Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Behavior and Preference Decision-Making in Response to Faces in High-Functioning Autism

How do we come to the decision that we like a face? This thesis investigates this important aspect of social processing and communication by examining preference decisions for faces and the role that visual behavior plays in the process. I present a series of studies designed to investigate face preference formation and gaze patterns using eye-tracking and self-reported preference ratings. I tested healthy control subjects and two clinical populations known to have deficits in social processing: people with autism and patients with amygdala lesions. In studies one and two, I explore whether known social cognition deficits in people with autism and amygdala lesions also impair subjective decision-making regarding the attractiveness of faces. In study three, I investigate the flexibility of rule-based visual strategies used by these populations during face perception. Additionally, I present a custom algorithm developed to process raw eyetracking data, which was used to analyze all eyetracking data in this thesis. People with autism and patients with amygdala lesions are known to have general deficits in social processing, including difficulty orienting toward and evaluating faces. Nevertheless, I find that their behavior is markedly similar in many areas where we would expect them to have abnormalities or deficiencies. Their preference decisions when judging facial attractiveness were highly correlated with those made by controls, and both groups showed the same biases for familiar faces over novel faces. In addition, people with autism exhibit the same visual sampling behavior linking preference and attentional orienting, but reach their decisions faster than controls and also appear insensitive to the difficulty of the choice. Finally, gaze to the eye region appears normal in the absence of an explicit decision-making task, but only when analyzed in a similar manner as previous studies. However, when face sub-regions were analyzed in greater detail, people with autism demonstrate abnormalities in face gaze patterns, failing to emphasize the most information-rich regions of the face. Furthermore, people with autism demonstrate impairments in their ability to update those gaze patterns to accommodate different viewing restrictions. Taken together, these findings support the idea that the normal formation of face preferences can be preserved in the presence of general social processing impairments. Patterns in the eyetracking and behavioral data indicate that this is made possible, in part, by compensatory atypical processing and visual strategies.
Geocommunity-Based Broadcasting for Data Dissemination in Mobile Social Networks

In this paper, we consider the issue of data broadcasting in mobile social networks (MSNets). The objective is to broadcast data from a superuser to other users in the network. There are two main challenges under this paradigm, namely 1) how to represent and characterize user mobility in realistic MSNets; 2) given the knowledge of regular users' movements, how to design an efficient superuser route to broadcast data actively. We first explore several realistic data sets to reveal both geographic and social regularities of human mobility, and further propose the concepts of geocommunity and geocentrality into MSNet analysis. Then, we employ a semi-Markov process to model user mobility based on the geocommunity structure of the network. Correspondingly, the geocentrality indicating the “dynamic user density” of each geocommunity can be derived from the semi-Markov model. Finally, considering the geocentrality information, we provide different route algorithms to cater to the superuser that wants to either minimize total duration or maximize dissemination ratio. To the best of our knowledge, this work is the first to study data broadcasting in a realistic MSNet setting. Extensive trace-driven simulations show that our approach consistently outperforms other existing superuser route design algorithms in terms of dissemination ratio and energy efficiency.
Religious Coping Across a Spectrum of Religious Involvement Among Jews

The present study examined the extent to which general religiousness moderated the relationship between religious coping and mental health in a sample of 572 Jewish adults. Hierarchical linear regression was utilized to examine interactions between religiousness and positive/negative religious coping and their effects on mental health. Results indicated that positive religious coping predicted higher subjective well-being, but not (higher or lower) depression or anxiety, and negative religious coping predicted lower subjective well-being, greater depression, and elevated anxiety. Surprisingly, general religiousness—regardless of how it was measured (e.g., religious practices, intrinsic religiosity, religious affiliation)—did not moderate relationships between religious coping and mental health. These findings suggest that religious coping associates with mental health across the entire spectrum of religious involvement for Jews, highlighting that religious coping is an important mental health variable in this population, irrespective of individual religious differences.
Application of Emotional Recognition in Intelligent Tutoring System

To meet the emotional need in traditional intelligent tutoring system (ITS), an intelligent tutoring system model based on emotional recognition technique was proposed in the paper. This model was added the emotional recognition module on the model of traditional intelligent tutoring system. The model of tutoring system based on emotional recognition was constructed with facial expression recognition and text recognition techniques, which can get, recognize and analyze emotional information of students' learning performance, then implement emotional stimulation and emotional tutoring according to different students' learning emotion.
Development and Preliminary Evaluation of a Multivariate Index Assay for Ovarian Cancer

