Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG feature extraction and analysis under drowsy state based on energy and sample entropy

In order to explore the effect of drowsiness on Electroencephalogram (EEG), EEG signals with bipolar lead C4-P4 are collected from 15 healthy subjects. There are six energy features and six sample entropy features of EEG signals under conscious and drowsy states extracted respectively. The study results show that the energy under drowsy state increase obviously while the sample entropy under drowsy state decrease obviously compare with those under conscious state (p<;0.05), it offers a new method for drowsiness detection based on EEG.
Validation of the spanish version of the consideration of future consequences scale

We tested the validity of a Spanish version of Strathman et al.’s (1994) Consideration of Future Consequences Scale (CFC-S). A sample of 158 Uruguayan university students completed the CFC-S scale and the Big Five Personality Inventory. We performed exploratory factor analyses that showed that the scale resembles the bi-factorial structure found in some of the samples with the English version, with a Future and an Immediate sub-scale. Results showed that the CFC Scale has acceptable psychometric properties though refi nements are needed: the item 5 presents some problems both in loadings and item-total correlations. Additionally, men scored higher than women on the CFC-I sub-scale and, conscientiousness is the most correlated personality trait with the scale.
Annotating Factive Verbs

We have created a scheme for annotating corpora designed to capture relevant aspects of factivity in verb-complement constructions. Factivity constructions are a well-known linguistic phenomenon that embed presuppositions about the state of the world into a clause. These embedded presuppositions provide implicit information about facts assumed to be true in the world, and are thus potentially valuable in areas of research such as textual entailment. We attempt to address both clear-cut cases of factivity and non-factivity, as well as account for the fluidity and ambiguous nature of some realizations of this construction. Our extensible scheme is designed to account for distinctions between claims, performatives, atypical uses of factivity, and the authority of the one making the utterance. We introduce a simple XML-based syntax for the annotation of factive verbs and clauses, in order to capture this information. We also provide an analysis of the issues which led to these annotative decisions, in the hope that these analyses will be beneficial to those dealing with factivity in a practical context.
EVA: A Framework for Network Analysis and Risk Assessment

EVA is an attack graph tool that allows an administrator to assess and analyze a network in a variety of fashions. Unlike other attack graph tools which just focus on visualizing the network or recommending a set of patches to secure the network, EVA goes beyond these modes to fully explore the power of attack graphs for a multitude of administrative and security tasks. EVA can be used to derive a set of hardening measures for a network, to perform strategic analysis of a network, to design a more secure network architecture, to assist in forensic evaluations after a security event and to augment an intrusion detect system with information about the likely targets of an attack. This paper summarizes the framework used by EVA, provides real-world results of using EVA and shows how EVA is scalable to large networks.
Maximizing a Sum of Sigmoids

The problem of maximizing a sum of sigmoidal functions over a convex constraint set arises in many application areas. This objective captures the idea of decreasing marginal returns to investment, and has applications in mathematical marketing, network bandwidth allocation, revenue optimization, optimal bidding, and lottery design. We dene the sigmoidal programming problem (SP) and show how it arises in each of these application areas. We show that the general problem is NP-hard, and propose an approximation algorithm (using a branch and bound method) to nd a globally optimal approximate solution to the problem. We show that this algorithm nds approximate solutions very quickly on problems of interest: in fact, for problems with few constraints, it frequently nds a solution within an acceptable error margin by solving a single convex optimization problem. To illustrate the power of this approach, we compute the positions which might have allowed the candidates in the 2008 United States presidential election to maximize their vote shares.
Lessons from a minimal middleware for IP-based in-car communication

Introducing the Internet Protocol to the in-car network, we need communication software for fast and robust development of future networked applications. The heterogeneity within the communication network concerning the differences in device capability and application requirements can be targeted by establishing a middleware consisting of several specifications. In this paper, we give evidence that a minimal middleware specification can be feasible even for smallest embedded Electronic Control Units but still largely interoperable with the more complex communication demands of powerful infotainment and driver assistance ones. We therefore present a prototype implementation, which is evaluated towards interoperability, resource consumption, and execution performance.
It's More about the Content than the Users! The Influence of Social Broadcasting on Stock Markets

