Reviewerly/exHarmony huggingface dataset:
Neural Face Models for Example-Based Visual Speech Synthesis

Creating realistic animations of human faces with computer graphic models is still a challenging task. It is often solved either with tedious manual work or motion capture based techniques that require specialised and costly hardware. Example based animation approaches circumvent these problems by re-using captured data of real people. This data is split into short motion samples that can be looped or concatenated in order to create novel motion sequences. The obvious advantages of this approach are the simplicity of use and the high realism, since the data exhibits only real deformations. Rather than tuning weights of a complex face rig, the animation task is performed on a higher level by arranging typical motion samples in a way such that the desired facial performance is achieved. Two difficulties with example based approaches, however, are high memory requirements as well as the creation of artefact-free and realistic transitions between motion samples. We solve these problems by combining the realism and simplicity of example-based animations with the advantages of neural face models. Our neural face model is capable of synthesising high quality 3D face geometry and texture according to a compact latent parameter vector. This latent representation reduces memory requirements by a factor of 100 and helps creating seamless transitions between concatenated motion samples. In this paper, we present a marker-less approach for facial motion capture based on multi-view video. Based on the captured data, we learn a neural representation of facial expressions, which is used to seamlessly concatenate facial performances during the animation procedure. We demonstrate the effectiveness of our approach by synthesising mouthings for Swiss-German sign language based on viseme query sequences.
Recognizing Chinese Sign Language Based on Deep Neural Network

Gesture recognition is ongoing attention in the field of human computer interaction (HCI). With development of deep neural network technology in computer vision, more complex sign languages are possible to recognize but, the research on Chinese language (CSL) recognition remain in discussion. Here we have performed our collected dataset and proposes a new solution to recognize CSL, and further insight on preliminary verification on CSL recognition using 2D image.This paper attempts to reduce the adverse impact of dataset itself on the image recognition network using continuously improved technical method. Present study addresses the following:1) Due to the lack of the CSL image dataset, we made a CSL dataset and used it in the following experiments to verify the usability of the dataset. 2) Using a self-made dataset, we combined the method of hand skeletal gesture recognition to reduce the impact of the gesture overlap and improve recognition accuracy. Finally, a network model was trained and tested on self-made dataset which include some overlapping gestures that are difficult to recognize and achieved the accuracy rate of 0.9324. 3) Put forward the idea of continuing the experiment to improve dataset and using fuzzy semantic recognition for trying to solve the time-domain problem of dynamic sign language recognition which needs linguistic studies.
A Probabilistic Online Policy Estimator for Autonomous Systems Planning and Decision Making

Partially Observable Markov Decision Process (POMDP) models are a popular probabilistic modeling method for continuous planning in systems that operate in partially observable, uncertain environments. This paper presents an online algorithm, N-Step Look-Ahead, for solving POMDP models with specific characteristics: flexible state-space, dynamic model parameters (e.g. probability distributions), and real-time constraints (e.g. response needed in fractions of seconds). This algorithm computes the best executable policy by creating a belief-tree starting from the current belief state and employing a look-ahead search over a finite time horizon. To address time-accuracy trade-offs, various heuristics in combination with a distance-based clustering technique is employed to expand and explore only high value belief nodes. To evaluate the accuracy of our algorithm, we compare the online policies computed from N-Step Look-Ahead with offline policies calculated using a customized Q-learning algorithm. We show that the online algorithm can compute optimal policies for beliefs, where belief probabilities are not normally distributed, by looking only a few steps ahead in the belief tree. We discuss computing online policies for normally distributed belief states using our algorithm and explain why they can be different from that obtained from the offline algorithm. Finally, we show how useful heuristics can be developed from Q-learning results to improve the N-Sep Look-Ahead in terms of computation time and accuracy, especially for large POMDP models.
On the imprecision of full conditional probabilities

The purpose of this paper is to show that if one adopts conditional probabilities as the primitive concept of probability, one must deal with the fact that even in very ordinary circumstances at least some probability values may be imprecise, and that some probability questions may fail to have numerically precise answers.
WITHDRAWN: Identify missing countries using GEEBLL iterative method for analyzing tweets opinion

