Reviewerly/exHarmony huggingface dataset:
Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution

Ultrasound imaging is most popular technique used for breast cancer screening. Lesion segmentation is challenging step in characterization of breast ultrasound (US) based Computer Aided Diagnosis (CAD) systems due to presence of speckle noise, shadowing effect etc. The aim of this study is to develop an automatic lesion segmentation technique in breast US with high accuracy even in presence of noises, artifacts and multiple lesions. This article presents a novel clustering method called Multi-scale Gaussian Kernel induced Fuzzy C-means (MsGKFCM) for segmentation of lesions in automatically extracted Region of Interest (ROI) in US to delimit the border of the mass. Further, a hybrid approach using MsGKFCM and Multi-scale Vector Field Convolution (MsVFC) is proposed to obtain an accurate lesion margin in breast US images. Initially, the images are filtered using speckle reducing anisotropic diffusion (SRAD) technique. Subsequently, MsGKFCM is applied on filtered images to segment the mass and detect an appropriate cluster center. The detected cluster center is further used by MsVFC to determine the accurate lesion margin. The proposed technique is evaluated on 127 US images using measures such as Jaccard Index, Dice similarity, Shape similarity, Hausdroff difference, Area difference, Accuracy, F-measure and analysis of variance (ANOVA) test. The empirical results suggest that the proposed approach can be used as an expert system to assist medical professionals by providing objective evidences in breast lesion detection. Results obtained are so far looking promising and effective in comparison to state-of-the-art algorithms.
Predicting Driver Attention in Critical Situations

Robust driver attention prediction for critical situations is a challenging computer vision problem, yet essential for autonomous driving. Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol—tracking eye movements during driving. Here, we first propose a new in-lab driver attention collection protocol and introduce a new driver attention dataset, Berkeley DeepDrive Attention (BDD-A) dataset, which is built upon braking event videos selected from a large-scale, crowd-sourced driving video dataset. We further propose Human Weighted Sampling (HWS) method, which uses human gaze behavior to identify crucial frames of a driving dataset and weights them heavily during model training. With our dataset and HWS, we built a driver attention prediction model that outperforms the state-of-the-art and demonstrates sophisticated behaviors, like attending to crossing pedestrians but not giving false alarms to pedestrians safely walking on the sidewalk. Its prediction results are nearly indistinguishable from ground-truth to humans. Although only being trained with our in-lab attention data, the model also predicts in-car driver attention data of routine driving with state-of-the-art accuracy. This result not only demonstrates the performance of our model but also proves the validity and usefulness of our dataset and data collection protocol.
Topic-enhanced emotional conversation generation with attention mechanism

Emotional conversation generation has elicited a wide interest in both academia and industry. However, existing emotional neural conversation systems tend to ignore the necessity to combine topic and emotion in generating responses, possibly leading to a decline in the quality of responses. This paper proposes a topic-enhanced emotional conversation generation model that incorporates emotional factors and topic information into the conversation system, by using two mechanisms. First, we use a Twitter latent Dirichlet allocation (LDA) model to obtain topic words of the input sequences as extra prior information, ensuring the consistency of content between posts and responses for emotional conversation generation. Second, the system uses a dynamic emotional attention mechanism to adaptively acquire content-related and affective information of the input texts and extra topics. The advantage of this study lies in the fact that the presented model can generate abundant emotional responses, with the contents being related and diverse. To demonstrate the effectiveness of our method, we conduct extensive experiments on large-scale Weibo post–response pairs. Experimental results show that our method achieves good performance, even outperforming some existing models.
A Thorough Trust and Reputation Based RBAC Model for Secure Data Storage in the Cloud

Cloud computing is a widespread technology, which has attracted much attention nowadays. Among the many criteria that must be considered for data storage in the cloud, access control plays a vital role. Role-based access control (RBAC) is a well-known technique for secure data storage in the cloud. Since the traditional RBAC models are improper for open and decentralized environments, recently, some works have integrated the trust concept into the RBAC model. Nevertheless, they have not fully addressed the required security metrics of a trust-based system. Therefore, in this paper, we first introduce the security goals that should be considered in an efficient trust-based system. Second, we propose a novel trust and reputation based RBAC model that not only can properly withstand the security threats of trust-based RBAC models, but also is scalable as it has reasonable execution time. Third, we evaluate the proposed model using the famous trust network of advogato dataset. Eventually, we compare the proposed model with recently-published ones in terms of mean absolute error, execution time of indirect trust computation, and provided features. The achieved results are indicative of the priority of the proposed model to be employed in real cloud environments.
Multiview Multitask Gaze Estimation With Deep Convolutional Neural Networks

