title: Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

abstract: Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision problem as blurs arise not only from multiple object motions but also from camera shake, scene depth variation. To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear. Moreover, recent machine learning based methods also depend on synthetic blur datasets generated under these assumptions. This makes conventional deblurring methods fail to remove blurs where blur kernel is difficult to approximate or parameterize (e.g. object motion boundaries). In this work, we propose a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources. Together, we present multi-scale loss function that mimics conventional coarse-to-fine approaches. Furthermore, we propose a new large-scale dataset that provides pairs of realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera. With the proposed model trained on this dataset, we demonstrate empirically that our method achieves the state-of-the-art performance in dynamic scene deblurring not only qualitatively, but also quantitatively.


title: Rolling Shutter Absolute Pose Problem with Known Vertical Direction

abstract: We present a solution to the rolling shutter (RS) absolute camera pose problem with known vertical direction. Our new solver, R5Pup, is an extension of the general minimal solution R6P, which uses a double linearized RS camera model initialized by the standard perspective P3P. Here, thanks to using known vertical directions, we avoid double linearization and can get the camera absolute pose directly from the RS model without the initialization by a standard P3P. Moreover, we need only five 2D-to-3D matches while R6P needed six such matches. We demonstrate in simulated and real experiments that our new R5Pup is robust, fast and a very practical method for absolute camera pose computation for modern cameras on mobile devices. We compare our R5Pup to the state of the art RS and perspective methods and demonstrate that it outperforms them when vertical direction is known in the range of accuracy available on modern mobile devices. We also demonstrate that when using R5Pup solver in structure from motion (SfM) pipelines, it is better to transform already reconstructed scenes into the standard position, rather than using hard constraints on the verticality of up vectors.


title: 3D finite difference computation on GPUs using CUDA

abstract: In this paper we describe a GPU parallelization of the 3D finite difference computation using CUDA. Data access redundancy is used as the metric to determine the optimal implementation for both the stencil-only computation, as well as the discretization of the wave equation, which is currently of great interest in seismic computing. For the larger stencils, the described approach achieves the throughput of between 2,400 to over 3,000 million of output points per second on a single Tesla 10-series GPU. This is roughly an order of magnitude higher than a 4-core Harpertown CPU running a similar code from seismic industry. Multi-GPU parallelization is also described, achieving linear scaling with GPUs by overlapping inter-GPU communication with computation.


title: Real-time geometric deformation displacement maps using programmable hardware

abstract: Striving for photorealism, texture mapping, and its more advanced variations, bump and displacement mapping, have all become fundamental tools in computer graphics. Recently, the introduction of programmable graphics hardware has enabled the employment of displacement mapping in real-time applications. While displacement mapping facilitates the actual modification of the underlying geometry, it is constrained by being an injective mapping. Further, it is also limited because it usually maps the geometry of the (low-resolution) smooth base surfaces, typically by displacing their vertices.


title: Fast and Accurate Analysis of Reflector Antennas With Phased Array Feeds Including Multiple Reflections Between Feed and Reflector

abstract: Several electrically large phased array feed (PAF) reflector systems are modeled to examine the mechanism of multiple reflections between parabolic reflectors and low-and high-scattering feeds giving rise to frequency-dependent patterns and impedance ripples. The PAF current is expanded in physics-based macro domain basis functions (CBFs), while the reflector employs the physical optics (POs) equivalent current. The reflector-feed coupling is systematically accounted for through a multiscattering Jacobi approach. An FFT expands the reflector radiated field in only a few plane waves, and the reflector PO current is computed rapidly through a near-field interpolation technique. The FEKO software is used for several cross validations, and the convergence properties of the hybrid method are studied for several representative examples showing excellent numerical performance. The measured and simulated results for a 121-element Vivaldi PAF, which is installed on the Westerbork Synthesis Radio Telescope, are in very good agreement.


title: Subdue: compression-based frequent pattern discovery in graph data

abstract: A majority of the existing algorithms which mine graph datasets target complete, frequent sub-graph discovery. We describe the graph-based data mining system Subdue which focuses on the discovery of sub-graphs which are not only frequent but also compress the graph dataset, using a heuristic algorithm. The rationale behind the use of a compression-based methodology for frequent pattern discovery is to produce a fewer number of highly interesting patterns than to generate a large number of patterns from which interesting patterns need to be identified. We perform an experimental comparison of Subdue with the graph mining systems gSpan and FSG on the Chemical Toxicity and the Chemical Compounds datasets that are provided with gSpan. We present results on the performance on the Subdue system on the Mutagenesis and the KDD 2003 Citation Graph dataset. An analysis of the results indicates that Subdue can efficiently discover best-compressing frequent patterns which are fewer in number but can be of higher interest.


title: Characterization of Uptake and Internalization of Exosomes by Bladder Cancer Cells

abstract: Bladder tumors represent a special therapeutic challenge as they have a high recurrence rate requiring repeated interventions and may progress to invasive or metastatic disease. Exosomes carry proteins implicated in bladder cancer progression and have been implicated in bladder cancer cell survival. Here, we characterized exosome uptake and internalization by human bladder cancer cells using Amnis ImageStreamX, an image cytometer. Exosomes were isolated by ultracentrifugation from bladder cancer culture conditioned supernatant, labeled with PKH-26, and analyzed on the ImageStreamX with an internal standard added to determine concentration. Exosomes were cocultured with bladder cancer cells and analyzed for internalization. Using the IDEAS software, we determined exosome uptake based on the number of PKH-26+ spots and overall PKH-26 fluorescence intensity. Using unlabeled beads of a known concentration and size, we were able to determine concentrations of exosomes isolated from bladder cancer cells. We measured exosome uptake by recipient bladder cancer cells, and we demonstrated that uptake is dose and time dependent. Finally, we found that uptake is active and specific, which can be partially blocked by heparin treatment. The characterization of cellular uptake and internalization by bladder cancer cells may shed light on the role of exosomes on bladder cancer recurrence and progression.


title: Adaptive SDN-based architecture using QoE metrics in live video streaming on Cloud Mobile Media

abstract: The increasing adoption of smart phones, the increased access to mobile broadband networks and the availability of public cloud infrastructures are aligning to the next generation of truly ubiquitous multimedia services, known as Cloud Mobile Media (CMM) services. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear in particular for live video streaming applications in order to keep a good Quality of Experience of the delivered video. Thus, to keep client's satisfaction at good levels in terms of Mean Opinion Score (MOS), we propose an adaptive Software Defined Networking (SDN)-based architecture running on CMM. We estimate MOS using an statistical method based on Factor Analysis (FA) and then the SDN controller is able to schedule different actions. The MOS estimation is based on different measured variables throughout the CMM infrastructure. Finally, we compare the accuracy of the estimated MOS against well known publicly available video quality algorithms as well as we propose an algorithm to run at the SDN controller to meet these requirements.


title: Biologically inspired speech emotion recognition

abstract: Conventional feature-based classification methods do not apply well to automatic recognition of speech emotions, mostly because the precise set of spectral and prosodic features that is required to identify the emotional state of a speaker has not been determined yet. This paper presents a method that operates directly on the speech signal, thus avoiding the problematic step of feature extraction. Furthermore, this method combines the strengths of the classical source-filter model of human speech production with those of the recently introduced liquid state machine (LSM), a biologically-inspired spiking neural network (SNN). The source and vocal tract components of the speech signal are first separated and converted into perceptually relevant spectral representations. These representations are then processed separately by two reservoirs of neurons. The output of each reservoir is reduced in dimensionality and fed to a final classifier. This method is shown to provide very good classification performance on the Berlin Database of Emotional Speech (Emo-DB). This seems a very promising framework for solving efficiently many other problems in speech processing.


title: Obsessive–compulsive disorder and gut microbiota dysregulation

abstract: Abstract Obsessive–compulsive disorder (OCD) is a debilitating disorder for which the cause is not known and treatment options are modestly beneficial. A hypothesis is presented wherein the root cause of OCD is proposed to be a dysfunction of the gut microbiome constituency resulting in a susceptibility to obsessional thinking. Both stress and antibiotics are proposed as mechanisms by which gut microbiota are altered preceding the onset of OCD symptomology. In this light, pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS) leading to episodic OCD is explained not by group A beta-hemolytic streptococcal infections, but rather by prophylactic antibiotics that are administered as treatment. Further, stressful life events known to trigger OCD, such as pregnancy, are recast to show the possibility of altering gut microbiota prior to onset of OCD symptoms. Suggested treatment for OCD would be the directed, specie-specific (re)introduction of beneficial bacteria modifying the gut microbiome, thereby ameliorating OCD symptoms. Special considerations should be contemplated when considering efficacy of treatment, particularly the unhealthy coping strategies often observed in patients with chronic OCD that may need addressing in conjunction with microbiome remediation.


