Reviewerly/exHarmony huggingface dataset:
Iterative Multiple Hypothesis Tracking With Tracklet-Level Association

This paper proposes a novel iterative maximum weighted independent set (MWIS) algorithm for multiple hypothesis tracking (MHT) in a tracking-by-detection framework. MHT converts the tracking problem into a series of MWIS problems across the tracking time. Previous works solve these NP-hard MWIS problems independently without the use of any prior information from each frame, and they ignore the relevance between adjacent frames. In this paper, we iteratively solve the MWIS problems by using the MWIS solution from the previous frame rather than solving the problem from scratch each time. First, we define five hypothesis categories and a hypothesis transfer model, which explicitly describes the hypothesis relationship between adjacent frames. We also propose a polynomial-time approximation algorithm for the MWIS problem in MHT. In addition to that, we present a confident short tracklet generation method and incorporate tracklet-level association into MHT, which further improves the computational efficiency. Our experiments on both MOT16 and MOT17 benchmarks show that our tracker outperforms all the previously published tracking algorithms on both MOT16 and MOT17 benchmarks. Finally, we demonstrate that the polynomial-time approximate tracker reaches nearly the same tracking performance.
Estimating VR Sickness and user experience using different HMD technologies: An evaluation study

This paper presents results of a user study of the effects of virtual reality technology on VR Sickness and User Experience. In our study the participants watched two different panoramic (360) videos, one with relaxing content (beach clip) and second one with action content (roller coaster video clip). Videos were watched on four different head mounted displays (HMDs) and on the 2D television as a reference display. To assess VR Sickness discomfort levels, we have used the Simulator Sickness Questionnaire (SSQ), and for user experience the User Experience Questionnaire (UEQ) was used. For quick assessments of VR Sickness discomfort levels, we have also used Subjective Units of Distress Scale (SUDS). We have found a strong correlation between SUDS and total SSQ score and between total SSQ score and SSQ-D score. Shown negative correlation between VR Sickness discomfort levels (assessed by SSQ and UEQ Questionnaire), and user experience (assessed by UEQ Questionnaire), indicates that presence of VR Sickness symptoms affects the user experience.
Changing power: Shifting the role of electricity consumers with blockchain technology – Policy implications for EU electricity law

One of the pressing legal questions of the energy transition is how to integrate “prosumers”, consumers who start producing electricity, in the electricity market. So far, their influence remains limited or fully absent because their role as independent market participants is barely or not facilitated as they are usually subject to regulated remuneration schemes. Blockchain technology offers changing the approach of “integration in the market” into “becoming the market” by enabling peer-to-peer transactions. Currently, transactions are facilitated by third parties, suppliers and system operators, whose main task is centrally compiling and coordinating information on loads and generation and contracting supply and distribution services. Instead, blockchain technology enables new ways of organising decentralised persons without the immediate need for one centrally connecting entity. This implies profound legal- and policy consequences. Based on information on first use cases of blockchain applications in the electricity sector, this article identifies those main policy implications for EU electricity law and thereby adds to the discussion how blockchain technology could facilitate “prosumers” to develop as independent market participants in the electricity sector from an energy law perspective.
Study of Blockchains’s Consensus Mechanism Based on Credit

Practical Byzantine fault tolerance (PBFT) is one of the most popular consensus protocols of the blockchain. However, in the PBFT, the enthusiasm of reliable nodes cannot be stimulated effectively, and a large amount of communication resources are used for data consistency. Therefore, a new consensus protocol-credit-delegated Byzantine fault tolerance (CDBFT)-is proposed in this paper. The CDBFT works as the following: 1) a voting rewards and punishments scheme and its corresponding credit evaluation scheme are proposed not only to stimulate enthusiasm of reliable nodes but also to reduce the participation of abnormal nodes in the consensus process, and the virtuous circle of the system can be founded and 2) consistency and checkpoint protocols based on PBFT are proposed to improve the efficiency and flexibility of system. From the simulation results, a conclusion can be drawn, the participation probability of abnormal nodes in the consensus process can be reduced to 5%, and the efficiency and stability of the system are improved greatly in the long-time running.
Blockchain Electricity Trading Under Demurrage

This letter proposes a novel demurrage mechanism for blockchain electricity marketplaces, whereby the redemptive value of energy-backed tokens declines with time. This mechanism is intended to reward organic price-responsive load shifting by incentivising the consumption of electricity when it is locally abundant. To demonstrate how such a demurrage mechanism might function in practice, this letter describes a mixed complementarity model of a notional token marketplace. These market simulations indicate that, in equilibrium and with rational actors, the demurrage mechanism creates price signals that temporally align the production and consumption of electricity.
Progress in context-aware recommender systems — An overview

