title: INTRAOPERATIVE AND POSTOPERATIVE COMPLICATIONS, ACCIDENTS AND FAILURES RELATED WITH DENTAL IMPLANT APPLICATIONS -

abstract: With revolutions in biotechnology, implant surgery has become a routine therapy in the last three decades for the rehabilitation of completely or partially edentulous patients. Despite implant surgery increasing popularity and high success rates of, complications due to implant therapy are still unavoidable. Complications can occur either intra-operative or post-operative period of the surgery. Intra-operative complications such as hematoma and ingestion or inhalation of mechanical components or instruments can be serious as life-threatening. Other complications that occur during implant placements are usually associated with local damages such as nerve injury, adjacent teeth damage and perforations of the nasal cavity or maxillary sinus. Also mandible fractures, sinusitis, peri-implant mucositis, peri-implantitis and periapical implant lesions are the complications that frequently encountered after surgery. Therefore implant therapy is a complicated procedure and the management of the complications associated with this therapy requires a special training and experience. ::: This review categorized complications associated with dental implant applications and discusses management options of these complications.


title: Traffic sign segmentation and classification using statistical learning methods

abstract: Abstract Traffic signs are an essential part of any circulation system, and failure detection by the driver may significantly increase the accident risk. Currently, automatic traffic sign detection systems still have some performance limitations, specially for achromatic signs and variable lighting conditions. In this work, we propose an automatic traffic-sign detection method capable of detecting both chromatic and achromatic signs, while taking into account rotations, scale changes, shifts, partial deformations, and shadows. The proposed system is divided into three stages: (1) segmentation of chromatic and achromatic scene elements using L ⁎ a ⁎ b ⁎ and HSI spaces, where two machine learning techniques (k-Nearest Neighbors and Support Vector Machines) are benchmarked; (2) post-processing in order to discard non-interest regions, to connect fragmented signs, and to separate signs located at the same post; and (3) sign-shape classification by using Fourier Descriptors, which yield significant advantage in comparison to other contour-based methods, and subsequent shape recognition with machine learning techniques. Experiments with two databases of real-world images captured with different cameras yielded a sign detection rate of about 97% with a false alarm rate between 3% and 4%, depending on the database. Our method can be readily used for maintenance, inventory, or driver support system applications.


title: Performance bounds for peer-assisted live streaming

abstract: Peer-assisted streaming is a promising way for service providers to offer high-quality IPTV to consumers at reasonable cost. In peer-assisted streaming, the peers exchange video chunks with one another, and receive additional data from the central server as needed. In this paper, we analyze how to provision resources for the streaming system, in terms of the server capacity, the video quality, and the depth of the distribution trees that deliver the content. We derive the performance bounds for minimum server load, maximum streaming rate, and minimum tree depth under different peer selection constraints. Furthermore, we show that our performance bounds are actually tight, by presenting algorithms for constructing trees that achieve our bounds.


title: Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination

abstract: Effective crisis management has long relied on both the formal and informal response communities. Social media platforms such as Twitter increase the participation of the informal response community in crisis response. Yet, challenges remain in realizing the formal and informal response communities as a cooperative work system. We demonstrate a supportive technology that recognizes the existing capabilities of the informal response community to identify needs (seeker behavior) and provide resources (supplier behavior), using their own terminology. To facilitate awareness and the articulation of work in the formal response community, we present a technology that can bridge the differences in terminology and understanding of the task between the formal and informal response communities. This technology includes our previous work using domain-independent features of conversation to identify indications of coordination within the informal response community. In addition, it includes a domain-dependent analysis of message content (drawing from the ontology of the formal response community and patterns of language usage concerning the transfer of property) to annotate social media messages. The resulting repository of annotated messages is accessible through our social media analysis tool, Twitris. It allows recipients in the formal response community to sort on resource needs and availability along various dimensions including geography and time. Thus, computation indexes the original social media content and enables complex querying to identify contents, players, and locations. Evaluation of the computed annotations for seeker-supplier behavior with human judgment shows fair to moderate agreement. In addition to the potential benefits to the formal emergency response community regarding awareness of the observations and activities of the informal response community, the analysis serves as a point of reference for evaluating more computationally intensive efforts and characterizing the patterns of language behavior during a crisis.


title: Setting up Virtual Reality and Augmented Reality Learning Environment in Unity

abstract: We propose a framework and a setup for presenting complex models for curriculum contents in both augmented reality and virtual reality environment. After constructing some three-dimensional models representing real world objects such as trees, stones, rivers, dams, and buildings, our workflow uses the Unity engine in combination with Virtual Reality headset devices to create interactive applications for both Virtual Reality and Augmented Reality environments to support students understanding the curriculum contents through their surrounding. Typical challenges are addressed when creating 3D curriculum contents, integrating these models into Unity and solutions are proposed where possible. The overall structure of the project is described with some functionalities added to Unity for visualization and interaction with the models.