Social broadcasting networks facilitate the public exchange of information and contain a large amount of stock-related information. This data is increasingly analyzed by research and practice to predict stock market developments. Insights from social broadcasting networks are used to support the decision-making process of investors and are integrated into automatic trading algorithms to react quickly to broadcasted information. However, a comprehensive understanding about the influence of social broadcasting networks on stock markets is missing. In this study, we address this gap by conceptualizing and empirically testing a model incorporating three dimensions of social broadcasting networks: users, messages, and discussion. We analyze 1.84 million stock-related Twitter messages concerning the S&P 100 companies between January and April 2014 and corresponding intraday stock market data from NYSE and NASDAQ. Our research model is constructed applying factor analyses and tested using a fixed effects panel analysis. The results show that the influence of social broadcasting on stock markets is driven by the message and discussion dimensions whereas the user dimension has no significant influence. Specifically, the influence of user mentions, financial sentiment, discussion reach, and discussion volume has the largest impact and should carefully be considered by investors making trading decisions.
Basic approaches in adaptive control system design for small turbo-compressor engines

Small turbo-compressor engines represent a specific class of such engines with application in small aircraft and as auxiliary energetic units. Energetic efficiency and high safety standards are nowadays key factors in practical applications of small turbo-compressor engines. One approach to achieve these aims is to design and utilize progressive control algorithms. As turbo-compressor engines represent a non-linear systems operating in a broad range of environments and inner conditions, taking the adaptive approach in controller design can be beneficial. The presented article tests and evaluates different basic approaches in adaptive controller design for a small turbo-compressor engine with modeled altitude and speed characteristics.
Duopoly Information Equilibrium : Cournot and

Consider a symmetric differentiated duopoly model in which firms have private market data about the uncertain demand. We analyze two types of duopoly information equilibrium, Cournot and Bertrand, which emerge, respectively, from quantity and price competition, and show that the incentives for information sharing and its welfare consequences depend crucially on the type of competition, the nature of the goods (substitutes or complements), and the degree of product differentation. The demand structure is linear and symmetric, and allows the goods to be substitutes, independent or complements. There is uncertainty only about the
Motion generation for human-robot collaborative pick and place based on non-obstructing strategy

This paper presents a model and motion generation method for the task of collaborative pick and place between a robot and a human. It often happens that the performance of a human is higher than that of a robot, in the case where the robot needs to recognize the environment as well as human behavior and move in a safe manner. This fact requires that the robot needs to make motions considering the timing of human motions and properly choose targets of manipulation beyond just considering one stroke of the human motion to enhance the total performance of the human-robot system. Accordingly, in this paper, we first express the robot and human using a state transition model. Then we present a method to generate a motion of the robot to increase future task executability of the human. This approach consists of two steps. In the first step, the system of the robot and human is simulated using 3D model till the near future. In the second step, the utility of the possible object-place layout is evaluated based on possible transportation hypotheses. We also evaluate the effectiveness of the proposed approach in simulation.
Information Retrieval on the World Wide Web and Active Logic: A Survey and Problem Definition

As more information becomes available on the World Wide Web (there are currently over 4 billion pages covering most areas of human endeavor), it becomes more difficult to provide effective search tools for information access. Today, people access web information through two main kinds of search interfaces: Browsers (clicking and following hyperlinks) and Query Engines (queries in the form of a set of keywords showing the topic of interest). The first process is tentative and time consuming and the second may not satisfy the user because of many inaccurate and irrelevant results. Better support is needed for expressing one's information need and returning high quality search results by web search tools. There appears to be a need for systems that do reasoning under uncertainty and are flexible enough to recover from the contradictions, inconsistencies, and irregularities that such reasoning involves. Active Logic is a formalism that has been developed with real-world applications and their challenges in mind. Motivating its design is the thought that one of the factors that supports the flexibility of human reasoning is that it takes place step-wise, in time. Active Logic is one of a family of inference engines (step-logics) that explicitly reason in time, and incorporate a history of their reasoning as they run. This characteristic makes Active Logic systems more flexible than traditional AI systems and therefore more suitable for commonsense, real-world reasoning. In this report we mainly will survey recent advances in machine learning and crawling problems related to the web. We will review the continuum of supervised to semi-supervised to unsupervised learning problems, highlight the specific challenges which distinguish information retrieval in the hypertext domain and will summarize the key areas of recent and ongoing research. We will concentrate on topic-specific search engines, focused crawling, and finally will propose an Information Integration Environment, based on the Active Logic framework.
Self-Coaching System Based on Recorded Driving Data: Learning From One's Experiences

