Reviewerly/exHarmony huggingface dataset:
Designing Android Based Education Game Aksara Jawa Using Shuffle Random Algorithm

Abstract The lack of appropriate supporting media in learning at the age of children makes the young generation now less familiar and still having difficulty learning Javanese script (Aksara Jawa called in indonesia). For this reason, we need appropriate media in the introduction of Javanese script, one of which is through interesting educational games and can provide information about Javanese script. Games are forms of application that can be used as learning media where the process can be done with the concept of learning while playing for children. In this study the algorithm used was the Shuffle Random Algorithm. With this randomization algorithm, it is expected to avoid repetition of the problem so that the learning process can run effectively. Furthermore, this application was tested using the Black Box, White Box and User Acceptance Test. With the Black Box and White Box testing get a percentage of 100% while with the User Acceptance Test this application has an average of 86%, so it can be concluded that this educational game is feasible to be used as a learning medium for Javanese script.
Automatic Missing-Child Recovery System using Eigenfaces

During the festival seasons, the crowds in the shopping malls become eight to ten times more than regular times. To impress maximum number of customers, business owners offer discounts, arrange raffle draws, game competitions, and many other events. They decorate their showrooms with eye-catching banners and lighting effects. These excellent marketing strategies work and increase customers’ engagements. However, the curious minds of children are attracted by these events more than those of adults. As a result, missing children in the shopping malls have become a common incident during festival seasons. Whenever a child is missing, shopping mall managements announce it though central sound system and normally if the people see the missing child, they bring it back to mall management’s office and the officials hand it over to the parents. This method depends on the generosity of the people and does not guarantee the return of the child. Moreover, the children kidnappers can take advantage of the announcement of missing child. In this paper, we are proposing a method that depends on eigenfaces which is fast, efficient yet simple to implement and ensure the recovery of the missing children in shortest possible time.
Fuzzy C-Mean Brain Tumor Image Segmentation Based on Multi-feature Fusion

A new Fuzzy C-Means algorithm based on multi-feature fusion FMFCM was proposed in this paper, aiming at the problem of poor anti-noise performance and low segmentation accuracy of Fuzzy C-Mean Clustering (FCM) algorithm. The brain tumor MR image was taken by the algorithm as the experimental object, extracted the gray value, spatial information and local binary pattern of the brain image as multi-dimensional features to segment, and tested the anti-noise performance of the image by adding different intensity of Gaussian noise and salt and pepper noise. Experiments show that compared with FLICM algorithm FRFCM and FCM-S2 algorithm, The proposed algorithm improves the partition coefficient (Vpc) by 6.9%, 10.3% and 13.2% respectively, and reduces the partition entropy (Vpe) by 16.2%, 11.8% and 15.2%, respectively. The segmentation accuracy is improved by 2.2%, 1.6% and 3% respectively. The proposed algorithm has better robustness to noise and can achieve better segmentation results.
Three-dimensional visualization of cribriform pattern in ductal carcinoma in situ with x-ray dark field computed tomography

Cribriform architecture is a histological pattern reminiscent of Swiss cheese that is commonly recognized in ductal carcinoma in situ (DCIS) of the breast observed by microscope. However, there are only a few three-dimensional studies to elucidate whether each glandular cavities of cribriform pattern are connected or not. The main reason for paucity of three-dimensional studies is that the conventional reconstruction based on histological sections requires laborious and time-consuming works. In this research, we first performed three-dimensional reconstruction of the cribriform pattern using crystal analyzer-based phase contrast technique, X-ray dark field computed tomography (XDFI-CT), which provides high contrast image of biological soft tissue with non-destructive and non-staining approach. Then, we propose a machine-learning-based method to extract the cavity from XDFI-CT images. Finally, we show that the useful information to analyze the cribriform patterns in DCIS such as the density and volume of the cavity can be obtained from the XDFI-CT images.
A Tool for Statistical Analysis of Alphabets and Words of Hindi

Natural language processing (NLP) is an approach to analyze and understand human language in a smart and useful way. Statistical analysis is an approach of mathematics to examine and perform analysis to map data in the form of numeric values. Frequency analysis of Hindi alphabets and words can help to examine occurrence frequency of words and alphabets in according to the provided word list. In this paper, an integrated model has been developed and implemented to perform statistical analysis of alphabets and words for the Hindi language. The complete model includes three modules for word-list preparation, frequency count and statistical analysis of alphabets and words. Hindi News articles of local and national paper have been considered as the input source. The tool evaluates the length, frequency of occurrence of practical and dictionary words.
Pedestrian Detection of Occlusion Based on Multi-marker method