title: Partial-parallel-repair (PPR): a distributed technique for repairing erasure coded storage

abstract: With the explosion of data in applications all around us, erasure coded storage has emerged as an attractive alternative to replication because even with significantly lower storage overhead, they provide better reliability against data loss. Reed-Solomon code is the most widely used erasure code because it provides maximum reliability for a given storage overhead and is flexible in the choice of coding parameters that determine the achievable reliability. However, reconstruction time for unavailable data becomes prohibitively long mainly because of network bottlenecks. Some proposed solutions either use additional storage or limit the coding parameters that can be used. In this paper, we propose a novel distributed reconstruction technique, called Partial Parallel Repair (PPR), which divides the reconstruction operation to small partial operations and schedules them on multiple nodes already involved in the data reconstruction. Then a distributed protocol progressively combines these partial results to reconstruct the unavailable data blocks and this technique reduces the network pressure. Theoretically, our technique can complete the network transfer in ⌈(log2(k + 1))⌉ time, compared to k time needed for a (k, m) Reed-Solomon code. Our experiments show that PPR reduces repair time and degraded read time significantly. Moreover, our technique is compatible with existing erasure codes and does not require any additional storage overhead. We demonstrate this by overlaying PPR on top of two prior schemes, Local Reconstruction Code and Rotated Reed-Solomon code, to gain additional savings in reconstruction time.


title: Group Touch: Distinguishing Tabletop Users in Group Settings via Statistical Modeling of Touch Pairs

abstract: We present Group Touch, a method for distinguishing among multiple users simultaneously interacting with a tabletop computer using only the touch information supplied by the device. Rather than tracking individual users for the duration of an activity, Group Touch distinguishes users from each other by modeling whether an interaction with the tabletop corresponds to either: (1) a new user, or (2) a change in users currently interacting with the tabletop. This reframing of the challenge as distinguishing users rather than tracking and identifying them allows Group Touch to support multi-user collaboration in real-world settings without custom instrumentation. Specifically, Group Touch examines pairs of touches and uses the difference in orientation, distance, and time between two touches to determine whether the same person performed both touches in the pair. Validated with field data from high-school students in a classroom setting, Group Touch distinguishes among users "in the wild" with a mean accuracy of 92.92% (SD=3.94%). Group Touch can imbue collaborative touch applications in real-world settings with the ability to distinguish among multiple users.


title: Concurrency and Privacy with Payment-Channel Networks

abstract: Permissionless blockchains protocols such as Bitcoin are inherently limited in transaction throughput and latency. Current efforts to address this key issue focus on off-chain payment channels that can be combined in a Payment-Channel Network (PCN) to enable an unlimited number of payments without requiring to access the blockchain other than to register the initial and final capacity of each channel. While this approach paves the way for low latency and high throughput of payments, its deployment in practice raises several privacy concerns as well as technical challenges related to the inherently concurrent nature of payments that have not been sufficiently studied so far. In this work, we lay the foundations for privacy and concurrency in PCNs, presenting a formal definition in the Universal Composability framework as well as practical and provably secure solutions. In particular, we present Fulgor and Rayo. Fulgor is the first payment protocol for PCNs that provides provable privacy guarantees for PCNs and is fully compatible with the Bitcoin scripting system. However, Fulgor is a blocking protocol and therefore prone to deadlocks of concurrent payments as in currently available PCNs. Instead, Rayo is the first protocol for PCNs that enforces non-blocking progress (i.e., at least one of the concurrent payments terminates). We show through a new impossibility result that non-blocking progress necessarily comes at the cost of weaker privacy. At the core of Fulgor and Rayo is Multi-Hop HTLC, a new smart contract, compatible with the Bitcoin scripting system, that provides conditional payments while reducing running time and communication overhead with respect to previous approaches. Our performance evaluation of Fulgor and Rayo shows that a payment with 10 intermediate users takes as few as 5 seconds, thereby demonstrating their feasibility to be deployed in practice.


title: LoRea: A Backscatter Architecture that Achieves a Long Communication Range

abstract: There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LOREA an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LOREA costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LOREA's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1 m distance from a 28 dBm carrier source while consuming 70 μW at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75 m, a significant improvement over existing RFID readers.


title: A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

abstract: Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures used within the field of edge computing. Then, we review recent literature, consisting of a wide range of papers and categorise them using 4 perspectives, namely resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the research currently conducted. While the combination of dynamic load changes and mobility would seem to require novel edge-based resource estimation and discovery methods, we found these areas less studied. As for resource types, we observe that energy and data as a resource are not as well-studied as computation and communication resources. Finally, we find that few works are dedicated to the study of the footprint of resource management techniques.


