Reviewerly/exHarmony huggingface dataset:
A Binary Grasshopper Optimization Algorithm for Feature Selection

Introduction: The aim of the feature selection is to find the most important information from a set of given features.It has been used widely in recent years.The set of features primarily has associated features that influence classification performance and increase training time.So, the feature selection is necessary to remove inappropriate features and improve classification performance.In this paper, a nature-inspired feature selection method is proposed based on the behavior of grasshoppers.Grasshopper optimization algorithm is expressed for continuous problems as standard.Methods: In this paper, continuous grasshopper optimization algorithm is converted into binary by using a nonlinear mapping function.Also, the binary grasshopper optimization algorithm will be converted into a powerful algorithm by combining and retaining exploration and exploitation.Result: the dataset taken from the UCI repository is used.By comparing the proposed method with GA, Binary Bat Algorithm (BBA), we indicate that it has had better performance than previous methods in terms of accuracy, reduction rate, precision, and computational time.Conclusion: In this paper, the feature selection problem is presented by the binary grasshopper optimization algorithm.The results are compared at different algorithms.It was concluded that the binary grasshopper optimization algorithm is the best method for feature selection
The Impact of Employees Motivation, Perceived Usefulness and Perceived Ease of Use on Employee Performance among Selected Public Sector Employees

The current study is titled "The Impact of Employee Motivation, Perceived Usefulness and Perceived Ease of Use on Employee Performance among Selected Public Sector Employees".Selected staff from public sector positions were used in order to examine and analyze the factors which contribute to employee performance levels.The independent variables in this case are motivation, perceived usefulness and perceived ease of use.The dependent variable is employee performance.Questionnaires were distributed among 201 respondents who are employed in the public sector.The resulting data was processed through frequency analysis, reliability analysis, descriptive analysis, correlation analysis and regression analysis by using SPSS version 21.The findings reveal that there exists a positive significant relationship between all the independent variables and the dependent variable, which is employee performance.The Research hypothesis is supported.
Multinomial Naïve Bayes untuk Klasifikasi Artikel Online tentang Gempa di Indonesia

Indonesia is a country where earthquake often occurs. This circumstances lead to many amount of news about earthquake in Indonesia was served. One popular way to provide news is through online article. Online article that contains information about earthquake often classified to Economic, Health, and Tourism category. Text classification can help the process of this article categorization. In this paper, a research about how multinomial naïve bayes performs on categorization of online article about earthquake in Indonesia was done. TF-IDF was used to determine weight of each feature. The testing was done by using unigram feature, bigram feature, and the combination of both . Furthermore, the testing was also done by removing stemming and stopwords removal from preprocessing. The highest F-measure obtained by 5-fold cross validation is 95.20% from a scenario where combination of both unigam and bigram feature used plus stemming and stopwords removal are included in preprocessing.
THE USE OF MIND MAPPING TO TEACH READING COMPREHENSION

The research was aimed at finding out the impact of using mind mapping toward students’ reading comprehension. This study was classified as a quasi experimental research design using two intact classes as an experimental and control group. The experimental group was treated using mind-mapping technique, while the control group was subjected to conventional learning technique. The sample of this study was the first year students of Nabi’ Nubu’ Islamic Senior High School. The instrument of this study employed reading test for students in both groups. The reading tests were distributed in the pre-test and post-test. The data analysis used descriptive and inferential statistic. The data were met the normal and homogeneous data. The result of the study showed that the mean scores of the experimental group (71.76) was higher than the control group (60.24) after treatment was given. Thus, the use of mind mapping has a significant effect towards students’ reading comprehension. It indicated that the technique can be an alternative technique to teach reading comprehension.
An Ontology Driven ESCO LOD Quality Enhancement

The labor market is a system that is complex and difficult to manage.To overcome this challenge, the European Union has launched the ESCO project which is a language that aims to describe this labor market.In order to support the spread of this project, its dataset was presented as linked open data (LOD).Since LOD is usable and reusable, a set of conditions have to be met.First, LOD must be feasible and high quality.In addition, it must provide the user with the right answers, and it has to be built according to a clear and correct structure.This study investigates the LOD of ESCO, focusing on data quality and data structure.The former is evaluated through applying a set of SPARQL queries.This provides solutions to improve its quality via a set of rules built in first order logic.This process was conducted based on a new proposed ESCO ontology.
D2SC-GAN: Dual Deep-Shallow Channeled Generative Adversarial Network, for Resolving Low-Resolution Faces for Recognition in Classroom Scenarios

