Miracle-dz/UniFAR-FTUs huggingface dataset:
High Mobility Control of Humanoid Robots Based on an Analogy of ZMP-COG Model and Carted Inverted Pendulum Model

A new control of the whole-body motion of humanoid robots is proposed. An analogy of ZMP-COG model and carted inverted pendulum represents the core dynamics of humanoids as strong nonlinear systems. It clues to the ZMP manipulation with a large number of degrees-of-freedom in the whole-body cooperated, and to the technique of a direct handling of dynamical constraints about the reaction force from the environment. As the result, the robot body is controlled just as if it were a simple inverted pendulum. And, COG Jacobian maps the motion of such an approximate model to the real multibody system. The advantage of proposed typically appears in the robustness against large perturbations, since it is suitable for a realtime implementation with a less computational cost.
Categorical Semantics of Reference Data Type

Reference types are very useful structures enabling handling with dynamic memory. In this paper we extend categorical model of type system with reference data type. We illustrate our approach on simple functional programming language T-NBL extended with reference type. After constructing parametric algebraic specification we construct categorical model of reference type and we show how our results can be implemented in functional programming language Ocaml.
Cube: a multi-view localization framework for 3D object analysis in computer vision applications

Precise object localization and volume estimation from a single viewpoint is a very difficult task. Furthermore, analysis of the real motion in only one view is (quasi)-impossible. To accomplish these valuable object analysis steps, information of multiple cameras is analyzed using a new localization framework. The framework merges the single-view detection results of the multiple cameras by homographic projection onto multiple horizontal and vertical planes, which slice the scene. The crossings of these slices create a 3D grid of virtual sensor points. Using this grid and subsequent spatial and temporal 3D clean-up filters, information about 3D object location, size, and motion can be instantly extracted from the video data. The novel aspect in the proposed framework is the 3D grid creation, which is a 3D extension of Arsic’s multiple plane homography. By using the grid, more accurate localization and object modelling is possible. Also the use of dynamic camera maps, and spatial and temporal 3D filters, provides a more reliable object analysis.
The New forms of embodiment, between Philosophy and Cognitive Neurosciences.

The term “embodiment” is normally used as a generic label to indicate a series of new approaches in the studies of mind. Focusing on the common grounds of these approaches, we will analyse the core meaning of “embodiment”, highlighting its intrinsic interdisciplinary attitude. More specifically, we will focus on its connection with the evolutionary perspective as well as the cognitive and relational neurosciences. We will therefore underline the highly heuristic value of embodiment as a neuro-biologically grounded epistemological paradigm for the study of the mind.
A Matlab Tutorial for Diffusion-Convection-Reaction Equations using DGFEM

We present a collection of MATLAB routines using discontinuous Galerkin finite elements method (DGFEM) for solving steady-state diffusion-convection-reaction equations. The code employs the sparse matrix facilities of MATLAB with "vectorization" and uses multiple matrix multiplications "MULTIPROD" (5) to increase the efficiency of the program.
Semi-Supervised Learning with Uncertainty

Summary The goal of semi-supervised learning is to utilize many unlabeled samples under a situation where a few labeled samples exist. Recently, researches of semi-supervised learning are evolving with deep learning technology development, because, in deep, models have powerful representation to make use of abundant unlabeled samples. In this paper, we propose a novel semi-supervised learning method with uncertainty . It naturally extends the consistency loss under the uncertainty and propose suitable regularizations for the uncertainty. Using two datasets CIFAR-10 and SVHN and with various experiments, we empirically demonstrate that the proposed method achieves competitive or higher performance in accuracy when compared to semi-supervised learning with the conventional consistency loss while our proposal can let a model generalize much faster.
Fractional PID controlled cascaded flyback switched mode power supply with enhanced time domain response

This work compacts with the modeling, simulation, and application of a Fractional Order Proportional Integral Differential (FOP-I-D) controlled Cascaded Flyback Switched Mode Power Supply (CFSMPS) system. It recommends Parallel cascaded flyback converter for the production of essential DC voltage from the input supply voltage. The output from CFSMPS is regulated by using closed loop configuration. The simulation of Closed-loop Proportional-Integral (PI) and FOP-I-D controlled CFSMPS system has been done and the results of the systems are related. The outcomes signify that the FOP-I-D based system has presented an enhanced response to represent as similar to the PI controlled CFSMPS system. The FOP-I-D controlled CFSMPS system has benefits like decreased steady-state error and enhanced time-domain response.
Self-organizing resource allocation for autonomic network

