Miracle-dz/UniFAR-FTUs huggingface dataset:
Audio Feature Extraction for DTW-based Audio-to-Score Alignment

Audio-to-score alignment is one of the music information retrieval (MIR) tasks that concerns the real world time when notes appeared in a corresponding audio. Although recent studies based on synthesizing MIDI to audio then applying audio feature extraction techniques and DTW-based alignment have achieved about 10 milliseconds in mean alignment error for piano music, evaluation in a real-world scenario for robustness is preferable. In this paper, we implemented a standard DTW-based Audio-to-score alignment system with audio feature extraction techniques for musical onset enhancement, and evaluated the robustness in a real-world scenario, namely for MIR database building. Considering this type of usage, we used 3 different synthesizers and real-world performance data from CrestMusePEDB in order to simulate the absence of prior information about audio recording conditions and velocity information. As for result, velocity from real-world performance and the choice of synthesizer can ruin DTW-based alignment system by almost doubling the average mean error in most cases. We also made a practical attempt at combining phase-based onset feature extraction and conventional MIDI-audio alignment framework on real-world flute aligning, indicating the protentional benefits of combining different type of audio features.
Human and group activity recognition from video sequences

A good solution to human activity recognition enables the creation of a wide variety of useful applications such as applications in visual surveillance, vision-based Human-Computer-Interaction (HCI) and gesture recognition. In this thesis, a graph based approach to human activity recognition is proposed which models spatio-temporal features as contextual space-time graphs. In this method, spatio-temporal gradient cuboids were extracted at significant regions of activity, and feature graphs (gradient, space-time, local neighbours, immediate neighbours) are constructed using the similarity matrix. The Laplacian representation of the graph is utilised to reduce the computational complexity and to allow the use of traditional statistical classifiers. A second methodology is proposed to detect and localise abnormal activities in crowded scenes. This approach has two stages: training and identification. During the training stage, specific human activities are identified and characterised by employing modelling of medium-term movement flow through streaklines. Each streakline is formed by multiple optical flow vectors that represent and track locally the movement in the scene. A dictionary of activities is recorded for a given scene during the training stage. During the testing stage, the consistency of each observed activity with those from the dictionary is verified using the Kullback-Leibler (KL) divergence. The anomaly detection of the proposed methodology is compared to state of the art, producing state of the art results for localising anomalous activities. Finally, we propose an automatic group activity recognition approach by modelling the interdependencies of group activity features over time. We propose to model the group interdependences in both motion and location spaces. These spaces are extended to time-space and time-movement spaces and modelled using Kernel Density Estimation (KDE). The recognition performance of the proposed methodology shows an improvement in recognition performance over state of the art results on group activity datasets.
Risk and return in the equity mutual fund industry: an unorthodox relationship and its application to new investment strategies

In 1980, Bowman proposed the Bowman paradox, a negative relationship between the risk and return in 85 U.S. industries, a contradiction with the high risk-high return doctrine. Examining the open-end equity mutual funds in Thailand, this study documents the negative relationship that can occasionally be seen between risk and return in the industry during 2003-2012. The study further examines the factors that will affect the probability that a fund will deliver an outstanding low-risk, high-return performance using unbalanced panel logistic regression on a binary dependent variable. The results showed that funds with high non-systematic risk, also called idiosyncratic risk, and/or older funds, were more likely to deliver a low-risk, high-return performance, and the funds that were managed by the company that managed a high number of funds were less likely to deliver such performance. This study proposes a new performance evaluation tool called the “risk-return matrix. ” This matrix suggested the funds with outstanding low-risk, high-return past performance.This study applied the results to three new investment strategies. All simulations demonstrated returns better than the industry’s average returns. JEL Classification: G11, G12
Short Review on Metamorphic Malware Detection in Hidden Markov Models

Metamorphic malware is well known for evading signature-based detection. To cope up with numerous malware which can emerge easily by using open source malware generator, efficient detection in terms of accuracy and runtime performance shall be considered during analysis. Detection strategies such as data mining combine with machine learning have been used by researchers for heuristically detecting malware. In this paper, we present Hidden Markov Model as an efficient metamorphic malware detection tool by exploring the common obfuscation techniques used in malware while reviewing and comparing the different studies that adopt HMM as a detection tool.
The Impact of Knowledge Management towards Employee’s Job Satisfaction

