Reviewerly/exHarmony huggingface dataset:
Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture

Finding Spatial High Utility Itemsets (SHUIs) in a spatiotemporal database is a challenging problem of great importance in many real-world applications. Most previous works focused on the sequential discovery of SHUIs in a database running on a single machine. Consequently, these works are not suitable for big data (or cloud-based) applications as they suffer from the scalability and fault tolerant problems. This paper proposes several novel pruning techniques to reduce the search space and present a more flexible distributed algorithm to find all desired itemsets from the database using Spark in-memory computing architecture. Our algorithm inherits several advantages of Spark, including low communication cost, fault tolerance, and high scalability. Experimental results demonstrate that the proposed algorithm has good scalability and performance on very large databases. Finally, we present a real-world navigation application in which SHUIs generated from the traffic congestion data have been employed to recommend alternative routes to the users.
The Fuzzy Control Automation Architecture of Parallel Action for the Intelligent Smart Grid Networks

Some technology introduction problems of Smart Grid in the electric networks of Ukraine are considered in the article. It is offered to apply the automats of parallel action in control system by a power supply. It is offered to make alteration in the structure of parallel action classical automat for the decision of problem vagueness detains. The general strategy is set for the construction of parallel action automats with fuzzy logic
Recognizing Five Major Dialects in Indonesia Based on MFCC and DRNN

Abstract Dialect is a variation of the language used by a group of people, sometimes in a particular region. It plays an essential role in automatic speech recognition (ASR). In general, an ASR gives high accuracy for a dialect-specific case, but it obtains a low accuracy for the multi-dialect application, such as for the Indonesian language that has hundreds of dialects. In this research, a system to recognize various dialects in Indonesia is developed. First, an utterance is preprocessed using both normalization and framing. Second, its features are then extracted using the Mel frequency cepstrum coefficients (MFCC), which is one of the feature extraction methods for the best acoustic signals. Finally, a deep recurrent neural network (DRNN) is used to learn and classify dialect characteristics. Evaluation of the dataset of five major dialects in Indonesia shows that the greater the Epoch and Bath Size, the greater the accuracy produced by the DRNN. However, accuracy is not directly proportional to the value of both parameters. The Epoch of 30 and Batch Size of 30 are the optimum parameters that yield the highest accuracy of 87.0% for the training set. Evaluation of the testing set shows that it gives an accuracy of 85.4% for the unseen dialects.
Dual Approach to Handling Imbalanced Class in Datasets Using Oversampling and Ensemble Learning Techniques

In the field of machine learning, the existence of class imbalances in the dataset will make the resulting model have less than optimal performance. Theoretically, the single classifier has a weakness for class imbalance conditions in the datasets because of the majority of single classifiers tend to work by recognizing patterns in the majority class the datasets are not balanced. So, the performance cannot be maximized. In this study, two approaches were introduced to deal with class imbalance conditions in the dataset. The first approach uses ADASYN as resampling while the second approach uses the Stacking algorithm as meta-learning. After conducting a test using 5 datasets with different imbalanced ratios, it shows that the proposed method produced the highest g-mean and AUC score compared to the other classification algorithms. The proposed method in this study is the stacking algorithm between the SVM and Random Forest algorithms and the addition of ADASYN in the resampling process. Hence, the proposed method can be a solution for handling class imbalance in datasets. However, this study has limitations such as the dataset used is a dataset with a binary class category. For this reason, for the future work, testing will be suggested using the imbalanced class dataset with the multiclass datasets.
Analysis K-Means Clustering to Predicting Student Graduation

Abstract The prediction of students’ graduation outcomes has been an important field for higher education institutions because it provides planning for them to develop and expand any strategic programs that can help to improve student academics performance. Data mining techniques can cluster student academics performance in predicting student graduation. The aim of this study is to analysis the performance of data mining techniques for predicting students’ graduation using the K-Means clustering algorithm. The data pre-processing used for data cleaning, and data reducing using Principle Component Analysis to determine any variables that affect the graduation time. This algorithm processes datasets of student academics performance numbering 241 students with 16 variables. Based on the clustering using K-means, the highest accuracy rate is 78.42% in the 3-cluster model and the smallest accuracy rate is 16.60% in the 4-cluster model. The influential variable in predicting student graduation based on the value of the loading factor is the GPA total of the 1st to 6th semester.
Cryptographic Engineering on COVID-19 Telemedicine: An Intelligent Transmission Through Recurrent Relation Based Session Key

