Reviewerly/exHarmony huggingface dataset:
Semi-supervised Depth Estimation from a Single Image Based on Confidence Learning

Recent advances in deep convolutional neural network have lead to significant improvement over depth estimation from a single image. However, training such networks usually needs a large amount of high-quality labeled training data which is difficult to collect. To alleviate it, a semi-supervised method based on confidence learning is proposed to improve the results of depth estimation with additional unlabeled data. We utilize the confidence map generated by a confidence network to predict the trusty regions of depth estimations on unlabeled data. These depth estimations are taken as pseudo ground truth and used to train the depth network together with labeled data. Experiments on NYU Depth dataset V2 show our proposed semi-supervised method outperforms fully-supervised method.
Moving Target Tracking and Classification Based on Micro-Doppler Signatures

This paper presents the results of accompanying classification of tracking for different classes of targets such as a car (moving non-rigid target), moving people (slow moving non-rigid target).The Data are collected using frequency modulation continuous wave (FMCW)radar, while different neural algorithms are considered for classification of targets recorded.A Doppler and Micro Doppler have been used as target features, while short time Fourier transform (STFT) has been used as a feature extraction algorithm.A novel combination between Kalman filter and tree-structured, as well as self-organizing map (SOM) neural network has been proposed as an estimator and classifier, respectively.The results show that the proposed approach is overperformed to the conventional classifier SVM by 1.6% and the gained classification is 92.6%, while the reduction error due to using Kalman filter is 95%.
ITS4S2: An Image Quality Dataset With Unrepeated Images From Consumer Cameras

This technical memorandum provides technical details for the image quality subjective experiment its4s2. This experiment contains 1,473 still images that were presented to subjects for 4 seconds each, as per still video. The goals are (1) to train no reference (NR) metrics that track the image quality of consumer grade cameras, (2) to understand spatial characteristics of moving objects that have complex interactions with video codecs, such as water and confetti, and (3) to begin to identify any differences in rating behaviors depending on whether the images are used for entertainment purposes or by public safety practitioners for job related tasks. The dataset is freely available for research and development purposes.
An open electromagnetic tracking framework applied to targeted liver tumour ablation

Electromagnetic tracking is a core platform technology in the navigation and visualisation of image-guided procedures. The technology provides high tracking accuracy in non-line-of-sight environments, allowing instrument navigation in locations where optical tracking is not feasible. EMT can be beneficial in applications such as percutaneous radiofrequency ablation for the treatment of hepatic lesions where the needle tip may be obscured due to difficult liver environments (e.g subcutaneous fat or ablation artefacts). Advances in the field of EMT include novel methods of improving tracking system accuracy, precision and error compensation capabilities, though such system-level improvements cannot be readily incorporated in current therapy applications due to the ‘blackbox’ nature of commercial tracking solving algorithms. This paper defines a software framework to allow novel EMT designs, and improvements become part of the global design process for image-guided interventions. An exemplary framework is implemented in the Python programming language and demonstrated with the open-source Anser EMT system. The framework is applied in the preclinical setting though targeted liver ablation therapy on an animal model. The developed framework was tested with the Anser EMT electromagnetic tracking platform. Liver tumour targeting was performed using the tracking framework with the CustusX navigation platform using commercially available electromagnetically tracked needles. Ablation of two tumours was performed with a commercially available ablation system. Necropsy of the tumours indicated ablations within 5 mm of the tumours. An open-source framework for electromagnetic tracking was presented and effectively demonstrated in the preclinical setting. We believe that this framework provides a structure for future advancement in EMT system in and customised instrument design.
FabAuth

We present a method we propose called FabAuth for identifying 3D-printed objects, which utilizes the differences in the resonant properties of such objects. We focus on changing the internal structures of each object made through a 3D printing process to assign a unique resonant property to it even if multiple objects have the same appearance. To identify the objects, the method identifies resonant property differences by using vibration that can pass through 3D-printed objects. The method can be applied even to low-filled 3D-printed objects as long as an acoustic wave can travel through the objects from one sensor to another. To validate the method's feasibility, we conducted a preliminary experiment to confirm whether it can be applied to low-filled 3D-printed objects and found that its average classification accuracy reached 92.2%.
UP-TreeRec: Building dynamic user profiles tree for news recommendation

