Reviewerly/exHarmony huggingface dataset:
Knowledge Based Chatbot with Context Recognition

More than 200 questions enter every day during the acceptance period of new students in EEPIS, where some of the questions use the same answer. A collection of incoming questions can indeed be answered entirely but the work is less effective and efficient because it makes the answerer answer the same question and it is wasting time. This condition make us to create a special chatbot for the admission of new students in EEPIS. This research processes information using the Text Mining method by improving it ability to handle words using synonym dictionary and stemmed stopword dictionary. In the Text Mining process other results will be generated in the form of unknown words which form the basis of dictionary learning synonyms. After the information has been processed, it become base knowledge for chatbot and the best answer search calculation will be performed by using context recognition for filtering related information. Then it will be calculated using binary cosine similiarity and the best calculation results will be used as answers. The results of this study showed an accuracy of 87.09% with testing 176 questions, 25 answers and creating new synonym dictionary in a case study of new student admissions in EEPIS.
Linear Approximation of Deep Neural Networks for Efficient Inference on Video Data

Sequential data such as video are characterized by spatio-temporal correlations. As of yet, few deep learning algorithms exploit them to decrease the often massive cost during inference. This work leverages correlations in video data to linearize part of a deep neural network and thus reduce its size and computational cost. Drawing upon the simplicity of the typically used rectifier activation function, we replace the ReLU function by dynamically updating masks. The resulting layer stack is a simple chain of matrix multiplications and bias additions, that can be contracted into a single weight matrix and bias vector. Inference then reduces to an affine transformation of the input sequence with these contracted parameters. We show that the method is akin to approximating the neural network with a first-order Taylor expansion around a dynamically updating reference point. The proposed algorithm is evaluated on a denoising convolutional autoencoder.
Non-Subsampled Shearlet Transform Based Seismic Data Denoising via Proximal Classifier with Consistency

The sparse transform based seismic denoising is one of the most effective and widely used approaches in seismic data processing. In this paper, we present a novel and unconventional seismic data denoising method based on the non-subsampled shearlet transform (NSST) and proximal classifier with consistency (PCC). NSST is an emerging and excellent multi-scale, multi-direction and optimal sparsity analysis method, which can provide nearly optimal approximation of the decomposed seismic effective signals. Unlike traditional sparse transform based methods that often use a thresholding operator and corresponding inverse transform to denoise seismic data, our proposed method employs a superior performance PCC to classify the NSST coefficients of seismic data before thresholding operator. The added step can effectively divide the NSST coefficients into reflected signal information-related coefficients and noise-related coefficients, which can preserve the edge of reflected signals and keep the information of events intact as much as possible. In addition, we also introduce an adaptive threshold computing method and a soft-thresholding method to achieve seismic data denoising better. A typical synthetic example is used to demonstrate the superior performance of the proposed method over two well-known sparse transform based denoising methods. Besides, we also apply the proposed method to real seismic data, achieving the satisfactory denoising results.
Impact of Non-Proportional Training Sampling of Imbalanced Classes on Land Cover Classification Accuracy with See5 Decision Tree

The accuracy of a supervised classification is highly dependent upon the training samples. This paper is concerned with the impact of non-proportional training data sampling of imbalanced classes on the land cover classification accuracy, using the See5 decision tree classifier. The purpose of this paper is 1) to examine experimentally how the training sampling ratio affects classification accuracy in the imbalanced class scenario; and 2) to determine the best training data sampling ratio for optimal classification performance using a See5 decision tree classifier. To better measure classification accuracy, we propose a balanced accuracy measure of a targeted class, which incorporates both False Positive and False Negative errors to truthfully reflect the accuracy of a targeted class. The study result indicates that balancing the training sample between classes does not necessarily improve the classification accuracy. Instead, selecting a training sample ratio which equals the actual ratio of the coverages of the imbalanced classes will yield the best classification performance.
Novel Technique for Ro-Reference Image Quality Assessment

In this paper, a new measure for no-reference assessment of image quality based on the use of the structural properties of an image is proposed. As a measure, it is proposed to use the estimate of the Weibull distribution shape parameter, obtained by the set of image gradient magnitudes. This measure was previously successfully used to estimate the blurriness of the image. To test the effectiveness of the proposed measure, we used the data from the well-known TID2013 image database, which includes images of various types of distortions and corresponding mean opinion scores of the humans. The ability of the proposed measure is shown to distinguish the types of image distortions, which change the structural properties of an image.
Augmented reality for interactive promotion media at Faculty of Computer Science and Information Technology Mulawarman University

