Reviewerly/exHarmony huggingface dataset:
Simplifying TugGraph using zipping algorithms

Graphs are an invaluable modelling tool in many domains, but visualising large graphs in their entirety can be difficult. Hierarchical graph visualisation – recursively clustering a graph’s nodes to view it at a higher level of abstraction – has thus become popular. However, this can hide important information that a user needs to understand a graph’s topology, e.g. nodes’ neighbourhoods. TugGraph addressed this by ‘separating out’ a given node’s neighbours from their hierarchy ancestors to visualise them independently. Its original implementation was straightforward, but copied parts of the hierarchy, making it slow and memory-hungry. An optimised later version, which we refer to as FastTug, avoided this, but at a cost in clarity. Optimising TugGraph without sacrificing clarity is difficult because of the need to keep every hierarchy node connected, a common challenge for graph hierarchy editing algorithms. Recently, this problem has been addressed by ‘zipping’ algorithms, multi-level split/merge algorithms that preserve hierarchy node connectedness and can be built upon for higher-level editing. In this paper, we generalise the original unzipping algorithms to implement SimpleTug, a simple, modular version of TugGraph that is easy to understand and implement, and even faster and more memory-efficient than FastTug. We formally prove its equivalence to FastTug, and show how both can be parallelised. Using our millipede hierarchical image segmentation system, we show experimentally that both the serial and parallel versions of SimpleTug are around 25% faster than their FastTug counterparts, whilst using considerably less memory. Finally, we discuss the interesting theoretical connections between TugGraph and zipping, and suggest ideas for further work.
Multi Object Tracking Based on Detection with Deep Learning and Hierarchical Clustering

Multi object tracking is one of the hotspots of computer vision research. Tracking-by-detection is a common approach to multi-object tracking. With the development of machine learning, especially of deep leaning method, the basis for a tracker becomes much more reliable. The method proposed in this paper is based on detection with convolutional neural network and tracking with hierarchical clustering. Experimental evaluation shows that the proposed method achieves overall competitive performance at high frame rates.
Feature Based Opinion Mining on Hotel Reviews Using Deep Learning

Social media and networks are being used excessively these days for commenting on any news, product, services etc. We have Facebook, Twitter, LinkedIn for sharing of information with others. The data on these social media sites are in the form of text and everyday many users are commenting on these networks hence we are producing zettabytes of data each day. These data need to be managed properly so that they can be used for the benefit of companies, product manufacturers etc. The analysis of data can be done and find whether people are commenting in favor or against any particular product or service. This is known as mining of opinions. The analysis of priorities of customer’s, their needs and their attitude towards any service or product, analyzing and extracting data from reviews of customers is the primary goal of this paper. For targeting the mentioned goal, the research has focused on the approach of deep learning and NN to find the polarity of reviews of customers in the Hotel domain. Research in this dissertation explores new techniques to aggregation, automated analysis, and extraction of opinions and features of customer reviews from text by using data mining and natural language processing techniques. It focuses on aspect-based opinion mining of customer reviews from hotel booking websites. It discusses about customer reviews characteristics and describes different approaches to extract aspects and their corresponding sentiments. The results of this research show that the proposed model using CNN networks, is capable to find the score as well as polarity of reviews with 98.22% accuracy of combined reviews, 95.345% accuracy for positive reviews and 96.145% accuracy for negative reviews and the output obtained is comparatively more accurate than other given techniques.
Workflow Complexity in Constructive Cost Model II

In developing software for the automation of an organization's business processes, it is important to include the complexity of business processes as one of the cost drivers when determining the estimate costs of project. In this paper, we propose a model for calculating the complexity of business processes using cyclometric complexity which was initially used as a quantitative measure of the logic complexity of a program. Furthermore, the results of measuring the complexity of the workflow are attached to one of the multiplier efforts in constructive cost model (COCOMO) II.
A demonstration for the detection of sign language

People having physical limitations such as speech and hearing impairment are often unable to convey their message properly, which leads to them being left out in many aspects of life. To help those people express themselves in a better and easier way we have developed the sign language detection application. We have developed a translator that takes hand gestures and input and give the equivalent alphabet as output, which will help those people to communicate. Convolutional neural network was used for image recognition and classification in order to detect the hand from other objects in the screen and classify the sign represented by the hand gesture at any given time, thus enabling us to translate the signs into English alphabets. Thus, this application can be used by the specially abled people to communicate with others in a more efficient and hassle freeway. The tools used were anaconda, python, Opencv, tensorflow, matplotlib, numpy, convolutional neural networks.
Reverse Engineering of Relational Database Schema to UML Model

