Reviewerly/exHarmony huggingface dataset:
Algorithmic and HCI Aspects for Explaining Recommendations of Artistic Images

Explaining suggestions made by recommendation systems is key to make users trust and accept these systems. This is specially critical in areas such as art image recommendation. Traditionally, artworks are sold in galleries where people can see them physically, and artists have the chance to persuade the people into buying them. On the other side, online art stores only offer the user the action of navigating through the catalog, but nobody plays the persuading role of the artist. Moreover, few works in recommendation systems provide a perspective of the many variables involved in the user perception of several aspects of the system such as domain knowledge, relevance, explainability, and trust. In this article, we aim to fill this gap by studying several aspects of the user experience with a recommender system of artistic images, from algorithmic and HCI perspectives. We conducted two user studies in Amazon Mechanical Turk to evaluate different levels of explainability, combined with different algorithms. While in study 1 we focus only on a desktop interface, in study 2 we attempt to understand the effect of explanations in mobile devices. In general, our experiments confirm that explanations of recommendations in the image domain are useful and increase user satisfaction, perception of explainability and relevance. In the first study, our results show that the observed effects are dependent on the underlying recommendation algorithm used. In the second study, our results show that these effects are also dependent of the device used in the study but with a smaller effect. Finally, using the framework by Knijnenburg et al., we provide a comprehensive model, for each study, which synthesizes the effects between different variables involved in the user experience with explainable visual recommender systems of artistic images.
Posed and Spontaneous Expression Distinction Using Latent Regression Bayesian Networks

Facial spatial patterns can help distinguish between posed and spontaneous expressions, but this information has not been thoroughly leveraged by current studies. We present several latent regression Bayesian networks (LRBNs) to capture the patterns existing in facial landmark points and to use those points to differentiate posed from spontaneous expressions. The visible nodes of the LRBN represent facial landmark points. Through learning, the LRBN captures the probabilistic dependencies among landmark points as well as latent variables given observations, successfully modeling the spatial patterns inherent in expressions. Current methods tend to ignore gender and expression categories, although these factors can influence spatial patterns. Therefore, we propose to incorporate this as a kind of privileged information. We construct several LRBNs to capture spatial patterns from spontaneous and posed facial expressions given expression-related factors. Facial landmark points are used during testing to classify samples as either posed or spontaneous, depending on which LRBN has the largest likelihood. We conduct experiments to showcase the superiority of the proposed approach in both modeling spatial patterns and classifying expressions as either posed or spontaneous.
A data privacy protection scheme for Internet of things based on blockchain

Abstract In recent years, the Internet of things (IoT) equipment has grown rapidly, and the scale of the IoT has also expanded. The IoT is deployed in the system in a centralized manner. At the same time that massive data has put a certain amount of pressure on the storage, the open network environment has not fully protected the privacy of the IoT data, which has become one of the important factors restricting the development of the IoT. Blockchain is a point‐to‐point distributed ledger technology based on cryptographic algorithms. The characteristics of decentralization, tamper resistance, anonymity, and public verifiability can alleviate data security issues in IoT. Ring signature and proxy reencryption are common encryption technologies in the field of privacy protection. Therefore, this article combines blockchain technology with ring signature and proxy reencryption to propose a privacy protection solution for the IoT. Through this solution, the data authorized for sharing in the IoT is transmitted in the system in the form of ciphertext, the identity information of the data sender is protected, and the distributed ledger eases the pressure of mass data storage on a centralized server. The correctness and safety of the proposed scheme are also analyzed.
Mobile Robot Navigation Algorithm Based on Ant Colony Algorithm with A* Heuristic Method

In the complex environment, the path planning of mobile robot should not only requires a short path, but also avoids problems such as excessive path turns and poor environmental adaptability. Therefore, an improved ACO is proposed in this paper to realize efficient search of path planning in complex maps of mobile robots. The improved ACO uses the evaluation function of A* algorithm as the initial pheromone concentration to accelerate the convergence rate of the algorithm. Secondly, turning points are taken as part of the pheromone concentration. Finally, the optimal route is smoothed to reduce the number of turning points and the length of the route. The simulation results show that the comprehensive performance of the improved algorithm on the complex map is greatly improved, and it has better global search ability and convergence, and the number of iterations, path length and turn times are better.
Text2SceneVR

