Reviewerly/exHarmony huggingface dataset:
Conditional semi‐fuzzy <i>c</i> ‐means clustering for imbalanced dataset

Fuzzy c-means algorithms have been widely utilised in several areas such as image segmentation, pattern recognition and data mining. However, the related studies showed the limitations in facing imbalanced datasets. The maximum fuzzy boundary tends to be located on the largest cluster which is not desirable. The overall fuzzy partition results in false grouping of edge objects and weakens the compactness of cluster. It is important the clusters are delineated by the maximum fuzzy boundary. In this study, a semi-fuzzy c-means algorithm is proposed by combining hard partition and soft partition. This study aims to provide an effective partition for the edge objects, such that the compactness of cluster can be improved. The proposed algorithm integrates the semi-fuzzy c-means method with the size-insensitive integrity-based fuzzy c-means algorithm. In particular, the latter algorithm has the ability to deal with imbalanced data. With the experiment validation, the proposed algorithm is robust and outperforms the two component algorithms by using synthetic and widely known benchmark datasets.
Five Stars Teaching Mode of Sports Training Based on APP Microcourse

With the development of teaching reform, methods and concepts, microcourse teaching has been recognized and developed extensively. However, micro-class pays more attention to the presentation of key contents, and pays less to the teaching design guided by teaching principles, causing the waste of microcourse resources to some extent and affecting the effect of microcourse teaching. Moreover, most of the sports training lectures still follow in the traditional educational pattern of teacher-blackboard-teacher-student, which cannot satisfy students' desire for knowledge and contradict with the demand of social development. Therefore, on the basis of the teaching design of five-star teaching principle, the study focuses on and solves classroom problems, emphasizes the application of knowledge points in different situations, and establishes the relationship between new and old knowledge. Meanwhile, the study combines a new type of Rope Skipping APP microcourse technology, and elaborates the teaching of knowledge rope skipping in the form of microcourse teaching, forming a learning method combined sound and image with pictures. Finally, the study takes learners as the evaluation subject and adopts the analytic hierarchy process to construct five first-level indicators, including value, learning, interaction, technology and art, which makes full use of the expert group's quantitative evaluation on the comparison of the importance of each layer's factors. By constructing the judgment matrix, it calculates the weight value of each index and forms an objective and scientific evaluation index system of microcourse. The results confirm that the teaching mode of the study can be used as the basis for microcourse improvement and auto-recommendation.
A Collaborative Approach to Build a KBS for Crop Selection: Combining Experts Knowledge and Machine Learning Knowledge Discovery

Selecting proper crops for farmland involves a sequence of activities.These activities and the entire process of farming require a help of expert knowledge.However, there is a shortage of skilled experts who provide advice for farmers at district level in developing countries.This study proposed designing knowledge based solution through the collaboration of experts' knowledge with the machine learning knowledge base to recommending suitable agricultural crops for a farm land.To design the collaborative approach the knowledge was acquired from document analysis, domain experts' interview and hidden knowledge were extracted from Ethiopia national meteorology agency weather dataset and from central statistics agency crop production dataset by using machine learning algorithms.The study follows the design science research methodology, with CommonKADS and HYBRID models; and WEKA, SWI-Prolog 7.32 and Java NetBeans tools for the whole process of extracting knowledge, develop the knowledge base and for developing graphical user interface respectively.Based on the objective measurement PART rule induction have the highest classifier algorithm which classified correctly 82.6087% among 9867 instances.The designed collaborative approach of experts' knowledge with the knowledge discovery for agricultural crop selections based on the domain expert, farmers and agriculture extension evaluation 95.23%, 82.2 % and 88.5 % overall performance respectively.
The Influence of Instructional Models and Learning Styles on the Ability to Read to Understand

The purpose of this study was to determine the effect of Instructional models and learning styles on learning outcomes of reading skills.Reading is part of the educational process of developing self-potential so that it can think rationally towards academic achievement.Researchers are of the view that teachers should introduce a variety of reading strategies that are relevant to the needs of students.This study aims to obtain a picture of the influence of think, talk, write learning models and picture to picture learning models (external factors) as well as visual and auditory learning styles (internal factors) on the ability of reading to understand the contents of the Baitussalam Integrated Islamic Qurrata'ain Elementary School in Bogor.Experimental research methods with quantitative research approaches are conducted to test the effect of a variable on other variables with the two-way Anava test.104 respondents were divided into four classes, each class consisting of 26 students.Each class is distinguished by instructional models and learning styles namely instructional models think, talk, write and picture to picture while the learning styles are visual and auditory.The hypothesis there is a positive influence of instructional models on reading skills, and there is a positive influence on learning styles and reading skills.The results of this research depicted that the picture of the instructional model and visual learning style significantly influence reading skills.
Directions of training bachelors of pedagogical education to the basics of robotics

