Miracle-dz/UniFAR-FTUs huggingface dataset:
Robotic Indoor Path Planning Using Dijkstra's Algorithm with Multi-Layer Dictionaries

Dijkstra's algorithm is a classic algorithm for finding the shortest path between two points due to its optimisation capability. The adjacency matrix is the naive storage structure of the algorithm. This storage structure has limited the use of the algorithm as it expands large storage space. A multi-layer dictionary is proposed in this work to enhance the storage structure. Previously, the algorithm was used to optimise single parameter (such as distance, time and fuel) for movement between two places. The path computed using the classic Dijkstra's algorithm is the shortest; however, it may not be the most feasible. The algorithm needs to optimise other factors such as energy consumption and the degree of turns a robot need to take. Junction degree of difficulty function is introduced to further improve the output. It is defined as the degree of difficulty of the path between two points in an open environment. The experimental result shows that the proposed path planning method produced the most optimal path between two points when applied to a map of any indoor terrain.
Reprocessing data to form QALYs.

OBJECTIVES--To determine whether reprocessing data from published sources into quality adjusted life years (QALYs), as recommended in The QALY Toolkit, is a useful method of helping purchasing authorities to determine the most cost effective pattern of care to buy for their populations. SETTING--United Kingdom. DESIGN--The method was tested for six elective surgical conditions; data from published studies were reprocessed into the Rosser index, to obtain values for change in quality of life. These were then used to form QALYs. A small validation exercise was carried out. MAIN OUTCOME MEASURES--QALYs formed from the Rosser index. RESULTS--Published data could not be found for three interventions (cataract surgery, inguinal hernia repair, varicose vein surgery). For the remainder (prostatectomy, hip replacement, and knee replacement) data were found which could be reprocessed to form QALYs, though it was often hard to compare data from different studies and many assumptions had to be made. CONCLUSION--The value of QALY results obtained by this method is questionable, given the large number of assumptions which had to be made. For many interventions published data are unlikely to be available.
The fertilization ability and developmental competence of bovine oocytes grown in vitro

In vitro growth culture systems for oocytes are being developed in several mammalian species. In these growth culture systems, in vitro grown oocytes usually have lower blastocyst formation than in vivo grown oocytes after in vitro fertilization. Furthermore, there have been a few reports that investigated the fertilization ability of in vitro grown oocytes in large animals. The purpose of this study was to investigate the fertilization process and developmental competence of bovine oocytes grown in vitro. Oocyte-granulosa cell complexes collected from bovine early antral follicles (0.4−0.7 mm in diameter) were cultured for growth with 17β-estradiol and androstenedione for 14 days and matured in vitro. These oocytes were then inseminated for 6 or 12 h, and further cultured for development up to 8 days in vitro. After growth culture, oocytes grew from 95 µm to around 120 µm and acquired maturation competence (79%). Although fertilization rates of in vitro grown oocytes were low after 6 h of insemination, 34% of in vitro grown oocytes fertilized normally after 12 h of insemination, having two polar bodies and two pronuclei with a sperm tail, and 22% of these oocytes developed into blastocysts after 8 days of culture. The fertilization and blastocyst formation rates were similar to those of in vivo grown oocytes. In addition, blastocyst cell numbers were also similar between in vitro and in vivo grown oocytes. In conclusion, in vitro grown bovine oocytes are similar to in vivo grown oocytes in fertilization ability and can develop into blastocysts.
Exemplar-based Learning in Adaptive Optical Music Recognition System

The learning process of an adaptive optical music recognition system (AOMR) is described here. By combining k-nearest neighbor classifier and genetic algorithm, the system can learn to recognize new music symbols and handwritten music notations, and it also continually improves the accuracy in recognizing these objects. Given the wide range of music notation styles, these are essential characteristics of a music recognizer.
Boolean Evaluation with a Pairing and Unpairing Function

