Reviewerly/exHarmony huggingface dataset:
A Conceptual Framework for Sensitive Big Data Publishing

Big data can be referred to as the massive quantity of data which is collected from numerous sources at high velocities in different data formats. Social network data, medical data and sensor data comprise the major sources of big data. One of the major concerns with data includes the privacy of the data owner while publishing the data. Various anonymization mechanisms have been proposed for handling the privacy concerns associated with relational databases. This paper proposes a conceptual big data framework for anonymizing social network data before publishing it. Natural language processing techniques have been combined with Lambda architecture for efficiently processing social network data and publishing.
Implementation of Intermediate Image Processing for Video from Closed Circuit Television Cameras

Surveillance cameras and CCTV cameras are crucial in the present day society. The installation of such cameras provides public safety and protection against theft and vandalism. Most surveillance camera doesn't have the capability to provide a clear footage. Therefore it has become a necessity to improve the quality of the video. The video processing provided in this paper would allow the user to get an enhanced quality video from a low quality video; this could be useful in identifying the perpetrator for the a fore known activities. Image processing is very important in ameliorating a raw image captured by a CMOS or CCD sensor. In this paper we have taken surveillance footage and extracted the frames from the video, each of these frames undergo automatic colour balance, interpolation on the filter which resides over the sensor, gamma correction. The enhanced images are then used to recreate the video. The video after processing gives an enhanced and a finer quality in the video. The proposed method is implemented in MATLAB.
Improving Photovoltaic Efficiency by Tilting Angle Tracking System (TATS) in South of Iraq

In this paper tilting angle tracking system is presented for city of Al-Samawa/Al-Mamlaha area at south of Iraq. The altitude α and azimuth angles γ of the sun is calculated for this region. Then the optimal tilting angle is evaluated at the best azimuth angles for each month during the year. Consequently, this work tracks the sun with two-fixed angles, one angle (tilting angle) for the vertical movement, while the second angle (azimuth angle γ) for the horizontal movement. The sun irradiances falling on the unit area for the tracking system is evaluated and it is compared with the sun irradiances that applied on the horizontal plane. The gain of energy is calculated for all months of the year. The result of angles and energy is calculated by MATLAB/Simulink software, while the figures of the results is drew by Microsoft/Excel software.
An Efficient and Secure Approach to the Traffic Fine System Using Hyperledger Fabric

Our current, mostly analog system for taking traffic fine and validating documents or certificates (e.g., RC paper, driving license, etc.) is sluggish, complex, and unpredictable. Moreover, recently proposed traffic fine ordinances give rise to bribery. To ensure traffic fine and document verification as a transaction at real-time cost and in a hassle-free way, this paper proposes the use of a permissioned model of blockchain, i.e., hyperledger fabric technology for executing smart contracts. This paper also describes a prototype for carrying out the traditional fine in an efficient, secure, and transparent manner using hyperledger composer. Digitized strategy for validation of documents before each transaction and permissioned technology could supplant the traditional traffic fine system.
Prediction of Photovoltaic Time Series by Recurrent Neural Networks and Genetic Embedding

The need of reliable prediction algorithms of energy production is increasing due to the spread of smart solution for grid, plant and resource management. Recurrent neural networks are a viable solution for prediction but their performance is somewhat insufficient when the time series is generated by an underlying process that behaves in a complex manner. In this paper, a new combination of echo state network and genetic algorithms is employed in order to improve the prediction accuracy of photovoltaic time series. The genetic algorithm is used to embed past samples of the time series to be used for predicting a new one. It aims at a feature extraction in order to regularize data being fed into a neural network model, so that it is able to learn more robust and generalizable prediction models. The experimental tests prove that the proposed approach is suited to the application focused in this paper.
Fine-Tuning Temperatures in Restricted Boltzmann Machines Using Meta-Heuristic Optimization

Restricted Boltzmann Machines (RBM) are stochastic neural networks mainly used for image reconstruction and unsupervised feature learning. An enhanced version, the temperature-based RBM (T-RBM), considers a new temperature parameter during the learning process that influences the neurons' activation. Nevertheless, the major vulnerability of such models concerns selecting an adequate system's temperature, which might lead them to inadequate training or even overfitting when wrongly set, thus limiting the network from predicting or working effectively over unseen data. This paper addresses the problem of selecting a suitable system's temperature through a meta-heuristic optimization process. Meta-heuristic-driven techniques, such as Particle Swarm Optimization, Bat Algorithm, and Artificial Bee Colony are employed to find proper values for the temperature parameter. Additionally, for comparison purposes, three standard temperature values and a random search are used as baselines. The results revealed that optimizing T-RBM is suitable for training purposes, primarily due to their complex fitness landscape, which makes fine-tuning temperatures a nontrivial task.
Facial Attribute Classification: A Comprehensive Study and a Novel Mid-Level Fusion Classifier

