Reviewerly/exHarmony huggingface dataset:
Gaze Estimation for Assisted Living Environments

Effective assisted living environments must be able to perform inferences on how their occupants interact with one another as well as with surrounding objects. To accomplish this goal using a vision-based automated approach, multiple tasks such as pose estimation, object segmentation and gaze estimation must be addressed. Gaze direction provides some of the strongest indications of how a person interacts with the environment. In this paper, we propose a simple neural network regressor that estimates the gaze direction of individuals in a multi-camera assisted living scenario, relying only on the relative positions of facial keypoints collected from a single pose estimation model. To handle cases of keypoint occlusion, our model exploits a novel confidence gated unit in its input layer. In addition to the gaze direction, our model also outputs an estimation of its own prediction uncertainty. Experimental results on a public benchmark demonstrate that our approach performs on par with a complex, dataset-specific baseline, while its uncertainty predictions are highly correlated to the actual angular error of corresponding estimations. Finally, experiments on images from a real assisted living environment demonstrate that our model has a higher suitability for its final application.
Defense Against the Dark Cars

As autonomous vehicles (AVs) become a reality on public roads, researchers and designers are beginning to see unexpected reactions from the public ranging from curiosity to vandalism. These behaviors are concerning, as AV platforms will need to know how to deal with people behaving unexpectedly or aggressively. We call this griefing of AVs, adopting the term from harassment in online gaming. We discuss several examples of griefing observed in on-road field studies using a Wizard-of-Oz driverless car. While Uber and Waymo have anecdotally mentioned vandalism towards AVs, we believe this to be the first public video available of AV griefing ranging from playful to aggressive. To stimulate discussion, we propose speculative design principles to address griefing.
Smart Contract Classification With a Bi-LSTM Based Approach

With the number of smart contracts growing rapidly, retrieving the relevant smart contracts quickly and accurately has become an important issue.A key step for recognizing the related smart contracts is able to classify them accurately.Different from traditional text, the smart contract is composed of several parts: source code, code comments and other useful information like account information.How to make good use of those different kinds of features for effective classification is a problem need to be solved.Inspired by this, we proposed a smart contract classification approach based on Bi-LSTM model and Gaussian LDA, which can use a variety of information as inputs of the model, including source code, comments, tags, account and other content information.Bi-LSTM is utilized to capture grammar rules and context information in source code, while Gaussian LDA model is employed to generate comments feature where the semantics of the comments are enriched by embeddings.We also use attention mechanism to focus on the more relevant features in smart contracts for tags and fuse account information to provide additional information for classification.The experimental results show that the classification performance of the proposed model is superior to other baseline models.
Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures

Modern neural network-based algorithms are able to produce highly accurate depth estimates from stereo image pairs, nearly matching the reliability of measurements from more expensive depth sensors. However, this accuracy comes with a higher computational cost since these methods use network architectures designed to compute and process matching scores across all candidate matches at all locations, with floating point computations repeated across a match volume with dimensions corresponding to both space and disparity. This leads to longer running times to process each image pair, making them impractical for real-time use in robots and autonomous vehicles. We propose a new stereo algorithm that employs a significantly more efficient network architecture. Our method builds an initial match cost volume using traditional matching costs that are fast to compute, and trains a network to estimate disparity from this volume. Crucially, our network only employs per-pixel and two-dimensional convolution operations: to summarize the local match information at each location as a lowdimensional feature vector, and to spatially process these "cost-signature" features to produce a dense disparity map. Experimental results on KITTI show that our method delivers competitive accuracy at significantly higher speeds- running at 48 frames per second on a modern GPU.
Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers.
Sinhala Hate Speech Detection in Social Media using Text Mining and Machine learning