Application-layer networks (ALN) are software architectures that allow the provisioning of services requiring a huge amount of resources by connecting large numbers of individual computers, e.g. grids and P2P-networks. Self-organization, like proposed by the autonomic computing concept, might be the key to controlling these systems. The CATNET project evaluates a decentralized mechanism for resource allocation in ALN, based on the economic paradigm of the Catallaxy. The economic model is based on self-interested maximization of utility and self-interested cooperation between software agents, who buy and sell network services and resources to and from each other.
Eighteenth-century Cholón

The main purpose of this book is to give a description of the Cholon language as represented in the Arte de la Lengua Cholona (ALC), a colonial grammar written in 1748 by a Franciscan friar, named Pedro de la Mata. The ALC is kept in the British Library in London. Nowadays, the Cholon language is probably extinct. It was spoken in North Peru in the valley of the Huallaga river. Cholon formed a small language family together with the neighbouring language Hibito. The description of eighteenth-century Cholon, the linguistic part of the book, is preceded by a description of secondary sources and of theories about genetic relations (chapter 1), by an ethnohistorical sketch (chapter 2), and by an analysis of the manuscript (chapter 3). The linguistic part starts with an analysis of the orthography used in the ALC and of the observations about certain sounds, in order to reconstruct a tentative sound system (chapter 4). Chapter 5 deals with morphonology. In this chapter attention is paid to syllable structure, to phenomena like vowel suppression and harmonization, and to stem-initial consonant changes. Nominal and verbal morphosyntax are discussed in chapters 6 and 7, respectively. Cholon is an agglutinative language. Besides nouns and verbs, which are the most important word categories, Cholon has a small class of adverbs (chapter 8) and interjections (chapter 9). In chapter 10 discourse markers, such as question and exclamation markers, are treated. Chapter 11 is dedicated to the negation. In chapter 12, a survey of the different subordinate clauses is given. The linguistic part ends with a lexicon.
Causal Inference for a Single Group of Causally-Connected Units Under Stratified Interference

The assumption that no subject's exposure affects another subject's outcome, known as the assumption of no interference, has long held a foundational position in the study of causal inference. However, this assumption may be violated in many settings, and in recent years has been relaxed considerably. Often this has been achieved with either the aid of knowledge of an underlying network, or the assumption that the population can be partitioned into separate groups, between which there is no interference, and within which each subject's outcome may be affected by all the other subjects in the group, but only as a function of the total number of subjects exposed (the stratified interference assumption). In this paper, we consider a setting in which we can rely on neither of these aids, as each subject affects every other subject's outcome. In particular, we consider settings in which the stratified interference assumption is reasonable for a single group consisting of the entire sample, i.e., a subject's outcome is affected by all other subjects' exposures, but only via the total number of subjects exposed. This can occur when the exposure is a shared resource whose efficacy is modified by the number of subjects among whom it is shared. We present a doubly-robust estimator that allows for incorporation of machine learning, and tools for inference for a class of causal parameters that includes direct effects and overall effects under certain interventions. We conduct a simulation study, and present results from a data application where we study the effect of a nurse-based triage system on the outcomes of patients receiving HIV care in Kenyan health clinics.
What is this painting about? Experiments on Unsupervised Keyphrases Extraction algorithms

A large number of cultural heritage archives are freely available on the web: they can be in Linked Open data format, or in any other format, such as databases, collections or archives, with some information for each object. To be really enjoyed and enjoyable by the users on the web, a set of scored keywords need to be associated with each item, manually or automatically. The overall problem here addressed is the automatic, unsupervised extraction of keywords/keyphrases from the items of cultural heritage archives, in different languages (English and Italian). The problem is very actual and in literature many papers are devoted to this topic and several approaches have been defined: we present here a work-in-progress, an experimentation done with the aim of automatically associating scored keywords/keyphrases to a painting archive. We have therefore tested five different methods present in literature, such as tf-idf, RAKE, TextRank, …, on two datasets, in English and in Italian, and evaluated the results - using recall and precision@n as the evaluation metrics.
Bidirectional LSTM-CRF Model and POS for Article Title Summarization

In this paper, we propose a method based on Bidirectional LSTM-CRF for article title summarization. And for the summary generated by the model is not compliant with the grammar rules problem, we use POS (Part of Speech) to revise the generated summary. In order to verity our method, we conducted an experiment with the article title of WeChat public number. The results show that our method is effective, and POS can make results consistent with grammar rules and read fluently.
Considerations for improving the accuracy of large-signal GaAs MESFET models to predict power amplifier circuit performance

Several approaches to large-signal GaAs MESFET modeling are considered as they apply to the design of high-efficiency portable communications equipment. The validity of each modeling approach is examined by comparing simulations to the fundamental and harmonic output power of a MESFET using load pull to represent typical amplifier load lines. Two effects which are believed to influence model accuracy are considered: 1) low-frequency dispersion in output conductance, and 2) modulation of the threshold voltage as a function of drain-source voltage. The results suggest that improved modeling of the modulation in threshold voltage with respect to drain-source voltage provides significantly more accurate predictions. In contrast, for these applications, when conventional approaches for adding low-frequency dispersion effects to the model were implemented, inaccurate predictions resulted. >
Temporal enhanced sentence-level attention model for hashtag recommendation