Online news recommendation systems aim to address the information explosion of news and make personalized recommendations for users. The key problem of personalized news recommendation is to model users' interests and track their changes. A common way to deal with the user modeling problem is to build user profiles from observed behavior. However, the majority of existing methods make static representations of user profiles and little research has focused on effective user modeling that could dynamically capture user interests in news topics. To address this problem, in this paper, we propose UP-TreeRec, a news recommendation framework based on a user profile tree (UP-Tree), which is a novel framework combining content-based and collaborative filtering techniques. First, by exploiting a novel topic model namely UI-LDA, we obtain the representation vectors for news content in a topic space as the fundamental bridge to associate user interests with news topics. Next, we design a decision tree with a dynamically changeable structure to construct a user interest profile from the user's feedback. Furthermore, we present a clustering-based multidimensional similarity computation method to select the nearest neighbor of the UP-Tree efficiently. We also provide a Map-Reduce framework-based implementation that enables scaling our solution to real-world news recommendation problems. We conducted several experiments compared to the state-of-the-art approaches on real-world datasets and the experimental results demonstrate that our approach significantly improves accuracy and effectiveness in news recommendation.
Empirical Analysis of Cervical and Breast Cancer Prediction Systems using Classification

Cancer is a life-threatening disease with high mortality rates.In the Indian subcontinent, women have a higher possibility to be diagnosed with cancer than men.The most common cancers identified in Indian women are Breast Cancer and Cervical Cancer.Both these cancers have high survival rates in case of early prediction.This paper reviews the attributes which are used in the existing datasets for prediction of these two cancers.The paper also proposes new attributes to overcome the limitations of existing ones, which will further increase the effectiveness of cancer prediction systems.The efficiency of existing and proposed attributes is compared by processing datasets through data mining algorithms using WEKA tool.The algorithms used for this study are -J48 (Decision Tree), Naï ve Bayes, Random Forest, Random Tree, KStar and Bagging Algorithm.The empirical analysis done in the paper reported improvement in the efficiency of cancer prediction over existing prediction systems.
Fast and Unbiased Estimation of Volume Under Ordered Three-Class ROC Surface (VUS) Based on Dynamic Programming

Receiver operating characteristic (ROC) analysis has been widely used in two-class problems. However, in practice, three-class problems are frequently encountered, especially in the area of medicine. To evaluate the performance of three-class classifiers, researchers have proposed the volume under the three-class ROC surface (VUS) as a figure-of-merit. Unfortunately, to the best of our knowledge, however, all the existing methods suffer heavy computational loads. In this paper, to overcome such an unsatisfactory problem, we develop an efficient dynamic programming-based algorithm for unbiased estimation of the VUS and the corresponding variance. The Monte Carlo simulations verified both the unbiasedness and computing efficiency of our algorithm compared with the state-of-the-art work proposed by Waegeman and co-authors.
Cipher Modification Against Steganalysis Based on NIST Tests

Part of our team proposed a new steganalytic method based on NIST tests at MMM-ACNS 2017 [1], and it was encouraged to investigate some cipher modifications to prevent such types of steganalysis. In the current paper, we propose one cipher modification based on decompression by arithmetic source compression coding. The experiment shows that the current proposed method allows to protect stegosystems against steganalysis based on NIST tests, while security of the encrypted embedded messages is kept. Protection of contemporary image steganography based on edge detection and modified LSB against NIST tests steganalysis is also presented.
Behavior Pattern-Driven Test Case Selection for Deep Neural Networks

With the widespread application of deep learning systems, the robustness of deep neural networks (DNNs) is received increasing attentions recently. By studying the distribution of neurons outputs in DNN models, we found that the behavior patterns of neurons are different for different kinds of DNNs' inputs, e.g. test cases generated by different adversarial attack techniques. In this paper, we extract the neuron behavior patterns of DNNs under different adversarial attack techniques, use them as the guidance for test case selection. Experimental results show that this method is more efficient than random technology.
Visual analytics of aftershock point cloud data in complex fault systems

Abstract. Aftershock point cloud data provide direct evidence for the characteristics of underground faults. However, there has been a dearth of studies using state-of-the-art visual analytics methods to explore the data. In this paper, we present a novel interactive visual analysis approach for visualizing the aftershock point cloud. Our method employs a variety of interactive operations, rapid visual computing functions, flexible display modes, and various filtering approaches to present and explore the desired information for the fault geometry and aftershock dynamics. The case study conducted for the 2016 Central Italy earthquake sequence shows that the proposed approach can facilitate the discovery of the geometry of the four main fault segments and three secondary fault segments. It can also clearly reveal the spatiotemporal evolution of the aftershocks, helping to find the fluid-driven mechanism of this sequence. An open-source prototype system based on the approach is also developed and is freely available.
Three-dimensional shape measurement based on binary fringe conventional projection

