Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex differences in attentional bias before and after stress induction : an event related potential study

Females have double the lifetime prevalence of anxiety disorders compared to males. Attentional bias to threat and arousal reactivity have been consistently implicated as a potential mechanism underlying the development and maintenance of anxiety disorders. Sex differences in attentional bias and arousal may contribute towards the prevalence for anxiety disorders in females. There is limited research exploring sex differences in attentional bias, and the literature is inconsistent, possibly due to relying on RT as a dependent measure for attentional bias, and not accounting for baseline arousal. One recent study found females displayed increased arousal and attentional bias to threat following acute stress induction. To replicate and extend these findings ERPs, RT and salivary alpha amylase (sAA, indexed noradrenaline) were measured to examine sex differences in P1 and N1 (reflecting early visual orientation) and P3 component (reflecting conscious allocation of visual resources). sAA results indicated that acute-stress induction produced significant increase in stress hormone noradrenaline, but females did not have heighted arousal reactivity. RT and ERP component analysis indicated no attentional bias to threat in females or males. These findings did not confirm the predictions of the study. The limitations of the present study and future research suggestions are also discussed.
Connecting the dots in IT service delivery: From operations content to high-level business insights

IT service delivery relies on intelligent data-driven insights to make strategic decisions. It is a highly complex business with many sub-organizations that focus on different aspects of delivery operations. High-level business insights that emerge from understanding the collective value of all these viewpoints are invaluable to achieving excellent service quality and solid profit margin. However, this is hindered by the inability to integrate data models and taxonomies across business components such as asset management, configuration management, and incident management. Innovative solutions are necessary to effectively "connect-the-dots", bridging the gaps between available content and higher-level business insights. In this paper, we describe several real-world business decisions in service delivery and logistics that suffer from this content-model gap. We propose a unified approach to bridge this gap, with an information system component called Business-Knowledge Discovery Component. We discuss key challenges, architectural framework and the text analytic techniques that are involved.
Constraint Query Languages Constraint Query Languages

We investigate the relationship between programming with constraints and database query languages. We show that eecient, declarative database programming can be combined with eecient constraint solving. The key intuition is that the generalization of a ground fact, or tuple, is a conjunction of constraints over a small number of variables. We describe the basic Constraint Query Language design principles and illustrate them with four classes of constraints: real polynomial inequalities, dense linear order inequalities, equalities over an innnite domain, and boolean equalities. For the analysis, we use quantiier elimination techniques from logic and the concept of data complexity from database theory. This framework is applicable to managing spatial data and can be combined with existing multi-dimensional searching algorithms and data structures.
Cut! that’s a wrap: regulating negative emotion by ending emotion-eliciting situations

Little is known about the potentially powerful set of emotion regulation (ER) processes that target emotion-eliciting situations. We thus studied the decision to end emotion-eliciting situations in the laboratory. We hypothesized that people would try to end negative situations more frequently than neutral situations to regulate distress. In addition, motivated by the selection, optimization, and compensation with ER framework, we hypothesized that failed attempts to end the situation would prompt either (a) greater negative emotion or (b) compensatory use of a different ER process, attentional deployment (AD). Fifty-eight participants (18–26 years old, 67% women) viewed negative and neutral pictures and pressed a key whenever they wished to stop viewing them. After key press, the picture disappeared (“success”) or stayed (“failure”) on screen. To index emotion, we measured corrugator and electrodermal activity, heart rate, and self-reported arousal. To index overt AD, we measured eye gaze. As their reason for ending the situation, participants more frequently reported being upset by high- than low-arousal negative pictures; they more frequently reported being bored by low- than high-arousal neutral pictures. Nevertheless, participants’ negative emotional responding did not increase in the context of ER failure nor did they use overt AD as a compensatory ER strategy. We conclude that situation-targeted ER processes are used to regulate emotional responses to high-arousal negative and low-arousal neutral situations; ER processes other than overt AD may be used to compensate for ER failure in this context.
The Concept and Design of Pleated Pneumatic Artificial Muscles

