Reviewerly/exHarmony huggingface dataset:
Time is of the Essence

In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several additions have been proposed for extending such models in order to handle specific problems or data. Two such extensions are 1.) modeling of inter-session relations for catching long term dependencies over user sessions, and 2.) modeling temporal aspects of user-item interactions. The former allows the session-based recommendation to utilize extended session history and inter-session information when providing new recommendations. The latter has been used to both provide state-of-the-art predictions for when the user will return to the service and also for improving recommendations. In this work, we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction. The model consists of a Hierarchical RNN for the inter-session and intra-session items recommendation extended with a Point Process model for the time-gaps between the sessions. The experimental results indicate that the proposed model improves recommendations significantly on two datasets over a strong baseline, while simultaneously improving return-time predictions over a baseline return-time prediction model.
Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking

While deep learning has achieved significant advances in accuracy for medical image segmentation, its benefits for deformable image registration have so far remained limited to reduced computation times. Previous work has either focused on replacing the iterative optimization of distance and smoothness terms with CNN-layers or using supervised approaches driven by labels. Our method is the first to combine the complementary strengths of global semantic information (represented by segmentation labels) and local distance metrics that help align surrounding structures. We demonstrate significant higher Dice scores (of 86.5 %) for deformable cardiac image registration compared to classic registration (79.0 %) as well as label-driven deep learning frameworks (83.4%).
Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms

Anti-discrimination is an increasingly important task in data science. In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data are used for predictive analysis (e.g., building classifiers). The main drawback of existing methods is that they cannot distinguish the part of influence that is really caused by discrimination from all correlated influences. In our approach, we make use of the causal graph to capture the causal structure of the data. Then, we model direct and indirect discrimination as the path-specific effects, which accurately identify the two types of discrimination as the causal effects transmitted along different paths in the graph. For certain situations where indirect discrimination cannot be exactly measured due to the unidentifiability of some path-specific effects, we develop an upper bound and a lower bound to the effect of indirect discrimination. Based on the theoretical results, we propose effective algorithms for discovering direct and indirect discrimination, as well as algorithms for precisely removing both types of discrimination while retaining good data utility. Experiments using the real dataset show the effectiveness of our approaches.
Does CIO risk appetite matter? Evidence from information security breach incidents

After a series of recent high-profile information security breach incidents, practitioners have engaged in heated debates about the role of the chief information officer (CIO), particularly his/her role in information security risk management. However, little is known in the academic literature about how a CIO's appetite for risk affects the effectiveness of information security management. We address this gap by examining how a CIO's risk appetite is associated with information security breach incidents. We show that the level of CIO risk aversion is negatively associated with the likelihood of breach incidents. Furthermore, we find that this association is stronger if the company's chief executive officer (CEO) is also risk averse. In additional analyses, we show that the relationship between CIO risk aversion and breach incidents varies depending on breach type and the strategic position of the company and is moderated by the CIO's power.
A new approach to modelling the effects of cognitive processing and threat detection on phishing susceptibility

Phishing threats are real and are ever increasing in their reach and devastating effects. This study delves into the role of cognitive processing in detecting and curtailing phishing attacks. The proposed model is grounded on the Elaboration Likelihood Model and is tested empirically using data from 192 cases. Data was collected through direct observations of phishing susceptibility and self-reported questionnaires after staging a phishing attack targeting a university population in Nairobi, Kenya. The model was found to have excellent fit and was able to account for 50.8% of a person's cognitive processing of a phishing attack, 69.5% of their ability to detect phishing threats and could predict 28% of their actual phishing susceptibility. Analysis was done to test 25 hypothesis, and to examine the mediating effects of cognitive processing and threat detection. In addition, multi-group moderation analysis was done to examine if the model was invariant based on the level of knowledge. Results indicate that threat detection has the strongest effect in reducing phishing susceptibility. Threat detection was found to be what explains why people who expend cognitive effort processing phishing communication are less likely to fall for phishing threats.
Weighted Tensor Rank-1 Decomposition for Nonlocal Image Denoising

