title: Hybrid cars now, fuel cell cars later.

abstract: We compare the energy efficiency of hybrid and fuel cell vehicles as well as conventional internal combustion engines. Our analysis indicates that fuel cell vehicles using hydrogen from fossil fuels offer no significant energy efficiency advantage over hybrid vehicles operating in an urban drive cycle. We conclude that priority should be placed on hybrid vehicles by industry and government.


title: Repeatability, reproducibility, and rigor in systems research

abstract: Computer systems research spans sub-disciplines that include embedded and real-time systems, compilers, networking, and operating systems. Our contention is that a number of structural factors inhibit quality research and decrease the velocity of science. We highlight some of the factors we have encountered in our work and observed in published papers and propose solutions that, if widely adopted, could both increase the productivity of researchers and the quality of their output.


title: Gender differences in the perception and use of E-mail: An extension to the technology acceptance model

abstract: This study extends the TAM model (Davis 1989) and the SPIR addendum (Straub 1994) by adding gender to an IT diffusion model. The technology acceptance model (TAM) has been widely studied in IS research as an explanation of the use of information systems across IS types and nationalities. While this line of research has found significant cross-cultural differences, it has ignored the effects of gender, even though in socio-linguistic research, gender is a fundamental aspect of culture. Indeed, socio-linguistic research has shown that men tend to focus discourse on hierarchy and independence, while women focus on intimacy and solidarity. This literature provides a solid grounding for conceptual extensions to the IT diffusion research and the technology acceptance model.Testing gender differences that might relate to beliefs and use of computer-based media, the present study sampled 392 female and male responses via a cross-sectional survey instrument. The sample drew from comparable groups of knowledge workers using e-mail systems in the airline industry in North America, Asia, and Europe.Study findings indicate that women and men differ in their perceptions but not use of e-mail. These findings suggest that researchers should include gender in IT diffusion models along with other cultural effects. Managers and coworkers, moreover, need to realize that the same mode of communication may be perceived differently by the sexes, suggesting that more favorable communications environments might be created, environments that take into account not only organizational contextual factors, but also the gender of users. The creation of these environments involves not only the actual deployment of communication media, but also organizational training on communications media.


title: Age effects on gray matter volume and attentional performance in Zen meditation

abstract: Zen meditation, a Buddhist practice centered on attentional and postural self-regulation, has been speculated to bring about beneficial longterm effects for the individual, ranging from stress reduction to improvement of cognitive function. In this study, we examined how the regular practice of meditation may affect the normal age-related decline of cerebral gray matter volume and attentional performance observed in healthy individuals. Voxel-based morphometry for MRI anatomical brain images and a computerized sustained attention task were employed in 13 regular practitioners of Zen meditation and 13 matched controls. While control subjects displayed the expected negative correlation of both gray matter volume and attentional performance with age, meditators did not show a significant correlation of either measure with age. The effect of meditation on gray matter volume was most prominent in the putamen, a structure strongly implicated in attentional processing. These findings suggest that the regular practice of meditation may have neuroprotective effects and reduce the cognitive decline associated with normal aging.


title: Posterior Wall Blowout in Anterior Cruciate Ligament Reconstruction

abstract: Violation of the posterior femoral cortex, commonly referred to as posterior wall blowout, can be a devastating intraoperative complication in anterior cruciate ligament (ACL) reconstruction and lead to loss of graft fixation or early graft failure. If cortical blowout occurs despite careful planning and adherence to proper surgical technique, a thorough knowledge of the anatomy and alternative fixation techniques is imperative to ensure optimal patient outcomes. This article highlights anatomic considerations for femoral tunnel placement in ACL reconstruction and techniques for avoidance and salvage of a posterior wall blowout.


title: NASARI: a Novel Approach to a Semantically-Aware Representation of Items

abstract: The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/.


title: Diagnosis and management of genital ulcers.

abstract: Herpes simplex virus infection and syphilis are the most common causes of genital ulcers in the United States. Other infectious causes include chancroid, lymphogranuloma venereum, granuloma inguinale (donovanosis), secondary bacterial infections, and fungi. Noninfectious etiologies, including sexual trauma, psoriasis, Behçet syndrome, and fixed drug eruptions, can also lead to genital ulcers. Although initial treatment of genital ulcers is generally based on clinical presentation, the following tests should be considered in all patients: serologic tests for syphilis and darkfield microscopy or direct fluorescent antibody testing for Treponema pallidum, culture or polymerase chain reaction test for herpes simplex virus, and culture for Haemophilus ducreyi in settings with a high prevalence of chancroid. No pathogen is identified in up to 25 percent of patients with genital ulcers. The first episode of herpes simplex virus infection is usually treated with seven to 10 days of oral acyclovir (five days for recurrent episodes). Famciclovir and valacyclovir are alternative therapies. One dose of intramuscular penicillin G benzathine is recommended to treat genital ulcers caused by primary syphilis. Treatment options for chancroid include a single dose of intramuscular ceftriaxone or oral azithromycin, ciprofloxacin, or erythromycin. Lymphogranuloma venereum and donovanosis are treated with 21 days of oral doxycycline. Treatment of noninfectious causes of genital ulcers varies by etiology, and ranges from topical wound care for ulcers caused by sexual trauma to consideration of subcutaneous pegylated interferon alfa-2a for ulcers caused by Behçet syndrome.


title: Survey on data science with population-based algorithms

abstract: This paper discusses the relationship between data science and population-based algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two categories of literature, which include population-based algorithms solving data analysis problem and utilizing data analysis methods in population-based algorithms. With the exponential increment of data, the data science, or more specifically, the big data analytics has gained increasing attention among researchers. New and more efficient algorithms should be designed to handle this massive data problem. Based on the combination of population-based algorithms and data mining techniques, we understand better the insights of data analytics, and design more efficient algorithms to solve real-world big data analytics problems. Also, the weakness and strength of population-based algorithms could be analyzed via the data analytics along the optimization process, a crucial entity in population-based algorithms.


title: Experience of Transfistula (TFARP) Repair for Congenital Recto-Vestibular Fistula

abstract: Background: The objective of this study is to assess the feasibility of aprimary transfistula anorectoplasty (TFARP) in congenital recto-vestibular fistula without a covering colostomy in the north of Iraq. Patients and Methods: Female patients having imperforate anus with congenital rectovestibular fistula presenting to pediatric surgical centres in the north of Iraq (Mosul & Erbil) between 1995 to 2011 were reviewed in a nonrandomized manner, after excluding those with pouch colon, rectovaginal fistula and patients with colostomy. All cases underwent one stage primary (TFARP) anorectoplasty at age between 1-30 months, after on table rectal irrigation with normal salinep no recurrence of fistula was noted. At 3 months postoperative, most patients had 1 - 3 stools per day with no episodes of soiling. Twelve (16.4%) patients had grade I-II constipation and 3 cases (4%) had partial anal mucosal prolapse. Conclusions: One–stage


title: Antifungal Potential of Extracellular Metabolites Produced by Streptomyces hygroscopicus against Phytopathogenic Fungi

abstract: Indigenous actinomycetes isolated from rhizosphere soils were assessed for in vitro antagonism against Colletotrichum gloeosporioides and Sclerotium rolfsii. A potent antagonist against both plant pathogenic fungi, designated SRA14, was selected and identified as Streptomyces hygroscopicus. The strain SRA14 highly produced extracellular chitinase and beta-1,3-glucanase during the exponential and late exponential phases, respectively. Culture filtrates collected from the exponential and stationary phases inhibited the growth of both the fungi tested, indicating that growth suppression was due to extracellular antifungal metabolites present in culture filtrates. The percentage of growth inhibition by the stationary culture filtrate was significantly higher than that of exponential culture filtrate. Morphological changes such as hyphal swelling and abnormal shapes were observed in fungi grown on potato dextrose agar that contained the culture filtrates. However, the antifungal activity of exponential culture filtrates against both the experimental fungi was significantly reduced after boiling or treatment with proteinase K. There was no significant decrease in the percentage of fungal growth inhibition by the stationary culture filtrate that was treated as above. These data indicated that the antifungal potential of the exponential culture filtrate was mainly due to the presence of extracellular chitinase enzyme, whereas the antifungal activity of the stationary culture filtrate involved the action of unknown thermostable antifungal compound(s).


title: A comparison of charge dynamics in the reverse-conducting RC IGBT and Bi-mode Insulated Gate Transistor BiGT

abstract: In this paper we present the analysis of charge dynamics in the reverse conducting (RC)-IGBT and the Bi mode Insulating Gate Transistor (BiGT). The differences and similarities between the two device concepts are analyzed with the aid of 2D device simulations using a realistic 3.3kV device model. The influence of the anode shorts dimensioning and buffer design on the on-state characteristics and the snap-back effect is discussed and the advantages of the hybrid BiGT structure are demonstrated. It is shown that the application of the BiGT structure decouples the conventional RC-IGBT design trade-offs with respect to the diode and IGBT areas in relation to the snap-back effect. Main design principles for optimal IGBT and diode performance are outlined.


