Miracle-dz/UniFAR-FTUs huggingface dataset:
Students’ perceptions of their first accounting class: implications for instructors

ABSTRACT The purpose of this action research study is to learn directly from undergraduate students, through focus groups, about their experiences in their first accounting class, especially about the students’ knowledge and practice of critical thinking and about which classroom experiences engaged their attention and enhanced learning. The findings show that students want to understand how the first accounting course connects to the business world and to their other classes. Participants were unsure about when and how they practiced critical thinking; for improved learning outcomes, instructors need to keep critical thinking front and center. Students also need instruction in how to study accounting and how to utilize effectively the resources provided in their textbooks and as supplemental tools (e.g. integrated accounting software). Accounting professors must adapt their teaching methods to help students to meet professional demands, such as critical thinking skills and ability to handle a complex global business environment.
Aster * x : Load-Balancing Web Traffic over Wide-Area Networks

Effective load-balancing systems for services hosted in unstructured networks need to take into account both the congestion of the network and the load on the servers. In this whitepaper, we propose a comprehensive load-balancing solution that works well for such networks. The system we propose, called Aster*x, tries to minimize response time by controlling the load on the network and the servers using customized flow routing. We hope to use the GENI infrastructure to understand how Aster*x works within a combined network and computation slice spanning multiple campuses across the country; the GENI infrastructure is well-suited for this purpose because of the distributed and realistic nature of the computing and networking substrate. Besides the base behavior, we plan to investigate the effect of dynamically adding and removing computing resources to the system, increasing the request arrival rate, altering the CPU or network load of each request, and changing load-balancing algorithms.
Landmark Recognition Using Machine Learning

Introduction As smartphones and mobile data become more prevalent in modern society, the possibilities for them to interact with the physical world also grow exponentially. Technologies such as Oculus Rift and Google Glass are attempting to bridge the gap between the virtual and the physical, and as enhancements in computer speed and image processing are made, the concept of Augmented Reality (AR) becomes more tangible. However, one difficulty with AR is the sheer complexity of image processing and feature recognition. A successful AR system must be able to distinguish among a large number of landmarks and should be able to adapt to the existence of new landmarks. Because of the adaptability requirement, AR algorithms naturally lend themselves to using machine learning. As such, the focus of this project is to develop, refine and document a machine learning algorithm that can distinguish landmarks from images using a database of known landmarks.
Anomaly Detection Using Unsupervised Profiling Method in Time Series Data

The anomaly detection problem has important applications in the field of fraud detection, network robustness analysis and intrusion detection. This paper is concerned with the problem of detecting anomalies in time series data using Peer Group Analysis (PGA), which is an unsupervised technique. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects and then to detect any differences in evolution between the expected pattern and the target. The experimental results demonstrate that the method is able to flag anomalous records effectively.
Improve Indirect Branch Prediction with Private Cache in Dynamic Binary Translation

Dynamic binary translation (DBT) is a just-in-time technology of compiler. It is used to support the binary translation, dynamic optimization, program instrumentation, virtualization and so on. How to improve performance is the core research issues about dynamic binary translation technology. Many researches show that how to handle the indirect branch instruction has a key impact about the performance of DBT. Some methods are proposed to handle the indirect branch. But there are some limitations for these methods. This paper analyzes the locality of target address of the indirect branch. The experiment indicates that there is a well locality about the distribution of target address. To make use of this feature, we propose a novel algorithm to quickly predict the target address for indirect branch. For a given indirect branch, we add a private buffer to cache its all previous target address. When translator meets an indirect branch, it predicts the target address from the private buffer firstly. This way increases the predication hit rate of the target address for indirect branch and reduce the number of context switching in a DBT system. It also effectively improves the whole performance for the dynamic binary translation technology.
A Weighted Constraint Optimization Approach to the Nurse Scheduling Problem

As a solution proposal for the Second International Nurse Rostering Competition (INRC-II) [1], we introduce in this paper an approach based on the weighted constraint satisfaction problem (WCSP). The WCSP, given a number of constraints (both hard constraints - ones that must not be violated, and soft constraints - ones that influence the degree of optimality of a possible solution) aims to minimize the total weight of all unsatisfied constraints. We propose an heuristic based approach to the WCSP, using a general constraint satisfaction solver whose configuration was specifically adapted to the nurse scheduling problem presented by the INRC-II competition. Several problems were solved using our approach in order to better tweak the heuristic settings, the results of which are presented in this document, as well as the best implementation of the solver found so far.
Cross-pollination of normalisation techniques from speaker to face authentication using Gaussian mixture models

