Reviewerly/exHarmony huggingface dataset:
Foreign Accent Conversion using Concentrated Attention

Foreign accent conversion is an important and challenging problem due to significant differences in the manner of articulation and the speech prosody of different regional speakers. In this paper, we propose a new method for the problem of foreign accent conversion that uses Phonetic Posteriorgrams (PPGs) and Log-scale Fundamental frequency (Log-F0) to address the mismatches of phonetic and prosody. Furthermore, we propose using concentrated attention to improve the alignment of input sequences and mel-spectrograms. The concentrated attention selects the top k highest score values in the attention matrix row by row. In this way, the attention weight of the content related to the current sequence will be the largest. Our approach first trains a PPG extractor using LibriSpeech Corpus, which uses an end-to-end hybrid CTC-attention model. Then, the modified Tacotron2 based on concentrated attention is trained to model the relationships between PPGs and mel-spectrograms. In our proposed framework, the input of Tacotron2 is the concatenation of PPG embedding and normalized Log-scale fundamental frequency (Log-F0). In the convert stage, WaveGlow is modeled to generate speech, which is a streaming structure. To better verify the effectiveness of our proposed method, we also add some objective evaluation methods. These include Mel spectral distance, Object_MOS score, speaker similarity, and similarity in the embedding space of the entire speech. Experiments shows that our proposed concentrated attention method delivers comparable or better results than the previous foreign accent conversion method in terms of voice naturalness, speaker similarity to the source speaker, and accent similarity to the target speaker.
Weighted Transformer for Dialect Speech Recognition

End-to-end automatic speech recognition (ASR) with transformer models has recently made significant progress, surpassing Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs) mainly because it can model larger context in a parameter-efficient way using self-attention and feed-forward layers. Among them, the self-attention mechanism has received great attention from scholars and it has demonstrated promising results in various natural speech recognition (ASR) tasks. However, the classical transformer-based approaches usually require a large number of parameters and training data, with many training iterations to converge well. In addition, dialect is a common phenomenon in our daily life, and one may not have a large amount of data to start with. More importantly, different dialects have obvious differences at the phonological level although they may share large similarities at the lexical level. Thus, efficient training of highly accurate ASR models for various dialects is very much desirable but remains a challenging problem. In this work, we introduce a weighted transformer that makes better use of the multi-head attention mechanism and obtains more accurate results for four spoken English dialects. We pretrain our base models including weighted transformers using the 960 hours LibriSpeech dataset and adapt them on English dialect data of Common Voice and LibriSpeech SLR83 speech datasets respectively. Experimental results further show that the added weight can distinguish different dialects to obtain better representation. And our proposed dialect-dependent ASR system is significantly more accurate than the classical transformer baseline. In addition, during the training process, we found that the training speed of the new model has been improved by 15%-30%.
Classify Malaria Dataset Human Blood Images Using Convolutional Neural Networks

Malaria is prevalent in regions around the globe, and human activity has contributed to more than 400,000 fatalities annually. A timely and accurate diagnosis is necessary for optimal disease treatment, given that malaria is a substantial problem on a global scale. Our research presents classification studies of malaria as a solution to the previously described problem. These studies use image datasets obtained from photo data online and attempt to detect malaria infected or non-infected by someone using a blood image via Convolutional Neural Network strategies and deep learning methods. The results of tests done with Convolutional Neural Network techniques show that the processes were successful, with the best model getting more than 95%. In the confusion matrix, their accuracy is 97.28%, their precision is 99.60%, their recall is 95.15%, their specificity is 99.62%, and their F1 Score is 97.32%. In addition, the prediction accuracy for identifying malaria was 100% when utilizing photographs or various datasets from the laboratory as test data.
An Optimization Approach to Automatic Construction of Browsable Concept Index for Organizing Online Educational Content

As learning becomes more dependent on online learning platforms, new challenges emerge. One major challenge is efficient access to educational content as most content is unstructured and scattered on the Web. Indeed, there is a great need for organizing educational content and presenting it in a semantically structured and browsable format that does not require the users' prior knowledge of relevant content. In this paper, we address this need by introducing a novel semantic structure, called Browsable Concept Index (BCI), to bring semantic structures to educational content. A BCI consists of a set of representative concepts that form a graph with each (weighted) edge indicating strength of association between two concepts and each concept node being linked to the text segments in the collection of documents that cover the concept. The BCI effectively integrates scattered textual content and enables efficient access to the content by users in multiple ways including: semantic browsing, exploratory and targeted information seeking, and collaborative information seeking. We further propose an optimization-based algorithm to automatically create a BCI based on a collection of educational textual content. Experiments on Coursera course content demonstrate that the our algorithm is effective, allowing the automatic construction of a useful BCI that can facilitate online learning.
A Generative Adversarial Net Assisted Method for User Intention Recognition on Imbalanced Dataset

