Reviewerly/exHarmony huggingface dataset:
Estimating Minimum Operation Steps via Memory-based Recurrent Calculation Network

To estimate time complexity for a given algorithm is important for algorithm designers. Usually, time complexity means the "analytical" time complexity which needs to be proved by strict math derivation. We propose to estimate the "numerical" time complexity (NTC), which measures the minimum number of operations an algorithm has to spend, as well as capture the intrinsic laws of time complexity. The unique challenges include: (1) How to make a machine learning model has the same ability as a real-world CPU (2) How to measure the minimum number of required arithmetic operations for a given problem. To tackle these challenges, we first propose a memory-based recurrent calculation network to mimic the functions of CPU and then we propose a self-adaptive selection gate for deciding when the mimic calculation process should stop. In addition, we use a symbolic learning method to find the time complexity formula. We train and test our model on four basic algorithms: long integer addition, 1-dim max-pooling, outer product, and sorting. Experiment results demonstrate that our model can precisely predict the numerical time complexity as well as the time complexity formula for each algorithm. We also conduct many visualizations to prove the effectiveness and correctness of our model.
Fast QLB algorithm and hypothesis tests in logistic model for ophthalmologic bilateral correlated data

In ophthalmologic or otolaryngologic studies, bilateral correlated data often arise when observations involving paired organs (e.g., eyes, ears) are measured from each subject. Based on Donner's model , in this paper, we focus on investigating the relationship between the disease probability and covariates (such as ages, weights, gender, and so on) via the logistic regression for the analysis of bilateral correlated data. We first propose a new minorization–maximization (MM) algorithm and a fast quadratic lower bound (QLB) algorithm to calculate the maximum likelihood estimates of the vector of regression coefficients, and then develop three large-sample tests (i.e., the likelihood ratio test, Wald test, and score test) to test if covariates have a significant impact on the disease probability. Simulation studies are conducted to evaluate the performance of the proposed fast QLB algorithm and three testing methods. A real ophthalmologic data set in Iran is used to illustrate the proposed methods.
Establishing a Reference Model for Requirements Elicitation Behavior

Abstract Eliciting the correct requirements for a given product, project or service is crucial for the success of the endeavour. The requirements engineering process in general and the requirements elicitation process is fraught with pitfalls. This paper proposes a mechanism that can be used to model the requirements elicitation behaviour. The results of the modelling process can then provide reference modes against which to compare the actual requirements elicitation process. The result of this comparison can enable the requirements engineer to observe elicitation challenges earlier in the requirements engineering process.
A systematic approach for English education model based on the neural network algorithm

This paper algorithms based on neural network model designed for English education, to develop a model education system with artificial intelligence, summarized the dimensions were can be used for data analysis related indicators. These indicators include not only the contents of the learning behavior, test behavior, cooperation behavior and resource search behavior and other human-computer interaction behavior data, also includes demographic background information, learning ability, learning attitude, and other characteristic data that affect the learning effect. We tried to collect relevant indicators to the maximum extent. An audiovisual fusion method based on Convolutional Neural Network (CNN) is proposed. The independent CNN structure is used to realize independent modeling of audiovisual perception and asynchronous information transmission and obtain the description of audiovisual parallel data in the high-dimensional feature space. Following the shared fully connected structure, it is possible to model the long-term dependence of audiovisual parallel data in a higher dimension. Experiments show that the AVSR system built using a CNN-based audiovisual fusion method can achieve a significant performance improvement, and its recognition error rate is relatively reduced by about 15%. The speech recognition system trained with the cross-domain adaptive method can obtain a significant performance improvement, and its recognition error rate is more than 10% lower than that of the baseline system..
Few Features Attack to Fool Machine Learning Models through Mask-based GAN

GAN (Generative Adversarial Networks, [1]) is a machine-learning-based generative approach, which can create artificial contents such as images, languages and speeches. Recent studies have shown that GAN can also be applied to generate adversarial attack examples ( [2], [3]) to fool the machine-learning models. In comparison with the non-learning adversarial attack examples approaches, the GAN-based adversarial attack example approach can generate the adversarial samples quickly when facing a new sample after training, but meanwhile needs to perturb the attack samples in great quantities. To address this issue, we propose a new approach, named Few-Features-Attack-GAN (FFA-GAN). FFA-GAN has a significant time-consuming advantage than the non-learning adversarial attack samples approaches as it is based on the GAN architecture, and also has a better non-zero-features performance than the GAN-based adversarial sample attack approaches because of the introduction of the mask mechanism in the generator of GAN to confine the perturbations. Experiments are made respectively on the structured data sets KDD-Cup 1999 and CIC-IDS 2017, in which the dimensions of the data are relatively low, and also on the unstructured data sets MNIST and CIFAR-10 in which the data have the relatively high dimensions. The results of the experiments demonstrate the effectiveness and the robustness of our proposed approach.
Conceptual Model of E-Voting in Indonesia