title: A New Trust Reputation System for E-Commerce Applications

abstract: Robust Trust Reputation Systems (TRS) provide a most trustful reputation score for a specific product or service so as to support relying parties taking the right decision while interacting with an e-commerce application. Thus, TRS must rely on an appropriate architecture and suitable algorithms that are able to improve the selection, storage, generation and classification of textual feedbacks. In this work, we propose a new architecture for TRS in e-commerce applications. In fact, we propose an intelligent layer which displays to each feedback provider, who has already given his recommendation on a product, a collection of prefabricated feedbacks related to the same product. Our main contribution in this paper is a Reputation algorithm which studies the user's attitude toward this selection of prefabricated feedbacks. As a result of this study, the reputation algorithm generates better trust degree of the user, trust degree of the feedback and a better global reputation score of the product.


title: The Emergence of Flexible Spatial Strategies in Young Children.

abstract: The development of spatial navigation in children depends not only on remembering which landmarks lead to a goal location but also on developing strategies to deal with changes in the environment or imperfections in memory. Using cue combination methods, the authors examined 3- and 4-year-old children’s memory for different types of spatial cues and the spatial strategies that they use when those cues are in conflict. Children were taught to search for a toy in 1 of 4 possible hiding locations. Children were then tested on transformations of the array of locations. The transformations dissociated the different types of cues by putting them in conflict with one another. The authors were especially interested in the use of a majority strategy, by which children choose to search in the location indicated by the greatest number of cue types rather than relying on a preferred cue type. Children’s memory for spatial cues and their strategies varied both by age and by experimental setup. In Experiment 1, both 3and 4-year-old children preferred to use the distinct landmarks coincident with the hiding locations over any other types of cues and showed no use of a majority strategy. However, in Experiment 2, when the coincident landmarks were moved adjacent to the hiding locations, both 3- and 4-year-old children preferred to search in the position of the hiding location relative to the array. Furthermore, 4-year-old children in Experiment 2 showed better memory for individual types of cues and the emergence of a majority strategy.


title: Two New CMOS Schmitt Trigger Circuits based on Current Sink and Pseudo Logic Structures

abstract: At the outset, a couple of new CMOS Schmitt triggers are introduced using Current Sink and Pseudo Logic structures and their characteristics are evaluated both analytically and numerically. The hysteresis curves of the proposed Schmitt triggers are also presented. The Schmitt triggers enlisted are most sought after for low-voltage and high-speed applications. The performances of the proposed new Schmitt triggers are examined using PSPICE and the model parameters of a 0.18 μm CMOS process. Current Sink and Pseudo Logic structures are suitable for VLSI implementation. Simulation results are presented.


title: Expressive expression mapping with ratio images

abstract: Facial expressions exhibit not only facial feature motions, but also subtle changes in illumination and appearance (e.g., facial creases and wrinkles). These details are important visual cues, but they are difficult to synthesize. Traditional expression mapping techniques consider feature motions while the details in illumination changes are ignored. In this paper, we present a novel technique for facial expression mapping. We capture the illumination change of one person's expression in what we call an expression ratio image (ERI). Together with geometric warping, we map an ERI to any other person's face image to generate more expressive facial expressions.


title: Data privacy through optimal k-anonymization

abstract: Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem.


News from OPUS — A Collection of Multilingual Parallel Corpora with Tools and Interfaces


title: Markov Logic Networks for Natural Language Question Answering

abstract: Our goal is to answer elementary-level science questions using knowledge extracted automatically from science textbooks, expressed in a subset of first-order logic. Given the incomplete and noisy nature of these automatically extracted rules, Markov Logic Networks (MLNs) seem a natural model to use, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. In the first, we simply use the extracted science rules directly as MLN clauses. Unlike typical MLN applications, our domain has long and complex rules, leading to an unmanageable number of groundings. We exploit the structure present in hard constraints to improve tractability, but the formulation remains ineffective. In the second approach, we instead interpret science rules as describing prototypical entities, thus mapping rules directly to grounded MLN assertions, whose constants are then clustered using existing entity resolution methods. This drastically simplifies the network, but still suffers from brittleness. Finally, our third approach, called Praline, uses MLNs to align the lexical elements as well as define and control how inference should be performed in this task. Our experiments, demonstrating a 15\% accuracy boost and a 10x reduction in runtime, suggest that the flexibility and different inference semantics of Praline are a better fit for the natural language question answering task.


title: cC-GAN: A Robust Transfer-Learning Framework for HEp-2 Specimen Image Segmentation

abstract: Human epithelial type 2 (HEp-2) cell images play an important role for the detection of antinuclear autoantibodies in autoimmune diseases. As the HEp-2 cell has hundreds of different patterns, none of currently available HEp-2 datasets contain all of the types. Therefore, existing automatic processing systems for HEp-2 cells, e.g., cell segmentation and classification, needs to be transferred between different data sets. However, the performances of transferred system often dramatically decrease, especially when transferring supervised-approaches, e.g., deep learning network, from large dataset to the small but similar ones. In this paper, a novel transfer-learning framework using generative adversarial networks (cC-GAN) is proposed for robust segmentation of different HEp-2 datasets. The proposed cC-GAN tries to solve the overfitting problem of most deep learning networks and improves their transfer-capacity. An improved U-net, so-called Residual U-net (RU-net), is developed to work as the generator for cC-GAN model. The cC-GAN was first trained and tested using I3A dataset and then directly evaluated using MIVIA dataset, which is much smaller than I3A. The segmentation result demonstrates the excellent transferring-capacity of our cC-GAN framework, i.e., a new state-of-the-art segmentation accuracy of 75.27% was achieved on MIVIA without finetuning.


title: Convolutional Recurrent Deep Learning Model for Sentence Classification

abstract: As the amount of unstructured text data that humanity produces overall and on the Internet grows, so does the need to intelligently to process it and extract different types of knowledge from it. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to natural language processing systems with comparative, remarkable results. The CNN is a noble approach to extract higher level features that are invariant to local translation. However, it requires stacking multiple convolutional layers in order to capture long-term dependencies, due to the locality of the convolutional and pooling layers. In this paper, we describe a joint CNN and RNN framework to overcome this problem. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then, the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via long-short-term memory. Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review data set with 93.3% accuracy, and the Stanford Sentiment Treebank data set with 48.8% fine-grained and 89.2% binary accuracy, respectively. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer as a substitute for the pooling layer. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance.


title: Metamorphic Model-Based Testing of Autonomous Systems

abstract: Testing becomes difficult when we cannot easily determine whether or not the system under test delivers the correct result. Autonomous systems are a case in point because it is difficult to determine whether a safety-critical autonomous system's behavior meets its specifications. To address the problem of testing autonomous drones, we have developed a framework for automated testing of a simulated autonomous drone system using metamorphic testing principles combined with model-based testing. Based on the results from using the framework to test the drone in the simulator using obstacles that do not move during flight, we have determined that this is a cost beneficial solution allowing for comprehensive testing without having to develop complex testing infrastructure to determine detailed test oracles. Our test cases are automatically generated from a set of testing models where each model encodes a certain scenario that can be varied according to metamorphic principles.


title: Foreign Subtitles Help but Native-Language Subtitles Harm Foreign Speech Perception

abstract: Understanding foreign speech is difficult, in part because of unusual mappings between sounds and words. It is known that listeners in their native language can use lexical knowledge (about how words ought to sound) to learn how to interpret unusual speech-sounds. We therefore investigated whether subtitles, which provide lexical information, support perceptual learning about foreign speech. Dutch participants, unfamiliar with Scottish and Australian regional accents of English, watched Scottish or Australian English videos with Dutch, English or no subtitles, and then repeated audio fragments of both accents. Repetition of novel fragments was worse after Dutch-subtitle exposure but better after English-subtitle exposure. Native-language subtitles appear to create lexical interference, but foreign-language subtitles assist speech learning by indicating which words (and hence sounds) are being spoken.


title: An introduction to NDN and its software architecture

abstract: The NDN project investigates Jacobson's proposed evolution from today's host-centric network architecture (IP) to a data-centric network architecture (NDN). This conceptually simple shift has far-reaching implications in how we design, develop, deploy and use networks and applications. The NDN design and development has attracted significant attention from the networking community. To facilitate broader participation in addressing NDN research and development challenges, this tutorial will describe the vision of this new architecture and its basic components and operations.


