Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying an index of financial conditions for South Africa

Purpose - – The purpose of this study is to construct a financial conditions index (FCI) for the South African economy to enable the gauging of financial conditions and to better understand the macro-financial linkages in the country. The global financial crisis that began in 2007-2008 demonstrated how severe the impact of financial markets’ stress on real economic activity can be. In the wake of the financial crisis, policy-makers and decision-makers across the world identified the critical need for a better understanding of financial conditions, and more importantly, their impact on the real economy. Design/methodology/approach - – The FCI is constructed using monthly data over the period 1966 to 2011, and is based on a set of 16 financial variables, which include variables that define the state of international financial markets, asset prices, interest rate spreads, stock market yields and volatility, bond market volatility and monetary aggregates. The authors explore different methodologies for constructing the FCI, including full sample and rolling-window principal components analysis. Furthermore, the authors investigate whether it is beneficial to purge the FCI of the real effects of inflation, economic growth and interest rates, and evaluate the performance of our constructed FCIs by comparing their ability to pick up turning points in the South African business cycle, and by running in-sample causality (forecast) tests. Findings - – The authors find that the estimated FCIs are good predictors of economic activity; with the rolling-window FCI being the “best” performing index. Causality tests indicate that this FCI is a good in-sample predictor of industrial production growth and the Treasury Bill rate, but a weak predictor of inflation. Practical implications - – The authors find that the resulting FCI can act as an “early warning system”. This, in turn, may serve to indicate that monetary policy should take broader financial conditions into account. Originality/value - – This study offers three main contributions to the existing literature on financial conditions in South Africa: the authors construct an FCI over a sample period that is three decades longer than existing indices, the FCI of this paper comprises a wider coverage of financial variables than others and the authors make use of rolling-window estimation techniques that allow them to account for parameter instability and to capture the real-time constraints faced by a policymaker.
A novel approach for Sentimental Analysis and Opinion Mining based on SentiWordNet using web data

Opinion mining is an art of tracking the mood of the public about a particular product or topic from a huge set of opinions or reviews publically available in web. In this work, a novel approach is proposed based on SentiWordNet, which generates count of score words into seven categories such as strong-positive, positive, weak-positive, neutral, weak-negative, negative and strong-negative words for the opinion mining task and evaluated using machine learning algorithms like Naïve Bayes, SVM and Multilayer Perception (MLP). The web data is collected using web crawler applied with different pre-processing techniques which include removal of stop-words from online reviews, then stemming is performed using Porter Stemmer algorithm, and then reviews are tagged using Stanford POS tagger. The proposed approach is experimented on movie and product web domains and obtained higher success rate in terms of accuracy measured by various tools like Kappa statistics with an accuracy of 77.7% and has lower error rates. Weighted average of different accuracy measures like Precision, Recall, TP Rate, F-Measure rate depicts higher efficiency rate and lower FP Rate for Naïve Bayes and MLP models. The experimental results of Ten-Fold cross validation on the training data shows that Naïve Bayes & MLP outperforms the SVM model. Thus, the former are used for the Sentimental Analysis of web data. The results demonstrate that the proposed novel approach has higher efficacy and it can be successfully used in Opinion Mining for the task of decision making by any web user.
A Comparative Study of Equalization Techniques for MIMO Systems

A detail analysis of the performance of 2X2 Multiple Input Multiple Output (MIMO) antenna system with STBC multiplexing and Alamouti coding and BPSK modulation for high data rate transmission has been carried out assuming flat fading Rayleigh channel. Combining different linear and non-linear detection techniques, Bit Error Rate (BER) performance have been studied in comparison to the optimally equalized 1X2 antenna Maximal Ratio Combining (MRC) techniques of receive diversity. The combination of linear techniques such as Zero Force (ZF) equalization methods and non-linear techniques such as Successive Interference Cancellation (SIC) shows that for BER ~10-3 there is an improvement of SNR by ~2.2 dB compared to ZF case. The combination of ZF, SIC and non-linear techniques such as optimal ordering showed further improvement in the SNR ~2dB. The combination of linear techniques such as Minimum Mean Square Equalization (MMSE) methods with SIC and optimal ordering indicates that for BER by 10-3 significant improvement in SNR ~ 5 dB over ZF case.
Optimal regularization for MR diffusion signal reconstruction

In this paper we address two problems related to the parametric reconstruction of the diffusion signal in the complete 3D Q-space. We propose a modified Spherical Polar Fourier (mSPF) basis to naturally impose the continuity of the diffusion signal on the whole space. This mathematical constraint results in a dimension reduction with respect to the original SPF basis. In addition, we derive the expression of a Laplace regularization operator in this basis, and compute optimal regularization weight using generalized cross validation (GCV). Experiments on synthetic and real data show that this regularization leads to a more accurate reconstruction than the commonly used low-pass filters.
Trade from the ground up

