Miracle-dz/UniFAR-FTUs huggingface dataset:
Distance Measures for Time Series in R: The TSdist Package

The definition of a distance measure between time series is crucial for many time series data mining tasks, such as clustering and classification. For this reason, a vast portfolio of time series distance measures has been published in the past few years. In this paper, the TSdist package is presented, a complete tool which provides a unified framework to calculate the largest variety of time series dissimilarity measures available in R at the moment, to the best of our knowledge. The package implements some popular distance measures which were not previously available in R, and moreover, it also provides wrappers for measures already included in other R packages. Additionally, the application of these distance measures to clustering and classification tasks is also supported in TSdist, directly enabling the evaluation and comparison of their performance within these two frameworks.
Specific acoustic models for spontaneous and dictated style in indonesian speech recognition

The performance of an automatic speech recognition system is affected by differences in speech style between the data the model is originally trained upon and incoming speech to be recognized. In this paper, the usage of GMM-HMM acoustic models for specific speech styles is investigated. We develop two systems for the experiments; the first employs a speech style classifier to predict the speech style of incoming speech, either spontaneous or dictated, then decodes this speech using an acoustic model specifically trained for that speech style. The second system uses both acoustic models to recognise incoming speech and decides upon a final result by calculating a confidence score of decoding. Results show that training specific acoustic models for spontaneous and dictated speech styles confers a slight recognition advantage as compared to a baseline model trained on a mixture of spontaneous and dictated training data. In addition, the speech style classifier approach of the first system produced slightly more accurate results than the confidence scoring employed in the second system.
Elements of economic culture as informal institutions in the economic development of Germany

Motivation: The author is interested in the problem of the economic development of a state wherein economic capital is described as a set of elements creating economic culture and informal institutions. Aim: Identification of the operational conditions and manifestations of institutional economics in Germany may obtain an accurate picture and understanding of the functioning their counterparts in Poland. Article is also a proposal for the development of research in the field of institutional economics and economic culture. Results: Putting the thesis that the German economy belongs to the group of the strongest in the world, the study revealed a pattern of results where the institutions were proposed as part of institutional economics, which determine economic development.
Life-like Brachiation Control of Underactuated Two-link Robot Based on Dynamical Servo

With dynamical servo control theory introduced,life-like brachiation control of underactuated two-link robot is studied.For the life-like brachiation control of underactuated two-link robot,the dynamical servo theory is introduced to solve the problem of tracking a given trajectory by the terminal of swing arm.According to the pendulum-like features of primate's brachiation,a feedback control law is designed based on Lyapunov stability theory,and then the singular point in the closed loop system and their influence on the system's convergence ability are analyzed.The experiment results on a physical underactuated two-link robot show the effectiveness of the designed control strategy.
An Efficient, Extensible, Hardware-aware Indexing Kernel

An Efficient, Extensible, Hardware-aware Indexing Kernel Mohammad Sadoghi Hamedani Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2013 Modern hardware has the potential to play a central role in scalable data management systems. A realization of this potential arises in the context of indexing queries, a recurring theme in real-time data analytics, targeted advertising, algorithmic trading, and data-centric workflows, and of indexing data, a challenge in multi-version analytical query processing. To enhance query and data indexing, in this thesis, we present an efficient, extensible, and hardware-aware indexing kernel. This indexing kernel rests upon novel data structures and (parallel) algorithms that utilize the capabilities offered by modern hardware, especially abundance of main memory, multi-core architectures, hardware accelerators, and
Online collaborative environment to run the eastern bluefin tuna stock assessment workflow

In this note, we describe an approach which makes possible to execute online the set of codes used for eastern bluefin tuna (BFT-E) stock assess-ment at ICCAT. In 2014, ICCAT BFT-E working group has been able to execute thousands of model runs by using parallelization of R and Fortran codes on a supercomputer. This new approach has a lot of scientific benefits, but very few participants would be able to reproduce it without specific skills. Since november 2015, the H2020 BlueBridge project is funding an engineer to set up a WebSite (or VRE for Virtual Research Environment) where users will be able to parametrize and execute various steps of the BFT-E stock assessment workflow. By repackaging codes provided by ICCAT BFT-E working group the same codes are now executable online. They can be parametrized, executed and edited by anybody from a simple web page and data outputs are delivered in standard data format. At this stage, this VRE comes with various collaborative Web Services: a workspace to share documents or data, Web pages or RStudio server to process data online and an automated report service to dynamically generate documents packaging these results. Such a collaborative environment enables to store and access the whole set of data and source codes to replicate past results or to try new parametrizations of the model with usual tools or simple web forms. Such an approach is expected to give more transparency and collaboration within working groups.
Computational Decipherment of Unknown Scripts