This paper addresses the problem of automatically classifying attributes related to a person's appearance near the face. Example attributes include presence or absence of eyeglasses, separation distance of the eyes (narrow or wide), and information concerning facial hair. We have implemented a 3-step approach in which the face is located and cropped within an image, features are extracted, and 40 separate facial attributes are classified. One goal of the research has been to compare a wide range of techniques for their effectiveness in classifying facial attributes. Another goal has been the development of the new classifier. Our novel system demonstrates better performance than previous state-of-the-art models. We present extensive results to compare our approach with 7 different systems using the CelebA dataset. To our knowledge, this paper presents the most comprehensive study to date concerning facial attributes.
A Matching Algorithm Based on the Fusion of SAD and Census

This paper analyzes the characteristics of SAD algorithm and Census algorithm, improves SAD algorithm and Census algorithm, respectively, merges them, and proposes a SAD and Census algorithm based on adaptive window and adaptive weight. The experimental results show that the algorithm has stronger robustness and higher matching accuracy than SAD algorithm and Census algorithm.
환자 안전 측면에서 살펴본 분할 불가 정제 의약품의 분할선 사용 현황

Scoring is marked by a groove on the tablet surface to facilitate easy division or splitting into two or more equal pieces. It allows easy swallowing...
Penalized maximum likelihood estimator for mixture of von Mises–Fisher distributions

Abstract The von Mises–Fisher distribution is one of the most widely used probability distributions to describe directional data. Finite mixtures of von Mises–Fisher distributions have found numerous applications. However, the likelihood function for the finite mixture of von Mises–Fisher distributions is unbounded and consequently the maximum likelihood estimation is not well defined. To address the problem of likelihood degeneracy, we consider a penalized maximum likelihood approach whereby a penalty function is incorporated. We prove strong consistency of the resulting estimator. An Expectation–Maximization algorithm for the penalized likelihood function is developed and experiments are performed to examine its performance.
Feature's Selection-Based Shape Complexity for Writer Identification Task

Writer Identification task has attracted a lot of research interests due to its wide variety of applications. Different approaches based on various features exist in the literature. However, all these approaches use all the information available in the handwritten sample to identify the writer (relevant or irrelevant). In this paper, we propose an original approach based on a double feature selection process, where the features are represented by graphemes resulting from a segmentation process. These features are analyzed based on their shape complexity, using the Fourier Elliptic transform, and the complexity score is assigned to each grapheme (FECS). The second phase of feature selection is to eliminate the redundancy among the resulting using a sequential clustering algorithm. Two similarity measures are proposed to evaluate the proposed system on 100 writers of the IAM dataset. We obtained a good identification rate of 96% using only 25 graphemes, which is equivalent to 3--4 words.
Study on RLS-Type Algorithm of GPS-Guided Range Correction

Abstract Mitigating the error of GPS measurement signals is essential for the range precision of GPS-guided range correction projectile. In order to improve the range precision, an online method based on recursive least square algorithm (RLS) is applied to calculate the deployment time of brake. First, RLS is utilized to mitigate the error of the velocity, position, angle, etc., measured by GPS. Then, the deployment time can be calculated accurately by means of the appropriate pretreated data. To confirm the effectiveness of the method, a comparison to using least square algorithm and non-algorithm is done. The experimental results demonstrate that the method in this paper takes advantages in terms of online, real-time and the range correction accuracy.
Formulating Hypotheses and Predictions and Designing a Study

A clearly defined research question allows us to formulate hypotheses that propose possible answers to that question. From these hypotheses, we can then derive specific, unambiguous predictions that allow us to test their validity with empirical data. Hypotheses and predictions serve to narrow down the infinite possibilities for data collection and determine the data we need to collect. In this chapter I cover formulating hypotheses and predictions, then explain that we often use proxies to test predictions and how practical constraints influence our thinking.
Discovering Numerous Strassen’s Equivalent Equations Using a Simple Micro Multimodal GA: Evolution in Action

