I Probe, Therefore I Am: Designing a Virtual Journalist with Human Emotions

By utilizing different communication channels, such as verbal language, gestures or facial expressions, virtually embodied interactive humans hold a unique potential to bridge the gap between human-computer interaction and actual interhuman communication. The use of virtual humans is consequently becoming increasingly popular in a wide range of areas where such a natural communication might be beneficial, including entertainment, education, mental health research and beyond. Behind this development lies a series of technological advances in a multitude of disciplines, most notably natural language processing, computer vision, and speech synthesis. In this paper we discuss a Virtual Human Journalist, a project employing a number of novel solutions from these disciplines with the goal to demonstrate their viability by producing a humanoid conversational agent capable of naturally eliciting and reacting to information from a human user. A set of qualitative and quantitative evaluation sessions demonstrated the technical feasibility of the system whilst uncovering a number of deficits in its capacity to engage users in a way that would be perceived as natural and emotionally engaging. We argue that naturalness should not always be seen as a desirable goal and suggest that deliberately suppressing the naturalness of virtual human interactions, such as by altering its personality cues, might in some cases yield more desirable results.Comment: eNTERFACE16 proceeding

Alpha-amylase inhibition kinetics by caulerpenyne

Many algae have important secretions which are generally used for defensive purposes. These secretions take attentions of a lot of researchers who are wondering if these metabolites can be used for medical researches or not. Among these metabolites, caulerpenyne (CYN) which is the main metabolite of Caulerpa species, have had an important place in Caulerpa researches since the results related to its determined properties such as cytotoxic, antiviral, antiproliferative and apoptotic effects have been proven by many scientific reports. In the present study, the inhibitory effect of CYN isolated from C. prolifera on alpha-amylase was investigated. The inhibition experiments were done with CYN by spectrophotometric determination method. In order to evaluate the type of inhibition Lineweaver–Burk plot was produced. The results obtained from enzyme kinetic studies exhibited an un-competitive type of inhibition, which is characterized by the difference of Vmax and KM from those of the free enzyme, of alpha-amylase in the presence of CYN. The present study showed that Caulerpa species can be a potential target for producing diabetic drugs in the light of the results obtained for CYN

Process Induced Defects in Liquid Molding Processes of Composites

Liquid Composite Molding (LCM) processes are cost efficient manufacturing alternatives to traditional autoclave technology for producing near-net shape structural composite parts. However, process induced defects often limit wider usage of LCM in structural applications. Thorough knowledge of these defects, as well as their formation mechanisms and prevention techniques is essential in developing improved LCM processes. In this article, process induced defects in liquid molding processes of composites, categorized into preform, flow induced and cure induced defects, are reviewed. Preform defects are further presented as fiber misalignment and fiber undulation (waviness and wrinkling). The respective causes, detrimental effects, and possible prevention methods of these defects are presented. Thereafter, flow induced defects are classified as voids and dry spots. Dry spot formation mechanisms in LCM processes and available prevention techniques are summarized. In addition, void formation mechanisms, adverse effects on composite properties, and removal techniques are presented. Cure induced defects include microcracks, void growth and geometrical distortions (warpage and spring-in). Each of these defects are discussed along with their underlying causes as well as their control and reduction schemes.Ye

Malicious code detection in android : the role of sequence characteristics and disassembling methods

The acceptance and widespread use of the Android operating system drew the attention of both legitimate developers and malware authors, which resulted in a significant number of benign and malicious applications available on various online markets. Since the signature-based methods fall short for detecting malicious software effectively considering the vast number of applications, machine learning techniques in this field have also become widespread. In this context, stating the acquired accuracy values in the contingency tables in malware detection studies has become a popular and efficient method and enabled researchers to evaluate their methodologies comparatively. In this study, we wanted to investigate and emphasize the factors that may affect the accuracy values of the models managed by researchers, particularly the disassembly method and the input data characteristics. Firstly, we developed a model that tackles the malware detection problem from a Natural Language Processing (NLP) perspective using Long Short-Term Memory (LSTM). Then, we experimented with different base units (instruction, basic block, method, and class) and representations of source code obtained from three commonly used disassembling tools (JEB, IDA, and Apktool) and examined the results. Our findings exhibit that the disassembly method and different input representations affect the model results. More specifically, the datasets collected by the Apktool achieved better results compared to the other two disassemblers

Prediction of moisture saturation levels for vinylester composite laminates : a data-driven approach for predicting the behavior of composite materials