A thesis submitted in partial fulﬁllment of the requirements for the degree of Abstract Algorithmic decipherment is a prime example of a truly unsupervised problem. This thesis presents several algorithms developed for the purpose of decrypting unknown alphabetic scripts representing unknown languages. We assume that symbols in scripts which contain no more than a few dozen unique characters roughly correspond to the phonemes of a language, and model such scripts as monoalphabetic substitution ciphers. We further allow that an unknown transposition scheme could have been applied to the enciphered text, resulting in arbitrary scrambling of letters within words (anagramming). We also consider the possibility that the underlying script is an abjad, in which only consonants are explicitly represented. Our decryption system is composed of three steps. The ﬁrst step in the decipherment process is the identiﬁcation of the encrypted language. We propose three methods for determining the source language of a document enciphered with a monoalphabetic substitution cipher. The best method achieves 97% accuracy on 380 languages. The second step is to map each symbol of the ciphertext to the corresponding letter in the identiﬁed language. We propose a novel approach to deciphering short monoalphabetic substitution ciphers which combines both character-level and word-level language models. Our method achieves a signiﬁcant improvement over the state of the art on a benchmark suite of short ciphers. The third step is to decode the resulting anagrams into readable text, which may involve the recovery of unwritten vowels. Our approach obtains an average decryption word accuracy of 93% on a set of 50 ciphertexts in 5 languages. Finally, we apply our new techniques to the Voynich manuscript, a centuries-old document written in an unknown script, which has resisted decipherment despite decades of study.
Synchronous tracking control of 6-DOF hydraulic parallel manipulator using cascade control method

自从水力的元素的包括增加系统的顺序，有自由(DOF ) 的六度的一个水力的平行操纵者的同步追踪控制问题是复杂的。 解决这个问题，有 inner/outer-loop 控制结构的串联控制方法被使用，它掩盖水力的动力学与内部环以便设计控制器机械动力学和操纵者的水力的动力学考虑。 而且，一条跨 coupling 控制途径被介绍给操纵者的同步追踪控制。 位置同步错误被基于同步目标考虑在每个致动器关节和它的邻近的之间的运动同步开发。 与位置错误和同步错误的反馈，然后，追踪被证明保证位置错误和同步错误 asymptotically 收敛到零。 而且，建议途径的有效性被与 6-DOF 执行的试验性的结果验证水力的平行操纵者。
On the Power Leakage Problem in Millimeter-Wave Massive MIMO With Lens Antenna Arrays

The emerging millimeter-wave massive multiple-input multiple-output (MIMO) with lens antenna arrays, which is also known as “beamspace MIMO,” can effectively reduce the required number of power-hungry radio frequency (RF) chains. Therefore, it has been considered as a promising technique for the upcoming 5G communications and beyond. However, most current studies on beamspace MIMO have not taken into account the important power leakage problem in beamspace channels, which possibly leads to a significant degradation in the signal-to-noise ratio (SNR) and the system sum-rate. To this end, in this paper, we propose a beam-aligning precoding method to handle the power leakage problem. First, a phase shifter network (PSN) structure is proposed, which enables each RF chain in beamspace MIMO to select multiple beams to collect the leakage power. Then, a rotation-based precoding algorithm is designed based on the proposed PSN structure, which aligns the channel gains of the selected beams toward the same direction for maximizing the received SNR at each user. Furthermore, we reveal some system design insights by analyzing the sum-rate and energy efficiency (EE) of the proposed beam-aligning precoding method. In simulations, the proposed approach is found to achieve the near-optimal sum-rate performance compared with that of the ideal case of no power leakage, and obtains a higher EE than those of the existing schemes with either a linear or planar array.
Tragic optimism: an integrative meaning-centred approach to trauma treatment

