title: Content-Based Top-N Recommendation using Heterogeneous Relations

abstract: Top-N recommender systems have been extensively studied. However, the sparsity of user-item activities has not been well resolved. While many hybrid systems were proposed to address the cold-start problem, the profile information has not been sufficiently leveraged. Furthermore, the heterogeneity of profiles between users and items intensifies the challenge. In this paper, we propose a content-based top-N recommender system by learning the global term weights in profiles. To achieve this, we bring in PathSim, which could well measure the node similarity with heterogeneous relations (between users and items). Starting from the original TF-IDF value, the global term weights gradually converge, and eventually reflect both profile and activity information. To facilitate training, the derivative is reformulated into matrix form, which could easily be paralleled. We conduct extensive experiments, which demonstrate the superiority of the proposed method.


title: A deep convolutional encoder-decoder model for robust speech dereverberation

abstract: Research shows that speech dereverberation (SD) with Deep Neural Network (DNN) achieves the state-of-the-art results by learning spectral mapping, which, simultaneously, lacks the characterization of the local temporal spectral structures (LTSS) of speech signal and calls for a large storage space that is impractical in real applications. Contrarily, the Convolutional Neural Network (CNN) offers a better modeling ability by considering local patterns and has less parameters with its weights sharing property, which motivates us to employ the CNN for SD task. In this paper, to our knowledge, a Deep Convolutional Encoder-Decoder (DCED) model is proposed for the first time in dealing with the SD task (DCED-SD), where the advantage of the DCED-SD model lies in its powerful LTSS modeling capability via convolutional encoder-decoder layers with smaller storage requirement. By taking the reverberant and anechoic spectrum as training pairs, the proposed DCED-SD is well-trained in a supervised manner with less convergence time. Additionally, the DCED-SD model size is 23 times smaller than the size of DNN-SD model with better performance achieved. By using the simulated and real-recorded data, extensive experiments have been conducted to demonstrate the superiority of DCED-based SD method over the DNN-based SD method under different unseen reverberant conditions.


Fighting evasive malware with DVasion


title: Mapping RUP Roles to Small Software Development Teams

abstract: In the last decades the complexity of software development projects had a significant increase. This complexity emerges from the higher degree of sophistication in the contexts they aim to serve and from the evolution of the functionalities implemented by the applications However, many software corporations have a reduced dimension (micro, small or medium) which imposes a considerable constraint to the number of individuals that might be involved in each project. This limitation has obvious consequences to the individual’s efficiency and effectiveness. In this paper we describe a Rational Unified Process (RUP) tailoring to simplify the number of RUP roles. With this tailoring we obtain one set of RUP roles that, without neglecting any critical role of the software development process, may easily be adopted by a small or medium software development team. In this paper, we present and justify a complete set of mapping rules between RUP roles and one possible configuration for small software development teams.


title: An autonomous, underactuated exoskeleton for load-carrying augmentation

abstract: Metabolic studies have shown that there is a metabolic cost associated with carrying load (T. M. Griffen, et al., 2003). In previous work, a lightweight, underactuated exoskeleton has been described that runs in parallel to the human and supports the weight of a payload (C. J. Walsh, et al., 2006). A state-machine control strategy is written based on joint angle and ground-exoskeleton force sensing to control the joint actuation at this exoskeleton hip and knee. The joint components of the exoskeleton in the sagittal plane consist of a force-controllable actuator at the hip, a variable-damper mechanism at the knee and a passive spring at the ankle. The control is motivated by examining human walking data. Positive, non-conservative power is added at the hip during the walking cycle to help propel the mass of the human and payload forward. At the knee, the damper mechanism is turned on at heel strike as the exoskeleton leg is loaded and turned off during terminal stance to allow knee flexion. The passive spring at the ankle engages in controlled dorsiflexion to store energy that is later released to assist in powered plantarflexion. Preliminary studies show that the state machines for the hip and knee work robustly and that the onset of walking can be detected in less than one gait cycle. Further, it is found that an efficient, underactuated leg exoskeleton can effectively transmit payload forces to the ground during the walking cycle


title: Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

abstract: Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job. Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices.


title: 3D path planning and stereo-based obstacle avoidance for rotorcraft UAVs

abstract: We present a synthesis of techniques for rotorcraft UAV navigation through unknown environments which may contain obstacles. D* Lite and probabilistic roadmaps are combined for path planning, together with stereo vision for obstacle detection and dynamic path updating. A 3D occupancy map is used to represent the environment, and is updated online using stereo data. The target application is autonomous helicopter-based structure inspections, which require the UAV to fly safely close to the structures it is inspecting. Results are presented from simulation and with real flight hardware mounted onboard a cable array robot, demonstrating successful navigation through unknown environments containing obstacles.


title: A Lightweight Vulnerability Mitigation Framework for IoT Devices

abstract: Many of today's Internet of Things (IoT) devices are vulnerable due to the large amount of overhead incurred when their operating systems are patched against emerging vulnerabilities. In addition, legacy IoT devices are no longer supported by their manufacturers, leaving customers with unpatched devices that can be easily exploited by attackers. Thus, there is an urgent need for a solution that provides a lightweight and low-cost mechanism for preventing exploitation of vulnerable IoT devices. In this paper, we propose an innovative cloud-based framework for protecting IoT devices. The proposed framework consists of a cloud service and a designated IoT security appliance. The security appliance controls the network traffic flowing to and from the vulnerable device and verifies that it does not violate a set of rules, represented by a vulnerability mitigation policy, that have been derived and synthesized by the cloud service from public corpora of Common Vulnerabilities and Exposures (CVE). We demonstrate how the proposed solution can be applied as a cost-effective solution capable of preventing exploitation of vulnerable IP cameras as part of a prominent botnet attack called Mirai.


title: Modeling and comparison of dissolution profiles.

abstract: Over recent years, drug release/dissolution from solid pharmaceutical dosage forms has been the subject of intense and profitable scientific developments. Whenever a new solid dosage form is developed or produced, it is necessary to ensure that drug dissolution occurs in an appropriate manner. The pharmaceutical industry and the registration authorities do focus, nowadays, on drug dissolution studies. The quantitative analysis of the values obtained in dissolution/release tests is easier when mathematical formulas that express the dissolution results as a function of some of the dosage forms characteristics are used. In some cases, these mathematic models are derived from the theoretical analysis of the occurring process. In most of the cases the theoretical concept does not exist and some empirical equations have proved to be more appropriate. Drug dissolution from solid dosage forms has been described by kinetic models in which the dissolved amount of drug (Q) is a function of the test time, t or Q=f(t). Some analytical definitions of the Q(t) function are commonly used, such as zero order, first order, Hixson-Crowell, Weibull, Higuchi, Baker-Lonsdale, Korsmeyer-Peppas and Hopfenberg models. Other release parameters, such as dissolution time (tx%), assay time (tx min), dissolution efficacy (ED), difference factor (f1), similarity factor (f2) and Rescigno index (xi1 and xi2) can be used to characterize drug dissolution/release profiles.


title: Anatomic Distribution of Nerves and Microvascular Density in the Human Anterior Vaginal Wall: Prospective Study

abstract: BACKGROUND ::: The presence of the G-spot (an assumed erotic sensitive area in the anterior wall of the vagina) remains controversial. We explored the histomorphological basis of the G-spot. ::: ::: ::: METHODS ::: Biopsies were drawn from a 12 o'clock direction in the distal- and proximal-third areas of the anterior vagina of 32 Chinese subjects. The total number of protein gene product 9.5-immunoreactive nerves and smooth muscle actin-immunoreactive blood vessels in each specimen was quantified using the avidin-biotin-peroxidase assay. ::: ::: ::: RESULTS ::: Vaginal innervation was observed in the lamina propria and muscle layer of the anterior vaginal wall. The distal-third of the anterior vaginal wall had significantly richer small-nerve-fiber innervation in the lamina propria than the proximal-third (p = 0.000) and in the vaginal muscle layer (p = 0.006). There were abundant microvessels in the lamina propria and muscle layer, but no small vessels in the lamina propria and few in the muscle layer. Significant differences were noted in the number of microvessels when comparing the distal- with proximal-third parts in the lamina propria (p = 0.046) and muscle layer (p = 0.002). ::: ::: ::: CONCLUSIONS ::: Significantly increased density of nerves and microvessels in the distal-third of the anterior vaginal wall could be the histomorphological basis of the G-spot. Distal anterior vaginal repair could disrupt the normal anatomy, neurovascular supply and function of the G-spot, and cause sexual dysfunction.


