Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion classification based on bio-signals emotion recognition using machine learning algorithms

Emotions are complex processes involving multiple response channels, including physiological systems, facial expressions and voices. Bio-signals reflect sequences of neural activity, which result in changes in autonomic and neuroendocrine systems induced by emotional events. Therefore in human-computer interaction researches, one of the most current interesting topics in emotion recognition is to recognize human's feeling using bio-signals. The aim of this study is to classify emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multichannel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmo-graph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using four algorithms, linear discriminant analysis, Naïve Bayes, classification and regression tree and support vector machine. The used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 56.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naïve Bayes was highest (53.9%) and lowest by support vector machine (39.2%). This means that Naïve Bayes is the best emotion recognition algorithm for basic emotions. This result can be helpful to provide the basis for the emotion recognition technique in human-computer interaction.
An Automated, Language-Based Approach to the Creation of Component Libraries

A major promise of component-based software engineering is the reduction of application development time and costs by reusing software components. The existence of and access to component libraries, as well as the ability to build such libraries in the first place, is therefore key in a component-based development infrastructure to facilitate the envisioned reuse. We believe that the future demand for component libraries will increase substantially because many companies are about to adopt the software product line ideas. Due to the lack of a general standard for component libraries and because of the restrictions of existing ones, it is very often the case that new reuse libraries are written from scratch to fit a user's specific requirements. An additional problem is the size and the focus of (existing) component libraries or component repositories. With an increasing size and a broadening focus of the library it becomes harder to retrieve the "right" components due to more complex classification structures, which hinders effective reuse. This will lead to a large number of specific libraries, rather than to the global all-encompassing single one. This work introduces a component library description language, which allows for the definition of a component library, its provided functionality, and its associated semantics on a high level of abstraction. The language compiler uses those definitions made in the (XML-based) language to automatically generate a component library, which is a user-defined, customized application, including a web-based user interface and a persistent storage facility to store the components handled by the library. The language approach allows for the rapid development of domain specific component libraries in a time and cost efficient manner and therefore supports the need for fast creation of reuse libraries with minimal up-front investment.
Evaluating interval forecasts of high-frequency financial data

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression-based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated. Copyright © 2003 John Wiley & Sons, Ltd.
On the Evaluation of Handwritten Text Line Detection Algorithms

Even if numerous text line detection algorithms have been proposed, the algorithms are usually compared on a single database and according to a single metric. In this paper, we study the performance of four different text line detection algorithms, on four databases containing very different documents, and according to three metrics (Zone Map, ICDAR and recognition error rate). Our goal is to provide a more comprehensive empirical evaluation of handwritten text line detection methods and to identify what are the key points in the evaluation. We show that the different algorithms yield very different results depending on the type of documents and that two of them are constantly better than the others. We also show that the Zone Map and the ICDAR metric are strongly correlated, but the Zone Map metric provides greater detail on the error types. Finally we show that the geometric metrics are correlated to the recognition error rate on easy to segment databases, but this has to be confirmed on difficult documents.
A Data Structure Centric Method and Execution Model for Partitioning Sequential Programs into Multiple Speculative Threads

Speculative Multithreading (SpMT) approaches have gained wide popularity because they can get more parallelism by allowing two pieces of potentially conflicting code to execute in parallel while ensuring the correctness of the results by recovering from conflicts. However, the dividing-along-the-control-flow-path method employed by the traditional SpMT approach is unsuitable for a number of programs in which operations on different data structures are interleaved in the control flow path. If we linearly divide the program into threads along the control flow path, the operations on the same data structure will be divided among the different threads that are doomed to be prone to conflict when being executed concurrently. To effectively parallelize these programs, we proposed a data structure centric approach, in which the objects of a program are organized into different groups and the operations on the objects within the same group are dispatched to the same thread to be executed, thereby reducing the likelihood of conflict on the same data structure.
The Determinants of Successful Financial Innovation: An Empirical Analysis of Futures Innovation on Liffe

This paper documents futures innovation on LIFFE by empirically analyzing the individual growth profiles of its futures contracts and the factors that determine contract success or failure. The paper documents considerable heterogeneity across contracts, and finds that contract success can not easily be inferred from the contract's first years of trading. As expected, contract success is highly correlated with the size of the underlying market, as well as with its volatility. The paper also confirms the existence of a first-mover advantage. There is little systematic correlation, however, between bid-ask spreads and futures volume. This suggests that there may be a critical level of trading activity beyond which bid-ask spreads and execution risk vary relatively little. It is further argued that liquidity seems less a cause of contract success (or lack of liquidity a cause of failure), but rather a consequence. These results may provide a useful perspective as exchanges prepare themselves for the planned monetary unification. Successful product innovation will be critical since exchanges may face a drop in demand with reduced monetary uncertainty, and a reduction of the current spectrum of interest rate contracts to Euro contracts only. A related question is whether a futures markets needs a well developed spot market to succeed, or whether the creation of a futures market could help to boost liquidity in a fledgling spot market.
Consecutive or non-consecutive recurrent miscarriage: is there any difference in carrier status?

