Reviewerly/exHarmony huggingface dataset:
Robot Exploration Using Knowledge of Inaccurate Floor Plans

Exploration is a task in which autonomous mobile robots incrementally discover features of interest in initially unknown environments. Usually, robots use exploration strategies to select their next best locations in partially explored environments. Most of the current exploration strategies ignore prior knowledge about the environments to explore that, in some practical cases, could be available. In this paper, we present a method that includes a priori knowledge in an exploration strategy for a mobile robot. Our exploration strategy selects the next best locations the robot should reach by exploiting the knowledge of the floor plan of the indoor environment that is being explored. Although the floor plan can be inaccurate (e.g., it typically does not include furniture and could represent a topology that does not fully match with that of the actual environment), we experimentally show, both in simulation and with real robots, that knowing the floor plan improves the exploration performance under a wide range of conditions.
Bolt positioning method based on active binocular vision

We propose a position and posture measurement method based on active binocular vision to improve localization accuracy and stability of bolts in substation fittings. Firstly, we obtain the color image and the depth image of bolts and use shape-based template matching method to obtain the image coordinates of the bolts. Secondly, we obtain the 3D coordinates of the bolts based on the binocular camera stereo vision model. Finally, based on the point-cloud information of bolts plane, we obtain the posture information of the bolts by using the least square method. The experimental results show that the binocular system designed in this paper can accurately identify the bolts and has good stability. Within 500 mm measurement range, the position measurement error rate of bolts is less than 0.8 %, and the angle measurement error of the fitting plane is less than 14°.
Co-learning Multiple Browsing Tendencies of a User by Matrix Factorization-based Multitask Learning

Predicting an online user's future behavior is beneficial for many applications. For example, online retailers may utilize such information to customize the marketing strategy and maximize profit. This paper aims to predict the types of webpages a user is going to click on. We observe that instead of building independent models to predict each individual type of web page, it is more effective to use a unified model to predict a user's future clicks on different types of web pages simultaneously. The proposed model makes predictions based on the latent variables that represent possible interactions among the multiple targets and among the features. The experimental results show that this method outperforms the carefully tuned single-target training models most of the time. If the size of the training data is limited, the model shows a significant improvement over the baseline models, likely because the hidden relationship among different targets can be discovered by our model.
Web Observatory Insights

In the present era of Big Data, with continuously increasing amounts of user-generated content, it is becoming a challenge to understand the relation between the content that is available on the Web and the users who are generating that content. Researchers have come up with many ways to understand today's Web better. One of the recently introduced concepts is a Web observatory (WO). This article provides a deep understanding about web observatories. It discusses the status of existing WO systems. The article investigates and gathers the common practices of WOs. This research has implications for researchers and communities in the adoption of the WO concept. The article highlights the challenges of WOs, such as data crawling, privacy and security. It also provides future research and development directions. The article provides a comparative analysis of existing WOs. It discusses the architecture of WOs. It presents components of a WO in a coherent manner and finally provides insights into challenges and limitations of WOs.
Impact of Aging on Three-Dimensional Facial Verification

Age progression is associated with poor performance of verification systems. Thus, there is a need for further research to overcome this problem. Three-dimensional facial aging modeling for employment in verification systems is highly serviceable, and able to acknowledge how variations in depth and pose can provide additional information to accurately represent faces. In this article, the impact of aging on the performance of three-dimensional facial verification is studied. For this purpose, we employed three-dimensional (3D) faces obtained from a 3D morphable face aging model (3D F-FAM). The proposed 3D F-FAM was able to simulate the facial appearance of a young adult in the future. A performance evaluation was completed based on three metrics: structural texture quality, mesh geometric distortion and morphometric landmark distances. The collection of 500 textured meshes from 145 subjects, which were used to construct our own database called FaceTim V.2.0, was applied in performance evaluation. The experimental results demonstrated that the proposed model produced satisfying results and could be applicable in 3D facial verification systems. Furthermore, the verification rates proved that the 3D faces achieved from the proposed model enhanced the performance of the 3D verification process.
Neighborhood of the supersingular elliptic curve isogeny graph at j = 0 and 1728

We describe the neighborhood of the vertex [E0] (resp. [E1728]) in the ℓ-isogeny graph Gℓ(Fp2,−2p) of supersingular elliptic curves over the finite field Fp2 when p>3ℓ2 (resp. p>4ℓ2) with E0:y2=x3+1 (resp. E1728:y2=x3+x) supersingular.
Similarity criteria: evaluating perceptual change for visual localization