Abstract Showing the design and layout of the building using a model, making this model requires time and accuracy because the model is built on a scale that has been adjusted to the building to be made later, a problem that often occurs in modeling is the suitability of the scale and detail of the model. Augmented Reality as a variation of the virtual environment. Users of AR technology can see the real world, with virtual objects added to the real world. So, users see virtual objects and real objects in the same place. Submission of information by utilizing the three-dimensional visualization technique is very interesting because it can display the 3D model of an object before it is built in real terms. The Faculty of Computer Science and Information Technology Mulawarman University Samarinda has an exterior building design of a new building, but there are no interactive media that can display the exterior of the building as a whole and in detail. This application is able to be used to apply the 3D model of the building as a whole and can be seen from various viewpoints.
Object Recognition Using Convolutional Neural Networks

This chapter intends to present the main techniques for detecting objects within images. In recent years there have been remarkable advances in areas such as machine learning and pattern recognition, both using convolutional neural networks (CNNs). It is mainly due to the increased parallel processing power provided by graphics processing units (GPUs). In this chapter, the reader will understand the details of the state-of-the-art algorithms for object detection in images, namely, faster region convolutional neural network (Faster RCNN), you only look once (YOLO), and single shot multibox detector (SSD). We will present the advantages and disadvantages of each technique from a series of comparative tests. For this, we will use metrics such as accuracy, training difficulty, and characteristics to implement the algorithms. In this chapter, we intend to contribute to a better understanding of the state of the art in machine learning and convolutional networks for solving problems involving computational vision and object detection.
Characterization of hybrid solar panel prototype using PV-TEG module

One limitation of solar panels as a high potential renewable energy is the efficiency decrease due to surface temperature increase of solar panels during the day. To overcome these problems a hybrid solar panel was developed, using a combination of photovoltaic (PV) and thermoelectric generator (TEG) modules. The developed prototype composed of 17cm x 11cm of solar panel, a SP1848-27145 Thermoelectric generator (TEG), heatsink, INA219 sensor, two units of DS18B20 temperature sensor, and Arduino Uno R3 microcontroller as a control system. Through sensor characterizations are obtained, that maximum relative error of voltage, current, and temperatures are 2.48%, 2.52%, 2.85%, and 2.75%, respectively. Whereas TEG has a power increasing rate of 1,015 mW/0C. Data acquisition of prototype was carried out in South Tangerang. The highest power produced by the hybrid solar panel of 847,26 mW is obtained at 12:15. The TEG’s additional increase the output power of hybrid solar panels by 5,45%.
Children Activity Descriptions from Visual and Textual Associations

Augmented visual monitoring devices with the ability to describe children's activities, i.e., whether they are asleep, awake, crawling or climbing, open up possibilities for various applications in promoting safety and well being amongst children. We explore children's activity description based on an encoder-decoder framework. The correlations between semantic of the image and its textual description are captured using convolution neural network (CNN) and recurrent neural network (RNN). Encoding semantic information as activation patterns of CNN and decoding textual description using probabilistic language model based on RNN can produce relevant descriptions but often suffer from lack of precision. This is because a probabilistic model generates descriptions based on the frequency of words conditioned by contexts. In this work, we explore the effects of adding contexts such as domain specific images and adding pose information to the encoder-decoder models.
HiCAN

Convolutional neural networks (CNN) are widely used on sequential data since it can capture local context dependencies and temporal order information inside sequences. Attention (ATT) mechanisms have also attracted enormous interests due to its capability of capturing the important parts of a sequence. These two neural networks can extract different features from sequences. In order to combine the advantages of CNN and ATT, we propose a convolutional attention network (CAN), which merges the structure of CNN and ATT into a single neural network and can serve as a new basic module in complex neural networks. Based on CAN, we then build a sequence encoding model with hierarchical structure, "hierarchical convolutional attention network (HiCAN)", to tackle sequence modeling problems. It can explicitly capture both the local and global context dependencies and temporal order information in sequences. Extensive experiments conducted on session-based recommendation (Recommender Systems) demonstrate that HiCAN is able to outperform state-of-the-art methods and show higher computational efficiency. Furthermore, we conduct extended experiments on text classification (Natural Language Processing). The results show that our model can also achieve competitive performance on NLP tasks.
Tensor Decomposition-based Node Embedding

