Reviewerly/exHarmony huggingface dataset:
The Influence of Information Sharing in the Supply Chain Process on Business Performance: An Empirical Study

Supply chain management has become one of the focal points of competition nowadays.The importance of information sharing in supply chain management is also increasing in terms of business performance improvement.In this study, five different models were developed to investigate the effect of information sharing in the supply chain process of businesses on cost, flexibility, response, delivery and financial performance.The relationships among these models are examined by means of a structural equation model (SEM).This paper is focused on supply chain process, supply chain flexibility, environmental uncertainties and information sharing latent structures whose cumulative effects on the determined performance indicators have been examined.The survey data obtained from the companies within the scope of the ISO (Istanbul Chamber of Industry) 1000 were used in the analyses conducted, whose results proved that all the suggested models were adequate in terms of validity and reliability.Furthermore, it was determined in all models that environmental uncertainties affect the supply chain process, while supply chain flexibility affects the supply chain process and information sharing.
A Hybrid Cryptosystem to Enhance Security in IoT Health Care System

Internet of Things (IoT) based health care system provides an essential interface between tiny devices and customers, who require customary checking by remedial focus.The technological innovation is important to guarantee the data protection and security among customer and devices, though it is vulnerable by various security assaults.The cryptographic technique is a prominent method for data protection in a healthcare management system.Single cryptographic algorithm based solution suffered to provide efficient security as its high probability of attacks.So this paper proposes a hybrid cryptographic algorithm that secures the sensitive medicinal information in IoT health care system.The proposed hybrid algorithm is dealing with various security attacks in a proficient way, and also increases its performance when compared to other cryptographic algorithms.
Waypoint-Based Generation of Guided and Optimal Trajectories for Autonomous Tracking Using a Quadrotor UAV

\ Waypoint-based trajectory tracking control of a quadrotor UAV with robust performance attributes requires the generation of dynamically feasible and smooth trajectories.The smoothness of these trajectories relies on the adopted interpolation methods along with the constraints being considered (e.g.velocity, acceleration, and input constraints etc.).This paper briefly discusses the optimal trajectory generation and investigates Runge's phenomenon by presenting various feasible interpolation methods.Runge's issue may become severe due to sampling between waypoints when corridor constraints are considered with higher ordered polynomial interpolation.Trajectory generation requires the system dynamics to be differentially flat, therefore, a detailed proof is presented as well which shows clearly that quadrotor underactuated dynamics are differentially flat.For corridor constraints, higher order polynomials generate the trajectories near boundary walls, which may result in collision.This problem is solved using a scheme that guides these polynomials from one waypoint to another by incorporating a guiding term.The degree of smoothness and the shape of the desired path can be adjusted through the afore-mentioned guiding term.The proposed trajectory generation scheme can effectively reduce Runge's phenomenon and still offer finite and smooth curvatures at waypoints.Furthermore, the quadrotor model along with classical linear controller is used to demonstrate the smooth and faithful tracking of the optimal trajectories generated.
Robust visual tracking for planar objects using gradient orientation pyramid

Direct visual tracking (DVT) for planar objects is a fundamental problem in computer vision. DVT methods often formulate tracking as an image registration problem, where image intensities are directly used to match two images. However, these methods are usually sensitive to illumination changes as they assume intensities are constant. The gradient orientation (GO) was proven to be insensitive to illumination variations in previous reports. We further confirmed that the GO’s robustness can be significantly improved when the pyramid technique is employed. We present a robust DVT method, named gradient orientation pyramid efficient second-order minimization (GOP-ESM), based on the proposed gradient orientation pyramid descriptor. GOP-ESM takes the advantages of the robust feature descriptors and the efficient second-order minimization method as to enhance tracking robustness and accuracy. We also published a tracking dataset for planar objects with illumination changes (POIC). The evaluations on the proposed POIC dataset and the other two public benchmark datasets demonstrated that GOP-ESM outperforms the state-of-the-art tracking methods against various environmental variations, especially illumination changes.
Clustering acoustic emission signals by mixing two stages dimension reduction and nonparametric approaches