Class imbalance is a classic problem in machine learning and deep learning. This problem bothers recognizing user intention in knowledge graph question answering domain. Some intentions would not be recognized well when the training set is unbalanced. Sampling and weighted loss methods are widely used to address it. However, these methods pay little attention to the form of question texts in feature space. We propose a novel method based on generative adversarial net to deal with imbalanced class problem, which firstly learns how a user question text of a certain class is represented in feature space and then generates samples. Our method designs a model that contains three parts: text encoder, generator, and discriminator. The text encoder transforms user question text into words vectors. The generator generates vectors that would converge to expected classes and the discriminator is responsible for recognizing classes. We split the training period into two stages. In the first stage, the discriminator is going to distinguish between samples from encoder and generator. In the second stage, the generator learns data distribution and generates related samples to enhance the ability of encoder. Experimental results show that our method can balance the model performance on each class and significantly outperforms traditional methods on unbalanced datasets for user intention recognition.
Development of Papaya Plant Automation Systems with the Internet of Things Concept Using Fuzzy Logic

- Papaya plantations in Bali are actually not fully cultivated in the actual appropriate climate or weather, so the life expectancy of papaya plants is very less, therefore efforts are needed in maintaining papaya plants, one of which is by creating an automatic papaya plants maintenance system. This maintenance includes watering, rain protection dan pest repellent. Therefore, through this research, a papaya plant maintenance system will be created that will be integrated with a website. This system uses NodeMCU ESP32 as the main controller in carrying out maintenance, receiving and sending data and orders via the MQTT protocol. Sensors used such as soil moisture, rain meter and PIR. For watering results, it will be compared first using Mamdani Fuzzy Logic to get control output to the pump. All results and controls can be accessed through a website interface whose maintenance and monitoring functions have an average response time of 15.2 seconds and can be accessed from anywhere provided that they are connected to the internet network. The system can perform all the automated maintenance functions as desired and is capable of passing device durability tests.
NetDet: Concept of Integrating Basic Computer Network Learning into Game Mechanics

A computer network has similarities with a game, which must meet several rules to achieve its goals. The process of finding the source of the problem and how to solve it on the network is similar to detective's work, namely looking for clues that will lead to a conclusion. This research tries to design and create an educational game concept about computer network learning that integrates learning materials into game mechanics. The stages of the research carried out are Inception which consists of: determining the target user, platform, genre and background story; Elicitation which consists of determining the type of gameplay and interaction; and Elaboration which is the stage of determining the objective of the game and the level stage. Integration of learning materials into game rules and mechanics can be done, especially on the subject of basic computer network learning. Computer networks that follow the rules to run and function can be poured into game mechanics. This integration can be accommodated in casual game genre types, point & click interactions and with stage levels. Concepts and ideas in integrating learning into the rules and mechanics of a game have several limitations, namely not all subjects and learning materials can be converted or changed into a form of game mechanic rules and delivery of material in a little time.
The Business Prospect in Metaverse and NFT Era (User, Accountant, and Gaming Community Perspectives)

Non-Fungible Token (NFT) has been growing extraordinarily in recent years. The concept of NFT is essentially a digital asset that operates based on smart contracts on a blockchain that were traded with cryptocurrencies such as Ethereum, Tezos, Solana, etc. Public attention toward NFTs has exploded in 2021. Despite its popularity and skyrocketing trading prices, little is known about the economics, people's perceptions, and experiences with NFTs. Therefore, This study aims to provide diverse understandings of NFT, the benefits & drawbacks of NFT, and the potential of NFT and metaverse according to them. In this paper, researchers perform a qualitative study which consists of 18 respondents, including users, accountants, and gaming community. This research uses a semi-structured interview and descriptive analysis method. The conclusion drawn from this study is that this research recommends readers for utilizing NFT and we expect readers to find this research useful.
Hotel Review Sentiment Analysis Using Indonesian Language Based on Machine Learning