Detection of occluded pedestrian in traffic scene is a difficult problem in pedestrian detection. Based on this, this paper proposes a new pedestrian detection method which is different from the traditional sliding window method. This method combines a gaussian mixture model (GMM) with local occlusion pattern capture method based on multi-label learning. This model extracts detection information from a small number of frames for unsupervised learning, each part of the mixed image represents the expectation target found in the image region. At run time, the window most likely to contain a pedestrian will be sampled from the widget and evaluated by a classifier. In the analysis of local occlusion, a group of decision trees are shared by component detectors, and the computation time is reduced by learning and combining the regional correlation. Experimental results show that the combined gaussian mixture model with local occlusion capture method can greatly reduce the computational complexity and provide better recognition performance for pedestrian occlusion.
A new chaotic encrypted image based on the trigonometric circle

In this paper, we propose a new idea to encrypt images using the piecewise linear chaotic map (PWLCM). We converted the plaintext image abstractly and geometrically in the form of a cylinder so as to form a series of superimposed circles see Figure 2. This imagination allows us to process and encrypt the image using trigonometric operations in a different way. PWLCM used as a pseudo-random generator to perturb the angles generated by our transformation. Our algorithm has high sensitivity in plaintext image and the secret key. Moreover, the results show that the characteristics of our approach have excellent performance, with high scores (NPCR = 99.614%, UACI = 33.422%, entropy (Ciphertext Image) ≈ 8, PSNR = 8,4008 and Correlation Coefficient ≈ 0). The experimental results of the security analysis confirm a high level of security of the proposed cipher text.
Reading Mammography with Multiple Prior Exams

Change is one of the strongest features for identifying abnormality in screening mammography exams. However, publicly available mammography datasets have so far lacked prior exams, and so the majority of algorithm development has focused on assessing isolated exams, without prior information. Recently, it was shown that a deep learning algorithm can improve its diagnostic accuracy by utilizing a single prior mammography image. In this work, we extend the previous result to address the issue of reading a variable number of prior exams. We compare two approaches: Random Forest, which requires that all inputs have the same size, and LSTM, which can handle a variable-size input. We demonstrate a significant performance improvement when using multiple priors, consistent with the standard workflow of breast imagers. We also found for both models that multiple priors improved performance over using a single prior. Interestingly, LSTM consistently outperformed the Random Forest model, and is more practical because it can naturally process any number of prior images that are available at the time of read. We expect that these results will generalize to other screening programs, such as colorectal cancer, where prior images are readily available.
On semi-supervised multiple representation behavior learning

Since Shahshahani and Landgrebe published their seminal paper (Shahshahani and Landgrebe, 1994) [1] in 1994, the study on semi-supervised learning (SSL) developed fast and has already become one of the main streams of machine learning (ML) research. However, there are still some areas or problems where the capability of SSL remains seriously limited. Firstly, according to our observation, almost all SSL researches are towards classification, regression or clustering tasks. More difficult tasks such as planning, construction, summarization, argumentation, etc. are rarely seen studied with SSL methods. Secondly, most SSL researches use only simple labels (e.g. a string, an identifier, a numerical value, etc.) to mark the text data. It is difficult to use such simple labels to characterize data with delicate information. This limitation might be the reason why current SSL technique is not appropriate in processing complex tasks. Thirdly, after entering the age of big data and big knowledge, SSL, like the other branches of ML, is now facing the challenge of learning big knowledge from big data. The shortage of traditional SSL as mentioned above became even more serious and we are looking forward to new technology of SSL. In this paper, we propose and discuss a novel paradigm of SSL: the semi-supervised multiple representation behavior learning (SSMRBL). It is towards matching the challenge to SSL stated above. SSMRBL should extend current SSL techniques to support complex task learning such as planning, construction, summarization, argumentation etc. In order to meet the challenge, SSMRBL introduces compound structured labels such as trees, graphs, lattices, etc. to represent complicated information of objects and tasks to be learned. Thus, to label an unlabeled datum is to construct a compound structured label for it. As a consequence, SSMRBL needs to have multiple representations. There may be one representation for compound structured labels, one for the target model which is the unification of all local models (labels), one for representing the process (behavior) of label construction, and one for the efficient computation during the learning process. This paper introduces also a typical circumstance of SSMRBL—semi-supervised grammar learning (SSGL), which learns a grammar from a set of natural language texts and then applies this grammar to parse new texts and to summarize its content. We provide also experimental results based on a variety of algorithms to show the reasonability of our ideas.
SGGS Decision Procedures