Gaze estimation, which aims to predict gaze points with given eye images, is an important task in computer vision because of its applications in human visual attention understanding. Many existing methods are based on a single camera, and most of them only focus on either the gaze point estimation or gaze direction estimation. In this paper, we propose a novel multitask method for the gaze point estimation using multiview cameras. Specifically, we analyze the close relationship between the gaze point estimation and gaze direction estimation, and we use a partially shared convolutional neural networks architecture to simultaneously estimate the gaze direction and gaze point. Furthermore, we also introduce a new multiview gaze tracking data set that consists of multiview eye images of different subjects. As far as we know, it is the largest multiview gaze tracking data set. Comprehensive experiments on our multiview gaze tracking data set and existing data sets demonstrate that our multiview multitask gaze point estimation solution consistently outperforms existing methods.
Quality assessment of answers with user-identified criteria and data-driven features in social Q&amp;A

The purpose of the current study is to identify the user criteria and data-driven features, both textual and non-textual, for assessing the quality of answers posted on social questioning and answering sites (social Q&A) across four different knowledge domains—Science, Technology, Art and Recreation. A comprehensive review of literature on quality assessment of information produced in social contexts was carried out to develop the theoretical framework for the current study. A total of 23 user criteria and 24 data features were proposed and tested with high-quality answers obtained from four social Q&A sites in Stack Exchange. Findings indicate that content-related criteria and user and review features were the most frequently used in quality assessments, while the importance of user criteria and data features was variable across the knowledge domains. In the Technology Q&A site containing mostly self-help questions, the utility class was the most frequently used group of criteria. The popularity of the socio-emotional class was more apparent in discussion-oriented topic categories such as Art and Recreation, where people seek others’ opinions or advice. Users of Art and Recreation Q&A sites in Stack Exchange appear to place more value on answerers’ efforts and time, good attitudes or manners, personal experience, and the same taste. The importance of user features and the emphasis on answerer's expertise on the Science Q&A site was observed. Examining the connection or gap between user quality criteria and data features across the knowledge domains could help to better understand users’ evaluation behaviors for their preferred answers, and identify the potential of social Q&A for user education/intervention in answer quality evaluation. This examination also offers practical guidance for designing more effective social Q&A platforms, considering how to customize community support systems, motivate contributions, and control content quality.
Pair-cat codes: autonomous error-correction with low-order nonlinearity

We introduce a driven-dissipative two-mode bosonic system whose reservoir causes simultaneous loss of two photons in each mode and whose steady states are superpositions of pair-coherent/Barut-Girardello coherent states. We show how quantum information encoded in a steady-state subspace of this system is exponentially immune to phase drifts (cavity dephasing) in both modes. Additionally, it is possible to protect information from arbitrary photon loss in either (but not simultaneously both) of the modes by continuously monitoring the difference between the expected photon numbers of the logical states. Despite employing more resources, the two-mode scheme enjoys two advantages over its one-mode cat-qubit counterpart with regards to implementation using current circuit QED technology. First, monitoring the photon number difference can be done without turning off the currently implementable dissipative stabilizing process. Second, a lower average photon number per mode is required to enjoy a level of protection at least as good as that of the cat-codes. We discuss circuit QED proposals to stabilize the code states, perform gates, and protect against photon loss via either active syndrome measurement or an autonomous procedure. We introduce quasiprobability distributions allowing us to represent two-mode states of fixed photon number difference in a two-dimensional complex plane, instead of the full four-dimensional two-mode phase space. The two-mode codes are generalized to multiple modes in an extension of the stabilizer formalism to non-diagonalizable stabilizers. The $M$-mode codes can protect against either arbitrary photon losses in up to $M-1$ modes or arbitrary losses and gains in any one mode.
Smart Musical Instruments: Vision, Design Principles, and Future Directions

Smart musical instruments (SMIs) are a family of Internet of Musical Things devices for music creation. They are characterized by sensors, actuators, embedded intelligence, and wireless connectivity to local networks and to the Internet. In this paper, we depict a vision for this recent research area, which merges the research fields on digital musical instruments and smart objects, and fosters new types of interactions between the player and the instrument, between the player and other players, and between the player and audience members. We propose a set of capabilities and technical features characterizing SMIs, as well as a design for a technological architecture supporting them. To illustrate possible applications enabled by this vision, we present a set of scenarios exploiting the intelligence embedded in SMIs. We also propose a set of guidelines that can help digital luthiers in the process of designing SMIs. Finally, we present a set of directions for future research.
Semi-supervised dimensional sentiment analysis with variational autoencoder