Hashtags of microblogs can provide valuable information for many natural language processing tasks. How to recommend reliable hashtags automatically has attracted considerable attention. However, existing studies assumed that all the training corpus crawled from social networks are labelled correctly, while large sample statistics on real social media shows that there are 8.9% of microblogs with hashtags having wrong labels. The notable influence of noisy data to the classifier is ignored before. Meanwhile, recency also plays an important role in microblog hashtag, but the information is not used in the existing studies. Some temporal hashtags such as World Cup will ignite at a particular time, but at other times, the number of people talking about it will sharply decrease. To address the twofold shortcomings above, the authors propose an long short-term memory-based model, which uses temporal enhanced selective sentence-level attention to reduce the influence of wrong labelled microblogs to the classifier. Experimental results using a dataset of 1.7 million microblogs collected from SINA Weibo microblogs demonstrated that the proposed method could achieve significantly better performance than the state-of-the-art methods.
An Efficient Mitigation Method for Timing Side Channels on the Web

Research has shown that timing side channels exist in web applications [1, 3, 5]. An obvious, but problematic, mitigation for timing attacks is to delay the execution time to the worst case execution time, so that all requests have the same response time. On the upside, this prevents timing attacks as there are no differences in the response time any more. On the downside, this approach has a negative effect on performance, which may render the approach useless for many practical systems. In this extended abstract, we propose a new strategy to prevent timing attacks in web applications with little impact on performance. Our approach offers a provable security gain that can be freely traded for a performance decrease. We compare our approach to other strategies using two characteristics: firstly the added cost for an attacker to perform a side channel attack, and secondly the performance impact on the system. The rest of this paper is organised as follows: In section 2, we describe timing side channel attacks on the web and review existing research for timing side channel mitigation. Section 3 provides a formal system model for the timing side channels we aim to mitigate. Section 4 compares existing timing side channel mitigation strategies with our own approach.
Stock Returns Prediction by Using Artificial Neural Netwok Model for Pakistan Stock Exchange

Artificial neural networks are extensively used to predict the financial time series. This study implements the neural network model for predicting the daily returns of the Pakistan Stock Exchange (PSE). Such an application for PSE is very rare. A multi-layer perception network is used for the model used in this study, while the network is trained using the Error Back Propagation algorithm. The results showed that the predictive power of the network was performed by the return of the previous day rather than the input of the first three days. Therefore, this study showed satisfactory results for PSE. In short, artificial intelligence can be used to give a better picture of stock market operators and can be used as an alternative or additional to predict financial variables.
A Neuro-Fuzzy Control Scheme for the Electrical Submersible Pumping in Oilfield Systems

This work presents the development and implementation of a hybrid intelligent system (neuro-fuzzy) based controller applied to a non-linear flow control process in oilfield equipped with electrical submersible pumping as artificial lifting method. The efficient control of industrial plants requires that the user apply unconventional techniques, particularly when using different set-points or when it is required to follow certain system reference trajectories. The need to obtain expert knowledge in inference fuzzy logic membership functions and parameters are set automatically, using for training their own application data. The neuro-fuzzy model obtained is used to generate control actions in series the process presenting a suitable dynamic response.
Searching time series using textual approximation

There has been much research work on similarity search in time series for many years. In this paper, we propose a new time series similarity search technique called TAX, which is using textual approximation method to apply existing document retrieval methods to time series database. The proposed textual approximation is a method that extracts set of terms called T-terms from a time series to approximate time series data using document retrieval methods from a time series database. The paper describes this novel similarity search technique using the textual approximation method, including T-term extraction and use of document retrieval methods. We will show that TAX is effective for classification as well as search in time series data set in our evaluations.
Reasoning with taxonomies

Taxonomies are prevalent in a multitude of fields, including ecology, linguistics, programming languages, databases, and artificial intelligence. In this thesis, we focus on several aspects of reasoning with taxonomies, including the management of taxonomies in computers, extensions of partial orders to enhance the taxonomic information that can be represented, and novel uses of taxonomies in several applications. The first part of the thesis deals with theoretical and implementational aspects of representing, or encoding, taxonomies. our contributions include (i) a formal abstraction of encoding that encompasses all current techniques; (ii) a generalization of the technique of modulation that enhances the efficiency of this strategy for encoding and reduces its brittleness for dynamic taxonomies; (iii) the development of sparse logical terms as a universal implementation for encoding that is supported by a theoretical and empirical analysis demonstrating their efficiency and flexibility. The second part explores our contributions to the application and extension of taxonomic reasoning in knowledge representation, logic programming, conceptual structures and ecological modeling. We formalize extensions to partial orders that increase the ability of systems to express taxonomic knowledge. We develop a generalization of equality constraints among logic variables that induces a partial order among equivalence classes of variables. For graphic knowledge representation formalisms, we develop techniques for organizing the derived hierarchy among graphs in the knowledge base. Finally, we organize abstract models of landscapes in a taxonomy that provides a framework for systematically cataloging and analyzing landscape patterns.
Guido's Guide to PROC FREQ - A Tutorial for Beginners Using the SAS® System