Purpose – The purpose of this paper is two‐fold. First, a case study is presented that examines a model of agricultural development created by Healing Hands International (HHI) as one answer to the United Nations' Food and Agriculture Organization call to reduce world hunger in half by 2015. Second, the study of HHI's agricultural model is used to identify some of the variables that might predict success in achieving sustainable agricultural systems in developing communities around the world. Specifically, training and establishing trust through social networks are explored as potential indicators in which HHI and other non‐governmental organizations (NGOs) can better determine whether they are meeting the goal of making trade work for the poor.Design/methodology/approach – A case study of HHI's agricultural program is conducted as a means to identify success criteria for similar programs designed to establish economic viability in developing countries.Findings – HHI has received global attention for its .
Battery Grouping with Time Series Clustering Based on Affinity Propagation

Battery grouping is a technology widely used to improve the performance of battery packs. In this paper, we propose a time series clustering based battery grouping method. The proposed method utilizes the whole battery charge/discharge sequence for battery grouping. The time sequences are first denoised with a wavelet denoising technique. The similarity matrix is then computed with the dynamic time warping distance, and finally the time series are clustered with the affinity propagation algorithm according to the calculated similarity matrices. The silhouette index is utilized for assessing the performance of the proposed battery grouping method. Test results show that the proposed battery grouping method is effective.
Leadership competencies for a global public service

The notion of a global public service has been put forth in the literature as a means of addressing a number of policy issues that can no longer be addressed by a nation-state in isolation. This article sets out to address whether it is possible to formulate and implement a leadership competency model that could be used to select, develop and reward these global public servants and, if so, what leadership competencies they would require. Evidence will be drawn from both the literature on the competencies required for global managers/leaders as well as various public sector leadership competency models. It is argued that more thought needs to be given to how a leadership competency framework might be fruitfully employed to buttress such a cadre of individuals. Points for practitioners Public services around the world have been embracing the use of leadership competency models as part of their human resources management frameworks for the past few decades. This research examines a number of the various models that have been employed with the intent of identifying key competencies that would be more universal in nature. Additional research needs to be conducted to ensure that such models reflect the distinctiveness of the public sector.
Reducing switching activity on datapath buses with control-signal gating

This paper presents a practical technique for saving power dissipation in large datapaths by reducing unnecessary switching activity on wide buses. Control signals on a datapath module are gated by the observability don't care condition of the bus driven by that module to stop unnecessary switching activity on the bus. A methodology for automatic generation of gating conditions and synthesis of gated control signals from the RTL description of a design is presented. The technique has very low overheads in terms of area, power, and designer effort. It was applied to one of the integer execution units of a 64-bit super-scalar RISC microprocessor. Experimental results of running various application programs on the microprocessor show on an average 26.6% reduction in dynamic switching power in the integer execution unit, with no increase in path delays.
21. UniGene: A Unified View of the Transcriptome

The task of assembling an inventory of all genes of Homo sapiens and other organisms began more than a decade ago with large-scale survey sequencing of transcribed sequences. The resulting Expressed Sequence Tags (ESTs) were a gold mine of novel gene sequences that provided an infrastructure for additional large-scale projects, such as gene maps, expression systems, and fulllength cDNA projects. In addition, untold numbers of targeted gene-hunting projects have benefited from the availability of these sequences and the physical clone reagents. However, the high level of redundancy found among transcribed sequences, not to mention a variety of common experimental artifacts, made it difficult for many people to make effective use of the data. This problem was the motivation for the development of UniGene [http://www.ncbi. nlm.nih. gov/UniGene/], a largely automated analytical system for producing an organized view of the transcriptome. In this chapter, we discuss the properties of the input sequences, the process by which they are analyzed in UniGene, and some pointers on how to use the resource.
International workshop on multimodal virtual and augmented reality (workshop summary)

Virtual reality (VR) and augmented reality (AR) are expected by many to become the next wave of computing with significant impacts on our daily lives. Motivated by this, we organized a workshop on “Multimodal Virtual and Augmented Reality (MVAR)” at the 18th ACM International Conference on Multimodal Interaction (ICMI 2016). While current VR and AR installations mostly focus on the visual domain, we expect multimodality to play a crucial role in future, next generation VR/AR systems. The submissions for this workshop reflect the potential of multimodality for VR and AR, illustrate interesting new directions, and pinpoint important issues. This paper gives a short motivation for the workshop and its aim, and summarizes the aforementioned trends and challenges identified from the submissions.
Automating sequence dataset generating by using SeqGen

