Reviewerly/exHarmony huggingface dataset:
A Multimodal Analysis of Uda and Uni Selection’ Poster Advertisement in Pesisir Selatan

This study discusses "A Multimodal Analysis of Uda and Uni Selection' Poster Advertisement in Pesisir Selatan" which is a talent search event poster that aims to promote tourism. Thus, to convey the message in the poster not only use visual elements but also verbal elements. The purpose of this research is to identify visual and verbal elements, as well as to describe the representation of verbal and visual elements represented in the message. This study used a qualitative descriptive method and the data sources were three posters of Uda Uni Election which were randomly selected on the internet. This analysis focused on Halliday's (2004) ideational function to analyze verbal texts, Kress' and van Leeuwen's (2006) representations to analyze visual texts, and Cheong's (2004) theory to identify the structure of poster advertisements. The results of the analysis show that the most common verbal elements used in posters were announcements and the most common visual elements in posters were emblems. The verbal elements are often represented by the material process, while the visual elements are represented by the Action process and Reactional Process.
Brain MR image registration based on an Improved CNN Model

Brain image registration is a key part of brain image processing, so the research of brain image registration technology has important value. This paper proposes a brain MR image registration algorithm based on an improved convolutional neural network (CNN). Firstly, the improved CNN is used to extract the feature points in the brain image. Thereafter, the feature pre-matching is performed by finding the nearest neighbors in the feature space. Finally, the registration result of the brain image is obtained by combining the space transformation and the interpolation function. The extensive experiments show the improved CNN method obtains the better results compared with the classic SIFT algorithm and SURF algorithm. At the same time, compared with the CNN algorithm, the mutual information (MI), normalized cross-correlation coefficient (NCC) and normalized mutual information (NMI) of the improved CNN are improved by 10.55%, 0.62% and 1.84%, respectively, while mean square difference (MSD) decreasing by 0.65%. The results show that the algorithm based on the improved CNN achieves higher quality of registration.
Movie Recommendation System Using Concurrent Hybrid Variational Autoencoders

Movie recommendation is a fundamental user requirement for online movie streaming platforms. This research proposes a robust hybrid pipeline that uses two variational autoencoders which can run parallelly to capture the user’s movie preference and genre preference from past data separately. This research uses a stable 1M Movielens dataset for training and testing. This research further explores the impact of the latent space dimension, the impact of the output layer activation function in movie recommendation tasks, and a suitable prediction matrix based on the activation function we used. Our proposed methodology was evaluated against AutoRec Model and was able to achieve 1.3939 in root mean squared error evaluation.
Fast 3D-ISAR Image based on Shooting and Bouncing Ray Technique

In this paper, the conventional 3D ISAR imaging method is improved. By defining the product of three sinc functions in the conventional 3D imaging equation as the ray propagation equation, we can transform the imaging equation into convolution form. For the convolution form of the imaging equation, FFT method is used to take the inverse Fourier transform of the product of the Fourier transform of the two equations, and the first-order Lagrange interpolation algorithm is used to interpolate to achieve uniform sampling. The proposed method can shorten the imaging time compared with the conventional method.
A Coverless Text Steganography by Encoding the Chinese Characters’ Component Structures

The current coverless text steganography methods have a low steganographic capacity, and yet some of them cannot assure a message can be concealed. How to achieve a high steganographic capacity has become the research hotspot in text steganography. This paper proposes a text coverless steganography method by encoding the Chinese characters’ component structures. Its main idea is that a binary bit string can be conveyed by the Chinese characters’ component structures. The positions of Chinese characters that carry a secret message will be expressed in two systems of the linear remainder equations, whose solutions will be secretly sent to the receiver to extract the secret message. In the method, a single Chinese character can express p bits. The analyses and statistics show that its capacity will be much higher when the same Chinese character is used more than once than existing methods, and it can conceal any message successfully. In addition, this method can also be employed in other languages.
Intelligent Repair Method of Old Movie Speckle Noise Based on AI Deep Learning

