Reviewerly/exHarmony huggingface dataset:
Clinical Results Using Virtual Reality

Virtual Reality (VR) technology offers new opportunities for the development of innovative clinical research, assessment, and intervention tools. VR-based testing, training, teaching, and treatment approaches that would be difficult, if not impossible to deliver using traditional methods are now being developed that take advantage of the assets that are available with VR technology. As research evidence continues to indicate clinical efficacy, VR applications are being increasingly regarded as providing innovative options for targeting the cognitive, psychological, motor, and functional impairments that result from various clinical health conditions. VR allows for the precise presentation and control of stimuli within dynamic multisensory 3-D computer generated simulations as well as providing advanced methods for capturing and quantifying behavioral responses. These characteristics support the rationale for the use of VR applications in clinical assessment, intervention, and training. This article begins with a brief review of the history and rationale for the use of VR with clinical populations. We then detail one use-case for the clinical application of VR—the exposure therapy treatment of anxiety disorders and post-traumatic stress disorder. While significant work is cited in other areas of Clinical VR (e.g., pain management, cognitive/physical assessment/rehabilitation, eating disorders, social skills/clinical training, etc.), a full overview of such a broad literature is beyond the scope of this article. Thus, we have opted to provide more in-depth analysis of one specific clinical area that clearly illustrates how VR has been successfully applied and is supported by an encouraging and evolving scientific literature.
Vision‐based traffic accident detection using sparse spatio‐temporal features and weighted extreme learning machine

Vision-based traffic accident detection is one of the challenging tasks in intelligent transportation systems due to the multi-modalities of traffic accidents. The first challenging issue is about how to learn robust and discriminative spatio-temporal feature representations. Since few training samples of traffic accidents can be collected, sparse coding techniques can be used for small data case. However, most sparse coding algorithms which use norm regularisation may not achieve enough sparsity. The second challenging issue is about the sample imbalance between traffic accidents and normal traffic such that detector would like to favour normal traffic. This study proposes a traffic accident detection method, including a self-tuning iterative hard thresholding (ST-IHT) algorithm for learning sparse spatio-temporal features and a weighted extreme learning machine (W-ELM) for detection. The ST-IHT algorithm can improve the sparsity of encoded features by solving an norm regularisation. The W-ELM can put more focus on traffic accident samples. Meanwhile, a two-point search strategy is proposed to adaptively find a candidate value of Lipschitz coefficients to improve the tuning precision. Experimental results in our collected dataset have shown that this proposed traffic accident detection algorithm outperforms other state-of-the-art methods in terms of the feature's sparsity and detection performance.
TPCKT: Two-Level Progressive Cross-Media Knowledge Transfer

As multimedia data have been the main form of big data, cross-media retrieval becomes a research hotspot. It provides a flexible retrieval paradigm across different media types, such as using an image query to retrieve the relevant text, video, and audio. An effective model to establish cross-media correlation is indispensable for retrieval. Existing methods usually rely on labeled data for model training, but it is extremely labor consuming to collect and label cross-media data. Under this situation, it is a key issue toward the real application to transfer knowledge from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">existing data</i> to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new data</i> , for reducing the human labor. However, little attention has been paid to knowledge transfer between two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-media domains</i> . Therefore, this paper proposes the approach of two-level progressive cross-media knowledge transfer (TPCKT), which transfers knowledge from large-scale cross-media data, to boost the retrieval accuracy on cross-media data of another domain. Its contributions are: First, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two-level adversarial transfer architecture</i> is proposed with domain discriminators in media-specific level and media-shared level, which have partially shared parameters to preserve cross-media consistency of transfer. The domain discrepancy between cross-media domains is fully reduced for boosting the retrieval accuracy. Second, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">progressive semantic transfer mechanism</i> is proposed to iteratively select semantically related categories in two cross-media domains for transfer. This drives the transfer process with ascending difficulties, for addressing the difficulty from different label spaces, and ensuring the robustness of transfer. For the experiment, the large-scale cross-media dataset PKU XMediaNet serves as the source domain, and three widely used small-scale datasets are adopted as the target domains to perform retrieval. Experimental results show the promising improvement gained by the proposed TPCKT.
Decentralized blacklistable anonymous credentials with reputation