title: Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture

abstract: In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional network that is able to adapt easily to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.


title: Tag2Word: Using Tags to Generate Words for Content Based Tag Recommendation

abstract: Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.


title: Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

abstract: The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer’s accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user’s accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.


title: Robotic forklifts for intelligent warehouses: Routing, path planning, and auto-localization

abstract: Today, robotic systems are bridging the gaps between global economy, social needs, and logistics focusing on sustainable development solutions. Everyday new robotic applications can be found in literature and media. Some of them are basically entertainment toys. Nevertheless, the great majority of them is used inside industries, performing several tasks (painting, welding, moving materials, etc.). In a scenario of global economy growth, any sustainable solution that can reduce the product final cost is welcome. This article presents researches on robotic forklifts for intelligent warehouses developed at the Mechatronics Lab at USP-EESC in Brazil. We show three keyroutines that determine the Automated Guided Vehicle (AGV) behavior: the routing algorithm (that computes the overall task execution time and the minimum global path of each AGV using a topological map of the warehouse), the local path planning algorithm (based on A∗ it searches for the local minimum path between two nodes of the warehouse topological map), and an auto-localization algorithm (that applies an Extended Kalman Filter — EKF — to estimate the AGVs actual positions). In order to validate the algorithms developed, several tests were carried out using the simulation software Player/Stage. The results obtained were encouraging and the router developed was able to solve traffic jams and collisions, before sending the final paths to the robots. In a near future all algorithms will be implemented using mini- robotic forklifts and a scaled environment built in our lab.


title: Detection of Violent Events in Video Sequences Based on Census Transform Histogram

abstract: Video surveillance systems have enabled the monitoring of complex events in several places, such as airports, banks, streets, schools, industries, among others. Due to the massive amount of multimedia data acquired by video cameras, traditional visual inspection by human operators is a very tedious and time consuming task, whose performance is affected by fatigue and stress. A challenge is to develop intelligent video systems capable of automatically analyzing long sequences of videos from a large number of cameras. This work describes and evaluates the use of CENTRIST-based features to identify violence context from video scenes. Experimental results demonstrate the effectiveness of our method when applied to two public benchmarks, Violent Flows and Hockey Fights datasets.


Pthreads programming - a POSIX standard for better multiprocessing


title: CYCLOPS: A micro service based approach for dynamic rating, charging & billing for cloud

abstract: In the present days of rapid adoption of cloud services and varying cost models it is imperative to have a system which converts the cloud resource usage data into a measurable and payable cost. The system should be able to collect the cloud resource usage data, process it against a dynamically determined rate to get a charge for which the customer could be billed upon. These operations need to be done in a secured manner with appropriate authentication and authorization checks. As the cloud services are still evolving, the rating, charging and billing components for cloud are at a nascent stage of its maturity. In this paper we are describing a novel modular and micro services based design & architecture for developing a dynamic rating, charging and billing for cloud service providers with emphasis being given to the validation of the architecture through mathematical modeling and the implementation experience of the micro services.


title: STABILITY INDICATING HPLC METHOD FOR THE ESTIMATION OF CINACALCET HYDROCHLORIDE API

abstract: A stability-indicating liquid chromatography method has been developed and validated for the determination of Cinacalcet hydrochloride in a laboratory mixture as well as in a tablet formulation developed in-house. Efficient chromatographic separation was achieved on phenomenex C18 column (150 mm×4.6 mm, 5.0 μm) with mobile phase containing Methanol: Water (70:30v/v) pH adjusted to 3.6 with dilute Orthophosphoric acid at a flow rate of 1.3 mL/min and the eluent was monitored at 271 nm using Shimadzu LC-10AT-VP & LC-20 AD with Spinotech (Winchrome) software. Linearity range was found to be between 50-300μg/ml and the linear regression coefficient was not more than 0.999. The values of % RSD are less than 2% indicating accuracy and precision of the method. The percentage recovery varies from 97-103%w/w. LOD and LOQ were found to be within limit. The system suitability parameters like tailing factor, number of theoretical plates, Asymmetry were found to be within limit. Forced degradation studies were performed by using this method. There was a significant degradation in the presence of 0.1M HCl, 0.1M NaOH, 30%H2O2, heat and light. The proposed method is simple, accurate and rapid and hence can be employed for


title: Testbed Evaluation of Virtual Environment Interaction Techniques

abstract: As immersive virtual environment (VE) applications become more complex, it is clear that we need a firm understanding of the principles of VE interaction. In particular, designers need guidance in choosing three-dimensional interaction techniques. In this paper, we present a systematic approach, testbed evaluation, for the assessment of interaction techniques for VEs. Testbed evaluation uses formal frameworks and formal experiments with multiple independent and dependent variables in order to obtain a wide range of performance data for VE interaction techniques. We present two testbed experiments, covering techniques for the common VE tasks of travel and object selection/manipulation. The results of these experiments allow us to form general guidelines for VE interaction, and to provide an empirical basis for choosing interaction techniques in VE applications. This has been shown to produce measurable usability gains in a real-world VE application.


title: Enhanced creativity in bipolar disorder patients: a controlled study.

abstract: Abstract Objective Associations between eminent creativity and bipolar disorders have been reported, but there are few data relating non-eminent creativity to bipolar disorders in clinical samples. We assessed non-eminent creativity in euthymic bipolar (BP) and unipolar major depressive disorder (MDD) patients, creative discipline controls (CC), and healthy controls (HC). Methods 49 BP, 25 MDD, 32 CC, and 47 HC (all euthymic) completed four creativity measures yielding six parameters: the Barron–Welsh Art Scale (BWAS-Total, and two subscales, BWAS-Dislike and BWAS-Like), the Adjective Check List Creative Personality Scale (ACL-CPS), and the Torrance Tests of Creative Thinking – Figural (TTCT-F) and Verbal (TTCT-V) versions. Mean scores on these instruments were compared across groups. Results BP and CC (but not MDD) compared to HC scored significantly higher on BWAS-Total (45% and 48% higher, respectively) and BWAS-Dislike (90% and 88% higher, respectively), but not on BWAS-Like. CC compared to MDD scored significantly higher (12% higher) on TTCT-F. For all other comparisons, creativity scores did not differ significantly between groups. Conclusions We found BP and CC (but not MDD) had similarly enhanced creativity on the BWAS-Total (driven by an increase on the BWAS-Dislike) compared to HC. Further studies are needed to determine the mechanisms of enhanced creativity and how it relates to clinical (e.g. temperament, mood, and medication status) and preclinical (e.g. visual and affective processing substrates) parameters.


title: Distributed Immersive Virtual Reality Simulation Development for Medical Education

abstract: Training professionals for real-world application of required knowledge and skills and assessing their competence are major challenges. Simulations are being used in education and training to enhance understanding, improve performance, and assess competence. Validated virtual reality (VR) simulations provide a means of making experiential learning reproducible and reusable. Advanced communication networks, such as Internet2 Access Grid, allow dissemination of these simulations and collaborative learning independent of distance. The prior experiences of our three universities led to an interdisciplinary collaboration to further develop and evaluate an integrated, fully immersive, interactive VR based system. This environment employs simulations that are visually three-dimensional and are driven dynamically by a rules-based artificial intelligence engine within Flatland, a virtual environments development software tool, and associated commodity hardware. Studies include usability and validation, deployment for distributed testing over Internet2, and evaluation of impact on training and performance using concept mapping and knowledge structure methods. Subject matter experts found face and content validity in our closed head injury simulation. Seven pairs of medical students participated collaboratively in problem solving and managing of the simulated patient in VR. Students stated that opportunities to make mistakes and repeat actions in VR were extremely helpful in learning specific principles and they felt more engaged than in standard text-based scenarios. 48 students participated in knowledge structure experiments pre and post simulation experiences. Knowledge structure relatedness ratings were significantly improved in those students with lower pre-VR relatedness ratings indicating a potential value of VR simulation in learning. This research cuts across the integration of computing, networking, human-computer interfaces, learning, and knowledge acquisition. VR creates a safe environment to make mistakes and could allow rapid deployment for just-in-time training or performance assessment.


