Reviewerly/exHarmony huggingface dataset:
Effective Information Filtering Mining of Internet of Brain Things Based on Support Vector Machine

The traditional Internet of Brain Things’ big data information filtering method ignores the extraction of big data features, the filtering effect, and the effect of denoising processing on the simulation results, resulting in low filtering accuracy and poor performance. An effective information filtering and mining algorithm for the Internet of Brain Things based on support vector machine (SVM) is proposed. First, the model construction and feature extraction of the Internet of Brain Things’ big data system are carried out. The correlation feature extraction is performed on the effective information features; the correlation factors of the effective information data are sorted; the main feature quantity of the relevance degree is extracted; and the filter non-association is designed. The information is reasonably filtered; all data are processed, converted to the same interval for processing; data protocol is implemented; and data effective information feature mining is implemented based on the SVM algorithm. The simulation results show that the algorithm is effective for filtering big data and has high precision and superior performance, which shows good application value.
Virtual and Augmented Reality

This chapter focuses on virtual reality (VR) and augmented reality (AR) as tools for teaching and learning. Attainment of skills and knowledge can be supported through the use of VR/AR applications that are being developed both in the commercial sector and at various research institutions. An overview of what differentiates VR and AR is provided to the reader along with considerations of how such applications might be used to support learning environments in the future. If instructional designers and programmers can synchronize their efforts it may be possible to make VA/AR a common feature across learning environments nationally. Common elements of a VR/AR system are discussed here as well as the need to incorporate instructional design practices into the design of learning applications that use VR/AR.
Recent advances in denoising of manifold-valued images

Modern signal and image acquisition systems are able to capture data that are no longer real-valued but may take values on a manifold. However, whenever measurements are taken, no matter whether manifold-valued or not, there occur tiny inaccuracies, which result in noisy data. In this chapter, we review recent advances in denoising of manifold-valued signals and images, where we restrict our attention to variational models and appropriate minimization algorithms. The algorithms are either classical as the subgradient algorithm or generalizations of the half-quadratic minimization method, the cyclic proximal point algorithm, and the Douglas–Rachford algorithm to manifolds. An important aspect when dealing with real-world data is the practical implementation. Here several groups provide software and toolboxes as the Manifold Optimization (Manopt) package and the manifold-valued image restoration toolbox (MVIRT).
Using ICTs and Mobile Devices to Assist Adult English-Language Learning

The use of information and communication technologies (ICTs) and mobile devices to assist language acquisition has expanded rapidly during the past decade given the prevalence of broadband networks and mobile phones. Many studies have examined how these technologies can facilitate English-language learning. The chapter will discuss the current research on using ICTs to create e-portfolios to support language learning, the role of mobile devices in the language-learning process, the impact of using ICTs on English-language learners' (ELLs) learning other than language acquisition (e.g., increasing their sense of a learning community), and the design procedure of such a learning environment. Future research in this area also will be discussed.
Cytopathology and the Smartphone

Since George Papanicolaou proposed the famous test pap about 60 years ago, and due to heavy impact on cervical cancer reduction, cytopathology became a popular discipline, and in some countries, a medical specialty. The microscope is still the primary device in cytopathology laboratories; however, modalities for molecular tests and networks hosting laboratory information systems and imaging systems produce enormous data. Nowadays, competing mobile devices are integrated with the environment, always connected and always on the user side. Therefore, it is expected that applications for the mobile device targeting cytopathology would flourish. There is potential for applications to various activities of the laboratory including and not limited to training, reporting of results, diagnosis and consultation, management of the laboratory, whole slide imaging and still image capture, quality control and assurance, and numerous channels for interactions between patient-doctor or among medical specialists. The mobile device can enhance the cytopathology laboratory and offer numerous benefits.
Adaptive convolutional layer selection based on historical retrospect for visual tracking

Visual tracking has recently gained a great advance with the use of the convolutional neural network (CNN). Usually, existing CNN‐based trackers exploit the features from a single layer or a certain combination of multiple layers. However, these features only characterise an object from an invariable aspect and cannot adapt to scene variation, which limits the performance of such trackers. To overcome this limitation, the authors study the problem from a new perspective and propose a novel convolutional layer selection method. To obtain robust appearance representation, they investigate the advantages of features extracted from different convolutional layers. To determine the correctness of the tracking prediction and updated model, they design a verification mechanism based on historical retrospect, which can estimate the deviation for each layer by bidirectionally locating the target. Meanwhile, the deviation works as the layer‐wise selection criteria. Extensive evaluations on the OTB‐2013, visual object tracking (VOT)‐2016 and VOT‐2017 benchmarks demonstrate that the proposed tracker performs favourably against several state‐of‐the‐art trackers.
Investigating the topographic influence on short-wave irradiance measurements: A case study for Kanzelhöhe Observatory, Austria

