Miracle-dz/UniFAR-FTUs huggingface dataset:
A Standard GA Approach to Native Protein Conformation Prediction

Finding the 3-D geometry or tertiary structure of an arbitrary protein is vital to understanding the functionality of that protein. The prediction of this structure, known as the protein folding problem, is very difficult and has been labeled one of the “grand challenge problems” for the scientific community. We report here on further work to determine tertiary structures via genetic algorithms. We build on work done first by Unger and Moult using a simplified protein model but improve on the application of GAs to this model. We show, using the same simplified model, that the genetic algorithm indeed appears effective for determining tertiary structure with far fewer computational steps than first reported.
Blind Clustering of Popular Music Recordings Based on Singer Voice Characteristics

Music is more than just a collection of notes, beats, chords or rhythmic words. It is an information rich medium in itself, capable of conveying various concepts ranging from concrete to abstract, such as compositions, themes, artistic styles, moods, sentiments, or even ethnic culture; concepts which cannot be exhaustively described and attached textually to every music recording. As music material encoded in digital formats rapidly grow in size and number, finding the desired music information from an enormous amount of available options can be, however, no easy task. This problem has consequently motivated recent research towards content‐based retrieval of music information. Many techniques have been developed for extracting information of interest from music residing in an audio signal, such as melody (Durey and Clements 2002; Akeroyd et al. 2002), instrument involved (Herrera et al. 2000; Eronen 2003), genre (Tzanetakis and Cook 2002; Xu et al. 2003), artist or singer (Berenzweig 2002; Kim and Whitman 2002; Liu and Huang 2002). As an independent capability or as part of a music information retrieval system, techniques to automatically organize a collection of music recordings are needed in order to lessen or replace human documentation efforts. In response to
Consumer control in Smart Grids

Information about electrical consumption characteristics of the different economic sectors is crucial for any countries’ electricity sector, and Brazil is not an exception. The country has experienced a continuous economic growth during the last years that has influenced the way electric energy is consumed. In this context, the present study aims to present the major findings of a literature review in order to characterize the Brazilian electricity sector. The characteristics of the residential, commercial and industrial subsectors that comprise the electricity sector are presented, as well as further information about their consumption profile. It is observed a higher amount of works devoted to study the residential sector, on the contrary there is a lack of newly researches focusing on the commercial and industrial sectors.
An iterative parameter estimation method for biological systems and its parallel implementation

One difficulty in building a mechanistic model of biological systems lies in determining correct parameter values. This paper proposes a novel parameter estimation method to infer unknown parameters, such as kinetic rates, from noisy experimental observations. Derived from the approximate Bayesian computation sequential Monte Carlo algorithm, our method predicts the distribution of each parameter rather than a single value via several intermediate distributions. Motivated by the computational intensity of the method, we improve the approximate Bayesian computation sequential Monte Carlo method in two aspects. First, to increase the efficiency, a windowing method is developed to reduce the parameter‐searching space, and an adaptive sampling weight mechanism is introduced to make the intermediate distributions converge to the target distributions in a much quicker manner. Second, to speed up the estimation process, we implement our method in a parallel computing environment to speed up the sampling process. Copyright © 2013 John Wiley & Sons, Ltd.
TURP: Managing Trust for Regulating Privacy in Internet of Things

—Internet of Things [IoT] applications, such as smart home or ambient assisted living systems, promise useful services to end users. Most of these services rely heavily on sharing and aggregating information among devices; many times raising privacy concerns. Contrary to traditional systems, where privacy of each user is managed through well-deﬁned policies, the scale, dynamism, and heterogeneity of the IoT systems make it impossible to specify privacy policies for all possible situations. Alternatively, this paper argues that handling of privacy has to be reasoned by the IoT devices, depending on the norms, context, as well as the trust among entities. We present a technique, where an IoT device collects information from others, evaluates the trustworthiness of the information sources to decide the suitability of sharing information with others. We demonstrate the applicability of the technique over an IoT pilot study.
Probability Distribution of Turbulent Kinetic Energy Dissipation Rate in Ocean: Observations and Approximations