title: Binary grey wolf optimization approaches for feature selection

abstract: In this work, a novel binary version of the grey wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the latest bio-inspired optimization techniques, which simulate the hunting process of grey wolves in nature. The binary version introduced here is performed using two different approaches. In the first approach, individual steps toward the first three best solutions are binarized and then stochastic crossover is performed among the three basic moves to find the updated binary grey wolf position. In the second approach, sigmoidal function is used to squash the continuous updated position, then stochastically threshold these values to find the updated binary grey wolf position. The two approach for binary grey wolf optimization (bGWO) are hired in the feature selection domain for finding feature subset maximizing the classification accuracy while minimizing the number of selected features. The proposed binary versions were compared to two of the common optimizers used in this domain namely particle swarm optimizer and genetic algorithms. A set of assessment indicators are used to evaluate and compared the different methods over 18 different datasets from the UCI repository. Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.


title: Behavioral Impact of Drivers' Roles in Automated Driving

abstract: This study explores the effects of minor changes in automation level on drivers' engagement in secondary activities. Three levels of automation were tested: manual, semi-autonomous, and fully-autonomous. Potential distractor items were present and participants were instructed they could use them if they felt it was safe. Hand positions and engagement in secondary activities were manually coded. Participants were significantly less likely to engage in a secondary activity in semi-autonomous than fully-autonomous mode. Likewise, they were significantly less likely to use two hands to interact with a secondary activity in semi-autonomous mode than fully-autonomous mode. Gaze classification for each of the driver roles revealed that increasing levels of automation resulted in an increasing percentage of off-road glance durations. These observations suggest that in the event of automation failures, a driver in semi-autonomous driving may be in a somewhat better position to retake control and avoid collisions than during fully autonomous driving.


Application of Data Mining Techniques in IoT: A Short Review


title: The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10.

abstract: The Mini-International Neuropsychiatric Interview (M.I.N.I.) is a short structured diagnostic interview, developed jointly by psychiatrists and clinicians in the United States and Europe, for DSM-IV and ICD-10 psychiatric disorders. With an administration time of approximately 15 minutes, it was designed to meet the need for a short but accurate structured psychiatric interview for multicenter clinical trials and epidemiology studies and to be used as a first step in outcome tracking in nonresearch clinical settings. The authors describe the development of the M.I.N.I. and its family of interviews: the M.I.N.I.-Screen, the M.I.N.I.-Plus, and the M.I.N.I.-Kid. They report on validation of the M.I.N.I. in relation to the Structured Clinical Interview for DSM-III-R, Patient Version, the Composite International Diagnostic Interview, and expert professional opinion, and they comment on potential applications for this interview.


title: A Unified Framework for Whole-Body Humanoid Robot Control With Multiple Constraints and Contacts

abstract: Physical interactivity is a major challenge in humanoid robot-ics. To allow robots to operate in human environments there is a pressing need for the development of control architectures that provide the advanced capabilities and interactive skills needed to effectively interact with the environment and/or the human partner while performing useful manipulation and locomotion tasks. Such architectures must address the robot whole-body control problem in its most general form: task and whole body motion coordination with active force control at contacts, under various constraints, self collision, and dynamic obstacles. In this paper we present a framework that addresses in a unified fashion the whole-body control problem in the context of multi-point multi-link contacts, constraints, and obstacles. The effectiveness of this novel formulation is illustrated through extensive robot dynamic simulations conducted in SAI, and the experimental validation of the framework is currently underway on the ASIMO platform.


Determination of cognitive hemispheric lateralization by "functional" transcranial Doppler cross-validated by functional MRI.


title: Open Information Extraction to KBP Relations in 3 Hours

abstract: We participated in both the English Slot Filling and Entity Linking in the 2013 TAC-KBP evaluation. Our Slot Filling system provides an answer to the following conjectures: Can Open Information Extraction (Open IE) form the basis of a high precision extractor for a set of target relations in an ontology? And, just as importantly, can this be done with a minimum of human knowledge engineering? We built rules to map Open IE extractions to KBP Slot Filling relations and found that just three hours of rule creation gave extractor precision of 0.79. Spending a total of 12 hours refining the rules increased precision slightly to 0.80 with recall near the median of other KBP systems.


title: A Laboratory-Based Study for First Documented Case of Urinary Myiasis Caused by Larvae of Megaselia scalaris (Diptera: Phoridae) in Saudi Arabia

abstract: Megaselia scalaris (Diptera: Phoridae) is one of the medically important insects. Maggots from a urine sample of a 5-year-old Saudi girl were examined microscopically for identification. These maggots were cultured to become adult flies. Larvae and adults were identified using standard keys. Protozoan flagellates were obtained from the gut of the larvae. This is the first report of M. scalaris as a causative agent of urinary human myiasis in Saudi Arabia.


title: A case study of a corporate open source development model

abstract: Open source practices and tools have proven to be highly effective for overcoming the many problems of geographically distributed software development. We know relatively little, however, about the range of settings in which they work. In particular, can corporations use the open source development model effectively for software projects inside the corporate domain? Or are these tools and practices incompatible with development environments, management practices, and market-driven schedule and feature decisions typical of a commercial software house? We present a case study of open source software development methodology adopted by a significant commercial software project in the telecommunications domain. We extract a number of lessons learned from the experience, and identify open research questions.


Programming by Optimisation


title: Automated Extension of Narrative Planning Domains with Antonymic Operators

abstract: AI Planning has been widely used for narrative generation and the control of virtual actors in interactive storytelling. Planning models for such dynamic environments must include alternative actions which enable deviation away from a baseline storyline in order to generate multiple story variants and to be able to respond to changes that might be made to the story world. However, the actual creation of these domain models has been a largely empirical process with a lack of principled approaches to the definition of alternative actions. Our work has addressed this problem and in the paper we present a novel automated method for the generation of interactive narrative domain models from existing non-interactive versions. Central to this is the use of actions that are contrary to those forming the baseline plot within a principled mechanism for their semi-automatic production. It is important that such newly created domain content should still be human-readable and to this end labels for new actions and predicates are generated automatically using antonyms selected from a range of on-line lexical resources. Our approach is fully implemented in a prototype system and its potential demonstrated via both formal experimental evaluation and user evaluation of the generated action labels.


title: Europeana Newspapers OCR Workflow Evaluation

abstract: This paper summarises the final performance evaluation results of the OCR workflow which was employed for large-scale production in the Europeana Newspapers project. It gives a detailed overview of how the involved software performed on a representative dataset of historical newspaper pages (for which ground truth was created) with regard to general text accuracy as well as layout-related factors which have an impact on how the material can be used in specific use scenarios. Specific types of errors are examined and evaluated in order to identify possible improvements related to the employed document image analysis and recognition methods. Moreover, alternatives to the standard production workflow are assessed to determine future directions and give advice on best practice related to OCR projects.


title: Gene Signatures of Progression and Metastasis in Renal Cell Cancer

abstract: Purpose: To address the progression, metastasis, and clinical heterogeneity of renal cell cancer (RCC). ::: ::: Experimental Design: Transcriptional profiling with oligonucleotide microarrays (22,283 genes) was done on 49 RCC tumors, 20 non-RCC renal tumors, and 23 normal kidney samples. Samples were clustered based on gene expression profiles and specific gene sets for each renal tumor type were identified. Gene expression was correlated to disease progression and a metastasis gene signature was derived. ::: ::: Results: Gene signatures were identified for each tumor type with 100% accuracy. Differentially expressed genes during early tumor formation and tumor progression to metastatic RCC were found. Subsets of these genes code for secreted proteins and membrane receptors and are both potential therapeutic or diagnostic targets. A gene pattern (“metastatic signature”) derived from primary tumor was very accurate in classifying tumors with and without metastases at the time of surgery. A previously described “global” metastatic signature derived by another group from various non-RCC tumors was validated in RCC. ::: ::: Conclusion: Unlike previous studies, we describe highly accurate and externally validated gene signatures for RCC subtypes and other renal tumors. Interestingly, the gene expression of primary tumors provides us information about the metastatic status in the respective patients and has the potential, if prospectively validated, to enrich the armamentarium of diagnostic tests in RCC. We validated in RCC, for the first time, a previously described metastatic signature and further showed the feasibility of applying a gene signature across different microarray platforms. Transcriptional profiling allows a better appreciation of the molecular and clinical heterogeneity in RCC.