Background Most women with a clinical presentation consistent with ovarian cancer have benign conditions. Therefore methods to distinguish women with ovarian cancer from those with benign conditions would be beneficial. We describe the development and preliminary evaluation of a serum-based multivariate assay for ovarian cancer. This hypothesis-driven study examined whether an informative pattern could be detected in stage I disease that persists through later stages. Methodology/Principal Findings Sera, collected under uniform protocols from multiple institutions, representing 176 cases and 187 controls from women presenting for surgery were examined using high-throughput, multiplexed immunoassays. All stages and common subtypes of epithelial ovarian cancer, and the most common benign ovarian conditions were represented. A panel of 104 antigens, 44 autoimmune and 56 infectious disease markers were assayed and informative combinations identified. Using a training set of 91 stage I data sets, representing 61 individual samples, and an equivalent number of controls, an 11-analyte profile, composed of CA-125, CA 19-9, EGF-R, C-reactive protein, myoglobin, apolipoprotein A1, apolipoprotein CIII, MIP-1α, IL-6, IL-18 and tenascin C was identified and appears informative for all stages and common subtypes of ovarian cancer. Using a testing set of 245 samples, approximately twice the size of the model building set, the classifier had 91.3% sensitivity and 88.5% specificity. While these preliminary results are promising, further refinement and extensive validation of the classifier in a clinical trial is necessary to determine if the test has clinical value. Conclusions/Significance We describe a blood-based assay using 11 analytes that can distinguish women with ovarian cancer from those with benign conditions. Preliminary evaluation of the classifier suggests it has the potential to offer approximately 90% sensitivity and 90% specificity. While promising, the performance needs to be assessed in a blinded clinical validation study.
Enhancement of Bandwidth of Rectangular Patch Antenna Using Two Square Slots Techniques

The research in wireless communication has spurred the development of extra ordinary range of antennas, each with its own advantage and limitations. There are many applications where space is at premium, and where there is an urgent need for an antenna with the flexibility to efficiently combine the capabilities of multiple antennas. In fact rapidly developing market in personal communication systems (PCS), mobile satellite communications, direct broadband television (DBS) wireless local area networks (WLANs) suggest that demand for Microstrip antennas and array will increase even further. Conventional Microstrip patch antennas has some drawbacks of low efficiency, narrow bandwidth (3-6%) of the central frequency, its bandwidth is limited to a few percent which is not enough for most of the wireless communication systems nowadays. In this paper one of the efficient methods used for the enhancement of patch antenna bandwidth is the loading of rectangular microstrip patch antenna with two square slots across its side surface. This type of Microstrip patch antenna meets the requirement of operation for wireless applications. The proposed configuration is simulated and analyzed using GENESYS software package. The VSWR, input impedance‚ and S11 performance are used for the analysis of the different configurations. The simulated results for square slots antenna structure give bandwidth of 311 MHZ. Feed point on the patch that gives a good match of 50 ohm. The results of the fabricated microstrip antenna give the bandwidth of 286 MHz.
Large-Sample Results for Batch Means

In analyzing the output process generated by a steady-state simulation, we often seek to estimate the expected value of the output. The sample mean based on a finite sample of size n is usually the estimator of choice for the steady-state mean; and a measure of the sample mean's precision is the variance parameter, i.e., the limiting value of the sample size multiplied by the variance of the sample mean as n becomes large. This paper establishes asymptotic properties of the conventional batch-means BM estimator of the variance parameter as both the batch size and the number of batches become large. In particular, we show that the BM variance estimator is asymptotically unbiased and convergent in mean square. We also provide asymptotic expressions for the variance of the BM variance estimator. Exact and empirical examples illustrate our findings.
Procedural differences in the calculation of the prevalence of reading difficulties in Spanish-speaking school children / Diferencias procedimentales en el cálculo de la prevalencia del retraso lector en escolares hispanoparlantes