Blacklistable anonymous credential systems provide service providers with a way to authenticate users according to their historical behaviors, while guaranteeing that all users can access services in an anonymous and unlinkable manner, thus are potentially useful in practice. Traditionally, to protect services from illegal access, the credential issuer, which completes the registration with users, must be trusted by the service provider. However, in practice, this trust assumption is usually unsatisfied. In this paper, we solve this problem and present the decentralized blacklistable anonymous credential system with reputation (DBLACR), which inherits nearly all features of the BLACR system presented in Au et al. (2012) but does not need a trusted party to register users.The new system also has extra advantages. In particular, it enables blacklist (historical behaviors) sharing among different service providers and is partially resilient to the blacklist gaming attack, where dishonest service providers attempt to compromise the privacy of users via generating blacklist maliciously. Technically, the main approach to achieve DBLACR system is a novel use of the blockchain technique, which serves as a public append-only ledger. The system can be instantiated from three different types of cryptographic systems, including the RSA system, the classical DL system, and the pairing based system. To demonstrate the practicability of our system, we also give a proof of concept implementation for the instantiation under the RSA system. The experiment results indicate that when authenticating with blacklists of reasonable size, our implementation can fulfill practical efficiency demands.
Blockchain-Based Management of Video Surveillance Systems

In this paper, we propose a video surveillance system based on blockchain system. The proposed system consists of a blockchain network with trusted internal managers. The metadata of the video is recorded on the distributed ledger of the blockchain, thereby blocking the possibility of forgery of the data. The proposed architecture encrypts and stores the video, creates a license within the blockchain, and exports the video. Since the decryption key for the video is managed by the private DB of the blockchain, it is not leaked by the internal manager unauthorizedly. In addition, the internal administrator can manage and export videos safely by exporting the license generated in the blockchain to the DRM-applied video player.
Spatial and Temporal Controls on the Distribution of Indium in Xenothermal Vein-Deposits: The Huari Huari District, Potosí, Bolivia

The Huari Huari deposit, Potosí Department in SW Bolivia, hosts polymetallic stratiform and vein mineralization of Miocene age with significant concentrations of the critical metal indium (In). Vein mineralization records document early crystallization of quartz and cassiterite followed by prominent associations of sulfides and sulfosalts. The earliest sulfide was arsenopyrite, followed by pyrrhotite, and progressively giving way to pyrite as the main iron sulfide, whereas Cu–Ag–Pb sulfosalts constitute late hypogene associations. Sphalerite is the chief ore mineral, and its crystallization is extended during most of the mineralization lifespan as evidenced by its initial cocrystallization with pyrrhotine, then with pyrite, and finally with Ag–Pb sulfosalts. The composition of sphalerite varies from early to late generations with a continuous decrease in FeS that attests to a decrease in temperature, which is constrained to vary from ~450 to &lt;200 °C, and/or an increase in f(S2), both congruent with the described paragenetic sequence. Indium concentrated mostly in the structure of Fe-rich sphalerite (up to 3.49 wt. %) and stannite (up to 2.64 wt. %) as limited solid solutions with roquesite in the (Zn,Fe)S–Cu2FeSnS4–CuInS2 pseudoternary system. In sphalerite, In shows a strong positive correlation with Cu at Cu/In = 1, suggesting its incorporation via a (Cu+ + In3+) ↔ 2Zn2+ coupled substitution, and it does not correlate with Fe. In stannite, In shows a moderate, negative correlation with Cu and Sn, and an In3+ ↔ (Cu+ + ½ Sn4+) coupled substitution is suggested. Coexisting sphalerite and stannite yielded the highest In concentrations and crystallized at temperatures between 350 and 250 °C. Copper activity probably played a major role in the accumulation of In in the structure of sphalerite since In-bearing sphalerite coexisted with the deposition of stannite, shows high concentrations of Cu (up to 0.13 atoms per formula unit (a.p.f.u.)) in its structure, and hosts exsolutions of stannite and chalcopyrite. Distribution on the district scale of In suggests an input of hydrothermal fluids richer in Cu in the central position of the mineralizing system, represented by the Antón Bravo vein.
Exploring the Potential of Using Augmented Reality and Virtual Reality for STEM Education

The purpose of this article is to focus on the potential of using new technologies such as Augmented Reality and Virtual Reality in science education in general and in STEM education in particular. Although Augmented Reality and Virtual Reality as a technology have strength and weaknesses, this article focus to show how those technologies may facilitate STEM education and how to improve the area of weaknesses. In this research, we address the need for STEM education and training, which is currently either limited or not interested for students. It is also overcome the distance limitation that students face in STEM education. Furthermore, Augmented Reality and Virtual Reality environments are an effective platform to encourage and attracting the new generation technology to STEM field. Technology virtualization has been used in a various way in STEM classroom to encourage creatively and innovation. However, it is also important to take in our consideration that students learning in this environment can be difficult in many directions.
Structural and behavioral reference model for IoT‐based elderly health‐care systems in smart home