For the evolution and maintenance of legacy systems, it is essential that they are reverse engineered. It is becoming important because in many Legacy systems, suitable documentation is not available which makes maintenance. The proposed approach extracts hidden information which is useful for maintaining a software. Application modeling and data modeling are essential for the development of the software and are used for designing application and database separately. Generally, in software systems application layer is the object-oriented programming language and database is the backend store. Modeling of these layers separately creates inconsistency. This paper proposes a technique to reverse engineer the database schema to UML model which will incorporate modeling of an object-oriented technology on top of purely relational database. Using this conversion of database schema to UML diagram, the gap between these layers is being bridged. It will provide the understanding of the legacy system in a better way. The database schema is converted into XMI format which can be imported in a UML modeling tool. The proposed approach converts the SQL database schema as well as more frequently used SELECT statements for the schema into UML model which is in the form of XMI. By importing the generated XMI file into MagicDraw, UML model is graphically represented.
Incingarette

Previous work in HCI about personal informatics and behavior change suggests that representing data in intuitive metaphors and meaningful stories on glace-able displays should be considered to complement typical data visualization for daily user reflection and understanding. Informed by insights from social psychology, providing information regarding one's behavior (i.e., feedback) should (1) link behavioral data to positively or negatively valued outcomes; (2) show changes in the outcomes over time; and (3) include measures for pursuing different outcomes. Grounded in metaphor and blending theories from embodied cognition, we suggest metaphorically mapping less intuitive behavior-outcome links with more direct cause-effect relations from seemingly unrelated yet familiar domains. A behavior and a comparable scenario are cognitively compressed into an "animated parable". This paper describes the theoretical framework and design guidelines, and reports the development of a blended concept, "incingarette" (cigarette and incinerator), and its prototype. The work-in-progress informs updates on design recommendations.
Stacked-SVM

Recent years witnesses the rampancy of telephone fraud along with the development of modern communication technology. The challenges from telephone fraud identification mainly exist in two aspects: (1) the telephone fraud records are typical imbalanced data due to the characteristic of heterogeneous spatial-temporal distribution, leading to bias towards predicting the majority class; (2) traditional evaluation metrics in imbalanced learning mainly rely on accuracy or precision, neglecting the completeness of telephone fraud identification in real-world implementations.
Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Weighted 3D Markov Random Field

Segmenting the cerebral vessels precisely from the time-of-flight magnetic resonance angiography (TOF-MRA) images is important for the diagnosis and therapy of the cerebrovascular diseases. Since the complex structures of cerebral vessels, the current cerebrovascular segmentation algorithms based on statistical model have less accuracy for stenotic vessels and are quite time-consuming. In this paper, we propose a novel automatic cerebrovascular segmentation algorithm based on focused Multi-Gaussians (FMG) model and weighted 3D Markov Random Field. As far as our knowledge, this is the first time to adopt multi-Gaussians distributions as vascular model with the purpose of modeling the vascular tissue more accurately. Furthermore, the fitting range is narrowed to local region related to vessels in order to make the model focus on the vascular tissue and simplify the finite mixture model. To incorporate precise local character of images to the model, we design a new weighted 3D MRF by a weighted neighborhood system (W-NBS). Finally, the particle swarm optimization (PSO) algorithm of parameter estimation has been implemented parallelly based on GPUs and the execution speed was improved by about 70 times. The experimental results show that the algorithm can produce detailed segmentation results especially for stenotic vessels.
Competence-Based Language Expert Network for Translation Business Process Management

Nowadays large companies have a lot of business processes that cannot be automated by existing information systems on the market. Some processes require development of smart methods and approaches to support them and reduce the human interaction. The paper considers a translation business process. The idea is that translation has to be implemented by the translation agency but it has to be approved by the local company experts. The business process requires task to find the expert group that can approve the translation, organize their communication during the work as well as to motivate them to implement the task in time. The presented approach allows increasing the motivation and improving the efficiency and productivity of employees without significant company expenses. Also, the competency management technology is widely used, and it provides efficient use of the employees and organizations strengths.
Liver Histopathological Image Retrieval Based on Deep Metric Learning