In the context of nuclear safety experiments, we consider curves issued from acoustic emission. The aim of their analysis is the forecast of the physical phenomena associated with the behavior of the nuclear fuel. In order to cope with the complexity of the signals and the diversity of the potential source mechanisms, we experiment innovative clustering strategies which creates new curves, the envelope and the spectrum, from each raw hits, and combine spline smoothing methods with nonparametric functional and dimension reduction methods. The application of these strategies prove that in nuclear context, adapted functional methods are effective for data clustering.
An Improved Recognition Approach for Noisy Multispectral Palmprint by Robust L2 Sparse Representation with a Tensor-Based Extreme Learning Machine

For the past decades, recognition technologies of multispectral palmprint have attracted more and more attention due to their abundant spatial and spectral characteristics compared with the single spectral case. Enlightened by this, an innovative robust L2 sparse representation with tensor-based extreme learning machine (RL2SR-TELM) algorithm is put forward by using an adaptive image level fusion strategy to accomplish the multispectral palmprint recognition. Firstly, we construct a robust L2 sparse representation (RL2SR) optimization model to calculate the linear representation coefficients. To suppress the affection caused by noise contamination, we introduce a logistic function into RL2SR model to evaluate the representation residual. Secondly, we propose a novel weighted sparse and collaborative concentration index (WSCCI) to calculate the fusion weight adaptively. Finally, we put forward a TELM approach to carry out the classification task. It can deal with the high dimension data directly and reserve the image spatial information well. Extensive experiments are implemented on the benchmark multispectral palmprint database provided by PolyU. The experiment results validate that our RL2SR-TELM algorithm overmatches a number of state-of-the-art multispectral palmprint recognition algorithms both when the images are noise-free and contaminated by different noises.
Automatic Detection and Diagnosis of Neurologic Diseases

This paper presents a hybrid proposal of a specialist system with a multicriteria decision analysis method, aiming to subsidize decisions in the early diagnosis of psychological disorders. Such disorders can even cause the inability of professionals from various fields of activity, notably of those related to innovation and automation of critical business processes, which exert more pressure on results. These cited disorders cause harm to the professional, to his family, to the company for which he works, to the productive system, and to the social security of a nation. The lack of early diagnosis and the undue attention given to the symptoms only provide reactive and late measures, when the losses have already occurred, in addition to the fact that the professional shows signs of incapacity for work and social life. This paper presents a model that facilitates the process of early diagnosis of various psychological disorders from the qualitative and comparative analysis of events and criteria, using multicriteria methodology associated with a specialist system. Therefore, the proposed model constitutes a modern and consistent tool that contributes to the decision to indicate diagnoses in psychological disorders. Among the various psychological disorders described and categorized in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association, this paper highlights the following: schizophrenia spectrum disorders, bipolar disorder, depressive disorders, anxiety disorders, obsessive-compulsive disorder, trauma-related disorders and stressors, dissociative disorders, and disorders related to substances and adverse disorders.
Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation

Temporal information plays an important role in Point-of-Interest (POI) recommendations. Most existing studies model the temporal influence by utilizing the observed check-in time stamps explicitly. With the conjecture that transition intervals between successive check-ins may carry more information for diversified behavior patterns, we propose a probabilistic factor analysis model to incorporate three components, namely, personal preference, distance preference, and transition interval preference. They are modeled by an observed third-rank transition tensor, a distance constraint, and a continuous latent variable, respectively. In our framework, the POI recommendation and the transition interval for user's very next move can be inferred simultaneously by maximizing the posterior probability of the overall transitions. Expectation Maximization (EM) algorithm is used to tune the model parameters. We demonstrate that the proposed methodology achieves substantial gains in terms of prediction on next move and its expected time over state-of-the-art baselines. Code related to this paper is available at: https://github.com/skyhejing/ECML-PKDD-2018 .
ProbeTools