Abstract This paper describes the design of a new type of Pneumatic Artificial Muscle (PAM), namely the Pleated Pneumatic Artificial Muscle (PPAM). It was developed as an improvement with regard to existing types of PAM, e.g. the McKibben muscle. Its principle characteristic is its pleated membrane. It can inflate without material stretching and friction and has practically no stress in the direction perpendicular to its axis of symmetry. Besides these it is extremely strong and yet very lightweight and it has a large stroke compared to most other designs. A general introduction on PAMs is given together with a short discussion and motivation for this new design. The concept of the PPAM is explained and a mathematical model is derived. This model proves its principle of operation. From the model, several characteristics, such as developed force, maximum contraction, diameter, volume and membrane tensile stress, are obtained. Material choices and dimensions of a typical PPAM are next discussed and its measured values of static force and diameter are compared to the model predicted values. The agreement between both is found to be very good.
Reflexive Scott Domains are Not Complete for the Extensional Lambda Calculus

A longstanding open problem is whether there exists a model of the untyped lambda calculus in the category CPO of complete partial orderings and Scott continuous functions, whose theory is exactly the least lambda-theory lambda-beta or the least extensional lambda-theory lambda-beta-eta. In this paper we analyze the class of reflexive Scott domains, the models of lambda-calculus living in the category of Scott domains (a full subcategory of CPO). The following are the main results of the paper: (i) Extensional reflexive Scott domains are not complete for the beta-eta-calculus, i.e., there are equations not in lambda-beta-eta which hold in all extensional reflexive Scott domains. (ii) The order theory of an extensional reflexive Scott domain is never recursively enumerable. These results have been obtained by isolating among the reflexive Scott domains a class of webbed models arising from Scott's information systems, called iweb-models. The class of iweb-models includes all extensional reflexive Scott domains, all preordered coherent models and all filter models living in CPO. Based on a fine-grained study of an ``effective'' version of Scott's information systems, we have shown that there are equations not in lambda-beta (resp. lambda-beta-eta) which hold in all (extensional) iweb-models.
A Framework for Web Host Quality Detection

With the rapid growth of World Wide Web, finding useful and desired information in a short amount of time becomes an important issue for Web users. Search engines and focused crawlers help people to navigate the internet. A user expresses her information need in the form of a query and there is huge number of Web pages returning to this query. However, the majority of users view only a single page (the top 10 Web pages as ranked by the search engine) returned by a search engine. Even if the returned Web pages do not provide the exact information they need, the users also do not refine their query based on the returning results of their initial query. Thus, not only finding relevant Web pages but also ranking them plays an important role for the search engines. For this reason, determining the quality of Web pages is one of the main priorities of search engines, since low quality Web pages cause search engines results to be extremely vague and flooded with irrelevant Web pages. In this paper, we propose a novel method for determining the quality of Web pages. The proposed method first identifies the genre of Web pages and then it determines the quality of Web pages based on their genre. Our experimental results show that our proposed method is very effective and efficient.
Evolutionary Decision Trees and Software Metrics for Module Defects Identification

— Software metric is a measure of some property of a piece of software or its specification. The aim of this paper is to present an application of evolutionary decision trees in software engineering in order to classify the software modules that have or have not one or more reported defects. For this some metrics are used for detecting the class of modules with defects or without defects.
Modeling the octanol–water partition coefficient of substituted phenols by the use of structure information

This work presents the abilities in estimation and prediction of the octanol–water partition coefficient of some para-substituted phenols through the integration of complex structures information by the use of an original molecular descriptors family on the structure–property relationship approach. The proposed approach uses the complex information obtained from para-substituted phenols structure in order to generate and calculate the molecular descriptors family. The structure–property relationship models were built based on the generated descriptors. The obtained multi-varied models (model with two and four descriptors, respectively) were validated through the assessment of the cross-validation leave-one-out score. The comparison between the multi-varied model with two and four descriptors was performed using Steiger's Z-test. The analysis of the statistical characteristics of the obtained models demonstrated that the model with four descriptors has greater ability to estimate and predict compared with the model with two descriptors. This observation was also sustained by the results of correlated-correlation analysis. The multi-varied model with four descriptors revealed that the octanol-water partition coefficient of studied para-substituted phenols is likely to be of geometry nature, it is strongly dependent on the partial charges of compounds and group electronegativity, and it is in relation to the elastic force. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2007
An Empirical Analysis of the Relationship between Tax Structures and Economic Growth in CEE Countries

