Reviewerly/exHarmony huggingface dataset:
Affinity Regularized Non-Negative Matrix Factorization for Lifelong Topic Modeling

Lifelong topic model (LTM), an emerging paradigm for never-ending topic learning, aims to yield higher-quality topics as time passes through knowledge accumulated from the past yet learned for the future. In this paper, we propose a novel lifelong topic model based on non-negative matrix factorization (NMF), called Affinity Regularized NMF for LTM (NMF-LTM), which to our best knowledge is distinctive from the popular LDA-based LTMs. NMF-LTM achieves lifelong learning by introducing word-word graph Laplacian as semantic affinity regularization. Other priors such as sparsity, diversity, and between-class affinity are incorporated as well for better performance, and a theoretical guarantee is provided for the algorithmic convergence to a local minimum. Extensive experiments on various public corpora demonstrate the effectiveness of NMF-LTM, particularly its human-like behaviors in two carefully designed learning tasks and the ability in topic modeling of big data. A further exploration of semantic relatedness in knowledge graphs and a case study on a large-scale real-world corpus exhibit the strength of NMF-LTM in discovering high-quality topics in an efficient and robust way.
Adaptive Architecture and Personal Data

Through sensors carried by people and sensors embedded in the environment, personal data is being processed to try to understand activity patterns and people's internal states in the context of human-building interaction. This data is used to actuate adaptive buildings to make them more comfortable, convenient, and accessible or information rich. In a series of envisioning workshops, we queried the future relationships between people, personal data and the built environment, when there are no technical limits to the availability of personal data to buildings. Our analysis of created designs and user experience fictions allows us to contribute a systematic exposition of the emerging design space for adaptive architecture that draws on personal data. This is being situated within the context of the new European information privacy legislation, the EU General Data Protection Regulation 2016. Drawing on the tension space analysis method, we conclude with the illustration of the tensions in the temporal, spatial, and inhabitation-related relationships of personal data and adaptive buildings, re-usable for the navigation of the emerging, complex issues by future designers.
Optimal autonomous multirotor motion planning in an obstructed environment

This paper addresses a methodology for autonomous motion planning of multirotor aircraft in obstructed environments. The control strategy allows the vehicle to online generate quasi-optimal trajectories with limited computational load while performing collision avoidance tasks. The problem is formulated in a model-predictive control architecture in which motion planning and trajectory tracking processes are solved separately. The first process is based on a spline path planning approach to generate smooth and safe trajectories. The second process elaborates trajectory inputs in terms of commanded thrust magnitude and desired attitude rates in order to steer the vehicle during the mission task. Results of both numerical simulations and, for the first time, an experimental validation are provided in order to assess the performance of the approach in the presence of external disturbances and unmodeled dynamics, provided adequate time horizon and update frequency are selected for the numerical optimization algorithm.
Offset regression networks for view plane estimation in 3D fetal ultrasound

Ultrasound (US) is the modality of choice for fetal screening, which includes the assessment of a variety of standardized growth measurements, like the abdominal circumference (AC). Screening guidelines define criteria on the scan plane, in which the measurement is taken. As US is increasingly becoming a 3D modality, approaches for automated determination of the optimal scan plane in a volumetric dataset would greatly improve the workflow. In this work, a novel framework for deep hyperplane learning is proposed and applied for view plane estimation in fetal US examinations. The approach is tightly integrated in the clinical workflow and consists of two main steps. First, the bounding box around the structure of interest is determined in the central slice (MPR). Second, offsets from the structure in the bounding box to the optimal view plane are estimated. By linear regression through the estimated offsets, the view plane coordinates can then be determined. The presented approach is successfully applied on clinical screening data for AC plane estimation and a high accuracy is obtained, outperforming or comparable to recent publications on the same application.
Compositional data techniques for the analysis of the container traffic share in a multi-port region

The statistical techniques based on compositional data are applied to investigate the evolution of the traffic share of the container throughput in a multi-port system. Compositional vectors are those which contain relative information of parts of some whole. The application of conventional statistical techniques to compositional data may lead to erroneous conclusions and spurious correlations. Therefore, compositional data (CoDa) should be treated taking into account their own mathematical structure. The so-called log-ratio approach provides a set of transformations that allow to apply conventional statistical techniques to the transformed compositional data samples. Thus, the objective of this paper is double. As a first stage it aims to introduce the CoDa formalism and highlight its potentiality in the port container throughput analysis as example of transport system providing an applied example: the container throughput evolution in the Spanish Mediterranean Ports system during the period 1976–2015. Second, based on the previous analysis, the aim is to characterize the container throughput in SpanishMed ports and its temporal evolution. The CoDa analysis clarifies the interpretation and data association of the container traffic throughput evolution in function of some selected change points: boom of containerization in 1990s and 2008 crisis. This contribution proves that the CoDa methodology is useful to investigate the complexity of the transport disciplines in order to understand and to manage the spatial integration that results from the movement of people and freight.
USER EXPERIENCE EVALUATION AND CREATIVITY STIMULATION WITH AUGMENTED REALITY MOBILE APPLICATIONS

