title: ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

abstract: The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals Picture Archiving and Communication Systems (PACS). On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high precision computer-aided diagnosis (CAD) systems. In this paper, we present a new chest X-ray database, namely ChestX-ray8, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially-located via a unified weakly-supervised multi-label image classification and disease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network based reading chest X-rays (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully-automated high precision CAD systems.


title: Depth augmented stereo panorama for cinematic virtual reality with focus cues

abstract: Cinematic virtual reality (VR) aims to provide immersive visual experiences of real-world scenes on head-mounted displays. Current cinematic VR systems support stereo cues, but not focus cues that are important for depth perception and comfortable viewing. We propose a new content representation, depth augmented stereo panorama (DASP), which permits generating light fields (LFs) across the observer's pupils, achieving an order of magnitude reduction in data requirements compared to the existing techniques. The LF generation and refocusing capabilities of DASP are evaluated for both, computer-generated and real scenes. Results indicate that DASP can successfully create stereo as well as support focus cues.


title: Abnormal Event Detection in Surveillance Video: A Compressed Domain Approach for HEVC

abstract: Recently, detecting abnormal events in surveillance videos has become one of the most important tasks of video analysis. There is a huge demand in developing fast and accurate abnormal event detection approach. However, traditional pixel-domain approaches are time-consuming and require fully decoding of the bit streams. On the other hand, the compression format may provide useful information to solve the challenge: compared to raw pixels, the advantage of the compression format is that it already contains some valuable clues for video analysis. In this work, we focus on anomaly detection in traffic video with the information provided in the HEVC compressed domain. There are mainly two contributions. The first is that we propose a novel feature namely motion energy intensity (MEI) to represent the motion intensity within coding unit, based on the MV fields, bit allocations and partitions of coding units in the HEVC compressed domain, which can provide energy and motion information of the LCU. The second contribution is that we have proposed an anomaly detection model based on the MEI. Furthermore, this approach can also serve as preprocess for pixel domain detection algorithms, by labeling suspicious part of the video which are likely to be abnormities. Figure 1 shows the flowchart of the proposed algorithm. Experimental results show that the proposed approach can obtain rather high detecting accuracy with very little overhead. The processing speed for detection is as fast as 1200 fps on 480x360 video. When served as a preprocess approach, the pixel-domain methods may also benefit with up to 50% time reduction.


title: Correction of Intensity Flicker in Old Film Sequences

abstract: Temporal intensity flicker is a common artifact in old film sequences. Removing disturbing temporal fluctuations in image intensities is desirable because it increases both the subjective quality and, where image sequences are stored in a compressed format, the coding efficiency. We describe a robust technique that corrects intensity flicker automatically by equalizing local frame means and variances in the temporal sense. The low complexity of our method makes it suitable for hardware implementation. We tested the proposed method on sequences with artificially added intensity flicker and on original film material. The results show a great improvement.


title: A Neural-Network-Based Model for the Dynamic Simulation of the Tire/Suspension System While Traversing Road Irregularities

abstract: This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be trained with experimental data obtained in the laboratory from simulated road profiles (cleats). The results obtained from the neural network demonstrate good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tire dynamics behavior. Although the neural network model, as a black-box model, does not provide a good insight of the physical behavior of the tire/suspension system, it is a useful tool for assessing vehicle ride and noise, vibration, harshness (NVH) performance due to its good computational efficiency and accuracy.


title: Power efficient, bandwidth optimized and fault tolerant sensor management for IOT in Smart Home

abstract: This paper proposes a localized data processing and decision making framework for IoT enabled environment. It constitutes a master unit which efficiently manages local sensor networks in Smart Homes. This master unit collects data from the network of sensors installed in different locations within and around the house and intelligently identifies the dependencies among them. Based on this, the set of sensors are classified as dependent whose output values can be predicted using data from remaining of them. This knowledge extracted locally is shared with the Cloud Server, thereby providing a refined picture of the sensory data. On the basis of locally extracted knowledge, the sensors are tuned in real time to minimize the redundancy in usage and power consumption. Moreover, in cases of failure of a sensor unit, approximate decisions can still be made based on previous patterns.


title: Particle Swarm Optimization-based method for generating healthy lifestyle recommendations

abstract: This paper addresses the problem of reducing the risk of developing cardiovascular diseases by proposing a nature-inspired method that generates healthy lifestyle recommendations. The method analyses the profile of a person including general health information, preferences and daily dietary and physical activities habits using a Naive Bayes classifier to identify unhealthy behaviours that increase the risk of developing cardiovascular diseases. If an unhealthy behaviour is detected, the method applies a Particle Swarm Optimization-based algorithm to generate healthy lifestyle recommendations that adjust the person's body mass index within healthy limits. The proposed method has been integrated into an experimental prototype and evaluated on a set of generated user profiles.


title: A Bandwidth-Dense, Low Power Electronic-Photonic Platform and Architecture for Multi-Tbps Optical I/O

abstract: We present a high bandwidth-density photonic interconnect platform that transmits 1.05 Tbps/mm2 using 0.83 pJ/bit, integrated in a 45nm SOI process, and a high-density multi-wavelength laser source.


title: Miniaturized 122 GHz short range radar sensor with antenna-in-package (AiP) and dielectric lens

abstract: This paper shows a SiGe single chip radar sensor with high efficient LCP off-chip antennas integrated in a standard QFN package at a center frequency of 122.5 GHz. The millimeter-wave interconnect is performed by a CPW bond-wire connection with subsequent matching network. For demonstration purposes a complete baseband processing and RF unit is implemented on a PCB and integrated in a plastic housing where a focusing HDPE lens with a directivity of 30.7 dBi for gain enhancement can be connected. Distance measurements in a real world scenario are performed from 0.4-18.25m and 0.6-14.5m for a metal plate and a wooden plate, respectively. Additionally, a velocity measurement from 0.5-5m/s in the laboratory is performed.


title: Corporate-Fed Planar 60-GHz Slot Array Made of Three Unconnected Metal Layers Using AMC Pin Surface for the Gap Waveguide

abstract: This paper presents the design of a high efficiency corporate-fed 8×8-slot array antenna in the 60 GHz band. The antenna is built using three unconnected metal layers based on Artificial Magnetic Conductor (AMC) in gap waveguide technology. A 2×2 cavity-backed slot subarray is designed in a groove gap waveguide cavity. The cavity is fed through a coupling slot from a ridge gap waveguide corporate-feed network in the lower layer. The subarray is numerically optimized in an infinite array environment. The corporate-feed network is realized by a texture of pins and a guiding ridge. There is very good agreement between simulated and measured results. The fabricated antenna shows a relative bandwidth of 14% with input reflection coefficient better than -10 dB and an overall aperture efficiency larger than 65% (i.e. – 2 dB) with about 25 dBi realized gain between 56.2 and 65.0 GHz.


title: Epidemiological modeling of news and rumors on Twitter

abstract: Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.


SOILLESS CULTURE SYSTEMS PRODUCE READY-TO-EAT CORN SALAD (VALERIANELLA OLITORIA L.) OF HIGH QUALITY


title: Herbal Medicines: Possible Risks and Benefits

abstract: Each type of medicine has many strengths and weaknesses. In order to protect and improve our health, it is important to become an informed medical consumer. Herbal drugs are used widely for preventive and therapeutic purposes. The manufacturers of these products are not required to submit proof of safety and efficacy before marketing, so the adverse effects associated with remedies are largely unknown. Also, herbal products are not regulated for purity and potency. Thus, some of adverse effects reported could be caused by impurities or batch to batch variability. The potency of herbal products may increase the possibility of adverse effects. This paper highlights potential benefits and possible risks associated with consumption of herbal product so that conventional treatments can be made more safe and effective.


title: CacheAudit: A Tool for the Static Analysis of Cache Side Channels

abstract: We present CacheAudit, a versatile framework for the automatic, static analysis of cache side channels. CacheAudit takes as input a program binary and a cache configuration and derives formal, quantitative security guarantees for a comprehensive set of side-channel adversaries, namely, those based on observing cache states, traces of hits and misses, and execution times. Our technical contributions include novel abstractions to efficiently compute precise overapproximations of the possible side-channel observations for each of these adversaries. These approximations then yield upper bounds on the amount of information that is revealed. In case studies, we apply CacheAudit to binary executables of algorithms for sorting and encryption, including the AES implementation from the PolarSSL library, and the reference implementations of the finalists of the eSTREAM stream cipher competition. The results we obtain exhibit the influence of cache size, line size, associativity, replacement policy, and coding style on the security of the executables and include the first formal proofs of security for implementations with countermeasures such as preloading and data-independent memory access patterns.


