Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of "Condition": A Novel Scientific Knowledge Graph Representation and Construction Model

Conditions play an essential role in scientific observations, hypotheses, and statements. Unfortunately, existing scientific knowledge graphs (SciKGs) represent factual knowledge as a flat relational network of concepts, as same as the KGs in general domain, without considering the conditions of the facts being valid, which loses important contexts for inference and exploration. In this work, we propose a novel representation of SciKG, which has three layers. The first layer has concept nodes, attribute nodes, as well as the attaching links from attribute to concept. The second layer represents both fact tuples and condition tuples. Each tuple is a node of the relation name, connecting to the subject and object that are concept or attribute nodes in the first layer. The third layer has nodes of statement sentences traceable to the original paper and authors. Each statement node connects to a set of fact tuples and/or condition tuples in the second layer. We design a semi-supervised Multi-Input Multi-Output sequence labeling model that learns complex dependencies between the sequence tags from multiple signals and generates output sequences for fact and condition tuples. It has a self-training module of multiple strategies to leverage the massive scientific data for better performance when manual annotation is limited. Experiments on a data set of 141M sentences show that our model outperforms existing methods and the SciKGs we constructed provide a good understanding of the scientific statements.
Facilitating Application-Aware Bandwidth Allocation in the Cloud with One-Step-Ahead Traffic Information

Bandwidth allocation to virtual machines (VMs) has a significant impact on the performance of communication-intensive big data applications hosted in VMs. It is crucial to accurately determine how much bandwidth to be reserved for VMs and when to adjust it. Past approaches typically resort to predicting the long-term network demands of applications for bandwidth allocation. However, lacking of prediction accuracy, these methods lead to the unpredictable application performance. Recently, it is conceded that the network demands of applications can only be accurately derived right before each of their execution phases. Hence, it is challenging to timely allocate the bandwidth to VMs with limited information. In this paper, we design and implement AppBag, an Application-aware Bandwidth guarantee framework, which allocates the accurate bandwidth to VMs with one-step-ahead traffic information. We propose an algorithm to allocate the bandwidth to VMs and map them onto feasible hosts. To reduce the overhead when adjusting the allocation, an efficient Lazy Migration (LM) algorithm is proposed with bounded performance. We conduct extensive evaluations using real-world applications, showing that AppBag can handle the bandwidth requests at run-time, while reducing the execution time of applications by 47.3 percent and the global traffic by 36.7 percent, compared to the state-of-the-art methods.
Object Classification by a Super-resolution Method and a Convolutional Neural Networks

Recently with many blurless or slightly blurred images, convolutional neural networks classify objects with around 90 percent classification rates, even if there are variable sized images. However, small object regions or cropping of images make object detection or classification difficult and decreases the detection rates. In many methods related to convolutional neural network (CNN), Bilinear or Bicubic algorithms are popularly used to interpolate region of interests. To overcome the limitations of these algorithms, we introduce a superresolution method applied to the cropped regions or candidates, and this leads to improve recognition rates for object detection and classification. Large object candidates comparable in size of the full image have good results for object detections using many popular conventional methods. However, for smaller region candidates, using our super-resolution preprocessing and region candidates, allows a CNN to outperform conventional methods in the number of detected objects when tested on the VOC2007 and MSO datasets.
Robust Representation Learning of Biomedical Names

Biomedical concepts are often mentioned in medical documents under different name variations (synonyms). This mismatch between surface forms is problematic, resulting in difficulties pertaining to learning effective representations. Consequently, this has tremendous implications such as rendering downstream applications inefficacious and/or potentially unreliable. This paper proposes a new framework for learning robust representations of biomedical names and terms. The idea behind our approach is to consider and encode contextual meaning, conceptual meaning, and the similarity between synonyms during the representation learning process. Via extensive experiments, we show that our proposed method outperforms other baselines on a battery of retrieval, similarity and relatedness benchmarks. Moreover, our proposed method is also able to compute meaningful representations for unseen names, resulting in high practical utility in real-world applications.
RADAR: Robust Algorithm for Depth Image Super Resolution Based on FRI Theory and Multimodal Dictionary Learning