We investigate terrain effects on short-wave radiation measurements at Kanzelhöhe Observatory (KSO), a mountainous radiation monitoring site in the Austrian Southern Alps, using three-dimensional (3-D) radiative transfer (RT) modeling and observations. The magnitude of terrain effects on global radiation is experimentally assessed through the comparison of 10-min measurement segments ex−/including terrain reflected radiation. The reflected component is ex−/included in global solar irradiance (GLO) measurements through a horizon shaped metal frame raised/lowered in 10-min intervals. Additionally we assess terrain influence in 3-D RT model simulations performed with an updated version of GRIMALDI, allowing for digital elevation model inclusion. The DEM used for terrain representation is the one arc second Shuttle Radar Topography Mission (SRTM) digital elevation model. For GRIMALDI simulations we consider an area averaged albedo of 0.30 for summer and 0.36 for winter (0.30 for forests and 0.90 for fresh snow), respectively. The influence of the horizon and surface albedo on a grid cell of interest (KSO location) is derived through comparison of the solar irradiance incident on a domain grid cell surface (SI-GCS) with SI-GCS reduced for the reflected component. Applying a 2σ threshold for the significance of terrain effects in GLO measurements, we find no significant influence of the surrounding during summertime. Also during wintertime, we find for the vast majority of observational increments no significant influence of the surrounding. Above a 2σ threshold, exceedances are small, on average 0.2 W m−2. Differences between 3-D RT model simulations including and excluding terrain effects are small during winter conditions, i.e. always below 1% and never exceeding 1 W m−2. Slightly larger differences are found for 3-D RT simulations including/excluding the reflected component during summertime, reaching at solar noon up to 6.1 W m−2. Both observations and model simulations indicate that KSO is well suited for radiation monitoring as terrain effects are small (close to negligible considering instrument specifications) throughout the year.
Chasing the Buzz; Exploring Sense Deprivation in Bodily Play

Physical games involving blindfolded players have a timeless appeal and the restricting of perceptual channels can be insightful for players and observers regarding embodied experience. Wireless, mobile and wearable technologies open up further opportunities for designing bodily play experiences through exploiting sensory deprivation. To better understand the potential for movement-based games in which vision and/or audio is restricted, we iteratively developed and play-tested a series of three to four player chasing games. Based on our tests, we suggest the importance of ambiguity, proximity, and freedom of movement to support designing sensory deprivation games.
Security Model of Internet of Things Based on Binary Wavelet and Sparse Neural Network

At present, the internet of things has no standard system architecture. According to the requirements of universal sensing, reliable transmission, intelligent processing and the realization of human, human and the material, real-time communication between objects and things, the internet needs the open, hierarchical, extensible network architecture as the framework. The sensation equipment safe examination platform supports the platform through the open style scene examination to measure the equipment and provides the movement simulated environment, including each kind of movement and network environment and safety management center, turning on application gateway supports. It examines the knowledge library. Under this inspiration, this article proposes the novel security model based on the sparse neural network and wavelet analysis. The experiment indicates that the proposed model performs better compared with the other state-of-the-art algorithms.
Backdoors to planning

Backdoors measure the distance to tractable fragments and have become an important tool to find fixed-parameter tractable (fpt) algorithms for hard problems in AI and beyond. Despite their success, backdoors have not been used for planning, a central problem in AI that has a high computational complexity. In this work, we introduce two notions of backdoors building upon the causal graph. We analyze the complexity of finding a small backdoor (detection) and using the backdoor to solve the problem (evaluation) in the light of planning with (un)bounded plan length/domain of the variables. For each setting we present either an fpt-result or rule out the existence thereof by showing parameterized intractability. For several interesting cases we achieve the most desirable outcome: detection and evaluation are fpt. In addition, we explore the power of polynomial preprocessing for all fpt-results, i.e., we investigate whether polynomial kernels exist. We show that for the detection problems, polynomial kernels exist whereas we rule out the existence of polynomial kernels for the evaluation problems.
Impact of inaccurate data on Differential Privacy

