Reviewerly/exHarmony huggingface dataset:
Identifying review spam with an unsupervised approach based on topic abuse

The harmfulness of review spam (also known as deceptive opinion) has long been recognized. However, due to the lack of supervised annotations, detecting these fake reviews is challenging ever since the dawn of this field. In this paper, by exploring distinct composing patterns between sincere reviewers and spammers, we propose a novel approach to examine review contents and hunt long spams. Correlation levels upon product metadata and nominated aspects are highlighted for feature selection. We take two highly acknowledged metrics, i.e., duplication and burstiness, to evaluate our approach. Comparative results upon the top two Chinese business-to-customer websites show that our approach is effective and outperforms state-of-the-art solutions.
Twofold Siamese Network with Attentional Feature Fusion for Object Tracking

Object tracking is still a critical and challenging problem in computer vision. More and more researchers pay attention to applying deep learning to obtain the powerful feature for robust tracking. Nowadays, feature fusion is an essential part of Siamese tracking architectures. However, the existing feature fusion methods usually provide a fixed linear aggregation of feature maps, and this combination may not be appropriate for a specific object. In this paper, a twofold Siamese network, named SD-Siam, is proposed to extract the features of the object effectively. The template branch and the search branch are both composed of a deep layer sub-network and a shallow layer sub-network, which is used for feature fusion of the different network layers. Moreover, an attentional feature fusion scheme is employed to better fuse scale-inconsistent features, where a multi-scale channel attention module is used to fuse different scales of features. In addition, we respectively evaluate similarity measures for the features of deep layer sub-networks and the fused features of the template branch and the search branch, and then these two similarity response maps are added to obtain the tracking result. Experiments show the proposed SD-Siam outperforms representative trackers on several challenging benchmarks.
Reinforcement Learning of Graph Matching

Graph matching not only has widespread application prospect and practical value, but also provides a broader perspective and new technologies for fundamental researches. The recently presented idea to learn heuristics or approximation algorithms often require significant specialized knowledge and trial-and-error. Therefore, we need better models and better ways to meet practical implementation. In this paper, a unique feature extraction combined with reinforcement learning procedure is proposed to tackle this challenge. There are three main contributions: 1. we introduce matrix symmetric compression to obtain global feature and Bi-directional Recurrent Neural Network (Bi-RNN) to extract local feature; 2. we transform graph matching to sequence-to-sequence problem based on the above feature; 3. we optimize parameters using Actor-Critic framework. Our experiments on synthetic and real databases reveal that reinforcement learning compares favorably to supervised case and traditional methods, both in terms of efficiency and quality.
Functional Structure of Tomsk Library Science Museum’s Book Collection (1919–1921)

The paper deals with the principles of the book fund organization in Library Science Museum (1919–1921), created by the future famous library scientist and bibliologist D. A. Balika during his professional formation in Tomsk. The Museum was created for didactic and methodological support of library professional education. The author shows the functional structure of the book collection and gives comments on possible dynamics of its composition, describes the methods to identify the source books and the sample books basing on the features of their publishing and printing form and various aspects of content (affiliation to a particular branch of knowledge, psychological type). She characterizes the ways to use two mentioned types of editions in educational process. (That is for the formation of knowledge and skills in library science, book studies, author studies and reader studies in a passive or active way – by the assimilation of the source books’ content or by the analytical description of the sample books). The author traces the biblio-psychological (Rubakin’s) traditions and biblio-pedagogic innovations in D. A. Balika’s vision of librarian’s “inner work” (“reading guidance” within the framework of the extracurricular education program). The transformation technology of a non-specialized fund into an educational one implemented by D. A. Balika seems to be relevant nowadays due to the strengthening (restoration) of the reader focus in the programs of higher library education in Russia and Ukraine at the beginning of the XXI century.
Interactive Semantic Segmentation With Weak Supervision