Depth image super-resolution is a challenging problem, since normally high upscaling factors are required (e.g., $16\times $ ), and depth images are often noisy. In order to achieve large upscaling factors and resilience to noise, we propose a Robust Algorithm for Depth imAge super Resolution (RADAR) that combines the power of finite rate of innovation (FRI) theory with multimodal dictionary learning. Given a low-resolution (LR) depth image, we first model its rows and columns as piece-wise polynomials and propose an FRI-based depth upscaling (FDU) algorithm to super-resolve the image. Then, the upscaled moderate quality (MQ) depth image is further enhanced with the guidance of a registered high-resolution (HR) intensity image. This is achieved by learning multimodal mappings from the joint MQ depth and HR intensity pairs to the HR depth, through a recently proposed triple dictionary learning (TDL) algorithm. Moreover, to speed up the super-resolution process, we introduce a new projection-based rapid upscaling (PRU) technique that pre-calculates the projections from the joint MQ depth and HR intensity pairs to the HR depth. Compared with the state-of-the-art deep learning-based methods, our approach has two distinct advantages: we need a fraction of training data but can achieve the best performance, and we are resilient to mismatches between training and testing datasets. The extensive numerical results show that the proposed method outperforms other state-of-the-art methods on either noise-free or noisy datasets with large upscaling factors up to $16\times $ and can handle unknown blurring kernels well.
Digital Camera Identification Using Sensor Pattern Noise

With the advance of IT technology, multimedia contents are widely used and distributed. Since novice uses them for illegal purpose, there are needs for protecting contents and blocking illegal usage through multimedia forensics. This paper presents a forensic technique for identifying digital camera using the sensor pattern noise. First, the way to acquire a sensor pattern noise which comes from the imperfection of photon detector against light is presented. Then, the way to identify the similarity of digital camera is explained after estimating the sensor pattern noises from the reference images and the unknown image. For the performance analysis of the proposed technique, 10 camera including DSLR and compact camera, smartphone, and camcorder are tested and analyzed quantitatively. Based on the results, the proposed technique can achieve the 99.6% identification accuracy.
Wavelet and Neural Network Method for Speech Enhancement Objective Evaluation

Wavelet Neural Network Evaluation method WNNEM is proposed as a powerful tool for enhanced speech signal evaluation. This objective evaluation measure utilizes Feed forward back Propagation Neural Network FFBNN to train the free of noise signal, and then enhanced signal is simulated to the training output results taken for given target. The distance between simulation and the target, over different wavelet sub bands is studied. Four known speech enhancement method for studying the performance of WNNEM are utilized. The advantage of this method is the evaluation of different band passes of frequency based on wavelet transform by neural network, which is very powerful classification tool. Several objective measures are used to investigate the WNNEM compatibility. Results proved the validity of the proposed method.
Modal testing of an unreinforced masonry house

There are a significant number of uncertainties in finite element models of unreinforced masonry structures related to the modelling assumptions and the properties of local materials. Therefore, it is necessary to implement calibration techniques for these models. Modal testing is a good option for assessing the dynamic properties of the structure. The experimental data is used to verify and improve the predicted response obtained by finite element model. The study presents the modal testing of a full-scale physical model of an unreinforced masonry house. The structure is tested under three different excitations: an impact by a calibrated hammer, a random excitation induced by a calibrated hammer, and a steppedsine excitation induced by a shaker. In addition, an operational modal test has been performed using ambient and random excitations. Two different methods are used for system identification: peak picking and stochastic subspace identification. The results of this research will be used in future studies for updating the model.
Thermal Prediction Models for Virtualized Data center Servers by using Thermal-profiles

The energy dissipated as heat for each utilization level of a data center server is empirically measured and stored as the thermal-profile. These thermal-profiles are used to predict the outlet temperatures of the related servers for current and future utilization. The predicted outlet temperature is an important parameter for energy efficient thermal-aware workload scheduling and workload migration in green data centers. This paper presents three models for outlet temperature prediction on virtualized data center servers based on thermal-profile. The best case scenario managed to predict the outlet temperature with a negligible error of 0.3 degree Celsius.
On the use of visual programming languages for communication system simulation

The important features of several software packages for communication system simulation are discussed. Whereas initial versions of SYSTID and similar packages are language-oriented and designed to operate in a batch mode, such packages as ICS, ICSSM, etc. are interactive and menu driven. The packages developed in recent years (BOSS, MODEM, LabVIEW, etc.) involve powerful workstation and personal computers with friendly computing environments and highly visual and graphical user interfaces. A number of issues arise when simulation is selected as a design or analysis tool. Important considerations are the simulation environment, computer accessibility and cost, simulation accuracy, processing speed, and capability of the simulation language. It is noted that a visual programming tool like LabView gives a very favorable response to these considerations. Although LabView cannot replace the simulation languages such as SYSTID or ICS in terms of speed, it can be a helpful tool for communication system designers an analysts. <<ETX>>
Exploring Smartphone Users’ Social Information Behavior