This paper applies score and feature normalisation techniques to parts-based Gaussian mixture model (GMM) face authentication. In particular, we propose to utilise techniques that are well established in state-of-the-art speaker authentication, and apply them to the face authentication task. For score normalisation, T-, Zand ZT-norm techniques are evaluated. For feature normalisation, we propose a generalisation of feature warping to 2D images, which is applied to discrete cosine transform (DCT) features prior to modelling. Evaluation is performed on a range of challenging databases relevant to forensics and security, including surveillance and access control scenarios. The normalisation techniques are shown to generalise well to the face authentication task, resulting in relative improvements in half total error rate (HTER) of between 17% and 62%.
Path-hop: efficiently indexing large graphs for reachability queries

Graph reachability is a fundamental research problem that finds its use in many applications such as geographic navigation, bioinformatics, web ontologies and XML databases, etc. Given two vertices, u and v, in a directed graph, a reachability query asks if there is a directed path from u to v. Over the last two decades, many indexing schemes have been proposed to support reachability queries on large graphs. Typically, those schemes based on chain or tree covers work well when the graph is sparse. For dense graphs, they still have fast query time but require large storage for their indices. In contrast, the 2-Hop cover and its variations/extensions produce compact indices even for dense graphs but have slower query time than those chain/tree covers. In this paper, we propose a new indexing scheme, called Path-Hop, which is even more space-efficient than those schemes based on 2-Hop cover and yet has query processing speed comparable to those chain/tree covers. We conduct extensive experiments to illustrate the effectiveness of our approach relative to other state-of-the-art methods.
On the Status of Hidden Variable Theories in Quantum Mechanics

1. Quantum theory vs. hidden variables It takes time for new paradigms to be generally digested and become known. It also takes time to change some of the old paradigms once they have been used for some time. So one has become accustomed to the conclusion , although deeply disturbing, that no hidden variable models can be introduced into quantum theory, i.e., there is a limit to our knowledge. One cites Bell's theorem and all the discussions taking place around this theme, notwithstanding the example of D. Bohm's trajec-tories. I want to show that the confrontations between standard quantum theory and hidden variable models has not been resolved once and for all, as many now think. On the contrary, there are very simple physical " hidden " variable models—not so " hidden " , actually—which provide a deterministic quantum mechanism, such that the standard probabilistic quantum mechanics is obtained after an average over an ensemble. It is like the paradigm of quarks as real physical constituents of hadrons, rather than group theoretical objects: very unnatural. It took a long time to get accustomed to this, but once one has become accustomed to them, it is very difficult to get across the idea that the real constituents of matter cannot be quarks. Bell's theorem is, of course, mathematically correct, but it has in it an implicit, tacit assumption about what hidden variables one is actually looking for. One already assumes what the single individual event is, namely, a spin that can have only two values, or a photon that can be detected or not, both dichotomic variables. The single events already have quantum properties. One thinks that if the light intensity is lowered down sufficiently one ends up with an individual single photon, whereas the photon is a statistical notion. One can calculate a Feyn-man probability amplitude as though light consisted of photons of various frequencies. It is not that each single atom emits a single photon (it could not, because the photon fills the whole space! ). Similarly, a single indivi dual electron cannot already have a quantized spin, up or down; it can have a spin vector pointing in any direction, but only for an ensemble can the average direction rel a-tive to some axis be up or down, or for a beam of electrons , 50% spin up and 50% spin down, for example. This is the meaning of …
A Polynomial-Based Time-Varying Filter Structure for the Compensation of Frequency-Response Mismatch Errors in Time-Interleaved ADCs

This paper introduces a structure for the compensation of frequency-response mismatch errors in M-channel time-interleaved analog-to-digital converters (ADCs). It makes use of a number of fixed digital filters, approximating differentiators of different orders, and a few variable multipliers that correspond to parameters in polynomial models of the channel frequency responses. Whenever the channel frequency responses change, which occurs from time to time in a practical time-interleaved ADC, it suffices to alter the values of these variable multipliers. In this way, expensive on-line filter design is avoided. The paper includes several design examples that illustrate the properties and capabilities of the proposed structure.
Distributed Convex Optimization A Study on the Primal-Dual Method of Multipliers

The Primal-Dual Method of Multipliers (PDMM) is a new algorithm that solves convex optimization problems in a distributed manner. This study focuses on the convergence behavior of the PDMM. For a deeper understanding, the PDMM algorithm was applied to distributed averaging and distributed dictionary learning problems. The results were compared to those of other state-of-the-art algorithms. The experiments show that the PDMM algorithm not only has a fast convergence rate but also robust performance against transmission failures in the network. Furthermore, on the basis of these experiments, the convergence rate of the PDMM was analyzed. Different attempts at proving the linear convergence rate were carried out. As a result, the linear convergence rate has been proven under certain conditions.
Elsevier Editorial System(tm) for Applied Soft Computing Manuscript Draft Manuscript Number: ASOC-D-12-00705R1 Title: A Novel Multi-swarm Algorithm for Optimization in Dynamic Environments Based on Particle Swarm Optimization