In recent years, node embedding algorithms, which learn low dimensional vector representations for nodes in a graph, have been one of the key research interests of the graph mining community. The existing algorithms either rely on computationally expensive eigendecomposition of the large matrices, or require tuning of the word embedding-based hyperparameters as a result of representing the graph as a node sequence similar to the sentences in a document. Moreover, the latent features produced by these algorithms are hard to interpret. In this paper, we present Tensor Decomposition-based Node Embedding (TDNE), a novel model for learning node representations for arbitrary types of graphs: undirected, directed, and/or weighted. Our model preserves the local and global structural properties of a graph by constructing a third-order tensor using the k-step transition probability matrices and decomposing the tensor through CANDECOMP/PARAFAC (CP) decomposition in order to produce an interpretable, low dimensional vector space for the nodes. Our experimental evaluation using two well-known social network datasets proves TDNE to be interpretable with respect to the understandability of the feature space, and precise with respect to the network reconstruction.
Spatial Enhanced-SSD For Multiclass Object Detection in Remote Sensing Images

Accurate multiclass object detection in remote sensing images is a challenging task, especially for small objects. Since the scales of objects in remote sensing images have a great variance, almost all of the advanced detection methods have shortcomings. Consequently, improving the accuracy of multiclass objects detection has always been the direction of researchers' efforts. In this paper, a spatial enhanced-Single Shot MultiBox Detector (SE-SSD) is proposed. First, to enhance the spatial information, we enlarge the input image channels with embedding oriented-gradients feature maps. Second, the multiple output layers in the backbone network are changed to reduce one pooling operation. Finally, we design a context module to enhance the receptive field for feature layer description in SE-SSD framework. Experimental results on DOTA dataset demonstrate that Spatial Enhanced-SSD method reaches a much higher mean average precision (mAP) than Faster R-CNN, SSD and other classic detection network.
Users’ Comment Mining for App Software’s Quality-in-Use

Only parts of app software users’ comments reflect software quality-in-use. A large number of useless users’ comments will affect the analysis of software quality-in-use. In order to mine the users’ comments about the quality-in-use from the massive app software users’ comments, a mining method of users’ comments based on comment seed is proposed. At first, the users’ comments reflecting the app software’s quality-in-use are mined through initial comment seeds. For the comments that cannot be matched with the comment seed, it will be determined whether they reflect quality-in-use according to the app software’s quality-in-use feature words. Then, the candidate comment modes are extracted from the comments that can be matched with the app software’s quality-in-use feature words. The new comment seeds are extracted based on the candidate comment mode library to further mine the users’ comments related to the quality-in-use. Finally, the experimental results show that the proposed method can effectively mine users’ comments reflecting app software quality-in-use with an average mining rate of 79.49%.
Towards the Gradient Vanishing, Divergence Mismatching and Mode Collapse of Generative Adversarial Nets

Generative adversarial network (GAN) is a powerful generative model. However, it suffers from gradient vanishing, divergence mismatching and mode collapse. To overcome these problems, we propose a novel GAN, which consists of one generator G and two discriminators (D1, D2). Focusing on the gradient vanishing, Spectral Normalization (SN) and ResBlock are first adopted in D1 and D2. Then, Scaled Exponential Linear Units (SELU) is adopted at last half layers of D2 to further address the problem. To divergence mismatching, relativistic discriminator is adopted in our GAN to make the loss function minimization in the training of generator equal to the theoretical divergence minimization. Concentrating on the mode collapse, D1 rewards high scores for the samples from the data distribution, while D2 favors the samples from the generator conversely. In addition, the minibatch discrimination is adopted in D1 to further address the problem. Extensive experiments on CIFAR-10/100 and ImageNet datasets demonstrate that our GAN can obtain the highest inception score (IS) and lowest Frechet Inception Distance (FID) compared with other state-of-the-art GANs.
Missing Data Imputation Techniques for Software Effort Estimation: A Study of Recent Issues and Challenges