SGGS (Semantically-Guided Goal-Sensitive reasoning) is a conflict-driven first-order theorem-proving method which is refutationally complete and model complete in the limit. These features make it attractive as a basis for decision procedures. In this paper we show that SGGS decides the stratified fragment which generalizes EPR, the PVD fragment, and a new fragment that we dub restrained. The new class has the small model property, as the size of SGGS-generated models can be upper-bounded, and is also decided by hyperresolution and ordered resolution. We report on experiments with a termination tool implementing a restrainedness test, and with an SGGS prototype named Koala.
Quantum Self-organizing Network and Routing Protocol for Cooperative Communication of Intelligent Robot Soccer Game

In the case of multiple intelligent robots working together, such as the robot soccer game, each robot is required to complete the common task with efficient, safe and fast cooperative communication. Compared with the traditional wireless communication, intelligent robots have the advantages of information security, large capacity, high efficiency and high speed if they interact with each other through quantum communication. In this paper, quantum communication and intelligent robot are combined, and a kind of quantum self-organizing network for robot soccer is proposed. In this network, each robot is both the sender and receiver of quantum information, and all players are communication nodes in the quantum self-organizing network. With the support of certain routing protocols, the network can collect, judge and make decisions on soccer position information, player movement information and soccer goal information. The simulation results show that compared with the traditional wireless method, this scheme not only has higher information security, but also ensures the effectiveness of information transmission. Therefore, this study provides a new idea for the design and development of communication network between soccer robots in the future.
Research on Panorama Reconstruction Technique of UAV Aerial Image Based on Improved ORB Algorithm

Classic ORB (oriented FAST and rotated BRIEF) algorithm usually extracts feature points globally, which will result in more false feature match pairs and longer computation time. In this paper, an improved ORB algorithm is proposed to reconstruct the panorama of aerial images. First, phase correlation method is used to obtain overlapping area between to-be-stitched image and reference image, and improved ORB is adopted to detect and describe feature points on overlapping regions. Second, K-nearest neighbor (KNN) algorithm is utilized to match feature points roughly, and false matching points are removed by Random Sample Consensus (RANSAC) algorithm. Finally, the improved weighted average fusion algorithm is adopted to eliminate ghosting, while realizing the seamless splicing of UAV images. Experimental results show that, compared with SIFT and classic ORB, the algorithm proposed in this paper can obtain higher registration accuracy, and also significantly improve the stitching accuracy and efficiency of UAV aerial images.
A Social Recommendation Based on Metric Learning and Network Embedding

Traditional recommender systems usually face data sparseness and cold-start problems. To alleviate these problems, social recommendations use social information between users to improve the quality of recommendations. At present, most social recommendation is based on matrix factorization, but with the combination of recommendation and metric learning, the user's preference is reflected by distance, which has proven to be an effective recommendation method. However, these social recommendation methods usually use simple similarity information as users' social regularization, which cannot analyze users' social relationship characteristics well. In order to overcome the shortcoming of social recommendation, we propose a new model for which combines network embedding and metric learning (SRMN) in this paper. SRMN believes that users and items have interactive behaviors in social space. The interaction can be regarded as the user's preferences for the item in the social space, and then combines with the use of metrics to reflect the user's preferences. Users' preferences for items are determined by both ratings and social information. Finally, the effects of different network embedding methods on the model are compared. Experimental results on three real data sets show that our method is effective in improving the accuracy of score prediction.
Class Incremental Learning With Task-Selection

Despite the success of the deep neural networks (DNNs), in case of incremental learning, DNNs are known to suffer from catastrophic forgetting problems which are the phenomenon of entirely forgetting previously learned task information upon learning current task information. To alleviate this problem, we propose a novel knowledge distillation-based class incremental learning method with a task-selective autoencoder (TsAE). By learning the TsAE to reconstruct the feature map of each task, the proposed method effectively memorizes not only the classes of the current task but also the classes of previously learned tasks. Since the proposed TsAE has a simple but powerful architecture, it can be easily generalized to other knowledge distillation-based class incremental learning methods. Our experimental results on various datasets, including iCIFAR-100 and iILSVRC-small, demonstrated that the proposed method achieves higher classification accuracy and less forgetting compared to the stateof-the-art methods.
Optimization of the Process of Parameter Adjustment: Image Processing as a Case Study

Selecting optimal parameters' values in a computer vision task is a challenge for users because of the multitude of possible cases. This paper discusses the use of artificial intelligence methods and techniques to optimize the process of finding the best values for parameters in image processing applications. We propose an automatic method based on multi-agent system and reinforcement learning. The proposed solution is proved by an experimentation of object extraction from an image.
A privacy-preserving multi-keyword ranked retrieval scheme in cloud computing