While customer information and knowledge need transform in the context of globalization and technological change, it is important for organizations to efficiently meet new and changing needs and stimulate consumer creativity through the use of augmented reality mobile applications. In order to solve this kind of problem, it is important to evaluate mobile applications with respect to user experience. The purpose of this study is to evaluate alternative research methods for user experience assessment in augmented reality, to determine whether the selected user experience survey method is suitable for augmented reality mobile applications’ evaluation, and to identify features, which would improve augmented reality mobile applications in order to enhance users’ creativity and positive attitude. The article presents the analysis of theoretical aspects of evaluation of augmented reality mobile applications, the concept of the augmented reality, the augmented reality mobile application evaluation results and the recommendations for the user’s creativity stimulation. Research methods such as scientific literature analysis and user experience survey are used to achieve the purpose of the article.
Visual analytical dashboards for comparative analytical tasks – a case study on mobility and transportation

Mobility, logistics and transportation are emerging fields of research and application. Humans' mobility behavior plays an increasing role for societal challenges. Beside the societal challenges these areas are strongly related to technologies and innovations. Gathering information about emerging technologies plays an increasing role for the entire research in these areas. Humans' information processing can be strongly supported by Visual Analytics that combines automatic modelling and interactive visualizations. The juxtapose orchestration of interactive visualization enables gathering more information in a shorter time. We propose in this paper an approach that goes beyond the established methods of dashboarding and enables visualizing different databases, data-sets and sub-sets of data with juxtaposed visual interfaces. Our approach should be seen as an expandable method. Our main contributions are an in-depth analysis of visual task models and an approach for juxtaposing visual layouts as visual dashboards to enable solving complex tasks. We illustrate our main outcome through a case study that investigates the area of mobility and illustrates how complex analytical tasks can be performed easily by combining different visual interfaces.
A sampling theorem with error estimation for S-transform

S-transform (ST) is an extension of wavelet transform (WT) and short-time Fourier transform (STFT). The ST is depended on a scalable and moving Gaussian window. It overcomes the low-resolution factor of STFT and tackles with a lack of phase in WT. The ST is a useful tool for signal processing and analysis. In this paper, the multiresolution analysis (MRA) related with ST is introduced and after that a sampling theorem associated with ST is proposed which is based on multiresolution subspace. Additionally, truncation and aliasing error generated due to the sampling process, are also derived. The theoretical determinations are exhibited and validated using simulation results.
QoE-driven resource allocation for massive video distribution

Massive video delivery systems employ the HTTP protocol and multiple Content Delivery Networks (CDNs), which serve the content to the end-users on behalf of the video providers and guarantee scalability and Quality of Experience (QoE). In this paper, a Video Control Plane (VCP) is presented which monitors the QoE delivered by any of the CDN belonging to its pool and selects the most performing one when a new video request is received. The VCP employs a continuously updated prediction of the CDNs performances based on the feedback sent by the video clients and computed through a k-NN regression algorithm. The proposed VCP has been evaluated through simulations and shows significant performance improvement in terms of QoE delivered to the user.
A Survey on RDF Data Store Based on NoSQL Systems for the Semantic Web Applications

Today the Resource Description Framework (RDF) that allows computers to understand and exploit Web data becomes very much in a progressive way, as well as the amount of web data that becomes very large. The storage and efficient management of this large RDF data is a real challenge in front of the classic RDF databases called triplestore. Recently, several researches focus on storing RDF data in triplestores based on NoSQL data management systems like HBase, Cassandra, Accumulo, and Couchbase. The majority of these researches are based on HBase. This NoSQL technology that is intended to handle this phenomenon of data explosion called Big Data, provided benefits like scalability and high availability compared to traditional triplestores. In this paper, we review existing works and systems that use NoSQL databases to store massive RDF data.
Gesture Recognition in Augmented Reality Assisted Assembly Training