This paper investigates the relationship between tax structures and econom-ic growth in selected CEE countries in the period from 1990 to 2010. The research basis on the data for 20 selected countries (EU-13 and selected former Soviet Union countries and Albania). We obtain empirical results by using the Pooled Mean Group estimator (PMG). The analysis focuses on the impact of structure of taxes on economic growth. All regressions contain the overall tax burden represented as a share of total tax revenues in GDP. The results show that all tax forms have a negative impact on economic growth. Personal income taxes proved to have the highest negative impact on economic growth, followed by corporate income taxes and property tax-es, which had the least negative impact. Consumption taxes showed to be statistically insignificant. Furthermore, the results indicate a significantly different impact observed countries’ tax structures had on economic growth to that of previous research on the dataset of OECD developed industrial countries.
UbiFlow: Mobility management in urban-scale software defined IoT

The growing of Internet of Things (IoT) devices has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist. Managing the multinetworks for mobile IoT access is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick configuration of network devices, but its application in multinetworks with frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for ubiquitous flow control and mobility management in multinetworks. UbiFlow adopts distributed controllers to divide urban-scale SDN into different geographic partitions. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on the per-device requirement and whole-partition capability. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance in each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks.
Providing flexible services for heterogeneous vehicles: an NFV-based approach

Currently, many automotive manufacturers including Mercedes Benz and BMW have released new products equipped with intelligent onboard systems (IOSs) that support interconnection between vehicles and the remote service center. With the help of IOSs, vehicles can obtain traffic information and enjoy location-based services, such as restaurant and nearby gas station recommendations. However, IOSs adopt a closed architecture and are very difficult to upgrade to support new services. NFV is introduced as a new methodology that offers a potential way out of this bottleneck. Utilizing virtualization technologies, NFV may execute services as software applications on standard IT platforms like servers, switches, and storage. Using NFV technology, closed IOSs can be transformed to open ones and upgraded with lower cost. In this article, we introduce an NFV-based framework for vehicular ad hoc networks, and discuss the benefits and challenges. We also present two use cases to demonstrate its strength.
Compressive Sampling of Binary Images

Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of binary images. A system is proposed whereby the image is split into non-overlapping blocks of equal size and compressive sampling is performed on selected blocks only using the orthogonal matching pursuit technique. The remaining blocks are sampled fully. This way, the complexity and the required sampling time is reduced since the orthogonal matching pursuit operates on a smaller number of samples, and at the same time local sparsity within an image is exploited. Our simulation results show more than 20% saving in acquisition for several binary images.
Terrorism and fear: do terrorists really want to scare?

ABSTRACT There is a widespread tendency among academics, state institutions and international organisations to regard intention to cause fear and intimidation as a key definitional characteristic of terrorism. This article argues that such a conceptualisation of terrorism is unfounded. A survey of mainstream terrorist doctrines (propaganda by the deed, foco theory–urban guerrilla warfare and armed propaganda, and jihadist doctrines) and writings of several pre-eminent terrorist ideologues provides little evidence to suggest that the main tactical goal of terrorism is to spread fear and intimidation. Rather, regardless of their ideological orientation and the historical period in which they operated, terrorists seem to be preoccupied with similar goals: mobilising what they regard to be their constituency, avenging their fallen comrades, extracting retribution for their allegedly wronged constituency, or, simply, with the physical destruction of their perceived enemies. This survey is complemented with a number of “crucial case studies”, where “most-likely cases” of terrorist justification of indiscriminate attacks and a detailed analysis of terrorist literature, which explicitly deals with the question of “fear”, are used to test the validity of the claim that terrorists aim to cause fear and intimidation. In both type of cases the hypothesis is invalidated. In cases of justification of indiscriminate attacks, there is no evidence to suggest that the aim of the terrorists is to spread fear and intimidation. Moreover, in the case of one particular terrorist ideologue who explicitly addresses the question of fear, the analysis of the texts indicates that fear among a population is not seen as an aim to be achieved but as an obstacle for mass mobilisation: an obstacle, which can only be overcome by the terrorist tactics. Thus, in this particular “most-likely case” the aim of terrorist tactics turns out to be encouraging masses for insurrection rather than intimidating them.
Phormidium autumnale Growth and Anatoxin-a Production under Iron and Copper Stress