Summary Increasing level of personal and social health and life expectancy has resulted in the growth of aged people population. Elderly care is an essential and costly issue in any society that should be addressed in researches. Elderly care has faced some problems such as elderly solitary, trained caregivers insufficiency, and increasing cost of late diagnosis of diseases and accident. Regarding to these problems, smart home technology can be used as an efficient solution. It provides an expertise, long‐term, and low‐cost care that empowers elderly to have an independent life. In this paper, we propose an architecture for a health‐care system in a smart home. In this architecture, rapid and timely diagnosis of environmental incidents and health risks causes reduction in costs of health care and relief. Given to the vital aspect of health‐care systems, the proposed architecture components and solutions are presented to meet quality attributes such as availability, performance, security, and interoperability. The proposed architecture evaluation is based on the Architecture Tradeoff Analysis Method (ATAM) scenario‐based approach. ATAM is a method for analysis and evaluation of software systems architecture. And quality attributes scenarios are examined to meet quality requirement. Compared with the previous works, more quality attributes are addressed in this paper.
Consistency-Preserving deep hashing for fast person re-identification

Numerous methods have been proposed for person re-identification (Re-ID) with promising performances. While most of them neglect the matching efficiency which is crucial in real-world applications. Recently, several hashing based approaches have been developed, which consider the importance of matching speed in large-scale datasets. Despite the considerable efficiency of these traditional and deep learning based hashing methods, the concomitant matching accuracy reduction is unacceptable in practical application. Towards this end, we propose a novel deep hashing framework, namely Consistency-Preserving Deep Hashing (CPDH), aiming to bridge the gap between the effective high-dimensional feature and low-dimensional binary vector by focusing on the consistency preservation of hash code. First, CPDH designs a new hash structure to extract the hash code. Next, a noise consistency cost is proposed to improve robustness of both hash code and high-dimensional feature. Finally, a topology consistency cost is provided to maintain the ordinal relation between the high-dimensional feature space and Hamming space. Comprehensive experimental results on three widely-used benchmark datasets demonstrate the superior performance of proposed method as compared with existing state-of-the-art approaches.
Representation learning using step-based deep multi-modal autoencoders

Deep learning techniques have been successfully used in learning a common representation for multi-view data, wherein different modalities are projected onto a common subspace. In a broader perspective, the techniques used to investigate common representation learning falls under the categories of 'canonical correlation-based' approaches and 'autoencoder-based' approaches. In this paper, we investigate the performance of deep autoencoder-based methods on multi-view data. We propose a novel step-based correlation multi-modal deep convolution neural network (CorrMCNN) which reconstructs one view of the data given the other while increasing the interaction between the representations at each hidden layer or every intermediate step. The idea of step reconstruction reduces the constraint of reconstruction of original data, instead, the objective function is optimized for reconstruction of representative features. This helps the proposed model to generalize for representation and transfer learning tasks efficiently for high dimensional data. Finally, we evaluate the performance of the proposed model on three multi-view and cross-modal problems viz., audio articulation, cross-modal image retrieval and multilingual (cross-language) document classification. Through extensive experiments, we find that the proposed model performs much better than the current state-of-the-art deep learning techniques on all three multi-view and cross-modal tasks.
Asymmetric Bidirectional Controlled Teleportation by Using Nine-Qubit Entangled State in Noisy Environment

In this paper, a novel protocol for asymmetric bidirectional controlled quantum teleportation (ABCQT) by using the nine-qubit entangled state as a quantum channel is proposed.Alice transmits an arbitrary three-qubit entangled state to Bob and at the same time, Bob transmits an arbitrary single-qubit entangled state to Alice under the permission of a third party as a controller.This protocol is based on the Hadamard (H) gates, controlled-not (CNOT) operations, appropriate unitary operations, and single-qubit Von Neumann measurements (SM) which are more efficient than multi-qubit joint measurements (JM).This scheme explicitly shows how the channel of nine-qubit entangled state prepares, and it is discussed in six types of noisy scenarios (amplitude-damping, phase-damping, bit-flip, phase-flip, bit-phase-flip, and depolarizing noisy scenarios).Then, we analytically derive the fidelities of the ABCQT process and show that they only depend on the amplitude parameter of the initial state and the decoherence noisy rate.Finally, compared with previous schemes, this scheme also shows remarkable advantages.
Modeling, Selection, and Teaching Staff Training at Higher School