AAs the popularity of social network services (SNS) grows, users’ online behavior is increasingly being affected by SNS browsing. Now, mobile device serve as a tool for social connection, provides an increasingly important communication channel in facilitating social connections. SNSs that are adequate for mobile devices have been developed. To clarify web user social information behavior, this study used social network sites to analyze smartphone user online behavior by collecting user clickstream data over a period of 3 months. The influence of SNS browsing on types of external sites visited—namely news, shopping, searches, forums, videos, and blogs—is examined. This study used a site-centric approach, wherein user data was examined by using association rules and the Jaccard Index to explore the relationship between SNS and other online activities. We collaborates with InsightXplorer to recruit smart phone users, collected 580 samples from the consumers who use the smartphone. First, user-centric approach will be conducted for correlation analysis to quantify the association between SNSs and other categories, and then use the results as the standard to conduct fixed effect model by exploring the duration of gender, age and weekdays/weekend at 24hrs. Second, site-centric approach will conduct correspondence analysis and association rules by using users' session to gather all categories in the graph and explore the relationship between the SNSs and the other categories. The result shows that Taiwanese smartphone users use Facebook (both Web and App) as a news-seeking, topic-seeking, video-seeking and word-of-mouth–seeking platform. That means Facebook will become a portal. When people want to find something interesting, the first thing is to open Facebook and searching their timeline. Finally, based on the findings, this study to provide a stepping stone for marketers and researchers to understand web users’ social information behavior. Keywords: Smartphone, Social information behavior, Web usage mining, Clickstream data, Mobile commerce To cite this document: Chiang, I. -P., Lin, C. -Y. , & Wu, Y. -J. (2019). Exploring smartphone users’ social information behavior. Contemporary Management Research, 15(1), 53-67. https://doi.org/10.7903/cmr.18461
Considerations of Translating Psychological Tests into Digital Mediums: A Case Study

This article details the construction of a computer program to test cognitive processing differences in adolescents engaged in a standard presentation of tests versus a fantasy-based game presentation. The article will discuss the challenges of creating a replication of traditional psychological tests into a new medium which holds comparable validity, the design of the play condition, the incorporation of the tests into a game, piloting the study, and technical issues encountered in the final development. Finally, guidance will be offered from this case study to other researchers attempting similar projects.
Organization of protection mechanisms for cloud storage services

Providing effective security mechanisms is an important requirement to any secure cloud solution. In this sense, it's proposed to use a hybrid approach to cloud security that includes both warning and detection mechanisms in order to minimize the possibility of a successful attack. According to that proposal, the customized attribute-based encryption (ABE) is a comprehensive access control solution for cloud storage services including user accountability and key revocation. The authors apply state-of-the-art signal processing techniques in order to detect the malicious activities and Man-in-the-Cloud ( MITC) attacks in the cloud environment.
iRho: An Imperative Rewriting Calculus [Extended Abstract]

Luigi Liquori, Bernard Serpette To cite this version: Luigi Liquori, Bernard Serpette. iRho: An Imperative Rewriting Calculus [Extended Abstract]. Proceedings of the 6th ACM SIGPLAN International Conference on Principles and Practice of Declarative Programming, Aug 2004, Verona, Italy. ACM, pp.167-178, 2004, <10.1145/1013963.1013983>. HAL Id: hal-01149659 https://hal.inria.fr/hal-01149659 Submitted on 7 May 2015 HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. iRho: An Imperative Rewriting Calculus [Extended Abstract] ∗ Luigi Liquori INRIA, France Luigi.Liquori@inria.fr Bernard Paul Serpette INRIA, France Bernard.Serpette@inria.fr
Learning and Reasoning Strategies for User Association in Ultra-dense Small Cell Vehicular Networks