At present, the most advanced semantic segmentation model training mainly relies on pixel-level annotation, that is, annotating the category of each pixel of an image. Such annotation usually is time-consuming and expensive, especially for special applications that require expert annotation. The weakly-supervised segmentation method using the point-level supervision information has been investigated which however has great problems that the supervision information is quite limited and the performance is far from fully supervised methods. In this paper, we proposes an novelty interactive image segmentation method based on weak supervision, which allows multiple feedbacks of easily obtained weakly supervised information and improves the efficiency of utility of the supervision information. In the downstream task (interactive image segmentation), supervised information at the point level is used for many times, which makes the connection between pixels in the upstream task become closer and improves the segmentation accuracy. First, image-level tags are used to train the classification network. Then the pseudo-semantic labels are generated and put into the interactive segmentation network for training, and an almost completely supervised CNN is obtained, which further improves the performance and provides operability for human-computer interaction. The proposed method achieves promising semantic segmentation results that are close to those obtained by strongly supervised segmentation methods on the PASCAL VOC 2012 datasets.
Weakly-supervised Salient Object Detection with Label Decoupling Siamese Network

Weakly-supervised salient object detection (SOD) does not require a lot of manually annotated pixel-level labels. To further improve the detection accuracy of weakly-supervised SOD, we introduce a label decoupling siamese network (LDSN), which implements weakly-supervised SOD by learning from scribble labels. Scribble labels are novel weakly-supervised labels, which can reduce the cost of manual annotating and provide good interaction for users. In previous methods, the use of scribble labels mainly focuses on the salient object region. With the label decoupling siamese network, we make more adequately use of the scribble labels and the complementary relationship between salient object and background. We also propose a complementary self-supervised loss (CSS Loss) to realize a self-supervision. To improve the accuracy of the edge detection and interpretability of the network, we design a gradient priors weakly-supervised edge detection module (GWEM), which combines traditional image processing and deep learning methods. We compare the proposed network with 6 state-of-the-art SOD methods on 5 widely used datasets. The experiment results show that the performance of LDSN is superior to existing weakly-supervised SOD methods, and is compared with several pixel-level label supervised methods.
User Engagement Continuum: From Art Exploration to Remixing Culture with Augmented Reality

The most common way to consume art is through observation and acknowledgement of its existence. From the viewpoint of preserving art and cultural heritage, such passive consumption seems adequate. However, throughout history art has always been the subject of endless reinterpretations and its reframing has a possibility to shed new perspectives on the original as well as the reinterpreted context. Novel technologies can enable mashups in real time and in the context where art is observed. Augmented reality (AR) is one of the most promising by offering the possibility of mixing physical artworks with digitally augmented users’ creations or/and curation of personalised exhibitions. In a similar way that the Web-enabled users to become active participants in content sharing, rating services and products, and deciding on the course of television shows in real time, AR can act as a medium to leave digital augmentation of artworks in real physical spaces and thus support remixing culture with re-appropriation of art. In this chapter, several AR ideas and solutions are presented with a common theme: each allows users to engage with art or cultural heritage in different ways and enables users to tinker with artworks. All presented prototypes can be placed on the user engagement continuum that spans from passive consumption to active creation. The chapter finishes with a discussion of implications such AR solutions would present in terms of copyright violation, curation of user-generated content, engagement with technology, ethical issues and others.
An Improved ROI Encryption Algorithm for Infrared Images with Logistic System

In order to improve the video monitoring capability in sensitive areas and the overall encryption speed of images, the concept of Region of interest (ROI) based encryption is proposed in this paper. The ROI of infrared image is encrypted using the improved Logistic Chaos system and the performance is compared with the traditional matrix transform encryption. The experimental results show that it has strong processing capability, high security and good encryption effect. Meanwhile, a parallel processing scheme which can achieve deep-level encryption, has put forward to improve the security of surveillance system in the paper. The experimental results show that it has strong processing capability, high security and good encryption effect.
Deployment of Intelligent White Balance on Smartphone Using Shufflenet-V1 and Adaptive Histogram Matching

In order to enhance the Automatic white balance (AWB) efficiency and performance, a ShuffleNet-v1 classifier is trained using the color-distorted dataset, which is constructed by histogram shifting from Pascal VOC2012. The distorted image is generated by randomly moving the histograms of the original normal image. Image identified as normal by this classifier is output directly, while the classified distorted image is processed by histogram matching with flexible reference channel, in which the reference channel is adaptively selected according to the mean gray level of the maximal channel. Finally, the intelligent AWB system is deployed on smartphone using the android application. Experiments show that the best deployment parameter is achieved by ShuffleNet-v1, which is 156.20% larger than the second best that achieved by MobileNet-v2. In addition, the overlap area is maximized and the visual effect is improved by adaptive histogram matching.
STKE: Temporal Knowledge Graph Embedding in the Spherical Coordinate System

