Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing The Sensing Capabilities Of Mobile And Embedded Systems

In this work, we aim to develop new sensors and sensing platforms that facilitate the development of new mobile and embedded devices. Mobile and embedded devices have become an integral part of our everyday lives and the sensing capabilities of these devices have improved throughout the years. Developing new and innovative sensors and sensing platforms will provide the building blocks for developing new sensing systems. In an effort to facilitate these innovations we have developed two new in-air sonar sensors and a new reconfigurable sensing platform. The first in-air sonar sensor is designed for ranging applications and uses the phone’s microphone and rear speaker to generate a wide beam of sound. The second in-air sonar sensor is an external module which uses a narrow beam of sound for high resolution ranging. This ranging information is then combined with orientation data from the phone’s gyroscope,magnetometer and accelerometer to generate a two dimensional map of a space. While researching ways of enhancing the sensing capabilities of mobile and embedded devices, we found that the process often requires developing new hardware prototypes. However, developing hardware prototypes is time-consuming. In an effort to lower the barrier to entry for small teams and software researchers, we have developed a new reconfigurable sensing platform that uses a code first approach to embedded design. Instead of designing software to run within the limited constraints of the hardware, our proposed code-first approach allows software researchers to synthesize the hardware configuration that is required to run their software.
Leveraging a Large Learner Corpus for Automatic Suggestion of Collocations for Learners of Japanese as a Second Language.

One of the challenges of learning Japanese as a Second Language (JSL) is finding the appropriate word for a particular usage. To address this challenge, we developed a collocational aid designed to suggest more appropriate collocations in Japanese. In particular, we address the problem of generating and ranking noun and verb candidates for correcting potential collocation errors in the learners’ text. Given a noun-verb construction as input, our system generates possible noun or verb correction candidates based on noun and verb corrections extracted from a large Japanese learner corpus. We use this corpus to investigate the learner's tendency to commit collocation errors, and to produce a smaller and more realistic set of candidates. After combining nouns or verbs with the generated candidates to form noun-verb pairs, the system uses the Weighted Dice coefficient as the association measure to filter out inappropriate noun-verb pairs and rank the proper collocations. We report the detailed evaluation and results on learner data. In addition, we show that our system statistically outperforms existing approaches to collocation error correction. Finally, we report a preliminary user study with JSL learners.
Benchmarking Replication in NoSQL Data Stores

The proliferation in Web 2.0 applications has increased both the volume, velocity, and variety of data sources which have exceeded the limitations and expected use cases of traditional relational DBMSs. Cloud serving NoSQL data stores address these concerns and provide replication mechanisms to ensure fault tolerance, high availability, and improved scalability. However, no research or studies exist which emperically explore the impact of replication on data store performance. As such this study paves the way for a new replication benchmarking tier for YCSB, a leading benchmark tool for cloud serving data stores, by benchmarking replication in four NoSQL data stores of variable categorization and replication strategy. These data stores include: Redis, a keyvalue store that uses a master-slave replication model; MongoDB, a document store with replica set replication; Cassandra, an extensible-record store with a multi-master replica architecture; and VoltDB, a distributed DBMS which utilizes a synchronous multi-master replication strategy. This study focuses on the impact of replication on performance and availability compared to non-replicated clusters on constant amounts of hardware. To increase the relevancy of this study to real-world use cases, experiments include different workloads, distributions, and tunable consistency levels, on clusters hosted on a private cloud environment. This study presents an in-depth analysis of the overall throughput, read latencies, and write latencies of all experiments per data store. Each analysis section concludes with a presentation and brief analysis of the latency histogram and CDF curves of reads and writes to aid predictions on how each data store should behave. Subsequently, a comparative analysis is performed between data stores to identify the most performant data store and replication model. This study should serve as a point of reference for companies attempting to choose the right data store and replication strategy for their use cases. It also highlights the need to investigate the effects of geo-replication in more detail due to its importance in ensuring high availability in the face of network partitions and data center failures. Additionally, the impact that replication has on the consistency of data is of importance and warrants further attention. By making the data collected in this study publicly available, we encourage further research in an area with considerable research gaps: performance modelling of NoSQL data stores.
Observational consequences of the Standard Model Higgs inflation variants