title: SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

abstract: Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage, and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto , to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata ) including the smartphone's orientation, position, and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Four optimization problems regarding the tradeoffs between photo utility and resource constraints, namely Max-Utility, online Max-Utility, Min-Selection, and Min-Selection with $k$ -coverage, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.


title: OWL, RDF, RDFS inference derivation using Jena semantic framework & pellet reasoner

abstract: Semantic web is an extension of World Wide Web. In Semantic web each resources and services is annotated with the help of web ontology language (OWL). Resource description framework (RDF) represents the web information into its properties and values. Resource description framework schema (RDFS) represents information resources into classes and properties are defined with the help of these classes. In this paper we create university ontology with the help of protege 4.3 alpha tool. We have proposed information retrieval framework that produce results with better precision, recall and information overkill. With the help of Jena semantic framework and pellet reasoner tools we analyze that OWL inference provide more relevant information compared to None, RDF and RDFS. We have developed five university domain ontologies but presented the result only for two ontologies. We have validated that our proposed framework provide more relevant information for these two ontologies.


title: Run-away dynamics of CDMA channels in an adaptive packet radio network

abstract: Radio communication offers the great advantage of mobility for users. At least two important systems exploit this feature: packet radio networks and personal communication systems. Since their radio links are subject to rapid changes in quality, the ability to adapt to these changes has much to offer in improving throughput, delay, and robustness characteristics. This paper addresses three areas. First, it presents the design fundamentals of an adaptive link-gain control algorithm for a multiple access radio channel. Second, it develops a probabilistic model for a related family of algorithms, which includes models for the effects of code-division multiple access interference. Third, it uses two sample algorithms to explore the transient behaviour of the system. The two algorithms are chosen to compare the relative merits of designs that emphasize adapting to channel noise versus adapting to multiple access interference. Transient analysis is sparse in the literature and is an important contribution of this paper.


title: Liturgical singing as ritual symbol

abstract: Liturgical singing is more than text and melody; it is also symbol and ritual. It is part of the ensemble of rituals within the worship service. As a ritual symbol, it is closely connected to the culture or subculture where it is conducted. Meaning is not only immanent in the text (lyrics), but assigned on a continuous base and differs from culture to culture. As a ritual symbol, liturgical singing does not only point to another reality, but presents the other reality within the cultural context of the worship service; within ritual the music and melody are more important than text and lyrics. Liturgical singing as a ritual symbol is never static, but in a continuous process of change.


title: Adaptive tetrapuzzles: efficient out-of-core construction and visualization of gigantic multiresolution polygonal models

abstract: We describe an efficient technique for out-of-core construction and accurate view-dependent visualization of very large surface models. The method uses a regular conformal hierarchy of tetrahedra to spatially partition the model. Each tetrahedral cell contains a precomputed simplified version of the original model, represented using cache coherent indexed strips for fast rendering. The representation is constructed during a fine-to-coarse simplification of the surface contained in diamonds (sets of tetrahedral cells sharing their longest edge). The construction preprocess operates out-of-core and parallelizes nicely. Appropriate boundary constraints are introduced in the simplification to ensure that all conforming selective subdivisions of the tetrahedron hierarchy lead to correctly matching surface patches. For each frame at runtime, the hierarchy is traversed coarse-to-fine to select diamonds of the appropriate resolution given the view parameters. The resulting system can interatively render high quality views of out-of-core models of hundreds of millions of triangles at over 40Hz (or 70M triangles/s) on current commodity graphics platforms.


title: Fast Calibration for Robot Welding System with Laser Vision

abstract: Camera calibration and robot hand-eye calibration are important parts in applications of robots with visions. A way to simultaneously carry out camera calibration and robot hand-eye calibration is presented in robot welding system with laser vision. It is based on a certain relation between hand-eye matrix and matrix of robot hand frame to robot base frame. Calibration experiments are performed in this way using specifically designed calibration objects. And all parameters to be calibrated are solved out in nonlinear least square optimization method. 3D world coordinates of 20 sample points selected from a v-groove seam are reconstructed with the calibration results. By contrasting their real coordinates, average error is 0.12 mm, which is sufficient to the requirement of robot seam tracking.


title: A Path Planning Method for Assistant Parallel Car-Parking

abstract: Path planning is one of the key technologies in an intelligent car-parking system. This paper presents a path planning method for parallel parking mode. a scheme of a two-arc parking path is proposed, with the start position given but the goal to be yielded. the parking process is formulated as a constrained nonlinear optimization problem, where two objective functions are studied. and we gained the optimized results by MATLAB Optimization Toolbox.


title: Measuring empathy in healthcare profession students using the Jefferson Scale of Physician Empathy: Health provider - student version

abstract: While empathy is commonly accepted as a mutually beneficial aspect of the health provider–patient relationship, evidence exists that many health profession students are unable to demonstrate this important skill. This study, the initial phase of a 2-year longitudinal series, examined measurement properties of the Jefferson Scale of Physician Empathy (JSPE) adapted for administration to health profession students (JSE-HPS version), and investigated group differences of empathy scores in the baccalaureate nursing (BSN) program within the College of Health Professions at a public university in the southeastern part of the USA. The 20-item survey and a demographic questionnaire were completed by 265 BSN students. Correlational analyses, t-test, and analysis of variance were used to examine internal relationships and group differences. Results showed the median item-total score correlation was statistically significant (0.42). The internal consistency of the scale (Cronbach’s coefficient α) was 0.78, falling w...


title: Framework for a Digital Forensic Investigation

abstract: Computer Forensics is essential for the successful prosecution of computer criminals. For a forensic investigation to be performed successfully there are a number of important steps that have to be considered and taken. The aim of this paper is to define a clear, step-by-step framework for the collection of evidence suitable for presentation in a court of law. Existing forensic models will be surveyed and then adapted to create a specific application framework for single computer, entry point forensics.


title: Paving green passage for emergency vehicle in heavy traffic: Real-time motion planning under the connected and automated vehicles environment

abstract: This paper describes a real-time multi-vehicle motion planning (MVMP) algorithm for the emergency vehicle clearance task. To address the inherent limitations of human drivers in perception, communication, and cooperation, we require that the emergency vehicle and the surrounding normal vehicles are connected and automated vehicles (CAVs). The concerned MVMP task is to find cooperative trajectories such that the emergency vehicle can efficiently pass through the normal vehicles ahead. We use an optimal-control based formulation to describe the MVMP problem, which is centralized, straightforward, and complete. For the online solutions, the centralized MVMP formulation is converted into a multi-period and multi-stage version. Concretely, each period consists of two stages: the emergency vehicle and several normal CAVs ahead try to form a regularized platoon via acceleration or deceleration (stage 1); when a regularized platoon is formed, these vehicles act cooperatively to make way for the emergency vehicle until the emergency vehicle becomes the leader in this local platoon (stage 2). When one period finishes, the subsequent period begins immediately. This sequential process continues until the emergency vehicle finally passes through all the normal CAVs. The subproblem at stage 1 is extremely easy because nearly all the challenging nonlinearity gathers only in stage 2; typical solutions to the subproblem at stage 2 can be prepared offline, and then implemented online directly. Through this, our proposed MVMP algorithm avoids heavy online computations and thus runs in real time.


A Study on Vulnerability Prevention Mechanism Due to Logout Problem Using OAuth


title: A 130 mV SRAM With Expanded Write and Read Margins for Subthreshold Applications

abstract: SRAM suffers read-disturb and write failures at a low supply voltage, especially at deep subthreshold operation. This study proposes a 9T-SRAM cell with a data-aware-feedback-cutoff (DAFC) scheme to enlarge the write margin and dynamic-read-decoupled (DRD) scheme to prevent read-disturb for achieving deep subthreshold operation. A 30 mV negative-pumped wordline scheme is employed to suppress bitline leakage current. The fabricated 90 nm 32 Kb 9T-SRAM macro achieves 130 mV VDDmin. All the 32 Kb 9T cells are stable across read and write operations when operated at 105 mV.


title: Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization

abstract: Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. A key to success for a bike sharing systems is the effectiveness of rebalancing operations, that is, the efforts of restoring the number of bikes in each station to its target value by routing vehicles through pick-up and drop-off operations. There are two major issues for this bike rebalancing problem: the determination of station inventory target level and the large scale multiple capacitated vehicle routing optimization with outlier stations. The key challenges include demand prediction accuracy for inventory target level determination, and an effective optimizer for vehicle routing with hundreds of stations. To this end, in this paper, we develop a Meteorology Similarity Weighted K-Nearest-Neighbor (MSWK) regressor to predict the station pick-up demand based on large-scale historic trip records. Based on further analysis on the station network constructed by station-station connections and the trip duration, we propose an inter station bike transition (ISBT) model to predict the station drop-off demand. Then, we provide a mixed integer nonlinear programming (MINLP) formulation of multiple capacitated bike routing problem with the objective of minimizing total travel distance. To solve it, we propose an Adaptive Capacity Constrained K-centers Clustering (AdaCCKC) algorithm to separate outlier stations (the demands of these stations are very large and make the optimization infeasible) and group the rest stations into clusters within which one vehicle is scheduled to redistribute bikes between stations. In this way, the large scale multiple vehicle routing problem is reduced to inner cluster one vehicle routing problem with guaranteed feasible solutions. Finally, the extensive experimental results on the NYC Citi Bike system show the advantages of our approach for bike demand prediction and large-scale bike rebalancing optimization.