Constraints imposed due to the cameo of the novel corona virus has abruptly changed the operative mode of medical sciences. Most of the hospitals have migrated towards the telemedicine mode of services for the non-invasive and non-emergency patients during the COVID-19 time. The advent of telemedicine services has remotely rendered health services to different types of patients from their isolates. Here, the patients' medical data has to be transmitted to different physicians/doctors in a safe manner. Such data are to be secured with a view to restore its privacy clause. Cardio vascular diseases (CVDs) are a kind of cardiac disease related to blockage of arteries and veins. Cardiac patients are more susceptible to the COVID-19 attacks. They are advised to be treated though cardiac telemedicine services. This paper presents an intelligent and secured transmission of clinical cardiac reports of the patients through recurrence relation based session key. Such reports were made through the following confusion matrix operations. The beauty of this technique is that confusion matrices are transferred to specified number of cardiologists with additional secret shares encapsulation. The case of robustness checking, transparency and cryptographic engineering has been tested under different set of inputs. The total cryptographic time observed here was noted as 469.92 ms, 3 ms 74.45 , 502.88 ms, 361.38 ms, 493.12 ms, and 660.16 ms, which is acceptable when compared with other classical techniques. The estimation of correlation coefficient in proposed variables has been recorded as - 0.362 . Different types of result and its analysis proves the efficiency of the proposed technique. It will provide more security in medical data transmission, especially in the needy hours of COVID-19 pandemic.
Zero-shot policy generation in lifelong reinforcement learning

Lifelong reinforcement learning (LRL) is an important approach to achieve continual lifelong learning of multiple reinforcement learning tasks. The two major methods used in LRL are task decomposition and policy knowledge extraction. Policy knowledge extraction method in LRL can share knowledge for tasks in different task domains and for tasks in the same task domain with different system environmental coefficients. However, the generalization ability of policy knowledge extraction method is limited on learned tasks rather than learned task domains. In this paper, we propose a cross-domain lifelong reinforcement learning algorithm with zero-shot policy generation ability (CDLRL-ZPG) to improve generalization ability of policy knowledge extraction method from learned tasks to learned task domains. In experiments, we evaluated CDLRL-ZPG performance on four task domains. And our results show that the proposed algorithm can directly generate satisfactory results without needing a trial and error learning process to achieve zero-shot learning in general.
Fuzzy Sets Theory Approach for Recognition Handwritten Signatures

In this paper we proposed the approach for recognition handwritten signatures based on dynamics characteristics and fuzzy sets theory. We suggested the formal model of human handwritten signature, which contains several fuzzy features. We also suggested handwritten signature reference template creation and handwritten signature recognition algorithms. Suggested formal model was used as a basis. Method of potentials was used to obtain membership functions of fuzzy features which makes it possible to create a reference template even under conditions of a small capacity of training set. The research was conducted on the MCYT_Signature_100 signature collection which includes 2500 genuine signatures and 2500 skillful fakes. Suggested approach makes it possible to recognize handwritten signatures even in case of their fuzzy character. Accuracy evaluation results showed FAR value 2.8%, FRR value 0.4% for random fake patterns and FRR value 0.8% for skillful fake patterns, which is better than evaluation results of many others approaches.
Non-linear scale-space based on fuzzy contrast enhancement: Theoretical results

This work presents a contrast enhancement operator based on a fuzzy-numerical description of images at the pixel level; this operator is further used to construct a scale-space, whose theoretical and practical properties are reviewed. A very remarkable feature of our scale-space is that, in contrast to many other scale-spaces, it converges to non-trivial stages. Within the study of our scale-space, we present a series of theoretical results that show that the convergence of the scale-space is closely related to the signal's convexity. Specifically, we prove formally that the intensities in convex signals tend to converge to the minimum intensity. As a result, our scale-space increases the contrast in the image and homogenizes images. In addition to theoretical results, we illustrate the scale-space's behaviour in ad-hoc 1D signals and in greyscale images. Finally, to validate the potential application of this theoretical approach, we show that the proposal can be used as a preprocessing that performed before a neural network technique, increasing the accuracy in a classification task.
HyGN: Hybrid Graph Engine for NUMA