A pairing function is a bijection f : N × N → N. Its inverse is called an em unpairing function. We show that boolean logic on bit vector variables can be expressed as compositions of pairing/unpairing operations which can emulate boolean evaluation of ordered binary decision trees (OBDTs) of a canonical form. Applications to enumeration and random generation of OBDTs and a generalization to Multi-Terminal Ordered OBDTs (MTOBDT) are also described. The paper is organized as a literate Haskell program (code available at http://logic.csci.unt.edu/tarau/research/2012/hOBDT.hs).
Discriminative DCT: An Efficient and Accurate Approach for Off-Line Signature Verification

In this paper, we proposed to combine the transform based approach with dimensionality reduction technique for off-line signature verification. The proposed approach has four major phases: Preprocessing, Feature extraction, Feature reduction and Classification. In the feature extraction phase, Discrete Cosine Transform (DCT) is employed on the signature image to obtain the upper-left corner block of size mX n as a representative feature vector. These features are subjected to Linear Discriminant Analysis (LDA) for further reduction and representing the signature with optimal set of features. Thus obtained features from all the samples in the dataset form the knowledge base. The Support Vector Machine (SVM), a bilinear classifier is used for classification and the performance is measured through FAR/FRR metric. Experiments have been conducted on standard signature datasets namely CEDAR and GPDS-160, and MUKOS, a regional language (Kannada) dataset. The comparative study is also provided with the well known approaches to exhibit the performance of the proposed approach.
Improving convergence of Belief Propagation decoding

The decoding of Low-Density Parity-Check codes by the Belief Propagation (BP) algorithm is revisited. We check the iterative algorithm for its convergence to a codeword (termination), we run Monte Carlo simulations to find the probability distribution function of the termination time, n_it. Tested on an example [155, 64, 20] code, this termination curve shows a maximum and an extended algebraic tail at the highest values of n_it. Aiming to reduce the tail of the termination curve we consider a family of iterative algorithms modifying the standard BP by means of a simple relaxation. The relaxation parameter controls the convergence of the modified BP algorithm to a minimum of the Bethe free energy. The improvement is experimentally demonstrated for Additive-White-Gaussian-Noise channel in some range of the signal-to-noise ratios. We also discuss the trade-off between the relaxation parameter of the improved iterative scheme and the number of iterations.
Controlling shop ̄ oor operations in a multi-family , multi-cell manufacturing environment through constant workin-process

This paper discusses pertinent issues in applying CONstant Work-In-Process (CONWIP) principles to control shop ̄oor operations in a manufacturing environment characterized by several product families processed along dierent routes in several production cells. The approach we take is to simultaneously answer two major questions: (1) what is the best WIP level? and (2) how to arrange the backlog list for a given system? The problem is posed as a mathematical programming model and solved via a simulated annealing heuristic. We design an experiment that captures essential elements of the systems under investigation. We then execute an extensive simulation to evaluate the eectiveness of various control schemes in a multi-cell, multi-family production environment. Speci®cally, we compare two variants of CONWIP control, one where containers are restricted to stay within given cells all the time and the other where containers are allowed to move through the entire system. We demonstrate the superiority of the latter in all the simulated scenarios.
A Channel Differential EZW Coding Scheme for EEG Data Compression

In this paper, a method is proposed to compress multi-channel electroencephalographic (EEG) signals in a scalable fashion. Correlation between EEG channels is exploited through clustering using a k-means method. Representative channels for each of the clusters are encoded individually while other channels are encoded differentially, i.e., with respect to their respective cluster representatives. The compression is performed using the embedded zero-tree wavelet encoding adapted to 1-D signals. Simulations show that the scalable features of the scheme lead to a flexible quality/rate tradeoff, without requiring detailed EEG signal modeling.
Thermal analysis of multi-MW two-level generator side converters with reduced common-mode-voltage modulation methods for wind turbines