This paper describes the development of a self-coaching system to improve driving behavior by allowing drivers to review a record of their own driving activity. By employing stochastic driver-behavior modeling, the proposed system is able to detect a wide range of potentially hazardous situations, which conventional event data recorders are not able to capture, including those involving latent risks, of which drivers themselves are unaware. By utilizing these automatically detected hazardous situations, our web-based system offers a user-friendly interface for drivers to navigate and review each hazardous situation in detail (e.g., driving scenes are categorized into different types of hazardous situations and are displayed with corresponding multimodal driving signals). Furthermore, the system provides feedback on each risky driving behavior and suggests how users can safely respond to such situations. The proposed system establishes a cooperative relationship between the driver, the vehicle, and the driving environment, leading to the development of the next generation of safety systems and paving the way for an alternative form of driving education that could further reduce the number of fatal accidents. The system's potential benefits are demonstrated through preliminary extensive evaluation of an on-road experiment, showing that safe-driving behavior can be significantly improved when drivers use the proposed system.
Selecting a small subset of informative genes from gene expression data by using a modified binary particle swarm optimisation

Gene expression technology, especially micro arrays, can be used to measure the expression levels of thousands of genes simultaneously in biological organisms. Gene expression data produced by micro arrays are expected to be useful for cancer classification. To select a small subset of informative genes for cancer classification, many researchers have analysed the gene expression data using various computational intelligence methods. However, due to the small number of samples compared with the huge number of genes (high-dimensional data), irrelevant genes, and noisy genes, many of the computational methods face difficulties in selecting the small subset. Thus, we propose a modified binary particle swarm optimisation to select a small subset of informative genes that are relevant for the cancer classification. In the proposed method, we introduce the particle speed and a rule for increasing the probability of bits in a particle's position to be zero. The method was empirically applied to a suite of four well-known benchmark gene expression data sets. The experimental results demonstrate that the proposed method outperforms the conventional version of binary particle swarm optimisation (BPSO) and other related works in terms of classification accuracy and the number of selected genes. In addition, this method also produces lower running times compared to BPSO.
The role of knowledge modelling techniques in software development: a general approach based on a knowledge management tool

The aim of the paper is to discuss the use of knowledge models to formulate general applications. First, the paper presents the recent evolution of the software field where increasing attention is paid to conceptual modeling. Then, the current state of knowledge modeling techniques is described where increased reliability is available through the modern knowledge acquisition techniques and supporting tools. The KSM (Knowledge Structure Manager) tool is described next. First, the concept of knowledge area is introduced as a building block where methods to perform a collection of tasks are included together with the bodies of knowledge providing the basic methods to perform the basic tasks. Then, the CONCEL language to define vocabularies of domains and the LINK language for methods formulation are introduced. Finally, the object oriented implementation of a knowledge area is described and a general methodology for application design and maintenance supported by KSM is proposed. To illustrate the concepts and methods, an example of system for intelligent traffic management in a road network is described. This example is followed by a proposal of generalization for reuse of the resulting architecture. Finally, some concluding comments are proposed about the feasibility of using the knowledge modeling tools and methods for general application design.
Karyotype characteristics, larval morphology and chromosomal polymorphism peculiarities of Glyptotendipes salinus Michailova, 1983 (Diptera, Chironomidae) from Tambukan Lake, Central Caucasus