Big data and cloud computing could bring security problems. In order to ensure data security and user privacy, people would choose to store data in the cloud with ciphertext. How to search data efficiently and comprehensively without decryption has become the focus of this paper. In this paper, we propose an efficient privacy protection scheme. In this scheme, Elliptic Curve Cryptography (ECC) is adopted to encrypt the data. It can reduce the computing cost of encryption and decryption uploading the encrypted files and indexes to the cloud server. Then it can authorize users to generate trap door using hash conflict function, and send it to Cloud Service Provider (CSP) for searching for matched ciphertext. The CSP uses the Apriori algorithm to extend keywords and search index to match the ciphertext. In this paper, we will use the Apriori algorithm to extend the keywords’ semantics, match the index list based on these keywords, and return the requested file-set which is more consistent with the user’s search. Experiments show that compared with traditional methods, files can be encrypted, decrypted, and recovered more quickly when we use this method. It can also ensure the privacy of data and reduce the communication overhead.
The optimalization of cosine similarity method in detecting similarity degree of final project by the college students

Abstract Technology development makes everything unlimited. Everyone easily to gets information. There are negative and positive impacts. The one negative impact is plagiarism the work of others. Of course this brings a bad impact. This study aims to examine the similarity of Student Final Reports at the Politeknik Unggul LP3M Medan. The method used is the Cosine Similarity Method. This method was chosen because it works based on mathematical calculations. How it works is by comparing the final project done by students with the final project that has been there before. With the Cosine Similitary Method, percentage of similarity will be obtained. If the similarity is high, the final project is said to be tracing.
Graph Convolutional Architectures via Arbitrary Order of Information Aggregation

Graph representation learning (GRL) has recently drawn a lot of attention due to its advantage in solving various machine learning tasks on graphs/networks, ranging from drug design to recommendation systems.One typical GRL approach is graph embedding, the purpose of which is to learn a map that encodes or represents network elements as points in a low-dimensional vector space so that downstream machine learning methods can be easily implemented.Initially, most graph embedding algorithms learn such a map independently from subsequent machine learning tasks.Therefore, they have limitations in solving supervised machine learning tasks on networks.Later, a great deal of graph convolutional networks (GCNs) have been proposed to learn node representations in an end-to-end manner based on different information aggregation mechanisms.By treating network structure as a computational layer in a GCN, the associated information of nodes with higher-order proximity can be aggregated by increasing the number of layers (i.e., depth) of the GCN.As a consequence, the computational overhead will increase and the representations will be projected towards a steady state.To solve this problem, in this paper, we propose a multi-channel graph convolutional network (MCGCN) that allows higher-order information aggregation by enriching the number of input channels.Based on the notion of Katz index, our model can further achieve an arbitrary order of information aggregation without increasing the computational overhead.Comprehensive experiments on several benchmark networks demonstrate the effectiveness of the proposed architecture by comparing it with the-state-of-art GRL methods in terms of node classification and computational efficiency.
Enhancing aspects of Thai chief complaint classification Performance

In this paper, we describe the aspects affecting in our experimental results of classifying Thai chief complaint (ThCC) into ICD-10 code. By merging our proposed Thai word separator to machine learning-based classifiers, ThCC have been converted into ICD-10 code which stands for International Classification of Diseases, Tenth Revision, and is a standard code used by physicians and other healthcare professionals to identify all diagnoses, signs and symptoms. At the beginning of experiments, the dataset from the sign and symptom description ranged in group R00 to R69 of ICD-10 have been used for training the classifiers. Subsequently the classifiers have been applied to the test dataset represented by 150 chief complaint cases in order to assign the related ICD-10 codes, and to evaluate classification accuracy. The experiment achieves 85% precision, 76% F1-measure, and 71% recall using our proposed Thai word separator with Classification and Regression Trees (CART) technique. However, we need to increase the precision which is strong enough to support our proposed separator. The additional experiment has been done by adding 50 chief complaint cases to the test dataset. We also have applied our proposed techniques including conflict element finding and classification criteria setting to improve the precision. Consequently, the later experimental results get higher classification accuracy by decreasing the false positives to mitigate the low recall problem.
An Effective Scheme for Copy-move Forgery Detection Using Polar Sine Transform

Nowadays, as rapid development of image processing technique, it is necessary to authenticate whether an image is an artificial image or not. Copy-move forgery is the frequently-used image manipulation means. In this paper, Polar Sine Transform (PST) and Locality Sensitive Hashing (LSH) are combined to detect copy-move forgery image. First, the detected image is divided into lots of overlapping blocks. And then, PST is applied to extract feature from each block. Subsequently, LSH is applied to classify these block features, and we search the similar features in same class as candidate block feature pairs. Finally, the post-processing operation using Euclidean distance is presented to filter out the weak block feature pairs. A series of experiments show that the proposed method has robustness to rotation and JPEG compression and is superior to the state-of-the-art methods.