Natural images often contain patches with high similarity. In this paper, to effectively utilize the local and nonlocal self-similarity for low-rank models, we propose a novel weighted tensor rank-1 decomposition method (terms as WTR1) for nonlocal image denoising. Although the low-rank approximation problem has been well studied for matrices, it remains elusive of the theoretically extension to tensors due to the NPhard tensor decomposition. To tackle this problem, the proposed WTR1 method designs a new efficient CANDECOMP/PARAFAC (CP) decomposition algorithm and constructs a straightforward low-rank tensor approximation strategy. This is achieved by elegantly manipulating the CP-rank, called intrinsic low-rank tensor approximation. Specifically, the WTR1 method first groups similar patches into a 3-D stack and converts the stack into a finite sum of rank-1 products. Then, we deploy the intrinsic low-rank tensor approximation to produce the final denoised image. The proposed WTR1 method can jointly exploit the local and nonlocal self-similarity, thus improving the nonlocal image denoising quality. Experimental results have shown that the proposed WTR1 outperforms several state-of-the-art denoising methods.
Addressing noise and pitch sensitivity of speech recognition system through variational mode decomposition based spectral smoothing

In this paper, we propose a novel front-end speech parameterization technique for automatic speech recognition (ASR) that is less sensitive towards ambient noise and pitch variations. First, using variational mode decomposition (VMD), we break up the short-time magnitude spectrum obtained by discrete Fourier transform into several components. In order to suppress the ill-effects of noise and pitch variations, the spectrum is then sufficiently smoothed. The desired spectral smoothing is achieved by discarding the higher-order variational mode functions and reconstructing the spectrum using the first-two modes only. As a result, the smoothed spectrum closely resembles the spectral envelope. Next, the Mel-frequency cepstral coefficients (MFCC) are extracted using the VMD-based smoothed spectra. The proposed front-end acoustic features are observed to be more robust towards ambient noise and pitch variations than the conventional MFCC features as demonstrated by the experimental evaluations presented in this study. For this purpose, we developed an ASR system using speech data from adult speakers collected under relatively clean recording conditions. State-of-the-art acoustic modeling techniques based on deep neural networks (DNN) and long short-term memory recurrent neural networks (LSTM-RNN) were employed. The ASR systems were then evaluated under noisy test conditions for assessing the noise robustness of the proposed features. To assess robustness towards pitch variations, experimental evaluations were performed on another test set consisting of speech data from child speakers. Transcribing children's speech helps in simulating an ASR task where pitch differences between training and test data are significantly large. The signal domain analyses as well as the experimental evaluations presented in this paper support our claims.
An integrated service recommendation approach for service-based system development

With the wide adoption of service-oriented computing and cloud computing, service-based systems (SBSs), a kind of software systems that can offer certain functionalities by leveraging one or more Web services, become increasingly popular. A challenging issue in SBS development is to find suitable services from a variety of available (semantics different) services. Towards this issue, we propose a new service recommendation approach that can integrate diverse information of SBSs and their component services. In this research, SBSs, services, their respective attributes (e.g. content and categories) and SBS-service composition relations are modeled as a heterogeneous information network (HIN); and several semantic similarities between SBSs are measured on a set of meta-paths in the HIN. Particularly, a word embedding technique is used to learn word vectors from the content of SBSs and services, which contribute to better functional similarities between SBSs. Afterwards, the combinational weights of different similarities are optimized using a Bayesian personalized ranking algorithm. Services are finally recommended based on collaborative filtering. We identify two recommendation scenarios with different SBS requirements. By conducting a series of experiments on a real-world dataset crawled from the ProgrammableWeb, we validate the effectiveness of our approach and find out the optimal combinations of SBS similarities for those two scenarios.
GERF: A Group Event Recommendation Framework Based on Learning-to-Rank