Some countries have adopted the technology of electronic voting into their democratic processes. Some of them successfully implemented e-voting in their elections, but some countries failed to transform traditional paper-based voting into a technology-based automated voting system. Indonesia is a developing country that carries out general elections every five years. The vote was conducted to elect presidential and vice-presidential candidates, national and regional legislatures, and members of the local representative council. Reducing errors and speed up the counting process are some of the electronic voting benefits. Electronic voting also eliminates the root cause of the controversies in every election, which is a vote that is deemed invalid or doubted, thus it still raises doubts, especially on the security, confidentiality of voter data, and ease of use of the system. This research aims to discover the readiness of the implementation of the e-voting system in Indonesia by conducting a quantitative survey and propose state-of-the-art electronic voting system architecture that is suitable for application in Indonesia. Our investigation shows that most of the respondents are ready to use the e-voting system.
Estimating the Importance of Relational Features by Using Gradient Boosting

With data becoming more and more complex, the standard tabular data format often does not suffice to represent datasets. Richer representations, such as relational ones, are needed. However, a relational representation opens a much larger space of possible descriptors (features) of the examples that are to be classified. Consequently, it is important to assess which features are relevant (and to what extent) for predicting the target. In this work, we propose a novel relational feature ranking method that is based on our novel version of gradient-boosted relational trees and extends the Genie3 score towards relational data. By running the algorithm on six well-known benchmark problems, we show that it yields meaningful feature rankings, provided that the underlying classifier can learn the target concept successfully.
Learning the Wireless Channel: A Deep Neural Network Approach

In this paper, we propose a new deep neural network (DNN)-based channel estimation method for the Rayleigh fading channel model. While deep learning has been considered for estimating channels in many communication scenarios, direct estimation of the basic wireless single-input single-output (SISO) communication channel coefficients has not been considered. The proposed DNN-based method can efficiently estimate the channel in real time. Extensive simulation results demonstrate that the proposed channel estimator outperforms conventional least square (LS) estimators in terms of bit error rate (BER) and mean square error (MSE). In addition, the proposed channel does not need channel statistics information or complex matrix computation, thereby reducing the amount of calculation significantly.
Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

Missing data imputation is an important task when dealing with crucial data that cannot be discarded such as medical data. This study evaluates and compares the impacts of two statistical and two machine learning imputation techniques when classifying breast cancer patients, using several evaluation metrics. Mean, Expectation-Maximization (EM), Support Vector Regression (SVR) and K-Nearest Neighbor (KNN) were applied to impute 18% of missing data missed completely at random in the two Wisconsin datasets. Thereafter, we empirically evaluated these four imputation techniques when using five classifiers: decision tree (C4.5), Case Based Reasoning (CBR), Random Forest (RF), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). In total, 1380 experiments were conducted and the findings confirmed that classification using imputation based machine learning outperformed classification using statistical imputation. Moreover, our experiment showed that SVR was the best imputation method for breast cancer classification.
Adaptive Graph Convolutional Networks with Attention Mechanism for Relation Extraction

In the relationship extraction task of NLP, how to effective use of the rich structural information on the dependency tree is a challenging research problem. To better learn the dependency relationship between nodes, we address the relationship extraction task by capturing rich contextual dependencies based on the attention mechanism, and using distributional reinforcement learning to generate optimal relation information representation. Unlike using an attention mechanism to effectively make use of relevant information, we propose a Dual Attention Graph Convolutional Network (DAGCN) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of GCN, which model the semantic interdependencies in spatial and relational dimensions respectively. The position attention module selectively aggregates the feature at each position by a weighted sum of the features at all positions of nodes internal features. Similar features would be related to each other regardless of their distances. Meanwhile, the relation attention module selectively emphasizes interdependent node relations by integrating associated features among all nodes. We sum the outputs of the two attention modules and use reinforcement learning to predict the classification of nodes relationship to further improve feature representation which contributes to more precise extraction results. The results on the TACRED and SemEval datasets show that the model can obtain more useful information for relational extraction tasks, and achieve better performances on various evaluation indexes.
Comparative Analysis of Artificial Hydrocarbon Networks versus Convolutional Neural Networks in Human Activity Recognition