Studies on planktonic cyanobacteria have shown variability in cyanotoxin production, in response to changes in growth phase and environmental factors. Few studies have investigated cyanotoxin regulation in benthic mat-forming species, despite increasing reports on poisoning events caused by ingestion of these organisms. In this study, a method was developed to investigate changes in cyanotoxin quota in liquid cultures of benthic mat-forming cyanobacteria. Iron and copper are important in cellular processes and are well known to affect growth and selected metabolite production in cyanobacteria and algae. The effect of iron (40–4000 μg L−1) and copper (2.5–250 μg L−1) on growth and anatoxin-a quota in Phormidium autumnale was investigated in batch culture. These concentrations were chosen to span those found in freshwater, as well as those previously reported to be toxic to cyanobacteria. Anatoxin-a concentrations varied throughout the growth curve, with a maximum quota of between 0.49 and 0.55 pg cell−1 measured within the first two weeks of growth. Growth rates were significantly affected by copper and iron concentrations (P < 0.0001); however, no statistically significant difference between anatoxin-a quota maxima was observed. When the iron concentrations were 800 and 4000 μg L−1, the P. autumnale cultures did not firmly attach to the substratum. At 250 μg L−1 copper or either 40 or 4000 μg L−1 iron, growth was suppressed.
Visual Tracking by Affine Kernel Fitting Using Color and Object Boundary

Kernel-based trackers aggregate image features within the support of a kernel (a mask) regardless of their spatial structure. These trackers spatially fit the kernel (usually in location and in scale) such that a function of the aggregate is optimized. We propose a kernel-based visual tracker that exploits the constancy of color and the presence of color edges along the target boundary. The tracker estimates the best affinity of a spatially aligned pair of kernels, one of which is color-related and the other of which is object boundary-related. In a sense, this work extends previous kernel-based trackers by incorporating the object boundary cue into the tracking process and by allowing the kernels to be affinely transformed instead of only translated and isotropically scaled. These two extensions make for more precise target localization. Moreover, a more accurately localized target facilitates safer updating of its reference color model, further enhancing the tracker's robustness. The improved tracking is demonstrated for several challenging image sequences.
Budgeted Batch Bayesian Optimization

Parameter settings profoundly impact the performance of machine learning algorithms and laboratory experiments. The classical trial-error methods are exponentially expensive in large parameter spaces, and Bayesian optimization (BO) offers an elegant alternative for global optimization of black box functions. In situations where the functions can be evaluated at multiple points simultaneously, batch Bayesian optimization is used. Current batch BO approaches are restrictive in fixing the number of evaluations per batch, and this can be wasteful when the number of specified evaluations is larger than the number of real maxima in the underlying acquisition function. We present the budgeted batch Bayesian optimization (B3O) for hyper-parameter tuning and experimental design - we identify the appropriate batch size for each iteration in an elegant way. In particular, we use the infinite Gaussian mixture model (IGMM) for automatically identifying the number of peaks in the underlying acquisition functions. We solve the intractability of estimating the IGMM directly from the acquisition function by formulating the batch generalized slice sampling to efficiently draw samples from the acquisition function. We perform extensive experiments for benchmark functions and two real world applications - machine learning hyper-parameter tuning and experimental design for alloy hardening. We show empirically that the proposed B3O outperforms the existing fixed batch BO approaches in finding the optimum whilst requiring a fewer number of evaluations, thus saving cost and time.
Extending the Path-Planning Horizon

The mobility sensors on a typical mobile robot vehicle have limited range. Therefore a navigation system has no knowledge about the world beyond this sensing horizon. As a result, path planners that rely only on this knowledge to compute paths are unable to anticipate obstacles sufficiently early and have no choice but to resort to an inefficient local obstacle avoidance behavior. To alleviate this problem, we present an opportunistic navigation and view planning strategy that incorporates look-ahead sensing of possible obstacle configurations. This planning strategy is based on a “what-if” analysis of hypothetical future configurations of the environment. Candidate sensing positions are evaluated based on their ability to observe anticipated obstacles. These sensing positions identified by this forward-simulation framework are used by the planner as intermediate waypoints. The validity of the strategy is supported by results from simulations as well as field experiments with a real robotic platform. These results show that significant reduction in path length can be achieved by using this framework.
Charting a Manifold

We construct a nonlinear mapping from a high-dimensional sample space to a low-dimensional vector space, effectively recovering a Cartesian coordinate system for the manifold from which the data is sampled. The mapping preserves local geometric relations in the manifold and is pseudo-invertible. We show how to estimate the intrinsic dimensionality of the manifold from samples, decompose the sample data into locally linear low-dimensional patches, merge these patches into a single low-dimensional coordinate system, and compute forward and reverse mappings between the sample and coordinate spaces. The objective functions are convex and their solutions are given in closed form.