title: Iris Biometric Cryptography for Identity Document

abstract: Currently, it is noticed that users tend to choose shorter password as their authentication which can be easily attacked. Biometric technologies such as fingerprint scanning, voice authentication, face recognition, signature, hand geometry and iris recognition is now playing an important role especially in application related to security issue. In this work, we present an approach to generate a unique and more secure cryptographic key from iris template. The iris images are processed to produce iris template or code to be utilized for the encryption and decryption tasks. AES cryptography algorithm are employed to encrypt and decrypt the identity data. Distance metric such as hamming distance and Euclidean distance are used for the template matching identification process. Experimental results show that this system can obtain a higher security with a low false rejection or false acceptance rate.


title: Two-Stage Isolated Switch-Mode Power Supply With High Efficiency and High Input Power Factor

abstract: This paper presents the conception and analysis of a switch-mode power supply (SMPS) with desirable characteristics of high-frequency isolation, high input power factor, low harmonic distortion, and high efficiency. Nearly unity input power factor can be obtained by using an interleaved boost converter associated with a nondissipative snubber, as high efficiency of the ac-dc front-end stage results. Additionally, a soft-switching full-bridge topology performs the dc-dc conversion, providing isolation to the SMPS by using a high-frequency transformer. By cascading both stages, the aforementioned characteristics are achieved. Theoretical background on each one of the converters is presented, and experimental results obtained from a laboratory prototype are presented and discussed in order to validate the proposal. In addition, the evaluation tests demonstrate the operation with nearly unity power factor, high efficiency, and good dynamic response over a wide load range.


PASSIVE TRANSFER OF DELAYED HYPERSENSITIVITY FOLLOWING SENSITIZATION TO TWO OR MORE ALLERGENIC AGENTS


title: Global minimum-jerk trajectory planning of robot manipulators

abstract: A new approach based on interval analysis is developed to find the global minimum-jerk (MJ) trajectory of a robot manipulator within a joint space scheme using cubic splines. MJ trajectories are desirable for their similarity to human joint movements and for their amenability to path tracking and to limit robot vibrations. This makes them attractive choices for robotic applications, in spite of the fact that the manipulator dynamics are not taken into account. Cubic splines are used in a framework that assures overall continuity of velocities and accelerations in the robot movement. The resulting MJ trajectory planning is shown to be a global constrained minimax optimization problem. This is solved by a newly devised algorithm based on interval analysis and proof of convergence with certainty to an arbitrarily good global solution is provided. The proposed planning method is applied to an example regarding a six-joint manipulator and comparisons with an alternative MJ planner are exposed.


title: Bayesian Learning via Stochastic Gradient Langevin Dynamics

abstract: In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the iterates will converge to samples from the true posterior distribution as we anneal the stepsize. This seamless transition between optimization and Bayesian posterior sampling provides an inbuilt protection against overfitting. We also propose a practical method for Monte Carlo estimates of posterior statistics which monitors a "sampling threshold" and collects samples after it has been surpassed. We apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients.


title: Stochastic Synchronization of Complex Networks With Mixed Impulses

abstract: In this paper, stochastic synchronization is studied for complex networks with delayed coupling and mixed impulses. Mixed impulses are composed of desynchronizing and synchronizing impulses. The delayed coupling term involves transmission delay and self-feedback delay. By using the average impulsive interval approach and the comparison principle, several conditions are derived to guarantee that exponential synchronization of complex networks is achieved in the mean square. The derived conditions are closely related to the impulsive strengths, the frequency of impulse occurrence, and the coupling structure of complex networks. Numerical simulations are presented to further demonstrate the effectiveness of the proposed approach.


Structural Dynamic Loadings Due to Impact and Whipping


title: A fast reconstruction algorithm for deterministic compressive sensing using second order reed-muller codes

abstract: This paper proposes a deterministic compressed sensing matrix that comes by design with a very fast reconstruction algorithm, in the sense that its complexity depends only on the number of measurements n and not on the signal dimension N. The matrix construction is based on the second order Reed- Muller codes and associated functions. This matrix does not have RIP uniformly with respect to all k-sparse vectors, but it acts as a near isometry on k-sparse vectors with very high probability.


title: System of Systems - the meaning of of

abstract: We present distinguishing characteristics (i.e. autonomy, belonging, connectivity, diversity, and emergence), that can help us to recognize or to realize a System of Systems (SoS). The principle differentiation that we make between a thing being either a 'system' or a SoS focuses on the nature of a system's composition. We will distinctly define this set of distinguishing characteristics which will include a set of cross references from our literature research where we believe others are articulating our chosen differentiating characteristics. We conclude by summarizing the difference in these terms in a fundamental sense, one that impacts their structure, behavior and realization, and the distinction comes from the manner in which parts and relationships are gathered together and therefore in the nature of the emergent whole.


title: Data-Driven Probabilistic Modeling and Verification of Human Driver Behavior

abstract: We address the problem of formally verifying quantitative properties of driver models. We first propose a novel stochastic model of the driver behavior based on Convex Markov Chains, i.e., Markov chains in which the transition probabilities are only known to lie in convex uncertainty sets. This formalism captures the intrinsic uncertainty in estimating transition probabilities starting from experimentally-collected data. We then formally verify properties of the model expressed in probabilistic computation tree logic (PCTL). Results show that our approach can correctly predict quantitative information about driver behavior depending on her state, e.g., whether he or she is attentive or distracted. Copyright © 2014, Association for the Advancement of Artificial Intelligence. All rights reserved.


title: Deep Model for Dropout Prediction in MOOCs

abstract: Dropout prediction research in MOOCs aims to predict whether students will drop out from the courses instead of completing them. Due to the high dropout rates in current MOOCs, this problem is of great importance. Current methods rely on features extracted by feature engineering, in which all features are extracted manually. This process is costly, time consuming, and not extensible to new datasets from different platforms or different courses with different characters. In this paper, we propose a model that can automatically extract features from the raw MOOC data. Our model is a deep neural network, which is a combination of Convolutional Neural Networks and Recurrent Neural Networks. Through extensive experiments on a public dataset, we show that the proposed model can achieve results comparable to feature engineering based methods.


title: Cross-Device User Linking: URL, Session, Visiting Time, and Device-log Embedding

abstract: Cross-Device User Linking is the task of detecting same users given their browsing logs on different devices (e.g., tablet, mobile phone, PC, etc.). The problem was introduced in CIKM Cup 2016 together with a new dataset provided by Data-Centric Alliance (DCA). In this paper, we present insightful analysis on the dataset and propose a solution to link users based on their visited URLs, visiting time, and profile embeddings. We cast the problem as pairwise classification and use gradient boosting as the leaning-to-rank model. Our model works on a set of features exacted from URLs, titles, time and session data derived from user device-logs. The model outperforms the best solution in the CIKM Cup by a large margin.


Advanced diagnostics and prognostics testbed


title: User experience evaluation: do you know which method to use?

abstract: High quality user experience (UX) has become a central competitive factor of product development in mature consumer markets. Although the term UX is widely used, the methods and tools for evaluating UX are still inadequate. This SIG session collects information and experiences about UX evaluation methods used in both academia and industry, discusses the pros and cons of each method, and ideates on how to improve the methods.


PASSIVE UHF RFID STRAIN SENSOR TAG FOR DETECTING LIMB MOVEMENT


title: Co-morbidity resolution in morbidly obese children and adolescents undergoing sleeve gastrectomy.

abstract: Abstract Background Bariatric surgery is becoming important for the reversal of co-morbidities in children and adolescents. We previously reported the safety and efficacy of laparoscopic sleeve gastrectomy (LSG) in the pediatric population. However, evidence pertaining to the effect of LSG on co-morbidities in this age group is scarce. The objective of this study was to assess the remission and improvement of co-morbidities (dyslipidemia, hypertension, diabetes, and obstructive sleep apnea) after LSG in children and adolescents. Methods Anthropometric changes, complications, remission, and improvement in co-morbidities were assessed over 3 years. OSA was diagnosed using the Pediatric Sleep Questionnaire (PSQ) and polysomnography and its resolution was assessed according to PSQ score alone. Diabetes, prediabetes, hypertension, prehypertension, and dyslipidemia were assessed using standard pediatric-specific definitions. Results The review yielded 226 patients; 74 patients were prepubertal (5–12 yr of age, mean: 9.8±2.3), 115 adolescents (13–17 yr of age, mean: 15.4±1.7), and 37 were young adults (18–21 yr of age, mean: 19.2±.8). Overall mean age was 14.4±4.0 years (range: 4.94–20.99), and 50.4% were females. Mean body mass index (BMI) and BMI z score were 48.2±10.1 kg/m 2 and 2.99±.35, respectively. Mean BMI z score at 1, 2, and 3 years postoperative was 2.01±.87, 2.00±1.07, and 1.66±.65, respectively. Mean preoperative height was 158.0±15.1 cm, and at 1, 2, and 3 years postoperative, it was 160.3±13.4, 161.4±14.1, and 163.2±11.1, respectively. All patients at different age groups experienced normal growth velocity. Within 2 years of follow-up, 90.3% of co-morbidities were in remission or improved, 64.9% of which were within the first 3 months postoperatively. No further improvement or remission was observed beyond 2 years, and there was no recurrence up to 3 years in patients who were seen in follow-up. The lost to follow-up in each of the 3 years was 4.2%, 7.6%, and 15.3%, respectively. Conclusion LSG performed on children and adolescents results in remission or improvement of>90% of co-morbidities within 2 years after bariatric surgery with few complications, no mortality, and normal growth.