PROC FREQ is an essential procedure within BASE SAS® used primarily for counting, displaying and analyzing categorical type data. It is such a powerful procedure that you will find it documented not only in BASE SAS but also in SAS®/STAT documentation. This Beginning Tutorial will touch upon both the uses of PROC FREQ in BASE SAS and SAS/STAT. Don’t worry, I promise that you do not need a statistical background to understand this procedure. This tutorial will teach you the basics of PROC FREQ and give you a framework to build upon and extend your knowledge of the SAS System.
Klasifikasi Anjuran, Larangan, dan Informasi pada Hadits Shahih Bukhari Menggunakan Naive Bayes Classifier

Hadits adalah segala sesuatu yang dinisbatkan kepada Nabi Muhammad S.A. W baik berupa perkataan, perbuatan, taqrir (sikap diam setuju) dsb. Umumnya hadits di koleksi oleh beberapa imam besar, salah satunya koleksi hadits yang disusun oleh Imam Bukhari. Namun, saat ini belum ada penelitian maupun inovasi pengklasifikasian data berupa hadits berdasarkan anjuran, larangan dan yang hanya sekedar informasi. Dalam penelitian klasifikasi metode probablistik yang paling sering digunakan adalah Naive Bayes Classifier karena memiliki keunggulan paling cepat dan sederhana. Naive Bayes Classifier merupakan klasifikasi yang mengasumsikan keberadaan atribut suatu kelas tidak terkait atau tidak akan mempengaruhi atribut di kelas lain, atau dikenal dengan class conditional independence Namun permasalahan yang sering muncul pada penelitian terkait adalah tingginya dimensi data (banyaknya fitur / kata dalam satu opini). Hal ini tentu akan sangat mempengaruhi proses dari klasifikasi itu sendiri. Oleh karena itu, untuk mengurangi banyaknya fitur/kata pada sebuh data harus dilakukan beberapa tahapan seperti feature selection salah satu metodenya yaitu Chi-square. Penelitian ini menggunakan metode Naive Bayes dengan menambahkan seleksi fitur pada penelitian ini menggunakan Chi-square. Kata kunci : Classification, Feature Selection, Naive Bayes, Chi-Square, Hadits.
Voltage Tracking Control of DC- DC Boost Converter Using Fuzzy Neural Network

This paper deals with voltage tracking control of DC- DC boost converter based on Fuzzy neural network. Maintaining the output voltage of the boost converter in some applications are very important, especially for sudden change in the load or disturbance in the input voltage. Traditional control methods usually have some disadvantages in eliminating these disturbances, as the speed of response to these changes is slow and thus affect the regularity of the output voltage of the converter. The strategy is to sense the output voltage across the load and compare it with the reference voltage to ensure that it follows the required reference voltages. In this research, fuzzy neural was introduced to achieve the purpose of voltage tracking by training the parameter of controller based on previous data. These data sets are the sensing input voltage of the converter and the value of the output load changes. To establish the performance of proposed method, MATLAB/SIMULINK environments are presented, simulation results shows that proposed method works more precisely, faster in response and elimination the disturbances
Key Management for Data Plane Encryption in SDN Using WireGuard

Software Defined Networks (SDNs) decouple the control plane and the data plane, congregating control functions in a designated entity in the network, the controller. The decoupling realizes a highly dynamic network which has benefits such as cost reductions and programmability. The OpenFlow protocol defines the communication between the controller and the network nodes. This thesis aims to construct a key management scheme for data plane encryption in SDN through an OpenFlow channel secured by TLS. Manual operations are usually embedded in key management solutions, but due to the dynamic nature of SDN, manual operations are not realizable in this architecture. Therefore, an automatic scheme needs to be designed. Consequently, this thesis proposes a scheme for key management where the controller manages and initializes encrypted connections in the data plane. Encryption between the nodes is enabled by the newly developed secure tunneling protocol, WireGuard. The key management procedures are carried out through the secure OpenFlow channel. OpenFlow does not provide functionality for key management operations, and therefore this thesis proposes an extension to the protocol which facilitates this.