Visual localization systems may operate in environments that exhibit considerable perceptual change. This paper proposes a method of evaluating the degree of appearance change using a similarity criteria based on comparing the subspaces spanned by the principal components of the observed image descriptors. We propose two criteria - θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> measures the minimum angle between subspaces and S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">total</sub> measures the total similarity between the subspaces. These criteria are introspective - they evaluate the performance of the image descriptor using nothing more than the image descriptor itself. Furthermore, we demonstrate that these similarity criteria reflect the ability of the image descriptor to perform visual localization successfully, thus allowing a measure of quality control on the localization output.
Cryptocurrencies in the global space: factors and prospects of promotion

The article considers the place of cryptocurrency markets in the global economy. The key risks of active implementation of IT achievements in the financial sector were defined. It was shown that cryptocurrencies are associated with settlement operations, investment activities and speculation in the currency markets. It was argued that the availability of equipment for the production of digital currencies, the spread of trust on the part of users and business representatives contribute to the promotion of cryptocurrencies and the penetration of digital currencies in various spheres of economic activity. The attraction of additional resources in the economy in the conditions of insignificant costs for entering the cryptocurrency market that become a factor in the spread of cryptocurrency assets was considered.
A Particle Swarm Optimization Algorithm Based on Dynamic Adaptive and Chaotic Search

Abstract Although the particle swarm optimization algorithm has the advantages of fast convergence, easy to use and strong versatility, the algorithm also has the defects of low search precision, poor local search ability and easy to fall into local optimal solution. Therefore, this paper proposes a particle swarm optimization algorithm based on dynamic adaptive and chaotic search to ensure the global search ability of the particle swarm while avoiding falling into the local optimal solution. The experimental results show that compared with the comparison algorithm, the DACSPSO has stronger global search ability, higher convergence precision, and can effectively avoid premature convergence.
Anticipating a future versus integrating a recent event? Evidence from eye-tracking

When comprehending a spoken sentence that refers to a visually-presented event, comprehenders both integrate their current interpretation of language with the recent event and develop expectations about future event possibilities. Tense cues can disambiguate this linking, but temporary ambiguity in these cues may lead comprehenders to also rely on further, experience-based (e.g., frequency or an actor's gaze) cues. How comprehenders reconcile these different cues in real time is an open issue. Extant results suggest that comprehenders preferentially relate their unfolding interpretation to a recent event by inspecting its target object. We investigated to what extent this recent-event preference could be overridden by short-term experiential and situation-specific cues. In Experiments 1-2 participants saw substantially more future than recent events and listened to more sentences about future-events (75% in Experiment 1 and 88% in Experiment 2). Experiment 3 cued future target objects and event possibilities via an actor's gaze. The event frequency increase yielded a reduction in the recent event inspection preference early during sentence processing in Experiments 1-2 compared with Experiment 3 (where event frequency and utterance tense were balanced) but did not eliminate the overall recent-event preference. Actor gaze also modulated the recent-event preference, and jointly with future tense led to its reversal in Experiment 3. However, our results showed that people overall preferred to focus on recent (vs. future) events in their interpretation, suggesting that while two cues (actor gaze and short-term event frequency) can partially override the recent-event preference, the latter still plays a key role in shaping participants' interpretation.
Machine Learning in Human-computer Nonverbal Communication

Nonverbal communication is an indispensable and omnipresent element of human behaviors which includes various ways such as human action, hand gesture, and facial expression, etc. As a basic means to express a person's attitudes, feelings, and emotions, it is essential in human's communication, and also play an important role in the multimodal interactions between human and computer system. Due to the richness, flexibility, and ambiguity existing in human's nonverbal expressions, as well as being affected by personalized behavior habits, the understanding and recognition of above human's expressions involves complex intelligent technologies such as visual analysis and situational awareness based on machine learning. This paper aims to provide a systematic summary and analysis of machine learning methods and technologies in human-computer nonverbal communication. It starts with the evolution of nonverbal communication research, goes on to present the definition and classification of nonverbal communication, proceeds to review and analyze the machine learning techniques which are frequently employed in human-computer nonverbal communication, and finally expounds the development of smart learning based on neural mechanism.
Privacy Preservation of Users in P2P E-Payment System