Speckle noise in old movies is caused by erasure or other reasons, which affects the quality of video images. Therefore, an intelligent repair method of speckle noise in old movies based on AI deep learning is proposed. Based on the analysis of the characteristics of speckle noise in old films, the image noise is filtered by bilateral filtering, and the image features are obtained by combining a convolution neural network to obtain the perceptual loss data. On this basis, the jump connection is added to the deep learning network structure of image restoration. Taking the loss function as the training object, the high-quality restoration of speckle noise is realized by optimizing the loss function. The test results show that the design method can ensure that the average PSNR value of the repaired image can reach more than 40 under lower and shorter training iterations, and the effect is obvious.
Personal Trajectory Verification Based on Blockchain and Zero-knowledge Proof

With the development of location-based services based on smart terminals and positioning technologies, location forgery and privacy leakage become an important issue, making it difficult for the existing methods of proof of location to guarantee the accuracy, authenticity and privacy of user trajectory information. By using the decentralized, non-tamperable and traceable characteristics of blockchain and combining with zero-knowledge proof method, this paper proposes a personal trajectory verification algorithm to provide a solution to solve the limitation of traditional location proof method for trajectory verification problems.
Low-resource machine translation based on fusion Drop method

Aiming at the over-fitting problem caused by the mismatch between coefficient data and large-scale neural network in low-resource translation tasks, a fusion drop method is proposed in this paper. On the basis of Dropout and Dopconnect, a mask matrix is constructed according to the Bernoulli distribution, and the weights and nodes are shielded, so that the model training avoids the disadvantages of loss of computing power and lack of semantic information caused by single nodes or weights. The experiment observed the changes in training loss and BLEU value of different methods on multiple low-resource tasks. The experiment tested the changes in training loss and BLEU value of different methods on multiple low-resource tasks. Experimental results show that for low-resource tasks, a reasonable node or weight distribution can expand the number of subnets participating in training. And when the mask matrix elements obey the Bernoulli distribution with a success rate of [0.2-0.3], the model training can achieve a better result.
Transformasi Wavelet Daubechis dan Fuzzy Subspace Clustering untuk Klasifikasi Misalignment pada Motor Induksi

Induction motors are the most widely used motors because they are sturdy and inexpensive. However, as the induction motor operates, mechanical damage will occur, one of which is bearing damage. Bearing damage can reach 50% compared to other types of damage. The causes include misalignment when installing the motor with a load or vibration when the motor is operating. In this study, the misalignment phenomenon will be classified based on the level of damage. The damage scenario is 1mm and 1.5mm misalignment. Vibration from normal motor and motor misalignment will be taken using a vibration sensor, then the vibration signal will be transformed using Daubechis wavelet transform. The output in the form of a high frequency signal from the Daubechis wavelet transform will be extracted based on the sum, range, and energy of the signal. Then, the performance of the Fuzzy Subspace Clustering method will be known after testing the data. As a comparison whether the Fuzzy Subspace Clustering method can classify induction motor conditions well, it will be compared with the K-Mean method. The results showed that the combination of Fuzzy Subspace Clustering or K-Means and the first-level Daubechis wavelet transform resulted in the best classification with an accuracy of 95.83%.
Technical Debt Measurement during Software Development using Sonarqube: Literature Review and a Case Study

Technical debt comprises the construction of poor software during the development process, potentially leading to several problems for organizations. For this reason, it is convenient to measure it and apply timely strategies to prevent unwanted consequences. Technical debt identification and measurement may be supported by static analysis tools, such as Sonarqube. This paper aims to analyze, evaluate, and apply the technical debt metrics proposed by Sonarqube. We present a literature review about technical debt measurement with this tool and describe the results of a case study. Based on the literature review and the case study results, we analyze the advantages, disadvantages, and limitations of using Sonarqube for technical debt measurement. We conclude that there are several threats to the validity on the proposed metrics, which may lead to inaccurate results. However, Sonarqube can still support technical debt management during the software development process.
Application of Process Metrics for Software Testing: A Case Study

In-process tracking and measurements play a critical role in software development, particularly for software testing. The in-process data and reports focus on design review and code inspection data, including testing data. These in-process metrics are effective for managing software testing and the in-process quality status of the project. These metrics have been used in the IBM Rochester software development laboratory and have been the main differentiator between other software testing metrics which lack usefulness and real-world industry implementation. In this study, three in-process metrics are applied to a small security application project within a private company which underwent a team resource change during the lifetime of the project. It was obtained great insight with the progress comparison between the changes occurring in the project and after its completion. This turned out to be an excellent proof of concept for applying this type of metrics in such challenging environments. Additionally, some recommendations based on the results are proposed for other organizations to encourage the application of these metrics within their projects.
Estimation for a student collaboration hours management system at the University of Costa Rica: a case study

