Reviewerly/exHarmony huggingface dataset:
Self-Adaptive Superpixels Based on Neural Network Models

In this paper, the self-adaptive superpixels are generated based on a neural network model. Superpixels are clusters of pixels, which can simplify the expression of images. Superpixels are widely used in the field of video/image processing. However, existing algorithms are mainly based on hand-crafted features, which will lose the details of the images. We use the neural network model to extract the deep features of the pixels instead of the hand-crafted features. A predicted object area is obtained according to the results of the neural network models. Self-adaptive superpixels are generated by the clustering algorithm based on the deep features of the pixels and the predicted object area. Finer superpixels are generated in the object area, and coarser superpixels are generated in background area. The generated self-adaptive superpixels can represent the image in a concise way and improve the segmentation accuracy. Experimental results show that the proposed algorithm outperforms several state-of-the-art methods on the BSDS500 dataset.
CO-CREATION WORKSHOP FOR INTERFACE DESIGN - DESIGNING INNOVATIVE HMI FOR AUTOMATED VEHICLES

&#x0D; &#x0D; &#x0D; The increasing growth in the development of new technology is changing the way users interact with products. Thus, designers must re-think the human-machine interfaces (HMI) for communicating information regularly. Co-creation – the process of joint creation of users and designers – is an approach to product and experience development that can substantially benefit the generation of innovative product proposals. The present paper aims to assess the feasibility of co-creation workshops for the development of proposals for interfaces of Artificial Intelligence derived products through a case study on interfaces for autonomous vehicles. For this purpose, two sessions of co-creation workshops using group brainstorming techniques were conducted with undergraduate and graduate Design students and professors, who are specialists in interaction and information design. The results showed that the interfaces proposed by the designers were communicated information in a wide range of ways, making use of existing technologies to present messages in an innovative manner, meeting autonomous vehicles’ users’ needs.&#x0D; &#x0D; &#x0D;
What is C++?

C++ is a programming language. A standardized, general-purpose, object-oriented, compiled language. C++ is accompanied by a set of functions and containers called the C++ Standard-Library. Bjarne Stroustrup created C++ as an extension to a C programming language. Still, C++ evolved to be a completely different programming language.
Chladni Figures in Modal Analysis of a Double-Panel Structure

Analysis of the structural vibration, under the sound excitation is an important part of the quality assurance during the design process of devices. One of the most commonly used method is Laser Doppler Vibrometry (LDV). However, under the rapid fluctuations of temperature, structural resonances are shifted into the other frequencies. In such situation LDV method may be inconvenient, due to the scanning time. In this paper the authors proposed Chladni figures to modal analysis of the double-panel structure, excited by the loudspeaker enclosed inside the casing with a rigid frame. Double-panel structure has been proven to be particularly useful for noise and vibration reduction applications. Vision images, obtained during the experiments are converted to binary patterns, using GLCM matrix, and compared with simulations performed in ANSYS.
Cancer Risk Analysis Based on Improved Probabilistic Neural Network

The problem of cancer risk analysis is of great importance to health-service providers and medical researchers. In this study, we propose a novel Artificial Neural Network (ANN) algorithm based on the probabilistic framework, which aims to investigate patients' pattern associated with their disease development. Compared to the traditional ANN where input features are directly extracted from raw data, the proposed probabilistic ANN manipulates original inputs according to their probability distribution. More precisely, the models of Naive Bayes and Markov chain are used to approximate the posterior distribution of the raw inputs, which provides a useful estimation of subsequent disease development. Later those distribution information is further leveraged as additional input to train ANN. Additionally, to reduce the training cost and boost the generalization capability, a sparse training strategy is also introduced. Experimentally, one of the largest cancer-related datasets is employed in this study. Compared to state-of-the-art methods, the proposed algorithm achieves a much better outcome, in terms of the prediction accuracy of subsequent disease development. The result also implies the potential impact of patients' disease sequence on their future risk management.
THE EFFECTIVENESS OF USING MEMRISE APPLICATION IN TEACHING VOCABULARY

This research is based on the use of the Memrise application in a vocabulary study of seventh-grade students. This research was carried out based on a preliminary study which showed that students had difficulty learning English because of the lack of understanding of the vocabulary they had, and the method used by the teacher to teach vocabulary is still in a conventional way. The subjects of this study were 35 students consisting of 28 male and 10 female seventh-grade students of SMPN 1 Cililin. The purpose of this study is to improve students' understanding and achievement of vocabulary through the use of the Memrise application. Vocabulary tests and questionnaires were used as the instruments of eliciting the data. The results of the test and questionnaire study showed an increase in grades, and students' interest and motivation after using the memrise application in vocabulary learning. From the results obtained, it was concluded that memrise application is recommended to be used as an alternative method in teaching vocabulary. Besides students, with this Memrise application, it can add to the teacher's insight about the use of methods in teaching in the classroom.
The Influence of Hadiths and Islamic Scholars’ Opinions on Current Miswak Practice

