Reviewerly/exHarmony huggingface dataset:
The novel classes of finite dimensional filters with non-maximal rank estimation algebra on state dimension four and rank of one

Ever since the technique of Kalman-Bucy filter was popularised, finding new classes of finite dimensional recursive filters has drawn much concern. The idea of using estimation algebra to construct finite-dimensional nonlinear filters was first proposed by Brockett and Mitter independently in the late 1970s, which has been proven an invaluable tool in tackling nonlinear filtering (NLF) problems. Once the estimation algebra is finite dimensional, one can construct the finite dimensional filters (FDFs) for NLF problems by Wei–Norman approach. In this paper, we give the construction of finite dimensional estimation algebra (FDEA) with state space dimension 4 and linear rank equal to 1, and further obtain a new class of NLF systems with FDFs. Importantly, we show that there is a class of polynomial FDF system in state space dimension 4 with linear rank one, but the coefficients in Wong's Ω-matrix are polynomials of degree two, or higher. In particular, these are the first examples of polynomial filtering systems not of Yau type (i.e. the drift term is not gradient plus affine functions) but with FDFs. Furthermore, we write down several easily satisfied sufficient conditions for the construction of more special classes of FDFs. Additionally, we derive the FDFs for the proposed NLF systems by using the Wei–Norman approach.
Optimal Overbooking Policy for Cloud Service Providers: Profit and Service Quality

A cloud federation is a current paradigm that enables partnered cloud providers to share idle capacities during low demand periods and to purchase spare resources during demand spikes. In this research, we propose an optimal overbooking policy to maximize federation members' profits and enhance cloud users' experiences. The proposed policy overcomes cloud providers' low utilization and increases their profits. Under the market-oriented cloud federation system, we use the number of idle resources in the cloud federation and the operational costs of those resources to help the provider decide on its instance exchange price. Under such a price mechanism, we develop an optimal overbooking model and identify the conditions necessary for optimal solutions. Through implementing the optimal mechanism, we observe that the proposed overbooking policy can improve a federated provider's profits and decrease the probability of service level agreement (SLA) violation. When a provider's capacity is relatively large and the provider adopts the proposed overbooking policy, it could achieve maximum profits and decline its SLA violation when it has unmet customer demands and there are idle resources in the cloud federation. Through establishing the cooperative game model of the cloud federation, we make a reasonable profit distribution based on Shapley value. The cloud provider's profits and the probability of the SLA violation change as the instance price, the distribution of unserved customers, the number of federation members and penalty cost change. Compared with the other overbooking policies and no overbooking mechanisms, our research improves profits and reduces cloud provider's overbooking risks, thereby presenting a win-win situation for both the individual providers and the cloud federation.
An Explainable Deep Model for Defect Prediction

Self attention transformer encoders represent an effective method for sequence to class prediction tasks as they can disentangle long distance dependencies and have many regularising effects. We achieve results substantially better than state of the art in one such task, namely, defect prediction and with many added benefits. Existing techniques do not normalise for correlations that are inversely proportional to the usefulness of the prediction but do, in fact, go further, specifically exploiting these features which is tantamount to data leakage. Our model is end-to-end trainable and has the potential capability to explain its prediction. This explainability provides insights and potential causes of a model's decisions, the absence of which has stopped defect prediction from gaining any traction in industry.
An Epipolar Volume Autoencoder With Adversarial Loss for Deep Light Field Super-Resolution

When capturing a light field of a scene, one typically faces a trade-off between more spatial or more angular resolution. Fortunately, light fields are also a rich source of information for solving the problem of super-resolution. Contrary to single image approaches, where high-frequency content has to be hallucinated to be the most likely source of the downscaled version, sub-aperture views from the light field can help with an actual reconstruction of those details that have been removed by downsampling. In this paper, we propose a three-dimensional generative adversarial autoencoder network to recover the high-resolution light field from a low-resolution light field with a sparse set of viewpoints. We require only three views along both horizontal and vertical axis to increase angular resolution by a factor of three while at the same time increasing spatial resolution by a factor of either two or four in each direction, respectively.
Private Transaction Retrieval for Lightweight Bitcoin Client