The automatic generation of digital scenes from texts is a central task of computer science. This task requires a kind of text comprehension, the automation of which is tied to the availability of sufficiently large, diverse and deeply annotated data, which is freely available. This paper introduces Text2SceneVR, a system that addresses this bottleneck problem by allowing its users to create a sort of spatial hypertexts in Virtual Reality (VR). We describe Text2SceneVR's data model, its user interface and a number of problems related to the implicitness of natural language in the manifestation of spatial relations that Text2SceneVR aims to address while trying to remain language independent. Finally, we present a user study with which we evaluated Text2SceneVR.
Joint Intermediate Domain Generation and Distribution Alignment for 2D Image-Based 3D Objects Retrieval

2D image-based 3D object retrieval provides a convenient way to manage 3D big data with easily accessed 2D images. It is also a challenging task due to the significant differences between 2D images and 3D objects. In this paper, we propose a 2D image-based 3D object retrieval method, which can reduce the distribution discrepancy between 2D images and 3D objects and learn invariant features between them. Specifically, we first construct an intermediate domain module based on maximum mean discrepancy (MMD) in an unsupervised way, which can reduce the 2D and 3D distribution discrepancy by marginal distribution constraint. Second, to further reduce conditional distribution discrepancy and learn invariant features, we use source domain labels as semantic information to dynamically guide distribution alignment. Moreover, in order to support the research in 3D object retrieval, we contribute a new dataset, MDI3D. We conducted extensive experiments on MDI3D and some popular datasets, such as MI3DOR and SHREC2013. The experimental results demonstrate the superiority of the proposed method by comparing with the state-of-the-art methods.
Efficient GPU thread mapping on embedded 2D fractals

This work proposes a new approach for mapping GPU threads onto a family of discrete embedded 2D fractals. A block-space map λ:ZE2↦ZF2 is proposed, from Euclidean parallel space E to embedded fractal space F, that maps in O(log2log2(n)) time and uses no more than O(nH) threads with H being the Hausdorff dimension of the fractal, making it parallel space efficient. When compared to a bounding-box (BB) approach, λ(ω) offers a sub-exponential improvement in parallel space and a monotonically increasing speedup n≥n0. The Sierpinski gasket fractal is used as a particular case study and the experimental performance results show that λ(ω) reaches up to 9× of speedup over the bounding-box approach. A tensor-core based implementation of λ(ω) is also proposed for modern GPUs, providing up to ∼40% of extra performance. The results obtained in this work show that doing efficient GPU thread mapping on fractal domains can significantly improve the performance of several applications that work with this type of geometry.
The Use of Modern E-learning Technologies at English Lessons in a Technical University Pedagogical Process

To create a competitive specialist new forms, methods and technologies in foreign language teaching are needed. This article deals with the use of E-learning technologies at foreign language practical lessons, the work experience of Foreign Languages Chair of Ryazan State Radio Engineering University named after V.F. Utkin for improving the quality of the university pedagogical process. The use of distance learning courses, educational electronic platforms in English are given. The examples of a distance learning course, e.g. “English for Electronics”, and an educational electronic platform, e.g. “Information Science and Computer Engineering” are presented in the article.
Layout and Text Extraction from Document Images using Neural Networks

This paper attempts to provide a new perspective for efficient text extraction techniques by including the layout information of the document images along with the text. The study proposes a novel approach of segmenting the image into smaller images based on its meta-data knowledge and then applying functions for recognition of text from the smaller images. Due to a lack of layout information of the text, poor results are generated during text searching by office automation tools. With this restriction usage of the text for various environments becomes limited and usage of extracted text may not be done effectively. The study proposes a technique to understand the structural and functional layout of the document image and using this knowledge to develop a better model. To verify the point of view, additional intelligence is attached to the data, making it capable to be used in varied environments. With this added quality, the new proposed system can extract and identify text or group of text into different entities within the document which the previous systems could not achieve. The proposal can be beneficial particularly for the development of various document processing tools.
E-commerce based on the Marketplace in efforts to sell agricultural products using Xtreme programming approach