title: A Doherty Amplifier With Modified Load Modulation Scheme Based on Load–Pull Data

abstract: We present the design and development of a Ka-band Doherty power amplifier (DPA) that achieves high efficiency at power backoff (PBO) using a new load modulation scheme and a proposed load–pull-based design technique. Using the proposed technique, the device size of the peaking amplifier and the impedance of the quarter-wavelength ( $\lambda $ /4) inverter are properly chosen to achieve high power-added efficiency (PAE) at a specific PBO level. The DPA is fabricated in a 0.15- $\mu {\text{m}}$ gallium arsenide pseudomorphic high-electron mobility transistor enhancement mode process and achieves a measured small signal gain of 10.5 dB, output power at 1-dB compression point ( $P_{1\,{\text{dB}}}$ ) of 27 dBm, and measured maximum PAE of 38%. The PAE at 6-dB PBO and 8-dB PBO are 32% and 28.5%, respectively. Furthermore, the PAE at 6-dB PBO maintains higher than 28.4% over a 1-GHz bandwidth from 29.25 to 30.25 GHz.


title: Early Stage Influenza Detection from Twitter

abstract: Influenza is an acute respiratory illness that occurs virtually every year and results in substantial disease, death and expense. Detection of Influenza in its earliest stage would facilitate timely action that could reduce the spread of the illness. Existing systems such as CDC and EISS which try to collect diagnosis data, are almost entirely manual, resulting in about two-week delays for clinical data acquisition. Twitter, a popular microblogging service, provides us with a perfect source for early-stage flu detection due to its real- time nature. For example, when a flu breaks out, people that get the flu may post related tweets which enables the detection of the flu breakout promptly. In this paper, we investigate the real-time flu detection problem on Twitter data by proposing Flu Markov Network (Flu-MN): a spatio-temporal unsupervised Bayesian algorithm based on a 4 phase Markov Network, trying to identify the flu breakout at the earliest stage. We test our model on real Twitter datasets from the United States along with baselines in multiple applications, such as real-time flu breakout detection, future epidemic phase prediction, or Influenza-like illness (ILI) physician visits. Experimental results show the robustness and effectiveness of our approach. We build up a real time flu reporting system based on the proposed approach, and we are hopeful that it would help government or health organizations in identifying flu outbreaks and facilitating timely actions to decrease unnecessary mortality.


title: A 77-79-GHz Doppler Radar Transceiver in Silicon

abstract: This paper presents the first 77 GHz single-chip direct-conversion transceiver in silicon. The transceiver, fabricated in a 0.13 mum SiGe BiCMOS technology with fT/fMAX of 170/200 GHz, consumes 740 mW, and occupies 1.3 mm x 0.9 mm. The receiver achieves 25.6 dB conversion gain, 9 dB noise figure, 90 dB dynamic range, and an IP1dB of -24 dBm. The transmitter provides +5.8 dBm of saturated output power at 77 GHz, and a divide14, static frequency divider is included on-die. A tuned, 77 GHz clock distribution network is used to distribute the VCO signal to the divider, power amplifier, and down-conversion mixer. Successful detection of a Doppler shift of 55 Hz at a range of 4 m is shown. The phase noise at IF is shown to be superior to the VCO, suggesting noise correlation between the transmitter and receiver.


title: Some New Parallel Mechanisms Containing the Planar Four-Bar Parallelogram

abstract: A parallelogram allows the output link to remain at a fixed orientation with respect to an input link, for which it acts as a unique role in the design of parallel mechanisms. In this paper, the unique role of a parallelogram is used completely to design some new parallel mechanisms with two to six degrees of freedom (DoFs). In these mechanisms, some with three DoFs possess the advantage of very high rotational capability and some with two DoFs have the translational output of a rigid body. More than that, the design concept is also applied first to some parallel mechanisms to improve the systems' rotational capability. The parallel mechanisms proposed in this paper have wide applications in industrial robots, simulators, micromanipulators, parallel kinematics machines, and any other manipulation devices in which high rotational capability and stiffness are needed. Especially, the paper provides new concepts of the design of novel parallel mechanisms and the improvement of rotational capability for such s...


title: A robust video based traffic light detection algorithm for intelligent vehicles

abstract: Recently, researches on intelligent vehicles which can drive in urban environment autonomously become more popular. Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, a robust and efficient algorithm to detect traffic lights based on video sequences captured by a low cost off-the-shelf video camera is proposed. The algorithm models the hue and saturation according to Gaussian distributions and learns their parameters with training images. From learned models, candidate regions of the traffic lights in the test images can be extracted. Post processing method which takes account of the shape information is applied to the candidate regions. Furthermore, detection results of the previous image frames are aggregated in order to provide a more robust result. Experimental results on several video sequences captured in typical urban environment prove the effectiveness of the proposed algorithm.


title: Asymmetry of congruency effects in spatial Stroop tasks can be eliminated

abstract: Three experiments examined whether asymmetry in interference can be eliminated in spatial Stroop tasks. In Experiment 1, responding to arrows or location words written in Chinese and to their locations created spatial Stroop effects of similar sizes. In Experiment 2, responding to a location word embedded in an outline drawing of arrow did not yield a spatial Stroop effect, but responding to the arrow's direction did yield an effect. In Experiment 3, responding to a location word flanked by an arrow and to the arrow rather than the word produced similar sizes of spatial Stroop effects. These results show that asymmetry in spatial Stroop interference can be eliminated in some situations. Although aspects of the results are consistent with predictions of translation and dimensional overlap models, they are in closest agreement overall with an account in terms of the relative strengths of the relevant and irrelevant stimulus-response associations. (C) 2013 Elsevier B.V. All rights reserved.