title: The fabric defect detection technology based on wavelet transform and neural network convergence

abstract: Methods to fabric defects detection are varied, but the common method to detect the fabric defect shapes is slow and poor accuracy. Based on wavelet transform and neural network convergence technologies, this paper presents a new detection method on the fabric defect, which takes full advantage of wavelet transform good time-frequency localization characteristics and multi-scale image analysis capabilities on the fabric defect extraction, neural network technology can against defects for precise identification of the fabric for rapid detection.


title: Autonomous indoor 3D exploration with a micro-aerial vehicle

abstract: In this paper, we propose a stochastic differential equation-based exploration algorithm to enable exploration in three-dimensional indoor environments with a payload constrained micro-aerial vehicle (MAV). We are able to address computation, memory, and sensor limitations by considering only the known occupied space in the current map. We determine regions for further exploration based on the evolution of a stochastic differential equation that simulates the expansion of a system of particles with Newtonian dynamics. The regions of most significant particle expansion correlate to unexplored space. After identifying and processing these regions, the autonomous MAV navigates to these locations to enable fully autonomous exploration. The performance of the approach is demonstrated through numerical simulations and experimental results in single and multi-floor indoor experiments.


title: Third-Order Ambisonic Extensions for Max/MSP with Musical Applications

abstract: This paper describes a package of extensions (externals) for Cycling ‘74’s Max/MSP software to facilitate the exploration of Ambisonic techniques of up to third order. Areas of exploration well suited to the Max/MSP environment and techniques for composition within the Ambisonic domain using the presented externals are described.


title: Superlattice-based thin-film thermoelectric modules with high cooling fluxes

abstract: Current thermoelectric modules provide cooling fluxes that are insufficient for high-heat flux applications. Here, the authors demonstrate thin-film-based thermoelectric modules capable of providing cooling fluxes more than double that of the current state-of-the-art.


title: A Survey on Attribute-based Encryption Schemes of Access Control in Cloud Environments

abstract: In Attribute-based Encryption (ABE) scheme, attributes play a very important role. Attributes have been exploited to generate a public key for encrypting data and have been used as an access policy to control users' access. The access policy can be categorized as either key-policy or ciphertext-policy. The key-policy is the access structure on the user's private key, and the ciphertext-policy is the access structure on the ciphertext. And the access structure can also be categorized as either monotonic or non-monotonic one. Using ABE schemes can have the advantages: (1) to reduce the communication overhead of the Internet, and (2) to provide a fine-grained access control. In this paper, we survey a basic attribute-based encryption scheme, two various access policy attribute-based encryption schemes, and two various access structures, which are analyzed for cloud environments. Finally, we list the comparisons of these schemes by some criteria for cloud environments.


title: Rectifying the bound document image captured by the camera: a model based approach

abstract: A model based approach for rectifying the camera image of the bound document has been developed, i.e., the surface of the document is represented by a general cylindrical surface. The principle of using the model to unwrap the image is discussed. Practically, the skeleton of each horizontal text line is extracted to help estimate the parameter of the model, and rectify the images. To use the model, only a few priori is required, and no more auxiliary device is necessary. Experiment results are given to demonstrate the feasibility and the stability of the method.


title: Feedback connection for deep neural network-based acoustic modeling

abstract: The use of auxiliary features is an effective way to improve the performance of deep neural network (DNN)-based acoustic models. Most approaches use auxiliary features that represent the speaker or the environment. These auxiliary features are usually computed independently of the acoustic model. This paper investigates a types of auxiliary features obtained from the output of a hidden layer that feeds back to the input layer of the network. Since the auxiliary features are extracted from the hidden layer of the network no external information is required such as the speaker or the environment. Experimentally, by forcing the extraction of the auxiliary features from the same networks, we can further improve the performance of the overall network and reduce the total number of parameters used. We tested this approach with different deep neural network architectures including: deep neural networks, convolutional neural networks and unfolded recurrent convolutional networks. We confirmed the effectiveness of this approach on the CHiME3 dataset.


title: Recurrent conditional random field for language understanding

abstract: Recurrent neural networks (RNNs) have recently produced record setting performance in language modeling and word-labeling tasks. In the word-labeling task, the RNN is used analogously to the more traditional conditional random field (CRF) to assign a label to each word in an input sequence, and has been shown to significantly outperform CRFs. In contrast to CRFs, RNNs operate in an online fashion to assign labels as soon as a word is seen, rather than after seeing the whole word sequence. In this paper, we show that the performance of an RNN tagger can be significantly improved by incorporating elements of the CRF model; specifically, the explicit modeling of output-label dependencies with transition features, its global sequence-level objective function, and offline decoding. We term the resulting model a “recurrent conditional random field” and demonstrate its effectiveness on the ATIS travel domain dataset and a variety of web-search language understanding datasets.


title: AlphaStar: an evolutionary computation perspective

abstract: In January 2019, DeepMind revealed AlphaStar to the world---the first artificial intelligence (AI) system to beat a professional player at the game of StarCraft II---representing a milestone in the progress of AI. AlphaStar draws on many areas of AI research, including deep learning, reinforcement learning, game theory, and evolutionary computation (EC). In this paper we analyze AlphaStar primarily through the lens of EC, presenting a new look at the system and relating it to many concepts in the field. We highlight some of its most interesting aspects---the use of Lamarckian evolution, competitive co-evolution, and quality diversity. In doing so, we hope to provide a bridge between the wider EC community and one of the most significant AI systems developed in recent times.


title: CognitiveEMS: a cognitive assistant system for emergency medical services

abstract: This paper presents our preliminary results on development of a Cognitive assistant system for Emergency Medical Services (CognitiveEMS) that aims to improve situational awareness and safety of first responders. CognitiveEMS integrates a suite of smart wearable sensors, devices, and analytics for real-time collection and analysis of in-situ data from incident scene and delivering dynamic data-driven insights to responders on the most effective response actions to take. We present the overall architecture of CognitiveEMS pipeline for processing information collected from the responder, which includes stages for converting speech to text, extracting medical and EMS protocol specific concepts, and modeling and execution of an EMS protocol. The performance of the pipeline is evaluated in both noise-free and noisy incident environments. The experiments are conducted using two types of publicly-available real EMS data: short radio calls and post-incident patient care reports. Three different noise profiles are considered for simulating the noisy environments: cafeteria, people talking, and emergency sirens. Noise was artificially added at 3 intensity levels of low, medium, and high to pre-recorded audio data. The results show that the i) state-of-the-art speech recognition tools such as Google Speech API are quite robust to low and medium noise intensities; ii) in the presence of high noise levels, the overall recall rate in medical concept annotation is reduced; and iii) the effect of noise often propagates to the final decision making stage and results in generating misleading feedback to responders.


title: Katyusha: the first direct acceleration of stochastic gradient methods

abstract: We introduce $\mathtt{Katyusha}$, the first direct stochastic gradient method that has an accelerated convergence rate. Given an objective that is an average of $n$ convex and smooth functions, $\mathtt{Katyusha}$ converges to an $\varepsilon$-approximate minimizer using $O((n + \sqrt{n \kappa})\cdot \log\frac{f(x_0)-f(x^*)}{\varepsilon})$ stochastic iterations, where $\kappa$ is the condition number. $\mathtt{Katyusha}$ is a direct primal method. In contrast, previous accelerated stochastic methods are either based on dual coordinate descent which are more restrictive, or based on outer-inner loops which make them"blind"to the underlying stochastic nature of the optimization process. $\mathtt{Katyusha}$ is the first algorithm that incorporates acceleration directly into the stochastic gradient updates. $\mathtt{Katyusha}$ supports proximal updates, non-Euclidean norm smoothness, non-uniform sampling, as well as mini-batch sampling. It also improves the best known convergence rates on many interesting classes of convex objectives, including smooth objectives (e.g., Lasso, Logistic Regression), strongly-convex objectives (e.g., SVM), and non-smooth objectives (e.g., L1SVM). The main ingredient behind our result is Katyusha momentum, a clever"negative momentum on top of momentum"that can be added on top of a variance-reduction based algorithm and speed it up. As a result, since variance reduction has been successfully applied to a fast growing list of practical problems, our paper suggests that in each of such cases, one had better hurry up and give Katyusha a hug.