The probability distribution of turbulent kinetic energy dissipation rate in stratified ocean usually deviates from the classic lognormal distribution that has been formulated for and often observed in unstratified homogeneous layers of atmospheric and oceanic turbulence. Our measurements of vertical profiles of micro-scale shear, collected in the East China Sea, northern Bay of Bengal, to the south and east of Sri Lanka, and in the Gulf Stream region, show that the probability distributions of the dissipation rate ɛ∼r in the pycnoclines (r ∼ 1.4 m is the averaging scale) can be successfully modeled by the Burr (type XII) probability distribution. In weakly stratified boundary layers, lognormal distribution of ɛ∼r is preferable, although the Burr is an acceptable alternative. The skewness Skɛ and the kurtosis Kɛ of the dissipation rate appear to be well correlated in a wide range of Skɛ and Kɛ variability.
Observations z SVM Threshold Detector ClassificationBayesian Filter SVM Output Expected Behavior

The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. Improving safety at intersections has also been identified as high priority due to the large number of intersection related fatalities. This paper focuses on developing algorithms for estimating driver behavior at road intersections. It introduces two classes of algorithms that can classify drivers as compliant or violating. They are based on 1) Support Vector Machines (SVM) and 2) Hidden Markov Models (HMM), two very popular machine learning approaches that have been used extensively for classification in multiple disciplines. The algorithms are successfully validated using naturalistic intersection data collected in Christiansburg, VA, through the US Department of Transportation Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) initiative.
Determination of Supplementary Water Requirements of Selected Food Crops per Growth Stage Using Climatic Indices

The present study examines the possibility of determining supplementary water requirements of selected food Crops per growth stage using the climatic indices alone. Rainfall and temperature datasets on high-resolution (0.5x0.5 degree) grids resolution were assembled from the Climatic Research Unit CRU TS 3.21 of the University of East Anglia, Norwich, United Kingdom for the period 1943-2012. To ascertain the suitability of the datasets for use in the study area the Pearson Product moment correlation was undertaken with measured rainfall data from Yelwa synoptic station, Nigerian Meteorological Agency. Melon, Beans, Millet, Sorghum, Soybean and Cucumber were used for estimating seasonal supplementary irrigation water needs in the Sokoto Rima, River Basin. The observed seasonal patterns of rainfall and PET shows that soil moisture surplus in the basin sets in from June – August while April to May is the soil water recharge periods. From October, water is withdrawn from the basin, paving way for moisture deficit which last from November-March. The study shows that even during the rainy months, supplementary irrigation is needed to compensate for deficit due to increased rate of evapo-transpiration. It is also possible to carry out an all year round cultivation in the basin. For an all-year-round cultivation of crops with greater yields, full scale irrigation is needed for a second cultivation season (from October to February) when the soil moisture storage of the basin is below the basin field capacity. In absence of spatial dataset, the use of CRU proves to be an alternative in climatic response and modelling studies.
PC Clusters for Virtual Reality

In the late 90’s, the emergence of high-performance 3D commodity graphics cards paved the way to the use of PC clusters for high-performance Virtual Reality (VR) applications. Today PC clusters are broadly used to drive multi-projector immersive environments, among other high-performance VR tasks such as tracking and sound synthesis. This survey presents the different approaches that have been developed to use PC clusters for VR applications. We review taxonomies and the most common software and hardware tools that enable us to take advantage of clusters. We also discuss some new trends for the near future.
UNIVERZA V LJUBLJANI FAKULTETA ZA MATEMATIKO IN FIZIKO Robert Hauko: AN ACOUSTIC ANALOG OF THE CASIMIR EFFECT SEMINAR

The Casimir effect is a phenomenon that has been exciting physics for five decades. In 1948 the Dutch physisist Hendrik Casimir theoretically proved the existence of the attractive force between the two perfectly conductive rigid plates on a small distance in a vacuum at zero temparature. The experimental verification came only after 50 years. In the first part of the seminar I described some important historical points for the later understanding of interatomic and intermolecular forces. I derived the Casimir force and showed some most succesful measurements in the recent years. In the second part I described acoustic analog of this phenomenon. First I compared it to EM Casimir effect and later I described the experiment into detail and analysed the measurement results. In the conclusion I showed some possible applications of the effect.
THE STRENGTH OF WEAK TIES YOU CAN TRUST: THE MEDIATING ROLE OF TRUST IN EFFECTIVE KNOWLEDGE TRANSFER.