In the context of modern vocational training of future teachers within the digitalization of education, the authors of the article consider such current direction as educational robotics. Having studied the content of the professional training of bachelors in the direction „Pedagogical Education”, offered today within the framework of Federal Standards, as well as in some experimental curricula and author ‘s approaches, the article systematizes the existing views on the problem. The authors propose options of directions, both for implementation of the recommended content of training and for possibly updated on account of inclusion of new elements in the content of training of future teachers of different profiles. The authors define targeted aspects and formulate general tasks of bachelors’ training in the context of forming their readiness for professional technical and technological activity. The result of the analysis of the subject training of bachelors of the profiles „mathematics and informatics”, „informatics and physics” directed to formation of technical and technological competence is presented. The role of educational robotics in solving problems of the formation of readiness for technical and technological activity in preparation of bachelors of pedagogical education is shown. The main directions of education to the basics of educational robotics are highlighted and the experience of implementation is presented for two training profiles „mathematics and informatics” in the university on the basis of educational practice, special discipline on the choice of the student, course and final qualification works, competitions on educational and sports robotics. This context also describes the approach and experience of the course „Basics of educational robotics” for bachelors of pedagogical education, the main result of which is the formation of professional readiness for technical and technological activities in the field of educational robotics. The authors specify this experience on the example of a bachelor with a second profile of informatics in the context of the development of digital technologies. As conclusions to the article, the authors update the study of scientific and methodological approaches to the development of the content of professional training in the field of educational robotics for bachelors of pedagogical education in other specializations.
Time-Sensitive Collaborative Filtering Algorithm with Feature Stability

In the recommendation system, the collaborative filtering algorithm is widely used. However, there are lots of problems which need to be solved in recommendation field, such as low precision, the long tail of items. In this paper, we design an algorithm called FSTS for solving the low precision and the long tail. We adopt stability variables and time-sensitive factors to solve the problem of user's interest drift, and improve the accuracy of prediction. Experiments show that, compared with Item-CF, the precision, the recall, the coverage and the popularity have been significantly improved by FSTS algorithm. At the same time, it can mine long tail items and alleviate the phenomenon of the long tail.
Hybrid image of three contents

Abstract A hybrid image allows multiple image interpretations to be modulated by the viewing distance. Originally, it can be constructed by combining the low and high spatial frequencies of two different images. The original hybrid image synthesis was limited to similar shapes of source images that were aligned in the edges, e.g., faces with a different expression, to produce an effective double image interpretation. In our previous work, we proposed a noise-inserted method for synthesizing a hybrid image from dissimilar shape images or unaligned images. In this work, we propose a novel method for adding an image to be seen from a middle viewing distance. The middle-frequency (MF) image is extracted by a special bandpass filter, which generates ringing while extracting only specified frequency bands. With this method, the middle frequency should be perceived as a meaningless pattern when viewed from a far distance and close up. A parameter tuning experiment was performed to determine the suitable cutoff frequencies for designing the filter for the MF image. We found that ringings of a suitable size could be used to make the middle frequency less noticeable when seen from far away.
Search-free Accelerator for Sparse Convolutional Neural Networks

Sparsification is an efficient solution to reduce the demand of on-chip memory space for deep convolutional neural networks (CNNs). Most of state-of-the-art CNN accelerators can deliver high throughput for sparse CNNs by searching pairs of nonzero weights and activations, and then sending them to processing elements (PEs) for multiplication-accumulation (MAC) operations. However, their PE scales are difficult to be increased for superior and efficient computing because of the significant internal interconnect and memory bandwidth consumption. To deal with this dilemma, we propose a sparsity-aware architecture, called Swan, which frees the search process for sparse CNNs under limited interconnect and bandwidth resources. The architecture comprises two parts: a MAC unit that can free the search operation for the sparsity-aware MAC calculation, and a systolic compressive dataflow that well suits the MAC architecture and greatly reuses inputs for interconnect and bandwidth saving. With the proposed architecture, only one column of the PEs needs to load/store data while all PEs can operate in full scale. Evaluation results based on a place-and-route process show that the proposed design, in a compact factor of 4096 PEs, 4.9TOP/s peak performance, and 2.97W power running at 600MHz, achieves 1.5-2.1× speedup and 6.0-9.1× higher energy efficiency than state-of-the-art CNN accelerators with the same PE scale.
Developing Students’ Reading Comprehension through Think-Pair-Share Strategy

