Reviewerly/exHarmony huggingface dataset:
REIN

In recent years, neural network has shown its great potential in autonomous driving systems. However, the theoretically well-train neural networks usually fail their performance when facing real-world examples with unexpected physical variations. As the current neural networks still suffer from limited generalization ability, those unexpected variations would cause considerable accuracy degradation and critical safety issues. Therefore, the generalization ability of neural networks becomes one of the most critical challenges for autonomous driving system design. In this work, we propose a robust training method to enhance neural network's generalization ability in various practical autonomous driving scenarios. Based on detailed practical variation modeling and neural network generation ability analysis, the proposed training method could consistently improve model classification accuracy by at most 25% in various scenarios (e.g. raining/fogy, dark lighting, and camera discrepancy). Even with adversarial corner cases, our model could still achieve at most 40% accuracy improvement over natural model.
Stochastic Programming Approach for Resource Selection Under Demand Uncertainty

Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand and availability of resources. In this paper, we present a stochastic optimization based framework for robust decision making in the selection of distributed resources over a planning horizon under demand uncertainty. We present a novel two-stage stochastic programming model for resource selection, and implement an L-shaped decomposition algorithm to solve this model. A Sample Average Approximation algorithm is integrated to enable stochastic optimization to solve problems with a large number of scenarios. Using the metric of stochastic solution, we demonstrate up to 30% cost reduction relative to solutions without explicit consideration of demand uncertainty for a 24-month problem. We also demonstrate up to 54% cost reduction relative to a previously developed solution for a 36-month problem. We further argue that the composition of resources selected is superior to solutions computed without explicit consideration of uncertainties. Given the importance of resource selection and scheduling of complex scientific workflows, especially in the context of commercial cloud computing, we believe that our novel stochastic programming framework will benefit many researchers as well as users of distributed computing resources.
LSD and Skeleton Extraction Combined with Farmland Ridge Detection

In the traditional agricultural machine-vision navigation, Hough transform was widely used to carry on the straight-line detection of the ridge line. As for the high complexity of Hough transform, amounts of pretreatment on the images need be done to reach the effective results, however, the detection results are unsatisfied. Plentiful false positive and false negative exist during the detection, so, it is difficult to attain the working accuracy of unmanned agricultural machine and hard to be used in the actual conditions. LSD (a Line segment detector) algorithm is a partial optimal detection algorithm, which bases the pixels gradient and obtains straight line through the regional growth, it owns some advantages such as rapid calculating, better testing result, rare false positive and false negative, etc. This paper attempted to apply the LSD algorithm to the agricultural machine-vision navigation, and advance the detection accuracy and results by extracting the skeleton of crops images. By experimenting on four different crops, the result demonstrates that the LSD algorithm is better than the Hough transform on the accuracy and effect, meanwhile, it has the better engineering-applied value.
Estimated Rating Based on Hours Played for Video Game Recommendation

This work presents a method to estimate ratings for video games based on the user’s playing hours. Based on these ratings, through collaborative filtering techniques, it is possible to make recommendations for video games without taking into account their popularity, solving the problem of long tail. The item-based k-NN algorithms and SVD++ are the ones that obtains the best results with the proposed estimation method, improving the original one and obtaining similar results in the rest of cases.
Investigating the Factors Affecting the Adoption of Cloud Computing in SMEs: A Case Study of Saudi Arabia

Cloud computing technology offers promising business advantages for small and medium-sized enterprises. In Saudi Arabia, the adoption of cloud computing by SMEs has so far occurred relatively slowly. This study investigates the factors affecting the adoption of cloud computing by SMEs in Saudi Arabia. A sample of fourteen interviews conducted with IT experts provides a foundation for exploring new factors that affect this decision, as incorporated into a conceptual model developed for this study. This model draws on the Technology-Organisation-Environment framework but adjusted based on new contextual factors. Factors such as the duration of software implementation, software integration, and software customisation guided this study. Furthermore, experts were of the view that other factors such as culture and technology infrastructure also impacted the adoption of cloud computing among SMEs. This study bridges the gap in the literature on cloud computing adoption with new evidence from the unexplored context of Saudi Arabia.
Bayesian Joint Modelling of Survival Time and Longitudinal CD4 Cell Counts Using Accelerated Failure Time and Generalized Error Distributions

Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts.Statistical modelling approaches are helpful towards this goal.This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients.Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015.The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters.Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software.The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution.The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases.Findings reveal that patients' health can be improved over time.Compared to the males, female patients gain more CD4 counts.Survival time of a patient is negatively affected by TB infection.Moreover, increase in number of opportunistic infection implies decline of CD4 counts.Patients' age negatively affects the disease marker with no effects on survival time.Improving weight may improve survival time of patients.Bayesian joint models with GED and AFT distributions are found to be useful How to cite this paper: Erango, M.
Real-time action recognition based on enhanced motion vector temporal segment network

At present, the method based on two-stream network has achieved good recognition performance in action recognition, however, its real-time performance is obstructed due to the high computational cost of optical flow. Temporal Segment Network (TSN), a successful example based on the two-stream network, achieves high recognition performance but cannot be processed in real time. In this paper, the motion vector TSN (MV-TSN) is proposed by introducing the motion vector into temporal segment networks, which greatly speeds up the processing speed of TSN. In order to solve the problem of performance degradation caused by the motion vectors lacking fine structure information, we propose a knowledge transfer strategy, which initializes the MV-TSN with the fine knowledge learned by optical flow. The experimental results show that the proposed method achieves a comparable recognition performance to the previous state-of-the-art approaches on UCF-101 and HMDB-51, and the processing speed is 206.2 fps, which is 13 times of the original TSN.
Bilateral Two-Dimensional Matrix Regression Preserving Discriminant Embedding for Corrupted Image Recognition

Nuclear-norm-based matrix regression (NMR) methods have been successfully applied for the recognition of corrupted images. However, most of these methods do not consider the label information and are classified as unsupervised learning methods. In this paper, we propose a new regression-based algorithm, named bilateral two-dimensional matrix regression preserving discriminant embedding (B2DMRPDE). The proposed algorithm constructs the within-reconstruction graph and between-reconstruction graph using NMR. Then, B2DMRPDE aims to seek a subspace in which the within-class reconstructive residual is minimized and the between-class reconstructive residual is maximized based on Fisher's criterion. Hence, B2DMRPDE can capture the potential discriminative information for classification. To enhance the classification effectiveness, we present a new NMR-based classifier to determine the class label of the testing sample. Extensive experiments on face image databases were performed, and the results validate the effectiveness of the proposed method.
Supervised Saliency Mapping for First-Person Videos With an Inverse Sparse Coding Framework

Saliency mapping is an efficient means of processing large amounts of incoming visual information from images and videos. Existing methods based on feature integration and construction error provide a rough saliency mapping and are sensitive to real-world noise. Furthermore, for first-person-vision video related to human activities, existing methods identify objects in the image without considering the human actions being performed. To address this issue, we propose a novel supervised saliency mapping based on a sparse coding framework. Different from the normal sparse representation, which uses dictionary atoms to represent signals, we use the inverse expression, whereby the use original image or video frame signals provide the normal dictionary, and these signals are used to represent the salience superpixel matrix, which is learned from a class of training images. We then construct the salient map by inverse sparse coding. In this paper, we first describe how the salience superpixel matrix is extracted by supervised selection, and then explain how to obtain the salient map through an inverse sparse coding framework. The experimental results show the enhanced accuracy of the proposed method compared with previous methods, as well as the time efficiency of the proposed method.
Generating value across academic and professional design practice in the Internet of Things

This article accounts for how value in design research can be derived from knowledge exchange that occurs through the collaborative discourse of doing design. To demonstrate this point, the article details the arc and impact of a design research collaboration that extended over several years with design practitioners in the field of internet-connected devices. As a result of this ongoing collaborative work, a programmatic set of design ideals and intentions concerning how to construct a more transparent and ethical relationship with technology was shaped. We examine this sustained and transformative collaboration as a series of knowledge exchanges. Within each particular collaborative project, worldviews and knowledge are exchanged among design researchers and practitioners, enabling them to traverse ‘through practices’ of academic and professional design. To unpack these exchanges and how they built and fed into each other, the article examines how collaboration unfolded and produced value on two levels: first on the scale of the academics and practitioners directly involved in a project; and secondly on the scale of the larger international community of design practitioners concerned with Internet-connected devices, with whom we had shared our work.
Perfectly secure message transmission over partially synchronous networks