Human activity recognition (HAR) has gained interest in the research communities in order to know the behavior and context of users for medical, sports performance evaluation, ambient assisted living and security applications. Recent works suggest that convolutional neural networks (CNN) are very competitive machine learning techniques for HAR. Nevertheless, CNN require many computational resources, high number of parameter tuning, and many data samples for training. In this paper, we present a comparative analysis of a novel technique, artificial hydrocarbon networks (AHN), with CNN on human activity recognition classification task. We choose to compare AHN with CNN given that it is a very well-suited machine learning technique for HAR. We show that AHN architecture is simpler to set up than CNN, it needs less hyper-parameter configuration and has a slightly better accuracy performance.
Least-squares Optimal Relative Planar Motion for Vehicle-mounted Cameras

A new closed-form solver is proposed minimizing the algebraic error optimally, in the least squares sense, to estimate the relative planar motion of two calibrated cameras. The main objective is to solve the over-determined case, i.e., when a larger-than-minimal sample of point correspondences is given - thus, estimating the motion from at least three correspondences. The algorithm requires the camera movement to be constrained to a plane, e.g. mounted to a vehicle, and the image plane to be orthogonal to the ground. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The solver obtains the motion parameters as the roots of a 6th degree polynomial. It is validated both in synthetic experiments and on publicly available real-world datasets that using the proposed solver leads to results superior to the state-of-the-art in terms of geometric accuracy with no noticeable deterioration in the processing time.
Axis projection for Kidney-Region-Of-Interest detection in computed tomography

The article presents an innovative method of scanning slices in computed tomography. The presented technique allows for automatically generation of three-dimensional projection, determined by X-Y-Z surfaces. Projections allow to calculate Kidney-Region-Of-Interest - a minimal envelope of kidney contours for all CT scans. The presented technique increases the accuracy of automatic identification and segmentation of kidneys and can be a good starting point for other techniques for identifying kidney areas on individual CT scans. This method significantly limits the area of kidney searching, thereby accelerates the operation of identification for any algorithm. The presented method is based on the technique of pixel intensity values averaging, area region-growing algorithms and morphological transformations. The presented technique has also been tested in implementation of the U-Net neural network system. Our presented solution of X-Y-Z projection is characterized by a high efficiency of visualization, comparable to the results obtained by a human expert.
Virtual bowling

This work proposes BowlingVR, an advanced Virtual Reality (VR) multiplayer game that tackles two main goals: the first one is to provide a realistic User eXperience (UX) to the user, by reproducing the dynamics and physical context of a real bowling challenge; the second one is to allow a remote, distributed, socially satisfying gameplay, providing the user the illusion of the real presence of the remote players. The prototype was evaluated using a modified version of SUXES, a kind of user interview schema that was originally devised for multimedia applications and that has been modified in order to better compare the responses of different users and get a more reliable estimation of user appreciation.
Detecting Adversarial Audio via Activation Quantization Error

The robustness and vulnerability of Deep Neural Networks (DNN) are quickly becoming a critical area of interest since these models are in widespread use across real-world applications (i.e., image and audio analysis, recommendation system, natural language analysis, etc.). A DNN's vulnerability is exploited by an adversary to generate data to attack the model; however, the majority of adversarial data generators have focused on image domains with far fewer work on audio domains. More recently, audio analysis models were shown to be vulnerable to adversarial audio examples (e.g., speech command classification, automatic speech recognition, etc.). Thus, one urgent open problem is to detect adversarial audio reliably. In this contribution, we incorporate a separate and yet related DNN technique to detect adversarial audio, namely model quantization. Then we propose an algorithm to detect adversarial audio by using a DNN's quantization error. Specifically, we demonstrate that adversarial audio typically exhibits a larger activation quantization error than benign audio. The quantization error is measured using character error rates. We use the difference in errors to discriminate adversarial audio. Experiments with three the-state-of-the-art audio attack algorithms against the DeepSpeech model show our detection algorithm achieved high accuracy on the Mozilla dataset.
Facial and Voice Recognition Based Ssecurity and Safety System in Car

In this proposed security system to detect the face of the driver and compare it with the previously saved face. When no one in the car and the car was parked anywhere then someone can theft the car. In this proposed system a small webcam will be hidden in the steering and when a person sits in the driver seat the webcam take the picture of the face of the person. Then it compares the face with the previously saved face if it matched then the person can access the steering wheel of the car. If the face of the person doesn't match with the previously saved images then the steering wheel and doors of the car will be locked automatically. Then the face of the unauthenticated person will be captured by the webcam and it will be sent to the owner's mobile and nearest police station with the help of GSM module and Police or the owner of the car can trace the car with the help of GPS system. This face detection system uses windows IoT software and detects the unauthenticated faces. Here we also used speech recognition for the safety purpose of the car.
Fast Portrait Segmentation With Highly Light-Weight Network