title: Generic Taxonomy of Social Engineering Attack

abstract: Social engineering is a type of attack that allows unauthorized access to a system to achieve specific objective. Commonly, the purpose is to obtain information for social engineers. Some successful social engineering attacks get victims’ information via human based retrieval approach, example technique terms as dumpster diving or shoulder surfing attack to get access to password. Alternatively, victims’ information also can be stolen using technical-based method such as from pop-up windows, email or web sites to get the password or other sensitive information. This research performed a preliminary analysis on social engineering attack taxonomy that emphasized on types of technical-based social engineering attack. Results from the analysis become a guideline in proposing a new generic taxonomy of Social Engineering Attack (SEA).


title: PyramidBox: A Context-assisted Single Shot Face Detector

abstract: Face detection has been well studied for many years and one of remaining challenges is to detect small, blurred and partially occluded faces in uncontrolled environment. This paper proposes a novel context-assisted single shot face detector, named \emph{PyramidBox} to handle the hard face detection problem. Observing the importance of the context, we improve the utilization of contextual information in the following three aspects. First, we design a novel context anchor to supervise high-level contextual feature learning by a semi-supervised method, which we call it PyramidAnchors. Second, we propose the Low-level Feature Pyramid Network to combine adequate high-level context semantic feature and Low-level facial feature together, which also allows the PyramidBox to predict faces of all scales in a single shot. Third, we introduce a context-sensitive structure to increase the capacity of prediction network to improve the final accuracy of output. In addition, we use the method of Data-anchor-sampling to augment the training samples across different scales, which increases the diversity of training data for smaller faces. By exploiting the value of context, PyramidBox achieves superior performance among the state-of-the-art over the two common face detection benchmarks, FDDB and WIDER FACE. Our code is available in PaddlePaddle: \href{https://github.com/PaddlePaddle/models/tree/develop/fluid/face_detection}{\url{https://github.com/PaddlePaddle/models/tree/develop/fluid/face_detection}}.


title: Enhanced segmentation and complex-sclera features for human recognition with unconstrained visible-wavelength imaging

abstract: Sclera recognition has received attention recently due to the distinctive features extracted from blood vessels within the sclera. However, uncontrolled human pose, multiple iris gaze directions, different eye image capturing distance and variation in lighting conditions lead to many challenges in sclera recognition. Therefore, we propose an enhanced system for sclera recognition with visible-wavelength eye images captured in unconstrained conditions. The proposed segmentation algorithm fuses multiple color space skin classifiers to overcome the noise factors introduced through acquiring sclera images such as motion, blur, gaze and rotation. We also propose a blood vessel enhancement and feature extraction method which we denote as complex-sclera features to increase the adaptability to noisy blood vessel deformations. The proposed system is evaluated using UBIRIS.v1, UBIRIS.v2 and UTIRIS databases and the results are promising in terms of accuracy and suitability in real-time applications due to low processing times.


title: Density Based Traffic Signal System

abstract: The project is designed to develop a density based dynamic traffic signal system. The signal timing changes automatically on sensing the traffic density at the junction. Traffic congestion is a severe problem in many major cities across the world and it has become a nightmare for the commuters in these cities. Conventional traffic light system is based on fixed time concept allotted to each side of the junction which cannot be varied as per varying traffic density [1]. Junction timings allotted are fixed. Sometimes higher traffic density at one side of the junction demands longer green time as compared to standard allotted time. The image captured in the traffic signal is processed and converted into grayscale image then its threshold is calculated based on which the contour has been drawn in order to calculate the number of vehicles present in the image. After calculating the number of vehicles we will came to know in which side the density is high based on which signals will be allotted for a particular side. Raspberry pi is used as a microcontroller which provides the signal timing based on the traffic density.


title: A Muddle of Models of Motivation for Using Peer-to-Peer Economy Systems

abstract: This paper reports on a study of motivations for the use of peer-to-peer or sharing economy services. We interviewed both users and providers of these systems to obtain different perspectives and to determine if providers are matching their system designs to the most important drivers of use. We found that the motivational models implicit in providers' explanations of their systems' designs do not match well with what really seems to motivate users. Providers place great emphasis on idealistic motivations such as creating a better community and increasing sustainability. Users, on the other hand are looking for services that provide what they need whilst increasing value and convenience. We discuss the divergent models of providers and users and offer design implications for peer system providers.


title: Contactless Slipring System based on rotating magnetic field principle for rotary applications

abstract: This paper presents a Contactless Slipring System (CSS) based on rotating magnetic field theory for transferring electrical power to a rotatable shaft. Besides the analytical expressions, a 3-D FEM (Finite Element Model) is developed for simulation study, and a practical circuit is built for system verification and performance evaluation. A method of canceling the rotational torque associated with the rotating magnetic field is proposed and practically verified by testing the prototype system. It has been shown that the proposed system can transfer power up to about 950W to the load at the secondary circuit with a practically achievable quality factor Qs of 5. The FEM simulation and practical results show that unlike the conventional rotating transformer solution, the added magnetic shield increases the power transfer capability by 20%. The uncompensated power receivable at the secondary is 157 VA and 198 VA for the cases without and with aluminum shield respectively.


title: Deep Learning Face Representation from Predicting 10,000 Classes

abstract: This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set. Moreover, the generalization capability of DeepID increases as more face classes are to be predicted at training. DeepID features are taken from the last hidden layer neuron activations of deep convolutional networks (ConvNets). When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons. The proposed features are extracted from various face regions to form complementary and over-complete representations. Any state-of-the-art classifiers can be learned based on these high-level representations for face verification. 97:45% verification accuracy on LFW is achieved with only weakly aligned faces.


title: Convolutional Feature Masking for Joint Object and Stuff Segmentation

abstract: The topic of semantic segmentation has witnessed considerable progress due to the powerful features learned by convolutional neural networks (CNNs). The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. This strategy introduces artificial boundaries on the images and may impact the quality of the extracted features. Besides, the operations on the raw image domain require to compute thousands of networks on a single image, which is time-consuming. In this paper, we propose to exploit shape information via masking convolutional features. The proposal segments (e.g., super-pixels) are treated as masks on the convolutional feature maps. The CNN features of segments are directly masked out from these maps and used to train classifiers for recognition. We further propose a joint method to handle objects and"stuff"(e.g., grass, sky, water) in the same framework. State-of-the-art results are demonstrated on benchmarks of PASCAL VOC and new PASCAL-CONTEXT, with a compelling computational speed.


title: Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensors

abstract: A software tool that facilitates the development of image reconstruction algorithms, and the design of optimal capacitance sensors for a capacitance-based 12-electrode tomographic flow imaging system are described. The core of this software tool is the finite element (FE) model of the sensor, which is implemented in OCCAM-2 language and run on the Inmos T800 transputers. Using the system model, the in-depth study of the capacitance sensing fields and the generation of flow model data are made possible, which assists, in a systematic approach, the design of an improved image-reconstruction algorithm. This algorithm is implemented on a network of trans- puters to achieve a real-time performance. It is found that the selection of the geometric param- eters of a 12-electrode sensor has significant effects on the sensitivity distributions of the capacitance fields and on the linearity of the capacitance data. As a consequence, the fidelity of the reconstructed images are affected. Optimal sensor designs can, therefore, be provided, by accommodating these effects.


ERP SYSTEM IMPLEMENTATION ISSUES AND CHALLENGES IN DEVELOPING NATIONS


title: Multi task sequence learning for depression scale prediction from video

abstract: Depression is a typical mood disorder, which affects people in mental and even physical problems. People who suffer depression always behave abnormal in visual behavior and the voice. In this paper, an audio visual based multimodal depression scale prediction system is proposed. Firstly, features are extracted from video and audio are fused in feature level to represent the audio visual behavior. Secondly, long short memory recurrent neural network (LSTM-RNN) is utilized to encode the dynamic temporal information of the abnormal audio visual behavior. Thirdly, emotion information is utilized by multi-task learning to boost the performance further. The proposed approach is evaluated on the Audio-Visual Emotion Challenge (AVEC2014) dataset. Experiments results show the dimensional emotion recognition helps to depression scale prediction.


