Miracle-dz/UniFAR-FTUs huggingface dataset:
Tangible Words are Recognized Faster: The Grounding of Meaning in Sensory and Perceptual Systems

Sensory experience rating (SER), a new variable motivated by the grounded cognition framework of conceptual processing (e.g., Barsalou, 2008), indexes the degree to which a word evokes sensory/perceptual experiences. In the present study, SERs were collected for over 2,850 words. While SER is correlated with imageability, age of acquisition, and word frequency, the latter variables (along with seven others) account for less than 30% of the variance in SER. Reanalyses of two large-scale studies demonstrate that SER significantly predicts lexical decision times when other established predictor variables are statistically controlled. These results suggest that conceptual processing is grounded in sensory systems. Additionally, a major benefit of this variable is that it allows psycholinguistic researchers to examine semantic–perceptual links for all word classes with a single rating.
What do a Million News Articles Look like?

We present a detailed description and analysis of the Signal Media One-Million Articles dataset. We have released this dataset to facilitate research on emerging news-related information retrieval (IR) challenges. In particular, we have observed over the past decade emerging novel paradigms for publishing and consuming news, where users can get updated on the go with news from multiple sources, and at the same time, news providers are increasingly using social media and citizen jour-nalism as powerful news sources. As a result, a number of news-related IR tasks have emerged and attracted attention in industry and academia. These include news ver-iﬁcation, temporal summarization of multiple news sources, and news recommendation among others. A number of news datasets were created and shared for news IR in the past. However, such datasets are often drawn from a single outlet, and heavily preprocessed and cleaned. Also, they have become out-dated and are not suitable any more for the emerging news IR challenges described above. Our dataset aims to address this because it is a recent collection from a wide range of sources reﬂecting many real-world issues in news collection and analysis. We present insights obtained from an analysis
Sensorimotor Plasticity after Music-Supported Therapy in Chronic Stroke Patients Revealed by Transcranial Magnetic Stimulation

Background Several recently developed therapies targeting motor disabilities in stroke sufferers have shown to be more effective than standard neurorehabilitation approaches. In this context, several basic studies demonstrated that music training produces rapid neuroplastic changes in motor-related brain areas. Music-supported therapy has been recently developed as a new motor rehabilitation intervention. Methods and Results In order to explore the plasticity effects of music-supported therapy, this therapeutic intervention was applied to twenty chronic stroke patients. Before and after the music-supported therapy, transcranial magnetic stimulation was applied for the assessment of excitability changes in the motor cortex and a 3D movement analyzer was used for the assessment of motor performance parameters such as velocity, acceleration and smoothness in a set of diadochokinetic movement tasks. Our results suggest that the music-supported therapy produces changes in cortical plasticity leading the improvement of the subjects' motor performance. Conclusion Our findings represent the first evidence of the neurophysiological changes induced by this therapy in chronic stroke patients, and their link with the amelioration of motor performance. Further studies are needed to confirm our observations.
Antecedents of Decisions to Outsource Production : A Manufacturing Strategy Perspective

We examine how key attributes of a business unit’s manufacturing strategy affected its plans to outsource manufacturing resources in the late 1990’s; a time when the outsourcing of production was at the forefront of manufacturing strategy. Drawing upon theory, we test a hierarchical regression model that includes the traditional transaction cost variables as well as operationsbased competitive capabilities and priorities. Consistent with theory, we find that high levels of product and process asset specificity significantly reduce plans to outsource. But, counter to transaction cost theory, we explain and show empirically that uncertainty can actually lead to an increase in outsourcing plans. We next examine the effect of the operations capabilities and priorities. Not surprisingly, companies with strong cost capability were less likely to be planning to outsource, and companies that placed a high priority on cost exhibited a higher propensity to outsource–controlling for their current capability. Consistent with real options theory and the management fads of the time, flexibility capability and priority also affected outsourcing plans. Counter to expectations, and interesting in light of recent events, the competitive dimensions of quality and new product development did not directly affect outsourcing plans. Taken together, and coupled with insights from competitive progression theory, our results provide insights into decisions to outsource production. In addition to the above, our results show that existing strategic management and economics theories on firm boundaries are incomplete as they generally fail to consider the role that competitive priorities play in outsourcing decisions.
Probabilistic model for code with decision trees

