Miracle-dz/UniFAR-FTUs huggingface dataset:
Probability density derivation and analysis of SINR in massive MIMO systems with MF beamformer

In massive MIMO systems, the matched filter (MF) beamforming is attractive technique due to its extremely low complexity of implementation compared to those high-complexity decomposition-based beamforming techniques such as zero-forcing, and minimum mean square error. A specific problem in applying these techniques is how to qualify and quantify the relationship between the transmitted signal, channel noise and interference. This paper presents detailed procedure of deriving an approximate formula for probability density function (PDF) of the signal-to-interference-and-noise ratio (SINR) at user terminal when multiple antennas and MF beamformer are used at the base station. It is shown how the derived density function of SINR can be used to calculate the symbol error rate of massive MIMO downlink. It is confirmed by simulation that the derived approximate expression for PDF is consistent with the simulated PDF in medium-scale and large-scale MIMO systems.
Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment

This paper presents a method along with a set of measures for evaluating learned ontologies against gold ontologies. The proposed method transforms the ontology concepts and their properties into a vector space representation to avoid the common string matching of concepts and properties at the lexical layer. The proposed evaluation measures exploit the vector space representation and calculate the similarity of the two ontologies (learned and gold) at the lexical and relational levels. Extensive evaluation experiments are provided, which show that these measures capture accurately the deviations from the gold ontology. The proposed method is tested using the Genia and the Lonely Planet gold ontologies, as well as the ontologies in the benchmark series of the Ontology Alignment Evaluation Initiative.
Best structure-recovering method with dictionary allocation and inconsistent HR-LR mapping

The coefficient of high resolution (HR) block and low resolution (LR) block is assumed to be equal in selecting the corresponding atoms of HR dictionary in previous super-resolution (SR) reconstruction algorithms, which may cause error matching and decrease the accuracy of HR coefficient estimation. A learning method of structural dictionary and mapping relation (LCDMR) is combined to compensate this bias assumption. This method ensures sparse coefficient correlation of homology HR/LR block through the synchronous training of HR/LR edge gradient and texture LBP dictionary pairs as well as the mapping relation of LR-HR sparse coefficient. Moreover, the mapping relation is used to modify the LR coefficient to increase the accuracy of HR coefficient prediction, while the optimization training process of the sparse coefficients reused algorithm is used to reduce training complexity caused by this adding mapping relation item. Experiment results show that, LCDMR enhances similarity between SR and original HR representation, in comparison with sparse coding super-resolution reconstruction (SCSR), LCDMR averagely improves PSNR, MSSIM and FSIM of test images by 0.6453dB, 0.0092 and 0.0082, respectively.
Parenting and young children’s emotional self-regulation in urban Korean families

The present study was designed to examine the relationship of Korean mothers’ psychological resources and discipline style to their children’s emotional self-regulation development. Structural equation modeling was conducted with a sample of 234 Korean mothers of kindergarten-aged children in the Incheon area in South Korea. The results indicated that maternal harshness negatively predicted and inconsistency positively predicted children’s emotional self-regulation development. Second, mothers’ psychological resources (i.e. parenting self-efficacy, enjoyment of parenting) predicted less harshness, inconsistency, and permissiveness and more responsiveness. Third, the direct path from enjoyment of parenting to permissiveness in parenting behaviors was moderated by the child’s gender. The results indicated that certain parenting behaviors, particularly permissiveness and inconsistency, may have culturally distinctive effects on children’s development in Korea.
Exploiting Common Object Usage in Test Case Generation

Generated test cases are good at systematically exploring paths and conditions in software. However, generated test cases often do not make sense. We adapt test case generation to follow patterns of common object usage, as mined from code examples. Our experiments show that generated tests thus (a) reuse familiar usage patterns, making them easier to understand and (b) focus on common usage, thus respecting implicit preconditions and avoiding meaningless tests.
Multiband distributed sensing based on compressed measurements via two AICs implementations

We develop a distributed multiband spectrum sensing detector for cognitive radios based on compressed measurements that does not rely on signal reconstruction. A fusion centre collects the measurements from different sensing nodes and then makes a sensing decision based on a simplified maximum likelihood criterion which is valid for both analog to information implemented in the paper (MWC and NUP) and does not require prior signal information. Simulation results for probability of erroneous detection and ROC curves show that the performance of the proposed detector is good. Plus, it has a low computational complexity.
Visual Tracking Based on Dynamic Coupled Conditional Random Field Model