The Augmented Reality assisted assembly training (ARAAT) is an effective and low-cost way in motor and electronic industry. In ARAAT, assembly operations are the processes which mainly use the AR device to recognize the gestures and match the virtual workpieces to the hand based on the consistency of time and space. Operations are recorded to the video by an AR device. By dealing with frames of the input video, operations can be distinguished. In this paper, a gesture recognition algorithm in ARAAT are proposed. The assembly training operations consist of several actions and actions we concerned are all conducted by gestures. Operations can be classified from the sequences of actions, that is, gestures. Every 20 frames of the input video are denoted as an action unit and the action unit slides with a time window. According to the 2D and 3D features of the action unit, a scorer trained by the samples of specific actions gives the optimal label of each frame to recognize the action. To avoid the disturbance of the transition and invalid action to the action boundary during the recognition, the boundary is iteratively optimized by the probability density distribution. The proposed algorithm implemented on HoloLens is compared with other algorithms. The experimental results indicate that the proposed algorithm achieves a high recognition rate and can reduce the computational complexity. The results prove the efficiency of recognition algorithm in ARAAT and ensure a friendly experience for the human-machine interaction.
The signless Laplacian state transfer in coronas

For two graphs G and H, the corona product G∘H is the graph obtained by taking one copy of G and |VG| copies of H, and joining the ith vertex of G with every vertex of the ith copy of H. In this paper, we study the state transfer of corona relative to the signless Laplacian matrix. We explore some conditions that guarantee the signless Laplacian perfect state transfer in G∘H. We prove that G∘Km has no signless Laplacian perfect state transfer for some special m. We also show that K2∘H has pretty good state transfer but no perfect state transfer relative to the signless Laplacian matrix for a regular graph H. Furthermore, we show that nK2¯∘K1 has signless Laplacian pretty good state transfer, where nK2¯ is the cocktail party graph.
Design and development of a DDDAMS-based border surveillance system via UVs and hybrid simulations

Despite the increasing use of sensor technologies in border surveillance applications, there is a lack of systematic methodology and its implementation with the effective control system. The challenge arises due to information heterogeneity and uncertainty caused by the usage of different sensors. This paper extends the authors' previous dynamic-data-driven adaptive multi-level simulation (DDDAMS) framework to overcome this challenge using unmanned vehicles (UVs), sensors, and multi-level simulation. Specifically, the detection and classification algorithms are employed to process real-time data generated by fixed (e.g. geophone) and mobile (e.g. UV camera) sensors for the adequate monitoring. Also, physics-based simulation (PBS) is utilized to provide uncertainties for robust planning and control of UVs. The environmental effects as well as target's proactive behavior against the surveillance system are incorporated into the framework using utility-based decision-making model. Finally, we provide a detailed description regarding field demonstration, where autonomous control of UVs and real-time communications among all system components are attained through PBS.
Frequentist, Bayes, or Other?

Both philosophically and in practice, statistics is dominated by frequentist and Bayesian thinking. Under those paradigms, our courses and textbooks talk about the accuracy with which true model parameters are estimated or the posterior probability that they lie in a given set. In nonparametric problems, they talk about convergence to the true function (density, regression, etc.) or the probability that the true function lies in a given set. But the usual paradigms' focus on learning the true model and parameters can distract the analyst from another important task: discovering whether there are many sets of models and parameters that describe the data reasonably well. When we discover many good models we can see in what ways they agree. Points of agreement give us more confidence in our inferences, but points of disagreement give us less. Further, the usual paradigms’ focus seduces us into judging and adopting procedures according to how well they learn the true values. An alternative is to judge models and parameter values, not procedures, and judge them by how well they describe data, not how close they come to the truth. The latter is especially appealing in problems without a true model.
Electricity Price-aware Consolidation Algorithms for Time-sensitive VM Services in Cloud Systems