title: Widespread Lateral Gene Transfer from Intracellular Bacteria to Multicellular Eukaryotes

abstract: Although common among bacteria, lateral gene transfer-the movement of genes between distantly related organisms-is thought to occur only rarely between bacteria and multicellular eukaryotes. However, the presence of endosymbionts, such as Wolbachia pipientis, within some eukaryotic germlines may facilitate bacterial gene transfers to eukaryotic host genomes. We therefore examined host genomes for evidence of gene transfer events from Wolbachia bacteria to their hosts. We found and confirmed transfers into the genomes of four insect and four nematode species that range from nearly the entire Wolbachia genome (>1 megabase) to short (<500 base pairs) insertions. Potential Wolbachia-to-host transfers were also detected computationally in three additional sequenced insect genomes. We also show that some of these inserted Wolbachia genes are transcribed within eukaryotic cells lacking endosymbionts. Therefore, heritable lateral gene transfer occurs into eukaryotic hosts from their prokaryote symbionts, potentially providing a mechanism for acquisition of new genes and functions.


title: Adolescent Idiopathic Scoliosis: Review and Current Concepts

abstract: Adolescent idiopathic scoliosis is present in 2 to 4 percent of children between 10 and 16 years of age. It is defined as a lateral curvature of the spine greater than 10 degrees accompanied by vertebral rotation. It is thought to be a multigene dominant condition with variable phenotypic expression. Scoliosis can be identified by the Adam’s forward bend test during physical examination. Severe pain, a left thoracic curve or an abnormal neurologic examination are red flags that point to a secondary cause for spinal deformity. Specialty consultation and magnetic resonance imaging are needed if red flags are present. Of adolescents diagnosed with scoliosis, only 10 percent have curves that progress and require medical intervention. The main risk factors for curve progression are a large curve magnitude, skeletal immaturity and female gender. The likelihood of curve progression can be estimated by measuring the curve magnitude using the Cobb method on radiographs and by assessing skeletal growth potential using Tanner staging and Risser grading. (Am Fam Physician 2001;64:111-6.)


title: Applications of real-time fMRI

abstract: The development of real-time fMRI has enabled us to watch our own brain in action 'live'. Christopher deCharms provides an overview of current and potential applications of this technique, including its use as a brain–machine interface and in learned control over brain activation.


title: Feature extraction for co-occurrence-based cosine similarity score of text documents

abstract: A major challenge in topic classification (TC) is the high dimensionality of the feature space. Therefore, feature extraction (FE) plays a vital role in topic classification in particular and text mining in general. FE based on cosine similarity score is commonly used to reduce the dimensionality of datasets with tens or hundreds of thousands of features, which can be impossible to process further. In this study, TF-IDF term weighting is used to extract features. Selecting relevant features and determining how to encode them for a learning machine method have a vast impact on the learning machine methods ability to extract a good model. Two different weighting methods (TF-IDF and TF-IDF Global) were used and tested on the Reuters-21578 text categorization test collection. The obtained results emerged a good candidate for enhancing the performance of English topics FE. Simulation results the Reuters-21578 text categorization show the superiority of the proposed algorithm.


title: Cost-effective printing of 3D objects with skin-frame structures

abstract: 3D printers have become popular in recent years and enable fabrication of custom objects for home users. However, the cost of the material used in printing remains high. In this paper, we present an automatic solution to design a skin-frame structure for the purpose of reducing the material cost in printing a given 3D object. The frame structure is designed by an optimization scheme which significantly reduces material volume and is guaranteed to be physically stable, geometrically approximate, and printable. Furthermore, the number of struts is minimized by solving an l0 sparsity optimization. We formulate it as a multi-objective programming problem and an iterative extension of the preemptive algorithm is developed to find a compromise solution. We demonstrate the applicability and practicability of our solution by printing various objects using both powder-type and extrusion-type 3D printers. Our method is shown to be more cost-effective than previous works.


Automatic Car Damage Recognition using Convolutional Neural Networks


title: Measuring service quality in higher education: HEdPERF versus SERVPERF

abstract: Purpose – This paper aims to test and compare the relative efficacy of three measuring instruments of service quality (namely Higher Education PERFormance (HEdPERF), SERVPERF and the moderating scale of HEdPERF‐SERVPERF) within a higher education setting. The objective was to determine which instrument had the superior measuring capability in terms of unidimensionality, reliability, validity and explained variance.Design/methodology/approach – After a pilot test, data were collected from students in two public universities, one private university and three private colleges in Malaysia between January and March 2004, by the “contact person” route. From a total of 560 questionnaires, 381 were usable: a response rate of 68.0 per cent. This sample of nearly 400,000 students in Malaysian tertiary institutions was in line with the generalized scientific guideline for sample size decisions. Data were subjected to regression analysis.Findings – A modified five‐factor structure of HEdPERF is put forward as the mos...


title: Acute caffeine administration impact on working memory-related brain activation and functional connectivity in the elderly: A BOLD and perfusion MRI study

abstract: In young individuals, caffeine-mediated blockade of adenosine receptors and vasoconstriction has direct repercussions on task-related activations, changes in functional connectivity, as well as global vascular effects. To date, no study has explored the effect of caffeine on brain activation patterns during highly demanding cognitive tasks in the elderly. This prospective, placebo-controlled crossover design comprises 24 healthy elderly individuals (mean age 68.8 +/- 4.0 years, 17 females) performing a 2-back working memory (WM) task in functional magnetic resonance imaging (fMRI). Analyses include complimentary assessment of task-related activations (general linear model, GLM), functional connectivity (tensorial independent component analysis, TICA), and baseline perfusion (arterial spin labeling). Despite a reduction in whole-brain global perfusion (-22.7%), caffeine-enhanced task-related GLM activation in a local and distributed network is most pronounced in the bilateral striatum and to a lesser degree in the right middle and inferior frontal gyrus, bilateral insula, left superior and inferior parietal lobule as well as in the cerebellum bilaterally. TICA was significantly enhanced (+8.2%) in caffeine versus placebo in a distributed and task-relevant network including the pre-frontal cortex, the supplementary motor area, the ventral premotor cortex and the parietal cortex as well as the occipital cortex (visual stimuli) and basal ganglia. The inverse comparison of placebo versus caffeine had no significant difference. Activation strength of the task-relevant-network component correlated with response accuracy for caffeine yet not for placebo, indicating a selective cognitive effect of caffeine. The present findings suggest that acute caffeine intake enhances WM-related brain activation as well as functional connectivity of blood oxygen level-dependent fMRI in elderly individuals. (c) 2013 IBRO. Published by Elsevier Ltd. All rights reserved.


title: Is Over-Education a Problem in Spain? Empirical Evidence Based on the EU-SILC

abstract: Spain is suffering an important economic slowdown which affects significantly to unemployment rises. For this reason, many individuals are considering to increase their educational level. However, this decision can lead to get worse labor market indicators. In this paper, we analyze the problem of over-education in Spain and its effect on earnings. Using the last four waves (2009–2012) of the European Union Statistics on Income and Living Conditions (EU-SILC, 2009–2012) this study analyses over-education situation of employed people and compares over-education rates and earnings of these individuals. Cross-sectional analysis (focused on a recent period of time) is performed. We apply different matching techniques based on propensity score methods to evaluate the impact of over-education on personal earnings. The results obtained in this paper add more empirical evidence supporting the effect of education on earnings together with occupational placement because over-education could be a great problem for many countries. In Spain, there exists a problem of over-education which varies among occupations. Overeducated workers are most likely to be found among “Service workers and shop and market sales workers” and “Elementary occupations”. Also, the majority of individuals working as “Professionals” are adequately educated. Besides, over-education has a significant negative impact on earnings. Although the results depend on the way we define over-education (interval or modal method), those individuals over-educated receive less earnings than they correspond to. In fact, the decrease can reach about 5000 euro per year.


title: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

abstract: Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the prescribed range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning. In addition, in order to achieve high object detection performance, we propose a new approach to extracting low-level visual features based on spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features. The result is the current best reported performance on the Caltech-USA pedestrian detection dataset.