Face Recognition using convolutional neural networks have achieved considerable success in constrained environments in the recent past. However, the performance of these methods deteriorates in case of mismatch of training and test distributions, under classroom/surveillance scenarios. These test (probe) samples suffer from degradations such as noise, poor illumination, pose variations, occlusion, low-resolution (LR), blur as well as aliasing, when compared to the crisp, rich training (gallery) set, comprising mostly of high-resolution (HR) mugshot images captured in laboratory settings. To cope with this scenario, we propose a novel dual deep-shallow channeled generative adversarial network (D2SC-GAN) which performs supervised domain adaptation (DA) by mapping LR degraded probe samples to their corresponding HR gallery-like counterparts to perform closed-set face recognition. D2SC-GAN uses a multi-component loss function comprising of multi-resolution patchwise MSE and normalized chi-squared distance loss functions, along with a Kullback-Leibler divergence based loss function. Moreover, we propose a novel classroom face dataset called the Indian Classroom Face Dataset (ICFD), which, to the best of our knowledge, is a first of its kind and will be helpful to explore the challenges of face recognition when used for automatically recording the attendance in classroom conditions. The proposed network achieves superior results on five real-world face datasets when compared with recent state-of-the-art deep as well as shallow supervised domain adaptation (DA), super-resolution (SR), and degraded face recognition (DFR) methods, which show the effectiveness of our proposed method.
Hyperspectral Imaging Based Method to Identify Potential Limestone Deposits

Surface mineral deposit identification using Hyperspectral imaging is important since it is economical. In this paper, an algorithm to detect potential limestone deposits is proposed with the use of a self-generated limestone reference. For this task images obtained by EO-1 satellite's Hyperion sensor are used. In the proposed algorithm, first, Principal Component Analysis is applied on the dataset for dimensionality reduction and a Euclidean distance based classification to identify pixels corresponding to soil regions. Next, Correlation Factor Analysis and Fisher Discriminant Analysis (FDA) are used to classify the regions based on their limestone availability. Finally, a reference signal is generated from the dataset to use with FDA to improve the accuracy of the results.
Branching time agents' logic, satisfiability problem by rules in reduced form

This paper considers the branching time logic on nontransitive intervals of agents' accessibility relations.The agents' accessibility relations are defined inside transitivity intervals and via neighboring limit points, they may be not complete and lose some states -the lacunas of forgotten time thought they may interfere.This approach is used for modeling computational processes and analysis of incomplete information for individual agents.A logical language for reasoning about models' properties which includes temporal and modal logical operations is suggested.Illustrative examples are provided.Mathematical part of the paper is devoted to the satisfiability and decidability problems for the suggested logic.We use instruments of reduced normal forms for rules and algorithms converting rules to such forms.We find algorithms solving the satisfiability problem.Some open problems are suggested.
Corrdrop: Correlation Based Dropout for Convolutional Neural Networks

Convolutional neural networks (CNNs) can be easily over-fitted when they are over-parametered. The popular dropout that drops feature units randomly can't always work well for CNNs, due to the problem of under-dropping. To eliminate this problem, some structural dropout methods such as SpatialDropout, Cutout and DropBlock have been proposed. However, these methods that drop feature units in continuous regions randomly, may have the risk of over-dropping, thus leading to degradation of performance. To address these issues, we propose a novel structural dropout method, Correlation based Dropout (CorrDrop), to regularize CNNs by dropping feature units based on feature correlation, which reflects the discriminative information in feature maps. Specifically, the proposed method first obtains correlation map based on the activation in the feature maps, and then adaptively masks out those regions with small average correlation. Thus, the proposed method can regularize CNNs well by discarding part of contextual regions. Extensive experiments on image classification demonstrate the superiority of our method compared with other counterparts.
ACER: detecting Shadowsocks server based on active probe technology

Anonymous server is created for hiding the information of hosts when they are surfing the Internet, such as Tor, Shadowsocks, etc. It is quite difficult to identify these servers, which provides potential criminals with opportunities to commit crime. Also, hackers can make use of these servers to threaten public network security, such as DDoS and Phishing attacks. Hence, the study of identifying these servers is pretty crucial. Current works on detecting Shadowsocks servers are mostly based on the features of servers’ data stream combined with machine learning. However, they are passive methods because they can only be established when the servers are in connection state. Therefore, we propose a new system named ACER, which AC means active and ER means expert, to detect these servers. Besides, we introduce XGBoost algorithm to process the data stream to optimize the detection. The method can recognize more Shadowsocks servers actively instead of monitoring the communication tunnel passively to identify the servers. The experiment result has achieved an accuracy of 94.63% by taking proposed framework and 1.20% more accurate than other existing solutions. We hope to provide a novel solution for those who are conducting research in this area, and provide a detection scheme for network censors to block illegal servers at the same time.
Unsupervised Clustering Based Real-time Shot Boundary Detection for Live Broadcasting