Item-Based Top-N Recommendation Algorithms


title: Sex Pheromones and Behavioral Biology of the Coniferophagous Choristoneura

abstract: Members of the coniferophagous Choristoneura genus can be found in all conifer-forested regions of North America. The distribution of its species is reasonably well established and most members of the group are taxonomically distinct. There has been one recorded case of natural hybridization between C. occidentalis and C. retiniana (68). In areas of sympatry among other species, the mechanisms of reproductive isolation are not well understood and little data is available to assume lack of hybridization. Pheromone differences may, in fact, be an important factor in species isolation (24). The two most economically important and widely distributed species, C. fumiferana, the spruce budworm, and C. occidentalis, the western spruce budworm, are believed to be geographically isolated and are the most in tensively studied of all the budworms. Spruce budworm, the most extensive in range of all the bud worms , exhibits population oscillations (35, 49 ) of very large amplitudes, with periods of extremely high densities when defoliation and tree mortality can occur (42) over extensive areas. Several reviews have summarized the biology (56) and the sex-pheromone chemistry (59, 60) of the coniferophagous Choristoneura. The pheromone mediated behavior of both C. fumiferana (3, 60) and C. occidentalis (2; J. Sweeney & J. McLean, unpublished data) has been studied in some detail, and this research may help decode the communication systems in the other


Designing Interfaces with Social Presence: Using Vividness and Extraversion to Create Social Recommendation Agents


title: Teacher Concerns During Initial Implementation of a One-to-One Laptop Initiative at the Middle School Level

abstract: AbstractMany schools are initiating projects that place laptop computers into the hands of each student and teacher in the school. These projects entail a great deal of planning and investment by all involved. The teachers in these schools are faced with significant challenges as they prepare for teaching in classrooms where every student has a computer. Using the Concerns-Based Adoption Model of change, this study investigated the concerns of teachers in the early stages of a one-to-one laptop initiative. The results of the study indicate that teachers fall into two relatively well-defined categories in terms of their concerns regarding the innovation. The majority of teachers have genuine concerns about how the introduction of laptop computers into the school environment will impact them personally. A lesser number have concerns about how they will be able to best use the laptops to meet the needs of the students. Implications for professional development include differentiating training based on teache...


title: Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data

abstract: Over the past decade, machine learning techniques and in particular predictive modeling and pattern recognition in biomedical sciences, from drug delivery systems to medical imaging, have become one of the most important methods of assisting researchers in gaining a deeper understanding of issues in their entirety and solving complex medical problems. Deep learning is a powerful machine learning algorithm in classification that extracts low-to high-level features. In this paper, we employ a convolutional neural network to distinguish an Alzheimers brain from a normal, healthy brain. The importance of classifying this type of medical data lies in its potential to develop a predictive model or system in order to recognize the symptoms of Alzheimers disease when compared with normal subjects and to estimate the stages of the disease. Classification of clinical data for medical conditions such as Alzheimers disease has always been challenging, and the most problematic aspect has always been selecting the strongest discriminative features. Using the Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimers subjects from normal controls, where the accuracy of testing data reached 96.85%. This experiment suggests that the shift and scale invariant features extracted by CNN followed by deep learning classification represents the most powerful method of distinguishing clinical data from healthy data in fMRI. This approach also allows for expansion of the methodology to predict more complicated systems.


title: Skip-Thought Vectors

abstract: We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice.


title: An Assessment of QR Code as a User Interface Enabler for Mobile Payment Apps on Smartphones

abstract: Mobile enabled payment technologies have seen an upsurge with advancement in smartphone technology. Several companies, including Google, Paypal, Amazon, Facebook have launched mobile payment solutions. Easy to adopt user interfaces play a pivotal role in wider adoption of such solutions. Typically, existing mobile payment solutions are designed for the user to type in the details of a transaction, like recipient id, amount, authorization code. Use of a pictorial mode to capture the transaction information can make the user interaction easier and faster during a transaction. We use a popular pictorial encoding format, called Quick-Response (QR) code, to auto-fill necessary details during a transaction, thus relieving the user from typing in the details. In this study using 48 participants from different literacy levels, we have compared the use of standard typing based system against inputs using pictorial format for enabling mobile payments. Our study indicates the benefits of QR code in designing UIs for mobile payment applications.


title: Attend to You: Personalized Image Captioning with Context Sequence Memory Networks

abstract: We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the users active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation tasks: hashtag prediction and post generation, on our newly collected Instagram dataset, consisting of 1.1M posts from 6.3K users. We propose a novel captioning model named Context Sequence Memory Network (CSMN). Its unique updates over previous memory network models include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information without suffering from the vanishing gradient problem, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the effectiveness of the three novel features of CSMN and its performance enhancement for personalized image captioning over state-of-the-art captioning models.


title: On dexterity and dexterous manipulation

abstract: This paper presents a high-level discussion of dexterity in robotic systems, focusing particularly on manipulation and hands. While it is generally accepted in the robotics community that dexterity is desirable and that end effectors with in-hand manipulation capabilities should be developed, there has been little, if any, formal description of why this is needed, particularly given the increased design and control complexity required. This discussion will overview various definitions of dexterity used in the literature and highlight issues related to specific metrics and quantitative analysis. It will also present arguments regarding why hand dexterity is desirable or necessary, particularly in contrast to the capabilities of a kinematically redundant arm with a simple grasper. Finally, we overview and illustrate the various classes of in-hand manipulation, and review a number of dexterous manipulators that have been previously developed. We believe this work will help to revitalize the dialogue on dexterity in the manipulation community and lead to further formalization of the concepts discussed here.


title: Parallelized stochastic gradient descent

abstract: With the increase in available data parallel machine learning has become an increasingly pressing problem. In this paper we present the first parallel stochastic gradient descent algorithm including a detailed analysis and experimental evidence. Unlike prior work on parallel optimization algorithms [5, 7] our variant comes with parallel acceleration guarantees and it poses no overly tight latency constraints, which might only be available in the multicore setting. Our analysis introduces a novel proof technique — contractive mappings to quantify the speed of convergence of parameter distributions to their asymptotic limits. As a side effect this answers the question of how quickly stochastic gradient descent algorithms reach the asymptotically normal regime [1, 8].


title: Effectiveness and Adoption of a Drawing-to-Learn Study Tool for Recall and Problem Solving: Minute Sketches with Folded Lists

abstract: Drawing by learners can be an effective way to develop memory and generate visual models for higher-order skills in biology, but students are often reluctant to adopt drawing as a study method. We designed a nonclassroom intervention that instructed introductory biology college students in a drawing method, minute sketches in folded lists (MSFL), and allowed them to self-assess their recall and problem solving, first in a simple recall task involving non-European alphabets and later using unfamiliar biology content. In two preliminary ex situ experiments, students had greater recall on the simple learning task, non-European alphabets with associated phonetic sounds, using MSFL in comparison with a preferred method, visual review (VR). In the intervention, students studying using MSFL and VR had ∼50-80% greater recall of content studied with MSFL and, in a subset of trials, better performance on problem-solving tasks on biology content. Eight months after beginning the intervention, participants had shifted self-reported use of drawing from 2% to 20% of study time. For a small subset of participants, MSFL had become a preferred study method, and 70% of participants reported continued use of MSFL. This brief, low-cost intervention resulted in enduring changes in study behavior.


A Principled Comparative Analysis of Dimensionality Reduction Techniques on Protein Structure Decoy Data


title: Recovering second-order statistics from compressive measurements

abstract: This paper focuses on the reconstruction of second order statistics of signals under a compressive sensing framework, which can be useful in many detection problems. More specifically, the focus is on general cyclostationary signals that are compressed using random linear projections, and using those compressive measurements, the cyclic power spectrum is retrieved. Subsequently, this can for instance be used to detect the occupation of specific frequency bands, which has applications in cognitive radio. Surprisingly, if the span of the random linear projections is larger than the period of the cyclostationary signals, the cyclic power spectrum can be recovered without putting any sparsity constraints on it, which allows for simple least squares reconstruction methods. This result indicates that significant compression can be realized by directly reconstructing the second-order statistics rather than the random signals themselves.


title: Acoustic window planning for ultrasound acquisition

abstract: Abstract ::: Autonomous robotic ultrasound has recently gained considerable interest, especially for collaborative applications. Existing methods for acquisition trajectory planning are solely based on geometrical considerations, such as the pose of the transducer with respect to the patient surface.


title: Vanquishing the XCB Question: The Methodological Discovery of the Last Shortest Single Axiom for the Equivalential Calculus

abstract: With the inclusion of an effective methodology, this article answers in detail a question that, for a quarter of a century, remained open despite intense study by various researchers. Is the formula XCB = e(x,e(e(e(x,y),e(z,y)),z)) a single axiom for the classical equivalential calculus when the rules of inference consist of detachment (modus ponens) and substitution? Where the function e represents equivalence, this calculus can be axiomatized quite naturally with the formulas e(x,x), e(e(x,y),e(y,x)), and e(e(x,y),e(e(y,z),e(x,z))), which correspond to reflexivity, symmetry, and transitivity, respectively. (We note that e(x,x) is dependent on the other two axioms.) Heretofore, thirteen shortest single axioms for classical equivalence of length eleven had been discovered, and XCB was the only remaining formula of that length whose status was undetermined. To show that XCB is indeed such a single axiom, we focus on the rule of condensed detachment, a rule that captures detachment together with an appropriately general, but restricted, form of substitution. The proof we present in this paper consists of twenty-five applications of condensed detachment, completing with the deduction of transitivity followed by a deduction of symmetry. We also discuss some factors that may explain in part why XCB resisted relinquishing its treasure for so long. Our approach relied on diverse strategies applied by the automated reasoning program OTTER. Thus ends the search for shortest single axioms for the equivalential calculus.