It is crucial to optimize the Naive Bayes technique because its level of accuracy still has flaws. In order to achieve a higher level of accuracy, optimization employs the right and best techniques for text grouping, particularly for hotel review classification. In order to increase the precision of sentiment analysis, this study compares the use of a dataset with 6 features and a dataset with 18 features in order to determine the impact on the classification accuracy of Naive Bayes with Chi-square. This is demonstrated when the Naive Bayesian algorithm is applied to a dataset with 18 features, which results in accuracy values of f-measure = 0.83 and ROC = 0.922 as opposed to 6 features, which have f-measure = 0.746 and ROC = 0.839. The Chi Square feature selection technique has the effect of improving the Nave Bayes algorithm's classification accuracy of Indonesian hotel review texts. Based on the f-measure and ROC values, which are f-measure = 0.831 and ROC = 0.92 for a dataset with 18 features, the accuracy value will be high if the Naive Bayes algorithm is combined with Chi Square feature selection. According to the results of this study, the accuracy value can be increased in both the 6-feature dataset and the 18-feature dataset by using the Naive Bayes algorithm in conjunction with Chi Square.
Analysis of Determination of Items Ordering Patterns By Using Apriori Method

This study is entitled Analysis of the Determination of Ordering Patterns of Items Using Apriori Method Case Study of Universitas Potensi Utama. The research conducted by the author uses data mining and uses the Apriori method which divides arranged items into two so that the more detailed types of items are often ordered so that it can help inventory to increase the stock of items needed by the staff of Universitas Potensi Utama. Ordering items is one of the activities carried out at the Universitas Potensi Utama which is usually used to process work activities in the office. The test results using a priori algorithm and a system built using 2 item sets showed 94% confidence results that have met the needs in determining the pattern of ordering items based on the tendency of ordering items carried out by each department.
Two is Better Than One: Dual Embeddings for Complementary Product Recommendations

Embedding based product recommendations have gained popularity in recent years due to its ability to easily integrate to large-scale systems and allowing nearest neighbor searches in real-time. The bulk of studies in this area has predominantly been focused on similar item recommendations. Research on complementary item recommendations, on the other hand, still remains considerably under-explored. We define similar items as items that are interchangeable in terms of their utility and complementary items as items that serve different purposes, yet are compatible when used with one another. In this paper, we apply a novel approach to finding complementary items by leveraging dual embedding representations for products. We demonstrate that the notion of relatedness discovered in NLP for skip-gram negative sampling (SGNS) models translates effectively to the concept of complementarity when training item representations using co-purchase data. Since sparsity of purchase data is a major challenge in real-world scenarios, we further augment the model using synthetic samples to extend coverage. This allows the model to provide complementary recommendations for items that do not share co-purchase data by leveraging other abundantly available data modalities such as images, text, clicks etc. We establish the effectiveness of our approach in improving both coverage and quality of recommendations on real world data for a major online retail company. We further show the importance of task specific hyperparameter tuning in training SGNS. Our model is effective yet simple to implement, making it a great candidate for generating complementary item recommendations at any e-commerce website.
Feature Selection Application to Classify Medicinal Plant Leaves using LVQ

Indonesia is a country famous for cultivating medicinal plants, but the large number of medicinal plants is inversely proportional to the number of people who have the knowledge and can recognize the types of medicinal plants. To solve this problem, a computer vision system was developed that aims to identify medicinal plants based on the image of their leaves. Several stages used in this research are image preprocessing, image segmentation, feature extraction, feature selection and classification using the Learning Vector Quantization (LVQ) method. The optimal features used are texture features B (ASM 0°, Contrast 0°, Contrast 45° and Contrast 90°) and a combination of shape and texture features B (Diameter, ASM 0°, Contrast 0°, Contrast 45° and Contrast 90°). The optimal parameter used in the LVQ method is the learning rate (a) which is 0.1, the decrease in the learning rate (dec a) is 0.75. When using the texture B feature, the training accuracy is 95.52% and the testing accuracy is 95%. When using a combination of shape and texture B features, the training accuracy is 94.40% and the testing accuracy is 92.50%.
Graph Reachability Pruning: Adaptive Data Reduction for Inexact Subgraph Matching