This paper proposes a novel approach to visual tracking of moving objects based on the dynamic coupled conditional random field (DcCRF) model. The principal idea is to integrate a variety of relevant knowledge about object tracking into a unified dynamic probabilistic framework, which is called the DcCRF model in this paper. Under this framework, the proposed approach integrates spatiotemporal contextual information of motion and appearance, as well as the compatibility between the foreground label and object label. An approximate inference algorithm, i.e., loopy belief propagation, is adopted to conduct the inference. Meanwhile, the background model is adaptively updated to deal with gradual background changes. Experimental results show that the proposed approach can accurately track moving objects (with or without occlusions) in monocular video sequences and outperforms some state-of-the-art methods in tracking and segmentation accuracy.
Toward a Perceptually Based Metric for BRDF Modeling

Measured materials are used in computer graphics to enhance the realism of synthetic images. They are often approximated with analytical models to improve storage efficiency and allow for importance sampling. However, the error metrics used in the optimization procedure do not have a perceptual basis and the obtained results do not always correspond to the best visual match. In this paper we present a first steps towards creating a perceptually-based metric for BRDF modeling. First, a set of measured materials were approximated with different error metrics and analytical BRDF models. Next, a psychophysical study was performed to compare the visual fidelity obtained using different error metrics and models. The results of this study show that the cube root metric leads to a better perceptual approximation than other RMS based metrics, independently of the analytical BRDF model used. More benefit of using the cube root metric compared to the RMS based metrics is obtained for sharp specular lobes, and as the specular lobe broadens the benefit of using the cube root metric decreases. The use of the cube root error metric will improve the visual fidelity of renderings made using BRDF approximations and expand the usage of measured materials in computer graphics.
BSA: A Hybrid Bees’ Simulated Annealing Algorithm To Solve Optimization & NP-Complete Problems

Swarm-based algorithms such as Bees Algorithm BA have proven to be very powerful computational techniques due to their search capabilities. Other methods which are useful in diverse application areas are simulated annealing, evolution strategies etc. The searching ability of these algorithms can be improved by properly blending their characteristic features. In this paper an attempt is made to intermix the search properties of BA and SA, in order to develop a hybrid algorithm which is equally applicable and has a better searching ability and power to reach a near optimal solution. This leads to the development of a fast method to solve complicated types of optimization and NP-complete problems.
Determinants of narrative risk disclosures in UK interim reports

Purpose - The purpose of this paper is to contribute to the existing disclosure literature by examining the determinants of narrative risk information in the interim reports for a sample of UK non-financial companies. Design/methodology/approach - This study uses the manual content analysis to measure the level of risk information in interim report narrative sections prepared by 72 UK companies. It also uses the ordinary least squares regression analysis to examine the impact of firm-specific characteristics and corporate governance mechanisms on narrative risk disclosures. Findings - The empirical analysis shows that large firms are more likely to disclose more risk information in the narrative sections of interim reports. In addition, the analysis shows that industry activity type is positively associated with levels of narrative risk disclosure in interim reports. Finally, the analysis shows statistically insignificant impact of other firm-specific characteristics (liquidity, gearing, profitability, and cross-listing) and corporate governance mechanisms on narrative risk disclosure. Practical implications - The study's findings have practical implications. It informs investors about the characteristics of UK companies that disclose risk information in their interim reports. For example, the findings show that narrative risk disclosures are affected by firm size and industry type rather than firms' risk levels (e.g. financing risk measured by the gearing ratio or liquidity risk measured by lower liquidity ratios). Practical implications for managers from these findings are that, in order to keep investors satisfied, companies with high levels of financing and liquidity risks should look at investors' demands for risk disclosure. This will help investors when making their investment decisions. Originality/value - The determinants of narrative risk disclosure in interim reports have not been explored so clearly in prior research and, therefore, this paper is the first of its kind to examine this research issue for a sample of UK companies.
Linguistic analysis of bug report titles with respect to the dimension of bug importance

We perform linguistic analysis of bug-report titles obtained from the publicly available Bugzilla defect tracking tool for the open-source Firefox browser (Mozilla project) and present the results of our analysis. Our motivation is to gain insights on how people describe software defects and do a feasibility study on the possibility of building a predictive model (a classifier) for categorizing bug report based only on the titles to one of the predefined severity levels (bug importance). We observed that in general bug titles do not contain enough information for automatically predicting its importance with high accuracy. However, we noticed that two of the bug importance categories such as critical and enhancement have characteristics or features in the title that can be exploited to assign the correct severity level. We perform statistical analysis on part-of-speech, word frequency and distribution across various severity levels.
Feature Initialization for Bearing-Only Visual SLAM Using Triangulation and the Unscented Transform