column Share on ProbeTools: unconventional cameras and audio devices for user research Authors: Andy Boucher Goldsmiths, University of London Goldsmiths, University of LondonView Profile , Dean Brown Goldsmiths, University of London Goldsmiths, University of LondonView Profile , Bill Gaver Goldsmiths, University of London Goldsmiths, University of LondonView Profile , Naho Matsuda Goldsmiths, University of London Goldsmiths, University of LondonView Profile , Liliana Ovalle Goldsmiths, University of London Goldsmiths, University of LondonView Profile , Andy Sheen Goldsmiths, University of London Goldsmiths, University of LondonView Profile , Michail Vanis Goldsmiths, University of London Goldsmiths, University of LondonView Profile Authors Info & Claims InteractionsVolume 26Issue 2March - April 2019 pp 26–35https://doi.org/10.1145/3305358Published:22 February 2019Publication History 3citation1,024DownloadsMetricsTotal Citations3Total Downloads1,024Last 12 Months198Last 6 weeks18 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Visual Content Recognition by Exploiting Semantic Feature Map with Attention and Multi-task Learning

Recent studies have shown that spatial relationships among objects are very important for visual recognition, since they can provide rich clues on object contexts within the images. In this article, we introduce a novel method to learn the Semantic Feature Map (SFM) with attention-based deep neural networks for image and video classification in an end-to-end manner, aiming to explicitly model the spatial object contexts within the images. In particular, we explicitly apply the designed gate units to the extracted object features for important objects selection and noise removal. These selected object features are then organized into the proposed SFM, which is a compact and discriminative representation with the spatial information among objects preserved. Finally, we employ either Fully Convolutional Networks (FCN) or Long-Short Term Memory (LSTM) as the classifiers on top of the SFM for content recognition. A novel multi-task learning framework with image classification loss, object localization loss, and grid labeling loss are also introduced to help better learn the model parameters. We conduct extensive evaluations and comparative studies to verify the effectiveness of the proposed approach on Pascal VOC 2007/2012 and MS-COCO benchmarks for image classification. In addition, the experimental results also show that the SFMs learned from the image domain can be successfully transferred to CCV and FCVID benchmarks for video classification.
Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.
Word Embeddings for Entity-Annotated Texts

Learned vector representations of words are useful tools for many information retrieval and natural language processing tasks due to their ability to capture lexical semantics. However, while many such tasks involve or even rely on named entities as central components, popular word embedding models have so far failed to include entities as first-class citizens. While it seems intuitive that annotating named entities in the training corpus should result in more intelligent word features for downstream tasks, performance issues arise when popular embedding approaches are naïvely applied to entity annotated corpora. Not only are the resulting entity embeddings less useful than expected, but one also finds that the performance of the non-entity word embeddings degrades in comparison to those trained on the raw, unannotated corpus. In this paper, we investigate approaches to jointly train word and entity embeddings on a large corpus with automatically annotated and linked entities. We discuss two distinct approaches to the generation of such embeddings, namely the training of state-of-the-art embeddings on raw-text and annotated versions of the corpus, as well as node embeddings of a co-occurrence graph representation of the annotated corpus. We compare the performance of annotated embeddings and classical word embeddings on a variety of word similarity, analogy, and clustering evaluation tasks, and investigate their performance in entity-specific tasks. Our findings show that it takes more than training popular word embedding models on an annotated corpus to create entity embeddings with acceptable performance on common test cases. Based on these results, we discuss how and when node embeddings of the co-occurrence graph representation of the text can restore the performance.
The Silent Majority Speaks: Inferring Silent Users' Opinions in Online Social Networks

With the blossoming of social networking platforms like Twitter and Facebook, how to infer the opinions of online social network users on specific topics they had not directly given yet, has received much attention. Existing solutions mainly rely on one's previous posted messages. However, recent studies show that over 40% of users opt to be silent all or most of the time and post very few messages. Consequently, the performance of existing solutions will drop dramatically when they are applied to infer silent users' opinions, and how to infer the opinions of these silent users becomes a meaningful while challenging task. Inspired by the collaborative filtering techniques in cold-start recommendations, we infer the opinions of silent users by leveraging the text content posted by active users and their relationships between silent users. Specifically, we first consider both observed and pseudo relationships among users, and cluster users into communities in order to extract various kinds of features for opinion inference. We then design a coupled sparse matrix factorization (CSMF) model to capture the complex relations among these features. Extensive experiments on real-world data from Twitter show that our CSMF model achieves over 80% accuracy for the inference of silent users' opinions.
Impact of Social Media Usage on Organizational Performance in the Jordanian Dead Sea Cosmetic Sector