Subgraph matching is a fundamental graph analysis capability that many fields in science and technology depend on to retrieve information from data that are structured as graphs. These fields span network and cyber security, social and digital media, and the physical and life sciences. Because subgraph isomorphism is an NP-Complete problem, with depth-first search approaches having factorial time complexity and breadth-first approaches having factorial space complexity, efficient search space reduction is fundamental to the timely execution of queries against large, real world graphs having hundreds of millions or more nodes and edges. In this paper, we introduce Graph Reachability Pruning (GRP), a neighborhood-based data reduction algorithm that discards all infeasible and many suboptimal regions of a knowledge graph prior to performing subgraph matching. The algorithm automatically adapts a missing neighbor tolerance parameter to enable structurally inexact matching without requiring cumbersome parameter tuning by an analyst and without relying on a space consuming pre-computed index. It outperforms the only known alternative algorithm, Inexact Local Constraint Checking (ILCC), which applies a single tolerance parameter uniformly across an entire query graph. In our real world case study, ILCC often fails to reduce knowledge graph data by more than a single digit percentage, whereas GRP often discards more than ninety-nine percent of the data. GRP operates independently from any search algorithm it might be paired with. Other data reduction methods are often integrated into, and operate concurrently with, a coupled search algorithm. This property of GRP gives it a versatility that is exceptionally useful.
Use of software and project management metrics in agile software development methodologies

Context: Agile software development is widespread in software development companies because of the benefits it provides. Design and project management metrics can be used during agile software development as a guide for taking decisions and applying corrective actions. Objective: The purpose of the paper is to present the reasons of using software and project management metrics in agile software development methodologies. There are many metrics and variations of these metrics and so this research will try to identify and classify the purposes of using metrics in the context of agile software development. Method: For the purposes of the research, a systematic mapping study was conducted. Results: The research turned out that metrics are used to achieve the following aims: (a) Improving agile processes, (b) Complying with protocols in agile methodologies, (c) Improving software quality during development, (d) Improving the quality of source code, (e) Improving estimation and planning, (f) Increasing productivity. Conclusions: This study provides researchers and practitioners with a basic overview of the use of software and project management metrics in agile software development methodologies, as well as the reasoning behind the use of such metrics.
A Novel Cooperative Parallel Multi-Population Optimization Algorithm

This work proposes a new parallel meta-heuristic optimization algorithm to deal with high dimensional optimization problems. We introduce a parallel and co-evolving multi population framework that mimics the hierarchical structure of grey wolves. We also propose using elite groups and a probabilistic mutation operator to improve the convergence speed and exploration ability. The algorithm is benchmarked on the twenty-eight functions of IEEE Congress of Evolutionary Computation (CEC) 2013 test suites and is compared with other meta-heuristic algorithms. Our proposed algorithm results show that our algorithm can find more optimal solutions at higher dimensions as compared to other meta-heuristic algorithms. Non-parametric statistical test also show the consistency in the obtained results.
Organizational governance: Resolving insufficient practice and quality expectation in Small Software Companies.

The quality of software products is among the most prevalent challenges threatening the software development primarily in small software companies (SSCs). These challenges are associated with insufficient practices affecting the production of software and the development processes. This paper explores the role of governance in streamlining software processes and practices to produce better quality software products. In a cross-sectional survey (n = 127), we reached out to software practitioners working in SSCs from four countries. We examined how SSCs engage in oversight and accountability and how SSCs perform management roles and activities, such as controlling, directing, and guiding in the process of developing software. Our findings indicate that although the SSCs minimally embrace governance practices, the smaller companies have a more challenging task embracing governance practices from the complexities arising out of these companies' structures. This study highlights the aspects of governance that need attention in the smaller category of SSCs. It proposes an organizational governance model to facilitate the SSCs in developing governance strategies to take advantage of the benefits of governance during software development.
An approach for the forecasting of the maintainability of system functionalities

The programming of system functionalities requires the development of components that must cooperate to satisfy the functional requirements of users, modularity, and reusability. Therefore, maintenance tasks demand developers to understand their internals and have knowledge about the dependency graphs that are formed by the interaction between components. Furthermore, they should be aware of the size, complexity, and maintainability of individual items, as well as of their aggregated weight for the complete coupling graph. Consequently, this paper aims to present an approach for calculating a forecasting indicator of the maintainability of system functionalities using as a base the indirect coupling graphs and a set of computed metrics for a group of code commits performed during a time period. Therefore, its contributions are a set of metrics for the calculation of a Maintainability Index of system functionalities and the individual elements in their dependency graphs, and to forecast the maintainability of system functionalities based on the sum of weights of the methods in the dependency graphs.