Abstract Along with the development of technology that enters all walks of life including agriculture provides an opportunity for farmers to be able to trade agriculture directly because of the problems that occur in the distribution of agricultural products for quite a long time so that they are directly in hand. Consumers sometimes make farmers quite disadvantaged because they get income that is not comparable if sold directly to consumers (end users). However, the limited space and lack of information make it a problem for farmers to sell it directly. E-commerce is one solution to this problem. E-Commerce is a forum that brings together sellers and buyers in buying and selling transactions directly using online electronic devices. The software development methodology that used in this study is the XP (Extreme Programming) method. This article aims to make e-commerce based on m-commerce to accommodate the needs of farmers in selling agricultural products directly and get greater profits, and customers get cheaper product prices. The results of this study are the form of e-commerce applications that can be used as a medium for promotion, communication, information, and provide direct benefits to farmers and consumers.
A Value-Driven Modeling Approach for Crossover Services

In recent years, crossover services have attracted wide attention as an emerging service mode in the modern service industry. Crossover services can offer values that cannot be provided by single-domain services, and they usually need to cross the boundaries of domains, organizations, and processes, which puts forward more challenges for requirements modeling and analysis under the crossover scenarios. Given the characteristics of crossover services, the authors propose a value-driven meta-model framework from multiple viewpoints to support the requirements analysis of crossover services, which consists of three parts: a value network, a goal network, and a service network. Based on the proposed meta-model framework, a value-driven crossover service modeling tool is developed to help requirements analysts in requirements analysis and design, and a case study is presented to illustrate the usage of the proposed approach. Finally, we evaluate our methods and tools using a controlled experiment, and the experimental results show the effectiveness of the approach.
Multi-modality Cascaded Fusion Technology for Autonomous Driving

A highly reliable sensor is crucial for autonomous driving, which draws more attention on multi-modality fusion. This paper proposes a general multi-modality cascaded fusion framework, exploiting the advantages of decision-level and feature-level fusion, utilizing target position, size, velocity, appearance and confidence to achieve accurate fusion results. In the fusion process, dynamic coordinate alignment(DCA) is conducted to reduce the error between sensors from different modalities. In addition, the calculation of affinity matrix is the core module of sensor fusion, we propose an affinity loss that improves the performance of deep affinity network(DAN). Last, compared to the end-to-end fusion methods, our step-by-step cascaded fusion framework is more interpretable and flexible. Extensive experiments on Nuscenes [1] dataset show that our approach achieves the state-of-the-art performance.
Identification of HEp-2 specimen images with mitotic cell patterns

In this paper, we propose and analyze a novel framework to identify the HEp-2 specimen images, consisting of mitotic spindle (MS) pattern cells. It is based on the fact that the cells showing MS patterns will always be present with other interphase type pattern cells, in a whole slide image (WSI) or specimen image, but the number of MS cells will be very small, unlike interphase patterns. Considering this fact, the work contributes in presenting a framework, using different strategies such as cells-based, region-based and complete image-based approaches. For cell-based approach, the distinctive characteristic of MS patterns is represented using morphology and texture-based features, followed by a traditional Support Vector Machine (SVM) based classifier, whereas the region-based approach uses a Convolutional Neural Network (CNN) as feature extractor and baseline classifier. Finally, for the image-based approach, the Faster Region-CNN (Faster-RCNN) based object detection framework has been applied, considering MS patterns as distinct objects. The region and image-based approaches also contribute in avoiding the requirement of DAPI based segmentation masks. Another contribution of this work is to use a novel and clearly specified threshold-based decision-making criteria for patterns declaration of the specimens with MS pattern cells. All the proposed strategies, integrated with decision-making criteria are validated on a publicly available dataset and across various experiments, we demonstrate good performance, i.e., max. MCC 0.92 in one case. Hence, the proposed framework proves to be an effective solution for the problem statement.
A Detailed Study on Generative Adversarial Networks