Recent vehicular ad hoc networks research has been focusing on providing intelligent transportation services by employing information and communication technologies on road transport. It has been understood that advanced demands such as reliable connectivity, high user throughput, and ultra-low latency required by these services cannot be met using traditional communication technologies. Consequently, this thesis reports on the application of artificial intelligence to user association as a technology enabler in ultra-dense small cell vehicular networks. In particular, the work focuses on mitigating mobility-related concerns and networking issues at different mobility levels by employing diverse heuristic as well as reinforcement learning (RL) methods. Firstly, driven by rapid fluctuations in the network topology and the radio environment, a conventional, three-step sequence user association policy is designed to highlight and explore the impact of vehicle speed and different performance indicators on network quality of service (QoS) and user experience. Secondly, inspired by control-theoretic models and dynamic programming, a real-time controlled feedback user association approach is proposed. The algorithm adapts to the changing vehicular environment by employing derived network performance information as a heuristic, resulting in improved network performance. Thirdly, a sequence of novel RL based user association algorithms are developed that employ variable learning rate, variable rewards function and adaptation of the control feedback framework to improve the initial and steady-state learning performance. Furthermore, to accelerate the learning process and enhance the adaptability and robustness of the developed RL algorithms, heuristically accelerated RL and case-based transfer learning methods are employed. A comprehensive, two-tier, event-based, system level simulator which is an integration of a dynamic vehicular network, a highway, and an ultra-dense small cell network is developed. The model has enabled the analysis of user mobility effects on the network performance across different mobility levels as well as served as a firm foundation for the evaluation of the empirical properties of the investigated approaches.
Gender Stereotypes Differ between Male and Female Writings

Written language often contains gender stereotypes, typically conveyed unintentionally by the author. To study the difference in how female and male authors portray people of different genders, we quantitatively evaluate and analyze the gender stereotypes in their writings on two different datasets and from multiple aspects. We show that writings by females on average have lower gender stereotype scores. We plan to study and interpret the distributions of gender stereotype scores of individual words, and how they differ between male and female writings. We also plan on using more datasets over the past century to study how the stereotypes in female and male writings evolved over time.
Optimal Feature Spaces for Noise-Robust Speech Recognition

June Declaration I, Rogier van Dalen, of Girton College, a candidate for the M.Phil. in Computer Speech, Text and Internet Technology, hereby declare that this dissertation and the work described in it are my own work, unaided except as specied, and that the dissertation does not contain material that has already been used to any substantial extent for any comparable purpose. I also declare that this dissertation contains words, including footnotes, appendices and bibliography, and that this is less than words, as prescribed in the Special Regulations of the M.Phil. examinations for which I am a candidate. Acknowledgements I would like to thank my supervisor, Mark Gales, for coming up with the idea of this project, and helping me along throughout with answers, advice and new ideas. I would also like to thank Hank Liao for giving me access to his experimental setups , for his comments, and for gure. on page .
An efficient programmable 2-D convolver chip

This paper proposes a new real-time 2-D convolution filter architecture to reduce the product of the hardware complexity and propagation delay. To meet the real-time image-processing requirement, several commercial 2-D convolver chips have many parallel multipliers, which occupy a large VLSI area. The implemented chip uses only one special shift-and-accumulator block instead of nine parallel multipliers. Hence the chip can reduce the chip size by more than 50% of existing 2-D convolvers. Moreover, due to a finite state machine, which is controlling input data sequences, the proposed chip does not require row buffers to store three row image data used in the commercial chips. We used the SOG cell library (KG60K). The implemented filter chip consists of only 3,893 gates, operates at 125 MHz and can meet the real-time image processing requirement, i.e., the standard of ITU-R BT.601.
Multi-Horizon Time Series Forecasting with Temporal Attention Learning

We propose a novel data-driven approach for solving multi-horizon probabilistic forecasting tasks that predicts the full distribution of a time series on future horizons. We illustrate that temporal patterns hidden in historical information play an important role in accurate forecasting of long time series. Traditional methods rely on setting up temporal dependencies manually to explore related patterns in historical data, which is unrealistic in forecasting long-term series on real-world data. Instead, we propose to explicitly learn constructing hidden patterns' representations with deep neural networks and attending to different parts of the history for forecasting the future. In this paper, we propose an end-to-end deep-learning framework for multi-horizon time series forecasting, with temporal attention mechanisms to better capture latent patterns in historical data which are useful in predicting the future. Forecasts of multiple quantiles on multiple future horizons can be generated simultaneously based on the learned latent pattern features. We also propose a multimodal fusion mechanism which is used to combine features from different parts of the history to better represent the future. Experiment results demonstrate our approach achieves state-of-the-art performance on two large-scale forecasting datasets in different domains.
IoT Attack Detection with Deep Learning

In this paper, we analyze the network attacks that can be launched against IoT gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We also present the principles and design of a deep learning-based approach for the online detection of network attacks. Empirical validation results on packet captures in which attacks were inserted show that the Deep Neural Network correctly detects attacks.