Knowledge management (KM) has been often claimed to be an important ingredient in building competitive advantage in an organization. Based on the previous studies KM adoption in Malaysia is relatively slow. Most organizations in Malaysia are unsure if the promised performance improvement through the implementation of KM practices is just passing a trend. Therefore, this paper aims to meet this need by developing conceptual framework. The conceptual framework for this study is outlined by the process of adapts and adopts from the past research studies. The KM practices that will be discussed in this paper are knowledge acquisition, knowledge sharing, knowledge creation and knowledge retention. It outlines the conceptual framework for this study and find out the factors that will impact the job satisfaction in organization. The conceptual framework section will explained the detailed of the conceptual framework used in this study which consists of four independent variables such as knowledge acquisition, knowledge sharing, knowledge creation and knowledge retention and one dependent variable is job satisfaction. Besides, the review will also cover on the impact of knowledge management practices on employee’s job satisfaction. The paper ends with recommendations on further research should be carried out in order to enhance the understanding of the concepts and the effect of KM towards employee’s job satisfaction.
Quantifying the Extent of Temporal Decay in Service Quality Ratings

This paper seeks to synthesise the disparate research to date that has been done on the temporal decay of service quality and satisfaction responses. We aim to verify if this measurement artefact exists and, if so, to see if its magnitude can be quantified and generalised across a range of conditions. Using the existing published results from various ad hoc studies to date that have looked at the issue, we quantify the reported decays in service quality and satisfaction scores. In addition, we extend the research into two new crosssectional studies in the catering and financial planning industries. Service quality ratings decay as the time since the service experience increases. The extent of this decay is generalisable to approximately a 10% decline in service scores over a one- to three-month period. The majority of the decay occurs within the first month of the service encounter and then this rate of decline slows down or stabilises. This research adds to the body of knowledge concerning moderators of service quality ratings. It makes an important managerial contribution through providing a generalised decay figure of about 10% that can be used by managers and researchers to separate the confounding effects of temporal decay from real changes in customers’ service quality perceptions.
Genetic algorithm and parallel computing

The paper presents an optimisation algorithm for the geometry of three-phase unscreened high-current busways with solid insulation. A genetic algorithm method and parallel computing were used for this purpose. Moreover, the paper discusses the methodology of defining an objective function to allow for phenomena occurring in static and dynamic states.
The Impact of Students’ Cultural Intelligence on Their Psychological Safety in Global Virtual Project Teams

ABSTRACT This study addresses a call for the design and implementation of course curricula that prepare students to develop their CQ and gain experience working with peers on global virtual project teams. We explored how US-based and Peru-based students’ cultural intelligence (CQ) impacted their sense of psychological safety (PS) during a month-long global, virtual team project. We also examined the students’ people-focused (PF) and task-focused (TF) behaviors as mediators of the CQ-PS relationship. The results of mediation analyses provide support for our hypothesis that the relationship between cultural intelligence and psychological safety will be mediated by people-focused behaviors. Finally, we provide a model and suggestions for virtually bringing together students from different countries to collaborate on a global virtual project, and avenues for future research. Here we encourage a focus on a curriculum that educates students about their cultural intelligence and ways to develop psychologically safe learning environments. We also highlight the potential learning for faculty teaching such courses, and note how our experience collaborating with our counterpart in Peru constituted a fractal of what our students were experiencing on their global projects.
NoCGIN: A Gamma Interconnection Network as NoC Interconnect

As billions of transistors can easily getting manufactured on small chips, multiple processing elements are also getting fabricated on these chips. This type of chip manufacturing caught attention of researchers from the domains like Parallel and Distributed Computing, Computer Aided Chip Manufacturing, Computer Design etc. Many researchers tried to utilize the boosted capacity of multiprocessor chips to implement time consuming, bulky, parallel algorithms. A strong communication network, which is reliable, robust and reusable is very much needed to achieve expected performance. This paper proposes a new Gamma Interconnection Network variant, namely NoCGIN, which act as interconnection network for Networks-on-Chip. The paper further gives information about the topology of NoCGIN and a simple routing algorithm for routing packets.
On an Improved Fuzzy C-Means Clustering Algorithm