Thermal performance influences both the cost of cooling system and the reliability of the power converter. Moreover, common-mode voltage in a generator drive may damage the bearing of the generator and also cause failure in a longer term. Therefore, both the thermal performance and common-mode voltage of the converter should be taken into account during the selection process of the modulation strategy. In this paper, two modulation strategies developed to reduce the common-mode-voltage reduced are compared based on the generator side converter of a 3 MW wind power system using the conventional-60° discontinuous PWM, where the common-mode voltage, power losses and thermal performance are all taken into account. The junction temperature and temperature of the power devices in the converter are obtained based on the thermal model by simulations and compared between the two modulation strategies.
Parametric Evaluation of Video Motion Tracking Data Sets

因为在哪个录像运动需要被追踪，环境广泛地基于申请被改变并且在追踪的系统的设计和表演上提出几限制，录像追踪是一个复杂问题。 没有彻底地分析环境强加的这些不同限制的效果，被用来评估并且比较录像运动追踪算法的当前的数据集使用一项累积性能措施。 但是它需要作为参数分析这些限制。 这份报纸的目的是识别这些参数并且定义量的措施让这些参数为运动追踪比较录像数据集。
Unbounded

Our aim is to enable software to take full advantage of the capabilities of emerging microprocessor designs without modifying the compiler. Towards this end, we propose a new approach to code generation and optimization. Our approach uses an SMT solver in a novel way to generate efficient code for modern architectures and guarantee that the generated code correctly implements the source code. The distinguishing characteristic of our approach is that the size of the constraints does not depend on the candidate sequence of instructions. To study the feasibility of our approach, we implemented a preliminary prototype, which takes as input LLVM IR code and uses Z3 SMT solver to generate ARMv7-A assembly. The prototype handles arbitrary loop-free code (not only basic blocks) as input and output. We applied it to small but tricky examples used as standard benchmarks for other superoptimization and synthesis tools. We are encouraged to see that Z3 successfully solved complex constraints that arise from our approach. This work paves the way to employing recent advances in SMT solvers and has a potential to advance SMT solvers further by providing a new category of challenging benchmarks that come from an industrial application domain. CCS Concepts • Software and its engineering→Compilers; • Theory of computation→ Logic and verification;
Can Social Screencasting Help Developers Learn New Tools?

An effective way to learn about software development tools is by directly observing peers' workflows. However, these tool knowledge transfer events happen infrequently because developers must be both colocated and available. We explore an online social screen casting system that removes the dependencies of colocation and availability while maintaining the beneficial tool knowledge transfer of peer observation. Our results from a formative study indicate these online observations happen more frequently than in-person observations, but their effects are only temporary. We conclude that while peer observation facilitates online knowledge transfer, it is not the only component -- other social factors may be involved.
Tracing Kernel Activity in Sunos 4.0

This paper describes a software tool for tracing kernel activity within the SunOS (cid:0)(cid:1)(cid:2) operating system(cid:1) The tool is designed as a pseudo(cid:3)controller with separate pseudo(cid:3)devices to capture event streams from various parts of the operating system(cid:1) A high resolution interval timer is attached to the target system(cid:4)s SCSI port in order to provide hardware support for timestamps(cid:1) Use of the timer also allows measurement of CPU usage by the instrumentation system(cid:1)
Logistic regression analysis with multidimensional random effects : A comparison of three approaches

This paper investigates the performance of three types of random coefficients logistic regression models; that is, models using parametric, semi-parametric, and nonparametric specifications of the distribution of the random effects. Whereas earlier studies focussed on models with a single random effect, here we look at models with multidimensional random effects (intercepts and slopes). Moreover, also the performance of a semi-parametric approach – using mixture regression models with number of latent classes is selected using the BIC – is investigated. One of the main conclusions of our study is that the good results obtained with the nonparametric approach in the unidimensional case do not generalize to the multidimensional case. Parametric and semi-parametric approaches are much better in terms of bias and relative efficiency than the nonparametric approach. For the fixed-effects estimation, a parametric approach is the preferred method when the underlying assumption of the parametric model holds. In other situations, the semi-parametric approach is the best choice.
Monitoring Recovery in Collegiate Strength and Conditioning