A three-dimensional (3D) shape measurement method based on binary fringe conventional projection is proposed. In traditional 3D shape measurement methods based on binary fringe projection, the binary fringe is used as an approximate sinusoidal fringe by defocusing projection or even directly used as an approximate sinusoidal fringe by nonlinear error suppression feature of large-step phase measuring profilometry (PMP). In the proposed method (Prop), neither defocusing projection is needed nor large-step PMP must be used. When the binary fringe is conventionally projected on the measured object, just by filtering operation in spatial frequency domain from the captured deformed pattern, the nearly unbroken sinusoidal deformed pattern can be extracted efficiently. While N ( N≥3) frames binary fringes with 1/ N period misalignment between each adjacent fringe are conventionally projected onto the measured object sequentially, the corresponding nearly unbroken N-step sinusoidal deformed patterns extracted from the N frames captured deformed patterns can be used to reconstruct the 3D shape of the measured object with N-step PMP algorithm. The experimental results show the feasibility and validity of the Prop. It is more flexible for its optional fringe period and optional phase-shifting steps. It has potential application in the real-time 3D shape measurement.
Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR), which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks, a GloVe word embedding model was used to vectorize words.In the NER task, a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task, the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset, the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks, where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover, the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios, laying the foundation for the subsequent work in the EMR field.
Logical Design of n-bit Comparators: Pedagogical Insight from Eight-Variable Karnaugh Maps

An -bit comparator is a celebrated combinational circuit that compares two -bit inputs and and produces three orthonormal outputs: G (indicating that is strictly greater than ), E (indicating that and are equal or equivalent), and L (indicating that is strictly less than ). The symbols ‘G’, ‘E’, and ‘L’ are deliberately chosen to convey the notions of ‘Greater than,’ ‘Equal to,’ and ‘Less than,’ respectively. This paper analyzes an -bit comparator in the general case of arbitrary and visualizes the analysis for on a regular and modular version of the 8-variable Karnaugh-map. The cases 3, 2, and 1 appear as special cases on 6-variable, 4-variable, and 2-variable submaps of the original map. The analysis is a tutorial exposition of many important concepts in switching theory including those of implicants, prime implicants, essential prime implicants, minimal sum, complete sum and disjoint sum of products (or probability-ready expressions).
A Novel Fault Prevention Model for Metro Overhead Contact System

As an indispensable part of metro traction power supply system, the metro overhead contact system (OCS) is complex in composition and has various causes of faults, so its fault prevention is too complicated to be handled by using traditional methods. However, over the last few years, the fault data of metro OCS has been through explosive growth. Consequently, the method of high utility mining, which is capable of pointedly mining fault association rules from historical fault data, is adapted to address the issue. According to the fault association rules, once the precursor of the specified fault occurs, we can immediately conduct preventive maintenance to achieve the purpose of fault prevention. In this paper, we proposed a novel fault prevention model for metro OCS. First, we proposed a concept hierarchical database structure with a detailed encoding method, which can efficiently store the massive fault data and facilitate the subsequent mining. Then an improved algorithm for mining multiple-level top-K high utility fault-sets (MTHU) for metro OCS is proposed. At last, a case study is conducted and the mining result is analyzed to reveal the inner connections between faults. Based on the analysis result, the detailed preventive maintenance suggestions are given to effectively achieve fault prevention for OCS.
Do consumers talk about the software in my product? An Exploratory Study of IoT Products on Amazon

Consumer product reviews are an invaluable source of data because they contain a wide range of information that could help requirement engineers to meet user needs. Recent studies have shown that tweets about software applications and reviews on App Stores contain useful information, which enable a more responsive software requirements elicitation. However, all of these studies' subjects are merely software applications. Information on system software, such as embedded software, operating systems, and firmware, are overlooked, unless reviews of a product using them are investigated. Challenges in investigating these reviews could come from the fact that there is a huge volume of data available, as well as the fact that reviews of such products are diverse in nature, meaning that they may contain information mostly on hardware components or broadly on the product as a whole. Motivated by these observations, we conduct an exploratory study using a dataset of 7198 review sentences from 6 Internet of Things (IoT) products. Our qualitative analysis demonstrates that a sufficient quantity of software related information exists in these reviews. In addition, we investigate the performance of two supervised machine learning techniques (Support Vector Machines and Convolutional Neural Networks) for classification of information contained in the reviews. Our results suggest that, with a certain setup, these two techniques can be used to classify the information automatically with high precision and recall.
Experimental Evaluation of the Virtual Environment Efficiency for Distributed Software Development

