Reviewerly/exHarmony huggingface dataset:
Identity construction of suspects in telecom and internet fraud discourse: from a sociosemiotic perspective

As a new kind of contactless crime, the telecom and internet fraud has become a public hazard, with criminals targeting massive numbers of innocent victims. It is a prominent criminal problem that currently affects social stability and the sense of security of the public. Since the six Ministries and Commissions jointly cracked down the crime in 2016, the number of cases and the number of people deceived have decreased, but the occurrence and frequency has still been at a high rate, and the situation is deteriorating. Regardless of the scientific and technological means used by fraudsters, the main medium is language. From a sociosemiotic perspective, this study identifies that the criminal suspects use the discourse resources to fabricate false information and construct false identities through discursive practice, with the aim of achieving the communicative purpose of obtaining victims’ trust, tricking, deceiving and manipulating their victims into giving out confidential information and funds. This study, taking telecom fraud discourse as one type of genre, examines the dynamic process of selecting and constructing identities by manipulating related discourse resources and strategies in the social-cultural context. This study further provides a sociosemiotic solution to telecom fraud prevention.
A Distributed Social-Aware Location Protection Method in Untrusted Vehicular Social Networks

An original sender's location-disclosure is a major privacy issue in vehicular social networks when people are interested in location-aware services. In this paper, we first propose a basic social-aware location-privacy protection (B-SLP) method in vehicular social networks. This method enables trustworthy users to obfuscate an original sender's location with the help of their regions. Then, we provide an improved SLP (I-SLP) to reduce the size of the cloaking area by covering at least k - 1 trustworthy users, while maintaining a required level of privacy protection. Due to disclosure of their locations, selfish users may not help the original sender as they put themselves at risks. To stimulate more trustworthy users to join in the location obfuscation, we then propose encouraging SLP (E-SLP) where each trustworthy user can increase and/or decrease its social tie according to its behavior (cooperation or no-cooperation). Experiments show that B-SLP, I-SLP, E-SLP can improve the query success ratio compared to the existing method, while maintaining a required level of privacy protection.
Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder

Diversity plays a vital role in many text generating applications. In recent years, Conditional Variational Auto Encoders (CVAE) have shown promising performances for this task. However, they often encounter the so called KL-Vanishing problem. Pervious works use heuristic methods to avoid KL-vanishing, but it is hard to find an appropriate degree to which these methods should be applied. In this paper, we propose an explicit optimizing objective function to guide the encoder towards the "best encoder" and directly pull the CVAE away from KL-vanishing. A labeling network is introduced to estimate the "best encoder It provides a continuous label in the latent space of CVAE to help build a close connection between latent variables and targets. The whole proposed method is named Self Labeling CVAE (SLCVAE). To boost the research of diverse text generation, we also propose a large native one-to-many dataset. Extensive experiments are conducted on two tasks, which show that our method largely improves the generating diversity while achieving comparable accuracy compared with state-of-the-art algorithms.
Understanding user behavior in naturalistic information search tasks

Understanding users' search behavior has largely relied on the information available from search engine logs, which provide limited information about the contextual factors affecting users' behavior. Consequently, questions such as how users' intentions, task goals, and substances of the users' tasks affect search behavior, as well as what triggers information needs, remain largely unanswered. We report an experiment in which naturalistic information search behavior was captured by analyzing 24/7 continuous recordings of information on participants' computer screens. Written task diaries describing the participants' tasks were collected and used as real‐life task contexts for further categorization. All search tasks were extracted and classified under various task categories according to users' intentions, task goals, and substances of the tasks. We investigated the effect of different task categories on three behavioral factors: search efforts, content‐triggers, and application context. Our results suggest four findings: (i) Search activity is integrally associated with the users' creative processes. The content users have seen prior to searching more often triggers search, and is used as a query, within creative tasks. (ii) Searching within intellectual and creative tasks is more time‐intensive, while search activity occurring as a part of daily routine tasks is associated with more frequent searching within a search task. (iii) Searching is more often induced from utility applications in tasks demanding a degree of intellectual effort. (iv) Users' leisure information‐seeking activity is occurring inherently within social media services or comes from social communication platforms. The implications of our findings for information access and management systems are discussed.
Quantum Phase Properties of Photon Added and Subtracted Displaced Fock States