Employee Emotional Intelligence and Employee Performance in the Higher Education Institutions in Saudi Arabia : A Proposed Theoretical Framework


title: Local path planning in a complex environment for self-driving car

abstract: This paper introduces an local path planning algorithm for the self-driving car in a complex environment. The proposed algorithm is composed of three parts: the novel path representation, the collision detection and the path modification using a voronoi cell. The novel path representation provides convenience for checking the collision and modifying the path and continuous control input for steering wheel rather than way point navigation. The proposed algorithm were applied to the self-driving car, EureCar(KAIST) and its applicability and feasibility of real time use were validated.


title: The Pieper-Zulkowski pressure ulcer knowledge test.

abstract: OBJECTIVE ::: To describe the development and initial testing of the Pieper-Zulkowski Pressure Ulcer Knowledge Test (PZ-PUKT). ::: ::: ::: DESIGN ::: Cross-sectional, instrument testing. ::: ::: ::: SETTING ::: Hospital association pressure ulcer educational program conference. ::: ::: ::: METHODS ::: Pressure ulcer research and guidelines from the last 5 years were examined for test item content. The initial PZ-PUKT had 115 items; response options were "true," "false," and "don't know." Registered nurses (N = 108) were randomly divided into 2 groups to take either the 60 prevention/risk and staging items or the 55 wound description items. Analyses of these responses resulted in 72 items, which were administered in total to a second cohort of 98 nurses for reliability. ::: ::: ::: RESULTS ::: Cronbach's α was .80 for the 72-item PZ-PUKT. Cronbach's α values for the subscales were as follows: staging, .67; wound description, .64; and prevention/risk, .56. The mean correct scores were as follows: total, 80%; prevention, 77%; staging, 86%; and wound description, 77%. Nurses with wound care certification scored significantly higher on the PZ-PUKT than did nurses with other clinical certifications or with nurses who lacked certification. ::: ::: ::: CONCLUSIONS ::: The PZ-PUKT has updated content about pressure ulcer prevention/risk, staging, and wound description. Reliability values are highest for the total test. Further use of the instrument in diverse settings will add to reliability testing and may provide direction for determination of a passing cutoff score.


title: 3D Wikipedia: using online text to automatically label and navigate reconstructed geometry

abstract: We introduce an approach for analyzing Wikipedia and other text, together with online photos, to produce annotated 3D models of famous tourist sites. The approach is completely automated, and leverages online text and photo co-occurrences via Google Image Search. It enables a number of new interactions, which we demonstrate in a new 3D visualization tool. Text can be selected to move the camera to the corresponding objects, 3D bounding boxes provide anchors back to the text describing them, and the overall narrative of the text provides a temporal guide for automatically flying through the scene to visualize the world as you read about it. We show compelling results on several major tourist sites.


title: Augmented −1 adaptive control of an actuated knee joint exoskeleton: From design to real-time experiments

abstract: This paper deals with the control of a lower limb exoskeleton acting at the knee joint level. Classical −1 adaptive control law is proposed to ensure assistance-as-needed and resistive rehabilitation following a desired trajectory that is defined by a therapeutic doctor. This control law introduces a time lag within the desired trajectory tracking due to the presence of a filter in its structure. In order to mitigate this drawback, the classical −1 adaptive control is augmented by a nonlinear proportional control. The classical and augmented −1 adaptive control laws are tested in real-time using the Exoskeleton Intelligently COmmunicating and Sensitive to Intention (EICOSI) of LISSI-lab. Real-time experimental results highlight the utility of these control laws in assistance-as-needed and resistive rehabilitation paradigms.


title: NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

abstract: This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.


title: Domain Adaptation for CNN Based Iris Segmentation

abstract: Convolutional Neural Networks (CNNs) have shown great success in solving key artificial vision challenges such as image segmentation. Training these networks, however, normally requires plenty of labeled data, while data labeling is an expensive and time-consuming task, due to the significant human effort involved. In this paper we propose two pixel-level domain adaptation methods, introducing a training model for CNN based iris segmentation. Based on our experiments, the proposed methods can effectively transfer the domains of source databases to those of the targets, producing new adapted databases. The adapted databases then are used to train CNNs for segmentation of iris texture in the target databases, eliminating the need for the target labeled data. We also indicate that training a specific CNN for a new iris segmentation task, maintaining optimal segmentation scores, is possible using a very low number of training samples.


title: Directional Joint Bilateral Filter for Depth Images

abstract: Depth maps taken by the low cost Kinect sensor are often noisy and incomplete. Thus, post-processing for obtaining reliable depth maps is necessary for advanced image and video applications such as object recognition and multi-view rendering. In this paper, we propose adaptive directional filters that fill the holes and suppress the noise in depth maps. Specifically, novel filters whose window shapes are adaptively adjusted based on the edge direction of the color image are presented. Experimental results show that our method yields higher quality filtered depth maps than other existing methods, especially at the edge boundaries.