title: A Grey System Modeling Approach for Sliding-Mode Control of Antilock Braking System

abstract: In this paper, a grey sliding-mode controller is proposed to regulate the wheel slip, depending on the vehicle forward velocity. The proposed controller anticipates the upcoming values of wheel slip and takes the necessary action to keep the wheel slip at the desired value. The performance of the control algorithm as applied to a quarter vehicle is evaluated through simulations and experimental studies that include sudden changes in road conditions. It is observed that the proposed controller is capable of achieving faster convergence and better noise response than the conventional approaches. It is concluded that the use of grey system theory, which has certain prediction capabilities, can be a viable alternative approach when the conventional control methods cannot meet the desired performance specifications.


title: Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds

abstract: Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with plain multi layer perceptrons (MLP) applied to image patches. We will show that by training on large image databases we are able to outperform the current state-of-the-art image denoising methods. In addition, our method achieves results that are superior to one type of theoretical bound and goes a large way toward closing the gap with a second type of theoretical bound. Our approach is easily adapted to less extensively studied types of noise, such as mixed Poisson-Gaussian noise, JPEG artifacts, salt-and-pepper noise and noise resembling stripes, for which we achieve excellent results as well. We will show that combining a block-matching procedure with MLPs can further improve the results on certain images. In a second paper, we detail the training trade-offs and the inner mechanisms of our MLPs.


Women in Intercollegiate Sport: A Longitudinal Study


Dysphagia Management and Stroke Units


title: The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies †

abstract: The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can't meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS.


title: Concepts before coding: non-programming interactives to advance learning of introductory programming concepts in middle school

abstract: ABSTRACTBackground and Context: Learners struggle with conceptual understanding of introductory programming concepts such as variables, expressions, and loops.Objective: We examine whether and how ...


title: Comparison of Different Methods for Next Location Prediction

abstract: Next location prediction anticipates a person's movement based on the history of previous sojourns. It is useful for proactive actions taken to assist the person in an ubiquitous environment. This paper evaluates next location prediction methods: dynamic Bayesian network, multi-layer perceptron, Elman net, Markov predictor, and state predictor. For the Markov and state predictor we use additionally an optimization, the confidence counter. The criterions for the comparison are the prediction accuracy, the quantity of useful predictions, the stability, the learning, the relearning, the memory and computing costs, the modelling costs, the expandability, and the ability to predict the time of entering the next location. For evaluation we use the same benchmarks containing movement sequences of real persons within an office building.


title: FlashCache: a NAND flash memory file cache for low power web servers

abstract: We propose an architecture that uses NAND flash memory to reduce main memory power in web server platforms. Our architecture uses a two level file buffer cache composed of a relatively small DRAM, which includes a primary file buffer cache, and a flash memory secondary file buffer cache. Compared to a conventional DRAM-only architecture, our architecture consumes orders of magnitude less idle power while remaining cost effective. This is a result of using flash memory, which consumes orders of magnitude less idle power than DRAM and is twice as dense. The client request behavior in web servers, allows us to show that the primary drawbacks of flash memory?endurance and long write latencies?can easily be overcome. In fact the wear-level aware management techniques that we propose are not heavily used.


title: A 65 nm 0.5 V DPS CMOS Image Sensor With 17 pJ/Frame.Pixel and 42 dB Dynamic Range for Ultra-Low-Power SoCs

abstract: Adding vision capabilities to wireless sensors nodes (WSN) for the Internet-of-Things requires imagers working at ultra-low power (ULP) in nanometer CMOS systems-on-chip (SoCs). Such performance can be obtained with time-based digital pixel sensors (DPS) working at ultra-low voltage (ULV), at the expense of lower dynamic range, higher fixed-pattern noise (FPN) and thus poorer image quality. To address this problem, three key techniques were developed in this work for DPS pixels: wide-range adaptive body biasing, low- $R_{{\rm on}}$ gating of the 2-transistor in-pixel comparator and digital readout performing delta-reset sampling with low switching activity and robust timing closure. These concepts were demonstrated by designing and fabricating a 128 $\times$ 128 CMOS image sensor array in a 65 nm low-power CMOS logic process. Operating at 0.5 V, it features an FPN of 0.66%, a dynamic range of 42 dB and a fill factor of 57% with a 4 µm pixel pitch, while consuming only 17 pJ/(frame.pixel) and 8.8 µW at 32 fps. These performances combined with the small silicon area of 0.69 mm 2 makes the imager perfectly suitable for integration in ULP SoCs, targeting WSN applications.


title: An Interactive Artificial Ant Approach to Non-photorealistic Rendering

abstract: We couple artificial ant and computer graphics techniques to create an approach to Non-Photorealistic Rendering (NPR). A user interactively takes turns with an artificial ant colony to transform a photograph into a stylized picture. In turn with the user specifying its control parameters, members of a colony of artificial ants scan the source image locally, following strong edges or wandering around flat zones, and draw marks on the canvas depending on their discoveries. Among a variety of obtained effects, two are painterly rendering and pencil sketching.


title: Text mining and visualization using VOSviewer

abstract: VOSviewer is a computer program for creating, visualizing, and exploring bibliometric maps of science. In this report, the new text mining functionality of VOSviewer is presented. A number of examples are given of applications in which VOSviewer is used for analyzing large amounts of text data.


title: Discriminative spatial saliency for image classification

abstract: In many visual classification tasks the spatial distribution of discriminative information is (i) non uniform e.g. person ‘reading’ can be distinguished from ‘taking a photo’ based on the area around the arms i.e. ignoring the legs and (ii) has intra class variations e.g. different readers may hold the books differently. Motivated by these observations, we propose to learn the discriminative spatial saliency of images while simultaneously learning a max margin classifier for a given visual classification task. Using the saliency maps to weight the corresponding visual features improves the discriminative power of the image representation. We treat the saliency maps as latent variables and allow them to adapt to the image content to maximize the classification score, while regularizing the change in the saliency maps. Our experimental results on three challenging datasets, for (i) human action classification, (ii) fine grained classification and (iii) scene classification, demonstrate the effectiveness and wide applicability of the method.


Detecting Signs of Dementia Using Word Vector Representations


title: Adversarial Domain Adaptation with a Domain Similarity Discriminator for Semantic Segmentation of Urban Areas

abstract: Existing semantic segmentation models of urban areas have shown to perform well in a supervised setting. However, collecting lots of annotated images from each city to train such models is time-consuming or difficult. In addition, when transferring the segmentation model from the trained city (source domain) to an unseen city (target domain), the performance will largely degrade due to the domain shift. For this reason, we propose a domain adaptation method with a domain similarity discriminator to eliminate such domain shift in the framework of adversarial learning. Contrary to the single-input adversarial network, our domain similarity discriminator, which consists of a Siamese network, is able to measure the similarity of the pairwise-input data. In this way, we can use more information about the pairwise-input to measure the similarity between different distributions so as to address the problem of domain shift. Experimental results demonstrate that our approach outperforms the competing methods on three different cities.


title: MDL-CW: A Multimodal Deep Learning Framework with CrossWeights

abstract: Deep learning has received much attention as of the most powerful approaches for multimodal representation learning in recent years. An ideal model for multimodal data can reason about missing modalities using the available ones, and usually provides more information when multiple modalities are being considered. All the previous deep models contain separate modality-specific networks and find a shared representation on top of those networks. Therefore, they only consider high level interactions between modalities to find a joint representation for them. In this paper, we propose a multimodal deep learning framework (MDLCW) that exploits the cross weights between representation of modalities, and try to gradually learn interactions of the modalities in a deep network manner (from low to high level interactions). Moreover, we theoretically show that considering these interactions provide more intra-modality information, and introduce a multi-stage pre-training method that is based on the properties of multi-modal data. In the proposed framework, as opposed to the existing deep methods for multi-modal data, we try to reconstruct the representation of each modality at a given level, with representation of other modalities in the previous layer. Extensive experimental results show that the proposed model outperforms state-of-the-art information retrieval methods for both image and text queries on the PASCAL-sentence and SUNAttribute databases.