BACKGROUND Carrier status of a structural balanced chromosome abnormality is associated with recurrent miscarriage. There is, at present, no evidence of the impact of the sequence of preceding pregnancies on the probability of carrier status. The aim of our study was therefore to examine whether the history of consecutive versus non-consecutive miscarriages in couples with two or more miscarriages has any impact on the probability of carrying a chromosome abnormality. METHODS A nested case-control study was performed in six centres for clinical genetics in the Netherlands. Couples referred for chromosome analysis after two or more miscarriages were included: 279 couples with a carrier of a structural chromosomal abnormality and 428 non-carrier couples who served as controls. Univariable and multivariable logistic regression analyses, corrected for known risk factors for carrier status, were performed. The main outcome measure was the probability of carrier status. RESULTS Two hundred and fifty-six of 279 (92%) carrier couples and 381 of 428 (89%) non-carrier couples had experienced consecutive miscarriages (P = 0.21). A history of two or three consecutive miscarriages did not alter the probability of carrier status when compared with two [odds ratio (OR) 0.90, 95% confidence interval (CI) 0.48-1.7] or three (OR 0.71, 95% CI 0.39-1.3) non-consecutive miscarriages. CONCLUSIONS The sequence of preceding pregnancies is not a risk factor for carrier status. Therefore, couples with miscarriages interspersed with healthy child(ren) should be managed the same as couples with consecutive miscarriages regarding chromosome diagnosis.
Resources for Nepali Word Sense Disambiguation

Word sense disambiguation (WSD) is a process of identifying proper meaning of words that may have multiple meanings. It is regarded as one of the most challenging problems in the field of natural language processing (NLP). Nepali Language also has words that have multiple meanings, thus giving rise to the problem of WSD in it. In this paper, we investigate the impact of NLP resources like morphology analyzer (MA) and machine readable dictionary (MRD) in ambiguity resolution. Our results show that the accuracy in WSD is better with the availability of NLP resources like morph analyzer, MRD etc. Lesk algorithm has been used to solve WSD problem using a sample Nepali WordNet containing few sets of Nepali nouns and the system is able to disambiguate these nouns only. The system was tested on a small set of data with limited number of nouns. The accuracy reading was between 50% - 70% depending on the sample data provided. When the same data was tested through manual morph analysis, the accuracy was seen to be considerably high (80%).
Hemispheric predominance of abnormal findings in electroencephalogram (EEG)

The EEGs of 13,560 patients have been reviewed in order to determine whether abnormal findings, epileptiform or not, have a hemispheric dominance. We have included outpatients and hospitalized patients as well. Eight hundred and thirty-five EEGs had generalized abnormal findings, and 414 EEGs had lateralized abnormal findings. The EEGs of 322 patients (77.7%) had a left predominance, and those of 92 patients (22.3%) had a right predominance, of abnormal findings. A strong left predominance has been noted for the epileptiform discharges, i.e. 128 (79%) vs. 34 (21%). These results raise the possibility that the left hemisphere may be more vulnerable to nosological processes.
Accuracy indicator for fingerprinting localization systems

Fingerprinting is a technique that records vectors of received power from several transmitters, and later matches these to a new measurement to position the new user. This paper proposes a new mechanism to calculate the (Dilution of Precision) DOP-like value and error estimation for fingerprinting localization by investigating relationship between real distance and “vector” distance between fingerprints used in earlier fingerprinting experiments in Wi-Fi and FM radio networks. However, because fingerprinting algorithms use nearest neighbor techniques, the relationship between real distance and “vector” distance of these nearby fingerprints was also examined and found to more helpful when estimating the position. The proposed method used only the information from the training stage of fingerprinting and when compared with previous literature, it was found to give the improved results.
0 Model Predictive Control Relevant Identification