Recommender Systems are the set of tools and techniques to provide useful recommendations and suggestions to the users to help them in the decision-making process for choosing the right products or services. The recommender systems tailored to leverage contextual information (such as location, time, companion or such) in the recommendation process are called context-aware recommender systems. This paper presents a review on the continual development of context-aware recommender systems by analyzing different kinds of contexts without limiting to any specific application domain. First, an in-depth analysis is conducted on different recommendation algorithms used in context-aware recommender systems. Then this information is used to find out that how these techniques deals with the curse of dimensionality, which is an inherent issue in such systems. Since contexts are primarily based on users’ activity patterns that leads to the development of personalized recommendation services for the users. Thus, this paper also presents a review on how this contextual information is represented (either explicitly or implicitly) in the recommendation process. We also presented a list of datasets and evaluation metrics used in the setting of CARS. We tried to highlight that how algorithmic approaches used in CARS differ from those of conventional RS. In that, we presented what modification or additions are being applied on the top of conventional recommendation approaches to produce context-aware recommendations. Finally, the outstanding challenges and research opportunities are presented in front of the research community for analysis
PTAS: Privacy-preserving Thin-client Authentication Scheme in blockchain-based PKI

Recent years have witnessed tremendous academic efforts and industry growth in Internet of Things (IoT). Security issues of IoT have become increasingly prominent. Public Key Infrastructure (PKI) can provide authentication service to IoT devices which is a crucial element to the security of IoT. However, the conventional PKIs are organized as a tree-like centralized structure which has demonstrated serious usability and security shortcomings such as the single point of failure. Blockchain has numerous desirable properties, such as decentralized nature, cryptographic technology and unalterable transaction record, these properties make it a potential tool to build a decentralized blockchain-based PKI. Nevertheless, the latest proposals for blockchain-based PKI did not take thin-clients into consideration where thin-clients indicate those users who cannot download the entire blockchain due to the limited storage capacity of their equipment (most IoT devices fall into this category). To settle this problem, we firstly present a Privacy-preserving Thin-client Authentication Scheme (PTAS) employing the idea of private information retrieval (PIR), which enables thin-clients to run normally like full node users and protect their privacy simultaneously. Furthermore, in order to enhance security, we further propose a (m-1)-private PTAS which means thin-client’s information can be protected against a collusion of at most (m-1) full node users. Besides, security analysis and functional comparison are performed to demonstrate high security and comprehensive functionality of our schemes. Finally, extensive experiments are conducted to compare computational overhead and communication overhead of PTAS and (m-1)-private PTAS.
Arsenic evolution as a tool for understanding formation of pyritic gold ores

Research Article| February 26, 2019 Arsenic evolution as a tool for understanding formation of pyritic gold ores Yanlu Xing; Yanlu Xing 1School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria 3800, Australia Search for other works by this author on: GSW Google Scholar Joël Brugger; Joël Brugger * 1School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria 3800, Australia *E-mail: joel.brugger@monash.edu Search for other works by this author on: GSW Google Scholar Andrew Tomkins; Andrew Tomkins 1School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria 3800, Australia Search for other works by this author on: GSW Google Scholar Yuri Shvarov Yuri Shvarov 2Faculty of Geology, Lomonosov Moscow State University, 119991, Moscow, Russia Search for other works by this author on: GSW Google Scholar Geology (2019) 47 (4): 335–338. https://doi.org/10.1130/G45708.1 Article history received: 15 Oct 2018 rev-recd: 27 Jan 2019 accepted: 29 Jan 2019 first online: 27 Feb 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Yanlu Xing, Joël Brugger, Andrew Tomkins, Yuri Shvarov; Arsenic evolution as a tool for understanding formation of pyritic gold ores. Geology 2019;; 47 (4): 335–338. doi: https://doi.org/10.1130/G45708.1 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGeology Search Advanced Search Abstract Arsenic (As) and gold (Au) are closely associated in many gold deposits, both being hosted in Fe-sulfide minerals (pyrite, marcasite, and arsenopyrite), partly because As geochemistry controls Au accumulation. Yet, the partitioning behavior of As between pyrite, arsenopyrite, and hydrothermal fluids remains poorly understood. Here, we introduce solid-solution models for As in pyrite and As in arsenopyrite into a thermochemical model of fluid-rock interaction, and use it to evaluate the effects of temperature, redox state, and fluid-flow dynamics on As—and Au by association—partitioning. We find that As concentrations in pyrite decrease with increasing temperature, despite the widening of the solid-solution composition range. This is related to the preferential partitioning of As into fluids at higher temperatures. Simulations of infiltration of rock-buffered H2O-CO2-As fluids into low-As pyrite (As:S = 0.01) ores reveal a continuous enrichment of As in pyrite with increasing fluid:rock ratio. The modeling suggests that upgrading of early-formed low-grade ores by multistage hydrothermal events can generate large gold deposits. In this scenario, an anomalously Au-rich fluid is not needed, but instead, prolonged fluid-rock interaction enriches pyrite in As, which promotes gold sequestration. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data