Modern shared-memory platforms embrace the Non-uniform Memory Access (NUMA) architecture - they have physically distributed, yet cache-coherent shared-memory. This paper explores the feasibility of a shared-memory graph processing engine for NUMA platforms inspired by designs that target zero-sharing platforms. This work exploits the characteristics of two processing modes, synchronous and asynchronous, in the context of the shared-memory NUMA platform. Depending on the algorithm, phase of execution, and graph topology, synchronous and asynchronous modes hold unique advantages over one another. We then explore a hybrid solution that combines synchronous and asynchronous processing within the same graph computation task and harness optimizations therein. An extensive evaluation using graphs with billions of edges and empirical comparisons with several state-of-the-art solutions demonstrate the performance advantages of our design.
Train1 vs. Train2: Tagging Word Senses in Corpus

Homonymy and polysemy have hampered information-retrieval accuracy. The use of a word with multiple meanings as a retrieval key might yield diluted results. So far efforts to alleviate this problem were hampered, first, by the practical problem of tagging words in coprus, and second, by the theoretical problem of substantially enhancing the information contents of a query using word-sense tags. To alleviate thess problems we have implemented a scheme for tagging word senses in a corpus. Accordingly, users’s queries are made specific to particular senses of words. In this chapter we show how word-sense tagging is carried out in the I.M.Toolset system. First, the text is pre-processed as to morphology and local syntax; second, signatures are generated for each manifestation of a word, based on the local context of the word; third, signatures are clustered and word senses are extracted. We discuss the advantages of this scheme, i.e., improved accuracy, and its limitations, i.e., word senses are specific to a narrow corpus over which training took place. 1
Smooth Soft-Balance Discriminative Analysis for imbalanced data

Imbalance classification is a challenging research topic in the community of machine learning, in which it is difficult to acquire the discriminative features. To date, a series of methods have been proposed but they still suffer from the following issues. The first issue is caused by the underrepresented data where the boundaries between classes are not clear. The second one is the complex structure in majority classes. To address these two issues, a Smooth Soft-Balance Discriminative Analysis method (S2BDA) is proposed to deal with imbalanced data. Among it, the underrepresented data is preprocessed via a smoothing technique so that the compact representation of each class can be obtained to make the boundaries between classes more explicit. To mine the structure of majority classes meanwhile keep the pattern hidden in the minority class, a soft-balance clustering model is designed to determine the subclasses from the majority class. Based on the balanced subclasses, S2BDA takes advantage of subclass-aware discriminant analysis to extract the discriminative features for imbalanced data classification. Extensive experiments are conducted on two synthetic data sets and sixteen real-world data sets with various imbalance ratios (from 4 to 39.18), data sizes (from 132 to 20000), number of categories (from 2 to 9) and dimensionalities (from 4 to 178). The experimental results have demonstrated that S2BDA outperforms the state-of-the-art methods in terms of the widely used evaluation metrics.
Multiclass Classification of Astronomical Objects in the Galaxy M81 using Machine Learning Techniques

Data in astronomy usually contain various classes of astronomical objects. In this study, we explore the application of multiclass classification in classifying astronomical objects in the galaxy MS1. Our objective is to specify machine learning techniques that are best suited to our data and our classification goal. We used the archival data retrieved from the Canada-France-Hawaii Telescope (CFHT) data archive. The imaging data were transformed into data tables, then classified based on their visual appearances into five classes, including star, globular cluster, rounded galaxy, elongated galaxy, and fuzzy object. The classified data were used for supervised machine learning model building and testing. We investigated seven classification techniques, including Random Forest, Multilayer Perceptron, Weightless neural network (WiSARD), Deep learning (Weka deep learning), Logistic Regression, Support Vector Machine (SVM), and Multiclass Classifier. Our experiments show that Random Forest and Multilayer Perceptron archived the highest overall performances and are the best-suited model for classifying astronomical objects in the CFHT data of the galaxy M81.
FarsBase-KBP: A knowledge base population system for the Persian Knowledge Graph

While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues working. In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabelled raw text, crawled from the Web. The proposed system consists of a set of state-of-the-art modules such as an entity linking module as well as information and relation extraction modules designed for FarsBase. Moreover, a canonicalization system is introduced to link extracted relations to FarsBase properties. Then, the system uses knowledge fusion techniques with minimal intervention of human experts to integrate and filter the proper knowledge instances, extracted by each module. To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of 22015 FarsBase triples and verified by human experts. The evaluation results demonstrate the efficiency of the proposed system.
Speaker recognition method for short utterance