Miswak has been used as a tooth cleaning tool for thousands of years throughout the world. Following the introduction of the toothbrush, the usage of miswak is reducing but still exist especially among Muslims. Muslims are still using it as it is part of sunnah and is highly recommended by the Prophet P.B.U.H. Although there are numerous hadiths on the importance of miswak use, there are limited authentic resources on the methods of its usage. The technique of using miswak nowadays might be different from the method recommended in those authentic resources. To investigate the practice of miswak among current users and the miswak practice according to hadiths and Islamic scholars. A cross-sectional descriptive study among miswak users in Sri Petaling, Selangor using purposive sampling. Questionnaires on various aspects of miswak practice as oral hygiene tools were distributed among consented participants. The results were analysed descriptively and compared with miswak practice mentioned in hadiths and scholar’s recommendation. There were 37 subjects participated in the study. Several routines and practices explained in hadiths and Islamic scholars’ recommendations were practised by the participants. The use of miswak prior to ablution and prayer was highly recommended by the Prophet P.B.U.H and was practised by all participants (100%). Half of the participants used miswak upon waking up from sleep (59%) and 76% of them clean their tongue with miswak. All participants use miswak that is not too dry or too wet as suggested by an Islamic scholar. Majority of them (83.8%) cut the miswak stick before they use it and 35% of them soaked the miswak stick before use. Most of the current miswak users are practising the miswak as oral hygiene tool following the guidelines mentioned in hadiths and recommendations by Islamic scholars although there are some parts of miswak practice mentioned in hadiths and scholars’ recommendations were found not to be practised by the subjects and vice versa.
Asessing Teacher’s Perception in the Use of Pre-Reading Activities in EFL Classroom

This study was aimed at finding out kinds of pre-reading activities commonly used, and teachers’ perceptions on the use of pre-reading activities. Survey method was used as research method. To collect data, questionnaire and observation checklist were used as research instruments. There were 33 English teachers from 11 Junior High Schools as participants. The result of the study revealed that pre-reading activities were used by English teachers to engage students in reading activities. Pre-reading activities such as asking questions, identifying text structure, previewing, showing picture, brainstorming, and making prediction were the preferable activities applied by teachers. The teachers believed that the use of pre-reading activities in teaching reading is an effective way to improve students’ reading comprehension.
Mining Productive Itemsets in Dynamic Databases

Discovering frequent itemsets is a data analysis task used in numerous domains. It consists of finding sets of items (itemsets) that frequently appear in a set of database records (also called transactions). Though discovering frequent itemsets is useful, it can produce a large amount of spurious patterns. As a result, the user may spend a great amount of time to analyze the itemsets found by a frequent itemset mining algorithm to find truly interesting patterns. Hence, in recent years, a key research topic has emerged which is to discover statistically significant patterns in databases. The most popular model for identifying itemsets that are statistically significant is to discover non-redundant productive itemsets. The state-of-the-art algorithm to extract this set of patterns is OPUS-Miner. A key drawback of that algorithm is that it is designed to be applied to a static database. Moreover, a second drawback of OPUS-Miner is that it discovers all patterns in a database. In other words, the user cannot search for itemsets containing some specific items. This paper addresses these issues by defining the novel problem of discovering targeted non redundant productive itemsets in dynamic databases. An algorithm named IDPI+ (Interactive Discovery of Productive Itemsets) is presented, storing transactions in a tree structure, which can then be interactively queried to identify productive and non redundant itemsets containing specific items. A structure named Query-Tree is also introduced to process many queries at the same time. Moreover, to handle dynamic databases, efficient transaction insertion and deletion algorithms are provided to update the tree. It was observed in an experimental evaluation on benchmark datasets containing various types of data that IDPI+ can handle thousands of queries per second on a desktop computer. Moreover, it was found that IPDI+ is more than an order of magnitude faster than a baseline algorithm.
On Incorporating Structural Information to improve Dialogue Response Generation