title: Efficiency optimization control for single-phase brushless dc fan motors

abstract: This paper proposes a modeling method for characterization of single-phase brushless dc (BLDC) fan motors in applications to information appliances. The nonlinear back-EMF induced by the rotor flux with stator winding is modeled by a look-up table. By parameter identification and computer simulation, this modeling method assists designers in waveform analysis and control loop design. Simulation results have been verified with the measured experimental results. Moreover, in order to improve the efficiency of the BLDC fan motor over the entire speed control range, this paper develops an efficiency optimization control method based on a closed-loop current control scheme by using the Hall sensor feedback. The proposed control scheme has been realized and compared with the conventional open-loop PWM control scheme. Experimental results show a 40% reduction of peak current and 18% reduction of RMS current have been achieved at rotation speed of 3000 RPM.


title: SeNet: Structured Edge Network for Sea–Land Segmentation

abstract: Separating an optical remote sensing image into sea and land areas is very challenging yet of great importance to coastline extraction and subsequent object detection. Traditional methods based on handcrafted feature extraction and image processing often face this dilemma when confronting high-resolution remote sensing images for their complicated texture and intensity distribution. In this letter, we apply the prevalent deep convolutional neural networks to the sea-land segmentation problem and make two innovations on top of the traditional structure. First, we propose a local smooth regularization to achieve better spatially consistent results, which frees us from the complicated morphological operations that are commonly used in traditional methods. Second, we use a multitask loss to simultaneously obtain the segmentation and edge detection results. The attached structured edge detection branch can further refine the segmentation result and dramatically improve edge accuracy. Experiments on a set of natural-colored images from Google Earth demonstrate the effectiveness of our approach in terms of quantitative and visual performances compared with state-of-the-art methods.


title: A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

abstract: The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.


title: Bone remodelling at a glance.

abstract: The bone remodelling cycle (see Poster panel “The bone remodelling cycle”) maintains the integrity of the skeleton through the balanced activities of its constituent cell types. These are the bone-forming osteoblast, a cell that produces the organic bone matrix and aids its mineralisation ([


title: Annotating temporal and event quantification

abstract: In this paper we propose someimprovements to the proposed ISO-TimeML standard for the semantic annotation of information about time and events. We argue that these improvements are called for, either in order to deal with suboptimal choices in the XML-based representation of annotation structures, or for resolving some of the difficulties that arise due to the impossibility to separate semantic phenomena relating to time and events, like temporal quantification, from their more general form. We indicate solutions for both types of cases.


title: Deep vs. shallow networks : An approximation theory perspective

abstract: The paper briefy reviews several recent results on hierarchical architectures for learning from examples, that may formally explain the conditions under which Deep Convolutional Neural Networks perform much better in function approximation problems than shallow, one-hidden layer architectures. The paper announces new results for a non-smooth activation function - the ReLU function - used in present-day neural networks, as well as for the Gaussian networks. We propose a new definition of relative dimension to encapsulate different notions of sparsity of a function class that can possibly be exploited by deep networks but not by shallow ones to drastically reduce the complexity required for approximation and learning.


title: Privacy-Enhanced Web Personalization

abstract: Consumer studies demonstrate that online users value personalized content. At the same time, providing personalization on websites seems quite profitable for web vendors. This win-win situation is however marred by privacy concerns since personalizing people's interaction entails gathering considerable amounts of data about them. As numerous recent surveys have consistently demonstrated, computer users are very concerned about their privacy on the Internet. Moreover, the collection of personal data is also subject to legal regulations in many countries and states. Both user concerns and privacy regulations impact frequently used personalization methods. This article analyzes the tension between personalization and privacy, and presents approaches to reconcile the both.


title: A Framework for Clustering Evolving Data Streams

abstract: The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a few one-pass clustering algorithms have been developed for the data stream problem. Although such methods address the scalability issues of the clustering problem, they are generally blind to the evolution of the data and do not address the following issues: (1) The quality of the clusters is poor when the data evolves considerably over time. (2) A data stream clustering algorithm requires much greater functionality in discovering and exploring clusters over different portions of the stream. ::: ::: The widely used practice of viewing data stream clustering algorithms as a class of one-pass clustering algorithms is not very useful from an application point of view. For example, a simple one-pass clustering algorithm over an entire data stream of a few years is dominated by the outdated history of the stream. The exploration of the stream over different time windows can provide the users with a much deeper understanding of the evolving behavior of the clusters. At the same time, it is not possible to simultaneously perform dynamic clustering over all possible time horizons for a data stream of even moderately large volume. ::: ::: This paper discusses a fundamentally different philosophy for data stream clustering which is guided by application-centered requirements. The idea is divide the clustering process into an online component which periodically stores detailed summary statistics and an offine component which uses only this summary statistics. The offine component is utilized by the analyst who can use a wide variety of inputs (such as time horizon or number of clusters) in order to provide a quick understanding of the broad clusters in the data stream. The problems of efficient choice, storage, and use of this statistical data for a fast data stream turns out to be quite tricky. For this purpose, we use the concepts of a pyramidal time frame in conjunction with a microclustering approach. Our performance experiments over a number of real and synthetic data sets illustrate the effectiveness, efficiency, and insights provided by our approach.


title: Humans Outperform Machines at the Bilingual Shannon Game

abstract: We provide an upper bound for the amount of information a human translator adds to an original text, i.e., how many bits of information we need to store a translation, given the original. We do this by creating a Bilingual Shannon Game that elicits character guesses from human subjects, then developing models to estimate the entropy of those guess sequences.


title: Simultaneous co-clustering and learning to address the cold start problem in recommender systems

abstract: Recommender Systems (RSs) are powerful and popular tools for e-commerce. To build their recommendations, RSs make use of varied data sources, which capture the characteristics of items, users, and their transactions. Despite recent advances in RS, the cold start problem is still a relevant issue that deserves further attention, and arises due to the lack of prior information about new users and new items. To minimize system degradation, a hybrid approach is presented that combines collaborative filtering recommendations with demographic information. The approach is based on an existing algorithm, SCOAL (Simultaneous Co-Clustering and Learning), and provides a hybrid recommendation approach that can address the (pure) cold start problem, where no collaborative information (ratings) is available for new users. Better predictions are produced from this relaxation of assumptions to replace the lack of information for the new user. Experiments using real-world datasets show the effectiveness of the proposed approach.


title: Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units

abstract: We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron's input. This stochastic regularizer is comparable to nonlinearities aided by dropout, but it removes the need for a traditional nonlinearity. The connection between the GELU and the stochastic regularizer suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.


title: Amygdala stimulation produces two types of hippocampal aftercharges in the urethane-anesthetized rat

abstract: How seizures spread during epileptiform events has been the subject of intensive investigation over the years. The present study explored the relationship between the amygdala and the hippocampus during afterdischarges. Stimulating electrodes were placed in the amygdala and CA3 regions on the left side in urethane-anesthetized rats. Recording microelectrodes were placed in the dentate gyrus on the left and the CA1 cell layer on the right. Afterdischarges were elicited by stimulus trains between 10 and 50 Hz to the amygdala. Most of the afterdischarges consisted of broad positive potentials in dentate gyrus and no shift of the DC potential. When the stimulus trains were repeated, the afterdischarge evolved, first by spreading to the contralateral side and then by the appearance of maximal dentate activation. The onset of maximal dentate activation was indicated by the appearance of bursts of large amplitude population spikes and a negative shift of the DC potential. These data demonstrate that two types of afterdischarges can be produced in the hippocampus of the anesthetized rat after amygdala stimulation. The observations support the hypothesis that maximal dentate activation represents synchronized reverberatory activity throughout the hippocampal-parahippocampal circuit and indicate that amygdala stimulation can access this circuit in the anesthetized animal.


title: Network Structure of Social Coding in GitHub

abstract: Social coding enables a different experience of software development as the activities and interests of one developer are easily advertised to other developers. Developers can thus track the activities relevant to various projects in one umbrella site. Such a major change in collaborative software development makes an investigation of networkings on social coding sites valuable. Furthermore, project hosting platforms promoting this development paradigm have been thriving, among which GitHub has arguably gained the most momentum. In this paper, we contribute to the body of knowledge on social coding by investigating the network structure of social coding in GitHub. We collect 100,000 projects and 30,000 developers from GitHub, construct developer-developer and project-project relationship graphs, and compute various characteristics of the graphs. We then identify influential developers and projects on this sub network of GitHub by using PageRank. Understanding how developers and projects are actually related to each other on a social coding site is the first step towards building tool supports to aid social programmers in performing their tasks more efficiently.