title: Negation Scope Delimitation in Clinical Text Using Three Approaches: NegEx; PyConTextNLP and SynNeg

abstract: Negation detection is a key component in clinical information extraction systems, as health record text contains reasonings in which the physician excludes different diagnoses by negating them. Many systems for negation detection rely on negation cues (e.g. not), but only few studies have investigated if the syntactic structure of the sentences can be used for determining the scope of these cues. We have in this paper compared three different systems for negation detection in Swedish clinical text (NegEx, PyConTextNLP and SynNeg), which have different approaches for determining the scope of negation cues. NegEx uses the distance between the cue and the disease, PyConTextNLP relies on a list of conjunctions limiting the scope of a cue, and in SynNeg the boundaries of the sentence units, provided by a syntactic parser, limit the scope of the cues. The three systems produced similar results, detecting negation with an F-score of around 80%, but using a parser had advantages when handling longer, complex sentences or short sentences with contradictory statements.


title: A Random Sampling Scheme for Path Planning

abstract: Several randomized path planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empirically observed success. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot’s configuration space. We then describe two previously developed planners as instances of planners based on this scheme, but applying very different sampling strategies. These planners are probabilistically complete: if a path exists, they will find one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the “goodness” of the robot’s free space, we show that the running time only grows as the absolute value of the logarithm of the probability of failure that we are willing to tolerate. We also show that it increases at a reasonable rate as the space goodness degrades. In the last section we suggest directions for future research.


title: Sales forecasting using WaveNet within the framework of the Kaggle competition

abstract: We took part in the Corporacion Favorita Grocery Sales Forecasting competition hosted on Kaggle and achieved the 2nd place. In this abstract paper, we present an overall analysis and solution to the underlying machine-learning problem based on time series data, where major challenges are identified and corresponding preliminary methods are proposed. Our approach is based on the adaptation of dilated convolutional neural network for time series forecasting. By applying this technique iteratively to batches of n examples, a big amount of time series data can be eventually processed with a decent speed and accuracy. We hope this paper could serve, to some extent, as a review and guideline of the time series forecasting benchmark, inspiring further attempts and researches.


title: A framework for designing cloud forensic-enabled services (CFeS)

abstract: Cloud computing is used by consumers to access cloud services. Malicious actors exploit vulnerabilities of cloud services to attack consumers. The link between these two assumptions is the cloud service. Although cloud forensics assists in the direction of investigating and solving cloud-based cyber-crimes, in many cases the design and implementation of cloud services fall back. Software designers and engineers should focus their attention on the design and implementation of cloud services that can be investigated in a forensic sound manner. This paper presents a methodology that aims on assisting designers to design cloud forensic-enabled services. The methodology supports the design of cloud services by implementing a number of steps to make the services cloud forensic enabled. It consists of a set of cloud forensic constraints, a modeling language expressed through a conceptual model and a process based on the concepts identified and presented in the model. The main advantage of the proposed methodology is the correlation of cloud services’ characteristics with the cloud investigation while providing software engineers the ability to design and implement cloud forensic-enabled services via the use of a set of predefined forensic-related tasks.


title: Beating heart motion prediction for robust visual tracking

abstract: In the context of minimally invasive cardiac surgery, robotic assistance has significantly helped surgeons to overcome difficulties related to the minimally invasive procedure. Recently, techniques have been proposed for active canceling the beating heart motion for improving the accuracy of the surgical gestures. In this scenario, computer vision techniques can be applied for estimating the heart motion based solely on natural structures on the heart surface. However, visual tracking is complicated by the particular lighting conditions and clutter (smoke, liquids, etc) during surgery. Another challenging problem are the occasional occlusions by surgical instruments. In order to overcome these problems, we exploit the quasi-periodicity of the beating heart motion for increasing the robustness of the visual tracking task. In this paper, a novel time-varying dual Fourier series for modeling the quasi-periodic beating heart motion is proposed. For estimating the series parameters, an Extended Kalman Filter (EKF) is used. The proposed method is applied in a visual tracking task for bridging tracking disturbances and automatically reestablish tracking in cases of occlusions. The efficiency of the prediction method and the sensible improvements in the visual tracking task are demonstrated through in vivo experiments.


title: Identification of robust focus measure functions for the automated capturing of focused images from Ziehl-Neelsen stained sputum smear microscopy slide.