With the rapid growth of Information technology and Computer Science, communication and presenting ideologies became easier than early decades. Since Social Media are available globally through the web, anyone can easily target a person or a group who belongs to a different culture or a different belief. Though everyone has a right to express his or her own ideas, it should not be harmful, as everyone has a right to be prevented from any kind of hate speeches. In Social Media, there are no automatic methods to detect a hate speech, so anyone can easily be targeted. Since social media service providers do not have good linguistic knowledge on some languages such as Sinhala, they may take a couple of days to remove hate related comments from the content once they noticed. Therefore, hate speech detection in Sinhala language is an urgent and important work to address. We propose lexicon based and machine learning based approaches to automatically detect Sinhala hate and offensive speeches that are being shared through Social Media. In our study, lexicon based approach was initiated with the lexicon generating process and corpus based lexicon gave 76.3% of accuracy for hate, offensive and neutral speech detection. Machine learning approach was begun with building a 3000 comments corpus which is evenly distributed among hate, offensive and neutral speeches. Using this comment corpus, we were able to identify best fitting feature groups and models for Sinhala hate speech detection. According to our experiments, character trigram with Multinomial Naïve Bayes gave the highest recall value as 0.84 with 92.33% accuracy.
BLOCIS: Blockchain-Based Cyber Threat Intelligence Sharing Framework for Sybil-Resistance

The convergence of fifth-generation (5G) communication and the Internet-of-Things (IoT) has dramatically increased the diversity and complexity of the network. This change diversifies the attacker’s attack vectors, increasing the impact and damage of cyber threats. Cyber threat intelligence (CTI) technology is a proof-based security system which responds to these advanced cyber threats proactively by analyzing and sharing security-related data. However, the performance of CTI systems can be significantly compromised by creating and disseminating improper security policies if an attacker intentionally injects malicious data into the system. In this paper, we propose a blockchain-based CTI framework that improves confidence in the source and content of the data and can quickly detect and eliminate inaccurate data for resistance to a Sybil attack. The proposed framework collects CTI by a procedure validated through smart contracts and stores information about the metainformation of data in a blockchain network. The proposed system ensures the validity and reliability of CTI data by ensuring traceability to the data source and proposes a system model that can efficiently operate and manage CTI data in compliance with the de facto standard. We present the simulation results to prove the effectiveness and Sybil-resistance of the proposed framework in terms of reliability and cost to attackers.
A deep learning-based framework for lung cancer survival analysis with biomarker interpretation

Abstract Background Lung cancer is the leading cause of cancer-related deaths in both men and women in the United States, and it has a much lower five-year survival rate than many other cancers. Accurate survival analysis is urgently needed for better disease diagnosis and treatment management. Results In this work, we propose a survival analysis system that takes advantage of recently emerging deep learning techniques. The proposed system consists of three major components. 1) The first component is an end-to-end cellular feature learning module using a deep neural network with global average pooling. The learned cellular representations encode high-level biologically relevant information without requiring individual cell segmentation, which is aggregated into patient-level feature vectors by using a locality-constrained linear coding (LLC)-based bag of words (BoW) encoding algorithm. 2) The second component is a Cox proportional hazards model with an elastic net penalty for robust feature selection and survival analysis. 3) The third commponent is a biomarker interpretation module that can help localize the image regions that contribute to the survival model’s decision. Extensive experiments show that the proposed survival model has excellent predictive power for a public (i.e., The Cancer Genome Atlas) lung cancer dataset in terms of two commonly used metrics: log-rank test ( p -value) of the Kaplan-Meier estimate and concordance index ( c -index). Conclusions In this work, we have proposed a segmentation-free survival analysis system that takes advantage of the recently emerging deep learning framework and well-studied survival analysis methods such as the Cox proportional hazards model. In addition, we provide an approach to visualize the discovered biomarkers, which can serve as concrete evidence supporting the survival model’s decision.
A Comprehensive Study of the Efficiency of Type-Reduction Algorithms

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there has been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karnik–Mendel (EKM) and the enhanced iterative algorithm with stopping condition (EIASC). In a previous paper, we found that the computational efficiency of an algorithm is closely related to the platform, and how it is implemented. In computer science, the dependence on languages is usually avoided by focusing on the complexity of algorithms (using big O notation). In this article, the main contribution is the proposal of two novel type-reduction algorithms. Also, for the first time, a comprehensive study on both existing and new type-reduction approaches is made based on both algorithm complexity and practical computational time under a variety of programming languages. Based on the results, suggestions are given for the preferred algorithms in different scenarios depending on implementation platform and application context.
Real-time image dehazing by superpixels segmentation and guidance filter

