Reviewerly/exHarmony huggingface dataset:
CBCT image segmentation of tooth-root canal based on improved level set algorithm

Image segmentation of the root canal is important basis for the establishment of a three-dimensional model for application value clinical diagnosis and education. A total of 1554 CBCT images of polymorphic roots of 10 in vivo and 8 in vitro teeth were preprocessed by the adaptive enhancement algorithm of CLAHS and Laplace-γ, respectively. An improved level set algorithm was then used to segment the tooth-root canal image. In the process of curve evolution and convergence of the improved level set algorithm, the evolution of tooth-root canal contour was constrained by adding a new regularization function. Based on the similarity of root canal contours between adjacent sections of a tooth, a self-qualifying method was established to determine the initial contour of the root canal. In addition, a process was set up to analyze the data of a neighboring tooth to improve the segmentation process. Using the stated improvements, a set of root canal image segmentation methods was established for a single tooth based on the improved level set algorithm. Experimental results show that the average accuracy of the improved level set algorithm for root canal image segmentation is 84.7%, while in clinical trials, the average qualified rate is 90.4%.
User Experience Evaluation Methodology in the Onboarding Process: Snapchat Case Study

User experience (UX) has been a hot topic and should be an integral part of the design process – from the early concept to the final product. This article introduces a UX evaluation methodology that helps collect the pragmatic and hedonic attributes of an application in the onboarding process. Moreover, the presented method helps not just to understand the UX (including customers’ unconscious need, desire, and pain points) but also to make the development points clear and straightforward to everyone in the product management team. We focus on a social media platform – Snapchat.
A Proposed Framework for Controlling Cyber- Crime

The usage of the internet as part of our daily routine has brought many advantages and has also facilitated our lifestyle, the activities that required hours or days to be solved like sending a message to a far person has been reduced to a short time. The Internet has not only brought advantages to our community but also disadvantages, among the drawbacks of the internet we have cyber-crime activity. Cyber-crime activity is one of the biggest challenges that the world is facing. It is the concern of everyone as the main goal is to bring cybersecurity to reduce the number of losses that the crime generates across the globe. Cybercrime is happening every second anywhere, and it is causing much damage not only in terms of data or privacy breaches but financially also. Many countries are lacking an excellent policy to tackle cybercrime, and for that reason, the number of crimes is increasing day by day. As much as the number of crimes is increasing, likewise, the number of financial losses is also increasing. In this paper, we proposed a framework that will help us fight against cybercrime regardless of the place we are located by monitoring the activities done on our electronic devices.
Where Is The Emotion? Dissecting A Multi-Gap Network For Image Emotion Classification

Image emotion recognition has become an increasingly popular research domain in the area of image processing and affective computing. Despite fast-improving classification performance in this task, the understanding and interpretability of its performance are still lacking as there are limited studies on which part of an image would invoke a particular emotion. In this work, we propose a Multi-GAP deep neural network for image emotion classification, which is extensible to accommodate multiple streams of information. We also incorporate feature dependency into our network blocks by adding a bidirectional GRU network to learn transitional features. We report extensive results on the variants of our proposed network and provide valuable perspectives into the class-activated regions via Grad-CAM, and network depth contributions by truncation strategy.
Person-Following Algorithm Based on Laser Range Finder and Monocular Camera Data Fusion for a Wheeled Autonomous Mobile Robot

Reliable human following is one of the key capabilities of service and personal assisting robots. This paper presents a novel person tracking and following approach for autonomous mobile robots that are equipped with a 2D laser rangefinder (LRF) and a monocular camera. The proposed method does not impose restrictions on a person’s clothes, does not require a head or an upper body to be within a camera field of view and is suitable for low height indoor robots as well. The algorithm is based on a metric that takes into an account parameters obtained directly from LRF and monocular camera data. The algorithm was implemented and tested in the Gazebo simulator. Next, it was integrated into a control system of the TIAGo Base mobile robot and successfully validated in university environment experiments with real people. In addition, this paper proposes a new criterion of algorithm performance estimation, which is a function of false positives number and traveled distances by a person and by a robot. Further this criterion is used to compare performance of the proposed method with the Multiple Instance Learning (MIL) tracker in simulated and in real world environments.
A Power Efficient Multi-Bit Accelerator for Memory Prohibitive Deep Neural Networks