Twitter is one of the social media platforms where people express ideas, thoughts and comments on day to day basis. For researchers using Twitter as their dataset, they pay or purchase dataset to perform research. The existing Twitter API and other data extraction techniques has lack of country and geo-code information, the reason for that lacking might due to unaware and incomplete information by twitter user in public platform for providing direct location. Approximately only 2% of extracted tweets contain its corresponding account’s country and geo-code information which is very less and inadequate for data analysis task. The proposed method GEEBLL, Geo Entities Extraction from Bio-data, Location and Language is iterative in nature. It extracts country as well as geo-code information using the existing twitter metadata for both primary and secondary accounts. Metadata such as profile bio-data, location and tweets are used to predict the country by applying techniques such as entity recognition, language detection, extraction of bio-data and location for secondary accounts by tracing primary account’s timeline. This technique is efficient in terms of data extraction cost; its simplicity while implementing the model by using free python modules and web crawling techniques.
A self-organizing multi-model ensemble for identification of nonlinear time-varying dynamics of aerial vehicles

This article presents a novel identification approach which can deal with nonlinear and time-varying characteristics of complex dynamic systems, especially an aerial vehicle in the entire flight envelope. A set of local sub-models are first developed at different operating points of the system, and subsequently a self-organizing multi-model ensemble is introduced to aggregate the outputs of the local models as a single model. The number of employed local models in the proposed multi-model ensemble is optimized using a novel self-organizing approach. Also, wavelet neural networks, which combine both the universal approximation property of neural networks and the wavelet decomposition capability, are used as the local models of the proposed method. In addition, a generalized online sequential extreme learning machine is adopted in the introduced approach to determine the optimal validity function of the local models at each time step. Finally, the introduced self-organizing multi-model ensemble is applied to the NASA Generic Transport Model as a complex nonlinear system to demonstrate the effectiveness of the proposed identification approach. Furthermore, the results obtained from the conventional artificial neural networks are carefully compared with those from the wavelet neural networks, which are employed as the local models of the introduced multi-model ensemble. The simulation results suggest that the introduced wavelet neural network–based self-organizing multi-model ensemble can be used satisfactorily as the prediction model of model-based control systems for long prediction horizons.
Privacy-Preserving Friend Recommendation in an Integrated Social Environment

Ubiquitous Online Social Networks (OSN)s play a vital role in information creation, propagation and consumption. Given the recent multiplicity of OSNs with specially accumulated knowledge, integration partnerships are formed (without regard to privacy) to provide an enriched, integrated and personalized social experience. However, given the increasing privacy concerns and threats, it is important to develop methods that can provide collaborative capabilities while preserving user privacy. In this work, we focus on friend recommendation systems (FRS) for such partnered OSNs. We identify the various ways through which privacy leaks can occur, and propose a comprehensive solution that integrates both Differential Privacy and Secure Multi-Party Computation to provide a holistic privacy guarantee. We analyze the security of the proposed approach and evaluate the proposed solution with real data in terms of both utility and computational complexity.
Document-Analysis Systems and Techniques

This chapter provides a survey of different document-analysis systems and techniques. Documents used to communicate information in the office can be created, distributed, and stored in paper or in electronic forms. An optical scanner for documents generally consists of a transport mechanism plus a sensing device that converts light intensity into gray-levels. Printed documents usually consist of black on a white background. Adaptive-thresholding schemes can be either global or local. A global method determines a threshold value based on an analysis of the histogram of image-intensity values. Component labeling is a well-known image-processing technique that associates adjacent black pixels to detect distinct objects in a field. Component labeling may be done either pixel-by-pixel or using run-lengths.
An Approach to Improve the Accuracy of Detecting Spam in Online Reviews

