Reviewerly/exHarmony huggingface dataset:
Multi-Speaker Voice Cloning in Indonesian Using Sequence to Sequence Speech Synthesis

Multi-Speaker Voice Cloning is a system that can produce various voices based on voice samples and text input. Voice cloning can be an alternative to get audio data quickly without recording, which requires a lot of time, cost, and effort. Sequence to Sequence Speech Synthesis produces sounds that are naturally similar to speech equivalent to the human voice. Sequence to Sequence Speech Synthesis is a technology for generating speech directly from the text, consisting of attention-based predictive network features. Multi-Speaker Voice Clone consists of speaker encoder, encoder-decoder, and vocoder models. Tacotron-2 as model encoder-decoder and WaveNet as a vocoder. The speaker encoder will produce a speaker embedding to recognize the sound from the speaker. Tacotron-2 will embed text into the mel scale spectrogram. WaveNet will synthesize the time domain waveform from the mel scale spectrogram and generate sound predictions based on the samples. This model uses a multi-speaker voice dataset in Indonesian. Combining the three models produces a value of 3.28 for speech naturalness and 1.94 for speech similarity.
IF: An Interactive Fusion Network for Targeted Aspect-Based Sentiment Analysis

Targeted aspect-based sentiment analysis (TABSA)is a very challenging task in sentiment analysis field, which aims to determine the sentiment polarity for an aspect of a certain target. TABSA task is usually divided into two subtasks: aspect category detection and aspect sentiment classification. One simple thought for them is to use a pipeline method, but it is difficult to use in application and will produce cascading errors. Therefore, some researchers proposed joint methods by sharing the encoding of inputs. However, they did not explicitly model the relevance between the two subtasks or fuse the acquired information deeply. To address this problem, we propose a novel Interactive Fusion (IF) model, which can enable direct interaction between two subtasks and generate aspect categories and their sentiments simultaneously. Specifically, the sentence representations are obtained from the encoding layer, and then a fusion layer is introduced to make the two subtasks interact multiple rounds to fuse the representations better. Experimental results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.
Intelligent Recognition of Automatic Production Line of Metal Sodium Rod

A scheme and algorithm for identifying and locating metal sodium bars and placing them into barrels intelligently were designed. In the detection and positioning of sodium bars, deep learning is mainly used to quickly identify sodium bars based on Open CV and YOLO convolutional neural network, and then morphological processing is carried out on each recognition frame of sodium bars to obtain the central location of sodium bars. When determining where the remaining sodium bars can be placed, it is assumed that the long axis of the sodium bar is tangent to the barrel wall and a cluster of circles is drawn to predict all possible positions of the sodium bars. Then, whether each circle has the intersection with other sodium bars is calculated one by one, so as to output the positions of the sodium bars that can be placed.
Similarity Principle and Bogdanov Tektology in Systems Analysis Evolution of Large Systems

It is proposed to use the principle of similarity in the system analysis of large systems to hypothesize about their structure, properties and evolution on the basis of analogy with similar, but more accessible to the study of other systems. Based on the principle of similarity and Bogdanov’s general theory of organization and disorganization of systems (Bogdanov’s tektology), we can judge the patterns of their evolution. The evolution of large systems towards sustainable development occurs with the formation of new types of their organization and restructuring of the former integrity under the influence of external. An example of a large system is our Universe, the origin, properties and evolution of which, despite the successes obtained in its study by astronomy and cosmology, still remain unclear. An analogy is drawn between the evolution of the Universe and Man, who can also be regarded as a “big system”. Based on the principle of similarity and the laws of development of systems discovered by Bogdanov, a number of hypotheses about the origin and evolution of our Universe are put forward.
An Intelligent Hough Transform with Jaya of Multipopulation Cooperation for Diameter Estimation of Red-Hot Workpiece

Diameter is a critical metric during producing circular workpieces, but it is always challenging to measure accurately due to the existence of noise. The intelligent Hough transform with Jaya (IHTJ) is developed to solve this issue recently. However, it suffers from some disadvantages including unsatisfactory accuracy and convergence rate caused by the problem of trapping into local optimization in the case of inaccurate priori knowledge. In this paper, an IHTJ of multipopulation cooperation (IHTJMC) is proposed to overcome these disadvantages. Firstly, we analyse the problem of IHTJ in the diameter measurement. Secondly, a ring structure of the multipopulation cooperation is designed. In this structure, the whole population is divided into several subpopulations which evolve independently all the time. This can ensure a few best and worst solutions guide the evolution of the whole population at the same time. Then, we propose a migration strategy to finish the cooperation among the different subpopulations, which selects a few individuals from each subpopulation randomly, and exchange them with each other. This strategy can make the different subpopulations share the information among the entire population and improve the global search ability. Finally, three experiments are introduced to test the effectiveness of the IHTJMC. The experimental results demonstrate that the IHTJMC can restrain the problem of falling into local optimization significantly. Compared with the existing methods, it has superior accuracy and robustness for the diameter measurement of a circular workpiece which is heated to red state.
Research Highlights (Required)Embedding Model Predicts that Analogies are Worth About One Bit of Information.Analogy Tests are Akin to Word Guessing Games with Proximity and Analogy Hints.Experimental Accuracies Yield Approx. Information Contents of Each Hint.Paradoxically, Some Analogies May Even Degrade Analogy Test Performances