At every software design stage nowadays, there is an acute need to solve the problem of effective choice of libraries, development technologies, data exchange formats, virtual environment systems, characteristics of virtual machines.Due to the spread of various kinds of devices and the popularity of Web platforms, lots of systems are developed not for the universal installation on a device (box version), but for a specific architecture with the subsequent provision of web services.Under these conditions, the only way for estimating the efficiency parameters at the design stage is to conduct various kinds of experiments to evaluate the parameters of a particular solution.Using the example of the Web platform of digital psychological tools, the methods for experimental parameter evaluation were developed in the article.The mechanisms and technologies for improving the efficiency of the Vagrant and Docker cloud virtual environment were also proposed in the paper.A set of basic criteria for evaluating the effectiveness of the configuration of the virtual development environment has been determined to be rapid deployment; increase in the speed and decrease in the volume of resources used; increase in the speed of data exchange between the host machine and the virtual machine.The results of experimental estimates of the parameters that define the formulated efficiency criteria are given as: processor utilization involved (percentage); the amount of RAM involved (GB); initialization time of virtual machines (seconds); time to assemble the component completely (Build) and to reassemble the component (Watch) (seconds).To improve the efficiency, a file system access driver based on the NFS protocol was studied in the paper.
INTELLIGENT CONTROL SYSTEM OF A WHEELCHAIR FOR PEOPLE WITH QUADRIPLEGIA PARALYSIS

Our system proposes a wheelchair controlled by head gestures for people with quadriplegia paralysis (QP) or for those suffering from amputation of their hands and legs during the war in Iraq. The wheelchair design has an intelligent control that works with the movement of a patient‘s head in order to enable the patient to drive the wheelchair in the desired direction. The system consists of two main parts. The first is the transmitter in which sensing signals are transmitted to the second part and it consists of a 3-axes accelerometer (ADXL-345), a 433 MHz RF wireless receiver transmitter module, and Arduino UNO. The ADXL-345 sensor is placed in front of a hat that the patient wears. The system detects the direction of the patient's head movement then sends appropriate instructions to the wheelchair’s control part (receiving part). The receiving part is the wheelchair control system that consists of a 433 MHz RF wireless receiver transmitter module and an Arduino UNO. The Arduino processes the signals coming from the first part and moves the wheelchair accordingly in the desired direction. The research also provides an obstacle avoidance system with a range of 50 cm that prevents the wheelchair from colliding with objects. The head movement angles have been selected to be 40, 35 degree for vertical and horizontal movement respectively so that the QP patient feels comfortable to tilt his head in all directions and control the wheelchair easily. The system provides a very effective performance and accuracy.&#x0D; ABSTRAK: Sistem ini bergantung kepada kerusi roda yang dikawal menggunakan isyarat kepala untuk mereka yang lumpuh kuadriplegia (QP) ataupun mereka yang dipotong tangan dan kaki semasa perang di Iraq. Ia merupakan reka bentuk pintar pada kerusi roda dan pesakit boleh mengawal pergerakan mereka dengan isyarat pada kepala. Sistem ini terdiri daripada dua bahagian utama iaitu yang pertama merupakan pemancar yang menghantar isyarat gerakan ke bahagian kedua dan ianya terdiri daripada 3 axis accelerometer (ADXL-345), modul penerima isyarat pemancar tanpa wayar 433 MHz RF dan Arduino UNO. Sensor ADXL-345 ini diletakkan di bahagian depan topi yang dipakai oleh pesakit. Sistem ini mengesan arah gerakan kepala pesakit dan kemudiannya arahan dihantar ke bahagian pengendali kerusi roda (bahagian penerimaan isyarat). Bahagian ini merupakan sistem pengendalian kerusi roda yang terdiri daripada modul pemancar penerimaan tanpa wayar 433 MHz RF dan Arduino UNO. Arduino memproses isyarat yang datang dari bahagian pertama bagi menggerakkan kerusi roda ke arah yang dikehendaki. Kajian ini turut menyediakan sistem mengelak halangan pada jarak 50 cm bagi menjauhi kerusi roda daripada melanggar objek. Sudut gerakan kepala telah dipilih pada 40, 35 darjah bagi gerakan menegak dan mendatar masing-masing, supaya pesakit QP merasa selesa bagi mengerakkan kepalanya ke semua arah dan mengawal kerusi roda dengan mudah. Sistem ini juga sangat cekap dan tepat dalam mengawal arah.