Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Fuzzy Logic and Neural Network Design for Mobile Robot Navigation

The mobile robot is the type of robot that emerges not only in industry but also in the domestic application, intended to substitute or assist human in a dull, dirty, or dangerous environment. The robot is designed to imitate or resemble human abilities to perform a physical task using a simple control theorem, or even sophisticated task by implementing artificial intelligent (AI) to create a smart robot. The most applied AI is Fuzzy Logic Controller (FLC) and Neural Network (NN). The main issue in the mobile robot is the navigation, defined as how to ensure the robot can finish the task safely without crushing to any obstacles. This paper investigates the application of FLC and NN in robot navigation and compares the performance in navigating the robot to the target. Sensors used in this paper is distance sensors and a camera. A robot is moved in several experimental setting, and the effectiveness of FLC and NN application is compared. The comparison is conducted in a simulation program named MobotSim, where several robots were designed in various environments. The simulation results show that NN application is more suitable confirmed by faster time in completing the task.
A call for introducing LegalTech in the classroom

Change is coming to the way the business of law is conducted. It is an unavoidable reality that the delivery of professional legal services is on the cusp of major disruption. The way law firms and in-house legal teams operate is predicted to change dramatically. It is theorised that a majority of the aforementioned change will come from the adoption of more sophisticated technology by law firms and the courts. Technological change has already made some lawyer hours obsolete, and this trend is only expected to continue. Given this incoming wave of change, there exists a strong justification for the inclusion of legaltech in the undergraduate LLB curriculum. This article asses the feasibility of such an inclusion and provides suggestions for what institutions could be doing to support their users.
Automatic detection of simulated motion blur in mammograms

Purpose To use machine‐learning algorithms and blur measure (BM) operators to automatically detect motion blur in mammograms. Motion blur has been reported to reduce lesion detection performance and mask small abnormalities, resulting in failure to detect them until they reach more advanced stages. Automatic detection of blur could support the clinical decision‐making process during the mammography exam by allowing for an immediate retake, thereby preventing unnecessary expense, time, and patient anxiety. Methods Blur was simulated mathematically to mimic the real blur seen in clinical practice. The blur point‐spread‐function (PSF) mask is generated by distributing pixel intensity of an image pixel moving under random motion within the range of blur effect (the maximum amount of tissue motion allowed). The random motion trajectory vector is generated on a super‐sampled image frame to accommodate smaller substeps; the vector was then sampled on a regular pixel grid using subpixel linear interpolation to generate the blur PSF mask. This randomly generated motion trajectory is constrained by several factors: the effects of variations in tissue elasticity, imaging exposure time, and size of blur effect (motion boundary in millimeters) were examined. The blur mask is convolved with a mammogram to create blur. Five motion blur magnitudes (0.1, 0.25, 0.5, 1.0, and 1.5 mm) were simulated on 244 and 434 mammograms from the INbreast and DDSM databases, respectively. Blur was quantified using nine BM operators for each mammogram and at each blur level. The mammograms were assigned to training (70%) and testing (30%) datasets to train three machine‐learning classifiers: Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, to distinguish five levels of blurred from unblurred mammograms, using six‐way classification. Results For the INbreast mammograms, the average classification accuracies were 87.7%, 85.7%, and 85.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively, and the average classification accuracies for DDSM were 93.5%, 93.6%, and 92.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively. Conclusions Preliminary results show the potential to detect simulated blur automatically using those methods. Although limited work has been done to quantify the effects of motion blur on radiologists’ performance, there is evidence that motion blur might not be detected visually by a human observer and could negatively affect radiologists’ lesion detection performance. As of this date, no other study has investigated the ability of machine‐learning classifiers and BM operators to detect motion blur in mammograms.
Efficient multi-cluster feature selection on text data

AbstractAbstractFeature selection methods are used to obtain relevant feature subset from the original feature space that is of high dimension for efficient classification and clustering of data. Most real world datasets are of multi-cluster nature with correlation amongst the features. This paper proposes a new method of multi-cluster feature selection, called Efficient Multi-Cluster Feature Selection (EMCFS). It obtains only the features that can best preserve the multiple cluster structure of the data. It employs the anchor graph to build the adjacency matrix of much reduced dimension than the feature space. The eigen vector values of the graph Laplacian model the underlying geometric structure of the data. The experimental result on TDT2 and Reuters-21578 text data set demonstrates the efficiency of the proposed method. A comparison of EMCFS with the original Multi-Cluster Feature Selection (MCFS) demonstrates its improved accuracy and reduced execution time, making it a promising method for real world high dimensional datasets.Subject Classification: Big DataData MiningArtificial IntelligenceKeywords: EMCFSMCFSFeature Selection
Compositional interaction design—changes in design practice and its implications for teaching and research