abstract: Capturing of the best-focused image using focus measure function (FMF) from a stack of images acquired at different focus distances is a crucial step in automatic microscopy development. Detection of bacilli present in Ziehl-Neelsen (ZN) stained sputum smears conventional microscope (CM) images is critical to disease diagnosis. Studies have revealed that the performances of FMFs are sensitive to image contents and background noise. In this article, 24 diverse FMFs were implemented on 31 stacks of CM's field of view images acquired from three different microscopes to determine the best-focused one. Seven FMFs achieved the accuracies of greater than 90%. Accuracy, focus error, and false maxima were calculated for each FMF, and overall score and ranking were also calculated for better interpretation. Preprocessing techniques such as filtering and image distortions (noise, contrast, saturation, illumination, etc.) were performed to evaluate the robustness of every FMF. Gaussian derivative, steerable filters, Tenengrad, and Hemli and Scherer's mean FMFs were identified as the most robust and accurate functions with the accuracy >90%. These FMFs have a relatively less focus error and false maxima rate. Full widths at half maximum of these four FMFs were also computed to determine their efficacy for the optimization process. These four FMFs can be implemented for automated capturing of the image from ZN-stained sputum smear slide. Gaussian derivative FMF can also be used effectively for both CM and fluorescence microscope's field of view image stacks to determine the best-focused one from each stack. © 2017 International Society for Advancement of Cytometry.


title: ISOODL: Instance segmentation of overlapping biological objects using deep learning

abstract: Image segmentation is an important first step for the quantitative analysis of biomedical images. We present a method to simultaneously segment and classify translucent overlapping objects in 2D images. For this we propose an approach using a fully-convolutional neural network simultaneously solving two tasks: object detection and instance segmentation. Object detection predicts reference points, object class labels and sizes. To solve the problem of multiple labels per location, we lift our label-space from 2D to 3D, resulting in a non-overlapping representation of the instance masks. To our knowledge it is the first method that handles overlapping biological objects using deep learning making it easily applicable to a large variety of challenging datasets.


title: A millimeter tapered slot antenna fed by SIW with broadband and high gain

abstract: A tapered slot antenna (TSA) operates at Ka band with high gain and broadband is designed in this paper. The TSA is printed on two side of the substrate. A SIW transformer between the microstrip feedline and the TSA is applied to minimize the feedline losses for forming antenna arrays. The simulated bandwidth of the reflection coefficient less than -10dB is 26GHz from 25GHz to 51GHz. The front-back ratio is 15dB and the half power beam width is 60°. This prototype is fabricated and measured to verify this design. It is easy for this antenna element to be expended to array and applied in millimeter wave power transmission and imaging.


title: Handling a trillion (unfixable) flaws on a billion devices: Rethinking network security for the Internet-of-Things

abstract: The Internet-of-Things (IoT) has quickly moved from the realm of hype to reality with estimates of over 25 billion devices deployed by 2020. While IoT has huge potential for societal impact, it comes with a number of key security challenges---IoT devices can become the entry points into critical infrastructures and can be exploited to leak sensitive information. Traditional host-centric security solutions in today's IT ecosystems (e.g., antivirus, software patches) are fundamentally at odds with the realities of IoT (e.g., poor vendor security practices and constrained hardware). We argue that the network will have to play a critical role in securing IoT deployments. However, the scale, diversity, cyberphysical coupling, and cross-device use cases inherent to IoT require us to rethink network security along three key dimensions: (1) abstractions for security policies; (2) mechanisms to learn attack and normal profiles; and (3) dynamic and context-aware enforcement capabilities. Our goal in this paper is to highlight these challenges and sketch a roadmap to avoid this impending security disaster.


title: A Virtual Reality Dance Training System Using Motion Capture Technology

abstract: In this paper, a new dance training system based on the motion capture and virtual reality (VR) technologies is proposed. Our system is inspired by the traditional way to learn new movements-imitating the teacher's movements and listening to the teacher's feedback. A prototype of our proposed system is implemented, in which a student can imitate the motion demonstrated by a virtual teacher projected on the wall screen. Meanwhile, the student's motions will be captured and analyzed by the system based on which feedback is given back to them. The result of user studies showed that our system can successfully guide students to improve their skills. The subjects agreed that the system is interesting and can motivate them to learn.


title: My Text in Your Handwriting

abstract: There are many scenarios where we wish to imitate a specific author’s pen-on-paper handwriting style. Rendering new text in someone’s handwriting is difficult because natural handwriting is highly variable, yet follows both intentional and involuntary structure that makes a person’s style self-consistent. The variability means that naive example-based texture synthesis can be conspicuously repetitive. We propose an algorithm that renders a desired input string in an author’s handwriting. An annotated sample of the author’s handwriting is required; the system is flexible enough that historical documents can usually be used with only a little extra effort. Experiments show that our glyph-centric approach, with learned parameters for spacing, line thickness, and pressure, produces novel images of handwriting that look hand-made to casual observers, even when printed on paper.


title: Towards wearable cognitive assistance

abstract: We describe the architecture and prototype implementation of an assistive system based on Google Glass devices for users in cognitive decline. It combines the first-person image capture and sensing capabilities of Glass with remote processing to perform real-time scene interpretation. The system architecture is multi-tiered. It offers tight end-to-end latency bounds on compute-intensive operations, while addressing concerns such as limited battery capacity and limited processing capability of wearable devices. The system gracefully degrades services in the face of network failures and unavailability of distant architectural tiers.