Model predictive control (MPC) is a multivariable feedback control technique used in a wide range of practical settings, such as industrial process control, stochastic control in economics, automotive and aerospace applications. As they are able to handle hard input and output constraints, a system can be controlled near its physical limits, which frequently results in performance superior to linear controllers (Maciejowski, 2002), specially for multivariable systems. At each sampling instant, predictive controllers solve an optimization problem to compute the control action over a finite time horizon. Then, the first of the control actions from that horizon is applied to the system. In the next sample time, this policy is repeated, with the time horizon shifted one sample forward. The optimization problem takes into account estimates of the system output, which are computed with the input-output data up to that instant, through a mathematical model. Hence, in MPC applications, a suitable model to generate accurate output predictions in a specific horizon is crucial, so that high performance closed-loop control is achieved. Actually, model development is considered to be, by far, the most expensive and time-consuming task in implementing a model predictive controller (Zhu & Butoyi, 2002).
Bayesian Inference for Duplication–Mutation with Complementarity Network Models

Abstract We observe an undirected graph G without multiple edges and self-loops, which is to represent a protein–protein interaction (PPI) network. We assume that G evolved under the duplication–mutation with complementarity (DMC) model from a seed graph, G0, and we also observe the binary forest Γ that represents the duplication history of G. A posterior density for the DMC model parameters is established, and we outline a sampling strategy by which one can perform Bayesian inference; that sampling strategy employs a particle marginal Metropolis–Hastings (PMMH) algorithm. We test our methodology on numerical examples to demonstrate a high accuracy and precision in the inference of the DMC model's mutation and homodimerization parameters.
Induction motor rotor fault detection using Artificial Neural Network

The present paper deals with the detection of broken rotor bar of an induction motor. The problem is approached through mathematical modeling of induction motor. Both the models, for healthy as well as faulty motor, are developed using MATLAB simulink. The model is used to simulate different conditions of fault with varying number of broken bars. Parameters like three-phase voltage, three-phase current and THD of all voltages and currents are acquired from the simulated model. The data thus generated is used to train Artificial Neural Network which diagnoses the condition of motor. The results obtained prove the effectiveness of proposed method.
Presynaptic Modulation of Spinal Nociceptive Transmission by Glial Cell Line-Derived Neurotrophic Factor (GDNF)

The role of glial cell line-derived neurotrophic factor (GDNF) in nociceptive pathways is still controversial, as both pronociceptive and antinociceptive actions have been reported. To elucidate this role in the mouse, we performed combined structural and functional studies in vivo and in acute spinal cord slices where C-fiber activation was mimicked by capsaicin challenge. Nociceptors and their terminals in superficial dorsal horn (SDH; laminae I–II) constitute two separate subpopulations: the peptidergic CGRP/somatostatin+ cells expressing GDNF and the nonpeptidergic IB4+ neurons expressing the GFRα1-RET GDNF receptor complex. Ultrastructurally the dorsal part of inner lamina II (LIIid) harbors a mix of glomeruli that either display GDNF/somatostatin (GIb)-IR or GFRα1/IB4 labeling (GIa). LIIid thus represents the preferential site for ligand-receptor interactions. Functionally, endogenous GDNF released from peptidergic CGRP/somatostatin+ nociceptors upon capsaicin stimulation exert a tonic inhibitory control on the glutamate excitatory drive of SDH neurons as measured after ERK1/2 phosphorylation assay. Real-time Ca2+ imaging and patch-clamp experiments with bath-applied GDNF (100 nm) confirm the presynaptic inhibition of SDH neurons after stimulation of capsaicin-sensitive, nociceptive primary afferent fibers. Accordingly, the reduction of the capsaicin-evoked [Ca2+]i rise and of the frequency of mEPSCs in SDH neurons is specifically abolished after enzymatic ablation of GFRα1. Therefore, GDNF released from peptidergic CGRP/somatostatin+ nociceptors acutely depresses neuronal transmission in SDH signaling to nonpeptidergic IB4+ nociceptors at glomeruli in LIIid. These observations are of potential pharmacological interest as they highlight a novel modality of cross talk between nociceptors that may be relevant for discrimination of pain modalities.
A computation trust model with confidence in unstructured P2P systems

Trust is a fundamental issue in Pervasive computing, especially when they are applied in unstructured P2P systems. The computational models of trust play an important role in determining who and how to interact in open and dynamic environments. However, current models are not efficient or scalable enough with the expansion of system. To this end, a computation trust model with confidence is proposed in our paper, in which peers can autonomously deal with the biased information and identify trust with confidence in unstructured P2P systems with low overhead and better efficiency.
A 0.1-8 GHz Low Noise Amplifier Design Using 0.18 μm CMOS Technology