Data are being generated and used to support all aspects of healthcare provision, from policy formation to the delivery of primary care services. Particularly, with the change of emphasis from curative to preventive medicine, the importance of data-based research such as data mining and machine learning has emphasized the issues of class distributions in datasets. In typical predictive modeling, the inability to effectively address a class imbalance in a real-life dataset is an important shortcoming of the existing machine learning algorithms. Most algorithms assume a balanced class in their design, resulting in poor performance in predicting the minority target class. Ironically, the minority target class is usually the focus in predicting processes. The misclassification of the minority target class has resulted in serious consequences in detecting chronic diseases and detecting fraud and intrusion where positive cases are erroneously predicted as not positive. This paper presents a new attribute selection technique called variance ranking for handling imbalance class problems in a dataset. The results obtained were compared to two well-known attribute selection techniques: the Pearson correlation and information gain technique. This paper uses a novel similarity measurement technique ranked order similarity-ROS to evaluate the variance ranking attribute selection compared to the Pearson correlations and information gain. Further validation was carried out using three binary classifications: logistic regression, support vector machine, and decision tree. The proposed variance ranking and ranked order similarity techniques showed better results than the benchmarks. The ROS technique provided an excellent means of grading and measuring the similarities where other similarity measurement techniques were inadequate or not applicable.
Scalable 360° Video Stream Delivery: Challenges, Solutions, and Opportunities

In recent years, virtual reality and augmented reality applications have seen a significant increase in popularity. This is due to multiple technology trends. First, the availability of new tethered and wireless head-mounted displays allows viewers to consume new types of content. Second, 360° omnidirectional cameras, in combination with production software, make it easier to produce personalized 360° videos. Third, beyond these new developments for creating and consuming such content, video sharing websites and social media platforms enable users to publish and view 360° video content. In this paper, we present challenges of 360° video streaming systems, give an overview of existing approaches for 360° video streaming, and outline research opportunities enabled by 360° video. We focus on the data model for 360° video and the different challenges and approaches of creating, distributing, and presenting 360° video content, including 360° video recording, storage, distribution, edge delivery, and quality-of-experience evaluation. In addition, we identify major research opportunities with respect to efficient storage, timely distribution, and cybersickness-free personalized viewing of 360° videos.
An efficient coding scheme for reversible data hiding in encrypted image with redundancy transfer

Recently, reversible data hiding in encrypted image (RDHEI) has attracted extensive attention, which can be used in secure cloud computing and privacy protection effectively. In this paper, a RDHEI scheme based on redundancy transfer and sparse block encoding is proposed. Content owner first utilizes a specific encryption method, including bit plane disordering, block and pixel scrambling, to encrypt each original block, which not only can transfer the redundancy from MSB to LSB, but also can mask image contents securely. Through an improved sparse matrix encoding method for different types of binary blocks, data hider embeds additional data into the vacated room within LSB of encrypted image efficiently. Separable operations of data extraction, image decryption and image recovery can be conducted on the receiver side based on encryption key and data hiding key. Experimental results demonstrate that our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.
Iterative Joint Image Demosaicking and Denoising Using a Residual Denoising Network