Histopathological image retrieval aims to search histopathological images sharing similar content with the query image, which could provide pathologists with an approach to easily obtain similar diagnostic cases for reference. Recent histopathological image retrieval methods are usually based on CNN feature extractors, which require a large amount of annotated data for training. Besides, most of existing methods could not define a reasonable similarity metric for histopathological images. In this paper, we apply deep metric learning to liver histopathological image retrieval. We construct a model based on mixed attention mechanism and train the model with a modified version of multi-similarity loss, which enables embedding vectors of similar images in the given metric space to be closer and dissimilar ones to be far from each other. Additionally, our model can be well fitted with limited data. Finally, we evaluate the proposed method with our own established liver histopathological image dataset. Compared with several published methods, our model shows higher performance.
Low Complexity Sigmoid Function Implementation Using Probability-Based Piecewise Linear Function

In this paper, a piecewise linear (PWL) sigmoid function approximation based on the neurons' value distribution probability in each layer is proposed to improve the network recognition accuracy with only addition circuit. The method divides the sigmoid function into three fixed regions, and then the curve in each region is segmented into different length sub-regions based on the neurons' value distribution probability to reduce the approximation error and improve the recognition accuracy. Experiments performed on FPGA-XC7A200T for MNIST and CIFAR-10 datasets show that the recognition accuracies of the proposed method in deep neural network on MNIST and convolutional neural network on MNIST and CIFAR are 97.45%, 98.42% and 63.14%, respectively, up to 0.84%, 0.57% and 1.99% higher than other methods with only addition circuit.
Comparative Study of Data Augmentation Strategies for White Blood Cells Classification

The paper is devoted to the study of strategies that can be applied to unbalanced data in solving the task of classifying white blood cells. The main goal of the proposed paper is to determine the best approach to combat data imbalance when working with images of blood cells. Description of classical and state-of-the-art methods to deal with imbalance is given. In the course of the research, biomedical data is collected, annotated, and preprocessed, as well as selected strategies are applied to form datasets. Base model of artificial neural network for image classification is selected and built. Also, the dependence of trained models accuracy on the applied strategies is studied. Thus, the best approach to data augmentation for white blood cells classification problem is determined.
Kirsch Direction Template Despeckling Algorithm of High-Resolution SAR Images-Based on Structural Information Detection

In order to overcome the drawback of the traditional Kirsch template despeckling usings fixed windows, an improved Kirsch direction template despeckling algorithm, based on structural information detection, is proposed for high-resolution synthetic aperture radar (SAR) images. First, the point targets are detected and preserved in the current region. Second, the window is enlarged adaptively based on the statistical characteristics of the local region. Finally, the window finally obtained is classified. The averaged filter is directly adopted if the region is homogeneous, or else the Kirsch template filter is used. Combining point target detection, adaptive windowing, and region classification, altogether the proposed algorithm can effectively improve the performance of the traditional Kirsch direction template despeckling. Despeckling experiments on simulated and real high-resolution SAR images demonstrate that the Kirsch direction template despeckling algorithm based on structural information detection can not only sufficiently suppress speckle in homogenous and edge regions, but also effectively preserve point targets and edge information, leading to good despeckling results.
Effectiveness of Haar-like Features and ViBe Algorithm for Detecting Jaywalkers

Despite many attempts, common techniques used in pedestrian detection still encounter problems such as high miss rate and false detection rates with images and videos. Several studies have been conducted in the field of object detection, and there are still other existing gaps such as, detection hampered due to lighting, object size distortion caused by angle and perspective projection while taking the video footage, and ghosting in certain frames due to sudden movements of static objects. This study focuses in devising a model that detects and classifies pedestrians crossing from other moving objects within a given region of interest (ROI). The proponents utilized ViBe algorithm that covered the process of segmenting the foreground objects (pedestrians crossing) from the background, while to process the segmented images, Haar-like features was utilized that focused on the characteristics of the different human body parts which determined if the segmented moving objects are pedestrians. The scope of this study did not include detection of pedestrians that are using skateboards, wheelchairs, canes, and other walking aids that are utilized while crossing the road. This study determines the volume of people who are really crossing in the designated pedestrian lane. A Region of Interest (ROI) which is the pedestrian lane of any design, was considered in quantifying the number of people crossing inside and outside the said ROI.
Hardware Implementation of Convolutional Neural Network for Face Feature Extraction

As an important feed-forward neural network in the field of deep learning, convolutional neural network (CNN) has been widely used in image classification, face recognition, natural language processing and document analysis in recent years. CNN has a large amount of data and many multiply and accumulate (MAC) operations. With the diversity of application files, the channel sizes and kernel sizes of CNN are diverse, while the existing hardware platform mostly adopts the average optimization technology, which causes the waste of computing resources. In this paper, a special configurable convolution computing array is designed, which contains 15 convolution units, each PE contains 6×6 MAC operations, it can be configured to calculate three different kernel sizes of 5×5, 3×3 and 1×1. At the same time, pipeline structure is used to synchronize convolution and pooling operations, which reduces the storage of intermediate results. We design the special hardware structure to optimize DeepID network. Tested on Altera Cyclone V FPGA, the peak performance of each convolution layer at 50 MHz is 27 GOPS, and the average utilization of the MAC is 92%.
Obstacle Avoiding Path Following based on Nonlinear Model Predictive Control using Artificial Variables