In this paper we introduce a new approach for learning precise and general probabilistic models of code based on decision tree learning. Our approach directly benefits an emerging class of statistical programming tools which leverage probabilistic models of code learned over large codebases (e.g., GitHub) to make predictions about new programs (e.g., code completion, repair, etc). The key idea is to phrase the problem of learning a probabilistic model of code as learning a decision tree in a domain specific language over abstract syntax trees (called TGen). This allows us to condition the prediction of a program element on a dynamically computed context. Further, our problem formulation enables us to easily instantiate known decision tree learning algorithms such as ID3, but also to obtain new variants we refer to as ID3+ and E13, not previously explored and ones that outperform ID3 in prediction accuracy. Our approach is general and can be used to learn a probabilistic model of any programming language. We implemented our approach in a system called Deep3 and evaluated it for the challenging task of learning probabilistic models of JavaScript and Python. Our experimental results indicate that Deep3 predicts elements of JavaScript and Python code with precision above 82% and 69%, respectively. Further, Deep3 often significantly outperforms state-of-the-art approaches in overall prediction accuracy.
Toward Stable Network Performance in Wireless Sensor Networks: A Multilevel Perspective

Many applications in wireless sensor networks require communication performance that is both consistent and of high quality. Unfortunately, performance of current network protocols can vary significantly because of various interferences and environmental changes. Current protocols estimate link quality based on the reception of probe packets over a short time period. This method is neither efficient nor accurate enough to capture the dramatic variations of link quality. Therefore, we propose a link metric called competence that characterizes links over a longer period of time. We combine competence with current short-term estimations in routing algorithm designs. To further improve network performance, we have designed a distributed route maintenance framework based on feedback control solutions. This framework allows every link along an end-to-end (E2E) path to adjust its link protocol parameters, such as transmission power and number of retransmissions, to ensure specified E2E reliability and latency under dynamic link qualities. Our solutions are evaluated in both extensive simulations and real system experiments. In real system evaluations with 48 T-Motes, our overall solution improves E2E packet delivery ratio over existing solutions by up to 40% while reducing transmission energy consumption by up to 22%. Importantly, our solution also achieves more stable and better transient performance than current approaches.
Recognition of Abstract Objects Via Neural Oscillators: Interaction Among Topological Organization, Associative Memory and Gamma Band Synchronization

Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.
Investigation of Fuzzy Models for the Valuation of Residential Premises Using the KEEL Tool

A series of experiments aimed to generate and learn fuzzy models for the valuation of residential premises was conducted using the KEEL tool (knowledge extraction based on evolutionary learning). Four regression and four post-processing algorithms were applied to several data sets. They referred to sales/purchase transactions of residential premises, which were derived from the cadastral system and the registry of real estate transactions. The results proved the usefulness of the tool to carry out laborious and tedious investigation in a relatively short time. Further research is needed to determine to what extent data coming from such sources allow to build the real estate valuation models.
A Smart TCP Acknowledgment Approach for Multihop Wireless Networks

Reliable data transfer is one of the most difficult tasks to be accomplished in multihop wireless networks. Traditional transport protocols like TCP face severe performance degradation over multihop networks given the noisy nature of wireless media as well as unstable connectivity conditions in place. The success of TCP in wired networks motivates its extension to wireless networks. A crucial challenge faced by TCP over these networks is how to operate smoothly with the 802.11 wireless MAC protocol which also implements a retransmission mechanism at link level in addition to short RTS/CTS control frames for avoiding collisions. These features render TCP acknowledgments (ACK) transmission quite costly. Data and ACK packets cause similar medium access overheads despite the much smaller size of the ACKs. In this paper, we further evaluate our dynamic adaptive strategy for reducing ACK-induced overhead and consequent collisions. Our approach resembles the sender side's congestion control. The receiver is self-adaptive by delaying more ACKs under nonconstrained channels and less otherwise. This improves not only throughput but also power consumption. Simulation evaluations exhibit significant improvement in several scenarios
ReAssert: a tool for repairing broken unit tests