title: FingerReader: A Wearable Device to Explore Printed Text on the Go

abstract: Accessing printed text in a mobile context is a major challenge for the blind. A preliminary study with blind people reveals numerous difficulties with existing state-of-the-art technologies including problems with alignment, focus, accuracy, mobility and efficiency. In this paper, we present a finger-worn device, FingerReader, that assists blind users with reading printed text on the go. We introduce a novel computer vision algorithm for local-sequential text scanning that enables reading single lines, blocks of text or skimming the text with complementary, multimodal feedback. This system is implemented in a small finger-worn form factor, that enables a more manageable eyes-free operation with trivial setup. We offer findings from three studies performed to determine the usability of the FingerReader.


title: Towards Topic Trend Prediction on a Topic Evolution Model with Social Connection

abstract: Hot topics are usually those breaking news discussed most at online forums, especially microblogging systems, such as twitter, which helps to learn user concentration and public opinion. This paper focuses on the problem of predicting emerging hot topics. Previous prediction models usually focus on building the content profile to discover the hot topics, they may neglect the social network function or overlook the keyword feature of the post. In this paper, we address this problem by introducing a combined model using the content and the connection information. We define the concept of topic hotness, introduce the algorithm calculating the hotness with content based hotness and connection based hotness, and finally we predict those emerging hot topics by the hotness evolution model.


6 . 858 : Hacking Bluetooth


title: Optimizing RPL Objective Function for Mobile Low-Power Wireless Networks

abstract: Supporting mobility in wireless sensor networks is one of the major requirements for future Internet of Things (IoT) applications. This work focuses on optimizing the objective function of Routing Protocol for Low-power and Lossy Networks (RPL) in mobile applications. RPL routing is the most common standard routing protocol designed for IoT applications. We optimized RPL objective function by combining several RPL parameters, such as (i) Expected Transmission Count (ETX), (ii) number of hops, and (iii) average Received Signal Strength Indicator (RSSI) as inputs in a fuzzy logic model. These parameters are more influenced in mobile applications. We applied the fuzzy decision to the mRPL (a hand-off enabled RPL mechanism). We fine-tuned the weighting scheme by running extensive simulations to achieve reliable data communication. We found that the fuzzy-based hand-off approach provides high reliability by successfully delivering nearly 100% of data packets, while achieving a very short hand-off delay.


title: A four-electrode low frequency impedance spectroscopy measurement system using the AD5933 measurement chip

abstract: This paper presents the design of a four electrode impedance measurement circuit dedicated to bioimpedance embedded applications. It extends the use of the AD5933 measurement chip to allow it to operate in a four electrode configuration in order to limit the effects of the parasitic impedances between the medium under test and the electrodes. The circuit has shown a good measurement accuracy on various test circuits. In association with a four microband electrode system it has been successfully used to characterize small physiological samples (50 μl) with conductivities ranging from 0.14 to 1.2 S m−1. It can be used as an alternative bioimpedance measurement approach for embedded applications operating in the four electrode configuration.


title: Derivation of Human Trophoblast Stem Cells

abstract: Summary Trophoblast cells play an essential role in the interactions between the fetus and mother. Mouse trophoblast stem (TS) cells have been derived and used as the best in vitro model for molecular and functional analysis of mouse trophoblast lineages, but attempts to derive human TS cells have so far been unsuccessful. Here we show that activation of Wingless/Integrated (Wnt) and EGF and inhibition of TGF-β, histone deacetylase (HDAC), and Rho-associated protein kinase (ROCK) enable long-term culture of human villous cytotrophoblast (CT) cells. The resulting cell lines have the capacity to give rise to the three major trophoblast lineages, which show transcriptomes similar to those of the corresponding primary trophoblast cells. Importantly, equivalent cell lines can be derived from human blastocysts. Our data strongly suggest that the CT- and blastocyst-derived cell lines are human TS cells, which will provide a powerful tool to study human trophoblast development and function.


title: Robot manipulator modeling in Matlab-SimMechanics with PD control and online gravity compensation

abstract: The objective of this article is to present a method to model the mechanics of robot manipulators. A complete description of the procedure to model and control a two-link planar robot arm is detailed and simulated using Matlab/Simulink from the generation of a mechanical model in SimMechanics Toolbox. One of the simplest position controllers for robot manipulators is the PD control with desired gravity compensation. The implementation of this method was verified on this model. The results of simulation show that SimMechanics can be used to model the mechanics of robot and the better performance can be obtained with the online gravity compensation method as compared to the case of constant gravity compensation method.


Visualizing Amazon . com ’ s Purchase Circles


title: The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

abstract: Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.


title: A CMOS low-power low-offset and high-speed fully dynamic latched comparator

abstract: This paper presents a novel dynamic latched comparator that demonstrates lower offset voltage and higher load drivability than the conventional dynamic latched comparators. With two additional inverters inserted between the input- and output-stage of the conventional double-tail dynamic comparator, the gain preceding the regenerative latch stage is improved. The complementary version of the regenerative latch stage, which provides larger output drive current than the conventional one at a limited area, is implemented. The proposed circuit is designed using 90nm CMOS technology and 1V power supply voltage, and it demonstrates up to 19% less offset voltage and 62% less sensitivity of the delay to the input voltage difference (17ps/decade) than the conventional double-tail latched comparator at approximately the same area and power consumption.


title: Information extraction from full text scientific articles: Where are the keywords?

abstract: BackgroundTo date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant.ResultsIn this work we addressed those questions showing that the keyword content of the different sections of a standard scientific article (abstract, introduction, methods, results, and discussion) is very heterogeneous.ConclusionsAlthough the abstract contains the best ratio of keywords per total of words, other sections of the article may be a better source of biologically relevant data.


title: Assigning bug reports using a vocabulary-based expertise model of developers

abstract: For popular software systems, the number of daily submitted bug reports is high. Triaging these incoming reports is a time consuming task. Part of the bug triage is the assignment of a report to a developer with the appropriate expertise. In this paper, we present an approach to automatically suggest developers who have the appropriate expertise for handling a bug report. We model developer expertise using the vocabulary found in their source code contributions and compare this vocabulary to the vocabulary of bug reports. We evaluate our approach by comparing the suggested experts to the persons who eventually worked on the bug. Using eight years of Eclipse development as a case study, we achieve 33.6% top-1 precision and 71.0% top-10 recall.


title: Convolutional Neural Networks for No-Reference Image Quality Assessment

abstract: In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature.


title: Reinforced Co-Training

abstract: Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted labels on the unlabeled data and select samples based on prediction confidence to augment the training. However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space. In this paper, we propose a novel method, Reinforced Co-Training, to select high-quality unlabeled samples to better co-train on. More specifically, our approach uses Q-learning to learn a data selection policy with a small labeled dataset, and then exploits this policy to train the co-training classifiers automatically. Experimental results on clickbait detection and generic text classification tasks demonstrate that our proposed method can obtain more accurate text classification results.


title: Audio Super Resolution using Neural Networks

abstract: We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks. Our model is trained on pairs of low and high-quality audio examples; at test-time, it predicts missing samples within a low-resolution signal in an interpolation process similar to image super-resolution. Our method is simple and does not involve specialized audio processing techniques; in our experiments, it outperforms baselines on standard speech and music benchmarks at upscaling ratios of 2x, 4x, and 6x. The method has practical applications in telephony, compression, and text-to-speech generation; it demonstrates the effectiveness of feed-forward convolutional architectures on an audio generation task.


title: Electromyographic Analysis of the Rotator Cuff and Deltoid Musculature During Common Shoulder External Rotation Exercises

abstract: Study Design Prospective single-group repeated-measures design. Objectives To quantify electromyographic (EMG) muscle activity of the infraspinatus, teres minor, supraspinatus, posterior deltoid, and middle deltoid during exercises commonly used to strengthen the shoulder external rotators. Background Exercises to strengthen the external rotators are commonly prescribed in rehabilitation, but the amount of EMG activity of the infraspinatus, teres minor, supraspinatus, and deltoid during these exercises has not been thoroughly studied to determine which exercises would be most effective to achieve strength gains. Methods and Measures EMG measured using intramuscular electrodes were analyzed in 10 healthy subjects during 7 shoulder exercises: prone horizontal abduction at 100° of abduction and full external rotation (ER), prone ER at 90° of abduction, standing ER at 90° of abduction, standing ER in the scapular plane (45° abduction, 30° horizontal adduction), standing ER at 0° of abduction, standing ER at 0...