Modern digital cameras rely on the sequential execution of separate image processing steps to produce realistic images. The first two steps are usually related to denoising and demosaicking where the former aims to reduce noise from the sensor and the latter converts a series of light intensity readings to color images. Modern approaches try to jointly solve these problems, i.e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise. While there are several machine learning systems that have been recently introduced to solve this problem, the majority of them relies on generic network architectures which do not explicitly take into account the physical image model. In this work we propose a novel algorithm which is inspired by powerful classical image regularization methods, large-scale optimization, and deep learning techniques. Consequently, our derived iterative optimization algorithm, which involves a trainable denoising network, has a transparent and clear interpretation compared to other black-box data driven approaches. Our extensive experimentation line demonstrates that our proposed method outperforms any previous approaches for both noisy and noise-free data across many different datasets. This improvement in reconstruction quality is attributed to the rigorous derivation of an iterative solution and the principled way we design our denoising network architecture, which as a result requires fewer trainable parameters than the current state-of-the-art solution and furthermore can be efficiently trained by using a significantly smaller number of training data than existing deep demosaicking networks. Code and results can be found at https://github.com/cig-skoltech/deep_demosaick
Short-Range Radar Based Real-Time Hand Gesture Recognition Using LSTM Encoder

Due to the development of short-range radar with high-resolution, the radar sensor has a high potential to be used in real human-computer interaction (HCI) applications. The radar sensor has advantages over optical cameras in that it is unaffected by illumination and it is able to detect the objects in an occluded environment. This paper proposes a hand gesture recognition system for a real-time application of HCI using 60 GHz frequency-modulated continuous wave (FMCW) radar, Soli, developed by Google. The overall system includes signal processing part that generates range-Doppler map (RDM) sequences without clutter and machine learning part including a long short-term memory (LSTM) encoder to learn the temporal characteristics of the RDM sequences. A set of data is collected from 10 participants for the experiment. The proposed hand gesture recognition system successfully distinguishes 10 gestures with a high classification accuracy of 99.10%. It also recognizes the gestures of a new participant with an accuracy of 98.48%.
The relationship between LGBT inclusion and economic development: Macro-level evidence

This study analyzes the relationship between social inclusion of lesbian, gay, bisexual, and transgender (LGBT) people and economic development. It uses legal and economic data for 132 countries from 1966 to 2011. Previous studies and reports provide substantial evidence that LGBT people are limited in their human rights in ways that also create economic harms, such as lost labor time, lost productivity, underinvestment in human capital, and the inefficient allocation of human resources. This analysis uses a fixed effects regression approach and a newly-created dataset – Global Index on Legal Recognition of Homosexual Orientation (GILRHO) – to assess how these detriments are related to the macroeconomy. Our study finds that an additional point on the 8-point GILRHO scale of legal rights for LGB persons is associated with an increase in real GDP per capita of approximately $2000. A series of robustness checks confirm that this index continues to have a positive and statistically significant association with real GDP per capita after controlling for gender equality. In combination with the qualitative evidence from previous studies and reports, our quantitative results suggest that LGBT inclusion and economic development are mutually reinforcing. Also, a back-of-the-envelope estimate suggests that about 6% to 22% of the finding could reflect the costs to GDP of health and labor market stigmatization of LGB people. Results from this study can help to better understand how the fuller enjoyment of human rights by LGBT people can contribute to a country’s economic development.
General twin support vector machine with pinball loss function

The standard twin support vector machine (TSVM) uses the hinge loss function which leads to noise sensitivity and instability. In this paper, we propose a novel general twin support vector machine with pinball loss (Pin-GTSVM) for solving classification problems. We show that the proposed Pin-GTSVM is noise insensitive and more stable for re-sampling. Further, the computational complexity of the proposed Pin-GTSVM is similar to that of the TSVM. Thus, the pinball loss function does not increase the computation time of the proposed Pin-GTSVM. Numerical experiments with different noise are performed on 17 UCI and KEEL benchmark real-world datasets and the results are compared with other baseline methods. The comparisons clearly show that the proposed Pin-GTSVM has better generalization performance for noise corrupted datasets.
A user sensitive subject protection approach for book search service

In a digital library, book search is one of the most important information services. However, with the rapid development of network technologies such as cloud computing, the server‐side of a digital library is becoming more and more untrusted; thus, how to prevent the disclosure of users' book query privacy is causing people's increasingly extensive concern. In this article, we propose to construct a group of plausible fake queries for each user book query to cover up the sensitive subjects behind users' queries. First, we propose a basic framework for the privacy protection in book search, which requires no change to the book search algorithm running on the server‐side, and no compromise to the accuracy of book search. Second, we present a privacy protection model for book search to formulate the constraints that ideal fake queries should satisfy, that is, (i) the feature similarity, which measures the confusion effect of fake queries on users' queries, and (ii) the privacy exposure, which measures the cover‐up effect of fake queries on users' sensitive subjects. Third, we discuss the algorithm implementation for the privacy model. Finally, the effectiveness of our approach is demonstrated by theoretical analysis and experimental evaluation.