In the current Bitcoin network, lightweight clients outsource most of the computational and storage tasks to full blockchain nodes in order to run on resource-constrained devices, such as mobile phones. In the interactions with the full node, the lightweight client leaks considerable information about the Bitcoin transactions or addresses associated with this client. Therefore, the benefit of using lightweight client comes at costs of privacy. Existing solutions cannot support the lightweight client to retrieve the relevant transactions from the full node in an efficient and privacy-preserving way. In this paper, we propose a new approach to protect the privacy of lightweight clients in Bitcoin. Our main idea is to use private information retrieval (PIR) to guarantee that the lightweight clients can fetch the relevant transactions from untrusted full nodes without revealing to those nodes which particular transactions are being fetched. Directly using existing PIR scheme incurs high communication overhead for lightweight clients. Therefore, we propose an optimized protocol utilizing two-step PIR to efficiently retrieve the queried transactions without leaking sensitive query information. We developed a prototype implementation to demonstrate the feasibility of our proposed scheme.
Topic Structure-Aware Neural Language Model

Our goal is to exploit a unified language model so as to explain the generative process of documents precisely in view of their semantic and topic structures. Because various methods model documents in disparate ways, we are motivated by the expectation that coordinating these methods will allow us to achieve this goal more efficiently than using them in isolation; we combine topic models, embedding models, and neural language models. As we focus on the fact that topic models can be shared among, and indeed complement embedding models and neural language models, we propose Word and topic 2 vec (Wat2vec), and Topic Structure-Aware Neural Language Model (TSANL). Wat2vec uses topics as global semantic information and local semantic information as embedding representations of topics and words, and embeds both words and topics in the same space. TSANL uses recurrent neural networks to capture long-range dependencies over topics and words. Since existing topic models demand time consuming learning and have poor scalability, both due to breaking the document?s structure such as order of words and topics, TSANL maintains the orders of words and topics as phrases and segments, respectively. TSANL reduces the calculation cost and required memory by feeding topic recurrent neural networks, and topic specific word networks with these embedding representations. Experiments show that TSANL maintains both segments and topical phrases, and so enhances previous models.
Geochemical relationships in Cretaceous bentonites as inferred from linear discriminant analysis

Bentonites serve as an important stratigraphic tool for facies correlation due to their instantaneous deposition and preservation across multiple depositional environments. Correlating one bed to another can be a complex issue limited by the 3-dimensional availability of outcrops and core. This process is reliant on the assumption that the geochemical signatures of ash beds do not vary enough to statistically alter the correlation coefficients that tie one ash-fall to another. This assumption may be faulty, however, and the problem compounds as bentonite bed thickness increases because the chemical composition varies through an eruption due to stratification of the magma chamber and through physical transport. Linear discriminant analysis (LDA) can significantly aid with this issue, allowing for the utilization of a multivariate dataset to statistically separate chemically-similar groups. Here we present a statistical approach to assessing the primary discriminatory variables on a suite of 87 Cretaceous bentonites deposited in the Bighorn Basin, Wyoming, USA, that span five geologic stages and seven formations, from the Cloverly Formation (110 Ma) to the Meeteetse Formation (70 Ma), and includes nine named ash beds. Using LDA, bentonites were sorted into groups based on geologic stage, formation, and unique beds using key elemental concentrations and radiogenic isotopes (e.g., 87Sr/86Sr, TiO2, Sc, Na, and Th). Additionally, distinct similarities in the discrimination pattern between formations reveal two periods of magmatism, the development of the Idaho batholith and the migration and development of the Boulder batholith and surrounding related plutons. With well-preserved, temporally-constrained ash, LDA can correlate ancient tephra deposits while also shedding light on the cyclical nature of geochemical trends in a migrating subduction zone.
Content-Adaptive Noise Estimation for Color Images With Cross-Channel Noise Modeling

Noise estimation is crucial in many image processing tasks such as denoising. Most of the existing noise estimation methods are specially developed for grayscale images. For color images, these methods simply handle each color channel independently, without considering the correlation across channels. Moreover, these methods often assume a globally fixed noise model throughout the entire image, neglecting the adaptation to the local structures. In this work, we propose a contentadaptive multivariate Gaussian approach to model the noise in color images, in which we explicitly consider both the contentdependence and the inter-dependence among color channels. We design an effective method for estimating the noise covariance matrices within the proposed model. Specifically, a patch selection scheme is first introduced to select weakly textured patches via thresholding the texture strength indicators. Noticing that the patch selection actually depends on the unknown noise covariance, we present an iterative noise covariance estimation algorithm, where the patch selection and the covariance estimation are conducted alternately. For the remaining textured regions, we estimate a distinct covariance matrix associated with each pixel using a linear shrinkage estimator, which adaptively fuses the estimate coming from the weakly textured region and the sample covariance estimated from the local region. Experimental results show that our method can effectively estimate the noise covariance. The usefulness of our method is demonstrated with several image processing applications such as color image denoising and noise-robust superpixel.
A Swarm Algorithm Inspired by the Collective Animal Behavior