title: Design and Validation of a Torque-Controllable Knee Exoskeleton for Sit-to-Stand Assistance

abstract: Individuals with poststroke hemiparesis often exhibit mobility deficits, particularly during tasks requiring high lower limb torques. The sit-to-stand transition is consistently marked by asymmetrical weight-bearing between the paretic and unaffected legs. One way to improve characteristics of stroke sit-to-stand may be to provide assistive knee extension torque with a powered exoskeleton. To perform research on the biomechanical effects of assisting sit-to-stand, a unilateral powered knee exoskeleton is required, which can accurately control torque. This paper introduces a novel series elastic actuator capable of producing the full torques and speeds required for sit-to-stand (80 Nm, 3 rad/s). It utilizes a unique transmission configuration with a series fiberglass beam spring that improves torque-control and reduces output impedance. The actuator is incorporated into a unilateral orthosis and a high-level sit-to-stand controller is implemented. A small validation study with three able-bodied subjects performing sit-to-stand is presented, demonstrating the ability to appropriately provide assistance.


title: A Simple Model for Calculating Transformer Hot-Spot Temperature

abstract: A simple model for calculating the hot-spot temperature is introduced. The model is based on the hot-spot to ambient gradient. The model considers the changes of the oil viscosity and winding losses with temperature. The results are compared with temperatures calculated by IEEE Annex G method and measured results at varying load for the following transformer units: 250-MVA ONAF, 400-MVA ONAF, and 605-MVA OFAF.


title: Answer Selection in Community Question Answering via Attentive Neural Networks

abstract: Answer selection in community question answering (cQA) is a challenging task in natural language processing. The difficulty lies in that it not only needs the consideration of semantic matching between question answer pairs but also requires a serious modeling of contextual factors. In this letter, we propose an attentive deep neural network architecture so as to learn the deterministic information for answer selection. The architecture can support various input formats through the organization of convolutional neural networks, attention-based long short-term memory, and conditional random fields. Experiments are carried out on the SemEval-2015 cQA dataset. We attain 58.35% on macroaveraged F 1 , which outperforms the Top-1 system in the shared task by 1.16% and improves the state-of-the-art deep-neural-network-based method by 2.21%.


title: Finding the face in the crowd : An anger superiority effect

abstract: Facial gestures have been given an increasingly critical role in models of emotion. The biological significance of interindividual transmission of emotional signals is a pivotal assumption for placing the face in a central position in these models. This assumption invited a logical corollary, examined in this article: Face-processing should be highly efficient. Three experiments documented an asymmetry in the processing of emotionally discrepant faces embedded in crowds. The results suggested that threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.


title: The elements of computer credibility

abstract: Given the importance of credibility in computing products, theresearch on computer credibility is relatively small. To enhanceknowledge about computers and credibility, we define key termsrelating to computer credibility, synthesize the literature in thisdomain, and propose three new conceptual frameworks for betterunderstanding the elements of computer credibility. To promotefurther research, we then offer two perspectives on what computerusers evaluate when assessing credibility. We conclude bypresenting a set of credibility-related terms that can serve infuture research and evaluation endeavors.


title: A New Metrics for Countries' Fitness and Products' Complexity

abstract: Classical economic theories prescribe specialization of countries industrial production. Inspection of the country databases of exported products shows that this is not the case: successful countries are extremely diversified, in analogy with biosystems evolving in a competitive dynamical environment. The challenge is assessing quantitatively the non-monetary competitive advantage of diversification which represents the hidden potential for development and growth. Here we develop a new statistical approach based on coupled non-linear maps, whose fixed point defines a new metrics for the country Fitness and product Complexity. We show that a non-linear iteration is necessary to bound the complexity of products by the fitness of the less competitive countries exporting them. We show that, given the paradigm of economic complexity, the correct and simplest approach to measure the competitiveness of countries is the one presented in this work. Furthermore our metrics appears to be economically well-grounded.


title: Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks

abstract: Age estimation from speech has recently received increased interest as it is useful for many applications such as user-profiling, targeted marketing, or personalized call-routing. This kind of applications need to quickly estimate the age of the speaker and might greatly benefit from real-time capabilities. Long short-term memory (LSTM) recurrent neural networks (RNN) have shown to outperform state-of-the-art approaches in related speech-based tasks, such as language identification or voice activity detection, especially when an accurate real-time response is required. In this paper, we propose a novel age estimation system based on LSTM-RNNs. This system is able to deal with short utterances (from 3 to 10 s) and it can be easily deployed in a real-time architecture. The proposed system has been tested and compared with a state-of-the-art i-vector approach using data from NIST speaker recognition evaluation 2008 and 2010 data sets. Experiments on short duration utterances show a relative improvement up to 28% in terms of mean absolute error of this new approach over the baseline system.


title: Self Adjusting Refresh Time Based Architecture for Incremental Web Crawler

abstract: Due to the deficiency in their refresh techniques (12), current crawlers add unnecessary traffic to the already overloaded Internet. Moreover there exist no certain ways to verify whether a document has been updated or not. In this paper, an efficient approach is being proposed for building an effective incremental web crawler (13). It selectively updates its database and/ or local collection of web pages instead of periodically refreshing the collection in batch mode thereby improving the "freshness" of the collection significantly and bringing new pages in more timely manner. It also detects web pages which frequently undergo up-dation and dynamically calculates the refresh time of the page for its next update. Index Terms - World Wide Web, Search engine, Incremental Crawler, Hypertext, Browser.


title: Processing of wood-based microfibrillated cellulose and nanofibrillated cellulose, and applications relating to papermaking: a review

abstract: As an emerging cellulosic nanomaterial, microfibrillated cellulose (MFC) and nanofibrillated cellulose (NFC) have shown enormous potential in the forest products industry. The forest products industry and academia are working together to realise the possibilities of commercializing MFC and NFC. However, there are still needs to improve the processing, characterisation and material properties of nanocellulose in order to realise its full potential. The annual number of research publications and patents on nanocellulose with respect to manufacturing, properties and applications is now up in the thousands, so it is of the utmost importance to review articles that endeavour to research on this explosive topic of cellulose nanomaterials. This review examines the past and current situation of wood-based MFC and NFC in relation to its processing and applications relating to papermaking.


title: Capturing and Reusing Functional and Non-functional Requirements Knowledge: A Goal-Object Pattern Approach

abstract: Pattern-based approach has proven to be an effective means for capturing and reusing past experience and best practices to facilitate communication and shorten the time required for software mdeling. However, current knowledge capture is limited to mostly functional knowledge. Knowledge reuse is also often manual and limited to low-scale small model fragments. This paper presents a goal-object pattern framework that captures both functional and non-functional requirements knowledge that is modeled using UML and a goal-oriented method. Patterns in this framework are a collection of model refinement methods, each defining a single model element generation step in the target model. The patterns are model-driven in that they are defined using a UML metamodel and then used as such. The framework also supports incremental knowledge capture in that large-grain patterns can be composed from finegrain ones, and also application-independent patterns can be specialized into application-specific ones. The framework is illustrated using a simplified on-line book-store application to demonstrate how large-sclae reuse may be possible.


title: Deep Monocular Depth Estimation via Integration of Global and Local Predictions

abstract: Recent works on machine learning have greatly advanced the accuracy of single image depth estimation. However, the resulting depth images are still over-smoothed and perceptually unsatisfying. This paper casts depth prediction from single image as a parametric learning problem. Specifically, we propose a deep variational model that effectively integrates heterogeneous predictions from two convolutional neural networks (CNNs), named global and local networks. They have contrasting network architecture and are designed to capture depth information with complementary attributes. These intermediate outputs are then combined in the integration network based on the variational framework. By unrolling the optimization steps of Split Bregman (SB) iterations in the integration network, our model can be trained in an end-to-end manner. This enables one to simultaneously learn an efficient parameterization of the CNNs and hyper-parameter in the variational method. Finally, we offer a new dataset of 0.22 million RGB-D images captured by Microsoft Kinect v2. Our model generates realistic and discontinuity-preserving depth prediction without involving any low-level segmentation or superpixels. Intensive experiments demonstrate the superiority of the proposed method in a range of RGB-D benchmarks including both indoor and outdoor scenarios.