The Design Space for Robot Appearance and Behaviour for Social Robot Companions


title: Adaptive Multimodal Fusion by Uncertainty Compensation With Application to Audiovisual Speech Recognition

abstract: While the accuracy of feature measurements heavily depends on changing environmental conditions, studying the consequences of this fact in pattern recognition tasks has received relatively little attention to date. In this paper, we explicitly take feature measurement uncertainty into account and show how multimodal classification and learning rules should be adjusted to compensate for its effects. Our approach is particularly fruitful in multimodal fusion scenarios, such as audiovisual speech recognition, where multiple streams of complementary time-evolving features are integrated. For such applications, provided that the measurement noise uncertainty for each feature stream can be estimated, the proposed framework leads to highly adaptive multimodal fusion rules which are easy and efficient to implement. Our technique is widely applicable and can be transparently integrated with either synchronous or asynchronous multimodal sequence integration architectures. We further show that multimodal fusion methods relying on stream weights can naturally emerge from our scheme under certain assumptions; this connection provides valuable insights into the adaptivity properties of our multimodal uncertainty compensation approach. We show how these ideas can be practically applied for audiovisual speech recognition. In this context, we propose improved techniques for person-independent visual feature extraction and uncertainty estimation with active appearance models, and also discuss how enhanced audio features along with their uncertainty estimates can be effectively computed. We demonstrate the efficacy of our approach in audiovisual speech recognition experiments on the CUAVE database using either synchronous or asynchronous multimodal integration models.


title: mFerio: the design and evaluation of a peer-to-peer mobile payment system

abstract: In this paper, we present the design and evaluation of a near-field communication-based mobile p2p payment application, called mFerio, that is designed to replace cash-based transactions. We first identify design criteria that payment systems should satisfy and then explain how mFerio, relative to those criteria, improves on the limitations of cash-based systems. We next describe mFerio's implementation and user interface design, focusing on the balance between usability and security. Finally, we present the results of a two-phase user study, involving a total of 104 people, that shows that mFerio has low cognitive load and is also fast, accurate, and easy to use - even outperforming cash in terms of speed and cognitive load in common payment situations.


title: Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling

abstract: In this paper, we investigate the application of recurrent neural network language models (RNNLM) and factored language models (FLM) to the task of language modeling for Code-Switching speech. We present a way to integrate partof-speech tags (POS) and language information (LID) into these models which leads to significant improvements in terms of perplexity. Furthermore, a comparison between RNNLMs and FLMs and a detailed analysis of perplexities on the different backoff levels are performed. Finally, we show that recurrent neural networks and factored language models can be combined using linear interpolation to achieve the best performance. The final combined language model provides 37.8% relative improvement in terms of perplexity on the SEAME development set and a relative improvement of 32.7% on the evaluation set compared to the traditional n-gram language model.


ETH Zurich at TREC Clinical Decision Support 2016


Event-driven contrastive divergence for spiking neuromorphic systems


title: Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition

abstract: Accurate and computationally efficient means for classifying human activities have been the subject of extensive research efforts. Most current research focuses on extracting complex features to achieve high classification accuracy. We propose a template selection approach based on Dynamic Time Warping, such that complex feature extraction and domain knowledge is avoided. We demonstrate the predictive capability of the algorithm on both simulated and real smartphone data.


title: Tagging Urdu Sentences from English POS Taggers

abstract: Being a global language, English has attracted a majority of researchers and academia to work on several Natural Language Processing (NLP) applications. The rest of the languages are not focused as much as English. Part-of-speech (POS) Tagging is a necessary component for several NLP applications. An accurate POS Tagger for a particular language is not easy to construct due to the diversity of that language. The global language English, POS Taggers are more focused and widely used by the researchers and academia for NLP processing. In this paper, an idea of reusing English POS Taggers for tagging non-English sentences is proposed. On exemplary basis, Urdu sentences are processed to tagged from 11 famous English POS Taggers. State-of-the-art English POS Taggers were explored from the literature, however, 11 famous POS Taggers were being input to Urdu sentences for tagging. A famous Google translator is used to translate the sentences across the languages. Data from twitter.com is extracted for evaluation perspective. Confusion matrix with kappa statistic is used to measure the accuracy of actual Vs predicted tagging. The two best English POS Taggers which tagged Urdu sentences were Stanford POS Tagger and MBSP POS Tagger with an accuracy of 96.4% and 95.7%, respectively. The system can be generalized for multi-lingual sentence tagging.


title: Reversible Data Hiding in Encrypted Images With Distributed Source Encoding

abstract: This paper proposes a novel scheme of reversible data hiding in encrypted images using distributed source coding. After the original image is encrypted by the content owner using a stream cipher, the data-hider compresses a series of selected bits taken from the encrypted image to make room for the secret data. The selected bit series is Slepian-Wolf encoded using low-density parity check codes. On the receiver side, the secret bits can be extracted if the image receiver has the embedding key only. In case the receiver has the encryption key only, he/she can recover the original image approximately with high quality using an image estimation algorithm. If the receiver has both the embedding and encryption keys, he/she can extract the secret data and perfectly recover the original image using the distributed source decoding. The proposed method outperforms the previously published ones.