Quantum phase properties of photon added and subtracted displaced Fock states (and a set of quantum states which can be obtained as the limiting cases of these states) are investigated from a number of perspectives, and it is shown that the quantum phase properties are dependent on the quantum state engineering operations performed. Specifically, the analytic expressions for quantum phase distributions and angular $Q$ distribution as well as measures of quantum phase fluctuation and phase dispersion are obtained. The uniform phase distribution of the initial Fock states is observed to be transformed by the unitary operation (i.e., displacement operator) into non-Gaussian shape, except for the initial vacuum state. It is observed that the phase distribution is symmetric with respect to the phase of the displacement parameter and becomes progressively narrower as its amplitude increases. The non-unitary (photon addition/subtraction) operations make it even narrower in contrast to the Fock parameter, which leads to broadness. The photon subtraction is observed to be a more powerful quantum state engineering tool in comparison to the photon addition. Further, one of the quantum phase fluctuation parameters is found to reveal the existence of antibunching in both the engineered quantum states under consideration. Finally, the relevance of the engineered quantum states in the quantum phase estimation is also discussed, and photon added displaced Fock state is shown to be preferable for the task.
Semantic representation for visual reasoning

In the field of visual reasoning, image features are widely used as the input of neural networks to get answers. However, image features are too redundant to learn accurate characterizations for regular networks. While in human reasoning, abstract description is usually constructed to avoid irrelevant details. Inspired by this, a higher-level representation named semantic representation is introduced in this paper to make visual reasoning more efficient. The idea of the Gram matrix used in the neural style transfer research is transferred here to build a relation matrix which enables the related information between objects to be better represented. The model using semantic representation as input outperforms the same model using image features as input which verifies that more accurate results can be obtained through the introduction of high-level semantic representation in the field of visual reasoning.
Autoencoder-based personalized ranking framework unifying explicit and implicit feedback for accurate top-N recommendation

Existing top-N recommendation models can be classified according to the following two criteria: way of optimization and type of data. In terms of optimization, the models can either minimize the mean squared error (MSE) of rating predictions, which is so-called pointwise learning, or maximize the likelihood of pairwise preferences over more preferred and less preferred items (e.g., rated and unrated items), which is so-called pairwise learning. According to the data type, the models use either explicit feedback or implicit feedback. Most existing models use one of the optimization methods with either explicit or implicit feedback. However, we believe that pairwise learning and pointwise learning (resp. using explicit and implicit feedback) are complementary, thus employing both optimization methods and both forms of data together would bring a synergy effect in recommendation. Along this line, we propose a novel, unified recommendation framework based on deep neural networks, in which the pointwise and pairwise learning are employed together while using both the users’ explicit and implicit feedback. The experimental results on four real-life datasets confirm the effectiveness of our proposed framework over the state-of-the-art ones.
Dynamic Student Classiffication on Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is the assessment of student's knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classification on Memory Networks (DSCMN) for knowledge tracing that enhances existing KT approaches by capturing temporal learning ability at each time interval in student's long-term learning process. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art KT modelling techniques.
Leveraging Robot Embodiment to Facilitate Trust and Smoothness

Interactions with social robots in public and private spaces are becoming more and more common and varied. As this trend continues, it is important to understand how a robot's embodiment influences its ability to calibrate trust and comfort with its users and behave in accordance with social norms. This is especially true when one social intelligence embodies multiple physical robots (re-embodiment). We have conducted two studies-one quantitative and and one qualitative-which shed light on the way robots should be embodied and re-embodied by intelligences during different types of social interactions. This paper outlines our previous work on elucidating the role of embodiment in social interactions and experimenting with re-embodiment as a design paradigm, and it describes the directions in which we plan to take this research in the near future.
Spatio-temporal object detection by deep learning: Video-interlacing to improve multi-object tracking

Tracking-by-detection have become a hot topic of great interest to some computer vision applications in the recent years. Generally, the existing tracking-by-detection frameworks have difficulties with congestion, occlusion, and inaccurate detection in crowded scenes. In this paper, we propose a new framework for Multi-Object Tracking-by-Detection (MOT-bD) based on a spatio-temporal interlaced encoding video model and a specialized Deep Convolutional Neural Network (DCNN) detector. The spatio-temporal variation of objects between images are encoded into “interlaced images”. A specialized “interlaced object” convolutional deep detector is trained to detect objects in interlaced images and a classical association algorithm to perform the association between detected objects, since interlaced objects are built to increase overlap during the association step which leads to improve the MOT performance over the same detector/association algorithm applied on non-interlaced images. The effectiveness and robustness of this contribution is demonstrated by experiments on popular tracking-by-detection datasets and benchmarks such as the PETS, TUD and the MOT17 benchmark. Experimental results demonstrate that interlacing video idea has many advantages to improve the tracking performances in terms of both precision and accuracy of tracking and illustrate that the “power of video-interlacing” outperforms several state-of-the-art tracking frameworks in multiple object tracking.
A comprehensive study of argumentation frameworks with sets of attacking arguments