title: Recent progress in multi-electrode spike sorting methods

abstract: In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.


title: Face Detection with End-to-End Integration of a ConvNet and a 3D Model

abstract: This paper presents a method for face detection in the wild, which integrates a ConvNet and a 3D mean face model in an end-to-end multi-task discriminative learning framework. The 3D mean face model is predefined and fixed (e.g., we used the one provided in the AFLW dataset). The ConvNet consists of two components: (i) The face proposal component computes face bounding box proposals via estimating facial key-points and the 3D transformation (rotation and translation) parameters for each predicted key-point w.r.t. the 3D mean face model. (ii) The face verification component computes detection results by pruning and refining proposals based on facial key-points based configuration pooling. The proposed method addresses two issues in adapting state-of-the-art generic object detection ConvNets (e.g., faster R-CNN) for face detection: (i) One is to eliminate the heuristic design of predefined anchor boxes in the region proposals network (RPN) by exploiting a 3D mean face model. (ii) The other is to replace the generic RoI (Region-of-Interest) pooling layer with a configuration pooling layer to respect underlying object structures. The multi-task loss consists of three terms: the classification Softmax loss and the location smooth \(l_1\)-losses of both the facial key-points and the face bounding boxes. In experiments, our ConvNet is trained on the AFLW dataset only and tested on the FDDB benchmark with fine-tuning and on the AFW benchmark without fine-tuning. The proposed method obtains very competitive state-of-the-art performance in the two benchmarks.


title: MoCoGAN: Decomposing Motion and Content for Video Generation

abstract: Visual signals in a video can be divided into content and motion. While content specifies which objects are in the video, motion describes their dynamics. Based on this prior, we propose the Motion and Content decomposed Generative Adversarial Network (MoCoGAN) framework for video generation. The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames. Each random vector consists of a content part and a motion part. While the content part is kept fixed, the motion part is realized as a stochastic process. To learn motion and content decomposition in an unsupervised manner, we introduce a novel adversarial learning scheme utilizing both image and video discriminators. Extensive experimental results on several challenging datasets with qualitative and quantitative comparison to the state-of-the-art approaches, verify effectiveness of the proposed framework. In addition, we show that MoCoGAN allows one to generate videos with same content but different motion as well as videos with different content and same motion.


Can Mobile Health Technologies Transform Health Care


title: Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling

abstract: In this work we propose a novel motion guided method for automatic and targetless calibration of a LiDAR and camera and use the LiDAR points projected on the image for real-time super-resolution depth estimation. The calibration parameters are estimated by optimizing a cost function that penalizes the difference in the motion vectors obtained from LiDAR and camera data separately. For super-resolution, we propose a simple, yet effective and time efficient formulation that minimizes depth gradients subject to an equality constraint involving measured LiDAR data. We perform experiments on real data obtained in urban environments and demonstrate that the proposed calibration technique is robust and the real-time depth super-resolution reconstruction outperforms the quality of state of the art approaches.


title: The emergence of geometric order in proliferating metazoan epithelia

abstract: The predominantly hexagonal cell pattern of simple epithelia was noted in the earliest microscopic analyses of animal tissues, a topology commonly thought to reflect cell sorting into optimally packed honeycomb arrays. Here we use a discrete Markov model validated by time-lapse microscopy and clonal analysis to demonstrate that the distribution of polygonal cell types in epithelia is not a result of cell packing, but rather a direct mathematical consequence of cell proliferation. On the basis of in vivo analysis of mitotic cell junction dynamics in Drosophila imaginal discs, we mathematically predict the convergence of epithelial topology to a fixed equilibrium distribution of cellular polygons. This distribution is empirically confirmed in tissue samples from vertebrate, arthropod and cnidarian organisms, suggesting that a similar proliferation-dependent cell pattern underlies pattern formation and morphogenesis throughout the metazoa.


title: A new recommendation algorithm combined with spectral clustering and transfer learning

abstract: Collaborative filtering (CF) recommendation algorithm has been successfully applied into recommender systems for years which can solve the problem of information overload. However, CF suffers from data sparsity and knowledge cannot transfer between multiple rating matrixes. In this paper, we propose a collaborative filtering recommendation algorithm combined with spectral clustering and transfer learning (RASCTL). RASCTL firstly uses spectral clustering to cluster the dimensions of users and items in the original rating matrix. In addition, RASCTL decomposes the rating matrix after clustering and gets the sharing group rating matrix. Finally, RASCTL makes rating forecasting and recommendations combined with the sharing group rating matrix and transfer learning. By the simulation experiments on Epinions and MovieLents data sets, the results show that RASCTL is able to obtain comparable or even better recommendation accuracy and generalization ability compared with other seven CF recommendation algorithms.


title: Identification of a Subtype of Hepatocellular Carcinoma with Poor Prognosis Based on Expression of Genes within the Glucose Metabolic Pathway

abstract: Hepatocellular carcinoma (HCC) is the most prevalent primary cancer and a highly aggressive liver malignancy. Liver cancer cells reprogram their metabolism to meet their needs for rapid proliferation and tumor growth. In the present study, we investigated the alterations in the expression of the genes involved in glucose metabolic pathways as well as their association with the clinical stage and survival of HCC patients. We found that the expressions of around 30% of genes involved in the glucose metabolic pathway are consistently dysregulated with a predominant down-regulation in HCC tumors. Moreover, the differentially expressed genes are associated with an advanced clinical stage and a poor prognosis. More importantly, unsupervised clustering analysis with the differentially expressed genes that were also associated with overall survival (OS) revealed a subgroup of patients with a worse prognosis including reduced OS, disease specific survival, and recurrence-free survival. This aggressive subtype had significantly increased expression of stemness-related genes and down-regulated metabolic genes, as well as increased immune infiltrates that contribute to a poor prognosis. Collectively, this integrative study indicates that expressions of the glucose metabolic genes could be used as potential prognostic markers and/or therapeutic targets, which might be helpful in developing precise treatment for patients with HCC.


Economic and environmental implications of sewage on river cauvery: a case study in Tiruchirappalli urban area


title: Agile Big Data Analytics for Web-Based Systems: An Architecture-Centric Approach

abstract: This article contributes an architecture-centric methodology, called AABA ( A rchitecture-centric A gile B ig data A nalytics), to address the technical, organizational, and rapid technology change challenges of both big data system development and agile delivery of big data analytics for Web-based Systems (WBS). As the first of its kind, AABA fills a methodological void by adopting an architecture-centric approach, advancing and integrating software architecture analysis and design, big data modeling and agile practices. This article describes how AABA was developed, evolved and validated simultaneously in 10 empirical WBS case studies through three CPR (Collaborative Practice Research) cycles. In addition, this article presents an 11th case study illustrating the processes, methods and techniques/tools in AABA for cost-effectively achieving business goals and architecture agility in a large scale WBS. All 11 case studies showed that architecture-centric design, development, and operation is key to taming technical complexity and achieving agility necessary for successful WBS big data analytics development. Our contribution is novel and important. The use of reference architectures, a design concepts catalog and architectural spikes in AABA are advancements to architecture design methods. In addition, our architecture-centric approach to DevOps was critical for achieving strategic control over continuous big data value delivery for WBS.


title: Exploiting Multiple-Antenna Diversity for Shared Secret Key Generation in Wireless Networks

abstract: Generating a secret key between two parties by extracting the shared randomness in the wireless fading channel is an emerging area of research. Previous works focus mainly on single-antenna systems. Multiple-antenna devices have the potential to provide more randomness for key generation than single-antenna ones. However, the performance of key generation using multiple-antenna devices in a real environment remains unknown. Different from the previous theoretical work on multiple-antenna key generation, we propose and implement a shared secret key generation protocol, Multiple-Antenna KEy generator (MAKE) using off-the-shelf 802.11n multiple-antenna devices. We also conduct extensive experiments and analysis in real indoor and outdoor mobile environments. Using the shared randomness extracted from measured Received Signal Strength Indicator (RSSI) to generate keys, our experimental results show that using laptops with three antennas, MAKE can increase the bit generation rate by more than four times over single-antenna systems. Our experiments validate the effectiveness of using multi-level quantization when there is enough mutual information in the channel. Our results also show the trade-off between bit generation rate and bit agreement ratio when using multi-level quantization. We further find that even if an eavesdropper has multiple antennas, she cannot gain much more information about the legitimate channel.