title: Matching pursuit-based signal processing method to improve ultrasonic flaw detection in NDT applications

abstract: A novel matching pursuit-based signal processing method is proposed to improve ultrasonic flaw detection in non-destructive testing (NDT) applications. The method utilises time-shifted Morlet functions as dictionary elements, since they are well matched with the ultrasonic pulse echoes obtained from the transducer used in the experiments. It is fast enough to be used in the signal processing stage of real-time inspection systems. Experimental results show meaningful SNR improvements for low input SNR's (below 0 dB).


title: Content-Oriented User Modeling for Personalized Response Ranking in Chatbots

abstract: Automatic chatbots (also known as chat-agents) have attracted much attention from both researching and industrial fields. Generally, the semantic relevance between users’ queries and the corresponding responses is considered as the essential element for conversation modeling in both generation and ranking based chat systems. By contrast, it is a nontrivial task to adopt the users’ information, such as preference, social role, etc., into conversational models reasonably, while users’ profiles play a significant role in the procedure of conversations by providing the implicit contexts. This paper aims to address the personalized response ranking task by incorporating user profiles into the conversation model. In our approach, users’ personalized representations are latently learned from the contents posted by them via a two-branch neural network. After that, a deep neural network architecture is further presented to learn the fusion representation of posts, responses, and personal information. In this way, the proposed model could understand conversations from the users’ perspective; hence, the more appropriate responses are selected for a specified person. The experimental results on two datasets from social network services demonstrate that our approach is hopeful to represent users’ personal information implicitly based on user generated contents, and it is promising to perform as an important component in chatbots to select the personalized responses for each user.


title: Social Contagion Theory: Examining Dynamic Social Networks and Human Behavior

abstract: Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a ‘three degrees of influence’ property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations. Copyright © 2012 John Wiley & Sons, Ltd.


title: Visually-Aware Fashion Recommendation and Design with Generative Image Models

abstract: Building effective recommender systems for domains like fashion is challenging due to the high level of subjectivity and the semantic complexity of the features involved (i.e., fashion styles). Recent work has shown that approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made more accurate by incorporating visual signals directly into the recommendation objective, using `off-the-shelf' feature representations derived from deep networks. Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i.e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features. Furthermore, we show that our model can be used \emph{generatively}, i.e., given a user and a product category, we can generate new images (i.e., clothing items) that are most consistent with their personal taste. This represents a first step towards building systems that go beyond recommending existing items from a product corpus, but which can be used to suggest styles and aid the design of new products.


title: Anonymizing healthcare data: a case study on the blood transfusion service

abstract: Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients' privacy. In this paper, we study the privacy concerns of the blood transfusion information-sharing system between the Hong Kong Red Cross Blood Transfusion Service (BTS) and public hospitals, and identify the major challenges that make traditional data anonymization methods not applicable. Furthermore, we propose a new privacy model called LKC-privacy, together with an anonymization algorithm, to meet the privacy and information requirements in this BTS case. Experiments on the real-life data demonstrate that our anonymization algorithm can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large datasets.


title: Origin and Functions of Tissue Macrophages

abstract: Macrophages are distributed in tissues throughout the body and contribute to both homeostasis and disease. Recently, it has become evident that most adult tissue macrophages originate during embryonic development and not from circulating monocytes. Each tissue has its own composition of embryonically derived and adult-derived macrophages, but it is unclear whether macrophages of distinct origins are functionally interchangeable or have unique roles at steady state. This new understanding also prompts reconsideration of the function of circulating monocytes. Classical Ly6c(hi) monocytes patrol the extravascular space in resting organs, and Ly6c(lo) nonclassical monocytes patrol the vasculature. Inflammation triggers monocytes to differentiate into macrophages, but whether resident and newly recruited macrophages possess similar functions during inflammation is unclear. Here, we define the tools used for identifying the complex origin of tissue macrophages and discuss the relative contributions of tissue niche versus ontological origin to the regulation of macrophage functions during steady state and inflammation.


title: Variance component model to account for sample structure in genome-wide association studies

abstract: Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.


title: Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter

abstract: We present a novel lane detection and tracking system using a fusion of Maximally Stable Extremal Regions (MSER) and Progressive Probabilistic Hough Transform (PPHT). First, MSER is applied to obtain a set of blobs including noisy pixels (e.g., trees, cars and traffic signs) and the candidate lane markings. A scanning refinement algorithm is then introduced to enhance the results of MSER and filter out noisy data. After that, to achieve the requirements of real-time systems, the PPHT is applied. Compared to Hough transform which returns the parameters ρ and Θ, PPHT returns two end-points of the detected line markings. To track lane markings, two kalman trackers are used to track both end-points. Several experiments are conducted in Ottawa roads to test the performance of our framework. The detection rate of the proposed system averages 92.7% and exceeds 84.9% in poor conditions.


title: Towards discovering and understanding task hijacking in android

abstract: Android multitasking provides rich features to enhance user experience and offers great flexibility for app developers to promote app personalization. However, the security implication of Android multitasking remains under-investigated. With a systematic study of the complex tasks dynamics, we find design flaws of Android multitasking which make all recent versions of Android vulnerable to task hijacking attacks. We demonstrate proof-of-concept examples utilizing the task hijacking attack surface to implement UI spoofing, denial-of-service and user monitoring attacks. Attackers may steal login credentials, implement ransomware and spy on user's activities. We have collected and analyzed over 6.8 million apps from various Android markets. Our analysis shows that the task hijacking risk is prevalent. Since many apps depend on the current multitasking design, defeating task hijacking is not easy. We have notified the Android team about these issues and we discuss possible mitigation techniques in this paper.


title: Simultaneous feature selection and clustering using mixture models

abstract: Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters.


title: Development of the Smart Spring for Active Vibration Control of Helicopter Blades

abstract: Significant structural vibration is an undesirable characteristic in helicopter flight that leads to structural fatigue, poor ride quality for passengers and high acoustic signature for the vehicle. Previous Individual Blade Control (IBC) techniques based on piezoelectric actuator schemes to reduce these effects have been hindered by electromechanical limitations of piezoelectric actuators. The Smart Spring is an active tunable vibration absorber using the IBC approach to adaptively alter the “structural impedance” at the blade root. In the paper, a mathematical model was developed to determine the response of the absorber under harmonic excitation. An adaptive notch algorithm using a DSP platform was developed to implement vibration control. Reference signal synthesis techniques were used to automatically track the shift in the fundamental vibratory frequency due to variations in flight conditions. Experiments using a mechanical shaker and wind tunnel tests conducted on the proof-of-concept hardware achi...


title: Hand pose estimation using HOG features from RGB-D data

abstract: Visual based recognition of hand gestures has been an active research field in recent years due to its efficiency in helping us achieve a more natural human-computer interaction. This paper presents a new approach to hand pose estimation using combined visual and geometric information obtained in a synchronized format from a RGB-D sensor. Firstly, we track the contour of the hand and recognize the fingertip positions. Then, Kernel Principal Component Analysis is used for selecting the most relevant elements from the histograms of oriented gradients feature vectors obtained on both color and depth data. We define an observation model based on the found fingertip positions and the selected principal components and we feed it as input for a Support Vector Machine classifier. Experimental results for the proposed method show that good detection percentages can be obtained with a small training dataset of real hand images and depth masks.


title: Applications and usage of visual cryptography: A review

abstract: Visual cryptography is a cryptographic technique which allows visual information to be encrypted in such a way that decryption becomes a mechanical operation that does not require a computer. In this paper, we intend to study the different application areas of Visual Cryptography. Visual Cryptography is a wide area of research used in data hiding, securing images, color imaging, multimedia and other such fields. Visual Cryptography comes in the field of data hiding used in cybercrime, file formats etc. This paper focuses on the application areas of visual cryptography from four different research papers/journals which talk about the most important application areas of visual cryptography.