We consider the possibility to observationally differentiate the Standard Model (SM) Higgs driven inflation with non-minimal coupling to gravity from other variants of SM Higgs inflation based on the scalar field theories with non-canonical kinetic term such as Galileon-like kinetic term and kinetic term with non-minimal derivative coupling to the Einstein tensor. In order to ensure consistent results, we study the SM Higgs inflation variants by using the same method, computing the full dynamics of the background and perturbations of the Higgs field during inflation at quantum level. Assuming that all the SM Higgs inflation variants are consistent theories, we use the MCMC technique to derive constraints on the inflationary parameters and the Higgs boson mass from their fit to WMAP7+SN+BAO data set. We conclude that a combination of the SM Higgs mass measurement by the LHC and accurate determination by the PLANCK satellite of the spectral index of curvature perturbations and tensor-to-scalar ratio will enable to distinguish among these models. We also show that the consistency relations of the SM Higgs inflation variants are distinct enough to differentiate among them.
Improved event‐triggered adaptive control of non‐linear uncertain networked systems

Over the past few years, networked control systems (NCSs) have shown rapid progress and have indeed been very popular in terms of research as well as industrial applications. The issue of limited resources has been a fundamental problem in the translation of modern control techniques to NCS design. In order to address the aforesaid design challenge, an improved event-triggered adaptive backstepping control scheme is presented in this study for a class of uncertain NCSs with non-Lipschitz non-linearities under limited resources. Rather than a preselected constant threshold assumption, a well-designed and systematic triggering rule is derived based on the Lyapunov approach in order to satisfy bandwidth limitation and ensure system stability with acceptable transient performance. Relative to existing strategies in the literature, the proposed method leads to a substantially reduced number of transmissions with longer inter-event time. Thereby, the proposed algorithm exhibits more efficiency in resource utilisation. Simulation results on a networked control based robotic manipulator system illustrate the efficacy of the proposed adaptive scheme compared to benchmark control algorithms intended for a similar application.
Human Gender and Motion Analysis with Ellipsoid and Logistic Regression Method

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.
Combined training strategy for low-resolution face recognition with limited application-specific data

Application-specific data for certain biometric applications are often not sufficiently available. The authors present a solution for face recognition with limited application-specific data. Existing methods often use a classifier with convolutional neural networks (CNNs) as feature extractors. The CNNs are trained with massive general (i.e. not application specific) data and the classifier is trained with application-specific data. Alternatively, the authors propose a combined training strategy to train the classifier on a balanced mixture of general and application-specific data, such that the recognition performance is maximised. The proposed method largely alleviates the needs for application-specific data. To prove its effectiveness, they apply the proposed method to low-resolution face recognition. Specifically, they use the heterogeneous joint Bayesian (HJB) classifier that is capable of comparing features from the same modality but with different characteristics. To further boost performance, the authors augment the training data by pre-processing it to resemble application-specific data. They conducted extensive experiments on challenging datasets, namely, SCface and COX. The results show that the proposed method improves the true match rate on SCface at a false match rate of 10% by ∼11% and the true match rate on COX at a false match rate of 1% by ∼12%.
Mapping the emission line strengths and kinematics of supernova remnant S147 with extensive LAMOST spectroscopic observations

We present extensive spectroscopic observations of supernova remnant (SNR) S147 collected with the Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST). The spectra were carefully sky-subtracted taking into account the complex filamentary structure of S147. We have utilized all available LAMOST spectra toward S147, including sky and stellar spectra. By measuring the prominent optical emission lines including Hα, [N ii] λλ 6548 and 6584 and [S ii] λλ 6717, 6731, we present maps of radial velocity and line intensity ratio covering the whole nebula of S147 with unprecedented detail. The maps spatially correlate well with the complex filamentary structure of S147. For the central 2° of S147, the radial velocity varies from −100 to 100 km s−1 and has peaks between ∼ 0 and 10 km s−1. The intensity ratios of Hα/[S ii] λλ 6717, 6731, [S ii] λ 6717/λ 6731 and Hα/[N ii] λ 6584 peak at about 0.77, 1.35 and 1.48, respectively, with a scatter of 0.17, 0.19 and 0.37, respectively. The intensity ratios are consistent with the literature values. However, the range of variations of line intensity ratios estimated here, which are representative of the whole nebula, is larger than previously estimated.
Real-time Scalable Video Coding for Surveillance Applications on Embedded Architectures

This thesis begins with a brief history of video surveillance, the migration from analog to digital systems, and presents the components of a modern video surveillance system. Then the focus is on video surveillance systems, where it is motivated why their requirements and observers differ from consumer video distribution systems, and what design constraints a practical surveillance system has to satisfy. From this we derive requirements for the coding system, which allows us to construct a detailed problem statement for this thesis. The outline of the individual chapters is presented in a structured manner to guide the reader throughout this thesis, and for each chapter the contributions and their related publications are listed.
Nonlinear mode decomposition for fluid dynamics