State of art deep neural network (DNN) models are both memory prohibitive and computationally intensive with millions of connections. Employing these models for an embedded mobile application is resource limited with large amount of power consumption and significant bandwidth requirement (to access the data from the external DRAM). In a custom FPGA hardware the bandwidth access from the DRAM is two to three times higher, compared to the MAC (Multiply-Accumulate) operation. In this paper, we propose a power efficient multi-bit neural network accelerator, where we employ the technique of truncating the partial sum (PSum) results from the previous layer before feeding it into the next layer. We demonstrate that, using our multi-bit accelerator, accuracy is maintained upto bit width of 12. The proposed truncation scheme has 50% power reduction and resource utilization was reduced by 16% for LUTs (Look-up tables), 9% for FFs (Flip-Flops), 19% for BRAMs (Block RAMs) and 7% for Digital Signal Processors (DSPs) when compared with the 32 bits architecture. A large network, AlexNet was used as a benchmark DNN model and Kintex-7 KC705 FPGA was used to test the architecture.
Graph Convolutional Networks for Target-oriented Opinion Words Extraction with Adversarial Training

The task of Target-oriented Opinion Words Extraction aims to extract the corresponding opinion words for a given opinion target from the sentence. Recently, the methods based on recurrent neural networks have shown promising results for this task. However, these approaches only considered the sequential information of the sentences and ignored the syntactic structure. In this paper, we propose a novel graph convolutional network with adversarial training to extract the opinion words. We present a graph convolutional network based on dependency tree to learn the syntactic representation of the input. Besides, we train our model with the mixture of original examples and adversarial examples, which can improve the robustness of the model. We conduct experiments on four benchmarking datasets and the results illustrate that our proposed model consistently outperforms the state-of-the-art methods.
Generating Images in Compressed Domain Using Generative Adversarial Networks

In this article, we present a generative adversarial network framework that generates compressed images instead of synthesizing raw RGB images and compressing them separately. In the real world, most images and videos are stored and transferred in a compressed format to save storage capacity and data transfer bandwidth. However, since typical generative adversarial networks generate raw RGB images, those generated images need to be compressed by a post-processing stage to reduce the data size. Among image compression methods, JPEG has been one of the most commonly used lossy compression methods for still images. Hence, we propose a novel framework that generates JPEG compressed images using generative adversarial networks. The novel generator consists of the proposed locally connected layers, chroma subsampling layers, quantization layers, residual blocks, and convolution layers. The locally connected layer is proposed to enable block-based operations. We also discuss training strategies for the proposed architecture including the loss function and the decoding between its generator and its discriminator. The proposed method is evaluated using the publicly available CIFAR-10 dataset and LSUN bedroom dataset. The results demonstrate that the proposed method is able to generate compressed data with competitive qualities.
Image Tiling For Clustering To Improve Stability Of Constant-Time Color Bilateral Filtering

Bilateral filtering is a typical edge-preserving smoothing and it is used in various applications. The main issue of bilateral filtering is the processing time. In order to solve this problem, constant-time bilateral filtering has been proposed. The constant-time bilateral filter is an effective method for grayscale images, but it takes high cost for color images because of the curse of dimensionality. Some algorithms specialize in constant-time color bilateral filtering for color images by using clustering. However, the clustering has randomness, and computational cost itself is also high. In this paper, we propose an acceleration method of clustering by using K-means ++, tiling, and subsampling, and also achieve improvement of the stability.
A Learning-based Data Augmentation for Network Anomaly Detection