title: Pose Guided RGBD Feature Learning for 3D Object Pose Estimation

abstract: In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation. Previous works have focused on learning feature embeddings based on metric learning with triplet comparisons and rely only on the qualitative distinction of similar and dissimilar pose labels. In contrast, we consider the exact pose differences between the training samples, and aim to learn embeddings such that the distances in the pose label space are proportional to the distances in the feature space. However, since it is less desirable to force the pose-feature correlation when objects are symmetric, we discuss the use of weights that reflect object symmetry when measuring the pose distances. Furthermore, end-to-end pose regression is investigated and is shown to further boost the discriminative power of feature learning, improving pose recognition accuracies. Experimental results show that the features that are learnt guided by poses, are significantly more discriminative than the ones learned in the traditional way, outperforming state-of-the-art works. Finally, we measure the generalisation capacity of pose guided feature learning in previously unseen scenes containing objects under different occlusion levels, and we show that it adapts well to novel tasks.


title: Is the free-energy principle neurocentric?

abstract: Recently, a free-energy formulation of brain function was reviewed in relation to several other neurobiological theories (The freeenergy principle: a unified brain theory? Nature Rev. Neurosci. 11, 127–138 (2010))1. Fiorillo raises some interesting questions about the formulation from a neurocentric perspective (A neurocentric approach to Bayesian inference. Nature Rev. Neurosci. 14 Jul 2010 (doi: 10.1038/nrn2787-c1))2:


title: SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling

abstract: Current software-based microarchitecture simulators are many orders of magnitude slower than the hardware they simulate. Hence, most microarchitecture design studies draw their conclusions from drastically truncated benchmark simulations that are often inaccurate and misleading. This paper presents the Sampling Microarchitecture Simulation (SMARTS) framework as an approach to enable fast and accurate performance measurements of full-length benchmarks. SMARTS accelerates simulation by selectively measuring in detail only an appropriate benchmark subset. SMARTS prescribes a statistically sound procedure for configuring a systematic sampling simulation run to achieve a desired quantifiable confidence in estimates.Analysis of 41 of the 45 possible SPEC2K benchmark/input combinations show CPI and energy per instruction (EPI) can be estimated to within ±3% with 99.7% confidence by measuring fewer than 50 million instructions per benchmark. In practice, inaccuracy in microarchitectural state initialization introduces an additional uncertainty which we empirically bound to ∼2% for the tested benchmarks. Our implementation of SMARTS achieves an actual average error of only 0.64% on CPI and 0.59% on EPI for the tested benchmarks, running with average speedups of 35 and 60 over detailed simulation of 8-way and 16-way out-of-order processors, respectively.


title: Determination of genetic diversity of South African intertidal limpets (Gastropoda: Siphonaria) with different reproductive modes using polyacrylamide gel electrophoresis of total cellular proteins

abstract: Abstract Using polyacrylamide gel electrophoresis of total soluble proteins, estimates of genetic variability were obtained from populations of seven species of southern African limpets. Among these sampled populations, greater levels of genetic variability were associated with the planktonic developing species, Siphonaria concinna, S. capensis and S. oculus with clear protein polymorphisms associated with S. concinna . The direct developing species, S. anneae, S. nigerrima, S. serrata and S. tenuicostulata were characterised by lower levels of genetic variability within a species and their mean value of genetic variability was lower than that of the planktonic developing species. All species had similar protein banding patterns and on a dendrogram produced from a similarity matrix they largely nested apart from each other and the three outgroups. The systematic relationships within Siphonaria were unclear. Six of the seven species fall into the subgenus Patellopsis but PAGE did not discriminate these from S. serrata of the subgenus Siphonaria . However, the earlier branching of the planktonic developing species with direct developing species nesting among them on the dendrogram suggests that the planktonic species may be primitive within the Patellopsis subgenus.


title: The relational model is dead, SQL is dead, and I don't feel so good myself

abstract: We report the opinions expressed by well-known database researchers on the future of the relational model and SQL during a panel at the International Workshop on Non-Conventional Data Access (NoCoDa 2012), held in Florence, Italy in October 2012 in conjunction with the 31st International Conference on Conceptual Modeling. The panelists include: Paolo Atzeni (Universita Roma Tre, Italy), Umeshwar Dayal (HP Labs, USA), Christian S. Jensen (Aarhus University, Denmark), and Sudha Ram (University of Arizona, USA). Quotations from movies are used as a playful though effective way to convey the dramatic changes that database technology and research are currently undergoing.


title: Erasure coded storage systems for cloud storage — challenges and opportunities

abstract: Erasure coded storage schemes offer a promising future for cloud storage. Highlights of erasure coded storage systems are that these offer the same level of fault tolerance as that of replication, at lower storage footprints. In the big data era, cloud storage systems based on data replication are of dubious usability due to 200% storage overhead in data replication systems. This has prompted storage service providers to use erasure coded storage as an alternative to replication. Refinements are required in various aspects of erasure coded storage systems to make it a real contender against data replication based storage systems. Streamlining huge bandwidth requirements during the recovery of failed nodes, inefficient update operations, effect of topology in recovery and consistency requirements of erasure coded storage systems, are some areas which need attention. This paper presents an in-depth study on the challenges faced, and research pursued in some of these areas. The survey shows that more research is required to improve erasure coded storage system from being bandwidth crunchers to efficient storage systems. Another challenge that has emerged from the study is the requirement of elaborate research for upgrading the erasure coded storage systems from being mere archival storage systems by providing better update methods. Provision of multiple level consistency in erasure coded storage is yet another research opportunity identified in this work. A brief introduction to open source libraries available for erasure coded storage is also presented in the paper.


title: Architecture support for accelerator-rich CMPs

abstract: This work discusses a hardware architectural support for accelerator-rich CMPs (ARC). First, we present a hardware resource management scheme for accelerator sharing. This scheme supports sharing and arbitration of multiple cores for a common set of accelerators, and it uses a hardware-based arbitration mechanism to provide feedback to cores to indicate the wait time before a particular resource becomes available. Second, we propose a light-weight interrupt system to reduce the OS overhead of handling interrupts which occur frequently in an accelerator-rich platform. Third, we propose architectural support that allows us to compose a larger virtual accelerator out of multiple smaller accelerators. We have also implemented a complete simulation tool-chain to verify our ARC architecture. Experimental results show significant performance (on average 51X) and energy improvement (on average 17X) compared to approaches using OS-based accelerator management.


title: A modular approach to soft robots

abstract: This paper describes a modular approach to creating soft robotic systems. The basis of these systems is an elastomeric actuation element powered by direct mechanical energy in the form of pressurized fluids. Fluidic elastomer actuators are fast and inexpensive to fabricate and offer safety and adaptability to robotic systems. Arrangements of these units can yield arbitrarily complex motions and achieve various functionalities. Actuation power can be generated on-board by a pneumatic battery, which harnesses the catalyzed chemical decomposition of hydrogen peroxide into oxygen gas, for mobile implementations. The modular nature of these robots enable distributed sensing and computation elements. Composition techniques of such soft robots are defined. Example systems are demonstrated and analyzed.


title: Class Distance Weighted Locality Preserving Projection for Automatic Age Estimation

abstract: We have developed new dimensionality reduction methods, extended from locality preserving projection (LPP), to estimate age using facial images. LPP seeks a linear transformation matrix such that optimally preserves the neighborhood structure of the data. Our focus has been on expanding LPP by making use of class label information. Specifically, one of our ideas is to assign weights only to the data with close class labels. A local scaling method is used for each class to compute the LPP affinity matrix. Another idea is to assign large weights to two samples with close class labels, i.e., close ages. By doing this, class label information for original data (i.e., age information) can be preserved. We thus call this "class distance weighted linear preserving projection" (CDLPP). Experimental results on a large database showed that CDLPP has more discriminative power than conventional methods such as PCA and LPP.


title: M*: A complete multirobot path planning algorithm with performance bounds

abstract: Multirobot path planning is difficult because the full configuration space of the system grows exponentially with the number of robots. Planning in the joint configuration space of a set of robots is only necessary if they are strongly coupled, which is often not true if the robots are well separated in the workspace. Therefore, we initially plan for each robot separately, and only couple sets of robots after they have been found to interact, thus minimizing the dimensionality of the search space. We present a general strategy called subdimensional expansion, which dynamically generates low dimensional search spaces embedded in the full configuration space. We also present an implementation of subdimensional expansion for robot configuration spaces that can be represented as a graph, called M*, and show that M* is complete and finds minimal cost paths.