Solving real-world complex optimization problems using simple metaheuristic algorithms is a challenging but attractive task. Making matrix multiplication efficient is one of the interesting problems. This time-consuming algebric operation is required in many applications in science and engineering, thus reducing its complexity targets more efficient computation. In fact, many of practical and theatrical complicated calculations can be modeled efficiently as matrix-based operations, therefore matrix multiplication is computationally expansive operator among all others. In this paper, a simple metaheuristic method based on Micro Genetic Algorithm is proposed to find Strassen's equivalent solutions which is an algebraic method to compute the product of two matrices with minimal number of multiplications. Since there are numerous optimal solutions, the modeled problem is a large-scale and highly multi-modal optimization problem. The proposed method could find more than 160, 000 valid solutions with same complexity as Strassen's in a large-scale search space. Among all discovered solutions found using the proposed method, there are 701 distinct solutions which is the maximum number of discovered Strassen's equivalent solutions to the best of our knowledge. The proposed algorithm is simple but very efficient to find more and more solutions, in fact, that is a great demonstration of “evolution in action” to tackle real-world complex problems like the current one, which just one set of its equations has been discovered by the Germen mathematicians and has remained mysterious for more than 50 years.
Prediction of Cardiovascular Risk Using Extreme Learning Machine-Tree Classifier on Apache Spark Cluster

Introduction: In the recent scenario, machine learning is considered a prevailing area in the diverse fields of science and technology include image processing, automobiles, banking, finance, etc. The availability of data and adverse improvements over machine learning techniques have become more feasible to understand and to work on various channels of real-time health analytics. Method: In this paper, a health status prediction system is proposed to detect cardiovascular diseases through patients’ tweets. Further, analytics will be carried on a distributed Apache Spark framework to reduce the time taken for both training and testing when compared with regular standalone machines. Results: Performance of the proposed framework with Extreme Learning Machine (ELM) - Tree classifier is evaluated on two different corpora, and which outperforms other classifiers such as Decision Trees, Naïve Bayes, and Linear SVC, DNN, etc. in both accuracy and time. Discussion: Social media streaming data is considered as one of the major sources for data in the proposed system. Based on the model, the attributes of the incoming user tweets are analyzed, and accordingly cardiovascular risk is predicted. Further, current health status is tweeted back as a reply to the respective user along with a copy to the family and caretakers. Conclusion: This proposed work provides the development of the alert-based system for heart status prediction by adding some additional features impacting the accuracy besides reducing the response time by using Big data Apache Spark Distributed Framework.
A Two-phase Evolutionary Algorithm for Solving the Accuracy-diversity Dilemma in Recommendation

Generating both accurate and diverse recommendations is required for modern recommender systems. Accurate recommendations can well meet the needs of users, while diverse recommendations can bring users novelty, discover the unknown preferences of users, and can also alleviate the long tail problem. However, these two metrics are conflicting and it is very hard to improve both at the same time. Thus, a compromise needs to be made. The easiest way is using hyperparameters to combine different objective functions or the recommendations of different recommendation algorithms, but it is difficult to determine parameters. Another common strategy is applying multi-objective evolutionary algorithm to provide multiple recommendations for each user, but how to choose the final recommendation becomes a new problem. To this end, we propose a two-phase evolutionary algorithm-based recommendation framework, named EARF. In EARF, a novel fitness function is designed, which converts the evaluation of accuracy and diversity into a single objective and automatically trades off these two goals. Based on the property of recommendation problem and fitness function, the genetic representation and operators are redefined. The experiments on real-world rating datasets indicate that the EARF is effective and the proposed evolutionary algorithm can achieve a good balance between accuracy and diversity.
Rancang Bangun Sistem Informasi Rekrutmen Karyawan Berbasis Web pada PT Megah Perkasa Teknologi

Admission of new employees is undoubtedly one of the programs for the company to gethuman resources who have the potential and qualifications according to company needs. Thepurpose of this study is to design an employee recruitment system at PT Megah PerkasaTeknologi that still utilizes social media and verbal communication from one person toanother so that only a few are aware of the recruitment needs. HRD still records and selectsincoming files both obtained via e-mail and sent through the delivery service. Therefore weneed a web-based employee recruitment system that can provide more comprehensiveinformation to the public, especially those who are looking for work. The system designedusing the Unified Modeling Language, and software developers use the Waterfall model withthe PHP and MySQL programming languages. The results of this study indicate that employeerecruitment information disseminated to the community and its requirements. Applicants canonly apply for one position in the employee recruitment period with the ease of filling outapplications. Also, file selection becomes more accessible and faster because the applicant isonly required to apply according to the desired application position. So with this web-basedrecruitment system, the HRD team's performance is expected to be more productive andefficient at every stage from file receipt, written test, interview test, to decision making.
Intersections of transformation

This forum is dedicated to exploring the notion of meaningfulness in design processes, taking the perspectives of community groups, nongovernmental organizations, and those who are marginalized in society as starting points. Authors will reflect conceptually and methodologically on practical engagements. --- Rosanna Bellini and Angelika Strohmayer, Editors
Sequential Neural Network Decoder For Convolutional Code With Large Block Sizes