Abstract Speaker recognition is a technology that uses identity information in the human voice for identity recognition, which owns many advantages in convenient information gathering, low gathering cost and high recognition accuracy. However, the difficulty in gathering messages within short utterance declines the voiceprint recognition function rapidly. We propose a recognition model based on SincNet in the aim of obtaining enough feature information in short utterance. The model used a set of learnable Sinc-based filter banks to extract feature directly from primordial voice in featured extraction layer, which enabled neural networks to discover more valuable voiceprint information; In the pooling layer, we designed the pooling method of dual attention mechanism, which combined multiple self-attention mechanism and self-attention mechanism to enrich the feature information and enhance the differentiation degree of key features so as to solve the defect of short speech with less information; choose ArcFace as the loss function, which can maximize the classification limit in the Angle space, thus improving the classification ability of the model. Experimental results demonstrate that the proposed model performs better than the benchmark model.
Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements

In recent years in mobile robotics, the focus has been on methods, in which the fusion of measurement data from various systems leads to models of the environment that are of a probabilistic type. The cognitive model of the environment is less accurate than the exact mathematical one, but it is unavoidable in the robot collaborative interaction with a human. The subject of the research proposed in this paper is the development of a model for learning and planning robot operations. The task of operations and mapping the unknown environment, similar to how humans do the same tasks in the same conditions has been explored. The learning process is based on a virtual dynamic model of a mobile robot, identical to a real mobile robot. The mobile robot’s motion with developed artificial neural networks and genetic algorithms is defined. The transfer method of obtained knowledge from simulated to a real system (Sim-To-Real; STR) is proposed. This method includes a training step, a simultaneous reasoning step, and an application step of trained and learned knowledge to control a real robot’s motion. Use of the basic cognitive elements language, a robot’s environment, and its correlation to that environment is described. Based on that description, a higher level of information about the mobile robot’s environment is obtained. The information is directly generated by the fusion of measurement data obtained from various systems.
A virtual environment for evaluation of computer vision algorithms under general airborne camera imperfections

Abstract This paper describes a camera simulation framework for validating machine vision algorithms under general airborne camera imperfections. Lens distortion, image delay, rolling shutter, motion blur, interlacing, vignetting, image noise, and light level are modelled. This is the first simulation that considers all temporal distortions jointly, along with static lens distortions in an online manner. Several innovations are proposed including a motion tracking system allowing the camera to follow the flight log with eligible derivatives. A reverse pipeline, relating each pixel in the output image to pixels in the ideal input image, is developed. It is shown that the inverse lens distortion model and the inverse temporal distortion models are decoupled in this way. A short-time pixel displacement model is proposed to solve for temporal distortions (i.e. delay, rolling shutter, motion blur, and interlacing). Evaluation is done by several means including regenerating an airborne dataset, regenerating the camera path on a calibration pattern, and evaluating the ability of the time displacement model to predict other frames. Qualitative evaluations are also made.
Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation

The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. The first layer involves the γ and λ, values which are introduced to describe the current condition of characteristics of the searched solution that diversifies the particles when it is converging too much on some optima. The second layer involves the partitioning of particles that tries to prevent premature convergence. With the two search mechanisms, the PSPSO presents a simpler way of making the particles communicate with each other without too much compromise of the computational time. The proposed algorithm was compared with different variants of particle swarm optimization (PSO) using benchmark functions as well as the IEEE 10-unit unit commitment problem. Results proved the effectiveness of PSPSO with different functions and proved its competitive advantage in comparison with published PSO variants.
Mining High-Utility Sequential Patterns in Uncertain Databases

During our research conducted in this paper, we demonstrate a successful mining progress to mine the sequential high-utility patterns of uncertain databases. A PUL-Chain structure is developed and built in this paper with several pruning methods to decrease the search space of required patterns for mining efficiency improvement. In contrast to the standard HUS-Span, our experimental results show clearly that both in runtime as well as in the number of candidates discovered, the developed algorithms showed the effectiveness of the discovered patterns and its mining efficiency compared to the elder HUS-Span model. We present the details of our research here in this paper and also focus our attention to future directions that this research may take in the years to come.
Ever-shifting roles in building, composing and performing with digital musical instruments

It is widely accepted that computational technologies shape the relationship of musicians, instrument builders and composers with music, affecting various socio-cultural realisms in music. In this article, I discuss in what ways music-making still emerges as a social construct, even as a result of the mutual cooperation with human musicians and AI-powered autonomous instruments. I argue that building, making, and performing with a digital musical instrument has undergone a gradual socio-technological change that has affected art, science, technology, culture and communities in general. I support my investigation through the current performance and composition practice of the autonomous AI-terity musical instrument.