The objective of the research was to find out whether or not Think-Pair-Share Strategy develops the students’ reading comprehension. The researcher applied quasi-experimental with the form of pre-test and post-test control group design and took the second year students of senior high school as the population. The researcher employed cluster random sampling consists of 60 students. The data were collected through pre-test and post-test in form of reading test. To find out whether or not Think-Pair-Share strategy develops the students’ reading comprehension, the researcher used t-test analysis. The result of the data analysis showed that the value of t-counted (5.747) was greater than the value of the t-table (2.002) at the level of significance (a = 0.05) and the degree of freedom df = 58 which meant that the null hypothesis (H0) was rejected and the alternative hypothesis (H1) was accepted. Based on the result of data analysis, the mean score of students’ post-test (84.44) was greater than the mean score of students’ pre-test (63.11). Based on the finding and discussion, the researcher concludes that Think-Pair-Share strategy is effective and gives positive effect to the students’ reading comprehension achievement.
Designing Efficient Shortcut Architecture for Improving the Accuracy of Fully Quantized Neural Networks Accelerator

Network quantization is an effective solution to compress Deep Neural Networks (DNN) that can be accelerated with custom circuit. However, existing quantization methods suffer from significant loss in accuracy. In this paper, we propose an efficient shortcut architecture to enhance the representational capability of DNN between different convolution layers. We further implement the shortcut hardware architecture to effectively improve the accuracy of fully quantized neural networks accelerator. The experimental results show that our shortcut architecture can obviously improve network accuracy while increasing very few hardware resources ( 0.11 × and 0.17 × for LUT and FF respectively) compared with the whole accelerator.
Ship Detection in Synthetic Aperture Radar Imagery Based on Discriminative Dictionary Learning

The ship target detection technology based on SAR image has important significance in military and civil field applications and is one of the research hotspots at this stage. In this paper, the research work on typical problems in SAR image ship target detection is carried out. A ship target detection algorithm based on discriminative dictionary learning is proposed, which mainly includes image denoising, candidate region extraction and candidate region identification. Firstly, an adaptive non-local filtering method is used to denoise the SAR image. Then the gradient feature map reconstruction algorithm is used to extract the candidate regions. Finally, the category constrained discriminative dictionary learning method is used to classify the candidate regions. The algorithm is based on GF-3 and Terra SAR data. The experimental results show that the proposed algorithm has strong robustness and adaptability.
Deck-Based Wide Block Cipher Modes and an Exposition of the Blinded Keyed Hashing Model

We present two tweakable wide block cipher modes from doubly-extendable cryptographic keyed (deck) functions and a keyed hash function: double-decker and docked-double-decker. Double-decker is a direct generalization of Farfalle-WBC of Bertoni et al. (ToSC 2017(4)), and is a four-round Feistel network on two arbitrarily large branches, where the middle two rounds call deck functions and the first and last rounds call the keyed hash function. Docked-double-decker is a variant of double-decker where the bulk of the input to the deck functions is moved to the keyed hash functions. We prove that the distinguishing advantage of the resulting wide block ciphers is simply two times the sum of the pseudorandom function distinguishing advantage of the deck function and the blinded keyed hashing distinguishing advantage of the keyed hash functions. We demonstrate that blinded keyed hashing is more general than the conventional notion of XOR-universality, and that it allows us to instantiate our constructions with keyed hash functions that have a very strong claim on bkh security but not necessarily on XOR-universality, such as Xoofffie (ePrint 2018/767). The bounds of double-decker and docked-double-decker are moreover reduced tweak-dependent, informally meaning that collisions on the keyed hash function for different tweaks only have a limited impact. We describe two use cases that can exploit this property opportunistically to get stronger security than what would be achieved with prior solutions: SSD encryption, where each sector can only be written to a limited number of times, and incremental tweaks, where one includes the state of the system in the variable-length tweak and appends new data incrementally.
Solving some affine equations over finite fields

Let l and k be two integers such that l|k. Define Tlk(X):=X+Xpl+⋯+Xpk−2l+Xpk−l and Slk(X):=X−Xpl+⋯+(−1)(k/l−1)Xpk−l, where p is any prime. This paper gives explicit representations of all solutions in Fpn to the affine equations Tlk(X)=a and Slk(X)=a, a∈Fpn. The case p=2 was solved very recently in [10]. The results of this paper reveal another solution.
IMPLEMENTASI ALGORITMA MODIFIED GUSTAFSON-KESSEL UNTUK CLUSTERING TWEETS PADA AKUN TWITTER LAZADA INDONESIA