Due to the curse of dimensionality, the training complexity of the neural network based channel-code decoder increases exponentially with the codeword length. Although computation power has made significant progresses, it is still hard to deal with convolutional code with large block sizes. In this paper, we proposed a neural network based decoder termed Sequential Neural Network Decoder (SNND). The SNND consists of multiple sub models, and it passes the last state of the current sub model to the following model as the initial state. The bit error rate (BER) performance of the SNND remains unchanged as the number of sub models increases. It achieves a performance close to that of the soft decision Viterbi decoder under Additive white Gaussian noise (AWGN) channel. However, the SNND's performance is found to decrease when the modulation order increases.
TRaNsMOGRiFiER: Fictional Narratives as Catalyst for Experimental Instrument-Building and Musical/Artistic Collaboration

Abstract The authors report on their artistic research project TRaNsMOGRiFiER and highlight musician-specific approaches to creating with technology that privilege the sharing of tools and practical knowledge. The use of fictional narratives as a catalyst for instrument-building and art-making is at the foreground of the discussion. By rethinking DIY/maker culture with an emphasis on collaboration and collective creativity, the authors highlight the tension between the production of fixed media output and practices that resist encapsulation. TRaNsMOGRiFiER underscores the benefits of hands-on learning and presents a collaborative artistic platform with a focus on open-ended processes and ongoing change.
A Comparative Study on Fibonacci-Galois Ring Oscillators for Random Number Generation

This paper presents a comparative study on random number generators(RNGs) based on regular sampling of irregular waveform method where the irregular signal is obtained from Fibonacci-Galois ring oscillators(FIGAROs) and classical ring oscillators(CROs) respectively. Both RNGs are implemented on FPGA and the RNG output is subjected to statistical randomness tests. The feedback polynomials defining the FIGAROs and the number of CROs are varied in order to assess the associated impact on the output randomness. It is shown that FIGARO based structures are capable of generating higher entropy with less elements compared to CRO based structures. To explain the reason behind this observation, power spectral analysis is conducted on both entropy sources. It is demonstrated that irregular signals generated by XORing FIGAROs have more flat and wide spectrum compared to irregular signals generated by XORing CROs, thus revealing the cause of the difference observed in statistical randomness test results.
A Preliminary Study on Evolutionary Clustering for Multiple Instance Learning

Since its beginnings, multiple instance learning studies have shown an excellent performance in the areas where it has been applied. This efficiency is due to multiple instance learning allows to represent a complex object by a set of feature vectors, being a more flexible representation to preserve more information than one based on single feature vector. This paper attempts to progress in this area carrying out a first study that introduces evolutionary algorithms for solving multiple instance cluster analysis. Specifically, we present four proposals of genetic algorithms for multi-instance partitional clustering: three of them are adaptations of existing algorithms for single-instance clustering, while the last one is a novel approach based on CHC evolutionary algorithm. Moreover, two classic non-genetic partitional algorithms are included in the final comparison. Experimental results considering ten representative datasets show promising results for our proposal.
Research on PID Control of Water Level in Pile Legs Based on Improved PSO

Abstract Aiming at the problem of poor water level control effect during mud dredging in pile legs, based on the research of traditional PID algorithm and particle swarm algorithm, a PID parameter optimization method based on improved PSO(IPSO)was proposed. This paper adopts a nonlinear dynamic adaptive inertial weight ( ω ) method and introduces a time factor to adjust the position update time. In this paper, a mathematical model of the water level control system in the leg is established, and the system is simulated by MATLAB / Simulink. By comparing the simulation results, PID controller optimized by improved PSO is better than traditional PID controller. The proposed method provides a theoretical reference for the water level control system during the mud dredging operation in the pile legs.
A Novel Velocity Reinforced Mechanism on Improving Particle Swarm optimization for Ill-conditioned Problems

Particle swarm optimization (PSO) in recent years has been widely applied to solve various real world problems. However, for ill conditioned problems with largely different sensitivity to the objective function, classical PSO cannot search for optimal solution efficiently due to the best position-guided strategy that wastes lots of source searching undesirable areas. Therefore, this paper proposes a novel velocity reinforced mechanism (VR) for solving m-conditional problems. Two implementations of the mechanism, velocity reinforced particle swarm optimization and velocity reinforced search, are introduced in this paper. VR updates its velocity by learning and correcting best velocity directly, instead of using classical best position-guided updating rules. In this way, it increases the possibility that finds better directions for m-conditional problems. Experiments indicate that the novel approaches improve the final results and efficiency.