Customers or user opinion is the most important and valuable information at online nowadays, especially in product reviews. Mostly customer used to make their decision for purchasing a particular product based on the other's customer reviews. Those reviews are increasing the rating of that e-commerce site. Normally, reviews are considered unbiased opinion of a person who has personal experience with a related specific product. The noticeable thing is that many reviewers reviews are not real or authentic. These kinds of feedback are usually called spam, and it is becoming a large problem in online and other electronic communication. As the value of online reviews is getting increased, the spammers are getting inspired to doing spam for them or promoting a specific e-commerce website. Also, they are demoting a specific site for payment. In this paper, we have discussed some traditional techniques for detecting spam in online public opinions. Next, we have used the stacking algorithm with some traditional classifiers for the detection of spam reviews. Finally, the performance of different classifiers has evaluated through a simulation experiment. From the experiments, we have seen that stacking classifier provides better accuracy than other traditional classifiers.
A New Efficient and Secure Secret Reconstruction Scheme (SSRS) with Verifiable Shares Based on a Symmetric Bivariate Polynomial

Secret sharing (SS) schemes have been widely used in secure computer communications systems. Recently, a new type of SS scheme, called the secure secret reconstruction scheme (SSRS), was proposed, which ensures that the secret can only be recovered by participants who present valid shares. In other words, if any outside adversary participated in the secret reconstruction without knowing any valid share, the secret cannot be recovered by anyone including the adversary. However, the proposed SSRS can only prevent an active attacker from obtaining the recovered secret, but cannot prevent a passive attacker from obtaining the secret since exchange information among participants is unprotected. In this paper, based on bivariate polynomials, we propose a novel design for the SSRS that can prevent both active and passive attackers. Furthermore, we propose a verification scheme which can verify all shares at once, i.e., it allows all shareholders to efficiently verify that their shares obtained from the dealer are generated consistently without revealing their shares and the secret. The proposed scheme is really attractive for efficient and secure secret reconstruction in communications systems.
Blockchain and Trusted Execution Environment Based Fairness Incentive Mechanism in Crowdsensing

We propose a novel scheme for blockchain-based crowdsensing applications by leveraging Trusted Execution Environments to address the issue that malicious requesters trick workers by publishing abnormal crowdsensing requirements, resulting in that requesters can get sensing data of the workers without needing to pay workers during a crowdsensing dealing. Experiments are performed to evaluate the processing time and network payload of our prototype system.
Fully Adaptive Resource Management in Radar Networks

We present a fully adaptive radar network testbed that consists of four monostatic X-band radar sensors and a centralized controller which uses a perception-action cycle to optimize radar parameter in real time. The sensor hardware and the control algorithms are presented and explained. In a second part of the paper, we present results from outdoor experiments where an optimized radar resource allocation has been considered. Given a predefined track performance, the radar network is able to save resources by reducing the bandwidth to the amount needed for an accurate tracking and by operating only the sensors with the best measurement contributions to the active tracks.
You Are What You Tweet: Profiling Users by Past Tweets to Improve Hate Speech Detection

Hate speech detection research has predominantly focused on purely content-based methods, without exploiting other contextual data. We briefly critique pros and cons of this task formulation. We then investigate profiling users by their past utterances as an informative prior to better predict whether new utterances constitute hate speech. To evaluate this, we augment three Twitter hate speech datasets with additional timeline data, then embed this additional context into a strong baseline model. Promising results suggest merit for further investigation.
Understanding Troll Writing as a Linguistic Phenomenon

The current study yielded a number of important findings. We built a neural network that achieved an accuracy score of 91% in classifying troll and genuine tweets. By means of regression analysis, we identified a number of features that make a tweet more susceptible to correct labelling and found that they are inherently present in troll tweets as a special type of discourse. We hypothesised that those features are grounded in the sociolinguistic limitations of troll writing, which can be best described as a combination of two factors: speaking with a purpose and trying to mask the purpose of speaking. Next, we contended that the orthogonal nature of these factors must necessarily result in the skewed distribution of language parameters of troll messages. Having chosen as an example distribution of the topics and vocabulary associated with them, we showed some very pronounced distributional anomalies, thus confirming our prediction.
Evaluation and scrutiny of XML files, XML empowered records and native XML records

Abstract With the expanding prevalence of XML information and a extraordinary requirement for an information base administration framework ready to store, recover and control XML-based information in an effective way, information base exploration networks and programming businesses have attempted to react to this necessity. XML-empowered information base and local XML information base are two methodologies that have been proposed to address this test. These two methodologies are an inheritance information base framework which are stretched out to store, recover and control XML-based information. The significant goal of this paper is to investigate and look at between the two methodologies and reach to a few measures to have a reasonable rule to choose the best approach in every situation. When all is said in done, local XML information base frameworks have greater capacity in correlation with XML-empowered information base framework for overseeing XML-based information.
Comparison of FPGA Implemented Sobel Edge Detector and Canny Edge Detector