Bitcoin ecosystem is supposed to be anonymous and untraceable. Nonetheless, Bitcoin offers weak anonymity in practice. The linkable pseudonymity of Bitcoin system raises privacy concerns for users. There are inputs and outputs of cryptocurrencies that link to Bitcoin public addresses. This is vulnerable to possible linkability and traceability of users’ identity, that can lead to information leakage. Input and output addresses of transactions miss unlinkability in Bitcoin. Several attempts to solve unlinkability and untraceability of users’ transactions found not to satisfy all requirements of a practical anonymity for users to transact business with bitcoins securely and privately. In this paper, the authors focused on preserving identity and transactional behaviour of users in bitcoin cryptocurrency transactions. A secure privacy-preserving scheme is presented. The paper incorporates bilinear pairing, elliptic curve (ECC), ring signature and Zero Knowledge Proof to curb users’ privacy breaches. The authors theoretically analysed, and evaluated proposed scheme, which is proven secure and robust to implement for preserving users’ privacy in bitcoin transactions. The proposal adds a compatible privacy-preserving layer on top of the Bitcoin blockchain, and consistent with the current Bitcoin architecture without offering any modification.
Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model

A recent Cell paper (Chang & Tsao, 2017) reports an interesting discovery. For the face stimuli generated by a pretrained active appearance model (AAM), the responses of neurons in the areas of the primate brain that are responsible for face recognition exhibit a strong linear relationship with the shape variables and appearance variables of the AAM that generates the face stimuli. In this letter, we show that this behavior can be replicated by a deep generative model, the generator network, that assumes that the observed signals are generated by latent random variables via a top-down convolutional neural network. Specifically, we learn the generator network from the face images generated by a pretrained AAM model using a variational autoencoder, and we show that the inferred latent variables of the learned generator network have a strong linear relationship with the shape and appearance variables of the AAM model that generates the face images. Unlike the AAM model, which has an explicit shape model where the shape variables generate the control points or landmarks, the generator network has no such shape model and shape variables. Yet it can learn the shape knowledge in the sense that some of the latent variables of the learned generator network capture the shape variations in the face images generated by AAM.
A Survey of Nonparametric Mixing Density Estimation via the Predictive Recursion Algorithm

Nonparametric estimation of a mixing density based on observations from the corresponding mixture is a challenging statistical problem. This paper surveys the literature on a fast, recursive estimator based on the predictive recursion algorithm. After introducing the algorithm and giving a few examples, I summarize the available asymptotic convergence theory, describe an important semiparametric extension, and highlight two interesting applications. I conclude with a discussion of several recent developments in this area and some open problems.
Enhanced Decentralized Management of Patient-Driven Interoperability Based on Blockchain

In healthcare, interoperability has been focused recently, in which, Electronic Health Record (EHR) is patient-centric. However, patient-centered interoperability brings new challenges and requirements, like security and privacy, advance technology, immutability, transparency and trust among applications. Data related to healthcare is an asset of a patient that must be controlled and owned by patient. In this paper, we have proposed a blockchain based patient-driven interoperability and discussed how we can leverage blockchain. Blockchain facilitates us in data liquidity, data immutability, data aggregation, patient identity, digital access rules, incentives and clinical data volume. Our system provides patients an immutable log and easy access to their health data across the healthcare organizations. Furthermore, patient authorize healthcare organizations to access their health data. Stakeholders (patients and healthcare organization) of EHRs are also incentivized if any organization wants to access their health data.
Low-Frequency Vibration Monitoring System Based on Optical Image Phase Method With Different Fringe Patterns

A rapid low-frequency vibration monitoring system based on fringe projection is introduced. Fourier transform profilometry is used to obtain the three-dimensional surface topography reconstruction of the vibrating object at each sampling moment. Then the height value of the selected point on the measured vibrating object at each moment is arranged by time and drawn as a trajectory curve. Finally, the frequency and the amplitude of the vibration could be calculated by the drawn trajectory curve and the sampling rate of the CCD camera. To confirm the reliability of this method, three different kinds of fringes are projected onto the measured object. It shows that the measurement results are more accurate when adopting sinusoidal wave as the fringe pattern, and the system could detect the vibration of whose frequency is above 0.01Hz or whose amplitude is more than 30μm. The study could be better introduced in this system to be applied to the field of industrial vibration measurement and dynamic vibration monitoring.
The Use of Augmented Reality to Represent Gamification Theory in User Story Training