Data preprocessing describes any type of processing performed on raw data to prepare it for another processing procedure. Commonly used as a preliminary data mining practice, data preprocessing transforms the data into a format that will be more easily and effectively processed. Sequential Pattern Mining finds interesting sequential patterns among the large database. Data acquired from the dataset may not be sequential. In this paper, we propose a SeqGen algorithm as preprocessing step in sequential pattern mining. The main objective of the algorithm is to generate sequences with timestamp on user personalization. The reference attribute is given as parameter for generating the sequences. Experimental results have shown that raw data in any form can be easily transformed into sequence dataset once the reference attribute is given.
Improved Graph-Based Lambda Lifting

Lambda lifting is a technique for transforming a program with local function definitions into a program consisting only of global function definitions. The best known lambda lifting algorithm computes the minimal set of extraneous parameters needed by each function in O(n) steps by solving a system of set equations which are recursive if the functions in the program are mutually recursive. Mutually recursive functions give rise to strongly connected components in the call graph of a program. Danvy and Schultz observed that all functions in a strongly connected component can be given the same set of free variables as extraneous parameters. Based on this observation, they developed an O(n) graph-based lambda lifting algorithm. This article illustrates how Danvy’s and Schultz’s algorithm is an approximation of Johnsson’s algorithm for a certain class of programs and describes an O(n) graph-based lambda lifting algorithm that yields the same results as Johnsson’s algorithm.
RES: A Robust and Efficient Snapshot Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSN) have been widely used in natural environment monitoring, military, health care, human life and other fields. Distributed snapshot, i.e. the global view/state of the whole system, is a fundamental block for many network control and data processing operations in WSNs. Although there have been quite a number of snapshot algorithms proposed, most of them are designed for traditional network environments. The characteristics of WSN in resource constraints, communication link and network topology require delicately new design. In this paper, we propose a new snapshot algorithm, named RES, which can tolerate packet loss with efficient communication cost. Basically, RES is executed based on the level of a node, which is determined by the hop count to the sink node. The snapshot query and snapshot data are propagated level by level. To tolerate packet loss, a node sends a packet by local broadcasting to all neighbors; to reduce communication cost, the feedback packet from lower level nodes will be merged with replicated data removed. Our design even does not require the knowledge of node constitution and neighborhood in prior, which makes RES more practical, especially for networks with high dynamics. We conduct extensive simulations via OMNET++ to evaluate the performance of RES and compare it with similar algorithms. The results show that RES can save both communication and time cost, and it can tolerate packet losses efficiently.
DETECTING AND PREVENTING PLAGIARISM IN PROJECTS

Projects play an important role in most Computer Science courses. Plagiarism in projects, how to discourage it and how to detect it have always been of concern to instructors. In this paper, we propose a technique for designing projects to detect and prevent plagiarism, called polymorphic projects. We also catalog several other techniques currently used to address plagiarism in projects.
American-Style Derivatives : Valuation and Computation

INTRODUCTION EUROPEAN CONTINGENT CLAIMS Definitions The Economy Attainable Contingent Claims Valuation of Attainable Claims Claims Involving Negative Payoffs The Structure of Contingent Claims' Prices Changes of Numeraire and Valuation Option and Forward Contracts Markets with Deterministic Coefficients Markets with Multiple Assets Appendix: Proofs AMERICAN CONTINGENT CLAIMS Contingent Claims with Random Maturity American Contingent Claims Exercise Premium Representations A Duality Formula: Upper Price Bounds American Options and Forward Contracts Multiple Underlying Assets Appendix: Proofs STANDARD AMERICAN OPTIONS The Immediate Exercise Region The Call Price Function Early Exercise Premium Representation A One-Dimensional Integral Equation Hedging Diffusion Processes Floating Strike Asian Options American Forward Contracts Appendix: Proofs BARRIER AND CAPPED OPTIONS Barrier Options Capped Options Diffusion Processes Appendix: Proofs OPTIONS ON MULTIPLE ASSETS Definitions, Examples and Literature The Financial Market Call Options on the Maximum of 2 Assets American Spread Options Options on an Average of 2 Assets Call Options on the Minimum of 2 Assets Options with n > 2 Underlying Assets Appendix A: Derivatives on Multiple Assets Appendix B: Proofs OCCUPATION TIME DERIVATIVES Background and Literature Definitions Symmetry Properties Quantile Options Parisian Options Cumulative Parisian Contingent Claims Step Options American Occupation Time Derivatives Multiasset Claims Appendix: Proofs NUMERICAL METHODS Numerical Methods for American Options Integral Equation Methods Exercise Time Approximations: LBA-LUBA Diffusion Processes Other Recent Approaches Performance Evaluation Methods for Multiasset Options Methods for Occupation Time Derivatives Appendix: Proofs Bibliography Index
On web accessibility evaluation environments