ii Chapter One: Introduction Statement of Problem . 1 Purpose of Study . 1 Significance of Study . 1 Limitations & Delimitations . 2 Basic Assumptions . 3 Definition of Terms . 3 Chapter Two: Review of Literature Importance of Periodization of Training & Impact of Monitoring Training . 8 Perceived Recovery Questionnaire . 9 Rate of Perceived Exertion . 14 Lifestyle Tracking (Nutrition & Sleep) . 14 Vertical Jump . 16 Heart Rate Variability . 17 Submaximal Heart Rate . 19 Chapter Three: Methodology Research Design . 21 Research Procedure . 21 Treatment of Research . 23 Chapter Four: Results . 24 Chapter Five: Discussion & Conclusion Discussion . 25 Practical Application . 28 Conclusion . 29 Bibliography: . 31 Appendix: Survey Outline . 36 Results Report . 38 IRB Checklist . 56 Qualtics Copy of Survey . 63 Email Sent for Survey . 69
Measurement Based Interference Models for Wireless Scheduling Algorithms

Modeling physical layer behavior of packet reception in the presence of interference is central to achieving efficient spectrum use in wireless sensor networks via spatial reuse. On one hand, analytic and simulations research has largely relied on assumptions of geometric path loss and isotropic transmission which have not been borne out in experiments. Experimental research, on the other hand, has not adopted theoretical models and instead focused on measuring the reality on the ground. We propose a new framework for wireless algorithms. First, distance-based path loss is replaced by an arbitrary gain matrix, typically obtained by measurements of received signal strength (RSS). This allows for the modeling of complex environments, e.g., with obstacles and walls. Second, a new parameter ζ indicates how close the gain matrix is to a distance metric, effectively measuring the complexity of the environment. We experimentally validate our framework on two indoors testbeds with 20 and 60 motes. The results validate the basic properties of the model, the predictive ability of packet reception, dominance over distance-based models, and the sensitivity of ζ to the nature of the environment. Theoretically, we show that all known SINR scheduling algorithms that work in general metric spaces carry over and achieve equivalent performance guarantees in the new model. The conclusions suggest that wireless theory can finally be grounded in experimental practice.
Non-linear Learning for Statistical Machine Translation

Modern statistical machine translation (SMT) systems usually use a linear combination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit model on the current data. In this paper, we propose a non-linear modeling for the quality of translation hypotheses based on neural networks, which allows more complex interaction between features. A learning framework is presented for training the non-linear models. We also discuss possible heuristics in designing the network structure which may improve the non-linear learning performance. Experimental results show that with the basic features of a hierarchical phrase-based machine translation system, our method produce translations that are better than a linear model.
On the Recursive Teaching Dimension of VC Classes

The recursive teaching dimension (RTD) of a concept class $C \subseteq \{0, 1\}^n$, introduced by Zilles et al. [ZLHZ11], is a complexity parameter measured by the worst-case number of labeled examples needed to learn any target concept of $C$ in the recursive teaching model. In this paper, we study the quantitative relation between RTD and the well-known learning complexity measure VC dimension (VCD), and improve the best known upper and (worst-case) lower bounds on the recursive teaching dimension with respect to the VC dimension. Given a concept class $C \subseteq \{0, 1\}^n$ with $VCD(C) = d$, we first show that $RTD(C)$ is at most $d 2^{d+1}$. This is the first upper bound for $RTD(C)$ that depends only on $VCD(C)$, independent of the size of the concept class $|C|$ and its~domain size $n$. Before our work, the best known upper bound for $RTD(C)$ is $O(d 2^d \log \log |C|)$, obtained by Moran et al. [MSWY15]. We remove the $\log \log |C|$ factor. We also improve the lower bound on the worst-case ratio of $RTD(C)$ to $VCD(C)$. We present a family of classes $\{ C_k \}_{k \ge 1}$ with $VCD(C_k) = 3k$ and $RTD(C_k)=5k$, which implies that the ratio of $RTD(C)$ to $VCD(C)$ in the worst case can be as large as $5/3$. Before our work, the largest ratio known was $3/2$ as obtained by Kuhlmann [Kuh99]. Since then, no finite concept class $C$ has been known to satisfy $RTD(C) > (3/2) VCD(C)$.
Sparse logistic regression with supervised selectivity for predictors selection in credit scoring