It has been argued that Dung's classical Abstract Argumentation Framework (AAF) model is not appropriate for capturing "joint attacks", a feature that is inherent in several contexts and applications. The model proposed by Nielsen and Parsons in [1], often referred to as "framework with sets of attacking arguments" (SETAF), fills this gap by introducing joint attacks as a generalisation of the standard attack relationship of AAFs, thus constituting a faithful generalization of Dung's model. Building on that work, we provide a more complete characterization of these frameworks, which includes the treatment of various semantics not considered in the original publication, a more fine-grained representation of all acceptability semantics using labellings, and two functions allowing the transition between extensions and labellings along with their properties. Moreover, we show that a variety of well-known results that apply to AAF can be migrated to the SETAF setting. To further associate the two frameworks, we provide a natural way to represent a SETAF as a Dung-style AAF, and show how the generated AAF behaves.
Marginal distribution covariance model in the multiple wavelet domain for texture representation

Filters are the commonly used techniques for texture feature extraction. In this paper, we present a texture feature extraction method based on multiple wavelet filters. For modeling the multiple wavelet coefficients, we develop Marginal Distribution Covariance Model (MDCM) in which the data points are projected into Cumulative Distribution Function (CDF) space and then the covariance model is constructed in the CDF space. MDCM can capture the dependence of variables, so it can be applied to model the texture features, in which the dependence exists, such as image intensities, color features and wavelet filter features. According to the characteristics of different wavelet filter features, we construct the different MDCMs in the three wavelet transform domains: Orthogonal Wavelet Transform (OWT), Dual Tree Complex Wavelet Transform (DTCWT) and Gabor Wavelet Transform (GWT). Experiments show the proposed method which uses multiple wavelet features can provide a promising performance compared with the state-of-the-art methods.
Crypto-watermarking scheme for tamper detection of medical images

Medical images of patients are often exchanged among specialist physicians, radiologists, hospital authorities and patients for remote monitoring and assessment as part of telemedicine. With the revolutions in the technology, the threat models and attackers are evolving on a daily basis and hence there is a constant need for the development of novel schemes that can protect personal information. In this paper, a non-blind fragile watermarking is developed to invisibly hide and integrate patient’s unique information such as biometrics in their radiological images for secure authentication, integrity verification and tamper detection purposes. The concept of compressive sensing theory is employed with discrete cosine transform to improve confidentiality. The performance of the proposed scheme is tested and evaluated on three types of medical images: X-ray, computed tomography and magnetic resonance imaging. The proposed scheme presents a means of verifying data integrity when medical images are subjected to attacks. The experimental results showed that the scheme invisibly hides high payloads of patient’s unique identities, apart from providing better tamper detection. The simulation results show that the proposed scheme provides high imperceptibility up to 92 dB and high payload capacity of up to 1 bpp.
One-Class Classification Using Generative Adversarial Networks

One-class classification (OCC) problem has drawn increasing attention in recent years. It expands the range of classification from pre-defined categories to undefined categories. Since the vast diversity of negative samples, it is hard to acquire complete knowledge of unknown classes and construct a negative set for training a one-class classifier, which remains a difficult problem. In this paper, we propose a new OCC model by modifying the generative adversarial network model to address the OCC problem. Taking the generator's outputs as outliers, the discriminator in our model is trained with these synthetic data and target training data, and it manages to distinguish them from each other. Moreover, a new evaluation protocol named classification recall index is put forward to indicate the classifier's performances on both positive and negative sets. The extensive experiments on the MNIST dataset and the Street View House Numbers (SVHN) dataset demonstrate that the proposed model is competitive over a variety of OCC methods.
Friend and POI recommendation based on social trust cluster in location-based social networks