A cluster is a gathering of similar objects which can exhibit dissimilarity to the objects of other clusters. Clustering algorithms may be classified as: Exclusive, Overlapping, Hierarchical, and Probabilistic; and several algorithms have been formulated for classification and found useful in different areas of application. The K-means, Fuzzy C-means, Hierarchical clustering, and Mixture of Gaussians are the most prominent of them. Our interest on this work is on the web search engines. In this paper, we examined the fuzzy c-means clustering algorithm in anticipation to improving upon its application area. On the Web, classification of page content is essential to focused crawling. Focused crawling supports the development of web directories, to topic-specific web link analysis, and to analysis of the topical structure of the Web. Web page classification can also help improve the quality of web search. Page classification is the process of assigning a page to one or more predefined category label. In all, the tendency for a web page to contain the qualities of two or more clusters could exist. Thus exclusive clustering would not be very useful in our case; so the need for overlapping clustering using Fuzzy C-means. It is worthy of note that the Fuzzy C-mean being an optimization problem, converges to a local minimum or a saddle point. The iteration in some cases becomes recurring. At such a point, one would assume the saddle point is reached and if the iteration is not terminated, the loop may continue to a stack-grab that may fault (increase running time, etc) the algorithm. In this work, we developed a modified fuzzy C-mean clustering algorithm with a sharp stopping condition which was tested on a demo data to ascertain its convergence and comparatively test its efficiency. Corel Q-pro optimizer was used on a timing macro. Our result(s) are quite interesting and challenging as they clearly show the presence of inter-lapping documents along the spectrum of two different clusters.
Privacy Preserving in Data stream classification using different proposed Perturbation Methods

– Data mining is extracts valuable knowledge from large amounts of data. Recently, data streams are emerging as a new type of data, which are different from traditional static data. The characteristics of data streams are: Data has timing preference; data distribution changes constantly with time; the amount of data is enormous; Data flows in and out with fast speed; and immediate response is required. Traditional algorithm is designed for the static database. If the data changes, it would be necessary to rescan the database, which leads to more computation time and inability to promptly respond to the user. The issue of privacy-preserving data mining has widely been studied and many techniques have been proposed. However, existing techniques for privacy-preserving data mining are designed for traditional static databases and are not suitable for data streams. So the privacy preservation issue of data streams mining is very important issue. We proposed methods and algorithms which extends the existing process of data streams classification to achieve privacy preservation .
Evaluation of Optimised Apriori Algorithm on HDFS using MapReduce in Hadoop Distributed Mode

— With a revolutionary change in data analytics it requires techniques that can equally extend with the trending data processing methods. To keep in pace with this elated progress in information evaluation, calibration and storage patterns, development and implementation of large scale algorithms for data processing is gaining importance. In datamining, association rule mining and classification is a wellutilised methodology for identifying overwhelming relations from data in large scale analytics. Apriori algorithm is one such crucial algorithm to mine the frequent item sets which form the basis for finding association rules among the data. Analyzing frequent item sets is a crucial step to find rules and association between them. This stands as a primary foundation for crucial decision making. With the advent of Hadoop Map-Reduce, parallel processing and efficient memory utilisation has come into order. This paper aims to identify the potential of Apriori Algorithm which is implemented as one-phase and k-phase Apriori algorithms in MapReduce framework and further an Optimised Apriori Algorithms(OAA) has been implemented which has a full-fledged MapReduce benefits and it has been identified that Optimised Apriori Algorithm has yielded better efficiency and reduced time complexity.
Four Things No One Will Tell You About ESG Data

As the ESG finance field and the use of ESG data in investment decision‐making continue to grow, we seek to shed light on several important aspects of ESG measurement and data. This article is intended to provide a useful guide for the rapidly rising number of people entering the field. We focus on the following: • The sheer variety, and inconsistency, of the data and measures, and of how companies report them. Listing more than 20 different ways companies report their employee health and safety data, the authors show how such inconsistencies lead to significantly different results when looking at the same group of companies. • ‘Benchmarking,’ or how data providers define companies' peer groups, can be crucial in determining the performance ranking of a company. The lack of transparency among data providers about peer group components and observed ranges for ESG metrics creates market‐wide inconsistencies and undermines their reliability. • The differences in the imputation methods used by ESG researchers and analysts to deal with vast ‘data gaps’ that span ranges of companies and time periods for different ESG metrics can cause large ‘disagreements’ among the providers, with different gap‐filling approaches leading to big discrepancies. • The disagreements among ESG data providers are not only large, but actually increase with the quantity of publicly available information. Citing a recent study showing that companies that provide more ESG disclosure tend to have more variation in their ESG ratings, the authors interpret this finding as clear evidence of the need for ‘a clearer understanding of what different ESG metrics might tell us and how they might best be institutionalized for assessing corporate performance. ’ What can be done to address these problems with ESG data? Companies should ‘take control of the ESG data narrative’ by proactively shaping disclosure instead of being overwhelmed by survey requests. To that end, companies should ‘customize’ their metrics to some extent, while at the same time seeking to self‐regulate by reaching agreement with industry peers on a ‘reasonable baseline’ of standardized ESG metrics designed to achieve comparability. Investors are urged to push for more meaningful ESG disclosure by narrowing the demand for ESG data into somewhat more standardized, but still manageable metrics. Stock exchanges should consider issuing—and perhaps even mandating—guidelines for ESG disclosures designed in collaboration with companies, investors, and regulators. And data providers should come to agreement on best practices and become as transparent as possible about their methodologies and the reliability of their data.
Human Walking Imitation based on Quadratic Programming