Due to the current debate on the positives and negatives of social media, most of the organizations are in an unclear state regarding the adoption of social media.Therefore, the purpose of this paper is to extend previous research on social media use comprising three variables: social media for marketing, social media for customer's relations and services, and social media for information accessibility and its impact on organizational performance in terms of rapid adaptation, time to market, cost reduction, and innovation in Jordan.The paper analyzed data obtained from a sample of 169 managers working at 23 different organizations in Dead Sea cosmetic sector in Jordan using a quantitative approach.Structural equation modelling used to test the hypotheses.Results showed that there is a strong positive impact of implementing and using social media on organizational performance in terms of rapid adaptation, cost reduction, and innovation.Also, social media usage is different among the companies' characteristics in terms of type, age, and size.This paper offers a clearer understanding of the importance of social media and its benefits.Also, this paper is one of the first studies conducted in Jordan and especially within the Dead Sea cosmetic sector by demonstrating the different areas of organizational performance that can be enhanced by using social media in different ways.
Determinants of &lt;i&gt;Bitcoin&lt;/i&gt; Expected Returns

In this study, we investigate the relationship between Bitcoin mining technology variables and Bitcoin returns, using a GARCH-M model.Additionally, we examine the predictive power of the mining technology variables on future Bitcoin returns.We find that mining difficulty and block size are inversely related to Bitcoin returns.Additionally, our findings signifying that the higher the block size the lower the Bitcoin price and consequently the lower the expected return.Second, our findings show that mining difficulty and block size are robust predictors of future Bitcoin returns.
Approximating Sample State Vectors Using the ESPT for Computationally Efficient Particle Filtering

The extrapolated single propagation technique (ESPT) based particle filter is proposed. Instead of propagating all the sample state vectors using multiple numerical integrations, only one sample state vector is propagated in the ESPT. Other sample states are computed at the next epoch with an accuracy of the second-order Taylor series expansion terms. It is theoretically shown that the particle filter algorithm, which uses the ESPT for state propagation, requires up to 90% reduced processing time than the conventional particle filter. Using a benchmark non-linear estimation problem the performance of the modified algorithm is compared with the particle filter. The experimental results confirm the theoretical analysis and demonstrate the effectiveness of the proposed method for non-linear state estimation. The proposed algorithm significantly reduces the processing time of the particle filter with a minor degradation in the estimation accuracy.
A Streaming Algorithm for k-Means with Approximate Coreset

For computing the [Formula: see text]-means clustering of the streaming and distributed big sparse data, we present an algorithm to obtain the sparse coreset for the [Formula: see text]-means in polynomial time. This algorithm is mainly based on the explicit form of the center of mass and the approximate [Formula: see text]-means. Because of the existence of the approximation, the coreset of the output inevitably has a factor, which can be controlled to be a very small constant.
The Comprehensive Assessment Method of Concrete Damage after Disastrous Fire Based on Game Theory-Normal Cloud Model

Rapidly and correctly assessing the extent of the damage to a concrete structure after a disastrous fire has great engineering significance. In this paper, an assessment method is proposed based on the game theory-normal cloud model. The surface colors of concrete, exfoliation, cracks, and hammering responses are chosen as the comprehensive assessment indices of concrete damage after fire to establish the corresponding assessment criteria. The normal cloud generator is used to calculate the certainty degree of the grading assessment index of concrete damage after fire. By adopting game theory, the objective weight obtained by the information entropy method and the subjective weight obtained by precedence chart are combined into a comprehensive weight. The comprehensive certainty degree value of concrete damage after fire is then calculated by certainty and weight matrices. The project case analysis shows the assessment results are both intuitive and realistic. This method provides a new approach to the assessment of concrete damage after fire which is more efficient and easier to apply than existing methods.
High PSNR based Image Steganography

Steganography is a method for inserting digital data within a different digital medium like text, pictures, sound signals, or film signals, while not exposing its occurrence in the medium.Information safekeeping is an essential necessary domain in correspondence medium over the web system.In this paper, working for improve the performance of Image Steganography.Results are showing comparison in between DCT-SVD and LWT-DCT-SVD.PSNR of proposed methodology is high as compare to DCT-SVD.