In this article, we focus on compositional interaction design as an emergent professional practice in interaction design. We propose that compositional interaction design practice can be defined as a practice of knowing, mapping configuring, customizing and connecting elements to form larger compositional wholes. We demonstrate how this practice demands new compositional skills, new methods and a changed division of labours. We suggest that this contemporary practice can be understood in terms of how different computational components are integrated to form interactive products and services. We illustrate this new practice with two concrete examples. Our first example illustrates compositional interaction design at a small scale, whereas our second example illustrates compositional design at a larger scale. We also discuss how this emergent practice relates to other streams of development (e.g. craft-oriented HCI and human-centred design) and we discuss what implications this shift may have to interaction design educational programs aimed at preparing students for this emerging professional practice.
Face Recognition Based on Two-stage CNN Combined with Transfer Learning

In recent years, pattern recognition has received wide attention, especially face recognition. Research shows that traditional methods have limits in face recognition under unrestricted environment because of illumination, facial expression, pose and so on. Deep learning is chosen to solve these problems. In this paper, we propose a complete method for face recognition under unrestricted environment by deep learning. First, we design a novel two-stage convolutional neural network for face detection and alignment. The first stage of the network aims at searching face windows and their bounding box regression vector. The second stage merges facial candidates which are covered highly. Then, the network finishes the task of face detection and alignment whose outputs are face regions and the position of the five facial landmarks. It achieves great performance on angle of faces in image and obtains accuracy of 96.1% on AFW and 98.9% on FDDB. Then faces are sent to convolutional neural network for transfer learning because of limited training samples. On the testing dataset, the accuracy of face recognition reaches to 95.3%, comparing with 89.1% of the original network. What’ more, our method is better than the traditional algorithms and general CNN.
A New Intra Fine-Tuning Method Between Histopathological Datasets in Deep Learning

This article presents a new fine-tuning framework for histopathological images analysis. Despite the most common solutions where the ImageNet models are reused for image classification, this research sets out to perform an intra-domain fine tuning between the trained models on the histopathological images. The purpose is to take advantage of the hypothesis on the efficiency of transfer learning between non-distant datasets and to examine for the first time these suggestions on the histopathological images. The Inception-v3 convolutional neural network architecture, six histopathological source datasets, and four target sets as base modules were used in this article. The obtained results reveal the importance of the pre-trained histopathological models compared to the ImageNet model. In particular, the ICIAR 2018-A presented a high-quality source model for the various target tasks due to its capacity in generalization. Finally, the comparative study with the other literature results shows that the proposed method achieved the best results on both CRC (95.28%) and KIMIA-PATH (98.18%) datasets.
A heuristic information cluster search approach for precise functional brain mapping

Abstract Detection of the relevant brain regions for characterizing the distinction between cognitive conditions is one of the most sought after objectives in neuroimaging research. A popular approach for achieving this goal is the multivariate pattern analysis which is currently conducted through a number of approaches such as the popular searchlight procedure. This is due to several advantages such as being automatic and flexible with regards to size of the search region. However, these approaches suffer from a number of limitations which can lead to misidentification of truly informative regions which in turn results in imprecise information maps. These limitations mainly stem from several factors such as the fact that the information value of the search spheres are assigned to the voxel at the center of them (in case of searchlight), the requirement for manual tuning of parameters such as searchlight radius and shape, and high complexity and low interpretability in commonly used machine learning‐based approaches. Other drawbacks include overlooking the structure and interactions within the regions, and the disadvantages of using certain regularization techniques in analysis of datasets with characteristics of common functional magnetic resonance imaging data. In this article, we propose a fully data‐driven maximum relevance minimum redundancy search algorithm for detecting precise information value of the clusters within brain regions while alleviating the above‐mentioned limitations. Moreover, in order to make the proposed method faster, we propose an efficient algorithmic implementation. We evaluate and compare the proposed algorithm with the searchlight procedure as well as least absolute shrinkage and selection operator regularization‐based mapping approach using both real and synthetic datasets. The analysis results of the proposed approach demonstrate higher information detection precision and map specificity compared to the benchmark approaches.
Image Denoising Using Graph-Based Frequency Domain Low-pass Filtering