Abstract The prevalence of reading difficulties (RD) reflects controversial data ranging from 3.1–3.2% to 17.5%. Possible explanations are partly based on the incidence of orthography-specific factors influencing the reading process, but also on methodological differences that hinder comparison of the reported results. For this reason, the present study aims to analyse the prevalence of RD in a sample of 1,408 Spanish-speaking school children by comparing different ways of calculating the prevalence rate. The results reflect a prevalence of 2.2−5.3%, consistent with data reported for predominantly transparent orthographies. Some of the procedures used to identify RD are more accurate in early school years than at more advanced moments of schooling. Furthermore, the consideration of students’ sex when calculating the prevalence of RD seems to represent a more sensitive way of identifying students with RD.
Automated Deduction: a Technological Point of View

There are recent successes of automated deduction techniques in various application domains, such as mathematics, classical and nonmonotonic logics, diagnosis, planning and within software engineering. We will briefly review some of them and observe that these successes have been made only after a careful analysis of the application domain as well as the deduction system under consideration. The purpose of this article is to argue that automated deduction systems can be usefully applied in practice, but it is necessary to have available a variety of deduction methods, to understand their properties and their computational power in order to tailor them for the application under consideration. In the early days of automated deduction, research concentrated on the development of general purpose deduction systems. “Applications” were toy examples, which did not scale up to realistically sized problems. Nowadays, the theorem prover community discovers applications again. The key to success quite often is the specific knowledge of the application domain which is used to optimise the deduction systems, i.e. the knowledge is used to guide the search for a proof or a model. Indeed, there are some recent impressive results which show that such deductive systems are even able to handle realistic problems. Domains to be mentioned here include mathematics (see Sections 1 and 4.1), planning [18] (see Section 3.1), model checking (see Section 3.2), diagnosis (see Section 3.3), software reuse [22] and verification (see Section 3.4), or view deletion in databases [2]. Another realistically sized problem solved by the Protein prover [5] was the analysis of banking fees rule sets, which was successfully tackled with constraints [44] (see Section 3.5).
Petri Net Plans A Framework for Collaboration and Coordination in Multi-Robot Systems

Programming the behavior of multi-robot systems is a challenging task which has a key role in developing effective systems in many application domains. In this paper, we present Petri Net Plans (PNPs), a language based on Petri Nets (PNs), which allows for intuitive and effective robot and multi-robot behavior design. PNPs are very expressive and support a rich set of features that are critical to develop robotic applications, including sensing, interrupts and concurrency. As a central feature, PNPs allow for a formal analysis of plans based on standard PN tools. Moreover, PNPs are suitable for modeling Multi-Robot Systems and the developed behaviors can be executed in a distributed setting, while preserving the properties of the modeled system. PNPs have been deployed in several robotic platforms in different application domains. In this paper, we report three case studies, which address complex single robot plans, coordination and collaboration.
Modulation of Visual Responses by Gaze Direction in Human Visual Cortex

To locate visual objects, the brain combines information about retinal location and direction of gaze. Studies in monkeys have demonstrated that eye position modulates the gain of visual signals with “gain fields,” so that single neurons represent both retinotopic location and eye position. We wished to know whether eye position and retinotopic stimulus location are both represented in human visual cortex. Using functional magnetic resonance imaging, we measured separately for each of several different gaze positions cortical responses to stimuli that varied periodically in retinal locus. Visually evoked responses were periodic following the periodic retinotopic stimulation. Only the response amplitudes depended on eye position; response phases were indistinguishable across eye positions. We used multivoxel pattern analysis to decode eye position from the spatial pattern of response amplitudes. The decoder reliably discriminated eye position in five of the early visual cortical areas by taking advantage of a spatially heterogeneous eye position-dependent modulation of cortical activity. We conclude that responses in retinotopically organized visual cortical areas are modulated by gain fields qualitatively similar to those previously observed neurophysiologically.
Effect of different root endophytic fungi on plant community structure in experimental microcosms