Successful software systems continuously change their requirements and thus code. When this happens, some existing tests get broken because they no longer reflect the intended behavior, and thus they need to be updated. Repairing broken tests can be time-consuming and difficult. We present ReAssert, a tool that can automatically suggest repairs for broken unit tests. Examples include replacing literal values in tests, changing assertion methods, or replacing one assertion with several. Our experiments show that ReAssert can repair many common test failures and that its suggested repairs match developers' expectations.
The Influence of Acculturation on Traumatic Stress Following Childbirth Among Hispanic Adolescents

Introduction: Many studies have explored the role of acculturation on health outcomes; however, no studies to date have examined relationships between acculturation and the traumatic stress of childbirth among Hispanic adolescents. Method: A convenience sample of 66 Hispanic adolescents 13 to 19 years of age completed the Acculturation Rating Scale for Mexican Americans–II (ARSMA-II), a demographic sheet, birth appraisal scale, and the Impact of Event Scale within 72 hours of birth. Results: Significant associations were found between the ARSMA-II subscales and acculturation proxy variables, excluding language; however, only the Mexican Orientation Subscale and generation proxy variable influenced the birth experience. The study findings showed that Hispanic adolescents reporting a more traumatic childbirth identified closer with the Mexican culture and reported fewer family generations residing in the United States. Conclusion: As an overlooked area of research, our findings support the need for additional research related to the traumatic stress of birth among Hispanic adolescents. Using multiple acculturation measurements, including the ARSMA-II, with larger, more diverse samples of adolescents, equally balanced between all categories of acculturation and placement within the five-tier generation matrix, can provide some insightful information and directed health care.
A greedy approach with criteria factors for QoS based web service discovery

Normally, the web service discovery mechanisms in web-sites have a trade-off between being Quality-driven or transaction cost-driven As goals of our paper, we decide upon several factors including the above-mentioned Quality and Transaction cost factors along with the parameters under each factor and use a priority-based greedy approach to involve minimum number of factors to achieve maximum efficiency in the web service discovery. We also present a model with QoS parameters which in a great way help in rating the web services with the end user's perspective on the basis of three levels of views, namely the business level view, Service level view and System level view. In this paper, we've broadly discussed around twenty eight parameters categorically falling under all the above-mentioned factors. To reduce the complexity of involving all the parameters we follow the mechanism of Greedy approach to evaluate the parameters and estimate the criteria rank for each and every parameter. The core principle of the Greedy approach, in general, is to involve minimum number of input data to achieve the maximum efficiency in the final optimized result.
Symbolic computation of differential equivalences

Ordinary differential equations (ODEs) are widespread in many natural sciences including chemistry, ecology, and systems biology, and in disciplines such as control theory and electrical engineering. Building on the celebrated molecules-as-processes paradigm, they have become increasingly popular in computer science, with high-level languages and formal methods such as Petri nets, process algebra, and rule-based systems that are interpreted as ODEs. We consider the problem of comparing and minimizing ODEs automatically. Influenced by traditional approaches in the theory of programming, we propose differential equivalence relations. We study them for a basic intermediate language, for which we have decidability results, that can be targeted by a class of high-level specifications. An ODE implicitly represents an uncountable state space, hence reasoning techniques cannot be borrowed from established domains such as probabilistic programs with finite-state Markov chain semantics. We provide novel symbolic procedures to check an equivalence and compute the largest one via partition refinement algorithms that use satisfiability modulo theories. We illustrate the generality of our framework by showing that differential equivalences include (i) well-known notions for the minimization of continuous-time Markov chains (lumpability), (ii)~bisimulations for chemical reaction networks recently proposed by Cardelli et al., and (iii) behavioral relations for process algebra with ODE semantics. With a prototype implementation we are able to detect equivalences in biochemical models from the literature that cannot be reduced using competing automatic techniques.
The Effect of Environment Light and Stimulation Frequency on Steady-State Visual Evoked Potentials