We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources.Specifically, given a conversation around a movie, the task is to generate the next response based on background knowledge about the movie such as the plot, review, Reddit comments etc.This requires capturing structural, sequential, and semantic information from the conversation context and background resources.We propose a new architecture that uses the ability of BERT to capture deep contextualized representations in conjunction with explicit structure and sequence information.More specifically, we use (i) Graph Convolutional Networks (GCNs) to capture structural information, (ii) LSTMs to capture sequential information, and (iii) BERT for the deep contextualized representations that capture semantic information.We analyze the proposed architecture extensively.To this end, we propose a plug-and-play Semantics-Sequences-Structures (SSS) framework which allows us to effectively combine such linguistic information.Through a series of experiments, we make some interesting observations.First, we observe that the popular adaptation of the GCN model for NLP tasks where structural information (GCNs) was added on top of sequential information (LSTMs) performs poorly on our task.This leads us to explore interesting ways of combining semantic and structural information to improve performance.Second, we observe that while BERT already outperforms other deep contextualized representations such as ELMo, it still benefits from the additional structural information explicitly added using GCNs.This is a bit surprising given the recent claims that BERT already captures structural information.Lastly, the proposed SSS framework gives an improvement of 7.95% BLEU score over the baseline.
Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context

In this work, we improve the intent classification in an English based e-commerce voice assistant by using inter-utterance context.For increased user adaptation and hence being more profitable, an e-commerce voice assistant is desired to understand the context of a conversation and not have the users repeat it in every utterance.For example, let a user's first utterance be 'find apples'.Then, the user may say 'i want organic only' to filter out the results generated by an assistant with respect to the first query.So, it is important for the assistant to take into account the context from the user's first utterance to understand her intention in the second one.In this paper, we present our approach for contextual intent classification in Walmart's e-commerce voice assistant.It uses the intent of the previous user utterance to predict the intent of her current utterance.With the help of experiments performed on real user queries we show that our approach improves the intent classification in the assistant.
Cross-spectral stereo matching for facial disparity estimation in the dark

Numerous applications on human faces hinge on depth information. Often, facial stereo matching provides an opportunity to estimate disparity without active projectors. However, existing algorithms are less effective at night due to unclear texture and severe noises in RGB images. In this paper, we address this problem by estimating facial disparity maps from NIR-RGB pairs. We develop a neural network composed of a multi-spectral transfer network (MSTN) and a disparity estimation network (DEN). MSTN is used to produce a pseudo-NIR image aligned with the RGB view using a spatially weighted sum on the NIR one by a kernel prediction network (KPN). As the pseudo-NIR and the NIR images share the same appearance, the facial disparity map is predicted by the proposed DEN with the same-spectral stereo pair. The whole network can be trained in an end-to-end manner and the experimental results demonstrate that it performs favorably against state-of-the-art algorithms on both synthetic and real data.
Sensitive Labels Matching Privacy Protection in Multi-Social Networks

In social networks, some private information, such as the personal name, age gender, the number of friends, can be obtained by others. This paper defines a combination degree-neighborhood label matching attack model based on group maps obtained from multi-social networks. Based on the heuristic combination degree attack model, the target combination degree and neighborhood labels are used as the background knowledge of the attacker to obtain the candidate vertices set. The singularity of the sensitive label matching results will expose the sensitive information of the vertex being attacked. In order to solve this privacy attack, this paper proposes a group graph sensitive label generalization L diversity algorithm. This algorithm reduces the probability of sensitive labels being identified by designing a group map sensitive label generalization tree. According to the background knowledge, the number of sensitive labels in the candidate set and the number of sensitive labels obtained by matching are not less than L, so as to protect the sensitive information of the attacked target. The algorithm was evaluated by using three sets of data with different ratios. The experiment results show that the privacy protection algorithm effectively prevents sensitive label privacy attacks consisting of combination degree-domain label matching and better maintains the availability of graph data.
Exploiting Magnitude and Phase Aware Deep Neural Network for Replay Attack Detection

Magnitude and phase aware deep neural network (MP aware DNN) based on Fast Fourier Transform information, has recently been received more attention to many speech applications. However, little attention has been paid to its aspect in terms of replay attack detection developed for the automatic speaker verification and countermeasures (ASVspoof 2017). This paper aims to investigate the MP aware DNN as a speech classification for detecting non-replayed (genuine) and replayed speech. Also, to exploit the advantage of the classifier-based complementary to improve the reliable detection decision, we propose a novel method by combining MP aware DNN with standard replay attack detection (that is, the use of constant Q transform cepstral coefficients-based Gaussian mixture model classification: CQCC-based GMM). Experiments are evaluated using ASVspoof 2017 and a standard measure of detection performance called equal error rate (EER). The results showed that MP aware DNN -based detection performed conventional DNN method using only the magnitude/phase features. Moreover, we found that score combination of CQCC-based GMM with MP aware DNN achieved additional improvement, indicating that MP aware DNN is very useful, especially when combined with the CQCC-based GMM for replay attack detection.
Tight Bounds on the Coefficients of Consecutive k-out-of-n:F Systems