Recent research suggests that people obtain complex, useful knowledge from other people with whom they work closely and frequently (i.e., strong ties). Yet there has been only limited systematic empirical work examining why strong ties are important for knowledge transfer. Based on a review of the social network, trust, and knowledge/organizational learning literatures, we propose a model whereby two-party (dyadic) trust mediates the relationship between strong ties and effective knowledge transfer. We tested this model with a two-stage survey in three companies in different countries and found strong support. First, the relationship between strong ties and effective knowledge transfer (as reported by the knowledge receiver) was mediated by competence- and benevolence-based trust. Second, once we controlled for these two dimensions of trust, it was actually weak ties that provided the most useful knowledge. This latter finding is consistent with prior research suggesting that weak ties provide access to n.
Performance Assessment of Convolutional Neural Networks for Semantic Image Segmentation

Convolutional neural networks are applied successfully for image classification and object detection. Recently, they have been adopted to semantic segmentation tasks and several new network architectures have been proposed. With respect to automotive applications, the Cityscapes dataset is often used as a benchmark. It is one of the biggest datasets in this field and consists of a training, a validation, and a test set. While training and validation allow the optimisation of these nets, the test dataset can be used to evaluate their performance. Our investigations have shown that while these networks perform well for images of the Cityscapes dataset, their segmentation quality significantly drops when applied to new data. It seems that they have limited generalisation abilities. In order to find out whether the image content itself or other image properties cause this effect, we have carried out systematic investigations with modified Cityscapes data. We have found that camera-dependent image properties like brightness, contrast, or saturation can significantly influence the segmentation quality. This papers presents the results of these tests including eight state-of-the-art CNNs. It can be concluded that the out-of-the-boxusage of CNNs in real-world environments is not recommended.
Relationship between Ocean Carbon and Heat Multidecadal Variability

AbstractThe global ocean serves as a critical sink for anthropogenic carbon and heat. While significant effort has been dedicated to quantifying the oceanic uptake of these quantities, less research has been conducted on the mechanisms underlying decadal-to-centennial variability in oceanic heat and carbon. Therefore, little is understood about how much such variability may have obscured or reinforced anthropogenic change. Here the relationship between oceanic heat and carbon content is examined in a suite of coupled climate model simulations that use different parameterization settings for mesoscale mixing. The differences in mesoscale mixing result in very different multi-decadal variability, especially in the Weddell Sea where the characteristics of deep convection are drastically changed. Although the magnitude and frequency of variability in global heat and carbon content is different across the model simulations, there is a robust anti-correlation between global heat and carbon content in all simula.
AIS Electronic Library (AISeL)

Value creation is the core purpose of organisations, and the Value Creation Logics (VCLs) describe how organisations create value for their customers through the provision of goods or services. VCLs can help organisations understand and model their business processes in order to fully utilise their resources and achieve optimal performances. Most organisations rely heavily on IS/IT for their value creation. Alignment between IS/IT and business strategies as well as with the VCLs thus plays a key role in the benefits realisation of IT investment. This paper investigates the empirical realisation of different VCLs, and the role of IS/IT therein, through a case study of a UK law firm. The findings show that the different types of value creation logics co-exist and that problems occur when the organisational structure does not support the various needs of the different logics. In order to support the logics, a Viable System Model-inspired organisational design is suggested. This is intended to drive the IS/IT strategy in order to support concurrently different value creation logics.
VIRTUAL LEARNING IN NIGERIAN UNIVERSITIES: A Panacea for Enhanced Academic Standards

Currently, local area network (LAN) is commonplace in the Nigerian tertiary institutions and can be a good platform for distributing and disseminating instructional materials. Thus, this paper proposes to improve the quality of academics through online provision of learning resources based on Free and Open Source Software (FOSS); wired and wireless access to contents; and availability of the system 24/7. The system is based on third party software or FOSS called phpBB and Windows 2003 Server Active Directory Services. Both are installed and configured on an intranet. It has a discussion forum which is accessed through Hypertext Transfer Protocol using a web browser; and directory services for files/folders upload and download based on a set of privilege levels in Discretionary Access Control List (DACL) as a way of improving security.
Team-KMUTT: Team Description Paper

This paper describes the design and development of the kidsized humanoid robots of the Team-KMUTT team for RoboCup 2019 Humanoid League (Sydney, Australia). This paper will include system overview, vision module and next goal for development. We also discuss about our improvement in color segmentation, ball detection and introducing landmark detection for self-localization.
MyUI: Mainstreaming Accessibility through Synergistic User Modelling and Adaptability