title: Physics-of-Failure-Based Prognostics and Health Management for High-Power White Light-Emitting Diode Lighting

abstract: Recently, high-power white light-emitting diodes (LEDs) have attracted much attention due to their versatility in applications and to the increasing market demand for them. So great attention has been focused on producing highly reliable LED lighting. How to accurately predict the reliability of LED lighting is emerging as one of the key issues in this field. Physics-of-failure-based prognostics and health management (PoF-based PHM) is an approach that utilizes knowledge of a product's life cycle loading and failure mechanisms to design for and assess reliability. In this paper, after analyzing the materials and geometries for high-power white LED lighting at all levels, i.e., chips, packages and systems, failure modes, mechanisms and effects analysis (FMMEA) was used in the PoF-based PHM approach to identify and rank the potential failures emerging from the design process. The second step in this paper was to establish the appropriate PoF-based damage models for identified failure mechanisms that carry a high risk.


title: Towards Agile Implementation of Test Maturity Model Integration (TMMI) Level 2 using Scrum Practices

abstract: the software industry has invested the substantial effort to improve the quality of its products like ISO, CMMI and TMMI. Although applying of TMMI maturity criteria has a positive impact on product quality, test engineering productivity, and cycle-time effort. But it requires heavy software development processes and large investments in terms of cost and time that medium/small companies do not deal with it. At the same time, agile methods deal with changing requirements and continuous delivery of valuable software by short time iterations. So that the aim of this paper is to improve the testing process by applying a detailed mapping between TMMI and Scrum practices and verifying this mapping with a study providing empirical evidence of the obtained results. The research has been evaluated on a sample of 2 large-sized TMMI certified companies. In conclusion, the experimental results show the effectiveness of using this integrated approach compared to other existing approaches.


title: To move through space: lines of vision and movement

abstract: Space syntax studies of pedestrian behaviour in building and urban environments have shown that there is a consistent correspondence between the configuration of space and the patterns of usage found within it. In particular, it has been shown that the topological relationships within a spatial system correlate to observed aggregate pedestrian movement. However, there is no proposed mechanism supporting the theory at the level of the individual. Although links between space syntax and individual movement decisions have been suggested through way-finding studies of building environments, virtual reality experiments, and agent-based models, none have proposed a formal link to the axial line analyses used within space syntax. Here we extend work on agent-based models to build a bridge between the line-based topological analyses of space syntax and visually directed agents, through the analysis of what we call ’through vision’. The decision rules for visually directed agents form a Markov transition matrix. We recap the mathematics of Markov chains in order to show that the steady state movement corresponds to an eigenvector of the transition matrix. As the agent transition matrix is extremely complex, we demonstrate that a good approximation of the eigenvector is achieved through the summation of the lines of vision through any one location within an environment. This set of lines forms a superset of the all-line axial map comprising the edges of the visibility graph, or lines of through movement. We show that the lines may be reduced in number (or bundled together) by an algorithmic process and connected into paths, thus making a direct connection between a moving individual with vision and the space syntactic topological analysis of space.


title: Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface

abstract: Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain–computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s−1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain–computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.


title: A unified model for stable and temporal topic detection from social media data

abstract: Web 2.0 users generate and spread huge amounts of messages in online social media. Such user-generated contents are mixture of temporal topics (e.g., breaking events) and stable topics (e.g., user interests). Due to their different natures, it is important and useful to distinguish temporal topics from stable topics in social media. However, such a discrimination is very challenging because the user-generated texts in social media are very short in length and thus lack useful linguistic features for precise analysis using traditional approaches. In this paper, we propose a novel solution to detect both stable and temporal topics simultaneously from social media data. Specifically, a unified user-temporal mixture model is proposed to distinguish temporal topics from stable topics. To improve this model's performance, we design a regularization framework that exploits prior spatial information in a social network, as well as a burst-weighted smoothing scheme that exploits temporal prior information in the time dimension. We conduct extensive experiments to evaluate our proposal on two real data sets obtained from Del.icio.us and Twitter. The experimental results verify that our mixture model is able to distinguish temporal topics from stable topics in a single detection process. Our mixture model enhanced with the spatial regularization and the burst-weighted smoothing scheme significantly outperforms competitor approaches, in terms of topic detection accuracy and discrimination in stable and temporal topics.


title: Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data

abstract: Composing fashion outfits involves deep under-standing of fashion standards while incorporating creativity for choosing multiple fashion items (e.g., jewelry, bag, pants, dress). In fashion websites, popular or high-quality fashion outfits are usually designed by fashion experts and followed by large audiences. In this paper, we propose a machine learning system to compose fashion outfits automatically. The core of the proposed automatic composition system is to score fashion outfit candidates based on the appearances and metadata. We propose to leverage outfit popularity on fashion-oriented websites to supervise the scoring component. The scoring component is a multimodal multiinstance deep learning system that evaluates instance aesthetics and set compatibility simultaneously. In order to train and evaluate the proposed composition system, we have collected a large-scale fashion outfit dataset with 195K outfits and 368K fashion items from Polyvore. Although the fashion outfit scoring and composition is rather challenging, we have achieved an AUC of 85% for the scoring component, and an accuracy of 77% for a constrained composition task.


title: Structural Design of Convolutional Neural Networks for Steganalysis

abstract: Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent ( TanH ) at early stages of the networks and reduce the strength of modeling using $1\times1$ convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.


title: Solving Maximum Clique Problem using a novel Quantum-inspired Evolutionary Algorithm

abstract: Maximum Clique Problem (MCP) is one of the most important NP-hard problems in the area of soft computing and it has many real world applications in numerous fields ranging from coding theory to the determination of the structure of a protein molecule. Different heuristic, Meta heuristic and hybrid solution approaches have been applied to obtain the solution. In this paper, we demonstrate a Quantum-inspired Evolutionary Algorithm (QEA) to solve MCP. We have used one dimensional arrays of Q-bits called Q-bit individuals to produce binary individuals. After production of binary individuals, we have repaired and improved them. Here, Q-gate is the main variation operator applied on Q-bit individuals. Our algorithm was tested on DIMACS benchmark graphs and 40 of them were tested. The results obtained here are extremely encouraging. For almost all of the datasets, we get the optimal results reported on DIMACS benchmark and also compared our results with other related works. For some cases we get better results than other works.


title: Comparing Hardware Description Languages

abstract: We describe a comparison of several existing hardware description languages to determine their advantages and disadvantages with regards to abstraction, code reuse, and usability. To make this comparison we will implement a FIR-lter and a FIFO-queue in every language and evaluate the result according to these criteria. The languages that will be compared are VHDL, C aSH, Kansas Lava and MyHDL.


title: Generalized Average Modeling of Dual Active Bridge DC–DC Converter

abstract: Full-order continuous-time average modeling and dynamic analysis of bidirectional dc-dc dual active bridge (DAB) converters are studied. The transformer current in DAB converter is purely ac, making continuous-time modeling difficult. The proposed full-order continuous-time average model uses the dc terms and first order terms of transformer current and capacitor voltage as state variables, resulting in a third-order model, if capacitor equivalent series resistance (ESR) is not considered, and a sixth-order model if ESR is considered. A control-to-output-voltage transfer function is derived for DAB converters. Experimental results confirm that the proposed model correctly predicts the small-signal frequency response and an even more accurate prediction can be obtained if capacitor ESR is taken into account.