The growing popularity of location-based systems (LBSs), which enable un-trusted servers to easily collect huge amounts of information regarding users’ locations, has recently raised serious privacy concerns. Differential privacy (DP) is one of the most prominent frameworks used to deal with disclosure prevention in publishing a database. However, the current state-of-the-art DP approaches have a serious drawback: they assume the real-world dataset is accurate, i.e., without errors. Due to the inaccurate positioning services and environmental aspects, location data typically contain errors. The impact of errors on the performance of DP has yet to be explored. To bridge the gap between theory and practice in data privacy, in this paper, we preliminarily analyze the impact of errors on DP and present a rigorous proof of the effect. Furthermore, we highlight the current mechanisms that should be evaluated in applying DP to an inaccurate dataset. The experimental results demonstrate that if errors exist, the released data will have a lower privacy guarantee than expected.
Ant Colony Optimization and image model-based robot manipulator system for pick-and-place tasks

A visual servo control system combines with the model-based image segmentation and an Ant Colony Optimization (ACO) algorithm to design an excellent six-Degree-of-Freedom (6-DOF) robot manipulator for solving the complicated combinations of pick-and-place tasks. A simple but efficient vision-based segmentation methodology is developed to extract the object information by getting appropriate feature of the controlled platform when the robot is tracking the manipulated image patterns. The evolutionary ACO learning algorithm explores the near-optimal path selections to drive the 6 ROF robot arm kinematics model for completing the Pick-and-Place tasks as soon as possible. Inverse orientation kinematic machine is proposed to successfully guide the robot manipulator into the desired position. Several software simulations include image segmentations, the shortest path selection, and the performance validation in various experiments. These results are described and presented to demonstrate that the designed image model-based robot manipulator wins the excellent Pick-and-Place task. Not only the software simulation, the practical robot synchronously performed in real-world to reach the higher feasible functions in the eye-to-hand experiments.
Multi-view non-negative matrix factorization for scene recognition

Accurately discriminating complicated sceneries from different categories is a useful technique in multimedia and computer vision. In this work, we propose a novel multi-view non-negative matrix factorization to detect human gaze behavior, which is subsequently integrated into an image kernel machine for scene categorization. More specifically, we first project regions from each scenery into the so-called perceptual space, which is established by combining color, texture, and semantic features. Then, a novel non-negative matrix factorization (NMF) algorithm is developed which decomposes the regions’ feature matrix into the product of the basis matrix and the sparse codes. The sparse codes indicate the saliency level of different regions which is used to constructed gaze shifting path. Thereby, the path from each scenery is derived and further incorporated into an image kernel for scene categorization. Comprehensive experiments on six scenery data sets have demonstrated the superiority of our method over a series of recognition models.
Research on Scene Understanding-Based Encrypted Image Retrieval Algorithm

Aiming to improve the precision and security, and reduce complexity of the encrypted image retrieval algorithm, a scene understanding-based encrypted image retrieval algorithm is proposed in this paper, which encrypts images by chaotic system-based image encryption algorithm, extracts image features-based on two-dimensional multi-scale hidden Markov model, and classifies the indoor scene to establish an offline database. In the online stage, the query image captured by the smart device is first encrypted, and the multi-scale feature extraction and the image scene matching are performed. Then images in the scene are matched one by one using manifold ranking. Finally, the optimal encrypted matching image is obtained by the homography-based image matching algorithm, and the plaintext image is also obtained by the decryption algorithm. Performance analysis and simulation results show that the proposed algorithm can improve the precision of image retrieval and protect the security of users’ privacy.
Parsing Chinese Sentences with Grammatical Relations

We report our work on building linguistic resources and data-driven parsers in the grammatical relation (GR) analysis for Mandarin Chinese. Chinese, as an analytic language, encodes grammatical information in a highly configurational rather than morphological way. Accordingly, it is possible and reasonable to represent almost all grammatical relations as bilexical dependencies. In this work, we propose to represent grammatical information using general directed dependency graphs. Both only-local and rich long-distance dependencies are explicitly represented. To create high-quality annotations, we take advantage of an existing TreeBank, namely, Chinese TreeBank (CTB), which is grounded on the Government and Binding theory. We define a set of linguistic rules to explore CTB’s implicit phrase structural information and build deep dependency graphs. The reliability of this linguistically motivated GR extraction procedure is highlighted by manual evaluation. Based on the converted corpus, data-driven, including graph- and transition-based, models are explored for Chinese GR parsing. For graph-based parsing, a new perspective, graph merging, is proposed for building flexible dependency graphs: constructing complex graphs via constructing simple subgraphs. Two key problems are discussed in this perspective: (1) how to decompose a complex graph into simple subgraphs, and (2) how to combine subgraphs into a coherent complex graph. For transition-based parsing, we introduce a neural parser based on a list-based transition system. We also discuss several other key problems, including dynamic oracle and beam search for neural transition-based parsing. Evaluation gauges how successful GR parsing for Chinese can be by applying data-driven models. The empirical analysis suggests several directions for future study.
Trace Ratio Criterion Based Large Margin Subspace Learning for Feature Selection