title: OpenWordNet-PT: An Open Brazilian Wordnet for Reasoning

abstract: Brazilian Portuguese needs a Wordnet that is open access, downloadable and changeable, so that it can be improved by the community interested in using it for knowledge representation and automated deduction. This kind of resource is also very valuable to linguists and computer scientists interested in extracting and representing knowledge obtained from texts. We discuss briefly the reasons for a Brazilian Portuguese Wordnet and the process we used to get a preliminary version of such a resource. Then we discuss possible steps to improving our preliminary version.1


title: Forming impressions of people versus inanimate objects: Social-cognitive processing in the medial prefrontal cortex

abstract: Recent neuroimaging research has linked the task of forming a bperson impressionQ to a distinct pattern of neural activation that includes dorsal regions of the medial prefrontal cortex (mPFC). Although this result suggests the distinctiveness of social cognition – the processes that support inferences about the psychological aspects of other people – it remains unclear whether mPFC contributions to this impression formation task were person specific or if they would extend to other stimulus targets. To address this unresolved issue, participants in the current study underwent fMRI scanning while performing impression formation or a control task for two types of target: other people and inanimate objects. Specifically, participants were asked to use experimentally-provided information either to form an impression of a person or an object or to intentionally encode the sequence in which the information was presented. Results demonstrated that activation in an extensive region of dorsal mPFC was greater for impression formation of other people than for all other trial types, suggesting that this region specifically indexes the social-cognitive aspects of impression formation (i.e., understanding the psychological characteristics of another mental agent). These findings underscore the extent to which social cognition relies on distinct neural mechanisms.


title: Brownian motion and anomalous diffusion revisited via a fractional Langevin equation

abstract: In this paper we revisit the Brownian motion on the basis of {the fractional Langevin equation which turns out to be a particular case of the generalized Langevin equation introduced by Kubo in 1966. The importance of our approach is to model the Brownian motion more realistically than the usual one based on the classical Langevin equation, in that it takes into account also the retarding effects due to hydrodynamic back-flow, i.e. the added mass and the Basset memory drag. We provide the analytical expressions of the correlation functions (both for the random force and the particle velocity) and of the mean squared particle displacement. The random force has been shown to be represented by a superposition of the usual white noise with a"fractional"noise. The velocity correlation function is no longer expressed by a simple exponential but exhibits a slower decay, proportional to t^{-3/2} for long times, which indeed is more realistic. Finally, the mean squared displacement is shown to maintain, for sufficiently long times, the linear behaviour which is typical of normal diffusion, with the same diffusion coefficient of the classical case. However, the Basset history force induces a retarding effect in the establishing of the linear behaviour, which in some cases could appear as a manifestation of anomalous diffusion to be correctly interpreted in experimental measurements.


title: A Fuzzy AHP Approach for Supplier Selection Problem: A Case Study in a Gear Motor Company

abstract: Suuplier selection is one of the most important functions of a purchasing department. Since by deciding the best supplier, companies can save material costs and increase competitive advantage.However this decision becomes compilcated in case of multiple suppliers, multiple conflicting criteria, and imprecise parameters. In addition the uncertainty and vagueness of the experts' opinion is the prominent characteristic of the problem. therefore an extensively used multi criteria decision making tool Fuzzy AHP can be utilized as an approach for supplier selection problem. This paper reveals the application of Fuzzy AHP in a gear motor company determining the best supplier with respect to selected criteria. the contribution of this study is not only the application of the Fuzzy AHP methodology for supplier selection problem, but also releasing a comprehensive literature review of multi criteria decision making problems. In addition by stating the steps of Fuzzy AHP clearly and numerically, this study can be a guide of the methodology to be implemented to other multiple criteria decision making problems.


title: Comparison of passive cell balancing and active cell balancing for automotive batteries

abstract: This paper presents a quantitative performance analysis of a conventional passive cell balancing method and a proposed active cell balancing method for automotive batteries. The proposed active cell balancing method was designed to perform continuous cell balancing during charge and discharge with high balancing current. An experimentally validated model was used to simulate the balancing process of both balancing circuits for a high capacity battery module. The results suggest that the proposed method can improve the power loss and extend the discharge time of a battery module. Hence, a higher energy output can be yielded.


title: In vitro digestion, physicochemical and morphological properties of low glycemic index rice flour prepared through enzymatic hydrolysis

abstract: ABSTRACT Low glycemic index (GI) rice flour was prepared through enzymatic hydrolysis. Slowly digestible and resistant starch contents increased significantly (p ≤ 0.05) by 195.82% and 204.03%, respectively, while as total, rapidly digestible and digestible starch contents decreased significantly (p ≤ 0.05) by 55.40%, 51.67%, and 18.74%, respectively, after enzymatic hydrolysis. Glycemic load of low GI rice flour was significantly (p ≤ 0.05) lower than untreated rice flour. The degradation temperature of low GI rice flour (~275°C) was lower compared to untreated rice flour (~298°C). Particle size distribution analysis indicated that both surface mean- D [3, 2] and volume mean diameter – D[4, 3] of low GI rice flour were higher than untreated rice flour. SEM results revealed numerous cracks in low GI rice flour whereas the surfaces of starch granules of untreated rice flour were smooth without scratches. Low GI rice flour presented A- and V-type polymorphs while as untreated rice flour showed only A-type crystallinity pattern.


Convolutional Networks for Images Speech and Time Series


Proposal Techniques and Solutions for Addressing Ransomware Attacks


title: A wave-to-wire model of ocean wave energy conversion system using MATLAB/Simulink platform

abstract: Renewable energy sources, unlike the conventional combustible fuels, are naturally distributed and extensively available in a boundless manner all over the world in different forms. Here, in this paper, authors elucidate the scopes and opportunities of the ocean wave to develop a low-cost, environmental friendly, and sustainable electrical power generation system. At the present time most technological modernizations aimed at exploiting such resources are at early stage of development, with only a handful of devices close to be at the commercial demonstration stage. None of them, though, operates converting the wave energy contents at its very origin: the orbital motion of water particles right below the ocean surface. The Sea spoon device catches the kinetic energy of ocean waves with favorable conversion proficiency, according to specific "wave-motion climate". In this letter, authors illustrate a possible methodology of converting this naturally exorbitant energy with efficient conversion methodology and simulating the conversion environment with MATLAB/Simulink platform.


title: Beam element verification for 3D elastic steel frame analysis

abstract: The paper describes the attributes that should be possessed by a benchmark example for verifying the beam elements used to carry out 3D linear buckling analysis and 3D second-order elastic analysis of steel frames. Based on the attributes described, the paper proposes a suite of benchmark examples selected from the literature. The necessary features of a beam element required to pass the proposed benchmark problems are given, and beam elements that possess these features are cited. The paper also explains the merits of linear buckling analysis examples, and provides a commentary on two well-known examples.


title: Studying Very Low Resolution Recognition Using Deep Networks

abstract: Visual recognition research often assumes a sufficient resolution of the region of interest (ROI). That is usually violated in practice, inspiring us to explore the Very Low Resolution Recognition (VLRR) problem. Typically, the ROI in a VLRR problem can be smaller than $16 \times 16$ pixels, and is challenging to be recognized even by human experts. We attempt to solve the VLRR problem using deep learning methods. Taking advantage of techniques primarily in super resolution, domain adaptation and robust regression, we formulate a dedicated deep learning method and demonstrate how these techniques are incorporated step by step. Any extra complexity, when introduced, is fully justified by both analysis and simulation results. The resulting \textit{Robust Partially Coupled Networks} achieves feature enhancement and recognition simultaneously. It allows for both the flexibility to combat the LR-HR domain mismatch, and the robustness to outliers. Finally, the effectiveness of the proposed models is evaluated on three different VLRR tasks, including face identification, digit recognition and font recognition, all of which obtain very impressive performances.


title: A Runtime System for Generalized Committed Choice

abstract: Traditional nondeterministic programming constructs (Dijkstra guards, CCP [6] and deep guards [10]) do not allow operations which modify the runtime environment without committing to a particular alternative. Generalized committed choice (GCC) allows speculative computations across different alternatives to execute in parallel and isolation. Speculation implicitly forks an environment into separate ones for the alternatives and later one of these environments can be committed to [3]. Speculations from concurrent processes can nevertheless interleave and synchronize against each other. In this paper, we present a concrete architecture, its implementation and optimizations for GCC where the store of the environment is in the form of record spaces. Our prototype implementation allows GCC to be embedded in traditional languages such as C/C++. Preliminary experimental results show that our runtime and GCC extension is a suitable coordination language for programming multiple distributed agents which employ speculation and choices.