From past decades, along with increase in computing power, different generative models have also been developed in the area of machine learning. Among all such generative models, one very popular generative model, called as Generative Adversarial Networks, has been introduced and researched upon, in past few years only. It is based on the concept of two adversaries constantly trying to outperform each other. Objective of this review is to extensively study the GAN related literature and provide a summarized form of the studied literature available on GAN, including the concept behind it, its objective function, proposed modifications in base model, and recent trends in this field. This paper will help in giving a thorough understating of GAN. This paper will give an overview of GAN and discuss the popular variants of this model, its common applications, different evaluation metrics proposed for it, and finally its drawbacks, conclusion of the paper and future course of action.
An acceleration-level visual servoing scheme for robot manipulator with IoT and sensors using recurrent neural network

With the rapid evolution of Internet of Things (IoT) and sensor technology, robot manipulators are able to acquire a great deal of necessary information to fulfill various tricky tasks. Based on the recurrent neural network method, this paper investigates the robot manipulator with visual sensors for the image-based visual servoing (IBVS) control at the acceleration level. In consideration of multiple-level joint constraints, the newly proposed acceleration-level IBVS scheme can drive the corresponding feature of the end-effector to the desired fixed feature with theoretical convergence proved. In order to verify the correctness of the theoretical analyses, simulations are conducted on different robot manipulators for the visual servoing task with excellent performance. In addition, comparison results between the proposed acceleration-level IBVS scheme and the existing method highlight the efficiency and the superiority of the proposed method. In short, the proposed method extends the application scenarios of the traditional visual servoing schemes by using acceleration commands, which also improves the safety of the visual servoing control of the redundant robot manipulator.
A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).
Building a dynamic theory of citizens’ awareness of European Cohesion Policy interventions

Since more than thirty years, the European Cohesion Policy aims to reduce economic disparities and support regional development by funding local-orientated projects. However, the citizens’ awareness of Cohesion Policy follows an unexpected longitudinal pattern characterised by a notable decrease after an initial increase. Although researchers have been investigating the relationship between policy implementation and public awareness, a lack of systemic comprehension of the underlying mechanisms is evident. Using system dynamics, we develop a causal model to explain the roots of the declining awareness towards policy interventions. The findings highlight how citizens initially manifest a high collective attention to Cohesion Policy that tends to decay over time. These dynamics, combined with the citizens’ inherent tendency to lose information saved in their long-term individual memory, could elucidate the system's behaviour. This novel system dynamics application provides policy-makers with operational guidelines for developing efficient communication strategies to improve policy awareness.
Semantic Information in Sensor Networks: How to Combine Existing Ontologies, Vocabularies and Data Schemes to Fit a Metrology Use Case

Self-describing sensors and measurements are a key component to establish (semi-)automated data-analysis in the context of Industry 4.0. In this contribution, multiple popular ontologies and vocabularies in the field of metrology are evaluated regarding their suitability for an industry-oriented metrological use case. The results are used to map necessary concepts from existing knowledge bases into a coherent new ontology that fulfills metrological requirements of sensor and measurement descriptions. We are considering use cases where aspects of sensor networks, network topology, network robustness, information fusion, calibration models for dynamic uncertainty, correct metrological representation and implementation performance are of interest.
Semiparametric mixture regression with unspecified error distributions

In fitting a mixture of linear regression models, normal assumption is traditionally used to model the error and then regression parameters are estimated by the maximum likelihood estimators (MLE). This procedure is not valid if the normal assumption is violated. By extending the semiparametric regression estimator proposed by Hunter and Young (J Nonparametr Stat 24:19–38, 2012a) which requires the component error densities to be the same (including homogeneous variance), we propose semiparametric mixture of linear regression models with unspecified component error distributions to reduce the modeling bias. We establish a more general identifiability result under weaker conditions than existing results, construct a class of new estimators, and establish their asymptotic properties. These asymptotic results also apply to many existing semiparametric mixture regression estimators whose asymptotic properties have remained unknown due to the inherent difficulties in obtaining them. Using simulation studies, we demonstrate the superiority of the proposed estimators over the MLE when the normal error assumption is violated and the comparability when the error is normal. Analysis of a newly collected Equine Infectious Anemia Virus data in 2017 is employed to illustrate the usefulness of the new estimator.