Haze and fog had a great influence on the quality of images, and to eliminate this, dehazing and defogging are applied. For this purpose, an effective and automatic dehazing method is proposed. To dehaze a hazy image, we need to estimate two important parameters such as atmospheric light and transmission map. For atmospheric light estimation, the superpixels segmentation method is used to segment the input image. Then each superpixel intensities are summed and further compared with each superpixel individually to extract the maximum intense superpixel. Extracting the maximum intense superpixel from the outdoor hazy image automatically selects the hazy region (atmospheric light). Thus, we considered the individual channel intensities of the extracted maximum intense superpixel as an atmospheric light for our proposed algorithm. Secondly, on the basis of measured atmospheric light, an initial transmission map is estimated. The transmission map is further refined through a rolling guidance filter that preserves much of the image information such as textures, structures and edges in the final dehazed output. Finally, the haze-free image is produced by integrating the atmospheric light and refined transmission with the haze imaging model. Through detailed experimentation on several publicly available datasets, we showed that the proposed model achieved higher accuracy and can restore high-quality dehazed images as compared to the state-of-the-art models. The proposed model could be deployed as a real-time application for real-time image processing, real-time remote sensing images, real-time underwater images enhancement, video-guided transportation, outdoor surveillance, and auto-driver backed systems.
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs

The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs). Although there has been a lot of research done on models and algorithmic optimizations of CNN, little attention has been paid to the efficient implementation of these algorithms on embedded CPUs, which usually have very limited memory and low power budget. This paper aims to fill this gap and focuses on the efficient implementation of Winograd or Cook-Toom based convolution on modern Arm Cortex-A CPUs, widely used in mobile devices today. Specifically, we demonstrate a reduction in inference latency by using a set of optimization strategies that improve the utilization of computational resources, and by effectively leveraging the ARMv8-A NEON SIMD instruction set. We evaluated our proposed region-wise multi-channel implementations on Arm Cortex-A73 platform using several representative CNNs. The results show significant performance improvements in full network, up to 60%, over existing im2row/im2col based optimization techniques.
An empirical study on using virtual reality for enhancing the youth's intercultural sensitivity in Hong Kong

Abstract According to the census data published by the government in 2016, 92% of the Hong Kong Special Administrative Region population is ethnic Chinese, while the number of ethnic minorities increased significantly by around 70% over the past decade. Enhancing the youth’s intercultural sensitivity is becoming critical. Although many of the schools start to provide intercultural sensitivity training as extra‐curriculum activities, students often find them unattractive while teachers question their effectiveness. In this paper, we present a virtual reality enabled approach for enhancing the youth’s intercultural sensitivity. The objective is to design and develop an approach that can benefit from using virtual reality to deliver the instructional materials, making the learning more effective. A total number of three different virtual reality learning scenarios have been created, covering topics such as views of marriage and family, religious beliefs, and language barrier. The empirical study found significantly increase of intercultural sensitivity among the 67 qualified participants after joining the session. The calculated linear regression model also suggested that the participants’ perceived motivation from the virtual reality learning content positively contributed to the intercultural sensitivity increase, while interestingly the perceived enjoyment during the virtual reality exposure may negatively affected the learning outcomes.
SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learning

We explore means to advance source camera identification based on sensor noise in a data-driven framework. Our focus is on improving the sensor pattern noise (SPN) extraction from a single image at test time. Where existing works suppress nuisance content with denoising filters that are largely agnostic to the specific SPN signal of interest, we demonstrate that a deep learning approach can yield a more suitable extractor that leads to improved source attribution. A series of extensive experiments on various public datasets confirms the feasibility of our approach and its applicability to image manipulation localization and video source attribution. A critical discussion of potential pitfalls completes the text.
The sociotechnical roots of smart mobility: Bike sharing since 1965

The search for “smart” or Information and Communication Technology (ICT) based mobility solutions goes back to at least the 1960s. The Provo anarchist Luud Schimmelpennink is well-known for designing mobility solutions and for being the driving force behind the 1965 “white-bike” experience. Less known is his 1968 project for shared electric cars (“Witkar”), which laid the foundations for the ICT-based bicycle sharing systems as we know today. By combining his talent for innovation with activism, he created a socially embedded design that could be part of the public transit system. Based on primary sources, we argue that these sociotechnical experiences paved the way for today’s mainstream bicycle sharing projects worldwide. We then show how since the 1990s, the Dutch railroad’s public transit bicycle (OV-fiets) has transformed Schimmelpennink’s original anarchist idea of bike sharing into a sustainable public transit system – a feat that has eluded other programmes worldwide: the integration of the bicycle’s share in a door-to-door experience.
Ensembling Artificial Bee Colony With Analogy-Based Estimation to Improve Software Development Effort Prediction