There are several ways to control a Brain Computer Interface (BCI). One of them is by using Steady State Visually Evoked Potentials (SSVEP): measuring the reaction of the brain to visual stimuli such as a flickering light. SSVEP BCIs are relatively easy to control, because a user merely has to focus on a stimulus and the BCI will pick up on it. However, there are certain factors that may influence the EEG (and possibly the SSVEP signal recognition). An example is light. SSVEP research takes place under controlled light circumstances, yet SSVEP-BCI should be usable in normal day-to-day life of users. Thus, this study aims to evaluate the influence of light on SSVEP signals. For this, an experiment was carried out on six test subjects who were presented with several stimuli of one of three frequencies, both in the light and in the dark. Unfortunately, the influence of light on the EEG signals seemed to be different for each test person. It was therefore concluded that light does have an influence on brain signals, but that it is not the same for everyone. To improve SSVEP BCIs, the BCI should be tailored to fit the needs of every individual user.
The Influence of Blanking Process by Superimposing Ultrasonic Vibrations

The application of ultrasonic vibrations superimposed on wire drawing, deep drawing, upsetting and rolling has been developed in the literature before. Few attempts have ever been made to develop the application of the ultrasonic vibration to blanking process. This paper reports an investigation into the effects of the superimposed ultrasonic vibrations of the punch during the blanking process by experimental design and response surface methodology. The effects of the ultrasonic vibration on the punch load and edge profile are systematically studied. The results show ultrasonic vibrations can decrease the punch load and improve the quality of edge profile.
Development of Fearfulness in Birds: Genetic Factors Modulate Non-Genetic Maternal Influences

The development of fearfulness and the capacity of animals to cope with stressful events are particularly sensitive to early experience with mothers in a wide range of species. However, intrinsic characteristics of young animals can modulate maternal influence. This study evaluated the effect of intrinsic fearfulness on non-genetic maternal influence. Quail chicks, divergently selected for either higher (LTI) or lower fearfulness (STI) and from a control line (C), were cross-fostered by LTI or STI mothers. Behavioural tests estimated the chicks' emotional profiles after separation from the mother. Whatever their genotype, the fearfulness of chicks adopted by LTI mothers was higher than that of chicks adopted by STI mothers. However, genetic background affected the strength of maternal effects: the least emotional chicks (STI) were the least affected by early experience with mothers. We demonstrated that young animal's intrinsic fearfulness affects strongly their sensitivity to non-genetic maternal influences. A young animal's behavioural characteristics play a fundamental role in its own behavioural development processes.
Simulating Cognitive Coping Strategies for Intelligent Support Agents

People react differently to stress. According to the Cognitive Motivational Relational Theory by Lazarus and Folkman, the appraisal of stress and the emotions related to it determine whether people cope with stress by focussing on altering the situation (problem focussed) or on changing the emotional consequences of the events (emotion focussed). These different coping strategies have different effects on the long term. The coping process can be described in a formal dynamic model. Simulations using this model show that problem focussed coping leads to better coping skills and higher decrease of long-term stress than emotion focussed coping. These results also follow from a mathematical analysis of the model. The presented model can form the basis of an intelligent support system that uses a simulation of cognitive processes in humans in stressful conditions.
Measurement of the wet-mass, moisture content, volume and density of single wheat grain kernels

The development of microwave techniques and a separate gas displacement method to measure the wet-mass, moisture content, volume and hence density of single wheat grain kernels are described. The perturbation of the circularly symmetric TM/sub 010/ mode inside a cylindrical cavity is used with a linear model to determine the single grain moisture content and wet-mass. A bellows loading mechanism is used with a calibrated gas-displacement pressure transducer to produce the volume data and hence grain density. Initially, a waveguide cell is used to evaluate the complex permittivity of compressed wheat and this data is used together with the finite element method to design the microwave cavity.
Stimulus-dependent variability and noise correlations in cortical MT neurons

Population codes assume that neural systems represent sensory inputs through the firing rates of populations of differently tuned neurons. However, trial-by-trial variability and noise correlations are known to affect the information capacity of neural codes. Although recent studies have shown that stimulus presentation reduces both variability and rate correlations with respect to their spontaneous level, possibly improving the encoding accuracy, whether these second order statistics are tuned is unknown. If so, second-order statistics could themselves carry information, rather than being invariably detrimental. Here we show that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves. We show that such tuning emerges in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multistability. Information theoretic analysis shows that second-order statistics carry information that can improve the accuracy of the population code.