This work presents a model predictive formulation for obstacle avoiding path following control for constrained vehicles. The obstacles are introduced as soft constraints in the value function, in order to maintain the convexity of state and output spaces. In this formulation, the path following and obstacle avoidance tasks may introduce local minima solutions -due to their competing costs- known as corner conditions. In order to address this problem, a heuristic switch in the form of additional decision variables is introduced into the cost function. The proposed solution is based on an extension of Model Predictive Control (MPC) by using Artificial Variables. An additional cost term is included in order to prevent early stops in the path following task. Simulations results considering an autonomous vehicle subject to input constraints are carried out to illustrate the performance of the proposed control strategy.
Why do Enterprises Adopt Natural Language Processing Services? Startups’ Landscape and Opportunities in Artificial Intelligence

The large number of internet users in Indonesia contributes to Indonesia's growth potential in general in the digital economy. This rapid growth urged the government to plan for the industrial 4.0 revolution with artificial intelligence (AI) technology as its basis. Innovations in the field of AI come from many startup companies. Despite many benefits, only 40 percent companies in Asia-Pacific utilized the AI technology in functional areas, such as chatbot services and customer service robots. This gap needs to be addressed considering that more than 87 percent of internet users in Indonesia utilize chat services and social media. AI utilization can be implemented by using the services of companies engaged in AI, including startups. However, AI-based startups in Indonesia are not yet mapped. Furthermore, practical impact of AI implementation needs to be surveyed as knowledge to implement AI in business. This research mapped 68 Indonesian startups of AI service providers. In addition, this study evaluates the implementation of AI in the Natural Language Processing (NLP) category for companies from the perspective of the service provider. Of all 8 mapped NLP service providers, 4 of them are chosen as the research objects. The impacts of its implementation are categorized into eight categories: motivation, profit, interest, change in strategy, competition, satisfaction, trust, and ethics. Recommendations are given to companies related to NLP with the most important things which are defining purposes and dare to try.
Geology and Petrology of Metavolcanic Rocks in the Neoarchean Mundo Novo Greenstone Belt, Eastern São Francisco Craton, NE Brazil: Tectonic Setting Considerations

The Neoarchean Mundo Novo greenstone belt (MNGB), in theeastern boundary of the Gaviao Block [1-3] and eastern portion ofthe Sao Francisco Craton, and lying within the Contendas-Jacobina..
Local-adaptive and outlier-tolerant image alignment using RBF approximation

Image alignment is a crucial step to generate a high quality panorama. The state-of-the-art approaches use local-adaptive transformations to deal with multi-view parallax, but still suffer from unreliable feature correspondences and high computational cost. In this paper, we propose a local-adaptive and outlier-tolerant image alignment method using RBF (radial basis function) approximation. To eliminate the visible artifacts, the input images are warped according to a constructed projection error function, whose parameters are estimated by solving a linear system. The outliers are efficiently removed by screening out the abnormal weights of RBFs, such that better alignment quality can be achieved compared to the existing approaches. Moreover, a weight assignment strategy is introduced to further address the overfitting issues caused by extrapolation, and hence the global projectivity can be well preserved. The proposed method is computationally efficient, whose performance is verified by comparative experiments on several challenging cases.
Robust Visual Tracking with Channel Weighted Color Ratio Feature

Robust visual tracking is an important and challenging problem due to various challenging factors and computational constraints. Recent studies have shown that taking advantage of color information is a simple and effective way to improve correlation-based tracker performance. In this paper, we propose a 1-channel color feature called color ratio (CR) feature inspired by mean-shift-based tracking algorithms, which is more efficient and effective than currently widely used 10-channel color-naming features. We then concatenate 1- channel CR, 13-channel HOG and 1-channel gray together to get totally 15-channel features for efficient DCF tracking. During feature concatenation process, we find that weighting between different feature channels can improve the tracking performance notably. Finally, correlation-based responses and CR-based responses are fused to further boost tracker robustness. Experimental results demonstrate that our feature and fusion strategy can achieve superior performance while attaining real-time performance.