Image denoising is always a very challenging task in image processing, which requires appropriate signal priori to regulate the problem. In recent years, the development of spectrum theory and graph signal has given us a new direction. By taking the image as a graph signal, we can construct a suitable underlying image to make the image relatively smooth, and combine the signal priori to image denoising. Based on this idea, and combined with graph Laplacian matrix,a method of image low-pass filtering in frequency domain is proposed in this paper. Experiments show that the proposed method is effective in image denoising, and superior to the traditional Wiener filtering and Gaussian filtering.
Elements of exploration geochemistry

This chapter presents the basic components of exploration geochemistry: rocks and their types, soil, regolith, geochemical elements, and a brief introduction of mineral exploration. The chapter begins by explaining the geochemical cycle; that is, processes within the earth’s interior, the surface, and the atmosphere all that lead to the formation of mineral deposits in rocks. The genesis and origin of igneous, sedimentary, and metamorphic rocks are described. The circulation of geochemical elements in rocks, soils, regolith, and plants is described. Soil and regolith formation and geochemical elements exchange, mobility and characteristic pattern within the earth’s surface processes are also explained. Further, chemical elements of the Periodic Table in terms of their abundance, classification, and significance in mineral exploration are highlighted. Finally, mineral exploration and its stages are described with emphasis on the role of exploration geochemistry.
Towards Generating Plausible Distractors for Code Comprehension Multiple-Choice Questions

Code comprehension is a critical skill that novice programmers must master. We develop a semi-automatic tool to help instructors rapidly create code comprehension Multiple-Choice Questions (MCQs). We conducted a preliminary survey with 10 MCQs and 10 respondents, and the results suggest that our tool can generate plausible distractors for these MCQs.
A Comparative Study of Z- Transform and Fourier Transform applied on Medical Images for Detection of Cancer Segments

A preliminary investigation is carried out by applying Z-transform technique on different types of images with sole intention to record the z-score of any binary image. Bandwidth of the images are computed, and compared with that obtained from Fourier technique. Length of z-score matrix is graphically represented. Result is also analyzed for smooth, shiny and rough images. This preliminary investigation will help in sending multimedia content, precisely images through network and in signal processing which helps for the segments of cancerous portion.
Detecting anomalies in fibre systems using 3-dimensional image data

Abstract We consider the problem of detecting anomalies in the directional distribution of fibre materials observed in 3D images. We divide the image into a set of scanning windows and classify them into two clusters: homogeneous material and anomaly. Based on a sample of estimated local fibre directions, for each scanning window we compute several classification attributes, namely the coordinate wise means of local fibre directions, the entropy of the directional distribution, and a combination of them. We also propose a new spatial modification of the Stochastic Approximation Expectation-Maximization (SAEM) algorithm. Besides the clustering we also consider testing the significance of anomalies. To this end, we apply a change point technique for random fields and derive the exact inequalities for tail probabilities of a test statistic. The proposed methodology is first validated on simulated images. Finally, it is applied to a 3D image of a fibre reinforced polymer.
The Control System of Kulkul Bali Based on Microcontroller

Kulkul is a traditional information medium that bridges Balinese information, death information, deliberation information, and disaster information. Kulkul is very sacred and not just anyone who can ring it. Obstacles when ringing the kulkul when the officer is not in the village and must ring the kulkul and must climb the kulkul hall building to ring the kulkul. Balai kulkul until now remains a communication medium about the activities in the banjar environment. In general, the kulkul hall has not been maximally utilized in the event of a disaster. Even though the kulkul hall as a traditional communication medium that is very trusted by the public, can become a valid information center especially in the event of a disaster. By maximizing the function of the kulkul hall as a medium of community communication even in the event of a disaster, it is expected that the kulkul hall can continue to survive amid the times. Seeing the potential for disasters and the superiority of the functions of the kulkul hall, an implementation of disaster mitigation technology with Balinese culture was made. So we need a tool that can control the kulkul remotely using an sms system and the kulkul controller using a keypad so that officers do not need to climb the kulkul hall building. In this study, the numbers registered on sim 900 alone can control the kulkul via sms and the kulkul control via the keypad only the correct password code can control the kulkul, if the code is wrong then the LCD displays the wrong code notification and cannot control the kulkul. This system can be controlled using a keypad and SMS remotely with the types of knock kulkul_kematian, kulkul_musyawarah, and kulkul_pancabaya, to determine the social impact on the community by distributing questionnaires.
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Constructing minimal linear codes is an interesting research topic due to their applications in coding theory and cryptography. Ashikhmin and Barg pointed out that wmin∕wmax>(q−1)∕q is a sufficient condition for a linear code over the finite field Fq to be minimal, where wmin and wmax respectively denote the minimum and maximum nonzero weights in a code. However, only a few families of minimal linear codes over Fq with wmin∕wmax≤(q−1)∕q were reported in the literature. In this paper, we obtain several families of minimal q-ary linear codes with wmin∕wmax≤(q−1)∕q. The weight distributions of all the constructed minimal linear codes are presented.
Motion Deblur of QR Code Based on Generative Adversative Network