In this chapter, a swarm algorithm for global optimization called the Collective Animal Behavior (CAB) is introduced. The algorithm is based on animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, that exhibit a variety of behaviors including swarming about a food source, milling around a central location or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic and to avoid predation. In the presented algorithm in this chapter, the searcher agents emulate a group of animals which interact to each other based on the biological laws of collective motion. The optimization method presented in this chapter has been compared to other well-known optimization algorithms. The results, experiments and practical examples confirm the high performance of the presented method to find a global optimum of several benchmark functions.
West Lake Tourist: A Visual Analysis System Based on Taxi Data

Mining the mobile pattern of the urban population plays an important role in city construction, and visual analysis is a powerful technique in studying mobile patterns. In this paper, based on the taxi trajectory data in Hangzhou, we share our design for an interactive visual analytic system, which helps analyzers leverage their domain knowledge to gain insight into travel patterns, including travel time rules of tourists and the distribution rules of pick-up and drop-off locations. Besides, our system can present the dynamic travel process and the Point of Interest (POIs) information of the origin and the destination. A case study has been conducted, which verifies that our system can provide tools for urban managers or urban experts on the design of scenic spot open entrances and exits and travel route planning.
User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements

Mobile video streaming has gained a lot of popularity in recent year with the introduction of large data plans for mobile phones. While users in non-mobile scenarios have become accustomed to high quality and few stalling events, this is not the case in mobile environments. People are more likely to tolerate stalling since they know high throughput coverage cannot always be guaranteed. Their behavior and their engagement are drastically different when watching videos on mobile devices. In this paper, we characterize mobile phone users who use a video streaming application. We present a data set of over 6,000 video views from a crowdsourced video measurement study. We investigate user activity and engagement during video streaming and how such metrics are correlated with each other. This is the first study that goes beyond user engagement and investigates the direct behavior of mobile video users outside the lab which is an important step towards mobile QoE management.
Matching Ontologies with Word2Vec-Based Neural Network

To date, knowledge engineering researchers have proposed a large number of ontology matching methods. In this paper, to solve the ontology mapping problem, it is proposed to use a vector language model based on the Word2Vec statistical model set. The vector model is implemented using a neural network based on the TensorFlow framework. The peculiarity of the method is the use of the baseline pre-trained Wor2Vec model based on texts from the English Wikipedia and Google News, which is consistently extended on the basis of relationships specific to the ontologies being matched. This approach allows the use of semantics of the language bypassing situations in which, due to the form of a word or a specific term, it is impossible to find a correspondence of ontology concepts.
CodersMUSE: Multi-Modal Data Exploration of Program-Comprehension Experiments

Program comprehension is a central cognitive process in programming. It has been in the focus of researchers for decades, but is still not thoroughly unraveled. Multi-modal psycho-physiological and neurobiological measurement methods have proved successful to gain a more holistic understanding of program comprehension. However, there is no proper tool support that lets researchers explore synchronized, conjoint multi-modal data, specifically designed for the needs in program-comprehension research. In this paper, we present CodersMUSE, a prototype implementation that aims to satisfy this crucial need.
Facial Makeup Transfer Combining Illumination Transfer

To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software. The makeup effects could present on the user's input image in real time, with an only single reference image. The input image and reference image are divided into three layers by facial feature points landmarked: facial structure layer, facial color layer, and facial detail layer. Except for the above layers are processed by different algorithms to generate output image, we also add illumination transfer, so that the illumination effect of the reference image is automatically transferred to the input image. Our approach has the following three advantages: 1) Black or dark and white facial makeup could be effectively transferred by introducing illumination transfer; 2) Efficiently transfer facial makeup within seconds compared to those methods based on deep learning frameworks, and; 3) Reference images with the air-bangs could transfer makeup perfectly.
An Integrated Approach for Modeling Ontology-Based Task Knowledge on an Incident Command System