Knowledge graph embedding (KGE) aims to learn the representation of entities and predicates in low-dimensional vector spaces which can complete the missing parts of the Knowledge Graphs (KGs). Nevertheless, temporal knowledge graphs (TKGs) that include time information are more consistent with real-world application scenarios. Meanwhile, the facts with time constraints make the results of reasoning over time more accurate. Because of these, we propose a novel temporal knowledge graph embedding (TKGE) model, namely Spherical Temporal Knowledge Graph Embedding (STKE), which embeds facts into a spherical coordinate system. We treat each fact as a rotation from the subject to the object. The entities and predicates in STKE are divided into three parts--the radial part, the azimuth part, and the polar part. The radial part aims to resize the modulus between two entities. The azimuth part is mainly used to distinguish entities with the same module length and the polar part aims to represent the transformation of the time embedding with the change of polar angle. We evaluate the proposed model via the link prediction task on four typical temporal datasets. Experiments demonstrate that STKE achieves a significant surpass compared with the state-of-the-art static knowledge graph embedding (SKGE) model and TKGE model. In addition, we analyze the representation ability of different facts in the spherical coordinate system and confirm that our model can better represent time-constrained facts.
Single-View 3d Reconstruction of Surface of Revolution

3D reconstruction from perspective images is normally done using a sequence of images, and few studies have been done to solve the problem of reconstruction using a single image. In this paper, we present a novel approach to reconstruct high-quality 3D models of axial symmetric objects using single images. The proposed algorithm solves an optimization problem to find the best 3D axis of rotational symmetry such that the projection of the 3D body matches the 2D input image. Moreover, we introduce a method for texture acquisition from a single image of surface of revolution to create a more realistic 3D model. We qualitatively and quantitatively show that the proposed framework reconstructs precise textured 3D models. A graphical user interface (GUI) is implemented to facilitate using the program.
Machine Learning and Symbolic Learning for the Recognition of Leukemia L1, L2 and L3

Leukemia is a health problem that affects to world population causing thousands of kills yearly, thus accurate and human-readable diagnostic methods are required. Symbolic learning uses methods based on high-level representations of problems, which is useful to design interpretable models to understand the solutions found to solve a problem. In this work, we analyze the performance of 3 classifiers used frequently in machine learning, which are independently embedded into a model of symbolic learning named brain programming. Results suggest that the classifiers as MLP and SVM are robust to noisy data, with the MLP demonstrating the most stable behavior into the symbolic learning model, which is fundamental in models of evolutionary vision as the brain programming.
End to End Residual Learning-Based Deep Neural Network Model Deployment for Human Activity Recognition

Human activity recognition (HAR) is a theme commonly explored in computer vision. A continuous intelligent control system for detecting human behaviour does a vast and growing demand for automated HAR in these sectors. The need to reduce the costs associated with manual surveillance has moved the research community to deep learning. A network of intelligent monitoring can be designed and developed to detect human activities. In this paper, a novel paradigm to identify human activity on the UTKinect dataset is implemented by using Residual learning-based Network ”ResNet-50” and transfer learning. The experimental results have shown an excellent generalization capability when tested on the validation set and obtained high accuracy of 98.60 per cent with a 0.02 loss score. The designed residual learning-based system indicates the efficiency of comparing with the other state-of-the-art models.
The UX Defined UX Designer Is No More

UX is now an acknowledged business process across several industries. Having grown out of Human Factors, Human-Computer Interaction and Interaction Design, UX is increasingly witnessing scholarly attention outside these historical roots. However, this popularity of UX also seems to have caused a broadening of the UX concept – where several non-UX professional roles and perspectives now shape its meaning. This makes its relationship with interaction design more ambiguous and possibly more diluted, potentially widening the gap between UX as an academic focus and its associated professions. Therefore, we seek to empirically briefly explore how UX is treated in HCI textbooks, as well as current design-related job postings. To that end, a research-practice analysis was conducted based on a literature review and an empirical analysis of job postings for UX design-related professions. Based on this limited study, we suggest that these multidisciplinary perspectives have transformed UX into a kind of placeholder for any 'interaction' experience, within the set of academic definitions and included job postings. Moreover, it becomes clear that within the scholarly HCI debate this transformation is accepted and fostered – as it borrows theories from other disciplines that are driving these different perspectives. To that end, it can be argued that UX has developed to be a holistic business perspective – rather than a product specific perspective – where a multitude of non-traditional UX-roles take part in a businesses' UX efforts. Resulting from this development, we argue that UX nominally has become somewhat decoupled from its roots in the interaction design profession.
Emotion Analysis Platform to Investigate Student-Teacher Interaction