While machine learning technologies have been remarkably advanced over the past several years, one of the fundamental requirements for the success of learning-based approaches would be the availability of high-quality data that thoroughly represent individual classes in a problem space. Unfortunately, it is not uncommon to observe a significant degree of class imbalance with only a few instances for minority classes in many datasets, including network traffic traces highly skewed toward a large number of normal connections while very small in quantity for attack instances. A well-known approach to addressing the class imbalance problem is data augmentation that generates synthetic instances belonging to minority classes. However, traditional statistical techniques may be limited since the extended data through statistical sampling should have the same density as original data instances with a minor degree of variation. This paper takes a learning-based approach to data augmentation to enable effective network anomaly detection. One of the critical challenges for the learning-based approach is the mode collapse problem resulting in a limited diversity of samples, which was also observed from our preliminary experimental result. To this end, we present a novel "Divide-Augment-Combine" (DAC) strategy, which groups the instances based on their characteristics and augments data on a group basis to represent a subset independently using a generative adversarial model. Our experimental results conducted with two recently collected public network datasets (UNSW-NB15 and IDS-2017) show that the proposed technique enhances performances up to 21.5% for identifying network anomalies.
GPU-accelerated image segmentation based on level sets and multiple texture features

Abstract In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.
The Deceptive Potential of Common Design Tactics Used in Data Visualizations

Visualizations effectively communicate data about important political, social, environmental, and health topics to a wide range of audiences; however, longstanding trust of graphs as conveyors of factual data makes them an easy means for spreading misinformation. Scholars in technical and professional communication have not yet conducted needed empirical research into people's perception and comprehension of data visualizations, especially when part of larger information texts [1]. Our study investigated the extent to which people exaggerated the differences between data points when reading graphs about non-controversial topics that used deceptive techniques and/or exaggerated titles. Participants (n=329) were randomly assigned to view one of four treatments for four different graph types (bar, line, pie, and bubble) and then asked to answer a question about each graph. Results show that deceptive techniques used in the graphs (including truncated axes, 3-D exaggeration, and arbitrary sizing), caused participants to misinterpret information in the deceptive vs. control visualizations for all of the graphs regardless of graph type, previous visualization coursework or comfort level with reading graphs. Results also showed that the presence of exaggerated vs. control titles that accompanied each graph did not significantly influence the extent of the misinterpretation. We will discuss the implication of these findings for technical communication as well as avenues of future research already underway that investigate these topics further.
Multi-label Feature Selection Method via Maximizing Correlation-based Criterion with Mutation Binary Bat Algorithm

Multi-label feature selection is a vital pre-processing step to reduce computational complexity, improve classification performance and enhance model interpretability, via selecting a discriminative subset of features from original high-dimensional features. Correlation-based feature selection (CFS) criterion measures the relevance between features and labels, and the redundancy among features, which has been combined with hill climbing and genetic algorithm to execute multi-label feature selection task. However, it is an open problem to search for more effective optimization tools for CFS. In this paper, through adding a mutation operation, we modify existing binary bat algorithm to build its mutation version (MBBA), to adjust the number of "1" components to be a fix size. Then a new multi-label feature selection approach is proposed via maximizing CFS criterion using MBBA, to select a fixed number of discriminative features. Our experiments on four data sets show that our proposed method is superior to three state-of-the-art approaches, according to four sample-based performance evaluation metrics for multi-label classification.
Generating Image Captions based on Deep Learning and Natural language Processing

This model enables an individual to input an image and output a description for the same. The research paper makes use of the functionalities of Deep Learning and NLP (Natural Language Processing). Image Caption Generation is an important task as it allows us automate the task of generating captions for any image. This functionality enables us to easily organize files without paying heed to the task of captioning. It is also important for making dynamic web pages. This paper is for people who are visually impaired or suffer from short sightedness. So, rather than looking at an image with trouble they can easily read the caption generated by this model in a larger format. It can also be used to give description of a video in real time on later implementation for a video.
Text Classification using Triplet Capsule Networks

Most existing methods only consider the local features of the samples, and their experimental results show better performance than traditional Non-deep learning methods. However, in these methods, the global features of the sample space are usually ignored, and these ignored global features will affect the classification accuracy. To solve this problem, a novel triple capsule network framework is proposed to text classification. The training in the first stage, to obtain a basic capsule network for obtaining local features. Then, three capsule networks sharing parameters are combined spatially, and the triplet loss function is used in the second stage of training. By comparative learning, the capsule network can learn global features that can represent the spatial distance between different categories. Through comparison experiments on six datasets and ten general benchmark algorithms, the results show that our results is the first in the four datasets.
A New Criterion on k-Normal Elements over Finite Fields