We introduce the direct linear triangulation (DLT) as a general technique for delayed feature initialization for bearing-only visual simultaneous localization and mapping (SLAM). Visual features are tracked over multiple frames, and triangulation is performed to recover a three-dimensional landmark position. The unscented transform is used to estimate the landmark covariance matrix. Our previous work in this field is extended with the novel usage of the DLT, as well incorporating observation information from the current SLAM map into the initialization process, resulting in more accurate landmark estimation. Our technique is simple and efficient, requiring no lengthy Jacobian calculations. We present results demonstrating the technique in a bearing-only SLAM system with a mobile robot.
Distorted sparse signal estimation from distributed sign measurements

A novel algorithm called Cooperative Binary Iterative Hard Thresholding (CB-IHT) based on distributed 1-bit compressive sensing is proposed in this paper. Taking advantage of the correlated nature of distributed signal processing, the proposed algorithm is aimed to fight against the error floor in the estimation of distorted sparse signal, with an array of agents recovering the target signal cooperatively. The principles of convex optimization, consistent reconstruction and greedy pursuit algorithm are combined in the algorithm design. With two joint sparsity models representing distortion of equivalent parallel AWGN channels and parallel fading channels separately, the algorithm is performed through extensive simulations, which show that with severe distortion and large bit-budget, estimation accuracy can be improved by simply increasing the array scale.
Towards interdomain transit traffic reduction in peer-assisted content delivery networks

Transit traffic exchanged with transit providers costs more for network providers compared to intra-domain traffic or traffic exchanged over peering links. We have measured and analyzed the peer distribution in BitTorrent, which is one of peer-assisted content delivery networks (CDNs). From the peer distribution, we show the potential of the high-cost transit traffic reduction. We then propose a peer selection preference which takes into account the economical relationships among Autonomous Systems (ASes) in peer-assisted CDNs to reduce the high-cost transit traffic. Since most commercial Internet service providers cannot disclose the relationships due to their commercial contract, we employ degree-based heuristics for inferring the relationships; degree can be approximated from publicly available BGP routing tables. We show that the peer selection method utilizing the proposed preference can reduce interdomain transit traffic exchanged with provider ASes by trace-driven computer simulation. The significance of this paper are 1) we show the potential of the transit traffic reduction from peer distribution analysis, and 2) the peer selection method with the proposed preference appropriately reduces the high-cost transit traffic with degree-based AS relationships inference heuristics even though there is no public AS relationships information.
USING REMOTE SENSING IMAGE AND MAP IMAGE

Remote sensing satellite image, topographical map and vegetation map images are helpful education material for environmental studies of college or high school students. Students can research actual information of environment by analysis of these images. However, it was difficult to analyze the images by a conventional programming method. To solve this problem, we have developed new education materials that include CSV formatted image data set and image analysis programming module using Visual Basic for Applications (VBA) scripts of Microsoft-Excel. The spreadsheet is a typical end-user programming tool, and appropriate to the education purpose. The spreadsheet programming is effective in the raster image analysis for students because of the dataflow graphs mapping method. We applied the spreadsheet programming to remote sensing image analysis with topographical map and vegetation map. In order to achieve for the applications effectively, we have developed worksheet image processing system and imagery data set that can access by Web browser.
Formulation and algorithmization of the interleaved vehicle routing problem

The problem of routing between two types of objects is considered. Two variants of the problem statement are considered: with a single point of collection and without it. An important aspect of the routes is the mandatory alternation of different types of objects. A mathematical model of the problem with boolean variables is proposed for each statement. A meaningful statement can be represented as the task of collecting haystacks in trucks afield. Two algorithms are developed to solve this problem. The first one is based on the “greedy” strategies, and the second one is based on the approach of Ant Colony Optimization (ACO) algorithms. To configure the parameters of the second algorithm it is carried out the computational experiment. The results of the experiment are provided. In conclusion, the main results of the work are presented.
3D reconstruction of tongue surface based on photometric stereo