A new approach is proposed to quantify, in bits of information, the actual relevance of analogies in analogy tests. This approach is based on accuracy measurements, but because small test sets may cause usual accuracy to significantly deviate from its average value, an alternative accuracy measurement was worked out, thus directly yielding entropy estimates with compensated biases. Some experimental results are obtained with pre-trained GloVe 300-D vectors, whose performances in analogy tests are around the best ones, and two analogy test sets, namely: the Google Analogy Test Set and the Bigger Analogy Test Set. All experiments corroborate the general perception noticed in publications since 2016 that the analogy is much less relevant to the tests than accuracy results would suggest, but under a new quantitative perspective which corroborates the theoretical prediction of a simple model also proposed in the paper.
Research on Improved Collaborative Filtering Recommendation Algorithm Based on User Behavior

Recommendation algorithms have been widely used in many fields. This paper studies an improved collaborative filtering recommendation algorithm based on user behavior to improve the accuracy. In this paper, user behavior is used as the influencing factor of item collaborative filtering recommendation and user collaborative filtering recommendation. Two improved collaborative filtering recommendation algorithms are proposed. The experimental results show that the improved collaborative filtering recommendation algorithm has higher recommendation accuracy for the recommended topic.
Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences

Sentence embedding methods offer a powerful approach for working with short textual constructs or sequences of words. By representing sentences as dense numerical vectors, many natural language processing (NLP) applications have improved their performance. However, relatively little is understood about the latent structure of sentence embeddings. Specifically, research has not addressed whether the length and structure of sentences impact the sentence embedding space and topology. This paper reports research on a set of comprehensive clustering and network analyses targeting sentence and sub-sentence embedding spaces. Results show that one method generates the most clusterable embeddings. In general, the embeddings of span sub-sentences have better clustering properties than the original sentences. The results have implications for future sentence embedding models and applications.
Automated Brain Segmentation for Guidance of Ultrasonic Transcranial Tissue Pulsatility Image Analysis

Background and Objective: Tissue pulsatility imaging is an ultrasonic technique that can be used to map regional changes in blood flow in the brain. Classification of regional differences in pulsatility signals can be optimized by restricting the analysis to brain tissue. For 2D transcranial ultrasound imaging, we have implemented an automated image analysis procedure to specify a region of interest in the field of view that corresponds to brain. Methods: Our segmentation method applies a K-means clustering algorithm that incorporates both echo strength and tissue displacement to create an image mask that designates brain tissue. Brain regions were extracted from the ultrasound data using different numbers of K-means clusters and multiple combinations of ultrasound data. Masks generated from ultrasound data were compared with reference masks derived from Computed Tomography data. Results: Improvement in the match between the ultrasound and Computed Tomography masks is seen with an increase in the number of clusters in the K-means analysis and with addition of the displacement data to the B-Mode data. Addition of an algorithm to identify shallow sources of shadowing improved the overall mask match.Conclusions: Several segmentation algorithms achieve a match of over 80% between the ultrasound and Computed Tomography brain masks. A final tradeoff can be made between processing complexity and the best match of the two data sets.
Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data

The joint use of atmospheric chemistry transport and transformation models and observational data makes it possible to solve a wide range of environment protection tasks, including pollution sources identification and reconstruction of the pollution fields in unobserved areas. Seamless usage of different measurement data types can improve the accuracy of air quality forecasting systems. The approach considered is based on sensitivity operators and adjoint equations solutions ensembles. The ensemble construction allows for the natural combination of various measurement data types in one operator equation. In the paper, we consider combining image-type, integral-type, pointwise, and time series-type measurement data for the air pollution source identification. The synergy effect is numerically illustrated in the inverse modeling scenario for the Baikal region.
THE USE OF GOOGLE FORM IN MAKING WRITING DESCRIPTIVE TEXT