ABSTRACT The purpose of this article is to first explain the concept of Tragic Optimism (TO) and why it is necessary for effective counselling and trauma therapy. It then explains how the TO model is based on the Positive Psychology 2.0 (P.P. 2.0) framework and examines the empirical support of its contribution to posttraumatic growth. It also discusses the psychometrics of the Life Attitudes Scale (LAS) – an instrument based on the TO components. Finally, it will consider the relevance of the dual-system model and the ABCDE strategy in Wong’s integrative meaning therapy in trauma treatment and concludes with a case study employing the Meaning Therapy with a TO emphasis.
Control Mechanisms and Agile Methodology Use: Data from the Industry

ABSTRACT This research investigates the relationship between the degree of agile methodology use and four types of control mechanisms that are widely used in the management of a software project and its personnel: outcome control, behavioral control, clan control, and self-control. We conducted two studies: a survey of software professionals and a follow-up interview in six organizations. We found the following: 1) software professionals reported high outcome and clan controls regardless of their levels of agile methodology use; 2) the high agile group reported some use of behavioral and self-controls while the low agile group did not; 3) even though behavioral control was minimum in the low agile group, all professionals we interviewed strongly agreed that using established materials and following existing procedures should be standardized practices in software development. Implications of this research are discussed.
Sacralizing the Tomb and Spring of Bagusan in the Development of Religious Tourism Village

This article aims to criticize the development of religious tourism which tends to create economic-oriented goals and neglect the sacredness of both religious sites and rituals. The declaration of religious tourism program in many villages tends to push local people further to take more serious attentions to some religious sites and rituals in order to gain more economic profit without considering other aspects, including the sacredness of those religious sites and rituals. Through the case study of managing Sendang and Makam Bagusan in Godean, Sleman, Special District of Yogyakarta, particularly considering on in-depth interviews and participant observations, the results of this research show how the sacredness of religious sites and rituals can be preserved and reproduced along with increasing the numbers of pilgrims. Several efforts in preserving and reproducing the sanctity of religious sites have effectively preserved the sacredness of both sites and rituals from economics-oriented behaviors in developing religious tourism projects across this country.
Design Classification Rules Using the PSO of Multiple Swarms

A new approach for classification rules using multi-particle swarm is proposed.In this way,the c(c≥2) classification problem is taken as c two-class problems,and c class rules are expressed by c particle swarms, and classfication rules with the continuous variable space is finished by the PSO algorithm.Experimental results on 5 data sets show its efficiency and feasibility and are compared with the C4.5 results.
Automatic helmet-wearing detection for law enforcement using CCTV cameras

The objective of this research is to develop an application for enforcing helmet wearing using CCTV cameras. The developed application aims to help law enforcement by police, and eventually resulting in changing risk behaviours and consequently reducing the number of accidents and its severity. Conceptually, the application software implemented using C++ language and OpenCV library uses two different angle of view CCTV cameras. Video frames recorded by the wide-angle CCTV camera are used to detect motorcyclists. If any motorcyclist without helmet is found, then the zoomed (narrow-angle) CCTV is activated to capture image of the violating motorcyclist and the motorcycle license plate in real time. Captured images are managed by database implemented using MySQL for ticket issuing. The results show that the developed program is able to detect 81% of motorcyclists on various motorcycle types during daytime and night-time. The validation results reveal that the program achieves 74% accuracy in detecting the motorcyclist without helmet.
Long-Term Associations Between Subjective Ratings of Wellness and Exertion and Parameters of Training Load in Elite Soccer Over Two Seasons

The purpose of this study was to investigate longitudinal relationships between subjective (self-reported) measures of readiness prior to training and exertion post-training with objective parameters of training performance measured via body-worn global positioning systems (GPS). Data was collected over a two-season period with 36 elite Premiership soccer players (16 players from the 2013/14 season; 20 players from the 2014/15). Subjective ratings of muscle soreness (MS) and fatigue (Fat) were collected prior to training sessions two days before a match. Sessional load (rating of perceived exertion x session duration) (sRPE) was measured following each session. GPS metrics of total distance covered (TD), high-speed running (HSR) and dynamic stress load (DSL), were significantly associated with subjective measures post-training. However, correlations between pre-training MS and HSR were discovered for the squad during the 2014/15 season. These observations were positive which showed increased muscle soreness correlated with increased HSR, which was not expected as increased muscle soreness would result in lower higher speed activity. Individual analyses revealed that 25% of the players demonstrated negative moderate or strong associations presenting that decreased muscle soreness is correlated with a decrease of HSR and TD. Post-training subjective ratings both presented strong associations at squad level between increased sRPE and increased DSL, HSR and TD. At the individual level 50% of players presented moderate to strong associations between sRPE and TD, and between HSR and DSL over both seasons. When examined over a combined two season period again squad pre-training subjective measures did not give any clear indication on performance levels within the following training session. There where individual instances of significant associations between pre-training subjective measures and GPS metrics. In addition, good associations between sRPE and both TD and DSL across both seasons provide new evidence for the use of post-session RPE as a robust indication of training performance within elite level soccer.
Analysis of C2C Website Competitiveness Based on Text Sentiment Analysis and Dynamic Fuzzy Computation