Event recommendation is an essential means to enable people to find attractive upcoming social events, such as party, exhibition, and concert. While growing line of research has focused on suggesting events to individuals, making event recommendation for a group of users has not been well studied. In this paper, we aim to recommend upcoming events for a group of users. We formalize group recommendation as a ranking problem and propose a group event recommendation framework GERF based on learning-to-rank technique. Specifically, we first analyze different contextual influences on user's event attendance, and extract preference of user to event considering each contextual influence. Then, the preference scores of the users in a group are taken as the features for learningto-rank to model the preference of the group. Moreover, a fast pairwise learning-to-rank algorithm, Bayesian group ranking, is proposed to learn ranking model for each group. Our framework is easily to incorporate additional contextual influences, and can be applied to other group recommendation scenarios. Extensive experiments have been conducted to evaluate the performance of GERF on two real-world datasets and demonstrate the appealing performance of our method on both accuracy and time efficiency.
Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes

There has been a rapid increase in the number of Internet of Things (IoT) devices in the last few years, providing a wide range of services such as camera feeds, light controls, and door locks for remote access. Access to IoT devices, whether within the same environment or remotely via the Internet, requires proper security mechanisms in order to avoid disclosing any secure information or access privileges. Authentication, on which other security classes are built, is the most important part of IoT security. Without ensuring that the authorized party is who it claims to be, other security factors would be useless. Additionally, with the increased mobility of IoT devices, traditional authentication mechanisms, such as a username and password, are less effective. Numerous security challenges in the IoT domain have resulted in the proposal of many different approaches to authentication. Many of these methods require either carrying an authentication token, such as a smartcard, or restricting access to a particular physical location. Considering that most IoT devices contain a wide array of sensors, a large amount of contextual information can be provided. Thus, real-time security mechanisms can protect user access by, for example, utilizing contextual information to validate requests. A variety of contextual information can be retrieved to strengthen the authentication process, both at the time of access request and throughout the entire access session, without requiring user interaction, which avoids the risk of being discovered by attackers of these features. In this paper, we introduce a continuous authentication framework that integrates contextual information for user authentication in smart homes. The implementation and evaluation show that the framework can protect smart devices against unauthorized access from both anonymous and known users, either, locally or remotely, in a flexible manner and without requiring additional user intervention.
Robust and Secure Image Fingerprinting Learned by Neural Network

Image fingerprinting is a technique that summarizes the perceptual characteristics of a digital image into an invariant digest, and it is one of the most effective solutions for digital rights management. Most conventional fingerprinting algorithms were developed by assembling manually designed feature extractor and quantizer, which requires extensive expert knowledge and may not capture the intrinsic or abstract visual characteristics of the digital image. Focusing on content identification related applications, we propose a data-driven image fingerprinting algorithm in this paper, where neural network is trained to automatically discover the optimal mapping from image to fingerprint. To ameliorate the difficulty of training, we start by training the fingerprint-computation network in a layer-wise manner to progressively improve its robustness against content-preserving distortions. Initialized by the states learned by layer-wise training, the network is then re-trained as a holistic unit, with the objective of maximizing its content identification accuracy. Moreover, we also develop a key-dependent version of the neural network-based fingerprinting algorithm. By quantifying its security using information-theoretic metrics, we have proved that the hierarchical architecture of neural network is beneficial to the security of fingerprinting algorithm. The experimental results on a large testing database show that the proposed work exhibits much higher content identification accuracy than state-of-the-art algorithms, and its execution speed is in the millisecond time scale.
UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition

Current UAV-recorded datasets are mostly limited to action recognition and object tracking, whereas the gesture signals datasets were mostly recorded in indoor spaces. Currently, there is no outdoor recorded public video dataset for UAV commanding signals. Gesture signals can be effectively used with UAVs by leveraging the UAVs visual sensors and operational simplicity. To fill this gap and enable research in wider application areas, we present a UAV gesture signals dataset recorded in an outdoor setting. We selected 13 gestures suitable for basic UAV navigation and command from general aircraft handling and helicopter handling signals. We provide 119 high-definition video clips consisting of 37151 frames. The overall baseline gesture recognition performance computed using Pose-based Convolutional Neural Network (P-CNN) is 91.9%. All the frames are annotated with body joints and gesture classes in order to extend the dataset’s applicability to a wider research area including gesture recognition, action recognition, human pose recognition and situation awareness.
Breast tumor classification using different features of quantitative ultrasound parametric images