Despite the salient feature of cloud computing, the cloud provider still suffers from electricity bill, which in part comes from 1) the power consumption of running physical machines (PMs) to guarantee the resource/time requirements of virtual machines (VMs), and 2) the dynamically varying electricity price offered by smart grids. In the literature, there exist viable solutions adaptive to electricity price variation to reduce the electricity bill. However, they are not applicable to serving time-sensitive VM requests. In serving time-sensitive VM requests, it is potential for the cloud provider to apply proper consolidation strategies to further reduce the electricity bill. Few prior works have provided theoretical solutions of VM consolidation strategies that are adaptive to electricity price variations in serving time-sensitive VM requests. In this work, to address this challenge, we develop electricity-price-aware consolidation algorithms for both the offline and online scenarios. For the offline scenario, we first develop a consolidation algorithm with constant approximation, which always approaches the optimal solution within a constant factor of 5. For the online scenario, we propose an <inline-formula><tex-math notation="LaTeX">$O(\log (\frac{L_{max}}{L_{min}}))$</tex-math></inline-formula> -competitive algorithm that is able to approach the optimal offline solution within a logarithmic factor, where <inline-formula><tex-math notation="LaTeX">$\frac{L_{max}}{L_{min}}$</tex-math></inline-formula> is the ratio of the longest length of the processing time requirement of VMs to the shortest one. Our trace-driven simulation results further demonstrate that the average performance of the proposed algorithms produce near-optimal electricity bill.
Siamese hierarchical attention networks for extractive summarization

In this paper, we present an extractive approach to document summarization based on Siamese Neural Networks. Specifically, we propose the use of Hierarchical Attention Networks to select the most relevant sentences of a text to make its summary. We train Siamese Neural Networks using document-summa ry pairs to determine whether the summary is appropriated for the document or not. By means of a sentence-level attention mechanism the most relevant sentences in the document can be identified. Hence, once the network is trained, it can be used to generate extractive summaries. The experimentation carried out using the CNN/DailyMail summarization corpus shows the adequacy of the proposal. In summary, we propose a novel end-to-end neural network to address extractive summarization as a binary classification problem which obtains promising results in-line with the state-of-the-art on the CNN/DailyMail corpus.
Novel Event Detection and Classification for Historical Texts

Event processing is an active area of research in the Natural Language Processing community, but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts Particularly in the current era of massive digitization of historical sources: Research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work, while having an impact also on the field of Natural Language Processing. Our work aims at shedding light on the complex concept of events when dealing with historical texts. More specifically, we introduce new annotation guidelines for event mentions and types, categorized into 22 classes. Then, we annotate a historical corpus accordingly, and compare two approaches for automatic event detection and classification following this novel scheme. We believe that this work can foster research in a field of inquiry as yet underestimated in the area of Temporal Information Processing. To this end, we release new annotation guidelines, a corpus, and new models for automatic annotation.
Formal Specification Technique in Smart Contract Verification

The block chain technology is changing rapidly. The block chain guarantees the integrity of the book through a specific consensus of the participants. In the past, the block chain technology had a limited range of applications. However, the use of block chain technology is gradually expanding as smart contracts that can formulate general business logic are mentioned. Already studied the components of smart contracts in other studies and proposed the possibility of extending them on the basis of ontology. And research on securing traceability of smart contract based on ontology has been carried out. However, research on various transactions constituting smart contracts is lacking. In this paper, the constituent elements of smart contract are analyzed and expressed by ontology. And the process of negotiating the components is represented by each transaction. Finally, we construct the component represented by the ontology as XML by including the state information in the transaction. In this way, the smart contract is represented in a formal language that contains state information. It also laid the foundation for a smart contract that can be reused and verified.
A New Quantum Blind Signature Scheme with BB84-State

The blind signature is widely used in cryptography applications because it can prevent the signer from gaining the original message. Owing to the unconditional security, the quantum blind signature is more advantageous than the classical one. In this paper, we propose a new provable secure quantum blind signature scheme with the nonorthogonal single-photon BB84-state and provide a new method to encode classical messages into quantum signature states. The message owner injects a randomizing factor into the original message and then strips the blind factor from the quantum blind signature signed by the blind signer. The verifier can validate the quantum signature and announce it publicly. At last, the analytical results show that the proposed scheme satisfies all of the security requirements of the blind signature: blindness, unforgeability, non-repudiation, unlinkability, and traceability. Due to there being no use of quantum entanglement states, the total feasibility and practicability of the scheme are obviously better than the previous ones.
An Access Control Model for Preventing Virtual Machine Hopping Attack

As a new type of service computing model, cloud computing provides various services through the Internet. Virtual machine (VM) hopping is a security issue often encountered in the virtualization layer. Once it occurs, it directly affects the reliability of the entire computing platform. Therefore, we have thoroughly studied the virtual machine hopping attack. In addition, we designed the access control model PVMH (Prevent VM hopping) to prevent VM hopping attacks based on the BLP model and the Biba model. Finally, we implemented the model on the Xen platform. The experiments demonstrate that our PVMH module succeeds in preventing VM hopping attack with acceptable loss to virtual machine performance.