Abstract Data on the karyotype characteristics, larval morphology and features of chromosomal polymorphism of a population of Glyptotendipes salinus Michailova, 1983 (Diptera, Chironomidae) from Tambukan Lake (on the northern macroslope of the central Caucasus) are presented. It was found that diagnostic larval characters of Glyptotendipes salinus from Caucasus in general are similar to those described in previous studies, but with some significant differences. By some morphological characteristics Caucasian larvae appeared to be closer to Glyptotendipes barbipes than to ones provided for European larvae of Glyptotendipes salinus by Contreras-Lichtenberg (1999). Obtained morphological data make possible to conclude that Caucasian population of Glyptotendipes salinus can be a markedly diverged population of the species, probably even subspecies. In the Caucasian population 12 banding sequences were found: two in arms A, B, C, E, and G, and one in arms D and F. Eight of these are already known for this species, and four, salA2, salB2, salEX, and salG3, are described for the first time. Genetic distances between all the previously studied populations of Glyptotendipes salinus were measured using Nei criteria (1972). The population of the central Caucasus occupies a distinct position on the dendrogram compared with populations from Altai and Kazakhstan. All the obtained morphological and cytogenetic data can indicate the plausible relative isolation and complexity of the Caucasus from the viewpoint of microevolution. More researches are required in other parts of Caucasus and other geographically distant regions for more specific allegations.
Prevalence of gastrointestinal helminth parasites of zoonotic significance in dogs and cats in lower Northern Thailand

Gastrointestinal zoonotic helminths of dogs and cats have a public health concern worldwide. We investigated the prevalence of gastrointestinal helminths of zoonotic significance in dogs and cats in lower Northern Thailand and utilized molecular tools for species identification of hookworms and Opisthorchis viverrini. Fecal samples of 197 dogs and 180 cats were collected. Overall prevalence of infection using microscopy was 40.1% in dogs and 33.9% in cats. Helminth infection found in both dogs and cats included hookworms, Spirometra spp. , Taenia spp. , Toxocara spp. , O. viverrini, Strongyloides spp. and Trichuris spp. Hookworms were the most common helminth in dogs, while Spirometra spp. were the most prevalent in cats. Among hookworm infection in dogs and cats, Ancylostoma ceylanicum was the most prevalent hookworm, being 82.1% in hookworm infected dogs and 95.8% in hookworm infected cats. Mixed-infection due to hookworms and Spirometra spp. was the most dominant in both dogs and cats. Our finding showed that zoonotic helminth infection is highly prevalent in dogs and cats in the lower Northern area of Thailand.
Estimating hyperparameters and instrument parameters in regularized inversion. Illustration for SPIRE/Herschel map making

We describe regularized methods for image reconstruction and focus on the question of hyperparameter and instrument parameter estimation, i.e. unsupervised and myopic problems. We developed a Bayesian framework that is based on the \post density for all unknown quantities, given the observations. This density is explored by a Markov Chain Monte-Carlo sampling technique based on a Gibbs loop and including a Metropolis-Hastings step. The numerical evaluation relies on the SPIRE instrument of the Herschel observatory. Using simulated and real observations, we show that the hyperparameters and instrument parameters are correctly estimated, which opens up many perspectives for imaging in astrophysics.
Preserving and memorialising relationships: exploring young people’s experiences of foster care through the lens of social capital

This article presents findings from research into how young people growing up in foster care in the UK manage the relationships in their social networks and gain access to social capital. It is a concept that highlights the value of relationships and is relevant to young people in care as they have usually experienced disruptions to their social and family life. Qualitative methods were used and the findings show that despite experiencing disruption to their social networks, the young people demonstrated that they were able to maintain access to their social capital. They achieved this in two ways. Firstly, they preserved their relationships, often through what can be seen as ordinary practices but in the extraordinary context of being in foster care. Secondly, they engaged in creative practices of memorialisation to preserve relationships that had ended or had been significantly impaired due to their experience of separation and movement. The article highlights implications for policy and practice, including the need to recognise the value of young people’s personal possessions. Furthermore, it stresses the need to support them to maintain their relationships across their networks as this facilitates their access to social capital.
The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir

Permeability is an important parameter connected with oil reservoir. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way out of this complexity through the design of a hybrid intelligent model. The model combines classification and regression. In order to handle the high range of the permeability value, a classification tree is utilized. ELM is used as a final predictor. Results demonstrate that this proposed model performs better when compared with support vector machines (SVM) and ELM in term of correlation coefficient. Moreover, the classification tree model potentially leads to better communication among petroleum engineers and has wider implications for oil reservoir management efficiency.