Analogy-Based Estimation (ABE) is one of the promising estimation models used for predicting the software development effort. Researchers proposed different variants of the ABE model, but still, the most suitable procedure could not be produced for accurate estimation. In this study, an artificial Bee colony guided Analogy-Based Estimation (BABE) model is proposed which ensembles Artificial Bee Colony (ABC) with ABE for accurate estimation. ABC produces different weights, out of which the most appropriate is infused in the similarity function of ABE during the stage of model training, which are later used in the testing stage for evaluation. There are six real datasets utilized for simulating the model procedure. Five of these datasets are taken from the PROMISE repository. The predictive performance is improved for BABE over the existing ones. The most significant of its performance is found on the International Software Benchmarking Standards Group (ISBSG) dataset.
Breast Cancer Classification and Proof of Key Artificial Neural Network Terminologies

Classification is one of the interesting areas in the academic field of Neural Networks. Artificial Neural Networks (ANNs) have been extensively used in pattern recognition and classification of data in the supervised and unsupervised environment. The ANNs use advanced concepts of computer science where a machine mimics human intelligence while learning from possible experience. To make a machine self-adaptive and autonomous, the machine is properly trained on a training data-set and then subsequently tested on new data. The excellent quality of training of ANNs typically depends on the underlying architecture of the network they employ, for a specific instance, a considerable number of deep layers, number of key nodes in each distinct layer, epoch size, and activation function. In this academic paper, the practical importance of these architectural components is carefully investigated. This paper is precisely about providing a solution that how ANNs can help us in Breast Cancer Classification. Furthermore, sufficient proofs of some extremely important terminologies used in ANNs are also discussed which will clarify the important concepts of ANNs.
Interpretable Machine Learning—Tools to Interpret the Predictions of a Machine Learning Model Predicting the Electrical Energy Consumption of an Electric Arc Furnace

Machine learning (ML) is a promising modeling framework that has previously been used in the context of optimizing steel processes. However, many of the more advanced ML models, capable of providing more accurate predictions to complex problems, are often impossible to interpret. This makes the domain experts in the steel industry, to a large extent, hesitant to adopt these models. The valuable increase in model accuracy is diminished by the lack of model interpretability. Herein, Shapley additive explanations (SHAP) is applied to an advanced ML model, predicting the electrical energy (EE) consumption of an electric arc furnace (EAF). The insights from SHAP reveal the contributions from each input variable on the EE for every single heat in the prediction domain. These contributions are then evaluated based on process metallurgical experience.
Intuitionistic fuzzy grey cognitive maps for forecasting interval-valued time series

In many real-world forecasting problems, the time series under investigation can be approximated. In that case, instead of dealing with its exact values, only their minima and maxima achieved in the predefined periods are considered. Such an approximation forms interval-valued time series (ITS). To forecast ITS, we propose a new method that relies on fuzzy cognitive maps (FCMs). We adapt standard FCMs to the forecasting of ITS using interval-valued intuitionistic fuzzy sets. In this way, we develop a forecasting model called the Intuitionistic Fuzzy Grey Cognitive Map (IFGCM). We validate our IFGCM using publicly available stock market data for 10 indexes for which the estimation of potential investment losses (minima) and gains (maxima) is crucial. The results of these experiments prove the high efficiency of the IFGCM, especially compared with state-of-the-art models.
Logical performance of 9 qubit compass codes in ion traps with crosstalk errors

We simulate four quantum error correcting codes under error models inspired by realistic noise sources in near-term ion trap quantum computers: $T_2$ dephasing, gate overrotation, and crosstalk. We use this data to find preferred codes for given error parameters along with logical error biases and a pseudothreshold which compares the physical and logical gate failure rates for a CNOT gate. Using these results we conclude that Bacon-Shor-13 is the most promising near term candidate as long as the impact of crosstalk can be mitigated through other means.