In this paper we compute the coefficients of the reliability polynomial of a consecutive-k-out-of-n:F system, in Bernstein basis, using the generalized Pascal coefficients. Based on well-known combinatorial properties of the generalized Pascal triangle we determine simple closed formulae for the reliability polynomial of a consecutive system for particular ranges of k. Moreover, for the remaining ranges of k (where we were not able to determine simple closed formulae), we establish easy to calculate sharp bounds for the reliability polynomial of a consecutive system.
Multiple Morphological Constraints-Based Complex Gland Segmentation in Colorectal Cancer Pathology Image Analysis

Histological assessment of glands is one of the major concerns in colon cancer grading. Considering that poorly differentiated colorectal glands cannot be accurately segmented, we propose an approach for segmentation of glands in colon cancer images, based on the characteristics of lumens and rough gland boundaries. First, we use a U-net for stain separation to obtain H-stain, E-stain, and background stain intensity maps. Subsequently, epithelial nucleus is identified on the histopathology images, and the lumen segmentation is performed on the background intensity map. Then, we use the axis of least inertia-based similar triangles as the spatial characteristics of lumens and epithelial nucleus, and a triangle membership is used to select glandular contour candidates from epithelial nucleus. By connecting lumens and epithelial nucleus, more accurate gland segmentation is performed based on the rough gland boundary. The proposed stain separation approach is unsupervised, and the stain separation makes the category information contained in the H&amp;E image easy to identify and deal with the uneven stain intensity and the inconspicuous stain difference. In this project, we use deep learning to achieve stain separation by predicting the stain coefficient. Under the deep learning framework, we design a stain coefficient interval model to improve the stain generalization performance. Another innovation is that we propose the combination of the internal lumen contour of adenoma and the outer contour of epithelial cells to obtain a precise gland contour. We compare the performance of the proposed algorithm against that of several state-of-the-art technologies on publicly available datasets. The results show that the segmentation approach combining the characteristics of lumens and rough gland boundary has better segmentation accuracy.
Discretization methods for Bayesian networks in the case of the earthquake

The Bayesian networks are a graphical probability model that represents interactions between variables. This model has been widely applied in various fields, including in the case of disaster. In applying field data, we often find a mixture of variable types, which is a combination of continuous variables and discrete variables. For data processing using hybrid and continuous Bayesian networks, all continuous variables must be normally distributed. If normal conditions unsatisfied, we offer a solution, is to discretize continuous variables. Next, we can continue the process with the discrete Bayesian networks. The discretization of a variable can be done in various ways, including equal-width, equal-frequency, and K-means. The combination of BN and k-means is a new contribution in this study called the k-means Bayesian networks (KMBN) model. In this study, we compared the three methods of discretization used a confusion matrix. Based on the earthquake damage data, the K-means clustering method produced the highest level of accuracy. This result indicates that K-means is the best method for discretizing the data that we use in this study.
Angstrom-Prescott Model for Predicting Global Solar Radiation in Mubi, Nigeria

Researchers In this study Angstrom-Prescott model was used to estimate the global solar radiation based on the monthly mean sunshine hour for Mubi town, Adamawa State.Several models have been proposed to estimate global solar radiation.The Angstrom constants a and b of Angstrom-type correlation used in estimating monthly average global solar radiation was estimated to be 0.27 and 0.54 respectively.The model developed has a good correlation coefficient with r = 0.87, obtained for this analysis shows the model best fits the data.The Angstrom-Prescott model developed in this study can also be applied to other cities to predict global solar radiation.The global solar radiation intensity predicted in this study can also be utilized in design, analysis and performance estimation of solar energy systems, which is gaining significant attention in Nigeria and the world at large.
Multiplicative-additive despeckling in SAR images

Visual and automatic analyses using synthetic aperture radar (SAR) images are challenging because of inherently formed speckle noise. Thus, reducing speckle noise in SAR images is an important research area for SAR image analysis. During speckle noise reduction, homogeneous regions should be smoothed while details such as edges and point scatterers need to be preserved. General speckle noise model contains gamma distributed multiplicative part which is dominant and Gaussian distributed additive part which is in low amount and mostly neglected in literature. In this study, a novel sparsity-driven speckle reduction method is proposed that takes both multiplicative noise model and additive noise model into consideration. The proposed speckle reduction method uses a cost function with multiplicative and additive data terms besides the total variation smoothness term. Also, an efficient and stable numerical minimization scheme is proposed for the proposed cost function that deals with multiplicative and additive noise. Speckle reduction performance of the proposed method is shown on synthetically generated SAR images and real-world SAR images.