The notion of normal elements for finite fields extension was generalized as k-normal elements by Huczynska et al. (2013). Several methods to construct k-normal elements were presented by Alizadah et al. (2016) and Huczynska et al. (2013), and the criteria on k-normal elements were given by Alizadah et al. (2016) and Antonio et al. (2018). In the paper by Huczynska, S., Mullen, G., Panario, D. and Thomson, D. (2013), the number of k-normal elements for a fixed finite field extension was calculated and estimated. In this paper the authors present a new criterion on k-normal elements by using idempotents and show some examples. Such criterion was given for usual normal elements before by Zhang et al. (2015).
Testing Ethereum Smart Contracts: A Comparison of Symbolic Analysis and Fuzz Testing Tools

Ethereum smart contract exploits have inflicted enormous monetary damage due to vulnerabilities introduced accidentally by the contract authors. Many of these errors can now be detected automatically by a growing number of security analysis tools that specifically target the most common vulnerabilities present in the Ethereum smart contract ecosystem. The aim of this work is to identify state-of-the-art security analysis tools that assist auditors in automatically testing and verifying real-world contracts. We compare two such symbolic executioners, Manticore (which we also extend) and Mythril, and one fuzz tester, Echidna, to evaluate their effectiveness when analysing a set of challenge contracts hosted online, as well as twenty of the most popular ERC-20 tokens found on the main Ethereum network. Our results showed that the tools were able to solve 24 of the 39 challenge contracts and both symbolic tools achieved on average more than 80% code coverage on successful evaluations of the popular ERC-20 token data. Code coverage plateaued after the second symbolic transaction, suggesting a good performance target for continuous integration environments.
Relative Lempel-Ziv Compression of Suffix Arrays

We show that a combination of differential encoding, random sampling, and relative Lempel-Ziv (RLZ) parsing is effective for compressing suffix arrays, while simultaneously allowing very fast decompression of arbitrary suffix array intervals, facilitating pattern matching. The resulting text index, while somewhat larger (5-10x) than the recent r-index of Gagie, Navarro, and Prezza (Proc. SODA '18)—still provides significant compression, and allows pattern location queries to be answered more than two orders of magnitude faster in practice.
A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation

We study the problem of multi-agent navigation in static environments when no centralized controller is present. Each agent is controlled individually and relies on three algorithmic components to achieve its goal while avoiding collisions with the other agents and the obstacles: i) individual path planning which is done by Theta* algorithm; ii) collision avoidance while path following which is performed by ORCA* algorithm; iii) locally-confined multi-agent path planning done by Push and Rotate algorithm. The latter component is crucial to avoid deadlocks in confined areas, such as narrow passages or doors. We describe how the suggested components interact and form a coherent navigation pipeline. We carry out an extensive empirical evaluation of this pipeline in simulation. The obtained results clearly demonstrate that the number of occurring deadlocks significantly decreases enabling more agents to reach their goals compared to techniques that rely on collision-avoidance only and do not include multi-agent path planning component.
Multi-phase and Multi-level Selective Feature Fusion for Automated Pancreas Segmentation from CT Images

CT images scanned in arterial and venous phases have been demonstrated to provide complementary information for accurate pancreas segmentation. In this paper, we propose a novel multi-phase and multi-level selective feature fusion network (MMNet) with a core component named adaptive cross refinement (ACR) module. Specifically, MMNet adopts two parallel encoders to extract features of the two phases respectively, which are then fused by ACR to excel each complementarity advantage. Unlike most existing fusion methods which only exchange and combine features of a single level with the same resolution between two phases/modalities, ACR module intelligently aggregates features of all levels in one phase as a multi-level prior, and then adaptively selects the most effective information from the multi-level prior to refine features at each level of the other phase. Such multi-phase, multi-level selective feature exchange and fusion strategy is bi-directional to mutually benefit segmentation of both phases. Experimental results on 141 cases of our private dataset demonstrate the effectiveness of our ACR module and superior performance to the state-of-the-art fusion methods.