With the global step into the era of large data, online review data show an explosive growth trend, and the resulting information overload has affected the consumer’s decision making. In terms of consumers, to tap the purchase decision-making information from the vast amount of information is a must-do before the tedious process; how to find the best service from a variety of shopping sites and find satisfied products from thousands of Products and how to help companies improve themselves, has become the forefront of e-commerce research topics. In this paper, data acquisition, data filtering, theme analysis, correlation analysis, sentiment analysis and dynamic fuzzy statistics are introduced to the user-generated content (UGC) fuzzy comprehensive evaluation. Firstly, collect the data, we use the correlation analysis and the stopped words to reduce the dimensionality of the data, and generate the revised candidate sets of themes. Then, we use emotion extraction, emotion polarity judgment and comment analysis to divide the UGC into several evaluation grades; then we get the fuzzy comprehensive evaluation matrix by the fuzzy statistic method. Finally, the comprehensive evaluation model is constructed to give users product score and the C2C website ranking.
Study of Linkages Between Outward Foreign Direct Investment (OFDI) and Domestic Economic Growth: an Indian Perspective

The inwards and outward FDI movement plays an important role in the economic development and growth of any economy. The OFDI have its impact on the home economy based on scale effect, competition effect and knowledge effect. It helps other domestic companies to expand their business and scale of firms by giving them technological and trade benefits. OFDI help the companies to improve efficiency and upgrade production processes. This paper is focusing on the Outward Foreign Direct investment (OFDI) and its linkage with the domestic growth in Indian perspective. It takes in to account various direct and indirect benefits which may spillover to domestic economy. This paper is examining the question whether OFDI is linked to production or it is regarded as a withdrawal of domestic capital. Further it has positive or negative impact on the GDP of the country. On the above question several studies have been conducted in developed countries but not in Indian context. So, this paper is attempting to answer the above question in Indian context. The research methodology for this paper is secondary in nature and the data collected for this paper is taken from the RBI sources. We have taken a period of 10 years from 2007 to 2016. For this paper, we used Augmented Dickey– Fuller (ADF) for testing the stationarity, Eigen value test, Trace test. Further we used the regression analysis in order to check the relationship of GDP and Outward Foreign Direct investment (OFDI). The results of the data analysis explain that there is no significant relationship between the OFDI and domestic economic growth in India. However, the results in case of some firms may not be the same.
Silhouette Threshold Based Text Clustering for Log Analysis

- Automated log analysis has been a dominant subject area of interest to both industry and academics alike. The heterogeneous nature of system logs, the disparate sources of logs (Infrastructure, Networks, Databases and Applications) and their underlying structure & formats makes the challenge harder. In this paper I present the less frequently used document clustering techniques to dynamically organize real time log events (e.g. Errors, warnings) to specific categories that are pre-built from a corpus of log archives. This kind of syntactic log categorization can be exploited for automatic log monitoring, priority flagging and dynamic solution recommendation systems. I propose practical strategies to cluster and correlate high volume log archives and high velocity real time log events; both in terms of solution quality and computational efficiency. First I compare two traditional partitional document clustering approaches to categorize high dimensional log corpus. In order to select a suitable model for our problem, Entropy, Purity and Silhouette Index are used to evaluate these different learning approaches. Then I propose computationally efficient approaches to generate vector space model for the real time log events. Then to dynamically relate them to the categories from the corpus, I suggest the use of a combination of critical distance measure and least distance approach. In addition, I introduce and evaluate three different critical distance measures to ascertain if the real time event belongs to a totally new category that is unobserved in the corpus.