This paper introduces a system that enable the collection of relevant data related to the emotional behavior and attention of both student and professor during exams. It exploits facial coding techniques to enable the collection of a large amount of data from the automatic analysis of students and professors faces using video analysis, advanced techniques for gaze tracking based on deep Learning, and technologies and the principles related to the Affective Computing branch derived from the research of Paul Ekman. It provides tools that facilitates the interpretation of the collected data by means of a dashboard. A preliminary experiment has been carried out to investigate whether such a system may help in assessing the evaluation setting and support reflection on the evaluation processes in the light of the different situations, so as to improve the adoption of inclusive approaches. Results suggest that information provided by the proposed system can be helpful in assessing the setting and the evaluation process.
Simple Flow-Based Contrastive Learning for BERT Sentence Representations

Natural language processing is a significant branch of machine learning, and pre-trained models such as BERT have been widely used in it. Previous research has shown that sentence embeddings from pre-trained language models without fine-tune have difficulty in capturing their exact semantics. The ambiguous semantics leads to poor performance on semantic text similarity (STS) tasks. However, fine-tune tends to skew the model toward high-frequency distributions due to the heterogeneous nature of word frequency and word sense distributions. Therefore, fine-tune is not a optimal choice. To address this issue, we propose an unsupervised flow-based contrastive learning model. The model maps sentence embedding distributions to smooth and isotropic Gaussian distributions, thus mitigating the impact caused by irregular word frequency distributions. To evaluate the performance of our model, we use an industry-recognized method that outperforms competing baselines in different sentence-related tasks.
Active Contour Model Based on Bias Correction and Pre-Fitting Function for Image Segmentation

Active contour model (ACM) is considered as a widely used image segmentation method. Plenty of existing ACMs are not effective for image segmentation with intensity inhomogeneity. In order to find a better way to solve these problems, we propose an ACM driven by bias correction and pre-fitting function (BCPFF). Firstly, we propose a pre-fitting piecewise function based on local bias field, which replaces the fitting constant in the assumption theory of image bias field. The piecewise function is computed using optimized K-means++ algorithm. Secondly, a local intensity energy function is defined by the calculated piecewise function and a global energy function is then derived. Thirdly, the above theory is combined with the ACM through the level set function. A novel variational level set function and gradient flow function are proposed for energy minimization. Finally, the optimized distance rule function and neighborhood average filtering method are used to regularize and smooth the level set function, respectively. Experiments on real images and synthetic images prove that the proposed method can effectively segment images with intensity inhomogeneity. Our model shows better robustness and segmentation efficiency than classical region-based models. Compared with bias correction model, the segmentation speed of this model is greatly improved.
Security Vulnerabilities Detection Through Assertion-Based Approach

Organizations and companies develop very complex software today. Errors and flaws can be introduced at different phases of the software development life cycle and can lead to exploitable vulnerabilities. Furthermore, considering that most systems are exposed to multiple users and environments, such flaws can lead to attacks (or actions) with unpredictable consequences in terms of damage and costs. Most research that deals with security-related issues of software focuses their efforts on coding errors and flaws, regardless of the infrastructure and platforms that run the software applications. Often, such analyses of software applications vulnerabilities may lack sufficient specification details, thus possibly miss larger systematic flaws, and consequently obscure the existence of serious vulnerabilities. Our research aims at developing a technique capable of discovering the security weaknesses, specifically buffer overflow vulnerabilities in C/C++ programs, through the analysis of source code combined with architecture specifications. The proposed approach relies on the notion of platform assertions that is, a collection of logical relationships used to characterize a platform (execution environment). In this paper, we focus on such assertions and show how vulnerabilities analysis of software applications can be performed with our assertion-based approach. Furthermore, the generation of assertion specifications as well as the construction of an assertion library including various platforms are explored.