In this paper, we describe a fast and light-weight portrait segmentation method based on a new highly light-weight backbone (HLB) architecture. The core element of HLB is a bottleneck-based factorized block (BFB) that has much fewer parameters than existing alternatives while keeping good learning capacity. Consequently, the HLB-based portrait segmentation method can run faster than the existing methods yet retaining the competitive accuracy performance with state-of-the-arts. Experiments conducted on two benchmark datasets demonstrate the effectiveness and efficiency of our method.
Multi-Receptive Atrous Convolutional Network for Semantic Segmentation

Deep Convolutional Neural Networks (DCNNs) have enhanced the performance of semantic image segmentation but many challenges still remain. Specifically, some details may be lost due to the downsampling operations in DCNNs. Furthermore, objects may appear in an image at different scales, and extracting features using convolutional filters with large sizes is costly in computation. Moreover, in many cases, contextual information, such as global and background features, is potentially useful for semantic segmentation. In this paper, we address these challenges by proposing a Multi-Receptive Atrous Convolutional Network (MRACN) for semantic image segmentation. The proposed MRACN captures the multi-receptive features and the global features at different receptive scales of the input. MRACN can serve as a module easily being integrated into existing models. We adapt the ResNet-101 model as the backbone network and further propose a MRACN segmentation model (MRACN-Seg). The experimental results demonstrate the effectiveness of the proposed model on two datasets: a benchmark dataset (PASCAL VOC 2012) and our industry dataset.
Does fixing bug increase robustness in deep learning?

Deep Learning (DL) based systems are utilized vastly. Developers update the code to fix the bugs in the system. How these code fixing techniques impacts the robustness of these systems has not been clear. Does fixing code increase the robustness? Do they deteriorate the learning capability of the DL based systems? To answer these questions, we studied 321 Stack Overflow posts based on a published dataset. In this study, we built a classification scheme to analyze how bug-fixes changed the robustness of the DL model and found that most of the bug-fixes can increase the robustness. We also found evidence of bug-fixing that decrease the robustness. Our preliminary result suggests that 12.5% and 2.4% of the bug-fixes in Stack Overflow posts caused the increase and the decrease of the robustness of DL models, respectively.
User Experience of Alexa, Siri and Google Assistant When Controlling Music – Comparison of Four Questionnaires

We evaluate user experience (UX) when users play and control music with three smart speakers: Amazon's Alexa Echo, Google Home and Apple's Siri on a HomePod. For measuring UX we use four established UX metrics (AttrakDiff, SASSI, SUISQ-R, SUS). We investigated the sensitivity of these four questionnaires in two ways: firstly, we compared the UX reported for each of the speakers, secondly, we compared the UX of completing easy single tasks and more difficult multi-turn tasks with these speakers. We find that the investigated questionnaires are sufficiently sensitive to show significant differences in UX for these easy and difficult tasks. In addition, we find some significant UX differences between the tested speakers. Specifically, all tested questionnaires, except the SUS, show a significant difference in UX between Siri and Alexa, with Siri being perceived as more user friendly for controlling music. We discuss implications of our work for researchers and practitioners.
Optimal Network Route Estimator Using Prediction Algorithms

Mobile networks have undergone a boost in the recent years, which has posed a big challenge to accurately predict to help in resource usage optimization , enhance the quality of service and save resources. Machine learning (ML) is profoundly used to extract the hidden deep features in the data which is helpful for understanding network traffic instability, but the performance of a only one ML model isn't quite efficient. The objective of this paper is to design a model using prediction algorithms to decide the best optimal path for network route to aid the efficient transmission of data from source IP address to the destination IP address. This will increase the efficiency of transmission based on quality of service parameters. In this paper, a Machine Learning algorithm called Random Forest is applied to train the prediction model. The algorithm is implemented by using python and its enriched library resources which include Pandas to load the data, NumPy to reshape the data, Matplotlib to plot the graphs, SciPy for advanced computation and SkLearn for data mining and data analysis. Real-life traffic dataset is fed into the model for the evaluation. Through experimental results, it is found that compared to other single ML algorithms the proposed model is much more efficient and improves the prediction performance. As per the information and papers available on the internet, the maximum achieved accuracy is found to be about 92% using XGBoost Model. During the initial model testing, the accuracy obtained was 72% using Random Forest Algorithm. Including other parameters and changing the model accordingly, the accuracy gradually increased to 98%.