Abstract Understanding the effects of root‐associated microbes in explaining plant community patterns represents a challenge in community ecology. Although typically overlooked, several lines of evidence point out that nonmycorrhizal, root endophytic fungi in the Ascomycota may have the potential to drive changes in plant community ecology given their ubiquitous presence, wide host ranges, and plant species‐specific fitness effects. Thus, we experimentally manipulated the presence of root endophytic fungal species in microcosms and measured its effects on plant communities. Specifically, we tested whether (1) three different root endophyte species can modify plant community structure; (2) those changes can also modified the way plant respond to different soil types; and (3) the effects are modified when all the fungi are present. As a model system, we used plant and fungal species that naturally co‐occur in a temperate grassland. Further, the soil types used in our experiment reflected a strong gradient in soil texture that has been shown to drive changes in plant and fungal community structure in the field. Results showed that each plant species responded differently to infection, resulting in distinct patterns of plant community structure depending on the identity of the fungus present. Those effects depended on the soil type. For example, large positive effects due to presence of the fungi were able to compensate for less nutrients levels in one soil type. Further, host responses when all three fungi were present were different from the ones observed in single fungal inoculations, suggesting that endophyte–endophyte interactions may be important in structuring plant communities. Overall, these results indicate that plant responses to changes in the species identity of nonmycorrhizal fungal community species and their interactions can modify plant community structure.
Improved Ant Colony Algorithm and its Applications in TSP

In the fields of ant colony optimization (ACO), models of collective intelligence of ants are transformed into useful optimization techniques. A kind of improved ACO (named PMACO) approach for traveling salesman problems (TSP) is presented. Aimed at the disadvantages existed in ACO, several new betterments are proposed and evaluated. In particular, the option that an ant hunts for the next step, the use of a combination of two kinds of pheromone evaluation models, the change of amount in the ant colony during the run of the algorithm, and the mutation of pheromone are studied. We tested ACO algorithm on a set of benchmark problems from the traveling salesman problem library. It performed better than the original and the other improved ACO algorithms
Ultra-dynamic Voltage scaling (UDVS) using sub-threshold operation and local Voltage dithering

Local voltage dithering provides near optimum savings when workload varies for fine-grained blocks. Combining this approach with sub-threshold operation permits ultra-dynamic voltage scaling from 1.1 V to below 300 mV for a 90-nm test chip. Operating at 330 mV provides minimum energy per cycle at 9/spl times/ less energy than ideal shutdown for reduced performance scenarios. Measurements from the test chip characterize the impact of temperature on the minimum energy point.
Inverse Identification Adaptive IIR Filtering: Convergence Speed Analysis and Successive Approximations Algorithm

The approach based on balanced realization theory, previously used to analyze the convergence speed of adaptive IIR filters in the identification configuration and to propose a faster algorithm (successive approximations algorithm), is now used in the inverse identification configuration. This case is of interest for applications such as channel equalization and system identification itself. We show that, while in an identification configuration the Hankel singular values of the system being identified have an important effect on convergence speed, in the inverse identification case it is the Hankel singular values of a certain system related to the system being identified that have this role. From this result, a condition for faster convergence speed is obtained as well as an inverse identification version of the successive approximations algorithm. As in the identification case, it can lead to much faster convergence with a relatively small increase in computational complexity.
Putting the Intelligent Collaboration Model in Practice within the Cleverpal Environment

As a way of overcoming a general feeling of isolation and consequent high student evasion in Virtual Learning Environments (VLE), this article presents results of an experience with the i-collaboration model that promotes the collaboration between users in VLE. I-Collaboration is based on the usage of the virtual companion agents’ (VLC) as collaboration monitors. The VLC are integrated with VLE collaborative tools and know each student personality (MBTI) and behavior in the VLE. Based on these information, the VLC provides students with a distinct perception and experience of the VLE. This fosters students’ motivation to keep learning and collaborating, based on their own needs.
Ontology-Based Information Extraction

Information Extraction (IE) aims to retrieve certain types of information from natural language text by processing them automatically. Ontology-Based Information Extraction (OBIE) has recently emerged as a subfield of Information Extraction. Here, ontologies which provide formal and explicit specifications of conceptualizations play a crucial role in the information extraction process. Because of the use of ontologies, this field is related to Knowledge Representation and has the potential to assist the development of the Semantic Web. This paper presents a survey of the current research work in this field including a classification of OBIE systems along different dimensions and an attempt to identify a common architecture among these systems. It also presents a definition for an OBIE system by taking several factors into consideration. In addition, this paper presents the details of some implementation work carried out by the author to explore the use of ontology-based information extraction. These include a project aimed at extracting information from a set of emails and a project aimed at using multiple ontologies to extract information from a set of university websites. The latter appears to be the first OBIE system to make use of multiple ontologies. Finally, the paper discusses possible directions for future research work on this field.