title: A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation

abstract: In this paper, we introduce a trie-structured Bayesian model for unsupervised morphological segmentation. We adopt prior information from different sources in the model. We use neural word embeddings to discover words that are morphologically derived from each other and thereby that are semantically similar. We use letter successor variety counts obtained from tries that are built by neural word embeddings. Our results show that using different information sources such as neural word embeddings and letter successor variety as prior information improves morphological segmentation in a Bayesian model. Our model outperforms other unsupervised morphological segmentation models on Turkish and gives promising results on English and German for scarce resources.


title: Microgrids for Enhancing the Power Grid Resilience in Extreme Conditions

abstract: This paper presents a framework for analyzing the resilience of an electric power grid with integrated microgrids in extreme conditions. The objective of this paper is to demonstrate that controllable and islandable microgrids can help improve the resiliency of power grids in extreme conditions. Four resilience indices are introduced to measure the impact of extreme events. Index $ {K}$ measures the expected number of lines on outage due to extreme events. Index loss of load probability measures the probability of load not being fully supplied. Index expected demand not supplied measures the expected demand that cannot be supplied. Index $ {G}$ measures the difficulty level of grid recovery. The mechanism of extreme events affecting power grid operation is analyzed based on the proposed mesh grid approach. The relationship among transmission grid, distribution grid, and microgrid in extreme conditions is discussed. The Markov chain is utilized to represent the state transition of a power grid with integrated microgrids in extreme conditions. The Monte Carlo method is employed to calculate the resilience indices. The proposed power grid resilience analysis framework is demonstrated using the IEEE 30-bus and 118-bus systems assuming all loads are within microgrids.


title: SmartSeed: Smart Seed Generation for Efficient Fuzzing

abstract: Fuzzing is an automated application vulnerability detection method. For genetic algorithm-based fuzzing, it can mutate the seed files provided by users to obtain a number of inputs, which are then used to test the objective application in order to trigger potential crashes. As shown in existing literature, the seed file selection is crucial for the efficiency of fuzzing. However, current seed selection strategies do not seem to be better than randomly picking seed files. Therefore, in this paper, we propose a novel and generic system, named SmartSeed, to generate seed files towards efficient fuzzing. Specifically, SmartSeed is designed based on a machine learning model to learn and generate high-value binary seeds. We evaluate SmartSeed along with American Fuzzy Lop (AFL) on 12 open-source applications with the input formats of mp3, bmp or flv. We also combine SmartSeed with different fuzzing tools to examine its compatibility. From extensive experiments, we find that SmartSeed has the following advantages: First, it only requires tens of seconds to generate sufficient high-value seeds. Second, it can generate seeds with multiple kinds of input formats and significantly improves the fuzzing performance for most applications with the same input format. Third, SmartSeed is compatible to different fuzzing tools. In total, our system discovers more than twice unique crashes and 5,040 extra unique paths than the existing best seed selection strategy for the evaluated 12 applications. From the crashes found by SmartSeed, we discover 16 new vulnerabilities and have received their CVE IDs.


title: Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

abstract: In recent years, the task of recommending hashtags for microblogs has been given increasing attention. Various methods have been proposed to study the problem from different aspects. However, most of the recent studies have not considered the differences in the types or uses of hashtags. In this paper, we introduce a novel nonparametric Bayesian method for this task. Based on the Dirichlet Process Mixture Models (DPMM), we incorporate the type of hashtag as a hidden variable. The results of experiments on the data collected from a real world microblogging service demonstrate that the proposed method outperforms stateof-the-art methods that do not consider these aspects. By taking these aspects into consideration, the relative improvement of the proposed method over the state-of-theart methods is around 12.2% in F1- score.


title: Friction Compensation in Robotics: an Overview

abstract: Friction effects are particularly critical for industrial robots, since they can induce large positioning errors, stick-slip motions, and limit cycles. This paper offers a reasoned overview of the main friction compensation techniques that have been developed in the last years, regrouping them according to the adopted kind of control strategy. Some experimental results are reported, to show how the control performances can be affected not only by the chosen method, but also by the characteristics of the available robotic architecture and of the executed task.


title: Secure Data Sharing in the Cloud

abstract: Cloud systems can be used to enable data sharing capabilities and this can provide an abundant of benefits to the user.


title: A highly secure quantum communication scheme for Blind Signature using qubits and qutrits

abstract: The advances in hardware speed has being rapidly increased rapidly in the recent years, which will lead to the ability to decrypt well known decryption algorithms in short time. This motivated many researchers to investigate better techniques to prevent disclosing and eavesdropping of communicated data. Quantum Cryptography is a promising solution, since it relies on the prosperities of quantum physics that ensure no change in the quantum state without the knowledge of the sender/receiver. Quantum Communication Scheme for Blind Signature with Two-Particle Entangled Quantum-Trits was proposed by Jinjing et al. [1] That scheme uses qutits during the communications and the process of the encryption is not clearly defined. In this paper we suggest a modification of Jinjing et al. protocol using qubits and qutrits during the encryption and decryption which proposed by Zhou et al. [2] The proposed algorithms enhances the efficiency of that scheme and creates a quantum cryptosystem environment to exchange the data in a secure way. During the communications, all the messages are encrypted using the the private key of the sender and a third party verifies the authenticity and the blindness of the signature.


title: A multihop data-gathering scheme using multiple AUVs in hierarchical underwater sensor networks

abstract: In this paper, we propose a scheme for multiple Autonomous Underwater Vehicles (AUVs) based data-gathering in large hierarchical underwater sensor networks where the AUVs constitute an intermittently connected multihop network through inter AUV synchronization. The variations in underwater currents severely affect the AUV mobility which affect the inter AUV synchronization and hence contributes to the delay in multihop communication. The proposed scheme reduces the AUV synchronization delay by using the neighboring AUVs information provided by sync-nodes at the static layer of the network. The performance of the proposed scheme is compared with conventional scheme using probability of AUV synchronization delay and average end-to-end packet delay. Simulation results show that the proposed scheme reduces the inter AUV synchronization delay caused by varying AUV mobility parameters.


title: On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning.

abstract: In this paper, we utilize results from convex analysis and monotone operator theory to derive additional properties of the softmax function that have not yet been covered in the existing literature. In particular, we show that the softmax function is the monotone gradient map of the log-sum-exp function. By exploiting this connection, we show that the inverse temperature parameter determines the Lipschitz and co-coercivity properties of the softmax function. We then demonstrate the usefulness of these properties through an application in game-theoretic reinforcement learning.


title: Multi-Person Pose Estimation via Multi-Layer Fractal Network and Joints Kinship Pattern

abstract: We propose an effective method to boost the accuracy of multi-person pose estimation in images. Initially, the three-layer fractal network was constructed to regress multi-person joints location heatmap that can help to enhance an image region with receptive field and capture more joints local-contextual feature information, thereby producing keypoints heatmap intermediate prediction to optimize human body joints regression results. Subsequently, the hierarchical bi-directional inference algorithm was proposed to calculate the degree of relatedness (call it Kinship) for adjacent joints, and it combines the Kinship between adjacent joints with the spatial constraints, which we refer to as joints kinship pattern matching mechanism, to determine the best matched joints pair. We iterate the above-mentioned joints matching process layer by layer until all joints are assigned to a corresponding individual. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art schemes and achieves about 1% and 0.6% increase in mAP on MPII multi-person subset and MSCOCO 2016 keypoints challenge.


title: A single neuron PID control for twin rotor MIMO system

abstract: This paper presents an intelligent control scheme which utilizes a single neuron PID controller to an experimental propeller setup called the twin rotor multi-input multi-output system (TRMS). The control objective is to make the TRMS move quickly and accurately to the desired attitudes. The pitch angle and the azimuth angle in the conditions of decoupled and cross-coupled between vertical and horizontal axes are considered. It is difficult to design a suitable controller because of the influence between two axes and nonlinear movement. For easy demonstrate the movement on the vertical plane and horizontal plane separately, the TRMS is decoupled first by the main rotor and tail rotor. After successful simulations in decoupled condition, the more difficult simulations of cross-coupled condition are performed. Simulation results show that the new approach can improve the tracking performance.


title: Improved Principal Component Regression for Face Recognition Under Illumination Variations

abstract: The uncontrollable illumination problem is a great challenge for face recognition. In this paper, we propose a novel face recognition framework, the improved principal component regression classification (IPCRC) algorithm, which could overcome the problem of multicollinearity in linear regression. The IPCRC approach first performs principal component analysis (PCA) process to project the face images onto the face space. The first n principal components are intentionally dropped to boost the robustness against illumination changes. Then, the linear regression classification (LRC) is executed on the projected data and the identity is determined by the minimum reconstruction error. Experiments carried out on Yale B and FERET facial databases reveal that the proposed framework outperforms the state-of-the-art methods and demonstrates promising abilities against severe illumination variation.