This research aims to produce development instruments and knowing the feasibility of development instrument by using google form application as a medium in the learning process of writing descriptive text in VII-A grade of SMP N 35 Bekasi. The research method that researcher use in is research and development (R&amp;D). The data collection in this research is test and interview. The population in this research is all VII grade students of SMP N 35 Bekasi the number of students is 40 students. From this population, researcher took sample using random sampling. Thus, the sample in this research was obtained by VII-A grade students with a total of 13 men and 11 women. The overall test results showed good results i.e. obtained a very decent category with details: (1) Cronbach's Alpha reliability test obtained a .747, (2) Cohen's Kappa inter-rater reliability test (k) appraiser 1 and appraiser 2 obtained a valid result of 100% missing 0%. Based on the results of the reliability test feasibility, it can be concluded that learning media using google form application is very feasible to be used in the online learning process during pandemic covid-19 as a medium in making descriptive text writing in grade VII SMP N 35 Bekasi.
Towards Dynamic Obstacle Avoidance for Robot Manipulators with Deep Reinforcement Learning

We use reinforcement learning (RL) to demonstrate an easily reproducible setup to learn dynamic obstacle avoidance for a robotic arm based on sensory input as it follows a pre-planned trajectory. Training takes place exclusively in a simulation environment with random obstacle movements around the robot. We show that training dynamic obstacle avoidance in simulation translates well to the real environment with a UR5 manipulator and yields similar performance and success without further tuning of the learned policy. This is a step towards learning general skills needed to enable robots to operate in dynamic environments shared with humans. Source code, data and application videos are available at: https://www.robogym.net .
Modeling and Predicting Student Academic Performance in Higher Education Using Data Mining Techniques

The primary goal of upper education foundations is to offer quality instruction to its understudies. One approach to accomplish most elevated level of import during advanced education framework is by finding learning for forecast with reference to enrollment of understudies in a specific course, estrangement of typical study hall showing model, discovery of out of line means that used in on-line assessment, identification of surprising qualities within the outcome sheets of the understudies, expectation concerning understudies' exhibition, etc. the educational is tucked away among the instructive infoal index and it's removable through information mining ways. This paper is intended to let the limits of data mining methodologies in setting of cutting edge training by giving a data mining model to higher education framework inside institute. During this exploration, the classification and prediction tasks are employed to assess understudy’s presentation and as there are various methodologies that are utilized; decision tree, clustering and neural network techniques are used here.
Team Formation Integrating Various Factors: Model and Solution Approach

Nowadays team formation has become a very active research topic and it is recognized as a key process for the success of organizations. This article describes a model for multiple team formation that, unlike existing models, it integrates a diversity of factors, both individual and collective and therefore it can be used in various contexts. The model includes the following factors: competences, workload, cost of communication in the team, incompatibility between members, cost of working at a distance, interest in playing the role, interest in working in the team and balance of psychological characteristics in the team. The model responds to a combinatorial optimization problem, for that reason to solve the problem it is used a library of classes of meta-heuristic algorithms. To demonstrate the applicability of the model, the results of using the model in the formation of student teams are presented.
Controllable Energy Angular Spectrum Method

A controllable energy method, which considers the undersampling issue of the transfer function and valid spectral energy of a source signal, is proposed to implement angular spectrum diffraction calculation in near and far fields. The proposed method provides an optimized frequency boundary f CEÂ within which it always keeps controllable energy to be diffracted. The controllable energy angular spectrum method significantly reduces the number of samples while having the same accuracy as previous angular spectrum methods, implying a higher calculation efficiency. The new perspective of analyzing spectral energy is shown to improve the performance of relevant diffraction calculations.
Symmetric Encryption Algorithms

In this chapter, we will discuss two types of symmetric encryption algorithms, Data Encryption Standard (DES) and Advanced Encryption Standard (AES). The goal of this chapter is to provide a starting point for implementing such algorithms from scratch without using third-party libraries, by using the theoretical concepts and translating them into a practical application using Java.
Generative Adversarial Minority Enlargement–A Local Linear Over-Sampling Synthetic Method

The imbalanced data problem is widely recognized in real-world datasets. To avoid learning bias on imbalanced data, the over-sampling strategy is well studied and adopted for generating synthetic minority samples. Wherein, the Synthetic Minority Over-sampling Technology and its improved algorithms become standard baselines. In recent years, the popular Generative Adversarial Networks and its enhanced variants, introduced from the computer vision community, are believed to generate better samples, by approximating the true data distribution. Yet, we notice that the synthetic data for the minority category in this line of designs is usually restrained in a limited feature space, which may mislead the classifiers trained on them to take data in the unsampled region of the minority category as from the majority category. Given such limitations, we propose a Generative Adversarial Minority Enlargement (GAME) method to intentionally extend the sampling margin during data generative and adversarial phases. This is accomplished by adjusting the parameters of a local linear model to approach the majority category. We conduct an extensive evaluation on 28 datasets of different domains, extracted from the UCI real-world datasets. The results show that GAME can achieve more balanced and stable results efficiently than 12 state-of-the-art methods.
A Novel Secure Speech Biometric Protection Method

