Improving the accuracy of automatic facial expression recognition in speaking subjects with deep learning

When automatic facial expression recognition is applied to video sequences of speaking subjects, the recognition accuracy has been noted to be lower than with video sequences of still subjects. This effect known as the speaking effect arises during spontaneous conversations, and along with the affective expressions the speech articulation process influences facial configurations. In this work we question whether, aside from facial features, other cues relating to the articulation process would increase emotion recognition accuracy when added in input to a deep neural network model. We develop two neural networks that classify facial expressions in speaking subjects from the RAVDESS dataset, a spatio-temporal CNN and a GRU cell RNN. They are first trained on facial features only, and afterwards both on facial features and articulation related cues extracted from a model trained for lip reading, while varying the number of consecutive frames provided in input as well. We show that using DNNs the addition of features related to articulation increases classification accuracy up to 12%, the increase being greater with more consecutive frames provided in input to the model

TEI INDEX MIGHT BE THE UNIQUE ECHOCARDIOGRAPHIC PARAMETER THAT DETECTS HYPERVISCOSITY SYNDROME: ACASE REPORT

Abstract: Hyperviscosity syndromes are disorders of infrequent prevalence in which changes of rheological characteristics cause increased resistance to blood flow, endothelial dysfunction, tissue ischemia and bleeding. Signs of hyperviscosity syndrome become clinically overt at the point of 4 centipoise units. We present a case of patient with hyperviscosity syndrome due to Waldenstrom’s macroglobulinemia with negative records on earlier cardiovascular illnesses. Laboratory diagnostic and standard echocardiography did not show any deviation towards increased cardiovascular risk, heart failure or ischemic heart disease. However, unique clinically significant change that could be indirectly related to hyperviscosity syndrome was found with the myocardium performance index (MPI). Tei-index showed median value of 0.75 corresponding to severe grades of myocardial dysfunction earlier described in the literature for other entities. Comprehensive roles of rheological changes in relation to echocardiography, pathophysiology of myocardial performance and cardiovascular continuum might be interesting point for further investigations

Flipping Engineering Courses: A School Wide Initiative

In the 2013-2014 school year, we implemented the "flipped classroom" as part of an initiative to drive active learning, student engagement and enhanced learning in our school. The flipped courses consisted of freshman through senior engineering classes in introductory programming, statics/mechanics, mechanical design, bio-thermodynamics, facilities layout/material handling, and chemical engineering dynamics and modeling. In the flipped classroom, students watch video lectures beforehand to obtain the foundational knowledge and then demonstrate skills during class. Our study set out to address the following research questions: (1) Does the flipped classroom promote student engagement during class, and does it positively impact the classroom environment? (2) Is the flipped classroom associated with increased student achievement and learning of content? and (3) What strengths, benefits, and drawbacks do students perceive with the flipped classroom? To address these, we used a mixed methods approach, including environment and evaluation surveys, instructor interviews, exam and homework results, video access data, and structured classroom observation. Based on our use of the College and University Classroom Environment Inventory (CUCEI), we found evidence that flipped instruction can positively impact the classroom environment. We also used a behavioral observation protocol--the Teaching Dimensions Observation Protocol (TDOP)--to assess student engagement and involvement during class. We compared our results to a national TDOP study of 58 lecture-based STEM classrooms, formally demonstrating the advantages of our flipped classrooms. Behaviors such as student discussion and questions and problem solving were significantly higher in our flipped classrooms (p< 0.0001). Our pre-flip versus flip exam and homework results were mixed from a statistical improvement standpoint. However, based on instructor interviews we noted enhanced higher-order skills such as problem solving and deeper engagement and proficiency in some courses and with some students. Unfortunately, we encountered challenges with our freshman and seniors. The great majority of freshmen did not use the videos for first-time instruction. The seniors expressed resistance to and dissatisfaction with this instructional change. Both freshmen and seniors rated their classroom environments statistically lower than the sophomores and juniors did. We uncovered other instances in the literature of these challenges. Nonetheless, we believe that flipped instruction is a valuable approach for promoting engagement and learning. We discuss lessons learned, including the need to educate students about the expectations of the flipped classroom

Meeting the expectation of industry: an integrated approach for the teaching of mechanics and electronics to design students