We present a new nonlinear mode decomposition method to visualize the decomposed flow fields, named the mode decomposing convolutional neural network (MD-CNN). The proposed method is applied to a flow around a circular cylinder at $Re_D=100$ as a test case. The flow attributes are mapped into two modes in the latent space and then these two modes are visualized in the physical space. Since the MD-CNNs with nonlinear activation functions show lower reconstruction errors than the proper orthogonal decomposition (POD), the nonlinearity contained in the activation function is considered the key to improve the capability of the model. It is found by applying POD to each field decomposed using the MD-CNN with hyperbolic tangent activation that a single nonlinear MD-CNN mode contains multiple orthogonal bases, in contrast to the linear methods, i.e., POD and MD-CNN with linear activation. The present results suggest a great potential for the nonlinear MD-CNN to be used for feature extraction of flow fields in lower dimension than POD, while retaining interpretable relationships with the conventional POD modes.
A System For High Resolution Ultrasonic Breast Imaging

The formation of high resolution ultrasonic breast images will require the use of two dimensional phased arrays with phase compensation for aberrating tissue layers in both dimensions. An analysis of the characteristies of two-dimensional spatial aberrators and the performance of phase correction algorithms indicates that a large number of phased array elements will be required for high resolution imaging. We have designed and performed simulations of a synthetic receive aperture system of a simple design which can be used to address a large number of phased array channels with a relatively small number of parallel receive channels. INTRODUCTION The potential image quality of ultrasonic imaging systems is not achieved in most clinical applications as a result of the degradation caused by tissueinduced phase errors. These aberrations, caused by spatial variations in the acoustic velocity of tissue, limit the dynamic range and resolution of ultrasonic images and are especially severe in systems using high ultrasonic frequencies and/or large imaging apertures. Several techniques for correcting these errors in phased array imaging systems have been proposed by ourselves (1, 2) and others (3, 4). To date successful clinical implementations using these techniques have not been reported. The efforts thus far have utilized one-dimensional phased arrays.
A General Framework for Comparing Predictions and Marginal Effects across Models

Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects because they are in the natural metric of the dependent variable and they avoid identification problems when comparing regression coefficients across logit and probit models. Despite advances that make it possible to compute marginal effects for almost any model, there is no general method for comparing these effects across models. In this article, the authors provide a general framework for comparing predictions and marginal effects across models using seemingly unrelated estimation to combine estimates from multiple models, which allows tests of the equality of predictions and effects across models. The authors illustrate their method to compare nested models, to compare effects on different dependent or independent variables, to compare results from different samples or groups within one sample, and to assess results from different types of models.
Tightly Arranged Four-Element MIMO Antennas for 5G Mobile Terminals

In this paper, a compact multiple-input multiple-output (MIMO) antenna is presented for 5G mobile terminals. The antenna consists of four tightly arranged elements and three lumped components. The edge-to-edge distance between adjacent elements is only 1 mm. It is found that adding an inductor at the current minimum location or a capacitor at the current maximum location of two elements can significantly reduce the mutual coupling. Based on high-isolated dual-element structures, a four-element MIMO antenna is experimentally analyzed for 3400–3600 MHz band. The size of the antenna is only <inline-formula> <tex-math notation="LaTeX">$40.8 \times 3$ </tex-math></inline-formula> mm<sup>2</sup>. The measured −6 dB bandwidth of the antenna is 135 MHz, with port isolation higher than 11.6 dB. In addition, a vertically placed four-element MIMO antenna is designed with a size of <inline-formula> <tex-math notation="LaTeX">$38.2\times3.2$ </tex-math></inline-formula> mm<sup>2</sup>. No ground clearance is needed between the antenna and the chassis ground.
MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data