In recent years, automatic speaker verification (ASV) has been widely used in speech biometrics. The ASV systems are vulnerable to various spoofing attacks, such as synthesized speech (SS), voice conversion (VC), replay attacks, twin attacks, and simulation attacks. The research to ensure the application of voice biometric systems in various security fields has attracted more and more researchers’ interest. The combination of credibility and voice is particularly important in this period when biometric systems are widely used. We propose a novel secure speech biometric protection method. This article also summarizes previous research on spoofing attacks, focusing on SS, VC, and replay, as well as recent efforts to improve security and develop countermeasures for spoofing speech detection (SSD) tasks. At the same time, it pointed out the limitations and challenges of SSD tasks.
Effect of Class Imbalanceness in Credit Card Fraud

With the expansion of latest technologies, credit card fraud has increased dramatically, making it the ideal target for fraudsters. As a result, financial institutions must be proactive in identifying and monitoring unethical behavior. Furthermore, the presence of class imbalanceness in the credit card usages poses a high challenge in identifying the genuine patterns. In this article, the authors have performed comprehensive experimental research to address such issues by proposing a novel hybrid fraud detection model that uses the machine learning techniques for identification of forged activities in transactional records. Extensive experiments have been done using a real-world dataset to show the efficiency of the proposed system. The authors have used an oversampling approach to remove the data imbalances present in the dataset. In addition, the efficiency of the proposed model on a balanced dataset over the imbalanced one has been presented.
Optimizing Distributed Multi-Sensor Multi-Target Tracking Algorithm Based on Labeled Multi-Bernoulli Filter

Labeled multi-Bernoulli (LMB) filter based on distributed multi-sensor (DMS) network is promising for multi-target tracking with an unknown number of targets. Specifically, the DMS network can effectively improve the capability of detection for moving targets. However, there is still an open problem on how to achieve the effective sensor data fusion by using the interactive information of interconnected sensors and how to solve the inconsistency of label space among the sensors. In this paper, we propose an improved distributed fusion algorithm under the LMB filter framework. First, a sensor ID and a new group variable are employed and extended into the LMB parameter set for recording the matching information of the interconnected sensor, then the matching LMB components of the survival targets between the sensors can be fused directly by checking the group variable, which can greatly reduce the time cost of the matching calculation and the interference from the newborn targets. Moreover, the Murty algorithm is employed and only performed once to find the matching relation of the newborn targets between the sensors. Finally, experimental results show that the proposed algorithm offers a better tracking performance than the state-of-the-art R-GCI-LMB algorithm with lower computational complexity and higher tracking accuracy.
A Survey of Recent Advances for Single Image Haze Removal Techniques

Single image haze removal has emerged out to be an innovative researching direction in various applications such as computer vision, computational photography, outdoor surveillances, scene analysis and classification, object detecting and tracking, etc. In addition, researchers involve themselves in developing algorithms or modifying existing algorithms which can improve the quality of hazy image. The scattering of the atmosphere usually degrades the quality of an image captured in bad climatic conditions, such as haze, fog, dust storm and rain. It reduces the contrast, makes the image color gray, makes the object features difficult to identify and creates inconvenience of post processing to the image. Therefore, hazy image needs to be enhanced and restored for improving the visual effects as well as convenience of post processing. In this paper, various techniques of single image haze removal have been surveyed and compared from year 2011 to 2021. We have precisely compared twenty-seven different research work done during the mentioned period for removing the haze from a single image. Initially, prior-based single image dehazing methods are discussed and then neural-network based methods have been explored for single image haze removal. Prior-based techniques include Dark Channel Prior (DCP), Color Attenuation Prior (CAP), guided-image filtering, total variation, etc. while neural-network based techniques include Convolutional Neural Networks (CNN), Ranking CNN, DehazeNet, Radial-basis CNN, MSCNN, etc. In order to get awareness about the existing methods for interested researchers, the paper thoroughly discuss about the important details, issues and results of each work. From the survey, it has been found that neural networks based methods have shown better performance in terms of removing haze and noise, and better quality image but the researchers find challenges in removing the haze completely from the image which contains sky region most. Also, it is observed that there is a requirement of large amount of training data to train the model which may implies to model failure in real time applications.