Dimensional sentiment analysis (DSA) aims to compute real-valued sentiment scores of texts in multiple dimensions such as valence and arousal. Existing methods for DSA are usually based on supervised learning. However, it is expensive and time-consuming to annotate sufficient samples for training. In this paper, we propose a semi-supervised approach for DSA based on the variational autoencoder model. Our model consists of three modules: an encoding module to encode sentences into hidden vectors, a sentiment prediction module to predict the sentiment scores of sentences, and a decoding module that takes the outputs of the preceding two modules as input and reconstructs the input sentences. In our approach, the sentiment prediction module is encouraged to accurately predict sentiment scores of both labeled and unlabeled texts to help the decoding module reconstruct such texts more accurately. Thus, our approach can exploit useful information in unlabeled data. Experimental results on three benchmark datasets show that our approach can effectively improve the performance of DSA with considerably less labeled data.
Efficient Variable Rate Image Compression With Multi-Scale Decomposition Network

While deep learning image compression methods have shown an impressive coding performance, most of them output a single-optimized-compression rate using a trained-specific network. However, in practice, it is essential to support the variable rate compression or meet a target rate with a high-coding performance. This paper proposes a novel image compression method, making it possible for a single convolutional neural network (CNN) model to generate the variable rate efficiently with an optimized rate-distortion (RD) performance. The method consists of CNN-based multi-scale decomposition transform and content adaptive rate allocation. Specifically, the transform network is learned to decompose the input image into several scales of representations while optimizing the RD performance for all scales. Rate allocation algorithms for two typical scenarios are provided to determine the optimal scale of each image block for a given target rate or quality factor. For a target rate, the allocation is adaptive based on content complexity. In addition, for a target quality factor which indicates a tradeoff between the rate and the quality, the optimal scale is determined by minimizing the RD cost. The experimental results have shown that our method has outperformed the JPEG2000 and BPG standards with high efficiency and the state-of-the-art RD performance as measured by the multi-scale structural similarity index metric. Moreover, our method can strictly control the rate to generate the target compression result.
Fixation-related Brain Potentials during Semantic Integration of Object–Scene Information

Abstract In vision science, a particularly controversial topic is whether and how quickly the semantic information about objects is available outside foveal vision. Here, we aimed at contributing to this debate by coregistering eye movements and EEG while participants viewed photographs of indoor scenes that contained a semantically consistent or inconsistent target object. Linear deconvolution modeling was used to analyze the ERPs evoked by scene onset as well as the fixation-related potentials (FRPs) elicited by the fixation on the target object (t) and by the preceding fixation (t − 1). Object–scene consistency did not influence the probability of immediate target fixation or the ERP evoked by scene onset, which suggests that object–scene semantics was not accessed immediately. However, during the subsequent scene exploration, inconsistent objects were prioritized over consistent objects in extrafoveal vision (i.e., looked at earlier) and were more effortful to process in foveal vision (i.e., looked at longer). In FRPs, we demonstrate a fixation-related N300/N400 effect, whereby inconsistent objects elicit a larger frontocentral negativity than consistent objects. In line with the behavioral findings, this effect was already seen in FRPs aligned to the pretarget fixation t − 1 and persisted throughout fixation t, indicating that the extraction of object semantics can already begin in extrafoveal vision. Taken together, the results emphasize the usefulness of combined EEG/eye movement recordings for understanding the mechanisms of object–scene integration during natural viewing.
An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering

A reliable nuclei segmentation is still an open-ended problem, especially in the breast cancer histology images. For the same, this paper proposes an intelligent gravitational search algorithm based superpixel clustering method for automatic nuclei segmentation. In the proposed method, a novel variant of gravitational search algorithm, intelligent gravitational search algorithm, is employed to obtain the optimal cluster centroids. The experimental and statistical results evince that the proposed variant surpasses existing meta-heuristic algorithms on 47 benchmark functions belonging to different problem categories i.e., unimodal, multimodal, and real-parameter single objective optimization problems of CEC, 2013. Further, the segmentation accuracy of the proposed method is examined on H&E stained estrogen receptor positive (ER+) breast cancer images. Experiments affirm that the proposed method is comparatively an efficacious and accurate method for segmenting the nuclei within breast cancer histology images.
Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation

In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling capacity in capturing multi-grained sequential characteristics over context information. Besides, existing methods often directly take the feature vectors derived from context information as auxiliary input, which is difficult to fully exploit the structural patterns in context information for learning preference representations. To address above issues, we propose a novel Taxonomy-aware Multi-hop Reasoning Network, named TMRN, which integrates a basic GRU-based sequential recommender with an elaborately designed memory-based multi-hop reasoning architecture. For enhancing the reasoning capacity, we incorporate taxonomy data as structural knowledge to instruct the learning of our model. We associate the learning of user preference in sequential recommendation with the category hierarchy in the taxonomy. Given a user, for each recommendation, we learn a unique preference representation corresponding to each level in the taxonomy based on her/his overall sequential preference. In this way, the overall, coarse-grained preference representation can be gradually refined in different levels from general to specific, and we are able to capture the evolvement and refinement of user preference over the taxonomy, which makes our model highly explainable. Extensive experiments show that our proposed model is superior to state-of-the-art baselines in terms of both effectiveness and interpretability.
"If HTTPS Were Secure, I Wouldn't Need 2FA" - End User and Administrator Mental Models of HTTPS

HTTPS is one of the most important protocols used to secure communication and is, fortunately, becoming more pervasive.However, especially the long tail of websites is still not sufficiently secured.HTTPS involves different types of users, e.g., end users who are forced to make security decisions when faced with warnings or administrators who are required to deal with cryptographic fundamentals and complex decisions concerning compatibility.In this work, we present the first qualitative study of both end user and administrator mental models of HTTPS.We interviewed 18 end users and 12 administrators; our findings reveal misconceptions about security benefits and threat models from both groups.We identify protocol components that interfere with secure configurations and usage behavior and reveal differences between administrator and end user mental models.Our results suggest that end user mental models are more conceptual while administrator models are more protocol-based.We also found that end users often confuse encryption with authentication, significantly underestimate the security benefits of HTTPS.They also ignore and distrust security indicators while administrators often do not understand the interplay of functional protocol components.Based on the different mental models, we discuss implications and provide actionable recommendations for future designs of user interfaces and protocols.
An experiential view to children learning in museums with Augmented Reality

Museums facilitate schoolchildren’s experiential learning, and when combined with Augmented Reality (AR) applications, schoolchildren can benefit from interactive, engaging learning experiences. Experiential learning is, therefore, situated in a context relevant to schoolchildren’s learning experience with digital technologies such as AR in museums, hence, it seems appropriate to employ Kolb’s (1984. Experiential Learning: Experience as the source of learning and development. NJ: Pearson Education) Experiential Learning Cycle as a theoretical base. A museum in the UK was used as a single case study, and experiments and three focus groups were conducted with 19 schoolchildren and data analysed using thematic analysis. This study revealed three new themes specific to schoolchildren’s experiential learning experiences with AR in museums including: (1) integrating AR could further enhance knowledge acquisition, (2) schoolchildren were able to identify their preferred learning style, and (3) schoolchildren are motivated to continue learning with AR in museums. Theoretical contributions and practical implications are presented, as well as suggestions for future research.
Mapping of single- and multi-element geochemical indicators based on catchment basin analysis: Application of fractal method and unsupervised clustering models

The study aimed to recognize significant single- and multi-elemental geochemical indicators associated with porphyry and skarn copper deposits in the Varzaghan district, NW Iran, from stream sediment geochemical data. In this regard, because of the diversity and complexity of geological processes that influence dispersion patterns of geochemical elements in different areas, sample catchment basin (SCB) modeling of stream sediments was applied. To judge the capability of each single-element geochemical indicator to predict exploration targets of mineral deposits of the type sought, prediction-area plot analysis was employed. Then, concentration-area fractal modeling was performed on the SCB models to delineate geochemical anomalies of each indicator element. Next, a Cu-Au-Mo-Bi association was considered to reflect multi-element indicator of porphyry-skarn Cu mineralization. Different populations of the SCB multi-element geochemical indicators were delineated by three unsupervised clustering methods, namely K-means, K-medoids and fuzzy c-means. Silhouette Width and Xie-Beni validation indices were calculated for selecting the optimal cluster number to optimize delineation of significant anomalies. Finally, for quantitative evaluation of the efficiency of single- and multi-element indicator anomalies in delineating exploration targets, we applied normalized density and success-rate curve based on the known Cu occurrences in the study area.