Abstract DBSCAN is an important spatial clustering technique that is widely adopted in numerous applications. As the size of datasets is extremely large nowadays, parallel processing of complex data analysis such as DBSCAN becomes indispensable. However, there are three major drawbacks in the existing parallel DBSCAN algorithms. First, they fail to properly balance the load among parallel tasks, especially when data are heavily skewed. Second, the scalability of these algorithms is limited because not all the critical sub-procedures are parallelized. Third, most of them are not primarily designed for shared-nothing environments, which makes them less portable to emerging parallel processing paradigms. In this paper, we present MR-DBSCAN, a scalable DBSCAN algorithm using MapReduce. In our algorithm, all the critical sub-procedures are fully parallelized. As such, there is no performance bottleneck caused by sequential processing. Most importantly, we propose a novel data partitioning method based on computation cost estimation. The objective is to achieve desirable load balancing even in the context of heavily skewed data. Besides, We conduct our evaluation using real large datasets with up to 1.2 billion points. The experiment results well confirm the e ciency and scalability of MR-DBSCAN.
Taxonomy for Identification of Security Issues in Cloud Computing Environments

ABSTRACT The emergence of Cloud Computing and consequential changes to infrastructure and work practices leaves little doubt that most computing platforms will be impacted by increased security concerns. Therefore, within the context of a security framework, issues surrounding the exploitation of hard or soft network elements in a Cloud architecture need to be acknowledged. To be applicable to any Cloud architecture, such a framework needs to play a role within the operational context of Cloud Computing. The framework is presented as a taxonomy of security threats specific to Cloud Computing environments.
Dynamic Simulations of Autoregressive Relationships in R with dynsim

This code snippet introduces the dynsim R package for calculating dynamic simulations with autoregressive time series using the approach laid out in Williams and Whitten (2012). dynsim depicts long-run simulations of dynamic processes for a variety of substantively interesting scenarios, with and without the presence of exogenous shocks. The package includes the dynsim function for calculating the dynamic simulations and provides a function (dynsimGG) for graphically depicting the simulations
A deep learning framework for hyperspectral image classification using spatial pyramid pooling

ABSTRACT In this letter, a new deep learning framework for spectral–spatial classification of hyperspectral images is presented. The proposed framework serves as an engine for merging the spatial and spectral features via suitable deep learning architecture: stacked autoencoders (SAEs) and deep convolutional neural networks (DCNNs) followed by a logistic regression (LR) classifier. In this framework, SAEs is aimed to get useful high-level features for the one-dimensional features which is suitable for the dimension reduction of spectral features, while DCNNs can learn rich features from the training data automatically and has achieved state-of-the-art performance in many image classification databases. Though the DCNNs has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large-scale variance of object. As a result, spatial pyramid pooling (SPP) is introduced into hyperspectral image classification for the first time by pooling the spatial feature maps of the top convolutional layers into a fixed-length feature. Experimental results with widely used hyperspectral data indicate that classifiers built in this deep learning-based framework provide competitive performance.
Phish and Chips: We're with Bill Gates -- We'll Never Fix this Problem without Smart Cards

The link is, of course, not to the bank but to the fraudsters' web site. Once the customer enters their details, the fraudsters whisk them away for their own use: this use generally being to loot the bank account as quickly as possible. If the fraudsters send out 10 million e-mails, and 1 in a 100 of the hapless recipients is a (for example) Citibank customer, and 1 in a 100 of them is fooled by the e-mail, that means that the fraudster could gain access to a hundred Citibank accounts. And they do. In the UK, Lloyds TSB, NatWest and Barclays have all admitted that customer accounts have been accessed and that money has been stolen but none of them would give a figure. APACS say that they think the figure is over a million and still growing [2]. And this stolen money isn't the only cost to the banks, who also have to pay to repair the damage: changing names and passwords, re-issuing cards, reassuring customers and so on. This has already cost some �60m in the UK [3] and, according to research company Gartner, more than a billion dollars in the US last year [4]. According to The Nilson Report, a respected industry newsletter, a list of 200 million e-mail addresses can be purchased for �20 or so and (of that fraction of the 200 million who are actually customers of the "target" institution, in fact around 7% give up data of sufficient value to be used or sold on). No wonder it's on the rise.
Linear-time algorithms for graphs with bounded branchwidth

We present an algorithmic framework (including a single data structure) that is extended into linear-time algorithms to solve several NP-complete graph problems (i.e., INDEPENDENT SET, M AXIMUM CUT, GRAPH COLORING, HAMILTONIAN CYCLE, and DISJOINT PATHS). The linearity is achieved assuming the provision of a branch decomposition of the instance graph. We then modify the framework to create a multithreaded framework that uses the existing problem-specific extensions without any revision. Computational results for the serial and parallel algorithms are provided. In addition, we present a graphical package called JPAD that can display a graph and branch decomposition, show their relationship to each other, and be extended to rim and display the progress and results of algorithms on graphs or on branch decompositions.