title: A Comprehensive Recommender System Model: Improving Accuracy for Both Warm and Cold Start Users

abstract: Sparsity of the ratings available in the recommender system database makes the task of rating prediction a highly underdetermined problem. This poses a limit on the accuracy and the quality of prediction. In this paper, we utilize secondary information pertaining to user’s demography and item categories to enhance prediction accuracy. Within the matrix factorization framework, we introduce additional supervised label consistency terms that match the user and item factor matrices to the available secondary information (metadata). Matrix factorization model—conventionally employed in collaborative filtering techniques—yields dense user and dense item factor matrices—the assumption is that users have an affinity toward all latent factors and items possess all latent factors. Our formulation, based on a recent work, aims to recover a dense user and a sparse item factor matrix—this is a more reasonable model. Human beings show a natural interest toward all the factors, but every item cannot possess all the factors; this leads to a sparse item factor matrix. A natural outcome of our proposal is a solution to the pure cold start problem. We utilize the label consistency map generated from the proposed model to make reasonable recommendations for new users and new items which have not (been) rated yet. We demonstrate the performance of our model for a movie recommendation system. We also design an efficient algorithm for our formulation.


abstract: Cosytec, Parc Club Orsay-University, 4, rue Jean-Rostand, 91893 Orsay Cedex, France Abstract Van Hentenryck, P., H. Simonis and M. Dincbas, Constraint satisfaction using constraint logic programming, Artificial Intelligence 58 (1992) 113-159. Constraint logic programming (CLP) is a new class of declarative programming lan- guages whose primitive operations are based on constraints (e.g. constraint solving and constraint entailment). CLP languages naturally combine constraint propagation with nondeterministic choices. As a consequence, they are particularly appropriate for solv- ing a variety of combinatorial search problems, using the global search paradigm, with short development time and efficiency comparable to procedural tools based on the same approach. In this paper, we describe how the CLP language cc(FD), a successor of CHIP using consistency techniques over finite domains, can be used to solve two practical applications: test-pattern generation and car sequencing. For both applications, we present the cc(FD) program, describe how constraint solving is performed, report experimental results, and compare the approach with existing tools. 1.


title: Meanings of Pragmatism: Ways to conduct information systems research

abstract: There is a growing interest in pragmatism within organisational and informational studies. Pragmatism has been seen as a viable alternative to positivism and anti-positivism. This paper explores the meanings of pragmatism in terms of implications for how to conduct research on information systems. Its main source of inspiration is the American philosophy of pragmatism. The paper describes the necessity of a focus on actions and practices, how to anchor abstractions in pragmatist ontology, the significance of practical knowledge interests and how to retain a pragmatic focus in inquiry processes. The paper concludes with the formulation of an action manifesto.


Mining Large Graphs


title: DARPA's Big Mechanism program

abstract: Reductionist science produces causal models of small fragments of complicated systems. Causal models of entire systems can be hard to construct because what is known of them is distributed across a vast amount of literature. The Big Mechanism program aims to have machines read the literature and assemble the causal fragments found in individual papers into huge causal models, automatically. The current domain of the program is cell signalling associated with Ras-driven cancers.


title: An experimental investigation into the influence of user state and environment on fading characteristics in wireless body area networks at 2.45 GHz

abstract: Using seven strategically placed, time-synchronized body worn receivers covering the head, upper front and back torso, and the limbs, we have investigated the effect of user state: stationary or mobile and local environment: anechoic chamber, open office area and hallway upon first and second order statistics for on-body fading channels. Three candidate models were considered: Nakagami, Rice and lognormal. Using maximum likelihood estimation and the Akaike information criterion it was established that the Nakagami-m distribution best described small-scale fading for the majority of on-body channels over all the measurement scenarios. When the user was stationary, Nakagami-m parameters were found to be much greater than 1, irrespective of local surroundings. For mobile channels, Nakagami-m parameters significantly decreased, with channels in the open office area and hallway experiencing the worst fading conditions.


title: An Alternative Model for Mixtures of Experts

abstract: We propose an alternative model for mixtures of experts which uses a different parametric form for the gating network. The modified model is trained by the EM algorithm. In comparison with earlier models--trained by either EM or gradient ascent--there is no need to select a learning stepsize. We report simulation experiments which show that the new architecture yields faster convergence. We also apply the new model to two problem domains: piecewise nonlinear function approximation and the combination of multiple previously trained classifiers.


title: Personality Traits, Emotional Intelligence and Academic Achievements of University Students

abstract: This research investigated the relationships between personality traits, emotional intelligence and academic achievements among 160 university students in Malaysia. Big Five Inventory (BFI) was used to measure the five dimensions of personality traits - extraversion, agreeableness, conscientiousness, neuroticism, openness; Schutte Emotional Intelligence Scale (SEIS) was used to measure emotional intelligence and students’ academic achievement was measured by Cumulative Grade Point Average (CGPA). Bivariate analysis using Pearson Correlation method indicated that extraversion (r=.311, p .05) was insignificantly associated with academic achievement. Future researches are recommended to employ Structural Equation Modeling analysis to determine how both personality traits and emotional intelligence have an impact on academic achievements.


Multivariate Aviation Time Series Modeling : VARs vs . LSTMs


title: Fader Networks: Manipulating Images by Sliding Attributes

abstract: This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images.


title: Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing

abstract: Decision support systems (DSS) are becoming increasingly more critical to the daily operation of organizations. Data warehousing, an integral part of this, provides an infrastructure that enables businesses to extract, cleanse, and store vast amounts of data. The basic purpose of a data warehouse is to empower the knowledge workers with information that allows them to make decisions based on a solid foundation of fact. However, only a fraction of the needed information exists on computers; the vast majority of a firm's intellectual assets exist as knowledge in the minds of its employees. What is needed is a new generation of knowledge-enabled systems that provides the infrastructure needed to capture, cleanse, store, organize, leverage, and disseminate not only data and information but also the knowledge of the firm. The purpose of this paper is to propose, as an extension to the data warehouse model, a knowledge warehouse (KW) architecture that will not only facilitate the capturing and coding of knowledge but also enhance the retrieval and sharing of knowledge across the organization. The knowledge warehouse proposed here suggests a different direction for DSS in the next decade. This new direction is based on an expanded purpose of DSS. That is, the purpose of DSS in knowledge improvement. This expanded purpose of DSS also suggests that the effectiveness of a DSS will, in the future, be measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s) and thereby how well it improves his/her decision making.


title: Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

abstract: We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics. This insight enables extension of HMC and slice sampling to a broader family of samplers, called Monomial Gamma Samplers (MGS). We provide a theoretical analysis of the mixing performance of such samplers, proving that in the limit of a single parameter, the MGS draws decorrelated samples from the desired target distribution. We further show that as this parameter tends toward this limit, performance gains are achieved at a cost of increasing numerical difficulty and some practical convergence issues. Our theoretical results are validated with synthetic data and real-world applications.


title: A Zero Knowledge Sumcheck and its Applications

abstract: Many seminal results in Interactive Proofs (IPs) use algebraic techniques based on low-degree polynomials, the study of which is pervasive in theoretical computer science. Unfortunately, known methods for endowing such proofs with zero knowledge guarantees do not retain this rich algebraic structure. ::: In this work, we develop algebraic techniques for obtaining zero knowledge variants of proof protocols in a way that leverages and preserves their algebraic structure. Our constructions achieve unconditional (perfect) zero knowledge in the Interactive Probabilistically Checkable Proof (IPCP) model of Kalai and Raz [KR08] (the prover first sends a PCP oracle, then the prover and verifier engage in an Interactive Proof in which the verifier may query the PCP). ::: Our main result is a zero knowledge variant of the sumcheck protocol [LFKN92] in the IPCP model. The sumcheck protocol is a key building block in many IPs, including the protocol for polynomial-space computation due to Shamir [Sha92], and the protocol for parallel computation due to Goldwasser, Kalai, and Rothblum [GKR15]. A core component of our result is an algebraic commitment scheme, whose hiding property is guaranteed by algebraic query complexity lower bounds [AW09,JKRS09]. This commitment scheme can then be used to considerably strengthen our previous work [BCFGRS16] that gives a sumcheck protocol with much weaker zero knowledge guarantees, itself using algebraic techniques based on algorithms for polynomial identity testing [RS05,BW04]. ::: We demonstrate the applicability of our techniques by deriving zero knowledge variants of well-known protocols based on algebraic techniques, including the protocols of Shamir and of Goldwasser, Kalai, and Rothblum, as well as the protocol of Babai, Fortnow, and Lund [BFL91].