The ultrasound B-mode-based morphological and texture analysis and Nakagami parametric imaging have been proposed to characterize breast tumors. Since these three feature categories of ultrasonic tissue characterization supply information on different physical characteristics of breast tumors, by combining the above methods is expected to provide more clues for classifying breast tumors.To verify the validity of the concept, raw data were obtained from 160 clinical cases. Six different types of morphological-feature parameters, four texture features, and the Nakagami parameter of benignancy and malignancy were extracted for evaluation. The Pearson's correlation matrix was used to calculate the correlation between different feature parameters. The fuzzy c-means clustering and stepwise regression techniques were utilized to determine the optimal feature set, respectively. The logistic regression, receiver operating characteristic curve, and support vector machine were used to estimate the diagnostic ability.The best performance was obtained by combining morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter, with an accuracy of 89.4%, a specificity of 86.3%, a sensitivity of 92.5%, and an area under receiver operating characteristic curve of 0.96. There was no significant difference between using fuzzy c-means clustering, logistic regression, and support vector machine based on the optimal feature set for breast tumors classification.Therefore, we verified that different physical ultrasonic features are functionally complementary and thus improve the performance in diagnosing breast tumors. Moreover, the optimal feature set had the maximum discriminating performance should be irrelative to the power of classifiers.
Joint Learning of Question Answering and Question Generation

Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in the literature. In this paper, we present two training algorithms for learning better QA and QG models through leveraging one another. The first algorithm extends Generative Adversarial Network (GAN), which selectively incorporates artificially generated instances as additional QA training data. The second algorithm is an extension of dual learning, which incorporates the probabilistic correlation of QA and QG as additional regularization in training objectives. To test the scalability of our algorithms, we conduct experiments on both document based and table based question answering tasks. Results show that both algorithms improve a QA model in terms of accuracy and QG model in terms of BLEU score. Moreover, we find that the performance of a QG model could be easily improved by a QA model via policy gradient, however, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Our algorithm that selectively assigns labels to generated questions would bring a performance boost.
RGB-D Salient Object Detection by a CNN With Multiple Layers Fusion

Recent saliency detectors use depth to improve the precision of the results. However, most of the existing RGB-D saliency detectors only treat depth as an additional feature, which cannot explore the distinguishing ability of the depth map. In this letter, we propose a deep convolutional neural network for RGB-D saliency detection. The network takes RGB-D as inputs and produces a saliency prediction in an end-to-end manner. To solve the scale problem, we fuse the features of higher layers to the features of lower layers gradually. Our method not only fully explores the information of the depth, but also makes the RGB and depth tightly coupled. Compared with the state-of-the-art RGB saliency detectors and depth-aware saliency detectors on two benchmark datasets, our method outperforms the competitors on mean absolute error and F-measure by large margins.
Emotional Processes of Foreign-Language Learning Situated in Real-Time Teacher Support

The dynamic turn in the field of psychology of foreign-language learning has inspired researchers to capture the nitty-gritty dynamics of development in inter-or intraindividual variables.Despite the growing number of techniques for analyzing dynamics, there is still a need for techniques that capture how intraindividual dynamics are situated in a changing context.One of the techniques that can contribute to this knowledge is a clustering technique called Kohonen's Self-Organizing Maps (SOM).In this study, we aimed to explore the intraindividual process of foreign-language enjoyment and foreign-language classroom anxiety in alignment with teachers' level of emotional support during teacher-student interactions for two dyads.The findings indicated the emergence of recurring patterns of teacher support, and student anxiety and enjoyment.These patterns highlight the self-organizing nature of these teacher-student interactions, the bidirectional nature of this process, and, in general, the notion of teachers and students as dynamic systems.Moreover, the specific nature of the emergent patterns suggests that the traditional positive association between teacher support and student affect may (mostly, but not always) be generalized to real-time and real-life processes.And finally, our results point toward the importance of the student in determining the affective nature of the interactions from moment to moment.