title: Benefits and costs of adaptive user interfaces

abstract: The paper examines the positive and the possible adverse effects of adaptive user interfaces (AUIs) in the context of an in-vehicle telematic system as a function of four factors: (1) four different levels of adaptivity (ranging from manual to fully adaptive with intermediate levels); (2) different tasks; (3) routine (familiar) and non-routine (unfamiliar) situations; and (4) different user age groups. Both experiments included three sessions during which participants drove a simple driving simulator and performed tasks with the telematic system at one of the adaptivity levels. We measured task performance times and lane position variance. Adaptivity was not always equally beneficial, and its benefits depended on a number of factors, including the frequency in which the tasks were performed, the user's age, the difficulty of the task and the user's involvement in the task. In familiar, routine situations, a fully adaptive system was beneficial for all participants, particularly older ones. In unfamiliar situations, to which the AUI was not adjusted, cognitive workload increased substantially, adversely affecting performance. Intermediate levels of adaptivity keep users involved in the task and help them become more proficient when performing both routine and non-routine tasks. However, intermediate levels of adaptivity should also be implemented with care, because they may also have adverse effects when users encounter non-routine situations.


title: The Computational Creativity Complex

abstract: We briefly examine the subject of computational creativity through the lenses of three different systems for producing creative artifacts in three different domains: music, visual art and cookery. From these we attempt to abstract something of a “general-purpose” computationally creative agent and examine how this agent might behave in the context of an archetypical “algorithm” for creativity. Finally, we consider this agent’s behavior from the point of view of the theory of (Turing) computability and suggest that computational creativity as a field provides an interesting opportunity for looking at computational complexity in new ways.


title: Transitional feedback schedules during computer-based problem-solving practice

abstract: Feedback has a strong influence on effective learning from computer-based instruction. Prior research on feedback in computer-based instruction has mainly focused on static feedback schedules that employ the same feedback schedule throughout an instructional session. This study examined transitional feedback schedules in computer-based multimedia instruction on procedural problem-solving in electrical circuit analysis. Specifically, we compared two transitional feedback schedules: the TFS-P schedule switched from initial feedback after each problem step to feedback after a complete problem at later learning states; the TFP-S schedule transitioned from feedback after a complete problem to feedback after each problem step. As control conditions, we also considered two static feedback schedules, namely providing feedback after each practice problem-solving step (SFS) or providing feedback after attempting a complete multi-step practice problem (SFP). Results indicate that the static stepwise (SFS) and transitional stepwise to problem (TFS-P) feedback produce higher problem solving near-transfer post-test performance than static problem (SFP) and transitional problem to step (TFP-S) feedback. Also, TFS-P resulted in higher ratings of program liking and feedback helpfulness than TFP-S. Overall, the study results indicate benefits of maintaining high feedback frequency (SFS) and reducing feedback frequency (TFS-P) compared to low feedback frequency (SFP) or increasing feedback frequency (TFP-S) as novice learners acquire engineering problem solving skills. An experiment investigated feedback schedules in problem solving instruction.Static stepwise feedback outperformed problem (summative) feedback on posttest.Stepwise-problem feedback schedule outperformed problem-stepwise feedback.Initial stepwise feedback is critical for problem-solving learning by novices.


title: Computational photography: Epsilon to coded photography

abstract: Computational photography combines plentiful computing, digital sensors, modern optics, actuators, and smart lights to escape the limitations of traditional cameras, enables novel imaging applications and simplifies many computer vision tasks. However, a majority of current Computational photography methods involves taking multiple sequential photos by changing scene parameters and fusing the photos to create a richer representation. Epsilon photography is concerned with synthesizing omnipictures and proceeds by multiple capture single image paradigm (MCSI).The goal of Coded computational photography is to modify the optics, illumination or sensors at the time of capture so that the scene properties are encoded in a single (or a few) photographs. We describe several applications of coding exposure, aperture, illumination and sensing and describe emerging techniques to recover scene parameters from coded photographs.


title: The Self-Reference Effect on Memory in Early Childhood

abstract: The self-reference effect in memory is the advantage for information encoded about self, relative to other people. The early development of this effect was explored here using a concrete encoding paradigm. Trials comprised presentation of a self- or other-image paired with a concrete object. In Study 1, 4- to 6-year-old children (N = 53) were asked in each trial whether the child pictured would like the object. Recognition memory showed an advantage for self-paired objects. Study 2 (N = 55) replicated this finding in source memory. In Study 3 (N = 56), participants simply indicated object location. Again, recognition and source memory showed an advantage for self-paired items. These findings are discussed with reference to mechanisms that ensure information of potential self-relevance is reliably encoded.


title: Temperament and characteristics related to nomophobia

abstract: Abstract Nomophobia is defined as the fear of being out of mobile phone contact and is considered to be a phobia of the modern age. The current study set out to establish the relationship between temperament and personality and the development of nomophobia. The sample was composed of 968 participants selected from the Andalusian population, of which there were 182 males and 785 females aged from 23.19 years. The instruments used were the Questionnaire to Assess Nomophobia (QANIP; Olivencia-Carrion et al., 2018) and the Temperament and Character Inventory Revised (TCI-R; Cloninger et al., 1993). We found that cooperation is a characteristic that significantly reduces nomophobic levels, particularly for the two factors of Mobile Phone Addiction and Negative Consequences. Furthermore, Reward Dependence appears to be positively related to two of the factors involved in nomophobia, namely Mobile Phone Addiction and Loss of Control , suggesting a relationship between Nomophobia and personality. These findings are discussed in terms of their usefulness for identifying the personality predictors of nomophobia in order to develop preventive and intervention strategies.


title: Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

abstract: Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations. However, there has not been a rigorous evaluation regarding the added value of sophisticated compositional functions. In this paper, we conduct a point-by-point comparative study between Simple Word-Embedding-based Models (SWEMs), consisting of parameter-free pooling operations, relative to word-embedding-based RNN/CNN models. Surprisingly, SWEMs exhibit comparable or even superior performance in the majority of cases considered. Based upon this understanding, we propose two additional pooling strategies over learned word embeddings: (i) a max-pooling operation for improved interpretability; and (ii) a hierarchical pooling operation, which preserves spatial (n-gram) information within text sequences. We present experiments on 17 datasets encompassing three tasks: (i) (long) document classification; (ii) text sequence matching; and (iii) short text tasks, including classification and tagging. The source code and datasets can be obtained from https:// github.com/dinghanshen/SWEM.


title: When technology meets technology: Retrained ‘Inception V3’ classifier for NGS based pathogen detection

abstract: Accurate characterization of pathogenic microbes that may be present in food or clinical samples is essential in the design of appropriate intervention strategies. Inherent genomic patterns (codon-biases and rate of evolution) do simplify the classification of microbes at most taxonomic levels (genus and above), but mostly blur classification at Species/Strain levels. Hence, their classification at these finer taxonomic levels requires high-resolution genomic-data that provide SNP (Single Nucleotide Polymorphism) level precision. Existing classification methods involve either targeted amplification of sero-specific genes (serotyping and MLST) or sequencing of the entire microbial genome, both of which require extra time and resources. We present a computational approach, which harnesses the power of the metagenomic NGS-data and object-detection abilities of Convolutional Neural Networks (CNN)(Inception V3), for precise classification of pathogens by converting genomic-data (NGS-reads) into images (nucleotide-by-color). A small scale retraining (<50 images/class) of ‘Inception V3’ resulted in a classifier with 100% and 96% validation and test accuracies, respectively, when classifying pathogens such as Campylobacter coli/jejuni and Escherichia coli (O157:H7 and Non O157-STECs). We aim to extend this protocol to the detection of several microbes (multiple-objects) in a metagenomic image (genomic image of an entire microbial community).


title: Which One to Read? Factors Influencing the Usefulness of Online Reviews for RE

abstract: Reviews for software products contain much information about the users' requirements and preferences, which can be very useful to the requirements engineer. However, taking advantage of this information is not easy due to the large and overwhelming number of reviews that is posted in various channels. Machine learning and opinion mining techniques have therefore been used to process the reviews automatically and to generate summaries of the data to the requirements engineer. However, one of the important challenges for these techniques lies in how to automatically assess the relevance of the reviews for the requirements engineer. So far, most techniques use intuition-based criteria for this task. In this work, we collect and present a list of factors that were found to impact the helpfulness of product reviews for customers. We then discuss to what extent these factors are likely to impact the usefulness of reviews for requirements engineering tasks. The factors can be used to support the automated identification of relevant reviews.