title: Convolutional Sketch Inversion

abstract: In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images. We first construct a semi-simulated dataset containing a very large number of computer-generated face sketches with different styles and corresponding face images by expanding existing unconstrained face data sets. We then train models achieving state-of-the-art results on both computer-generated sketches and hand-drawn sketches by leveraging recent advances in deep learning such as batch normalization, deep residual learning, perceptual losses and stochastic optimization in combination with our new dataset. We finally demonstrate potential applications of our models in fine arts and forensic arts. In contrast to existing patch-based approaches, our deep-neural-network-based approach can be used for synthesizing photorealistic face images by inverting face sketches in the wild.


title: Electric load forecasting: Literature survey and classification of methods

abstract: A review and categorization of electric load forecasting techniques is presented. A wide range of methodologies and models for forecasting are given in the literature. These techniques are classified here into nine categories: (1) multiple regression, (2) exponential smoothing, (3) iterative reweighted least-squares, (4) adaptive load forecasting, (5) stochastic time series, (6) ARMAX models based on genetic algorithms, (7) fuzzy logic, (8) neural networks and (9) expert systems. The methodology for each category is briefly described, the advantages and disadvantages discussed, and the pertinent literature reviewed. Conclusions and comments are made on future research directions.


title: Improving RDF Data Through Association Rule Mining

abstract: Linked Open Data comprises very many and often large public data sets, which are mostly presented in the Rdf triple structure of subject, predicate, and object. However, the heterogeneity of available open data requires significant integration steps before it can be used in applications. A promising and novel technique to explore such data is the use of association rule mining. We introduce “mining configurations”, which allow us to mine Rdf data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. We present rule-based approaches for predicate suggestion, data enrichment, ontology improvement, and query relaxation. On the one hand we prevent inconsistencies in the data through predicate suggestion, enrichment with missing facts, and alignment of the corresponding ontology. On the other hand we support users to handle inconsistencies during query formulation through predicate expansion techniques. Based on these approaches, we show that association rule mining benefits the integration and usability of Rdf data.


title: Readiness Assessment of Electronic Health Records Implementation

abstract: Introduction: During the past 20 years, with huge advances in information technology and particularly, in the areas of health, various forms of electronic records have been discussed, designed or implemented. Although making health records automatically has many advantages but unfortunately in some cases, creation of an Electronic Health Record (EHR) system seems to be complicated. E-health (Electronic health) readiness assessment, as a part of the assessment before implementation is considered essential and prior to implementation. Readiness assessment aims to evaluate preparedness of each organizational component. This process can lead to the correct decision making. Therefore, identifying areas and requirements for such an assessment is so essential. Using the results of this assessment can identify deficiencies in the existing electronic health records to plan their strategies. The aim of this study was first; to show the situation of readiness assessment in EHR implementation roadmap, second, to recognize requirements associated with electronic readiness assessment and main areas of EHR readiness assessment. Results and discussion: This study reviewed the literature on EHR readiness assessment with the help of library and also searches engines available at Google. For our searches, we employed the following keywords and their combinations: readiness, assessment, implementation, Electronic Health Record (EHR), Information Technology, road map in the searching areas of title, keywords, abstract, and full text. In this study, more than 100 articles and reports were collected and 45 of them were selected based on their relevancy.


title: Radial basis function network configuration using genetic algorithms

abstract: Abstract Most training algorithms for radial basis function (RBF) neural networks start with a predetermined network structure which is chosen either by using a priori knowledge or based on previous experience. The resulting network is often insufficient or unnecessarily complicated and an appropriate network structure can only be obtained by trial and error. Training algorithms which incorporate structure selection mechanisms are usually based on local search methods and often suffer from a high probability of being trapped at a structural local minima. In the present study, genetic algorithms are proposed to automatically configure RBF networks. The network configuration is formed as a subset selection problem. The task is then to find an optimal subset of n c terms from the N t training data samples. Each network is coded as a variable length string with distinct integers and genetic operators are proposed to evolve a population of individuals. Criteria including single objective and multiobjective functions are proposed to evaluate the fitness of individual networks. Training based on a practical data set is used to demonstrate the performance of the new algorithms.


title: Efficient CNTFET-based design of quaternary logic gates and arithmetic circuits

abstract: A new voltage mode design is presented for quaternary logic using CNTFETs. This architecture with presentation of a new structure for voltage division can be applied on any four-valued logic implementation. To ensure the functionality of this promising proposed architecture, basic gates, half-adder, and full-adder are implemented using voltage divider. Moreover, a decoder is considered to enhance the parameters of half-adder such as power consumption, delay, and number of transistors. The designs are simulated using Hspice simulation tool. In comparison with prior works, our half-adder design is optimized by 75.2%, 7.8% and 77% in power consumption, delay and PDP parameters, respectively.


title: Double SiC Oxidation Protective Coating on C/C Composites Prepared by Spark Plasma Sintering

abstract: Easy oxidation of carbon limits applications of carbon-carbon composites in an oxygen-containing environment. In this study, a two-layer SiC coating was prepared on carbon-carbon composites by a Spark plasma sintering technology at 1350°C for 1 min. The coating was mainly composed of β-SiC and Si and well bonded with the substrate. The double SiC coating could effectively protect the C/C composites from oxidation at 1600°C for 120 h, and the corresponding weight loss was only 2.62%.


title: litewi: A combined term extraction and entity linking method for eliciting educational ontologies from textbooks

abstract: Major efforts have been conducted on ontology learning, that is, semiautomatic processes for the construction of domain ontologies from diverse sources of information. In the past few years, a research trend has focused on the construction of educational ontologies, that is, ontologies to be used for educational purposes. The identification of the terminology is crucial to build ontologies. Term extraction techniques allow the identification of the domain-related terms from electronic resources. This paper presents LiTeWi, a novel method that combines current unsupervised term extraction approaches for creating educational ontologies for technology supported learning systems from electronic textbooks. LiTeWi uses Wikipedia as an additional information source. Wikipedia contains more than 30 million articles covering the terminology of nearly every domain in 288 languages, which makes it an appropriate generic corpus for term extraction. Furthermore, given that its content is available in several languages, it promotes both domain and language independence. LiTeWi is aimed at being used by teachers, who usually develop their didactic material from textbooks. To evaluate its performance, LiTeWi was tuned up using a textbook on object oriented programming and then tested with two textbooks of different domains-astronomy and molecular biology.


title: Inner Direction, Other Direction and Achievement Motivation

abstract: Concepts of inner direction and of other direction [RIESMAN et al ., 1953] are related to sex differences in correlates and antecedents of achievement motivation and behavior. The da


title: Detection, classification and tracking of moving objects in a 3D environment

abstract: In this paper, we present a framework based on 3D range data to solve the problem of simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic environments. The basic idea is to use an octree based Occupancy Grid representation to model dynamic environment surrounding the vehicle and to detect moving objects based on inconsistencies between scans. The proposed method for the discrimination between moving and stationary objects without a priori knowledge of the targets is the main contribution of this paper. Moreover, the detected moving objects are classified and tracked using Global Nearest Neighbor (GNN) technique. The proposed method can be used in conjunction with any type of range sensors however we have demonstrated it using the data acquired from a Velodyne HDL-64E LIDAR sensor. The merit of our approach is that it allows for an efficient three dimensional representation of a dynamic environment, keeping in view the enormous amount of information provided by 3D range sensors.


title: How SNARE molecules mediate membrane fusion: Recent insights from molecular simulations.

abstract: SNARE molecules are the core constituents of the protein machinery that facilitate fusion of synaptic vesicles with the presynaptic plasma membrane, resulting in the release of neurotransmitter. On a molecular level, SNARE complexes seem to play a quite versatile and involved role during all stages of fusion. In addition to merely triggering fusion by forcing the opposing membranes into close proximity, SNARE complexes are now seen to also overcome subsequent fusion barriers and to actively guide the fusion reaction up to the expansion of the fusion pore. Here, we review recent advances in the understanding of SNARE-mediated membrane fusion by molecular simulations.