Online social media is a new kind of media which is steadily growing and has become publicly popular. Due to its ability to spread informations rapidly and its easiness to access for internet users, social media provides new alternative to conduct advertising and product segmentation. Twitter is one of the most favored social media with 19.5 million users in Indonesia to the date. In this research, the application of text mining to cluster tweets from the @LazadaID Twitter account is done using the Modified Gustafson-Kessel clustering algorithm. The clustering process is executed five times with the number of cluster starts from two to six cluster. The results of this research indicate that the optimum number of clusters formed based on the Partition Coefficient and Classification Entropy validation index are three clusters. Those three clusters are tweets containing electronic stuff offers, discounts, and prize quizes. Tweets with the most retweets and likes are prize quiz tweets. PT Lazada Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @LazadaID Twitter account followers.Keywords: Twitter, advertising, Lazada Indonesia, Gustafson-Kessel Clustering algorithm, validation index
PREDIKSI HARGA EMAS MENGGUNAKAN FEED FORWARD NEURAL NETWORK DENGAN METODE EXTREME LEARNING MACHINE

The prediction of gold price aims to find out the gold price in the future on the basis of historical data on gold prices in the past, so it can be used as a consideration by gold investors to investing in gold. Prediction methods that do not require assumptions, one of which is Artificial Neural Networks. In this study, using Artificial Neural Networks, Feed Forward Neural Network with Extreme Learning Machine (ELM). ELM is a non-iterative algorithm so ELM has advantages in process speed. The input weight and bias for this method are determined randomly. After that, to find the final weight using the Moore-Penrose Generalized Inverse calculation on the hidden layer output matrix. The best model selection criteria uses the Mean Absolute Percentage Error (MAPE). This study shows that the results of the training and testing process from the model 1 input neuron and 7 hidden neurons are very good, because it produces MAPE training = 0.6752% and MAPE testing = 0.8065%. Also gives a very good prediction result because it has MAPE = 0.5499% Keywords: gold price, Extreme Learning Machine, MAPE
Deep Neural Networks for Designing Integrated Photonics

We present two different approaches to apply deep learning to inverse design for nanophotonic devices. First, we use a regression model, with device parameters as inputs and device responses as outputs, or vice versa. Second, we use a novel generative model to create a series of improved designs. We demonstrate them to design nanophotonic power splitters with multiple splitting ratios.
Using Artificial Intelligence Techniques for Prediction and Estimation of Photovoltaic System Output Power

Two artificial intelligence methods, namely, support vector machines (SVM) and gene expression programming (GEP), were explored for prediction and estimation of the Photovoltaic (PV)output power. Measured values of temperature T (°C) and irradiance E (kWh/㎡) were used as inputs (independent variables) and PV output power P (Kw) was used as output (dependent variable). The statistical metrics were used to assess the predictive performances of the methods. The results of the two models were estimated and compared. The results showed that the two techniques performances are better and similar. Using GEP technique, the relationships between the two parameters and output power were established. Importance of each parameter as contributor to PV output power was also investigated. The results indicated that the SVM and GEP would become the powerful tools that could help estimate the PV output power capacity reserve.
A high capacity reversible data hiding through multi-directional gradient prediction, non-linear regression analysis and embedding selection

Abstract The technique of reversible data hiding enables an original image to be restored from a stego-image with no loss of host information, and it is known as a reversible data hiding algorithm (RDH). Our goal is to design a method to predict pixels effectively, because the more accurate the prediction is, the more concentrated the histogram is, and it minimizes shifting to avoid distortion. In this paper, we propose a new multi-directional gradient prediction method to generate more accurate prediction results. In embedding stage, according to the embedding capacity of information, we generate the best decision based on non-linear regression analysis, which can differentiate between embedding region and non-embedding region to reduce needless shifting. Finally, we utilize the automatic embedding range decision. With sorting by the amount of regional variance, the easier predicted region is prior for embedding, and the quality of the image is improved after embedding. To evaluate the proposed reversible hiding scheme, we compared other methods on different pictures. Results show that the proposed scheme can embed much more data with less distortion.
Recognition of sign language hand gestures using leap motion sensor based on threshold and ANN models

Hand gesture recognition is a topic that is still investigated by many scientists for numerous useful aspects. This research investigated hand gestures for sign language number zero to nine. The hand gesture recognition was based on finger direction patterns. The finger directions were detected by a Leap Motion Controller. Finger direction pattern modeling was based on two methods: threshold and artificial neural network. Threshold model 1 contained 15 rules based on the range of finger directions on each axis. Threshold model 2 was developed from model 1 based on the behavior of finger movements when the subject performed hand gestures. The ANN model of the system was designed with four neurons at the output layer, 15 neurons at the input layer, seven neurons at the first hidden layer and 5 neurons at the second hidden layer. The artificial neural network used the logsig as the activation function. The result shows that the first threshold model has the lowest accuracy because the rule is too complicated and rigid. The threshold model 2 can improve the threshold model, but it still needs development to reach better accuracy. The ANN model gave the best result among the developed model with 98% accuracy. LMC produces useful biometric data for hand gesture recognition.