In a distributed network, we consider two special nodes called the sender S and the receiver R that are connected by n node-disjoint (except for S and R) bi-directional wires. Out of these n wires, the adversary can control at most t wires (of its choice) in Byzantine fashion. In this setting, our goal is to design a message transmission protocol Π that assures the following two conditions hold: (1) by the end of the protocol Π, R gets the correct message m transmitted by S without any error (perfect reliability), and (2) the adversary learns no information about m, whatsoever, in information theoretic sense (perfect secrecy). Protocols that satisfy these two conditions are known as the Perfectly Secure Message Transmission (PSMT) protocols.
Circular interpolation and chronological-whale optimization based privacy preservation in cloud

Privacy preservation is one of the major challenges in the cloud environment during data mining. Securing the privacy of the information in the cloud is necessary to gain popularity among the users. The cloud model provides a virtualized environment and hence, it is necessary for the cloud service providers to establish a secure environment, and prevent malicious users from accessing the data content. In this paper, Circular interpolation and the Chronological-Whale Optimization algorithm (CIC-WOA) is proposed for preserving the privacy of the data before providing the data to the third party in the cloud. The proposed privacy preservation model identifies the key coefficient based on the proposed Chronological-WOA with the maximum privacy and utility. The proposed Chronological-WOA algorithm utilizes the Chronological concept, where the position information of the past events is employed, for the generation of the key. The simulation environment for the implementation of the proposed model uses three databases, such as Cleveland, Switzerland, Hungary, and VA. From the simulation results, it is noted that the proposed CIC-WOA model achieved overall better performance with the values of 0.2893, 0.65265, and 0.0455 for fitness measure, privacy, and utility, respectively.
Deep Modular Multimodal Fusion on Multiple Sensors for Volcano Activity Recognition

Nowadays, with the development of sensor techniques and the growth in a number of volcanic monitoring systems, more and more data about volcanic sensor signals are gathered. This results in a need for mining these data to study the mechanism of the volcanic eruption. This paper focuses on Volcano Activity Recognition (VAR) where the inputs are multiple sensor data obtained from the volcanic monitoring system in the form of time series. And the output of this research is the volcano status which is either explosive or not explosive. It is hard even for experts to extract handcrafted features from these time series. To solve this problem, we propose a deep neural network architecture called VolNet which adapts Convolutional Neural Network for each time series to extract non-handcrafted feature representation which is considered powerful to discriminate between classes. By taking advantages of VolNet as a building block, we propose a simple but effective fusion model called Deep Modular Multimodal Fusion (DMMF) which adapts data grouping as the guidance to design the architecture of fusion model. Different from conventional multimodal fusion where the features are concatenated all at once at the fusion step, DMMF fuses relevant modalities in different modules separately in a hierarchical fashion. We conducted extensive experiments to demonstrate the effectiveness of VolNet and DMMF on the volcanic sensor datasets obtained from Sakurajima volcano, which are the biggest volcanic sensor datasets in Japan. The experiments showed that DMMF outperformed the current state-of-the-art fusion model with the increase of F-score up to 1.9% on average.
Using Two Meaningful Shadows to Share Secret Messages with Reversibility

A subtopic of visual secret sharing (VSS) is information hiding-based VSS (IH-VSS), which embeds secret messages into images using an information hiding technique. In the IH-VSS scheme, stego-images are divided into shadows under the guidance and constraint of some predetermined approaches. In order to achieve the purpose of security and reliability, the hidden information cannot be recovered unless a certain amount or all of the credible shadows work together. In this paper, we propose a (2, 2) IH-VSS scheme with reversibility and friendliness. In the shadow generation phase, two meaningful shadow images are produced and then distributed. In the extraction and restoration phase, the hidden secret information and cover image, respectively, can be reconstructed credibly and correctly. No complex computation of shadow generation is involved, but high security is achieved. Moreover, a satisfying peak-signal-to-noise ratio (PSNR) is obtained with the high embedding capacity of 1.59 bpp in a very simple and effective way.
Heterogeneous Face Matching Using Robust Binary Pattern of Local Quotient: RBPLQ