Optimization in dynamic environment is considered amongst prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization approach, in which several mechanisms were employed to face the challenges in this domain. In this algorithm, an improved multi-swarm approach has been used for finding peaks in the problem space and tracking them after an environment change in an appropriate time. Moreover, a novel method based on change in velocity vector and particle positions was proposed to increase the diversity of swarms. For improving the efficiency of the algorithm, a local search based on adaptive quantum particle around the best found position as well as a novel Awakening-Sleeping mechanism were utilized. The experiments were conducted on Moving Peak Benchmark which is the most wellknown benchmark in this domain and results have been compared with those of the state-of-the art methods. The results show the superiority of the proposed method.
Vietnamese Parsing with an Automatically Extracted Tree-Adjoining Grammar

This paper presents the construction and evaluation of a deep syntactic parser based on Lexicalized Tree-Adjoining Grammars for the Vietnamese language. This is a complete system integrating necessary tools to process Vietnamese text, which permits to take as input raw texts and produce syntactic structures. A dependency annotation scheme for Vietnamese and an algorithm for extracting dependency structures from derivation trees are also proposed. At present, this is the first Vietnamese parsing system capable of producing both constituency and dependency analyses with encouraging performances: 69.33% and 73.21% for constituency and dependency analysis accuracy, respectively.
Algorithmic Aspects for Bi-Objective Multiple-Choice Hardware/Software Partitioning

Designing embedded systems has become a challengingprocess due to the increasing complexity of the applications. In addition, there is a need to meet multiple conflicting constraints such as speed, power and cost. These factors have led to an explosion in the design space as each task in the application can have various implementation options (software and a range of hardware customizations), where each implementation option is associated with different speed, power and cost. In this paper, we propose hardware-software (HW/SW) partitioning algorithms that are capable of managing the large design space by taking into account the multiple implementation choices. In particular, we focus on multiple-choice HW/SW partitioning with the following objectives: minimizing execution time and power consumption, while meeting the area constraint. Two algorithms will be presented: 1) a heuristic method that is based on the bi-objective knapsack problem to rapidly generate an approximate solution, 2) a dynamic programming algorithm to calculate the exact solution. Simulation results show that the heuristic method produces results that are very close to the exact ones.
Diffusion-weighted MR imaging of metastatic disease of the spine: assessment of response to therapy.

BACKGROUND AND PURPOSE In cases of metastatic disease of the spine, monitoring the response to medical therapy with plain radiography, bone scanning, and conventional spin-echo sequence MR imaging is unsatisfactory because of the insensitivity or nonspecific findings of these imaging modalities. The purpose of this study was to investigate signal intensity changes of bone marrow after therapy by using diffusion-weighted MR imaging to monitor the response to medical therapy in cases of metastatic disease of the spine. METHODS Twenty-four patients with metastatic disease of the spine were examined with MR imaging. Diffusion-weighted MR imaging and spin-echo MR imaging were performed in all patients before and after radiation therapy. Follow-up diffusion-weighted MR imaging and spin-echo MR imaging were performed for comparison purposes in nine cases at 1 month, in seven cases at 2 months, in seven cases at 3 months, and in three cases at 6 months after therapy. The diffusion-weighted MR imaging sequences were based on a steady-state free precession with a low b value (165 s/mm(2)) and a single shot stimulated echo-acquisition mode with a high b value (650 s/mm(2)). Apparent diffusion coefficient maps were obtained using two different b values incorporated in a diffusion-weighted single shot stimulated echo-acquisition mode sequence. Apparent diffusion coefficient maps were obtained in three cases. Signal intensity changes of the metastatic disease of the vertebral bone marrow before and after therapy on conventional spin-echo sequence and diffusion-weighted MR images were evaluated. RESULTS As shown by diffusion-weighted MR imaging, metastatic disease of the vertebral bone marrow included in our study before therapy was hyperintense to normal vertebral bodies. In 23 patients with clinical improvement, metastatic disease of the spine after therapy was hypointense relative to normal vertebral bodies on the follow-up diffusion-weighted MR images. In one patient with hepatocellular carcinoma, the clinical symptoms did not improve and follow-up bone scanning performed 6 months after therapy showed increased uptake. Persistent hyperintense bone marrow after therapy was also noted on diffusion-weighted MR images. Decreased signal intensity of the metastatic disease of the spine on diffusion-weighted MR images was observed >1 month after therapy. CONCLUSION Diffusion-weighted MR imaging shows that, with successful therapy, there is decreased signal intensity of metastatic disease of the vertebral bone marrow.
Challenges to development of multipurpose global federated testbed for Future Internet experimentation