Software effort estimation is one the critical aspects of software engineering. It revolves around predicting the required efforts needed to complete a software task. However, any estimation technique or model relies on an input data in which it defines and predicts future values. Missing data and values within such data is a common occurrence in the software development industry and thus it leads to inaccurate predictions or misleading results. Thus, Missing Data is an important aspect of effort estimation models that is required to be addressed. However, Missing Data is not without its gaps and issues. This review aims at elaborating the recent issues and gaps that exist within the missing data and software effort estimation field. This may allow future researchers to get a better grasp and understanding of the inner workings of Missing Data and the methods through which these challenges can be addressed.
Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection

Prototype-based methods are of the particular interest for domain specialists and practitioners as they summarize a dataset by a small set of representatives. Therefore, in a classification setting, interpretability of the prototypes is as significant as the prediction accuracy of the algorithm. Nevertheless, the state-of-the-art methods make inefficient trade-offs between these concerns by sacrificing one in favor of the other, especially if the given data has a kernel-based (or multiple-kernel) representation. In this paper, we propose a novel interpretable multiple-kernel prototype learning (IMKPL) to construct highly interpretable prototypes in the feature space, which are also efficient for the discriminative representation of the data. Our method focuses on the local discrimination of the classes in the feature space and shaping the prototypes based on condensed class-homogeneous neighborhoods of data. Besides, IMKPL learns a combined embedding in the feature space in which the above objectives are better fulfilled. When the base kernels coincide with the data dimensions, this embedding results in a discriminative features selection. We evaluate IMKPL on several benchmarks from different domains which demonstrate its superiority to the related state-of-the-art methods regarding both interpretability and discriminative representation.
Digital -is- Physical: How Functional Fabrication Disrupts Ubicomp Design Principles

Ubiquitous computing has long explored design through the conceptual separation of digital and physical materials. We describe how the emergence of the fabrication community in HCI will challenge these conceptual principles. The idea of digital material in ubicomp ‘hides’ lower level abstractions such as physical architectures and materials from designers. As new fabrication techniques make these abstractions accessible to makers, physical materials are being used to encode digital functionality. Form (traditionally physical) and function (traditionally digital) can be mutually expressed within material design. We outline how emerging printed electronics techniques will enable functional fabrication, current limitations and opportunities for end-user fabrication of functional devices, and implications for new principles that emphasise combined physical design of form and function.
Some Aspects of Implementation of Web Services in Load Balancing Cluster-Based Web Server

We have designed, developed, and implemented SOAP-based web services in load balancing cluster-based web server and carried out load testing over the system. The roles of web services, such as client, broker, and service provider are segregated among different services. The system is monitored through a load testing tool, Mercury LoadRunner. The recorded system metrics are evaluated to study the overall performance and reliability aspects against different massive level of users. This article presents in detail the system architecture, testing methodology, and recorded system metrics. The statistical analysis is carried out to validate and correlate the overall assessment. This article also provides insights of some aspects of system metrics for deploying web services with segregated roles by using a cluster-based web server. It is observed that service with segregated roles is better than the service with merged roles. As a result, performance and reliability of the proposed system is observed to be better than other generic techniques for such deployment.
A Painting Image Retrieval Approach Based on Visual Features and Semantic Classification

Traditional content-based image retrieval technology does not support semantic-based image retrieval, and the target image based on visual features often lacks the understanding of its image semantics. This article mainly studies the use of visual and semantic features of images synthetically and realize the classification and retrieval of image database based on semantic classification of image database. In the visual feature extraction of image, three visual features are introduced. Then, the learning method of support vector machine(SVM) is applied to image classification, and the visual feature retrieval results are filtered by the classification results of image database. A multi-class classifier based on SVM is designed, which uses semantic and visual features to represent image content, trains and learns SVM, and classifies image database semantically by the image classification model. Finally, an image retrieval system is designed and the experiments on color, texture and comprehensive features are performed. The results show that the classification retrieval approach is effective and the retrieval accuracy is improved.
A Finely Focusing Method of SAR Using Very Deep Neural Network

Doppler-parameter estimation is a basic auto-focusing method of SAR. However, as the stability of the platform decreases, the Doppler-parameter method cannot achieve high estimation accuracy, and thus leads to the degradation of image quality. In order to solve the problems, this paper proposed a model of SAR signal finely focusing based on Very Deep Neural Network. After roughly focused, SAR signal is sent to pre-trained network to get finely focused result. Experiments on simulated data and actual data shows that the image quality has been greatly improved, meanwhile, the PSLR and ISLR of the result are -14.5494dB and -13.8190dB, which indicates that this method is suitable for solving SAR signal finely focusing problem.