title: Machine learning and deep neural network — Artificial intelligence core for lab and real-world test and validation for ADAS and autonomous vehicles: AI for efficient and quality test and validation

abstract: Autonomous vehicles are now the future of automobile industry. Human drivers can be completely taken out of the loop through the implementation of safe and intelligent autonomous vehicles. Although we can say that HW and SW development continues to play a large role in the automotive industry, test and validation of these systems is a must. The ability to test these vehicles thoroughly and efficiently will ensure their proper and flawless operation. When a large number of people with heterogeneous knowledge and skills try to develop an autonomous vehicle together, it is important to use a sensible engineering process. State of the art techniques for such development include Waterfall, Agile & V-model, where test & validation (T&V) process is an integral part of such a development cycle. This paper will propose a new methodology using machine learning & deep neural network (AI-core) for lab & real-world T&V for ADAS (Advanced driver assistance system) and autonomous vehicles. The methodology will initially connect T&V of individual systems in each level of development and that of complete system efficiently, by using the proposed phase methodology, in which autonomous driving functions are grouped under categories, special T&V processes are carried on simulation as well as in HIL systems. The complete transition towards AI in the field of T&V will be a sequence of steps. Initially the AI-core is fed with available test scenarios, boundary conditions for the test cases and scenarios, and examples, the AI-core will conduct virtual tests on simulation environment using available test scenarios and further generates new test cases and scenarios for efficient and precise tests. These test cases and scenarios are meant to cover all available cases and concentrate on the area where bugs or failures occur. The complete surrounding environment in the simulation is also controlled by the AI-core which means that the system can attain endless/all-possible combinations of the surrounding environment which is necessary. Results of the tests are sorted and stored, critical and important tests are again repeated in the real-world environment using automated cars with other real subsystems to depict the surrounding environment, which are all controlled by the AI-core, and meanwhile the AI-core is always in the loop and learning from each and every executed test case and its results/outcomes. The main goal is to achieve efficient and high quality test and validation of systems for automated driving, which can save precious time in the development process. As a future scope of this methodology, we can step-up to make most parts of test and validation completely autonomous.


title: Biometrics solutions for blind person authentication

abstract: Biometrie authentication becomes very popular in recent years because it is more secure than other authentication methods. There are different types of biometrics like, fingerprint, finger veins and hand veins. An eye contains many different biometrics like, sclera, iris and retina. Blind person need to authenticate in many different areas. The problem is in the steps of retina and iris recognition; there is no consideration for blind person and how to authenticate this person. This paper studies the structure of iris and retina to authenticate the blind person. Hence, it proposes an innovative step for blind person authentication that contains different cases of blind person. The paper proposes four solutions for authenticating blind person which are sclera, palmprint, hand veins and facial thermogram. A comparison between the four proposed solutions is made to show the best solution for blind person.


title: Efficient minimum-cost network hardening via exploit dependency graphs

abstract: In-depth analysis of network security vulnerability must consider attacker exploits not just in isolation, but also in combination. The general approach to this problem is to compute attack paths (combinations of exploits), from which one can decide whether a given set of network hardening measures guarantees the safety of given critical resources. We go beyond attack paths to compute actual sets of hardening measures (assignments of initial network conditions) that guarantee the safety of given critical resources. Moreover, for given costs associated with individual hardening measures, we compute assignments that minimize overall cost. By doing our minimization at the level of initial conditions rather than exploits, we resolve hardening irrelevancies and redundancies in a way that cannot be done through previously proposed exploit-level approaches. Also, we use an efficient exploit-dependency representation based on monotonic logic that has polynomial complexity, as opposed to many previous attack graph representations having exponential complexity.


title: SYNAPSE 39:32–41 (2001) Amphetamine-Type Central Nervous System Stimulants Release Norepinephrine More Potently

abstract: A large body of evidence supports the hypothesis that mesolimbic do- pamine (DA) mediates, in animal models, the reinforcing effects of central nervous system stimulants such as cocaine and amphetamine. The role DA plays in mediating amphetamine-type subjective effects of stimulants in humans remains to be established. Both amphetamine and cocaine increase norepinephrine (NE) via stimulation of release and inhibition of reuptake, respectively. If increases in NE mediate amphetamine-type subjective effects of stimulants in humans, then one would predict that stimulant medications that produce amphetamine-type subjective effects in humans should share the ability to increase NE. To test this hypothesis, we determined, using in vitro methods, the neurochemical mechanism of action of amphetamine, 3,4-methyl- enedioxymethamphetamine (MDMA), (1)-methamphetamine, ephedrine, phentermine, and aminorex. As expected, their rank order of potency for DA release was similar to their rank order of potency in published self-administration studies. Interestingly, the results demonstrated that the most potent effect of these stimulants is to release NE. Importantly, the oral dose of these stimulants, which produce amphetamine-type sub- jective effects in humans, correlated with the their potency in releasing NE, not DA, and did not decrease plasma prolactin, an effect mediated by DA release. These results suggest that NE may contribute to the amphetamine-type subjective effects of stimu- lants in humans. Synapse 39:32- 41, 2001. Published 2001 Wiley-Liss, Inc. †


title: Bringing Identity Theory into Environmental Sociology

abstract: In an effort to explain pro-environmental behavior, environmental sociologists often study environmental attitudes. While much of this work is atheoretical, the focus on attitudes suggests that researchers are implicitly drawing upon attitude theory in psychology. The present research brings sociological theory to environmental sociology by drawing on identity theory to understand environmentally responsive behavior. We develop an environment identity model of environmental behavior that includes not only the meanings of the environment identity, but also the prominence and salience of the environment identity and commitment to the environment identity. We examine the identity process as it relates to behavior, though not to the exclusion of examining the effects of environmental attitudes. The findings reveal that individual agency is important in influencing environmentally responsive behavior, but this agency is largely through identity processes, rather than attitude processes. This provides an important theoretical and empirical advance over earlier work in environmental sociology.


title: Deca-nanoscale maximum gate length of plasma wave transistor for operating terahertz emitter based on strained silicon platform

abstract: In this work, we have shown that plasma-wave transistor (PWT) operates as a terahertz (THz) emitter below maximum gate length (Lmax). Because the channel mobility (μ) of strained silicon (sSi) is higher than silicon (Si), we investigate how emission frequency range and Lmax of sSi PWT THz emitter are improved compared to Si PWT THz emitter by analyzing the effect of momentum relaxation time (τp) and electron effective mass (m) on a design window of PWT THz emitter.


title: An empirical exploration of CTC acoustic models

abstract: The connectionist temporal classification (CTC) loss function has several interesting properties relevant for automatic speech recognition (ASR): applied on top of deep recurrent neural networks (RNNs), CTC learns the alignments between speech frames and label sequences automatically, which removes the need for pre-generated frame-level labels. CTC systems also do not require context decision trees for good performance, using context-independent (CI) phonemes or characters as targets. This paper presents an extensive exploration of CTC-based acoustic models applied to a variety of ASR tasks, including an empirical study of the optimal configuration and architectural variants for CTC. We observe that on large amounts of training data, CTC models tend to outperform state-of-the-art hybrid approach. Further experiments reveal that CTC can be readily ported to syllable-based languages, and can be enhanced by employing improved feature front-ends.


title: Big Healthcare Data Analytics: Challenges and Applications

abstract: Increasing demand and costs for healthcare, exacerbated by ageing populations and a great shortage of doctors, are serious concerns worldwide. Consequently, this has generated a great amount of motivation in providing better healthcare through smarter healthcare systems. Management and processing of healthcare data are challenging due to various factors that are inherent in the data itself such as high-dimensionality, irregularity and sparsity. A long stream of research has been proposed to address these problems and provide more efficient and scalable healthcare systems and solutions. In this chapter, we shall examine the challenges in designing algorithms and systems for healthcare analytics and applications, followed by a survey on various relevant solutions. We shall also discuss next-generation healthcare applications, services and systems, that are related to big healthcare data analytics.


title: Finally, a Downloadable Test Collection of Tweets

abstract: Due to Twitter's terms of service that forbid redistribution of content, creating publicly downloadable collections of tweets for research purposes has been a perpetual problem for the research community. Some collections are distributed by making available the ids of the tweets that comprise the collection and providing tools to fetch the actual content; this approach has scalability limitations. In other cases, evaluation organizers have set up APIs that provide access to collections for specific tasks, without exposing the underlying content. This is a workable solution, but difficult to sustain over the long term since someone has to maintain the APIs. We have noticed that the non-profit Internet Archive has been making available for public download captures of the so-called Twitter "spritzer" stream, which is the same source as the Tweets2013 collection used in the TREC 2013 and 2014 Microblog Tracks. We analyzed both datasets in terms of content overlap and retrieval baselines to show that the Internet Archive data can serve as a drop-in replacement for the Tweets2013 collection, thereby providing the research community with, finally, a downloadable collection of tweets. Beyond this finding, we also study the impact of tweet deletions over time and how they affect the test collections.


title: Towards autonomous habitat classification using Gaussian Mixture Models

abstract: Robotic agents that can explore and sample in a completely unsupervised fashion could greatly increase the amount of scientific data gathered in dangerous and inaccessible environments. Our application is imaging the benthos using an autonomous underwater vehicle with limited communication to surface craft. Robotic exploration of this nature demands in situ data analysis. To this end, this paper presents results of using a Gaussian Mixture Model (GMM), a Hidden Markov Model (HMM) filter, an Infinite Gaussian Mixture Model (IGMM) and a Variation Dirichlet Process model (VDP) for the classification of benthic habitats. All of the models are trained using unsupervised methods. Furthermore, the IGMM and VDP are trained without knowing the the number of classes in the dataset. It was found that the sequential information the HMM filter provides to the classification process adds lag to the habitat boundary estimates, reducing the classification accuracy. The VDP proved to be the most accurate classifier of the four tested, and also one of the fastest to train. We conclude that the VDP is a powerful model for entirely autonomous labelling of benthic datasets.