Nowadays, one of the smart university teaching staff’s particularities and features is the unique ability of a teacher’s personality to rapidly adjust their professional activity to the new learning conditions characterized by the use of “smart technologies”. In connection therewith, the university teachers’ team is considered to be seen as a staff of a self-learning organization where readiness and willingness for mastering and implementation new material, new digital and information technologies, the transformation of acquired knowledge, and its further diffusion come up to one of the top places in a critical assessment of a teacher’s compliance to the requirements of the new tertiary school. In the paper, the authors reveal the conceptual approach to the university teachers’ selection, assessment. The methodology of evaluating a self-learning organization’s personnel is revealed and proposed. The expert methods and the methods of mathematical statistics are applied to validate the indicators assessing the level of the teaching staff’s personal achievements. The scientific article represents the results of the experiment which is conducted on the base of Humanity Institute and Institute of Economics, Finance and Management of Togliatti State University.
A seismic facies classification method based on the convolutional neural network and the probabilistic framework for seismic attributes and spatial classification

In the early stage of oil and gas exploration, due to the lack of available drilling data, the automatic seismic facies classification technology mainly relies on the unsupervised clustering method combined with the seismic multiattribute. However, the clustering results are unstable and have no clear geologic significance. The supervised classification method based on manual interpretation can provide corresponding geologic significance, but there are still some problems such as the discrete classification results and low accuracy. To solve these problems, inspired by hyperspectral and spatial probability distribution technology, we have developed a classification framework called the probabilistic framework for seismic attributes and spatial classification (PFSSC). It can improve the continuity of the classification results by combining the classification probability and the spatial partial probability of the classifier output. In addition, the convolutional neural network (CNN) is a typical classification algorithm in deep learning. By convolution and pooling, we could use it to extract features of complex nonlinear objects for classification. Taking advantage of the combination of PFSSC and CNN, we could effectively solve the existing problems mentioned above in seismic facies classification. It is worth mentioning that we select seismic the multiattribute by maximal information coefficient (MIC) before the seismic facies classification. Finally, using the CNN-PFSSC and MIC methods, we can obtain high accuracy in the test set, reasonable continuity within the same seismic facies, clear boundaries between different seismic facies, and seismic facies classification results consistent with sedimentological laws.
Reversible data hiding in encrypted images with additive and multiplicative public-key homomorphism

Public-key homomorphism is an efficient technique of reversible data hiding in encrypted images (RDH-EI). The existing public-key homomorphism based RDH-EI schemes benefit from the additive homomorphism of public-key encryption. In this paper, a theoretical analysis of public-key homomorphism based RDH-EI is given, which makes both additive homomorphism and multiplicative homomorphism available. Thus more homomorphic public-key encryptions can be used to construct RDH-EI schemes. By the theoretical analysis, lossless recovery of directly decrypted image is obtained. And the embedding rate can reach 1 bit per pixel (bpp) or more regardless of the pixel distribution of natural images. In addition, two novel public-key homomorphism based RDH-EI schemes using additive homomorphism and multiplicative homomorphism, respectively, are introduced. Experimental results are presented to illustrate the effectiveness and superiority of the proposed methods.
A semi-quantum authentication protocol for message and identity

A semi-quantum authentication protocol based on a GHZ-like state and W state is proposed to determine the identities of two participants and to ensure the credibility of the important information. The proposed semi-quantum authentication protocol does not require all communicators to own quantum capability, which not only improves the possibility of implementation but also greatly reduces the hardware requirements. In addition, analyses indicate that the proposed semi-quantum authentication protocol under limited resources is more secure and effective than some typical quantum authentication protocols.
Natural Language Querying of Complex Business Intelligence Queries

Natural Language Interface to Database (NLIDB) eliminates the need for an end user to use complex query languages like SQL by translating the input natural language statements to SQL automatically. Although NLIDB systems have seen rapid growth of interest recently, the current state-of-the-art systems can at best handle point queries to retrieve certain column values satisfying some filters, or aggregation queries involving basic SQL aggregation functions. In this demo, we showcase our NLIDB system with extended capabilities for business applications that require complex nested SQL queries without prior training or feedback from human in-the-loop. In particular, our system uses novel algorithms that combine linguistic analysis with deep domain reasoning for solving core challenges in handling nested queries. To demonstrate the capabilities, we propose a new benchmark dataset containing realistic business intelligence queries, conforming to an ontology derived from FIBO and FRO financial ontologies. In this demo, we will showcase a wide range of complex business intelligence queries against our benchmark dataset, with increasing level of complexity. The users will be able to examine the SQL queries generated, and also will be provided with an English description of the interpretation.