Aiming at the problem that the QR codes cannot be recognized due to factors such as motion blur caused by the hand tremor and environmental noise in the QR codes of the packaged product, this paper proposes an algorithm based on the Generative Adversative Network to remove QR codes motion blur. Compared with the traditional deblurring algorithm, this method eliminates the process of blur kernel estimation, directly restores the motion blurred image. Gradient L1 regularization is added on the basis of the traditional GAN, which enhances the edge features of the restored image and adds the perceptual loss term, so that the image generated by the generator is closer to the global structure of the target clear image. The results show that the generator can remove the blur and generate a QR code with high recognition rate.
Attention-based Recurrent Neural Model for Named Entity Recognition in Chinese Social Media

NER (Named Entity Recognition) is one of the major tasks in NLP (natural language processing). Although there are already some methods to solve NER problem, it is rare in Chinese social media, and less of them can make good use of external information. The POS (part-of-speech) of a word can convey a lot of information in NER, and the attention mechanism can help us successfully focus on these contents. For the above reasons, we propose an attention-based bidirectional LSTM (Long Short-Term Memory) model to address NER problem in Chinese social media. The attention mechanism utilizes the POS of every character of a sentence in the learning process. Experiments on the dataset of Chinese social media demonstrated the performance of the proposed neural model.
Serious Game Design for Terengganu Brassware Craft Heritage

Serious games are increasingly used in various fields such as cultural heritage. The Terengganu brassware craft is becoming extinct due to several factors such as lack of interest from the younger generation and lack of apprentice. The use of serious games in cultural heritage is significant to attract and engage the younger generation to preserve and conserve the cultural heritage. In order to develop an effective serious game, a good design is required. This study focused on the design phase to develop a serious game for Terengganu brassware craft cultural heritage. There are several activities involved in designing this game including the preparation of game design document, level design, character design and game interface design. The outcome of this design is a low fidelity prototype - a storyboard using sketching technique. The testing phase would be carried out on the low fidelity prototype to ensure that the design meets the requirements of the target user.
Bacterial Named Entity Recognition Based on Language Model

Interactions among microorganisms have been the key to understand microbial communities. As an important member of microorganisms, bacteria are closely related to human diseases. Therefore, studying the interaction between bacteria plays an important role in microbiome research. There are a large number of published medical literatures that contain small-scale data about the interactions between bacteria. These literatures often record the bacteria interactions discovered by co-cultural experiments for two or more species. Mining and organizing them into databases will provide reliable support for microbiome research. Named entity recognition (NER) is an essential step of interaction extraction (IE) by automatically identifying bacterial entities in the text. In this paper, we propose a method based on language model for identifying bacteria named entities. Using the language model to learn the semantic information between words, the F1 score reaches 96.14%, which is the best performance in bacteria NER compared with the previous experimental results.
Classification of Segmented Milkfish Eyes using Cosine K-Nearest Neighbor

The classification of milkfish freshness based on the eyes is supported by the results of correct fish eye segmentation. Generally, the problems faced by segmentation results are many objects with similar characters and similar gray intensity. Segmentation with K-Means produces K layers of binary image according to the selected K cluster. As a result, many objects appear as the result of segmentation. Among all objects appear, the fish eye has a special character, where it is round black. The problem faced by classic K-Nearest Neighbor (KNN) in classification is sensitive to noise when using low K while using high K the classification performance falls to the most class that shouldn't as the result. We propose Cosine KNN (CosKNN) to solve the classic KNN problem where the classification results aren't taken from the most class of nearest neighbor. CosKNN gives soft value that represents the belonging level of each class to the testing data. To evaluate the performance of CosKNN, we use precision and recall. The experiment result shows that the CosKNN achieves performance both precision and recall of 97.93% and 91.15%, respectively, all with shape features. Especially on precision performance, CosKNN achieves the highest performance compared to other methods, CosKNN, classic KNN and K-SVNN achieve 97.93%, 96.20%, and 95.77%. While in recall performance, KNN achieves the highest performance compared to other methods, CosKNN, classic KNN and K-SVNN achieve 91.15%, 92.10%, and 90.76%, respectively, all with shape features.