title: From Community Detection to Community Profiling

abstract: Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.


title: Neural Network Model for Earthquake Prediction Using DMETER Data and Seismic Belt Information

abstract: The mechanism of the earthquake remains to be investigated, though some anomalies associated with earthquakes have been found by DEMETER satellite observations. The next step is to use the DEMETER data for earthquake prediction. It is a useful and feasible way to use the self-adaptive artificial neural network to construct relations between various symptom factors and earthquake occurrences. The back-propagation neural network is quite suitable to express the nonlinear relation between earthquake and various anomalies. In this paper a series of physical quantities measured by the DEMETER satellite including Electron density, Electron temperature, ions temperature and oxygen ion density, together with seismic belt information are used to form sample sets for a back-propagation neural network. The neural network model then can be used to conduct the prediction. In the end, validation tests are performed based on those important seismic events happened in 2008.


title: Oracle performance for visual captioning

abstract: The task of associating images and videos with a natural language description has attracted a great amount of attention recently. Rapid progress has been made in terms of both developing novel algorithms and releasing new datasets. Indeed, the state-of-the-art results on some of the standard datasets have been pushed into the regime where it has become more and more difficult to make significant improvements. Instead of proposing new models, this work investigates the possibility of empirically establishing performance upper bounds on various visual captioning datasets without extra data labelling effort or human evaluation. In particular, it is assumed that visual captioning is decomposed into two steps: from visual inputs to visual concepts, and from visual concepts to natural language descriptions. One would be able to obtain an upper bound when assuming the first step is perfect and only requiring training a conditional language model for the second step. We demonstrate the construction of such bounds on MS-COCO, YouTube2Text and LSMDC (a combination of M-VAD and MPII-MD). Surprisingly, despite of the imperfect process we used for visual concept extraction in the first step and the simplicity of the language model for the second step, we show that current state-of-the-art models fall short when being compared with the learned upper bounds. Furthermore, with such a bound, we quantify several important factors concerning image and video captioning: the number of visual concepts captured by different models, the trade-off between the amount of visual elements captured and their accuracy, and the intrinsic difficulty and blessing of different datasets.


title: Digital control of a half-bridge LLC resonant converter

abstract: Recently, LLC resonant converters are gaining more and more interest in DC/DC conversion due to their high efficiency and power density. Usually, they are used in low power applications, i.e. in the range of few hundred Watt and with constant output load, however they are very appealing in high power and renewable energy applications as well as in many other applications affected by wide variations of the load and/or the source voltage. This paper presents and discusses a novel half bridge DC/DC resonant converter with digital control and fuzzy logic. The proposed system is compared with a more traditional PID controller. A variable frequency control algorithms are implemented using a low cost, high performance Texas Instrument TMS320F28069 DSP for the regulation of the output voltage. Simulation results using MATLAB are presented to evaluate the quality of the obtained result. A laboratory prototype has been realized and experimental results obtained using both PID and Fuzzy controllers are shown and discussed.


title: Screening by coral green fluorescent protein (GFP)-like chromoproteins supports a role in photoprotection of zooxanthellae

abstract: Green fluorescent protein (GFP)-like pigments are responsible for the vivid colouration of many reef-building corals and have been proposed to act as photoprotectants. Their role remains controversial because the functional mechanism has not been elucidated. We provide direct evidence to support a photoprotective role of the non-fluorescent chromoproteins (CPs) that form a biochemically and photophysically distinct group of GFP-like proteins. Based on observations of Acropora nobilis from the Great Barrier Reef, we explored the photoprotective role of CPs by analysing five coral species under controlled conditions. In vitro and in hospite analyses of chlorophyll excitation demonstrate that screening by CPs leads to a reduction in chlorophyll excitation corresponding to the spectral properties of the specific CPs present in the coral tissues. Between 562 and 586 nm, the CPs maximal absorption range, there was an up to 50 % reduction of chlorophyll excitation. The screening was consistent for established and regenerating tissue and amongst symbiont clades A, C and D. Moreover, among two differently pigmented morphs of Acropora valida grown under identical light conditions and hosting subclade type C3 symbionts, high CP expression correlated with reduced photodamage under acute light stress.


title: An Indoor Wireless System for Personalized Shopping Assistance

abstract: By integrating wireless, video, speech and real-time data access technologies, a unique shopping assistant service can be created that personalizes the attention provided to a custorner based on individual needs, without limiting his movement, or causing distractions from others in the shopping center. We have developed this idea into a service based on two products: a very high volume hand-held wireless communications device. the PSA (Personal Shopping Assistant), that the customer owns (or rnay be provided to a customer by the retailer), and a centralized server located in the shopping center to which the custorner communicates using the PSA. The centralized server maintains the customer database. the store database and provides audio/visual responses to inquiries fronr tens to hundreds of customers in real-time over a snrull areo wrteless network.


title: A Symbolic Intruder Model for Hash-Collision Attacks

abstract: In the recent years, several practical methods have been published to compute collisions on some commonly used hash functions. In this paper we present a method to take into account, at the symbolic level, that an intruder actively attacking a protocol execution may use these collision algorithms in reasonable time during the attack. Our decision procedure relies on the reduction of constraint solving for an intruder exploiting the collision properties of hush functions to constraint solving for an intruder operating on words.


title: A Computer Assisted Method for Nuclear Cataract Grading From Slit-Lamp Images Using Ranking

abstract: In clinical diagnosis, a grade indicating the severity of nuclear cataract is often manually assigned by a trained ophthalmologist to a patient after comparing the lens' opacity severity in his/her slit-lamp images with a set of standard photos. This grading scheme is often subjective and time-consuming. In this paper, a novel computer-aided diagnosis method via ranking is proposed to facilitate nuclear cataract grading following conventional clinical decision-making process. The grade of nuclear cataract in a slit-lamp image is predicted using its neighboring labeled images in a ranked image list, which is achieved using a learned ranking function. This ranking function is learned via direct optimization on a newly proposed approximation to a ranking evaluation measure. Our proposed method has been evaluated by a large dataset composed of 1000 different cases, which are collected from an ongoing clinical population-based study. Both experimental results and comparison with several existing methods demonstrate the benefit of grading via ranking by our proposed method.


title: Silver Nanowire Networks as Flexible, Transparent, Conducting Films: Extremely High DC to Optical Conductivity Ratios

abstract: We have used aqueous dispersions of silver nanowires to prepare thin, flexible, transparent, conducting films. The nanowires are of length and diameter close to 6.5 μm and 85 nm, respectively. At low thickness, the films consist of networks but appear to become bulk-like for mean film thicknesses above ∼160 nm. These films can be very transparent with optical transmittance reaching as high as 92% for low thickness. The transmittance (550 nm) decreases with increasing thickness, consistent with an optical conductivity of 6472 S/m. The films are also very uniform; the transmittance varies spatially by typically <2%. The sheet resistance decreases with increasing thickness, falling below 1 Ω/◻ for thicknesses above 300 nm. The DC conductivity increases from 2 × 105 S/m for very thin films before saturating at 5 × 106 S/m for thicker films. Similarly, the ratio of DC to optical conductivity increases with increasing thickness from 25 for the thinnest films, saturating at ∼500 for thicknesses above ∼160 nm. We...


title: LiSeg: Lightweight Road-object Semantic Segmentation In 3D LiDAR Scans For Autonomous Driving

abstract: LiDAR based perception module plays an important role in autonomous driving. However, the present CNN models are designed for image processing but not LiDAR point clouds. The performances of such models are limited by the great memory consumption and heavy computation cost. In this work, a lightweight CNN model, Liseg, is proposed to perform real-time road-object semantic segmentation on LiDAR point cloud scans for autonomous driving. The model size of Liseg is several times smaller than others, while achieving high accuracy.