This paper presents the TTIPP methodology, an integration of task analysis, task ontology, integration definition function modeling (IDEF0), Petri net, and Petri net mark language (PNML), to organize and model the task knowledge in the form of natural language expressions acquired during the knowledge-acquisition process. The goal of the methodology is to make the tasks more useful, accessible, and sharable through the web for a variety of stakeholders interested in solving a problem which is expressed mostly in linguistic form, and to shed light on the nature of problem-solving knowledge. This study provides a core epistemology for the knowledge engineer while developing the task ontology for a generic task. The proposed model overcomes the drawbacks of IDEF0, which are its static nature and Petri net which has no concept of hierarchy. A good number of countries lie on the typhoon and earthquake belts, which make them vulnerable to natural calamities. However, a practical incident command system (ICS) that provides a common framework to allow emergency responders of different backgrounds to work together effectively for standardized, on-the-scene, incident management has yet to be developed. There is a strong need to explicitly share, copy, and reuse the existing problem-solving knowledge in a complex ICS. As an example, the TTIPP model is applied to the task of emergency response for debris-flow during a typhoon as a part of an ICS.
A Scalable Virtual Document-Based Keyword Search System for RDF Datasets

RDF datasets are becoming increasingly useful with the development of knowledge-based web applications. SPARQL is the official structured query language to search and access RDF datasets. Despite its effectiveness, the language is often difficult to use for non-experts because of its syntax and the necessity to know the underlying data structure of the database queries. In this regard, keyword search enables non-expert users to access the data contained in RDF datasets intuitively. This work describes the TSA+VDP keyword search system for effective and efficient keyword search over large RDF datasets. The system is compared with other state-of-the-art methods on different datasets, both real-world and synthetic, using a new evaluation framework that is easily reproducible and sharable.
Effective Decision Making in Self-adaptive Systems Using Cost-Benefit Analysis at Runtime and Online Learning of Adaptation Spaces

Self-adaptation is an established approach to deal with uncertainties that are difficult to predict before a system is deployed. A self-adaptative system employs a feedback loop that tracks changes and adapts the system accordingly to ensure its quality goals. However, making effective adaptation decisions at runtime is challenging. In this chapter we tackle two problems of effective decision making in self-adaptive systems. First, current research typically focusses on the benefits adaptaton can bring but ignores the cost of adaptation, which may invalidate the expected benefits. To tackle this problem, we introduce CB@R (Cost-Benefit analysis @ Runtime), a novel model-based approach for runtime decision-making in self-adaptive systems that handles both the benefits and costs of adaptation as first-class citizens in decision making. Second, we look into the adaptation space of self-adaptive systems, i.e. the set of adaption options to select from. For systems with a large number of adaptation options, analyzing the entire adaptation space is often not feasible given the time and resources constraints at hand. To tackle this problem, we present a machine learning approach that integrates learning with the feedback loop to select a subset of the adaption options that are valid in the current situation. We evaluate CB@R and the learning approach for a real world deployed Internet of Things (IoT) application.
Rao‐Blackwellised particle filtering for low‐cost encoder/INS/GNSS integrated vehicle navigation with wheel slipping

This study presents a Rao-Blackwellised particle filter (RBPF)-based encoder/inertial navigation system (INS)/global navigation satellite system (GNSS) integration method for improving the navigation performance of an autonomous land vehicle (ALV) with wheel slipping. In contrast to traditional integration methods, the proposed integration method introduces an overall wheel slip consideration for the ALV, which greatly improves the accuracy of the velocity estimation, especially when the inertial sensor is low cost. Additionally, the proposed integrated system uses double-difference pseudorange measurements instead of single point positioning results provided by low-cost GNSS receivers, which greatly improves the accuracy of the position estimation. To verify the navigation performance of the proposed integrated system, comparisons between the states estimated by the proposed system, the EKF-based integrated system and the joint wheel-slip and motion-estimation system are provided. The results of the experiment show that the proposed integrated system has the highest accuracy in both the position estimation and the velocity estimation among the three compared systems, and can improve the navigation performance during GNSS signals outages.
What's Holding Back Blockchain Finance? On the Possibility of Decentralized Autonomous Finance

Despite the past decade’s rapid innovation in adapting blockchain technology to new uses, financial intermediation remains elusive except in basic and highly collateralized forms. We introduce the concept of the technical frontier to delimit the kinds of interactions that can feasibly be structured algorithmically among pseudonymous agents, as on a blockchain, and show that lending and financial intermediation – unlike monetary exchange – lie outside it, even in simple forms. The path forward for truly blockchain-native financial applications, therefore, must involve the integration of real-world identity information in order to disincentivize defection. We discuss several potential technologies for doing so, and conclude that such integration is possible without compromising pseudonymity, provided real-world identity is available in the breach.