Friend and point-of-interest (POI) recommendation are two primary individual services in location-based social networks (LBSNs). Major social platforms such as Foursquare and Instagram are all capable of recommending friends or POIs to individuals. However, most of these social websites make recommendations only based on similarity, popularity, or geographical influence; social trust among individuals has not been considered in those recommendation system. Recently, trust relationship has been proved to be helpful in collaborative recommendation. In this paper, we first propose algorithm to identify trust clusters and then give a trust prediction method based on these trust clusters. Then we combine the trust value and similarity among individuals to recommend friends to the target user. As for the POI recommendation, we devise a hybrid framework that integrates user preference, geographical influence, and trust relationship to improve the recommendation quality. In order to validate the effectiveness and efficiency of our methods, a series of experiments on two real social networks Foursquare and Instagram are conducted. The experiment results show that the trust cluster-based recommendation approach outperforms the baseline recommendation approaches in precision and recall.
Feature Fusion of Deep Spatial Features and Handcrafted Spatiotemporal Features for Human Action Recognition

Human action recognition plays a significant part in the research community due to its emerging applications. A variety of approaches have been proposed to resolve this problem, however, several issues still need to be addressed. In action recognition, effectively extracting and aggregating the spatial-temporal information plays a vital role to describe a video. In this research, we propose a novel approach to recognize human actions by considering both deep spatial features and handcrafted spatiotemporal features. Firstly, we extract the deep spatial features by employing a state-of-the-art deep convolutional network, namely Inception-Resnet-v2. Secondly, we introduce a novel handcrafted feature descriptor, namely Weber's law based Volume Local Gradient Ternary Pattern (WVLGTP), which brings out the spatiotemporal features. It also considers the shape information by using gradient operation. Furthermore, Weber's law based threshold value and the ternary pattern based on an adaptive local threshold is presented to effectively handle the noisy center pixel value. Besides, a multi-resolution approach for WVLGTP based on an averaging scheme is also presented. Afterward, both these extracted features are concatenated and feed to the Support Vector Machine to perform the classification. Lastly, the extensive experimental analysis shows that our proposed method outperforms state-of-the-art approaches in terms of accuracy.
Image caption model of double LSTM with scene factors

In this paper, an image semantic understanding model combining scene factors is proposed to solve the problem that the accuracy rate of the description sentence is low in the current image semantic understanding model which is incorrect or ignores the scene recognition. This model first identifies the corresponding theme (scene information) through the text volume of the LDA analysis corpus. We get the vocabulary used in this scene. Then we use the ResNet to extract the global feature of the image, and use the Places365-CNNs to extract the feature of the deep scene. Finally, the model uses the picture scene information and the corpus scene information. In the description statement of the picture generation, it uses the words related to the picture scene in large probability and in the statement. In the process of generation, double LSTM is used to adjust the parameters to improve the accuracy of statement generation. This model is trained and tested in the Flickr8K, Flickr30K and MSCOCO image sets. The model is verified with different evaluation methods. The experimental results show that the proposed model can effectively improve the image language compared with other models. The accuracy of meaning understanding can solve these problems effectively.
Word Embedding for the French Natural Language in Health Care: Comparative Study

Word embedding technologies, a set of language modeling and feature learning techniques in natural language processing (NLP), are now used in a wide range of applications. However, no formal evaluation and comparison have been made on the ability of each of the 3 current most famous unsupervised implementations (Word2Vec, GloVe, and FastText) to keep track of the semantic similarities existing between words, when trained on the same dataset.The aim of this study was to compare embedding methods trained on a corpus of French health-related documents produced in a professional context. The best method will then help us develop a new semantic annotator.Unsupervised embedding models have been trained on 641,279 documents originating from the Rouen University Hospital. These data are not structured and cover a wide range of documents produced in a clinical setting (discharge summary, procedure reports, and prescriptions). In total, 4 rated evaluation tasks were defined (cosine similarity, odd one, analogy-based operations, and human formal evaluation) and applied on each model, as well as embedding visualization.Word2Vec had the highest score on 3 out of 4 rated tasks (analogy-based operations, odd one similarity, and human validation), particularly regarding the skip-gram architecture.Although this implementation had the best rate for semantic properties conservation, each model has its own qualities and defects, such as the training time, which is very short for GloVe, or morphological similarity conservation observed with FastText. Models and test sets produced by this study will be the first to be publicly available through a graphical interface to help advance the French biomedical research.