Result of image processing in computer vision varies with algorithms. In this study, we compare FPGA implemented Sobel edge detector and Canny edge detector. We design edge detectors with Verilog HDL and program Xilinx Vertex-7 FPGA. The goal of this study is to compare output result and resource usage of both edge detection algorithm.
Prediction of the Visual Similarity between Photos Making Use of Deep Learning

By using the objective visual similarity matrix P that is given only to some photo pairs and the image similarity matrix Q that can be calculated automatically for all photo pairs, the visual similarity of the missing parts of P can be predicted. To make Q we use 1000 class classification and semantic segmentation of photos by pre-trained Convolutional Neural Networks (CNNs). Minimizing Kullback-Leibler divergence between two Gaussian distributions with mean 0, covariance matrix P and Q respectively, P can be completed. 200 photos were collected from students at the University of Tokyo and the visual similarity between all photo pairs was measured. 184 photos were also collected from Meiji University students and the visual similarity between photos was not measured. The visual similarity between all 384 photos was predicted by the proposed method.
Towards a cognitive architecture for self-supervised transfer learning for objects detection with a Humanoid Robot

Robots are becoming more and more present in our daily life operating in complex and unstructured environments. To operate autonomously they must adapt to continuous scene changes and therefore must rely on an incessant learning process. Deep learning methods have reached state-of-the-art results in several domains like computer vision and natural language processing. The success of these deep networks relies on large representative datasets used for training and testing. But one limitation of this approach is the sensitivity of these networks to the dataset they were trained on. These networks perform well as long as the training set is a realistic representation of the contextual scenario. For robotic applications, it is difficult to represent in one dataset all the different environments the robot will encounter. On the other hand, a robot has the advantage to act and to perceive in the complex environment. As a consequence when interacting with humans it can acquire a substantial amount of relevant data, that can be used to perform learning. The challenge we addressed in this work is to propose a computational architecture that allows a robot to learn autonomously from its sensors when learning is supported by an interactive human. We took inspiration on the early development of humans and test our framework on the task of localisation and recognition of objects. We evaluated our framework with the humanoid robot iCub in the experimental context of a realistic interactive scenario. The human subject naturally interacted with the robot showing objects to the iCub without supervision in the labelling. We demonstrated that our architecture can be used to successfully perform transfer learning for an object localisation network with limited human supervision and can be considered a possible enhancement of traditional learning methods for robotics.
Dynamic VR Display System of Digital Garment Display Design Based on 5G Virtual Reality Technology

The digital clothing mall is the product of the rapid development of 5G virtual reality technology and network technology. It surpasses the existing Internet e-commerce platform and realizes the “human-machine integration” of e-commerce and the immersive three-dimensional effect. The analysis of the differences and similarities between the dynamic VR display of digital clothing display design and traditional animated clothing design provides a theoretical basis for the combination of VR and clothing display system design, which has important theoretical and practical significance. This article analyzes the advantages of 5G virtual reality technology based on the current status of product display on the Internet, attempts to apply the traditional display design concepts to the network-based virtual display system, builds a network-based digital clothing store virtual display system, and creates A new three-dimensional product display method. In this paper, on the Virtools development platform, the user’s autonomous roaming in the scene and the interactive script of the product are designed in detail. Secondly, the principle of the core collision detection technology in 5G virtual reality technology is explained, and a script program for collision detection is added to the interactive function program of the clothing mall to avoid distortion and distortion. In-depth research was conducted on the display and dissemination methods of VR for apparel malls, building a display and dissemination model of apparel malls based on VR technology, and combining design elements of user experience to derive relevant design principles and communication strategies. And through a large number of questionnaires and interviews, the user’s needs were initially obtained and the needs were finally graded using the kano model. Survey research shows that 35% of people understand the dynamic display of clothing VR through the media, and the public’s understanding of the dynamic display of clothing VR has shown an upward trend in recent years.