— A T-coil network can be implemented as a high order filter for bandwidth extension. This technique is incorporated into the design of the input matching and output peaking networks of a low-noise amplifier. The intrinsic capacitances within the transistors are exploited as a part of the wideband structure to extend the bandwidth. Using the proposed topology, a wideband low-noise amplifier with a bandwidth of 0.1–8 GHz, a maximum gain of 18 dB and noise figure of 6 dB (min) is achieved. The total power consumption of the wideband low-noise amplifier from the 1.8Vpower supply is 4.5 mW. For design of this type of wideband low-noise amplifier 0.18 μm CMOS technology is used.
A Hand gesture recognition system based on difference image entropy

In this paper, we propose a real-time hand gesture recognition system based on the difference image entropy obtained using a stereo camera. In existing systems, hand detection has been primarily conducted in a constrained environment. However in this system, we implement a recognition system for incoming hand images in real-time. In the detection step, we implement a depth map using the SAD method based on right-left images acquired using a stereo camera. This system perceives the foreground object and performs hand detection. The hand gesture recognition system uses the difference image entropy of the input image and the average image. To evaluate the performance of the proposed technology, we implemented a recognition experiment using a hand gesture database (size 240). The experiment results show that the proposed method has an average recognition rate of 85%.
Requirement Driven Service Agent Collaboration and QoS Based Negotiation

Composing existing services for satisfying new requirements has gained daily expanding attentions and interests. Existing techniques assume that services are passively waiting for being discovered and invoked. However, it might be more attractive when services become active entities (service agents) distributed in Internet which can recognize the newly emergent requirements. Generally, some service agents collaborate to satisfy the requirements, and some service agents compete with each other for realizing (part of) the requirements. This is the idea of requirement driven service agent collaboration. Along with our previous work, this paper proposes an agent collaboration model and a QoS based negotiation framework. Collaboration is modeled as an optimization problem, and subsequently enhanced by a QoS based negotiation framework. Through negotiation, the chosen solution satisfies both the QoS requirement and service agents' payoffs.
Runking: A mobile social persuasion system for running exercise

Running regularly is a good way to keep fit. However, people's laziness usually stop them from keep up. Motivated by the competition and achievement system in many video games, we propose Runking, a mobile social network based persuasion system for running exercise. Runking can rank users' exercise records among friends and push those who are lazier than others. Meanwhile, if users keep on active, different achievements can be unlocked, which can further incent users to exercise regularly. The system is build upon smartphones. By utilizing sensory data from smartphones, it can track and measure people's daily running record without human interaction. Evaluations show that the system has a high recognition rate for identifying users' activity and can reduce the network communication by using disconnected operations. Therefore, it can help users improve their fitness effectively.
An integrated MEG acquisition system with CMOS instrumentation amplifier and offset cancellation

We propose a CMOS-based Magnetoencephalography(MEG) acquisition system. The MEG acquisition system consists of a small-sized high inductance coil sensor and an instrumentation amplifier (IA). The small-sized high inductance coil sensor is fabricated in 0.18 μm standard CMOS process. Output signal sensitivity of MEG acquisition sensor chip is 3.25fT/μV and noise figure of 34.9fT/(√Hz). The IA is designed along with low-pass and high-pass filters to reduce noise. It draws only 540 μA from a standard 3.3V battery, making it suitable for use in portable systems. By using current feedback techniques it achieves a common mode rejection ratio of 120 dB while the total noise referred to input is kept below 0.87 μV.
A 3-D self-reconfigurable structure

A three-dimensional, self-reconfigurable structure is proposed. The structure is a fully distributed system composed of many identical 3-D units. Each unit has functions of changing local connection, information processing, and communication among neighborhood units. Groups of units cooperate to change their connection so that the shape of the whole solid structure transforms into an arbitrary shape. Also, the structure can repair itself by rejecting faulty units, replacing them with spare units. This kind of self-maintainability is essential to structure's longevity in hazardous or remote environments such as space or deep sea where human operators cannot approach. We have designed and built a prototype unit to examine the feasibility of the 3-D self-reconfigurable concept. The design of the unit, method of reconfiguration, hardware implementation, and results of preliminary experiments are shown. In the last part of the paper, distributed software for self-reconfiguration is discussed.