title: An iBeacon primer for indoor localization: demo abstract

abstract: Providing an accurate, low cost estimate of sub-room indoor positioning remains an active area of research with applications including reactive indoor spaces, dynamic temperature control, wireless health, and augmented reality, to name a few. Recently proposed indoor localization solutions have required anywhere from zero additional infrastructure to customized RF hardware and have provided room-level to centimeter-level accuracy, typically in that respective order. One emerging technology that is proving a pragmatic solution for scalable, accurate localization is that of Bluetooth Low Energy beaconing, spearheaded by Apple's recently introduced iBeacon protocol. In this demo, we present a suite of localization tools developed around the iBeacon protocol, providing an in-depth look at Bluetooth Low Energy's viability as an indoor positioning technology. Our system shows an average position estimation error of 0.53 meters.


title: A Video Game Level Analysis Model Proposal

abstract: This paper presents a preliminary model proposal for the analysis of video game level scenarios consisting of four elements: video game, players, metrics, and gaming experience. The selection of parameters from each of these elements defines a specific video game level analysis context. Initial validation of the model is being carried out with a First-person shooter video game and selected metrics (including eye movement behavior) to understand player behavior (both interactive and visual) within a game level. An additional analysis technique based on visual representation techniques applied in this validation context is also described. After validated, this model may be applied in the analysis of player behavior in game levels, in several different situations predicted in the model itself. Information acquired from the application of this model may posteriorly be used in the design (or redesign) of game levels and scenarios driven for optimal use which should generate a greater gaming experience.


title: Tracking multi-object using tracklet and Faster R-CNN: PhD Forum

abstract: In this work, we propose an original approach for tracking using tracklets (mini-trajectories) and Faster R-CNN. In fact, the major tracking problem is how an object can keep the same ID during the entire trajectory. To solve problems of tracking, we proceed on the one hand by the definition of a specific signature for each detected object to build tracklets. On the other hand, we associate these mini-trajectories in order to have complete trajectory.


title: Tractable learning for complex probability queries

abstract: Tractable learning aims to learn probabilistic models where inference is guaranteed to be efficient. However, the particular class of queries that is tractable depends on the model and underlying representation. Usually this class is MPE or conditional probabilities Pr(x|y) for joint assignments x, y. We propose a tractable learner that guarantees efficient inference for a broader class of queries. It simultaneously learns a Markov network and its tractable circuit representation, in order to guarantee and measure tractability. Our approach differs from earlier work by using Sentential Decision Diagrams (SDD) as the tractable language instead of Arithmetic Circuits (AC). SDDs have desirable properties, which more general representations such as ACs lack, that enable basic primitives for Boolean circuit compilation. This allows us to support a broader class of complex probability queries, including counting, threshold, and parity, in polytime.


title: Patterns of temporal variation in online media

abstract: Online content exhibits rich temporal dynamics, and diverse realtime user generated content further intensifies this process. However, temporal patterns by which online content grows and fades over time, and by which different pieces of content compete for attention remain largely unexplored. We study temporal patterns associated with online content and how the content's popularity grows and fades over time. The attention that content receives on the Web varies depending on many factors and occurs on very different time scales and at different resolutions. In order to uncover the temporal dynamics of online content we formulate a time series clustering problem using a similarity metric that is invariant to scaling and shifting. We develop the K-Spectral Centroid (K-SC) clustering algorithm that effectively finds cluster centroids with our similarity measure. By applying an adaptive wavelet-based incremental approach to clustering, we scale K-SC to large data sets. We demonstrate our approach on two massive datasets: a set of 580 million Tweets, and a set of 170 million blog posts and news media articles. We find that K-SC outperforms the K-means clustering algorithm in finding distinct shapes of time series. Our analysis shows that there are six main temporal shapes of attention of online content. We also present a simple model that reliably predicts the shape of attention by using information about only a small number of participants. Our analyses offer insight into common temporal patterns of the content on theWeb and broaden the understanding of the dynamics of human attention.


title: Learning Descriptors for Object Recognition and 3D Pose Estimation

abstract: Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.


title: Learning Music Embedding with Metadata for Context Aware Recommendation

abstract: Contextual factors can benefit music recommendation and retrieval tasks remarkably. However, how to acquire and utilize the contextual information still need to be studied. In this paper, we propose a context aware music recommendation approach, which can recommend music appropriate for users' contextual preference for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require songs to be described by features beforehand, but it learns music pieces' embeddings (vectors in low-dimensional continuous space) from music playing records and corresponding metadata and infer users' general and contextual preference for music from their playing records with the learned embedding. Then, our approach can recommend appropriate music pieces. Experimental evaluations on a real world dataset show that the proposed approach outperforms baseline methods.


title: CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services

abstract: Cloud computing focuses on delivery of reliable, secure, fault-tolerant, sustainable, and scalable infrastructures for hosting Internet-based application services. These applications have different composition, configuration, and deployment requirements. Quantifying the performance of scheduling and allocation policy on a Cloud infrastructure (hardware, software, services) for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is an extremely challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: a new generalized and extensible simulation framework that enables seamless modelling, simulation, and experimentation of emerging Cloud computing infrastructures and management services. The simulation framework has the following novel features: (i) support for modelling and instantiation of large scale Cloud computing infrastructure, including data centers on a single physical computing node and java virtual machine; (ii) a self-contained platform for modelling data centers, service brokers, scheduling, and allocations policies; (iii) availability of virtualization engine, which aids in creation and management of multiple, independent, and co-hosted virtualized services on a data center node; and (iv) flexibility to switch between space-shared and time-shared allocation of processing cores to virtualized services.


title: BoneJ: Free and extensible bone image analysis in ImageJ

abstract: Bone geometry is commonly measured on computed tomographic (CT) and X-ray microtomographic (μCT) images. We obtained hundreds of CT, μCT and synchrotron μCT images of bones from diverse species that needed to be analysed remote from scanning hardware, but found that available software solutions were expensive, inflexible or methodologically opaque. We implemented standard bone measurements in a novel ImageJ plugin, BoneJ, with which we analysed trabecular bone, whole bones and osteocyte lacunae. BoneJ is open source and free for anyone to download, use, modify and distribute.


title: Getting the Cocktail Party Started: Masking Effects in Speech Perception

abstract: Spoken conversations typically take place in noisy environments, and different kinds of masking sounds place differing demands on cognitive resources. Previous studies, examining the modulation of neural activity associated with the properties of competing sounds, have shown that additional speech streams engage the superior temporal gyrus. However, the absence of a condition in which target speech was heard without additional masking made it difficult to identify brain networks specific to masking and to ascertain the extent to which competing speech was processed equivalently to target speech. In this study, we scanned young healthy adults with continuous fMRI, while they listened to stories masked by sounds that differed in their similarity to speech. We show that auditory attention and control networks are activated during attentive listening to masked speech in the absence of an overt behavioral task. We demonstrate that competing speech is processed predominantly in the left hemisphere within the same pathway as target speech but is not treated equivalently within that stream and that individuals who perform better in speech in noise tasks activate the left mid-posterior superior temporal gyrus more. Finally, we identify neural responses associated with the onset of sounds in the auditory environment; activity was found within right lateralized frontal regions consistent with a phasic alerting response. Taken together, these results provide a comprehensive account of the neural processes involved in listening in noise.


title: CUDA vs OpenACC: Performance Case Studies with Kernel Benchmarks and a Memory-Bound CFD Application

abstract: OpenACC is a new accelerator programming interface that provides a set of OpenMP-like loop directives for the programming of accelerators in an implicit and portable way. It allows the programmer to express the offloading of data and computations to accelerators, such that the porting process for legacy CPU-based applications can be significantly simplified. This paper focuses on the performance aspects of OpenACC using two micro benchmarks and one real-world computational fluid dynamics application. Both evaluations show that in general OpenACC performance is approximately 50\% lower than CUDA. However, for some applications it can reach up to 98\% with careful manual optimizations. The results also indicate several limitations of the OpenACC specification that hamper full use of the GPU hardware resources, resulting in a significant performance gap when compared to a fully tuned CUDA code. The lack of a programming interface for the shared memory in particular results in as much as three times lower performance.