The development of mobile Internet greatly facilitates information communication. The mobile live broadcasting platform has made video-sharing popular. In a long live broadcast, it is necessary to generate a video summary or some pieces of news for secondary information spreading. In order to produce a video summary in real-time, shot boundary detection (SBD) of video stream is badly needed. Traditionally, shot boundaries are determined by a threshold of feature distance in adjacent frames. It is difficult to decide a threshold applied in kinds of media content with good performance. Another, researchers have proposed Support Vector Machines (SVM) to classify shot boundaries, whose model relies much on the characteristics of training data. In terms of live broadcasting, real-time signal processing is required. This paper proposes an Unsupervised Clustering based Real-time SBD method (UCR-SBD), in which consideration is given to both detection efficiency and accuracy. Experiments show that the F value of SBD reaches 97.24% and the detection speed is 17 ms per frame on average. Finally, the proposed algorithm has been successfully applied in a real-time news reporting system for the live broadcast of CUC TV station.
The Difference in Emoji Usage Between Genders

This study investigated the use of emoji between male and female genders.Using mixed-method research, the study collected messages with emojis from 20 male and 20 female college students in one of the cities in Indonesia.The study also used brief interviews to learn the reasons and factors for using emojis.The findings show that while there are no signs in terms of intensity of the emoji use, there are some differences in the use of emojis between males and females especially in the kinds of emoji selections.Both genders reported that they used emojis to have to strengthen the meaning of their messages.Besides, the use of emoji is considered to not gender-specific because it functioned to create a comfortable atmosphere in online communication devices for both genders.
Quantum interface between light and a one-dimensional atomic system

We investigate optimal conditions for the quantum interface between a signal photon pulse and one-dimensional chain consisting of a varied number of atoms. The tested object is physically designed as an atomic array of tripod-type atoms confined with a nanoscale dielectric waveguide and experiencing its fundamental $\mathrm{HE}{}_{11}$ mode. The efficiency of interaction is mainly limited by achieved overlap and coupling of the waveguide evanescent field with the trapped atoms. We verify those physical conditions when the coupling within the main scattering channels would be sufficient for further providing various elementary interface protocols such as light storage, light-matter entanglement, preparation of a few photon states on demand.
Finding good quantum codes using the Cartan form

We present a simple and fast numerical procedure to search for good quantum codes for storing logical qubits in the presence of independent per-qubit noise. In a key departure from past work, we use the worst-case fidelity as the figure of merit for quantifying code performance, a much better indicator of code quality than, say, entanglement fidelity. Yet our algorithm does not suffer from inefficiencies usually associated with the use of worst-case fidelity. Specifically, using a near-optimal recovery map, we are able to reduce the triple numerical optimization needed for the search to a single optimization over the encoding map. We can further reduce the search space by using the Cartan decomposition, focusing our search over the nonlocal degrees of freedom resilient against independent per-qubit noise, while not suffering much in code performance.
Affine Geometrical Region CNN for Object Tracking

The state-of-the-art trackers using deep learning technology have little special strategy to gain the bounding box well when the target suffers drastic geometric deformation. In this paper, we take full use of the convolutional neural network (CNN) features of the deepest layer to represent the semantic feature model, and affine transformation to be as the space information model. A tracking method based on geometrical transformation region CNN is proposed. Firstly, affine transformation is applied to predict possible locations of a target, and the candidate bounding boxes obtained by affine transformation sampling can locate the possible geometric regions of the target more effectively before extracting features from CNN. Furthermore, RoI pooling with different sizes and shapes are designed to describe the geometric deformation region of the target. Then, multi-tasks loss function including the affine transformation regression is designed to refine the affine bounding box. Finally, the affine transformation NMS (Non-maximum suppression) is used to ensure the tracking bounding box having the largest IoU value. Extensive experimental results show that the proposed algorithm performs favorably against the compared methods in the public benchmarks.
A Probabilistic Decision Engine for Navigation of Autonomous Vehicles under Uncertainty