The purpose of this study is to train students in the university's design department to organize and demonstrate the story of a user's situational intentions in the design process. The user context investigation is an integral step in any design activity, and it helps to better understand the target of the design, meet the users' needs, and provide the correct design direction in order to reduce the failure rate after a product goes on the market. Students in the design department have often been found to lack complete product design thinking, and the students' design is often out of touch with reality and cannot meet the needs of end users. The aim of this research is to investigate how to combine physical objects and augmented reality by randomly generating three-dimensional objects combining people, objects and scenes, and having students tell the user stories based on the results produced. Through logic and insight, the future is then applied to the observation of actual scenes. In this study, augmented reality and 3D printing techniques were used to create three teaching aids, each presented in a hexahedral physical form. Each teaching aid contained six randomly generated 3D objects. The random control was thus the students who had a total of 216 permutations. The results of the application are more integrated and more effective in the classroom, and students were found to be highly interested in interacting with the system. With the application of augmented reality and the uncertainty caused by random probability, the effect of gamification was found to increase the desire to learn, and strengthened cognitive and observational powers which were helpful in describing the user's story.
Wide-View Sidewalk Dataset Based Pedestrian Safety Application

As the increasing number of people watching mobile phones while walking, sidewalk accidents occur more frequently.Using mobile camera instead of distracted pedestrians to monitor the road conditions ahead can effectively prevent pedestrian accidents.In order to develop this kind of applications, a widefield sidewalk dataset becomes a necessity.In this paper, three major contributions are concluded.Firstly, a dataset quality evaluation model is proposed, which directs the establishment of a wide-view dataset named PESID for the sidewalk environment.PESID currently contains more than 1.9K labeled images which cover more than 5 districts, 10 communal facilities, 6 typical roads.Secondly, a criterion is presented to evaluate 9 up-to-date object detection algorithms in order to train a mobile feasible obstacle detection model.Finally, a reliable and low-cost framework is designed for obstacle detection based pedestrian safety application.The proposal is able to avoid 71.4% collisions on average by evaluating the mean average precision (MAP), the dangerous reminder omission rate and the false detection rate of the model.
Secure data stream outsourcing with publicly verifiable integrity in cloud storage

Recent technological developments in Internet of Things has led to enormous amount of data stream. Limited by storage and computation capabilities, increasing companies and individuals outsource their data stream to powerful cloud servers, from which they can query the data when it is needed. Nevertheless, it presents a challenge in order to ensure the cloud server can comply with their advertised policies and return the correct query results. In this paper, we focus in particular on the scenario where a data owner wishes to (i) outsource its unbounded data stream to a cloud server; (ii) enable anyone who has the public key to retrieve the outsourced data stream; (iii) ensure public verification of the integrity for query results. To this end, we propose a secure data stream outsourcing scheme with publicly verifiable integrity in cloud storage (DSO-PVI), which builds upon the well-established techniques of Chameleon Vector Commitment (CVC) to guarantee the integrity of query results. Moreover, we introduce a formal security model of DSO-PVI and prove that it is secure against malicious cloud servers. Our analyses and experimental results demonstrate that the proposed scheme is greatly improved in terms of functionality and performance.
Uncertainty decomposition of quantum networks in SLH framework

Summary This paper presents a systematic method to decompose uncertain linear quantum input‐output networks into uncertain and nominal subnetworks, when uncertainties are defined in SLH representation. To this aim, two decomposition theorems are stated, which show how an uncertain quantum network can be decomposed into nominal and uncertain subnetworks in cascaded connection and how uncertainties can be translated from SLH parameters into state‐space parameters. As a potential application of the proposed decomposition scheme, robust stability analysis of uncertain quantum networks is briefly introduced. The proposed uncertainty decomposition theorems take account of uncertainties in all three parameters of a quantum network and bridge the gap between SLH modeling and state‐space robust analysis theory for linear quantum networks.
Galisonian logic devices and data availability: revitalising Upper Palaeolithic cultural taxonomies

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Deep Embedding GAN-based Model for Anomaly Detection on High-dimensional Sparse Data

The use of Generative Adversarial Nets (GAN) for anomaly detection has been explored recently. However, in the case of high-dimensional sparse data, existing GAN-based anomaly detection models suffer from inefficient dimensionality reduction, computationally costly data reconstruction, and suboptimal performance limited by the training objective. In this paper, a deep embedding GAN-based model is developed for anomaly detection on high-dimensional sparse data. In the model, dimensionality reduction of input data is performed by embeddings efficiently. With the bidirectional Wasserstein GAN, data reconstruction is conducted in the input dense representation space at a low computational cost. The objective function defined by the Wasserstein distance and Lipschitz continuity constraints stabilizes training and improves model performance. Experimental results on public datasets show that, the developed model has comparable or superior performance over the competing techniques, and achieves up to 8.81% relative improvement based on the area under the Receiver Operating Characteristics curve (AUC).