title: Comparative evaluation of latency reducing and tolerating techniques

abstract: Techniques that can cope with the large latency of memory accesses are essential for achieving high processor utilization in large-scale shared-memory multiprocessors. In this paper, we consider four architectural techniques that address the latency problem: (i) hardware coherent caches, (ii) relaxed memory consistency, (iii) softwareconuolled prefetching, and (iv) multiple-context suppon. We some studies of benefits of the individual techniques have been done, no Study evaluates all of the techniques within a consistent framework. This paper attempts to remedy this by providing a comprehensive evaluation of the benefits of the four techniques, both individually and in combinations, using a consistent set of architectural assumptions. The results in this paper have been obtained using detailed simulations of a large-scale shared-memory multiprocessor. Our results show that caches and relaxed consistency UNformly improve performance. The improvements due to prefetching and multiple contexts are sizeable, but are much more applicationdependent. Combinations of the various techniques generally amin better performance than each one on its own. Overall, we show that using suitahle combinations of the techniques, performance can be improved by 4 to 7 dmes


title: Integrating Wireless Sensor Networks with Cloud Computing

abstract: Wireless Sensor Networks (WSN) has been a focus for research for several years. WSN enables novel and attractive solutions for information gathering across the spectrum of endeavour including transportation, business, health-care, industrial automation, and environmental monitoring. Despite these advances, the exponentially increasing data extracted from WSN is not getting adequate use due to the lack of expertise, time and money with which the data might be better explored and stored for future use. The next generation of WSN will benefit when sensor data is added to blogs, virtual communities, and social network applications. This transformation of data derived from sensor networks into a valuable resource for information hungry applications will benefit from techniques being developed for the emerging Cloud Computing technologies. Traditional High Performance Computing approaches may be replaced or find a place in data manipulation prior to the data being moved into the Cloud. In this paper, a novel framework is proposed to integrate the Cloud Computing model with WSN. Deployed WSN will be connected to the proposed infrastructure. Users request will be served via three service layers (IaaS, PaaS, SaaS) either from the archive, archive is made by collecting data periodically from WSN to Data Centres (DC), or by generating live query to corresponding sensor network.


title: Holistic Processing Predicts Face Recognition

abstract: The concept of holistic processing is a cornerstone of face-recognition research. In the study reported here, we demonstrated that holistic processing predicts face-recognition abilities on the Cambridge Face Memory Test and on a perceptual face-identification task. Our findings validate a large body of work that relies on the assumption that holistic processing is related to face recognition. These findings also reconcile the study of face recognition with the perceptual-expertise work it inspired; such work links holistic processing of objects with people's ability to individuate them. Our results differ from those of a recent study showing no link between holistic processing and face recognition. This discrepancy can be attributed to the use in prior research of a popular but flawed measure of holistic processing. Our findings salvage the central role of holistic processing in face recognition and cast doubt on a subset of the face-perception literature that relies on a problematic measure of holistic processing.


title: Track level fusion of extended objects from heterogeneous sensors

abstract: The detection and tracking of extended targets is a challenging problem. The accuracy of the detections and the tracking is dependent on the granularity and the quality of the data obtained from the sensors. The systems specialised for automotive environment perception task are mostly a mix of high and low resolution sensors, equipped with their own target state estimation module. The sensors provide the fusion center, the target state estimates along with information on the target shape such as length and width. This paper focuses on deriving fused extended shapes from the set of tracked objects obtained from heterogeneous environment perception sensors. The concept and the procedure described in this paper is unique for the fusion of target extension and shape information. The algorithm is analytically investigated to analyse its performance bounds. A detailed Monte Carlo simulation has been carried out with different trajectories and noise sources to ascertain the performance. The interesting part of this paper is the results obtained using real world sensor data in a real traffic condition using high precision ground truth sensors for the sensor platform and the target vehicle.


title: Route Improvement Algorithm for Vehicle Routing Problem with Time-Dependent Travel Times

abstract: Congestion creates a substantial variation in travel speeds during peak morning and evening hours. Local search approaches and metaheuristics that have been successfully applied to traditional constant speed routing problems are not suitable for settings where there is a significant variation of travel speed. This research presents a new solution approach for the time-dependent vehicle routing problem (TDVRP) with hard or soft time windows. Improvements are obtained at a route level, hence the proposed approach does not rely on any type of local improvement procedure. Leveraging on the well known Solomon instances, new test problems that capture the typical speed variations of congested urban settings are proposed. Results in terms of solution quality as well as computational time as a function of the number of customers and time periods are presented.


title: Super-high-speed switched reluctance motor for automotive traction

abstract: This paper presents a Switched Reluctance Motor (SRM) capable of running up to 50,000rpm whilst producing an output power of 60kW. The high speed operation allows for a smaller overall size, which in turn leads to lower manufacturing costs. It is demonstrated that, at very high speeds, merely shrinking the motor is not enough and certain mechanical considerations should be taken into account. A rotor with a flywheel type design that uses bolts to secure the laminations rather than feeding them onto a central shaft is introduced. Electromagnetic and mechanical simulation results for the motor are presented for key operating points, the results confirm that the stresses on the rotor in the proposed topology are below the yield strength of the material with a safety factor of 1.2. This combined with high output power might make a viable design for use in mass production hybrid electric vehicles.


title: Robust Distance Metric Learning via Bayesian Inference

abstract: Distance metric learning (DML) has achieved great success in many computer vision tasks. However, most existing DML algorithms are based on point estimation, and thus are sensitive to the choice of training examples and tend to be over-fitting in the presence of label noise. In this paper, we present a robust DML algorithm based on Bayesian inference. In particular, our method is essentially a Bayesian extension to a previous classic DML method—large margin nearest neighbor classification and we use stochastic variational inference to estimate the posterior distribution of the transformation matrix. Furthermore, we theoretically show that the proposed algorithm is robust against label noise in the sense that an arbitrary point with label noise has bounded influence on the learnt model. With some reasonable assumptions, we derive a generalization error bound of this method in the presence of label noise. We also show that the DML hypothesis class in which our model lies is probably approximately correct-learnable and give the sample complexity. The effectiveness of the proposed method1 is demonstrated with state of the art performance on three popular data sets with different types of label noise.1A MATLAB implementation of this method is made available at http://parnec.nuaa.edu.cn/xtan/Publication.htm


title: An Introduction to Probabilistic Programming

abstract: This document is designed to be a first-year graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages. ::: We start with a discussion of model-based reasoning and explain why conditioning as a foundational computation is central to the fields of probabilistic machine learning and artificial intelligence. We then introduce a simple first-order probabilistic programming language (PPL) whose programs define static-computation-graph, finite-variable-cardinality models. In the context of this restricted PPL we introduce fundamental inference algorithms and describe how they can be implemented in the context of models denoted by probabilistic programs. ::: In the second part of this document, we introduce a higher-order probabilistic programming language, with a functionality analogous to that of established programming languages. This affords the opportunity to define models with dynamic computation graphs, at the cost of requiring inference methods that generate samples by repeatedly executing the program. Foundational inference algorithms for this kind of probabilistic programming language are explained in the context of an interface between program executions and an inference controller. ::: This document closes with a chapter on advanced topics which we believe to be, at the time of writing, interesting directions for probabilistic programming research; directions that point towards a tight integration with deep neural network research and the development of systems for next-generation artificial intelligence applications.


title: Factors influencing internet addiction tendency among middle school students in Gyeong-buk area.

abstract: PURPOSE ::: This study was conducted to understand the degree of internet addiction tendency and to find out the factors influencing this addiction tendency among middle school students in Gyeong-buk area. ::: ::: ::: METHODS ::: A total of 450 middle school students in the Daegu and Gyeong-buk area were surveyed in this study. Data collection was conducted through the use of questionnaires. ::: ::: ::: RESULTS ::: Internet addiction among middle school students was relatively low (Average user). In the overall ratio distribution, however, students who were classified as either addicted or at risk of addiction accounted for a high percentage, 27%. A positive correlation was found between Internet addiction and Internet expectation, depression and parent control over Internet use. A negative correlation was found between Internet addiction and interpersonal relationship, parent support and self-control. Multiple regression analysis revealed that the most powerful predictor of Internet addiction tendency was depression. ::: ::: ::: CONCLUSION ::: Through the above results, it would be necessary to develop an Internet addiction prevention program for adolescents taking into account for the psychological factors such as depression and Internet use habits. In the future study, the need assessment will be useful for developing this prevention program.


title: Leveraging Sparse and Dense Feature Combinations for Sentiment Classification

abstract: Neural networks are one of the most popular approaches for many natural language processing tasks such as sentiment analysis. They often outperform traditional machine learning models and achieve the state-of-art results on most tasks. However, many existing deep learning models are complex, difficult to train and provide a limited improvement over simpler methods. We propose a simple, robust and powerful model for sentiment classification. This model outperforms many deep learning models and achieves comparable results to other deep learning models with complex architectures on sentiment analysis datasets. We publish the code online.