Present Internet architecture is facing a number of challenges related to security, scalability, ubiquitous computing, management etc. Many initiatives are being carried out globally on designing new architecture for Future Internet which will address these issues. Large scale testbeds are required to validate new architectural designs. In this regard, two major initiatives being carried out are the European Union's (EU) Future Internet Research and Experimentation (FIRE) and USA's Global Environment for Network Innovations (GENI). Apart from these, other countries like Japan, China and Korea are also working towards their own testbeds. This paper gives an overview of large scale testbed initiatives across the world and discusses the major challenges to the development of the global Future Internet testbed. It also compares two major regional testbed architectures and suggests development of formal architecture for worldwide global testbed which will eliminate improvident investments of time, resources and duplicate efforts.
Efficient re-indexing of automatically annotated image collections using keyword combination

This paper presents a framework for improving the image index obtained by automated image annotation. Within this framework, the technique of keyword combination is used for fast image re-indexing based on initial automated annotations. It aims to tackle the challenges of limited vocabulary size and low annotation accuracies resulting from differences between training and test collections. It is useful for situations when these two problems are not anticipated at the time of annotation. We show that based on example images from the automatically annotated collection, it is often possible to find multiple keyword queries that can retrieve new image concepts which are not present in the training vocabulary, and improve retrieval results of those that are already present. We demonstrate that this can be done at a very small computational cost and at an acceptable performance tradeoff, compared to traditional annotation models. We present a simple, robust, and computationally efficient approach for finding an appropriate set of keywords for a given target concept. We report results on TRECVID 2005, Getty Image Archive, and Web image datasets, the last two of which were specifically constructed to support realistic retrieval scenarios.
Delphinidin sensitizes prostate cancer cells to TRAIL-induced apoptosis, by inducing DR5 and causing caspase-mediated HDAC3 cleavage

TRAIL can induce apoptosis in some cancer cells and is an immune effector in the surveillance and elimination of developing tumors. Yes, some cancers are resistant to TRAIL. Delphinidin, a polyphenolic compound contained in brightly colored fruits and vegetables, has anti-inflammatory, anti-oxidant, and anti-tumorigenic activities. Here we showed that delphinidin sensitized TRAIL-resistant human prostate cancer cells to undergo apoptosis. Cells treated with delphinidin and TRAIL activated the extrinsic and intrinsic pathways of caspase activation. TRAIL-induced apoptosis in prostate cancer cells pretreated with delphinidin was dependent on death receptor 5 (DR5) and downstream cleavage of histone deacetylase 3 (HDAC3). In conclusion, delphinidin sensitizes prostate cancer cells to TRAIL-induced apoptosis by inducing DR5, thus causing caspase-mediated HDAC3 cleavage. Our data reveal a potential way of chemoprevention of prostate cancer by enabling TRAIL-mediated apoptosis.
The Effect of Simulated Flash-Heat Pasteurization on Immune Components of Human Milk

A pasteurization temperature monitoring system has been designed using FoneAstra, a cellphone-based networked sensing system, to monitor simulated flash-heat (FH) pasteurization. This study compared the effect of the FoneAstra FH (F-FH) method with the Sterifeed Holder method currently used by human milk banks on human milk immune components (immunoglobulin A (IgA), lactoferrin activity, lysozyme activity, interleukin (IL)-8 and IL-10). Donor milk samples (N = 50) were obtained from a human milk bank, and pasteurized. Concentrations of IgA, IL-8, IL-10, lysozyme activity and lactoferrin activity were compared to their controls using the Student’s t-test. Both methods demonstrated no destruction of interleukins. While the Holder method retained all lysozyme activity, the F-FH method only retained 78.4% activity (p < 0.0001), and both methods showed a decrease in lactoferrin activity (71.1% Holder vs. 38.6% F-FH; p < 0.0001) and a decrease in the retention of total IgA (78.9% Holder vs. 25.2% F-FH; p < 0.0001). Despite increased destruction of immune components compared to Holder pasteurization, the benefits of F-FH in terms of its low cost, feasibility, safety and retention of immune components make it a valuable resource in low-income countries for pasteurizing human milk, potentially saving infants’ lives.
Self-calibration algorithm under varying cameras using the linear projective reconstruction method

We present a practical self-calibration algorithm that only requires a linear projective reconstruction. Recently, many self-calibration algorithms that use only the information in the image have been proposed. But most algorithms require bundle adjustments in the projective reconstruction or in the nonlinear minimization. We overcome the sensitivity of the self-calibration algorithms due to the image noises by adding another constraint on the position of the principal point. We also propose a linear initialization method based on the property of the absolute quadric. Experimental results using real and synthetic images demonstrate the feasibility of the proposed algorithm.