This work presents a procedure of imitating human walking motion online for a humanoid robot. Two aspects are essential for a successful walking imitation: stable footprints and motion similarity. The human footprints are recognized from the captured motion data and imitated by the robot through conventional zero-moment point (ZMP) control scheme. For the motion similarity we focus on similar knee joint trajectories, which are related to knee stretching and swing leg motion. After human motion capturing and preprocessing, the walking imitation problem is formulated as a quadratic programming (QP) problem with inequality constraints and dynamic equality constraints. The continuity of the control law is ensured by introducing a task activation buffer and position dependent velocity limit. Finally we evaluate the effectiveness of the proposed approach on the DLR humanoid robot TORO in dynamics simulation.
Increasing the Capacity of Signalized Intersections

1 Separate turn phases are often used on the approach legs to intersections with heavy left turns. This 2 wastes capacity on the approach because some of its lanes cannot discharge during its green phases. The 3 paper shows that the problem can be eliminated by reorganizing traffic on all the lanes upstream of an 4 intersection using a mid-block pre-signal. If drivers behave deterministically, the capacity that can be 5 achieved is the same as if there were no left turns. However, the reorganization is so drastic that it may be 6 counterintuitive to drivers. This can be remedied by reorganizing traffic on fewer lanes. It is shown that 7 such partial reorganization still increases capacity significantly, even if drivers behave randomly and only 8 one lane is reorganized. The paper shows how to optimize the design of a pre-signal system for a generic 9 intersection. It also identifies both, the potential benefits of the proposed system for a broad class of 10 intersections, and the domain of application where the benefits are most significant. 11 TRB 2011 Annual Meeting Original paper submittal not revised by author. Xuan, Daganzo, Cassidy 3
To repeat or not to repeat : A temporal perspective on repeated collaboration in R&D consortia

This study investigates the implicit assumption in embeddedness theory research that timing of repeated collaboration is homogeneous and mainly occurs in a sequential fashion, which leads to social embeddedness over time. Results of an event history analysis of the establishment of 1715 multi-partner R&D consortia over 22 years show that timing of repeated collaboration is heterogeneous, and concentrate at two points in time: the consortium start (parallel timing) and the consortium end (sequential timing). The timing of repeated ties is associated with two distinct time utilization strategies (time extension and time compression strategy), and depends on the type and number of actors involved in repeated collaboration. The study contributes to a temporal explanation of inter-organizational collaboration and presents an extension of embeddedness theory. Previous versions of this chapter were presented at the 27th EGOS colloquium (Gothenburg, 2011), the 2nd Tilburg Conference on Innovation (Oisterwijk, 2012), the 28th EGOS colloquium (Helsinki, 2012), and the 2015 Annual Meeting of the Academy of Management (Vancouver, 2015). 2.
Advanced Direct Power Control for Grid-connected Distribution Generation System Based on Fuzzy Logic and Artificial Neural Networks Techniques

This paper proposes an improvement of the direct power control (DPC) scheme of a grid connected three phase voltage source inverter based on artificial neural networks (ANN) and fuzzy logic (FL) techniques for the renewable energy applications. This advanced control strategy is based on two intelligent operations, the first one is the replacement of the conventional switching table of a three phase voltage source inverter (VSI) by a selector based on artificial neural networks approach, and the second one is the replacement of the hysteresis comparators by fuzzy logic controllers for the instantaneous active and reactive power errors. These operations enable to reduce the power ripples, the harmonic disturbances and increase the response time period of the system. Finally, the simulation results were obtained by Matlab/Simulink environment, under a unity power factor (UPF). These results verify the transient performances, the validity and the efficiency of the proposed DPC scheme.
An Analysis of the Intellectual Structure of Research on the Financial Economics of Precious Metals

We examine the literature on the financial economics of precious metals from a bibliometric and scientometric perspective. We surface the main trends and authors in the area over the last two decades, and provide estimates of the density of the networks of both researchers and research. Clear evidence is provided of an area dominated by a number of high output researchers, with distinct and clear areas of research with little overlap. A probabilistic topic modelling approach is further applied to determine the main topics of research in the area. We conclude that there are significant areas of potential synergy as yet unexploited in this research domain. We also highlight the current topics that can be a particularly important focus for these synergy efforts.