title: FOUP purge performance improvement using EFEM flow converter

abstract: Front Opening Unified Pod (FOUP) purging with nitrogen or clean dry air (CDA) is well known as the most effective method to protect wafers inside FOUPs from internal as well as external contaminations, e.g. particles, airborne molecular contaminants, humidity and oxygen, which can potentially damage integrated circuits (IC) and significantly impact manufacturing yields. Entegris, Inc. developed a diffuser purging concept that has demonstrated exceptional purge performance over conventional methods under the FOUP door open purging condition, however some unexpected test results were reported in field tests because the diffuser purge performance is significantly affected by Equipment Front End Module (EFEM) flow patterns. Since the EFEM flow is much stronger than the purging flow, in some cases the purging flow cannot overcome the EFEM flow deflected toward the FOUP opening. Furthermore, most of the EFEM flow patterns cannot be easily defined due to the variety of the EFEM designs and operation conditions, which makes it difficult to design a universal diffuser that works with all EFEMs in the field. Therefore, a need exists to develop a simple way to mitigate the effect of the EFEM air flow on the FOUP purging flow. This research was conducted to investigate the feasibility of the EFEM flow converter (EFC) concept, which is a screen mesh installed above the FOUP opening. The test results demonstrated that the EFC can dramatically improve the diffuser purge performance by converting non-uniform EFEM flow in front of the FOUP opening into uniform downward laminar flow that is favorable to FOUP purging.


title: An Evaluation of Microarray Visualization Tools for Biological Insight

abstract: High-throughput experiments such as gene expression microarrays in the life sciences result in large datasets. In response, a wide variety of visualization tools have been created to facilitate data analysis. Biologists often face a dilemma in choosing the best tool for their situation. The tool that works best for one biologist may not work well for another due to differences in the type of insight they seek from their data. A primary purpose of a visualization tool is to provide domain-relevant insight into the data. Ideally, any user wants maximum information in the least possible time. In this paper we identify several distinct characteristics of insight that enable us to recognize and quantify it. Based on this, we empirically evaluate five popular microarray visualization tools. Our conclusions can guide biologists in selecting the best tool for their data, and computer scientists in developing and evaluating visualizations


title: Variation in FTO contributes to childhood obesity and severe adult obesity

abstract: We identified a set of SNPs in the first intron of the FTO (fat mass and obesity associated) gene on chromosome 16q12.2 that is consistently strongly associated with early-onset and severe obesity in both adults and children of European ancestry with an experiment-wise P value of 1.67 x 10(-26) in 2,900 affected individuals and 5,100 controls. The at-risk haplotype yields a proportion of attributable risk of 22% for common obesity. We conclude that FTO contributes to human obesity and hence may be a target for subsequent functional analyses.


title: Leveraging the linkedin social network data for extracting content-based user profiles

abstract: In the last years, hundreds of social networks sites have been launched with both professional (e.g., LinkedIn) and non-professional (e.g., MySpace, Facebook) orientations. This resulted in a renewed information overload problem, but it also provided a new and unforeseen way of gathering useful, accurate and constantly updated information about user interests and tastes. Content-based recommender systems can leverage the wealth of data emerging by social networks for building user profiles in which representations of the user interests are maintained. The idea proposed in this paper is to extract content-based user profiles from the data available in the LinkedIn social network, to have an image of the users' interests that can be used to recommend interesting academic research papers. A preliminary experiment provided interesting results which deserve further attention.


title: Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning

abstract: Current Neural Network learning algorithms are limited in their ability to model non-linear dynamical systems. Most supervised gradient-based recurrent neural networks (RNNs) suffer from a vanishing error signal that prevents learning from inputs far in the past. Those that do not, still have problems when there are numerous local minima. We introduce a general framework for sequence learning, EVOlution of recurrent systems with LINear outputs (Evolino). Evolino uses evolution to discover good RNN hidden node weights, while using methods such as linear regression or quadratic programming to compute optimal linear mappings from hidden state to output. Using the Long Short-Term Memory RNN Architecture, the method is tested in three very different problem domains: 1) context-sensitive languages, 2) multiple superimposed sine waves, and 3) the Mackey-Glass system. Evolino performs exceptionally well across all tasks, where other methods show notable deficiencies in some.


title: Basic Local Alignment Search Tool

abstract: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.


title: Branch and bound for informative path planning

abstract: We present an optimal algorithm for informative path planning (IPP), using a branch and bound method inspired by feature selection algorithms. The algorithm uses the monotonicity of the objective function to give an objective function-dependent speedup versus brute force search. We present results which suggest that when maximizing variance reduction in a Gaussian process model, the speedup is significant.


title: Bipolar Fuzzy Trees

abstract: Connectivity has an important role in different discipline s of computer science including computer network. In the design of a network, it is important to analyze connections by the levels. The structural properties of bipolar fuzzy graphs provide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of bipolar fuzzy bridges, bipolar fuzzy cut-vertices, bipolar fuzzy cycles and bipolar fuzzy trees in bipolar fuzzy graphs, and investigate some of their properties. Most of these various types are defined in terms of levels. We also describe comparison of these types.


title: An interactive speech therapy session using linear predictive coding in Matlab and Arduino

abstract: Speech Therapy has become an efficient tool to bring back proper speech for patients suffering from various speech disorders. Patients are more benefitted when the speech therapy session is interactive where there is a visible change in the environment. Hence, the proposed system aims at manipulating devices when the user input is correct and also indicates if the user input is incorrect. Speech recognition has been done using the concept if Linear predictive coding and Arduino Uno board is used for hardware interface.


title: Effectiveness of Cognitive-Functional (Cog-Fun) Intervention With Children With Attention Deficit Hyperactivity Disorder: A Pilot Study

abstract: The executive function (EF) deficits of children with attention deficit hyperactivity disorder (ADHD) hinder their performance of complex daily functions. Despite the existing evidence-based pharmacological interventions for ADHD symptoms, no intervention has yet been found that deals directly with EFs in daily tasks. Fourteen children and their parents participated in the Cognitive‐Functional (Cog‐Fun) program in occupational therapy, which is tailored to the executive dysfunction of ADHD and focuses on enabling cognitive strategies for occupational performance. The study included initial assessment of EFs (Behavior Rating Inventory of Executive Functions; Tower of London DX ), occupational performance (Canadian Occupational Performance Measure), 10 sessions of Cog‐Fun intervention with each child‐parent dyad, and postintervention and 3-month follow-up assessments. We found significant improvements with medium to large effects on outcome measures after intervention, and most effects were maintained at follow-up. The findings warrant controlled studies examining the effectiveness of this intervention for children with ADHD. Hahn-Markowitz, J., Manor, I., & Maeir, A. (2011). Effectiveness of cognitive‐functional (Cog‐Fun) intervention with children with attention deficit hyperactivity disorder: A pilot study. American Journal of Occupational Therapy, 65, 384‐392. doi: 10.5014/ajot.2011.000901


title: PLL with Piecewise Judgement Function for SMO Beased Sensorless Control of PMSM

abstract: Sliding-mode observer (SMO) is widely used in the sensorless control strategy of PMSM due to its robustness. And phase locked loop (PLL) is usually applied to reduce the chattering introduced by SMO. However, the conventional PLL shows a type of intrinsic error during speed reversal or startup. To solve the problem, a novel PLL with modified control function is proposed and it can achieve excellent performance in simulation.


title: An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network

abstract: In this paper, we present a new automatic diagnosis method of facial acne vulgaris based on convolutional neural network. This method is proposed to overcome the shortcoming of classification types in previous methods. The core of our method is to extract features of images based on convolutional neural network and achieve classification by classifier. We design a binary classifier of skin-and-non-skin to detect skin area and a seven-classifier to achieve the classification of facial acne vulgaris and healthy skin. In the experiment, we compared the effectiveness of our convolutional neural network and the pre-trained VGG16 neural network on the ImageNet dataset. And we use the ROC curve and normal confusion matrix to evaluate the performance of the binary classifier and the seven-classifier. The results of our experiment show that the pre-trained VGG16 neural network is more effective in extracting image features. The classifiers based on the pre-trained VGG16 neural network achieve the skin detection and acne classification and have good robustness.


title: Uncertainty Visualization Methods in Isosurface Rendering

abstract: We describe two techniques for rendering isosurfaces in multiresolution volume data such that the uncertainty (error) in the data is shown in the resulting visualization. In general the visualization of uncertainty in data is difficult, but the nature of isosurface rendering makes it amenable to an effective solution. In addition to showing the error in the data used to generate the isosurface, we also show the value of an additional data variate on the isosurface. The results combine multiresolution and uncertainty visualization techniques into a hybrid approach. Our technique is applied to multiresolution examples from the medical domain.


title: Peak Detection based Spread Spectrum Audio Watermarking using Discrete Wavelet Transform

abstract: This paper proposes a robust spread spectrum based audio watermarking scheme using Discrete wavelet transform (DWT). Here we use a peak detection algorithm to obtain high robustness. In this watermarking scheme, watermarks are embedded into the peak value of detail coefficients of, transformed audio signal. It is a blind watermarking technique which is used for providing copyright protection and content authentication for digital content. Tests shows that the proposed watermarking scheme is robust against signal processing attacks like resampling, requantization, MP3 compression, noise addition. Performance of this technique is analyzed by calculating the percentage match and SNR values.