This paper proposes a robust binary scheme for representing and matching near-infrared (NIR)–visible (VIS) and sketch–photo heterogeneous faces. It is termed as robust binary pattern of local quotient (RBPLQ). RBPLQ provides illumination-invariant and noise-resistant features in coarse level. At first, a local quotient (LQ) is extracted for representing illumination-invariant image. Then, a robust local binary feature is proposed to capture the variations of LQ. The proposed technique is applied to different benchmark databases of NIR-VIS and sketch–photo images. Recognition accuracy of 60.72% is achieved in the NIR-VIS database. In the CUFSF database, which is a viewed sketch–photo database, the recognition accuracy of 96.24% is achieved. Extended Yale B database is also tested for verifying the illumination-invariant property of RBPLQ, and it achieved recognition accuracy of 94.20%. Finally, RBPLQ also provides good performance in the case of noisy situations.
Improving preliminary cost estimation in Indonesia using support vector regression

A typical preliminary cost estimation for a building construction project in Indonesia may take weeks and have an error rate varying from around −13 to +27%. The research objectives of this study were thus to determine the factors that influence cost estimation in Indonesia and to develop a support vector regression model to improve the accuracy and reduce the work hours for preliminary cost estimation. The literature review identified 14 factors that have the most influence on cost estimation in Indonesia. Data collection was carried out to gather information randomly on 104 building cases in Indonesia deemed to contain valid information for the proposed model. The model with a radial basis function kernel was established after data trimming, analysis and normalisation. The proposed model was then evaluated and implemented using fivefold cross-validation and yielded an average accuracy of 96% for preliminary cost estimation of building construction projects. The model’s average accuracy was 87%, it was efficient and resulted in significant time savings.
Policies to Regulate Distributed Data Exchange

Data sharing is becoming an integral part of many aspects of our daily lives. We propose a method for controlling access to data and knowledge through fine-grained, user-specified explicitly represented policies. We present an overview of a policy formalism and mechanisms to facilitate distributed data sharing. We provide a breakdown of how our approach defines compliance and violation, specifically providing a new outlook on violation of permissions within the context of data sharing. We also examine how our mechanisms have been adapted to support socially responsible interactions between participants, whilst still providing them with control over their own data. We also explore a series of planned experiments investigating how users understand and interact with policies in a simplified version of our formalism.
Catch-phrase based Document Representation for Improved Prior Art Search

With rising number of patent infringement cases, it is vital to both individual researchers and patent lawyers to search and assess existing patents similar to a patent before its filing. This paper proposes a query reduction based methodology that furnishes a set of ranked patent documents similar to a given input document. It utilizes catch phrase based document representation to measure patent similarity. A new measure for quantifying the similarity between overlapping catch phrases is proposed. Different document similarity measures utilizing the catch phrase similarity score are also presented. Proposed measures are evaluated using Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (nDCG).
Feature Extraction Shape Kawi Numbers and Java Images Using The Zernike Moment

The writing of Kawi and Javanese numeric characters has different forms, in order to find out the extraction value of the image form features of Kawi and Javanese numerals, the Moment Zernike method is free from rotation. To prove the Zernike Moment method feature extraction, a number of rare research processes are carried out, the steps are (1) image input, (2) grayscale, (2) threshold and region growing segmentation process, (3) extraction of the Zernike Moment feature. The results obtained from the test from the nearest value approach using city block extraction feature Kawi and Java numeric images with extraction features of rotated features 90° and 180°. The results obtained are the same as 0.01 for the Kawi 1 and 0.02 numbers for Java number 2.
Minimum Database Determination and Preprocessing for Machine Learning

The exploitation of large databases implies the investment of expensive resources both in terms of the storage and processing time. The correct assessment of the data implies that pre-processing steps be taken before its analysis. The transformation of categorical data by adequately encoding every instance of categorical variables is needed. Encoding must be implemented that preserves the actual patterns while avoiding the introduction of non-existing ones. The authors discuss CESAMO, an algorithm which allows us to statistically identify the pattern preserving codes. The resulting database is more economical and may encompass mixed databases. Thus, they obtain an optimal transformed representation that is considerably more compact without impairing its informational content. For the equivalence of the original (FD) and reduced data set (RD), they apply an algorithm that relies on a multivariate regression algorithm (AA). Through the combined application of CESAMO and AA, the equivalent behavior of both FD and RD may be guaranteed with a high degree of statistical certainty.