title: A Compact, Wideband Circularly Polarized Co-designed Filtering Antenna and Its Application for Wearable Devices With Low SAR

abstract: A compact circularly polarized (CP) co-designed filtering antenna is reported. The device is based on a patch radiator seamlessly integrated with a bandpass filter composed of coupled stripline open-loop resonators, which are designed together as a system. In the proposed design, the patch functions simultaneously as the radiator and the last stage resonator of the filter, resulting in a low-profile integrated radiating and filtering module with a small overall form factor of $\mathbf{0.53{\lambda _0} \times 0.53{\lambda _0} \times 0.07{\lambda _0}}$ . It is shown that the filtering circuit not only ensures frequency selectivity but also provides impedance matching functionality, which serves to broaden both the impedance and axial ratio bandwidths. The designed filtering antenna was fabricated and measured, experimentally achieving an $\mathbf{{S_{11}} , an axial ratio of less than 3 dB and a gain higher than 5.2 dBi over a bandwidth from 3.77 to 4.26 GHz, i.e., around 12.2%, which makes it an excellent candidate for integration into a variety of wireless systems. A linearly polarized version of the integrated filtering antenna was also demonstrated. In addition, further full-wave simulations and experiments were carried out to verify that the designed CP filtering antenna maintains its properties even when mounted on different positions of the human body with various body gestures. The stable impedance and radiation properties also make it a suitable candidate as a wearable antenna for off-body wireless communications.


title: Design on the low-leakage diode string for using in the power-rail ESD clamp circuits in a 0.35-/spl mu/m silicide CMOS process

abstract: A new design of the diode string with very low leakage current is proposed for use in the ESD clamp circuits across the power rails. By adding an NMOS-controlled lateral SCR (NCLSCR) device into the stacked diode string, the leakage current of this new diode string with six stacked diodes at 5 V (3.3 V) forward bias can be reduced to only 2.1 (1.07) nA at a temperature of 125/spl deg/C in a 0.35 /spl mu/m silicide CMOS process, whereas the previous designs have a leakage current in the order of mA. The total blocking voltage of this new design with NCLSCR can be linearly adjusted by changing the number of the stacked diodes in the diode string without causing latch-up danger across the power rails. From the experimental results, the human-body-model ESD level of the ESD clamp circuit with the proposed low-leakage diode string is greater than 8 kV in a 0.35 /spl mu/m silicide CMOS process by using neither ESD implantation nor the silicide-blocking process modifications.


title: Microstrip Patch Antennas—Basic Characteristics and Some Recent Advances

abstract: The basic geometry of a microstrip patch antenna (MPA) consists of a metallic patch printed on a grounded substrate. Three commonly used feeding methods are coaxial feed, stripline feed, and aperture-coupled feed. The patch antenna idea was first proposed in the early 1950s, but it was not until the late 1970s that this type of antenna attracted serious attention of the antenna community. The microstrip patch antenna offers the advantages of low profile, conformability to a shaped surface, ease of fabrication, and compatibility with integrated circuit technology, but the basic geometry suffers from narrow bandwidth. In the last three decades, extensive studies have been devoted to improve the performance of this antenna and the MPA has found numerous applications in both the military and the commercial sectors. This article begins with a brief description of the modeling techniques and basic characteristics of the MPA. Methods of broadbanding, dual and multiband designs, size-reduction techniques, and design for circular polarization are then reviewed. The paper ends with some concluding remarks.


title: Fractal trajectories for online non-uniform aerial coverage

abstract: We propose a novel method for non-uniform terrain coverage using Unmanned aerial vehicles (UAVs). The existing methods for coverage path planning consider a uniformly interesting target area and hence all the regions are covered with high resolution. However in many real world applications items of interest are not uniformly distributed but form patches of locally high interest. Therefore, with sparse sampling of uninteresting sections of the environment and high resolution sampling of interesting patches, the trajectory of the robot can become shorter. In this paper, we present a novel coverage strategy based on Space-Filling Curves that can accomplish non-uniform coverage of regions in the target area. Simulations and real robot experiments indicate that with the new strategy, travel time / cost of the task can be (almost) always less than a regular ‘lawnmower’ coverage pattern.


title: Inverted indexes vs. bitmap indexes in decision support systems

abstract: Bitmap indexes are widely used in Decision Support Systems (DSSs) to improve query performance. In this paper, we evaluate the use of compressed inverted indexes with adapted query processing strategies from Information Retrieval as an alternative. In a thorough experimental evaluation on both synthetic data and data from the Star Schema Benchmark, we show that inverted indexes are more compact than bitmap indexes in almost all cases. This compactness combined with efficient query processing strategies results in inverted indexes outperforming bitmap indexes for most queries, often significantly.


title: Dynamic path planning in sensor-based terrain acquisition

abstract: The terrain acquisition problem is formulated as that of continuous motion planning, and no constraints are imposed on obstacle geometry. Two algorithms are described for acquiring planar terrains with obstacles of arbitrary shape. Estimates of the algorithm performance are derived as upper bounds on the lengths of generated paths. >


title: An ontology for representing financial headline news

abstract: This paper presents the development of an ontology to represent financial headline news. This ontology is developed using the On-To-Knowledge methodology where the focus is on the design steps of the Knowledge Meta Process. This development is part of an ongoing project which aims to design a virtual stock market simulator based on multi-agent systems. The proposed ontology has 31 concepts and includes 201 attributes. The testing results conducted on reliable headline news show that 99% of these headline news can be properly represented by the attributes of the right category in the ontology. Unreliable headline news characterized by news having uncertainties, incompleteness, ill-definition, or imprecision cannot be represented by the proposed ontology. Approaches for representing these unreliable headline news are discussed.


title: EgoSet: Exploiting Word Ego-networks and User-generated Ontology for Multifaceted Set Expansion

abstract: A key challenge of entity set expansion is that multifaceted input seeds can lead to significant incoherence in the result set. In this paper, we present a novel solution to handling multifaceted seeds by combining existing user-generated ontologies with a novel word-similarity metric based on skip-grams. By blending the two resources we are able to produce sparse word ego-networks that are centered on the seed terms and are able to capture semantic equivalence among words. We demonstrate that the resulting networks possess internally-coherent clusters, which can be exploited to provide non-overlapping expansions, in order to reflect different semantic classes of the seeds. Empirical evaluation against state-of-the-art baselines shows that our solution, EgoSet, is able to not only capture multiple facets in the input query, but also generate expansions for each facet with higher precision.


title: RIOT-ROS2: Low-Cost Robots in IoT Controlled via Information-Centric Networking

abstract: In the future, IoT devices will be part of the robotics ecosystem, and the border between IoT and robotics will blur. Already today, we observe converging trends between low-end IoT devices and minibots (i.e. tiny, cheap robots) concerning their hardware, and open source software. In this paper, we explore the potential of programming minibots with the open source robotics software framework ROS2, running on top of the IoT operating system RIOT; we call the fruitful association of both elements RIOT-ROS2. In this article, the emphasis is particularly on the networking layer: using an information-centric networking (ICN) paradigm, we design and implement the communication primitives for RIOT-ROS2. We further evaluate the performance of our design on prototype minibots based on cheap, off-the-shelf hardware elements. We show that RIOT-ROS2 fits on low-end robotics hardware such as a System-on-Chip costing under $2, based on an ARM Cortex-M0+ microcontroller. Our experiments also show that the latency incurred with our information-centric approach is acceptable for minibot control, even on a low-throughput IEEE 802.15.4 radio.


title: A 1.5–3.0GHz wideband VCO with low gain variation

abstract: A wideband LC Voltage Control Oscillator (VCO) implemented on SMIC 65nm CMOS process is presented. The proposed VCO has a low gain variation by using the improved capacitor bank technology. In order to reduce the phase noise caused by the tail current and make the wave form more symmetry, a complementary structure without the tail current is adopted. The testing results show that the VCO has the tuning range of 66.7% from 1.511GHz to 3.057GHz; the gain variation is 49.5%. The phase noise of the output ranges from −115.1dBc/Hz to −120.3dBc/Hz at 1 MHz offset. The power dissipation is 1.02mW under 1.2V power supply. The whole layout occupies 0.58×0.67 mm2.


title: Fine-Grained Image Classification With Gaussian Mixture Layer

abstract: Fine-grained image classification aims at recognizing different subordinates in one basic-level category, for example, distinguishing species of birds. Compared with basic-level classification, it has both low inter-class and high intra-class variances. Therefore, utilization of discriminative parts is crucial for fine-grained classification. In this paper, we propose a Gaussian mixture model, which fuses part features by Gaussian mixture layer. More specifically, it first generates a set of part proposals by selective search. Then, we extract image feature maps from mid-layers of convolutional neural networks. Feature maps and part proposals are used for calculating part features via spatial pyramid pooling. Next, Gaussian mixture layer treats part features as data points and uses several Gaussian components to model their distribution. It finds clusters for input and generates output features based on combination of cluster center. Finally, the output feature can represent the whole image and is used for classification. Training process of the model consists of two loops. The outer loop is the optimization of the whole network, and the inner loop is about the EM algorithm used in Gaussian mixture layer. Experiments demonstrate higher or similar performance on four fine-grained data sets compared with the state-of-the-arts. More discussions on Gaussian mixture layer are also provided.


Acute hand weakness as a regional variant of Guillain–Barré syndrome