Credit scoring is a fundamental and one of the most complex tasks that financial institutions have to deal with. Commonly this problem is considered as default probability prediction for the lenders counterparts. Knowing predictors that significantly contribute to default prediction has recently emerged as a crucial issue of credit risk analysis. Default prediction and default predictor selection are two related issues, but many existing approaches address them separately. In this paper a unified procedure is proposed that is based on the regularization approach with logistic regression as an underlying model, which simultaneously selects the default predictors and optimizes all the parameters within the model. We give the strong probabilistic statement of shrinkage criterion for features selection. The proposed regularization do not corrupt the relevant predictors, can select correlated predictors into model, gives stable subset of relevant features. Experimental results show that the proposed framework is competitive on both artificial data and publicly available data sets.
Regular-look eyeglasses can monitor chewing

We monitor chewing using 3D-printed eyeglasses with surface Electromyography (EMG) and skull vibration sensors integrated at the eyeglasses frame. We demonstrate the feasibility of recording chewing of different foods using the prototype. Users can explore the effect on EMG and vibration when chewing different food samples, e.g. apples, potato chips, and the system's ability to identify food categories. The prototype showed promising results detecting chewing and compensating for non-chewing activities.
Symmetric caging formation for convex polygon object transportation by multiple mobile robots

In this paper, the problem of object caging and transporting is considered by multiple mobile robots using object closure technology. With the consideration of minimizing the number of the robots and decreasing the rotation of the object, the proper points are calculated and assigned to the multiple mobile robots to form the symmetric caging formation. The caging formation guarantees that all of the Euclidean distances between two adjacent robots are smaller than the minimal width of the polygon object and the object cannot escape. Finally, the simulation results represent multiple mobile robots which cage and transport the polygon object to the goal position.
Geared Robot Manipulators with a Jointed Unit: Topological Synthesis and Its Application

An efficient and systematic methodology for the topological synthesis of geared robot manipulators (GRMs) with a jointed unit is developed. The approach is based on the idea that the kinematic structure of a GRM with a jointed unit is composed of an equivalent open-loop chain (EOLC), disjointed mechanical transmission lines (MTLs), and jointed MTLs. It is shown that the jointed MTLs can be decomposed as a two-DoF nonfractionated jointed unit and several one-DoF disjointed units, connected in series. The characteristics of the jointed unit are laid out, and admissible jointed units are enumerated from the existing atlas accordingly. A systematic methodology is developed to enumerate admissible GRMs with a jointed unit of preferred DoF and number of links. It is also shown that the concept of jointed MTLs leads to the design of GRMs with decoupled joint motion by proper choices of gear ratios in the drive train.
Fused Potts Models

Generalizing the mapping between the Potts model with nearest neighbour interaction and the six vertex model, we build a family of ”fused Potts models” related to the spin k/2 Uqsu(2) invariant vertex model and quantum spin chain. These Potts models have still variables taking values 1, . , Q ( p Q = q + q −1 ) but they have a set of complicated multi spin interactions. The general technique to compute these interactions, the resulting lattice geometry, symmetries, and the detailed examples of k = 2,3 are given. For Q > 4 spontaneous magnetizations are computed on the integrable first order phase transition line, generalizing Baxter’s results for k = 1. For Q � 4, we discuss the full phase diagram of the spin one (k = 2) anisotropic and Uqsu(2) invariant quantum spin chain (it reduces in the limit Q = 4 (q = 1) to the much studied phase diagram of the isotropic spin one quantum spin chain). Several critical lines and massless phases are exhibited. The appropriate generalization of the Valence Bond State method of Affleck et al. is worked out.