Presented at the 34th International Conference of the Polymer Processing Society, May 24, 2018.This paper introduces a comprehensive, data-driven method to predict the properties of composite materials, such as thermo-mechanical properties, moisture saturation level, durability, or other such important behavior. The approach is based on applying data mining techniques to the collective knowledge in the materials field. In this article, first, a comprehensive database is compiled from published research articles. Second, the Random Forests algorithm is used to build a predictive model that explains the investigated material response based on a wide variety of material and process variables (of different data types). This advanced statistical learning approach has the potential to drastically enhance the design of composite materials by selecting appropriate constituents and process parameters in order to optimize the response for a specific application. This method is demonstrated by predicting the moisture saturation level for vinylester-based composite laminates. Using 90% of the available published data available as the training dataset, the Random Forests algorithm is used to develop a regression model for the moisture saturation level. Variables considered by the model include the manufacturing process, the fiber type and architecture, the fiber and void contents, the matrix filler type and content, as well as the conditioning environment and temperature. On this training data, the model proved to be a good fit with a prediction accuracy of R^2(training)=94.96%. When used to predict the moisture saturation level for the remaining unseen 10% of the compiled data, the model exhibited a prediction accuracy of R^2(test)=85.28%. Furthermore, the Random Forests model allows the assessment of the impact of the different variables on the moisture saturation level. The fiber type is found to be the most important determinant on the moisture saturation level in vinylester composite laminates.YesPeer reviewed for the proceedings of the 34t

ASME IMECE2003 -43837 FORMATION OF MICROSCOPIC VOIDS IN RESIN TRANSFER MOLDED COMPOSITES

ABSTRACT Performance of composite materials usually suffers from process-induced defects such as dry spots or microscopic voids. While effects of void content in molded composites have been studied extensively, knowledge of void morphology and spatial distribution of voids in composites manufactured by resin transfer molding (RTM) remains limited. In this study, through-the-thickness void distribution for a diskshaped, E-glass/epoxy composite part manufactured by resin transfer molding is investigated. Microscopic image analysis is conducted through-the-thickness of a radial sample obtained from the molded composite disk. Voids are primarily found to concentrate within or adjacent to the fiber preforms. More than 93% of the voids are observed within the preform or in a so-called transition zone, next to a fibrous region. In addition, void content was found to fluctuate through-the-thickness of the composite. Variation up to 17% of the average void content of 2.15% is observed through-the-thicknesses of the eight layers studied. Microscopic analysis revealed that average size of voids near the mold surfaces is slightly larger than those located at the interior of the composite. In addition, average size of voids that are located within the fiber preform is observed to be smaller than those located in other regions of the composite. Finally, proximity to the surface is found to have no apparent effect on shape of voids within the composite

Computerized Nurse Charting

journal articleBiomedical Informatic

Study of Short-distance Spin and Charge Correlations and Local Density-of-States in the CMR regime of the One-Orbital Model for Manganites

The metal-insulator transition, and the associated magnetic transition, in the colossal magnetoresistance (CMR) regime of the one-orbital model for manganites is here studied using Monte Carlo (MC) techniques. Both cooperative oxygen lattice distortions and a finite superexchange coupling among the $t_{\rm 2g}$ spins are included in our investigations. Charge and spin correlations are studied. In the CMR regime, a strong competition between the ferromagnetic metallic and antiferromagnetic charge-ordered insulating states is observed. This competition is shown to be important to understand the resistivity peak that appears near the critical temperature. Moreover, it is argued that the system is dynamically inhomogeneous, with short-range charge and spin correlations that slowly evolve with MC time, producing the glassy characteristics of the CMR state. The local density-of-states (LDOS) is also investigated, and a pseudogap (PG) is found to exist in the CMR temperature range. The width of the PG in the LDOS is calculated and directly compared with recent scanning-tunneling-spectroscopy (STS) experimental results. The agreement between our calculation and the experiment suggests that the depletion of the conductance at low bias observed experimentally is a reflection on the existence of a PG in the LDOS spectra, as opposed to a hard gap. The apparent homogeneity observed via STS techniques could be caused by the slow time characteristics of this probe. Faster experimental methods should unveil a rather inhomogeneous state in the CMR regime, as already observed in neutron scattering experiments.Comment: 13 pages, 10 figure

Accurate characterization of moisture absorption in polymeric materials

The importance of using the exact solution of the hindered diffusion model is demonstrated on experimental data from a nanoclay/epoxy composite.Ye

Gold Coast diagnostic criteria increase sensitivity in amyotrophic lateral sclerosis

Objective: This study evaluates diagnostic accuracy of the proposed ‘Gold Coast’ (GC) diagnostic criteria for amyotrophic lateral sclerosis (ALS). Methods: Five European centres retrospectively sampled consecutive patients referred for electromyography on suspicion of ALS. Patients were classified according to the GC criteria, the revised El Escorial (rEE) criteria and the Awaji (AW) criteria without and with the ‘Possible’ category (+ Poss). Reference standard was ALS confirmed by disease progression at follow-up. Results: Of 404 eligible patients 272 were diagnosed as ALS, 94 had mimicking disorders, 35 were lost for follow-up, and three had insufficient data. Sensitivity for the GC criteria was 88.2% (95% CI: 83.8-91.8%), which was higher than for previous criteria, of which the AW + Poss criteria reached the highest sensitivity of 77.6% (95% CI: 72.2–82.4%) (p < 0.001). Specificity was high for all criteria. The increase in sensitivity for the GC criteria was mainly due to the inclusion of 28 patients with progressive muscular atrophy (PMA). Conclusions: The simpler GC criteria increase the sensitivity, primarily due to considering PMA as a form of ALS with high specificity preserved. Significance: This validation study supports that GC criteria should be used in clinical practice and may be used for inclusion in trials