.
A Defensive Approach against Adversarial Examples Based on Manifold Learning

Deep neural network has been shown to be vulnerable to adversarial examples. It means that a well-trained network can be fooled to make wrong predictions by adding imperceptible changes to clean images. Research has found that adversarial examples mostly change the irrelevant features which have less impact on classification. In this paper, a new approach is proposed to defend adversarial examples by masking the irrelevant features based on manifold learning. Specially, Isomap is used for dimensionality reduction, which is one of the manifold learning algorithms. Then the approach analyzes the respective correlations between the result and each feature, and filters the features with lower correlation coefficient. Compared with other defensive approaches, the proposed method is easy to implement and does not rely on adversarial examples. Through experiments on MNIST, CIFAR-10, CIFAR-100 and our own traffic sign dataset, it shows that the proposed method can increase the robustness of classifiers against adversarial examples effectively.
Improving Student Success through an Articulation Program in Software Engineering

The swift advancement of computerization has resulted in an increased need for individuals skilled in a variety of highly technical fields. This demand is currently inadequately met by the United States? domestic workforce and rural areas are underserved by technological resources and opportunities. Due to political and legislative volatility, fixing this workforce shortage is not feasible. Instead, the long-term solution is the cultivation of domestic workforce with high technical expertise. Therefore, we propose a software engineering curriculum that follows the 2 + 2 educational model to meet the needs of community college students in North Carolina as a model for similar initiatives on a regional and national scale. In this paper, we discuss the current status of computer science and software engineering education in North Carolina community colleges, including the current degree programs and relevant curriculum content. Finally, we discuss our proposed curriculum for a 2 + 2 articulation program in software engineering.