This deliverable defines the requirements for user interface adaptation for MyUI. The deliverable contains a literature review on the variability of elderly people, considering their abilities and limitations. It covers their physical/motor, sensory, cognitive and environmental degradations due to age and age-related conditions, and specifically including stroke. User requirements were derived based upon the literature review; empirical data from focus groups with the elderly in the UK and Spain; interviews with care professionals and subject matter expertise and technological expertise within the consortium. User requirements were defined for general, physical/motor, sensory, cognitive, and environmental aspects. From the user requirements corresponding functional, technical and non-functional requirements were derived.
[Re] Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks

As part of the NeurIPS 2019 Reproducibility Challenge, we chose to attempt re1 produce the attack algorithm proposed in “Subspace Attack: Exploiting Promising 2 Subspaces for Query-Efficient Black-box Attacks”. Our reported results are better 3 than the original paper in terms of the median number of queries per attack, but 4 worse in terms of failure rate. A concise assessment of our implementation is also 5 included. 6
Constraint based Clustering in Feature Subset Selection Algorithm

Constraint based clustering that satisfies set of user-defined constraint. Constraint-based clustering is an example of a mining task where flexibility is desirable. It is a generalization of standard clustering in which the user can impose constraints on the clustering to be found, such as similar and dissimilar constraints. Feature selection involves recognizing a subset of the most constructive features that produces companionable outcomes as the innovative intact set of traits. Feature selection, as a data for preliminary considered. Features are processing step is effectual in reducing the spatial property, confiscate irrelevant data, mounting learning accuracy. We proposed an algorithm for the Region of Influence (ROI). The algorithm is numb to the order in which the pairs are divided into cluster by using relative neighborhood graphs (RNGs).
An integrated approach to measuring Semantic Similarity between Words using Information available on the Web Blind

Measuring semantic similarity between words is vital for various applications in natural language processing, such as language modeling, information retrieval, and document clustering. We propose a method that utilizes the information available on the Web to measure semantic similarity between a pair of words or entities. We integrate page counts for each word in the pair and lexico-syntactic patterns that occur among the top ranking snippets for the AND query using support vector machines. Experimental results on MillerCharles’ benchmark data set show that the proposed measure outperforms all the existing web based semantic similarity measures by a wide margin, achieving a correlation coefficient of0.8129. Moreover, the proposed semantic similarity measure significantly improves the accuracy (Fmeasure of 0.78) in a named entity clustering task, proving the capability of the proposed measure to capture semantic similarity using web content.
An approximation of the error back-propagation algorithm in a predictive coding network with local Hebbian synaptic plasticity

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error back-propagation algorithm. However, in the back-propagation algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of pre-synaptic and post-synaptic neurons. Several models have been proposed that approximate the back-propagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the back-propagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.
The Debate About the Sources of Growth in East Asia after a Quarter of a Century: Much Ado About Nothing

This paper reviews what the profession has learned during the last 25 years about East Asia’s growth using growth accounting exercises and estimations of production functions. The publication of Alwyn Young’s (1992, 1994, 1995) and Jong-Il Kim and Lawrence Lau’s (1994) studies, and Paul Krugman’s (1994) popularization of the “zero total factor productivity growth” thesis, led to a very important debate within the profession. The paper demystifies this literature by pointing out a major methodological problem inherent in these approaches. This is that the variables used, namely, output, employment, the stock of capital, and the factor shares, are related definitionally through an accounting identity in value terms. As a consequence, results that are believed to reflect a true technological or production relationship are no more than the outcome of manipulating this identity. We show that total factor productivity calculated with value data is not a measure of productivity. We conclude that the debate about the sources of growth in East Asia was much ado about nothing.
Self-Optimizing Random Forests

Random Forests (RFs) have become a standard pick of data scientists for classification and 4 regression tasks over the last two decades. Several modifications of RFs have been proposed 5 to eliminate the limitation of standard RFs to axis-aligned splits through the notion of 6 Oblique Random Forests (ORFs), which allow that splits are defined over linear combinations 7 of attributes rather than a single attribute. However, like there is no single best learner for 8 all supervised learning problems, there is also not a single best RF type. In this paper, we 9 present self-optimizing random forests (SORFs). SORFs incrementally create a forest by 10 growing different tree types and identifying the best tree type based on extrapolations of the 11 forest performance curves obtained from out-of-bag performance estimate. Our exhaustive 12 empirical evaluation shows that SORFs consistently achieve the maximum performance 13 across three considered types of RFs while requiring only half the time for training all 14 these forests on average. At the same time, SORFs outperform standard RFs statistically 15 significantly and by at least 0.01 in accuracy on 25% of the considered datasets and even 16 substantially beyond 0.35 in two cases.