Modern Web sites leverage several techniques (e.g. DOM manipulation) that allow for the injection of new content into their Web pages (e.g., AJAX), as well as manipulation of the HTML DOM tree. This has the consequence that the Web pages that are presented to users (i.e., browser environment) are different from the original structure and content that is transmitted through HTTP communication (i.e., command line environment). This poses a series of challenges for Web accessibility evaluation, especially on automated evaluation software. This paper details an experimental study designed to understand the differences posed by accessibility evaluation in the Web browser. For that, we implemented a Javascript-based evaluator, QualWeb, that can perform WCAG 2.0 based accessibility evaluations in both browser and command line environments. Our study shows that, in fact, there are deep differences between the HTML DOM tree in both environments, which has the consequence of having distinct evaluation results. Furthermore, we discovered that, for the WCAG 2.0 success criteria evaluation procedures we implemented, 67% of them yield false negative answers on their applicability within the command line environment, whereas more than 13% of them are false positives. We discuss the impact of these results in the light of the potential problems that these differences can pose to designers and developers that use accessibility evaluators that function on command line environments.
An intelligent lighting communication system combined with microwave Doppler technology

This paper introduces intelligent control of LED lighting by using the Doppler circuit to detect the moving people and vehicles. Visible light communication (VLC) is activated by the opening of LED. STM32 can process Doppler signals and judge the necessity of communication according to the time of day or night to guarantee the intelligent switch of LED and green communication. The paper describes the system's practicability and reliability from two aspects: the simulation of 8-IPPM (Inverse Pulse Position Modulation) modulation and the design and test of the circuit, thus finally completing over 20m Doppler detection and its intelligent control of LED lighting, 8.5m transmission of 3Mbps non-return-to-zero on-off keying (NRZ-OOK) signal with a bit-error-ratio (BER) of up to 10-7 level in free space, as well as lighting indicators qualified with the national standards.
Adaptive Multiscale Discretizations for Vision

Variational problems in vision are solved numerically on the pixel lattice because it provides the simplest computational grid to discretize the input images even though a uniform grid is seldom wellmatched to the complexity of the solution or the resolution power of the model. To address this issue, we introduce multiresolution discretizations that locally adapt the resolution of a piecewise polynomial solution to the resolving power of the variational model or complexity of its solution. Besides classic triangular finite elements, we investigate quadand octree element grids that efficiently locate pixels and produce multiresolution representations of the solution. We combine our discretization with the optimization algorithms of finite differences to solve the nondifferentiable functionals that characterize vision and develop algorithms easy o parallelize. Our 2 and 3D experiments in image segmentation, optical flow, stereo, and depth fusion validate our method as achieving significant computational savings with a minimal loss of accuracy.
Amplified and Homozygously Deleted Genes in Glioblastoma: Impact on Gene Expression Levels

Background Glioblastoma multiforme (GBM) displays multiple amplicons and homozygous deletions that involve relevant pathogenic genes and other genes whose role remains unknown. Methodology Single-nucleotide polymorphism (SNP)-arrays were used to determine the frequency of recurrent amplicons and homozygous deletions in GBM (n = 46), and to evaluate the impact of copy number alterations (CNA) on mRNA levels of the genes involved. Principal Findings Recurrent amplicons were detected for chromosomes 7 (50%), 12 (22%), 1 (11%), 4 (9%), 11 (4%), and 17 (4%), whereas homozygous deletions involved chromosomes 9p21 (52%) and 10q (22%). Most genes that displayed a high correlation between DNA CNA and mRNA levels were coded in the amplified chromosomes. For some amplicons the impact of DNA CNA on mRNA expression was restricted to a single gene (e.g., EGFR at 7p11.2), while for others it involved multiple genes (e.g., 11 and 5 genes at 12q14.1–q15 and 4q12, respectively). Despite homozygous del(9p21) and del(10q23.31) included multiple genes, association between these DNA CNA and RNA expression was restricted to the MTAP gene. Conclusions Overall, our results showed a high frequency of amplicons and homozygous deletions in GBM with variable impact on the expression of the genes involved, and they contributed to the identification of other potentially relevant genes.