Software estimation is a tool that seeks to provide organizations with a means to know the risks, costs and benefits that software development implies. The goal of this study is to estimate the effort required for a student collaboration hours management system in the context of the University of Costa Rica. For this, the IFPUG function point count standard and estimation models such as COCOMO II, the regression technique, the analogy technique and the comparison technique were used. The results show that the COCOMO II estimation method has the highest values of effort and duration and the analog technique the lowest values. For the regression and comparison techniques, the values were similar, located between the values obtained with COCOMO II and analogy, therefore, it was considered as a good estimation option for the system.
Deep Reinforcement Learning Using a Low-Dimensional Observation Filter for Visual Complex Video Game Playing

Deep Reinforcement Learning (DRL) has produced great achievements since it was proposed, including the possibility of processing raw vision input data. However, training an agent to perform tasks based on image feedback remains a challenge. It requires the processing of large amounts of data from high-dimensional observation spaces, frame by frame, and the agent's actions are computed according to deep neural network policies, end-to-end. Image pre-processing is an effective way of reducing these high dimensional spaces, eliminating unnecessary information present in the scene, supporting the extraction of features and their representations in the agent's neural network. Modern video-games are examples of this type of challenge for DRL algorithms because of their visual complexity. In this paper, we propose a low-dimensional observation filter that allows a deep Q-network agent to successfully play in a visually complex and modern video-game, called Neon Drive.
FINDING SYNONYMS IN A SYNTACTICALLY CONSTRAINED VECTOR SPACE MODEL

Distributional semantics in a vector space model plays an important role in natural language engineering.Apart from the word co-occurrences in plain context, the role of syntactic dependencies in deriving distributional semantics has not yet been fully investigated.We systematically investigate the salience of syntactic dependencies in accounting for distributional similarity.We first categorize the syntactic dependencies of words into four raw co-occurrence matrices that are respectively transformed into the second-order compressed matrices, then we systematically evaluate them in the TOEFL synonym test.Our results show that the semantic features of nouns mostly consist of their modifiers and their head nouns, whereas the semantic features of verbs are mostly explained by verb-modifiers and verb-objects.The syntactically conditioned contexts can interpret lexical semantics better than the unconditioned one.
Employee’s Performance in Small and Medium Enterprises: A Human Resource Management Critical Analysis

Performance management is an important step in the organization’s human resource management because it hones and improves employee performance. Determining the extent of functional flexibility in human resource management, organizational communication, leadership behaviors, and employee performance, including their significant relationship, as well as the significant effects, is the aim of this study. A descriptive survey and correlational research design were utilized to gather the said information through a questionnaire with 498 respondents. It was revealed through weighted mean, Pearson correlation, and multiple linear regression analysis that there was a significant low positive correlation between functional flexibility in human resource management and employee performance, organizational communication and employee performance, transformational leadership behaviors and employee performance, and transactional leadership behaviors with employee performance. Given the overall results, the study concluded that all four (4) independent variables had significant effects on employee performance in Small and Medium Enterprises.
An Online Road Transport Booking System

Road transportation bookings have traditionally been done over the counter in transportation terminals but with the exponential growth of e-commerce, this has changed. This study focuses on automating the Road Transport Booking System to allow travellers (passengers) and employees to buy and sell tickets online. The study also discusses issues that consumers and administrators face, such as long wait times to book a trip, unsafe environments and more. The project investigates several implementation problems and makes suggestions about how to successfully incorporate an Online Road Transport Booking System. This web portal would aid future development of a fully integrated system that connects transportation company employees to consumers, employees to employees, employees to other types of transportation providers, employees to companies, and employees to government agencies. The development tools used in the development of this research are PHP, CSS, HTML, JavaScript, MYSQL database, and XAMPP server.
Face Recognition Efficiency for Different Environmental Influence Conditions

his article concentrated on the study of modern deep learning algorithms for face recognition, to determine methods for as-sessing the effectiveness of detection. On the basis of the most optimal in terms of hardware algorithm implementation to de-termine the impact of capturing conditions on the resulting characteristics of the algorithm with establishing the most opti-mal conditions for its usage in real environment. For major task of the study the hardware complex was implemented based on typical IP camera and specialized computer. The obtained re-sults can be used during implementation of such systems in CCTV applications and improving deep learning face recognition algorithms during scientific studies.
When is Continuous Integration Useful? Empirical Study on Team Size and Reporters in Development