In the objective research field of traditional Chinese medicine (TCM) tongue diagnosis, 2D tongue image is regarded as the research object for feature extraction and analysis in most current methods. However, 2D tongue image always can not visually express detail information of tongue 3D surface, such as lingual papilla, prick and fissure of tongue. It brings certain limitations to perceive real tongue surface. It is limited for the doctor to give accurate diagnosis. To solve this problem, a method to reconstruct the 3D surface of tongue based on photometric stereo is proposed. First, the surface normals and texture reflectance are obtained using photometric stereo technology in this paper, then calculate the depth values of tongue surface, and generate 3D depth map. It has been proved that the method is viable through lots of experiments and doctors may observe the 3D information of tongue surface from multiple perspectives. Detail information of tongue surface also may be expressed quantitatively and visually.
Web-based Learning Support based on Implicit Feedback

In many applications concerning adaptive presentation of documents (be it web-based learning or open web in general), identification of interesting fragments is useful feature for various tasks like rewriting the content or recommending to the user where to focus. We presented a method using several implicit interest indicators with emphasis on read wear and gaze tracking to identify such fragments and implemented it in the widely used web browser with use of common webcam. Also, several scenarios employing identified fragments, specifically in context of web-based learning, are presented.
Flexible Self-Aligned Amorphous InGaZnO Thin-Film Transistors With Submicrometer Channel Length and a Transit Frequency of 135 MHz

Flexible large area electronics promise to enable new devices such as rollable displays and electronic skins. Radio frequency (RF) applications demand circuits operating in the megahertz regime, which is hard to achieve for electronics fabricated on amorphous and temperature sensitive plastic substrates. Here, we present self-aligned amorphous indium-gallium-zinc oxide-based thin-film transistors (TFTs) fabricated on free-standing plastic foil using fabrication temperatures . Self-alignment by backside illumination between gate and source/drain electrodes was used to realize flexible transistors with a channel length of 0.5 μm and reduced parasitic capacities. The flexible TFTs exhibit a transit frequency of 135 MHz when operated at 2 V. The device performance is maintained when the TFTs are bent to a tensile radius of 3.5 mm, which makes this technology suitable for flexible RFID tags and AM radios.
Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting-lyy8 the Midas Human Performance Model

A unique software tool for conducting human factors analyses of complex human-machine systems has been developed at NASA Ames Research Center. Called the Man-Machine Integration Design and Analysis System (MIDAS), this simulation system contains models of human performance that can be used to evaluate candidate procedures, controls, and displays prior to more expensive and time consuming hardware simulators and human subject experiments. While this tool has been successfully applied to research issues in several domains, particularly in aeronautics, a desire to expand its functionality and its ease of use has led to the construction of a new object-oriented system. This new version of MIDAS contains a substantially modified human performance model, one that is aimed at being more consistent with empirical data on human behavior and more natural for designers to apply to the analyses of complex new designs. This paper offers a summary of this new human performance model, together with justifications for some of its main components, and indicates plans for its subsequent verification and validation.
A robust multihop underwater network for sensing applications — Implementation and experimental evaluation

Various novel applications of underwater acoustic sensor networks have emerged or been proposed in recent years. In this paper, we present a complete system implementation of a robust multihop underwater network for sensing applications. At the core of the system is a robust data delivery scheme which uses opportunistic automatic repeat request (ARQ) with bidirectional overhearing. We demonstrate the modular and hardware-independent nature of our implementation by porting and deploying the software architecture and underwater network stack onto hydroacoustic modems from different vendors. We integrate off-the-shelf GPS receivers and temperature sensors and evaluate the performance of the data delivery scheme by transmitting actual sensor data over two hops in shallow underwater environments in Singapore. Received sensor data are then visualized using Google Earth as they arrive at the sink node.
WHAT IS APPROPRIATE RESOLUTION FOR SIMULATIONS OF THUNDERSTORMS ? AN ANSWER FROM A TURBULENCE PERSPECTIVE

Continuing advances in computer resources now allow researchers to simulate thunderstorms with very high resolution. For example, it is possible to simulate deep moist convection (including an entire mesoscale convective system) with grid spacing on the order of 100 m. It is natural to ask whether such high resolution is necessary, and at what point can simulations of convective processes be considered “well resolved”. Examination of the subgrid-scale turbulence parameterizations used in cloud models provides some guidance in the debate over adequate resolution. Although not widely acknowledged, the subgrid turbulence closures in most cloud models are taken directly from large eddy simulation (LES) models. Since the subgrid turbulence schemes from LES have been included in cloud models for decades, it is important to review whether these assumptions are valid for models with the commonly used grid spacing of order 1km.