Navigation or route planning under uncertainty is a very challenging but important task for autonomous vehicles such as self-driving cars, drones, unmanned aerial systems (UAS), etc. Probabilistic methods to the modeling and optimization of these systems are attractive in quantitatively capturing the uncertainty present in their dynamic environment. This paper will present a novel probabilistic decision engine that can serve as the core for the navigation of autonomous vehicles under uncertain conditions. This probabilistic decision engine takes in a network connection matrix (based on maps and graph theory) and a cost matrix (with entries of the cost's mean values and probability distributions) as its input and generates the probability distributions of the optimal routes as its output. The proposed probabilistic decision engine consists primarily of a stochastic network standardization module, a stochastic network decomposition module and a probabilistic solver (i.e., decision kernel). A deterministic network reduction method based upon Dijkstra's algorithm is first used to derive a standard, reduced network, augmented by a stochastic network reduction process. The standard network is then decomposed into a series of stochastic subnetworks by using sequential convolution, PDF (probability distribution function) shifting and reshaping techniques. A purely-analytical probabilistic solver is finally used to solve the stochastic decision-making problem. In this paper, the principle of operation and implementation methods of the entire probabilistic decision engine will be discussed in detail. Some representative simulation results will be provided to demonstrate the effectiveness of the proposed computational methodology and compared with the traditional Monte-Carlo simulation method to validate the analytical results. This study suggests that the time needed to find the solution using the proposed decision engine can be reduced by three to four orders of magnitude, compared with the Monte-Carlo simulation method.
LSB Color Image Embedding Steganography Based on Cyclic Chaos

Image steganography is a technique for hiding secret data in cover image. In recent years, chaotic systems have become increasingly important in steganography. This paper proposes an embedding technique based on cyclic chaos, which effectively embeds secret messages into color images. The cyclic chaos is divided into two parts: the chaotic function of generating seeds and the pseudo random number generator (PRNG). A pixel is found through the chaotic cycle system, and a three-bit secret message is embedded into the R, G, and B channels of the pixel through a specific embedding algorithm. Compared with other color image embedding methods, the proposed algorithm has higher visual quality and improved security.
EMPLOYING MULTIMEDIA-BASED LEARNING TO IMPROVE ENGLISH SPEAKING SKILLS

Regarding language learning today, everyone is increasingly awarded that traditional language teaching is no longer effective in supporting students need. Learning speaking skills in the classroom carry out by the lecturers are still using traditional methods conducting learning in the classroom using whiteboards, markers, and other manual tools. Meanwhile, in the current global era, multimedia based learning is a demand along with the development of technology. Therefore, this article elaborates the mechanism of multimedia used in learning speaking skills. The type of research was Classroom Action Research (CAR) with the subject were the second semester students of The University of Qomaruddin Gresik. This Classroom Action Research process was taken in two cycles with 4 procedure; planning, implementation, observation, and reflection. The result of the cycle one shown that 10 students or 50% of 20 students spoke English too slow and repeated the same sentence many times so the listener could not understand the meaning, so their speaking skill were in poor level. Meanwhile, 50% or 10 the rest of the students got good level. Since the result of the cycle one was below the target, so the researcher continued to conduct the second cycle. After the second cycles were conducted, the result shown that 25% students was in good level, and 75% students were in very good level. After the second cycle was conducted, finally the level of the students’ speaking skill was increasing, so the researcher ended the research.
Measuring transmitted wavefronts for non-circular apertures in broad bandwidths using discrete points

Transmitted wavefronts are usually measured by a laser interferometer of the corresponding design wavelength. The Zernike coefficient can be used to describe the transmitted wavefront of a circular aperture. However, it has some limitations in optical systems with non-circular apertures. In this paper, a method, based on discrete points, for measuring transmitted wavefronts in a broad bandwidth is presented. Simulations of monochromatic, achromatic and apochromatic systems verify the method. The system with off-axis field of view is also analyzed. The Conrady dispersion formula and apochromatic characteristic formula (ACF) are used to represent the relationship between the optical path difference (OPD) at each discrete point of the transmitted wavefront and the wavelength. We also experimentally verify this method by measuring the wavefront of the optical system with a square aperture. The results demonstrate that the method can be used to measure the non-circular wavefront in a broad bandwidth.
An Approach of Utilizing Binary Bat Algorithm for Pattern Nulling

This paper proposes a pattern nulling approach using binary bat algorithm (BBA)-based beamformer and amplitude-only control of antenna array weights. The resulting weights are discrete binary signals which fit very well with current digital attenuators. Furthermore, it helps to avoid the quantization error compared to the continuous values. In order to evaluate the effectiveness of the proposed method in pattern synthesis of smart antenna, three scenarios, which are single, multi, and broad nulls, were implemented. The simulation results show the superiority of convergence speed and nulling depth in pattern synthesis of BBA compared to binary particle swarm optimization.