This is an Accepted Manuscript of an article published by Taylor & Francis in European Journal of Engineering Education on 19th January 2015, available online: http://dx.doi.org/10.1080/03043797.2014.1001813This paper examines the traditional engineering-based provision delivered to Product Design and Technology (B.Sc.) undergraduates at the Loughborough Design School and questions its relevancy against the increasing expectations of industry. The paper reviews final-year design projects to understand the level of transference of engineering-based knowledge into design practice and highlights areas of opportunity for improved teaching and learning. The paper discusses the development and implementation of an integrated approach to the teaching of Mechanics and Electronics to formalise and reinforce the key learning process of transference within the design context. The paper concludes with observations from the delivery of this integrated teaching and offers insights from student and academic perspectives for the further improvement of engineering-based teaching and learning

Anomaly detection from log files using unsupervised deep learning

Computer systems have grown in complexity to the point where manual inspection of system behaviour for purposes of malfunction detection have become unfeasible. As these systems output voluminous logs of their activity, machine led analysis of them is a growing need with already several existing solutions. These largely depend on having hand-crafted features, require raw log preprocessing and feature extraction or use supervised learning necessitating having a labeled log dataset not always easily procurable. We propose a two part deep autoencoder model with LSTM units that requires no hand-crafted features, no preprocessing of data as it works on raw text and outputs an anomaly score for each log entry. This anomaly score represents the rarity of a log event both in terms of its content and temporal context. The model was trained and tested on a dataset of HDFS logs containing 2 million raw lines of which half was used for training and half for testing. While this model cannot match the performance of a supervised binary classifier, it could be a useful tool as a coarse filter for manual inspection of log files where a labeled dataset is unavailable

Trends of Dithiocarbamate Residues in Raspberries in the Republic of Serbia over the Period 2007/2010

Dithiocarbamates (DTC) are among the most commonly used in the world. In the Republic of Serbia almost 300 active substances are registered including five dithiocarbamate fungicides (ziram, mancozeb, metiram, propineb and tiram). For raspberry protection from fungal diseases, only mancozeb combined with metalaxylM (Ridomil Gold MZ 68-WG; mankozeb 640 g/kg + metalaxyl-M 40 g/kg), of all dithiocarbamates, is authorized for use in the Republic of Serbia. Dithiocarbamates present in food can be of health concern. They have at least three modes of toxic action. These include: (a) the capacity to generate carbon disulfide (CS2), (b) biotransformation into ethylenethiourea (ETU), (c) the ability to chelate physiologically important ions (e. g., copper). The Serbian government authorities adapted the maximum residue levels (MRLs) to the MRLs set by the European Union in 2010 (The Official gazette 27/2010) but dithiocarbamate levels were not set for all agricultural products. The MRLs for dithiocarbamates in raspberries (dithiocarbamates expressed as CS2, including maneb, mancozeb, metiram, propineb, thiram and ziram) in the EU and the Republic of Serbia is 0.05 mg/kg. The paper presents the method validation results and the results of dithiocarbamate monitoring study. Our study comprised the determination of the dithiocarbamate residues content in 103 raspberry samples from the market of the Republic of Serbia taken during 2007/2010. Over 20% of samples were above the MRLs (0.05 mg/kg), with the highest detected concentration of 0.8 mg/kg. The headspace GC/MS multi-residue method, based on the analysis of carbon disulphide obtained after the treatment of analytes with hydrochloric acid in the presence of tin (II) chloride was used. The averaged recovery from raspberries was spiked in the range of 0.05 - 1.25 mg/kg, is 91.35%. The limits of detection (LOD = 3 x sigma baseline noise) and quantification (LOQ = 10 x sigma baseline noise) were approximately 0.01 and 0.03 mg/kg, respectively. The linearity obtained without matrix was R-2=0.992, and with matrix was R-2=0.999. The matrices did not have interfering peaks in the time of CS2 elution

Electronic Nose: A First Sensors Array Optimization for Pesticides Detection Based on Wilks' A-Statistic

This paper describes an E-Nose aimed to Pesticide Detection which uses an array of different commercial gas sensors. In order to optimize the array reducing the redundancies due to similar sensors answers, Wilks’ A-statistic has been used