Continuous Integration (CI) is a tool that automatically builds and tests code in software development. Although the benefits of CI have been reported, the situations where CI is highly effective remains unclear. This study investigates the effectiveness of CI on issue resolution duration under different conditions. The results indicate that the larger teams may control the lengthening of the issue resolution duration by using CI.
Universal Pre-Calculating Structure: Reducing Complexity of Ising Chips with Arbitrary Connectivity

Combinatorial optimization problems widely exist in real-world application fields, such as intelligence computation. Solving combinatorial optimization problems is a great challenge for Von Neumann-architecture computers. Alternatively, the Ising chip is a promising solution for combinatorial optimization problems. However, the complexity of the next-spin calculation module greatly increase the hardware cost of the Ising chip. In this paper, we propose universal pre-calculating structures to simplify the next-spin calculating module for the Ising chip with arbitrary connectivity ranging from local to full connectivity. We establish a cost model to provide 1) fast and precise evaluation of hardware cost, and 2) guidelines to select the most suitable structure towards a certain design target. The hardware cost evaluation on FPGA agrees well with our cost model, and shows that our pre-calculating structure can reduce the total cost of Ising chips by 20&#x0025;.
Dichotomous Radial Basis Tanimoto Network to Predict Delivery Mode in Maternal Care Domain

Pregnancy delivery mode prediction is an important one for doctors to provide timely treatment. Some research works have been developed for pregnancy delivery mode prediction using machine learning techniques. But the accuracy of prediction was not improved with less time. In order to perform accurate delivery prediction, dichotomous radial basis Tanimoto network prediction (DRBTNP) method is proposed to enhance the process of pregnancy delivery mode prediction with higher accuracy. The proposed DRBTNP method comprises different types of layers for performing delivery mode prediction with less time and space utilization. Experimental evaluation is performed with different factors such as prediction accuracy, prediction time, and space utilization with respect to patient data. The observed result shows that the presented DRBTNP method increases the prediction accuracy up to 9% with the reduction of prediction time and space utilization up to 20% and 19% over the state-of-the-art methods.
A New Neural Networks-Based Integrated Model for Aspect Extraction and Sentiment Classification

The aspect-based sentiment analysis (ABSA) task consists of two closely related subtasks: aspect extraction and sentiment classification. However, the majority of previous studies looked into each task separately, limiting their effectiveness. In contrast, the integration of aspect extraction and sentiment classification into a single model improves results. The main focus in this work is to manage these two tasks into a new collapsed model. The proposed model relies upon the bidirectional long short-term memory (Bi-LSTM) architecture. On the one hand, it combines a multi-channel convolution layer with an optimization method for handling the aspect extraction task. On the other hand, it includes an attention mechanism based on the residual block and aspect position information for predicting the appropriate opinion orientation of an aspect. The experimental results demonstrate that the model achieved the best performance.
Model AI Assignments 2011

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of three AI assignments from the 2011 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.
Impostor Recognition Based Voice Authentication by Applying Three Machine Learning Algorithms

As compared to other conventional biometrics systems, voice is a unique and important metric, where it is used in many vital fields as the security and communication domains that do not need to be expensive to achieve. The purpose of this article is to see how machine learning (ML) algorithms perform for speaker Authentication to recognize impostors. To boost the audios usable in real environments, it was suggested the preprocessing of audio, like noise decreasing and voiced improving. Mel Frequency Cepstral Coefficients (MFCC) and the four features (Amplitude, Zero Crossing, Mean, and Standard Division) are extracted for all audio metrics, straight beside their differentials and accelerations. Then, Vector Quantization (VQ) is applied to these files. The algorithms were prepared and examined on two datasets, by applying k-fold cross-validation. The preparation for testing and comparing the three (ML) approaches are as follows: Support Vector Machine (SVM), One Rule (One-R), Linear Regression (LR). The result of the (SVM) algorithm average accuracy of 96.33 percent was superior. Index Terms— ML, Speaker Authentication, SVM, LR, ONE.R, Impostor.