In this paper, we propose a novel feature selection model based on subspace learning with the use of a large margin principle. First, we present a new margin metric described by a given instance and its nearest missing and nearest hit, which can be explained as the nearest neighbor with a different label and the same label, respectively. Specifically, for a given instance, the margin is the ratio of the distance of the nearest missing to that of the nearest hit rather than the difference of distances, which contributes to better balance since the distance to the nearest missing is usually much larger than the nearest hit. The proposed model seeks a subspace in which the margin metric is maximized. Moreover, considering that the nearest neighbors of a given sample are uncertain in the presence of many irrelevant features, we treat them as hidden variables and estimate the expectation of margin. To perform the feature selection, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm is imposed on the subspace projection matrix to enforce row sparsity. The resulting trace ratio optimization problem, which can be connected to a nonlinear eigenvalue problem, is hard to solve. Thus, we design an efficient iterative algorithm and present a theoretical analysis of the convergence. Finally, we evaluate the proposed method by comparing it against several other state-of-the-art methods. The extensive experiments on real-world datasets show the superiority of our proposed approach.
Achieving Semantic Secure Search in Cloud Supported Information-Centric Internet of Things

With the support of cloud computing, the scalability of the information-centric Internet of Things (IC-IoT) can be increased greatly. In the cloud-supported IC-IoT, how to make search more secure and efficient is a key issue to deal with. Searchable encryption is a potential technique to solve the above problem. In the cloud-supported IC-IoT, sensitive data are provided by multiple data users and can be accessed by specific data users. Then, the expression ability, search authorization, dynamic updates, and search efficiency become major concerns of search in the cloud-supported IC-IoT. In this paper, SASDQ, a semantic and authorized search scheme with dynamic updates and quick response, is proposed. In SASDQ, by constructing users’ personal dictionaries, dynamic updates and efficient index generation are achieved, and by leveraging ciphertext policy attribute-based encryption and conceptual graphs, semantic search with file-level fine-grained access authorization can be realized. In addition, quick search response is implemented by the preprocessing of time-consuming authorization tasks. The analysis shows that the proposed scheme can meet the data security requirement of the cloud-supported IC-IoT, and it also has less cost compared with other popular models.
On the Separating Redundancy of the Duals of First-Order Generalized Reed-Muller Codes

The separating redundancy is an important property in the analysis of the error-and-erasure decoding of a linear block code. In this work, we investigate the separating redundancy of the duals of first-order generalized Reed-Muller (GRM) codes, a class of nonbinary linear block codes that have nice algebraic properties. The dual of a first-order GRM code can be specified by two positive integers m and q and denoted by R(m,q), where q is the power of a prime number and q≠2. We determine the first separating redundancy value of R(m,q) for any m and q. We also determine the second separating redundancy values of R(m,q) for any q and m=1 and 2. For ≥3, we set up a binary integer linear programming problem, the optimum of which gives a lower bound on the second separating redundancy of R(m,q).
“I’m Your Personal Co-Driver—How Can I Assist You?” Assessing the Potential of Personal Assistants for Truck Drivers

User acceptance is widely recognized as a major factor for predicting the intention to use a technical device. As such, personal assistants for truck drivers pose significant design and function challenges. Long, solitary hours and comprehensive interaction with the vehicle open various possibilities for an assistant’s service. Including drivers’ requirements early in development is hence beneficial. This paper describes the result of an online survey intended to assess truck drivers’ attitudes toward personal assistants in the truck cabin. Its authors investigate the potential and the predicted acceptance of personal conversation, virtual, or robotic agents as interaction partners. They furthermore analyze how hedonic and pragmatic attributes affect the intention to use these personal assistants.
Augmented Materials for Tangible Interfaces: Experimenting with Young Designers. Outcomes and Analysis

This paper deals with the design of emerging, augmented, ICS materials in the framework of a Design-Driven Material Innovation approach, presenting a selection of concepts of Tangible Interfaces, developed during a workshop held at Politecnico di Milano. In the attempt to include digital technologies in product and interior design, enhancing human experience and new perspectives for next interactive products, young designers were asked to explore the new possibilities offered by these novel materials in an Experiencing Prototyping and Smart Aesthetics framework. The core of the paper focuses on the outcomes and the analysis of this experience between research and educational activity. The last part refers to the ongoing shift within Design Schools, where the educational mission is increasingly promoting workshops and partnerships with design oriented companies. The novel technological landscape implies indeed this sort of collaborations, as Design Schools can effectively assist companies in making evidence based decisions.