title: A novel method for non-linear control of wheel slip in anti-lock braking systems

abstract: Anti-lock braking system (ABS) provides active safety for vehicles during braking by regulation of the wheel slip at its optimum value. Due to the non-linear characteristics and model uncertainties in vehicle dynamics, a non-linear controller with increased robustness should be designed for ABS. In this paper, to achieve this aim, an optimization-based braking torque control law is developed for ABS using the prediction of the wheel slip response from a continuous non-linear vehicle dynamics model. To increase the robustness of the controller, the integral feedback technique is appended to the design method. The derived control law and its special cases are evaluated and discussed. At the end, the performance of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulations of braking on dry and slippery roads. The simulation results indicate that, the wheel slip tracking error is remarkably decreased by the proposed controller. Moreover, the achieved control input is entirely smooth and suitable for implementation.


title: Training deep neural-networks based on unreliable labels

abstract: In this study we address the problem of training a neural network based on data with unreliable labels. We introduce an extra noise layer by assuming that the observed labels were created from the true labels by passing through a noisy channel whose parameters are unknown. We propose a method that simultaneously learns both the neural network parameters and the noise distribution. The proposed method is compared to standard back-propagation neural-network training that ignores the existence of wrong labels. The improved classification performance of the method is illustrated on several standard classification tasks. In particular we show that in some cases our approach can be beneficial even when the labels are set manually and assumed to be error-free.


title: Aligning IT with Firm Business Strategies Using the Balance Scorecard System

abstract: High levels of investment in IT and related products and services over the last two decades have produced only mixed results. Research has shown that one of the most significant determinants of successful IT investment is the alignment between IT and the competitive strategies of a firm. Yet, it is largely unclear to both researchers and practitioners how to achieve such alignment in a complex business environment. In this paper, using the IT alignment model of Reich and Benbasat as the underlying theory, we present the preliminary findings of a case study on how one company used a well established strategic management tool, the Balanced Scorecard, as the framework for aligning its IT initiatives with business strategies, that resulted in financial success for the company.


title: A machine-learning based approach to privacy-aware information-sharing in mobile social networks

abstract: Contextual information about users is increasingly shared on mobile social networks. Examples of such information include users' locations, events, activities, and the co-presence of others in proximity. When disclosing personal information, users take into account several factors to balance privacy, utility and convenience - they want to share the "right" amount and type of information at each time, thus revealing a selective sharing behavior depending on the context, with a minimum amount of user interaction. In this article, we present SPISM, a novel information-sharing system that decides (semi-)automatically, based on personal and contextual features, whether to share information with others and at what granularity, whenever it is requested. SPISM makes use of (active) machine-learning techniques, including cost-sensitive multi-class classifiers based on support vector machines. SPISM provides both ease of use and privacy features: It adapts to each user's behavior and predicts the level of detail for each sharing decision. Based on a personalized survey about information sharing, which involves 70 participants, our results provide insight into the most influential features behind a sharing decision, the reasons users share different types of information and their confidence in such decisions. We show that SPISM outperforms other kinds of policies; it achieves a median proportion of correct sharing decisions of 72% (after only 40 manual decisions). We also show that SPISM can be optimized to gracefully balance utility and privacy, but at the cost of a slight decrease in accuracy. Finally, we assess the potential of a one-size-fits-all version of SPISM.


title: FACe! 3D Facial Animation System based on FACS

abstract: In this paper we present a 3D facial animation system named FACe! It is able to generate different expressions of the face throughout punctual and combined activation of Action Units, defined by Facial Acting Coding System(FACS). This system is implemented on a 3D human head controlled by bones, riggers and skinning to deform the ::: geometry. The bone system is implemented in order to move single or combined Action Units, so that they can implement superior layers such as expressions, phonemes, words, emotions and the synchronization of all them ::: together.


title: Taking Control: Design and Implementation of Botnets for Cyber-Physical Attacks with CPSBot

abstract: Recently, botnets such as Mirai and Persirai targeted IoT devices on a large scale. We consider attacks by botnets on cyber-physical systems (CPS), which require advanced capabilities such as controlling the physical processes in real-time. Traditional botnets are not suitable for this goal mainly because they lack process control capabilities, are not optimized for low latency communication, and bots generally do not leverage local resources. We argue that such attacks would require cyber-physical botnets. A cyber-physical botnet needs coordinated and heterogeneous bots, capable of performing adversarial control strategies while subject to the constraints of the target CPS. In this work, we present CPSBot, a framework to build cyber-physical botnets. We present an example of a centralized CPSBot targeting a centrally controlled system and a decentralized CPSBot targeting a system distributed control. We implemented the former CPSBot using MQTT for the C&C channel and Modbus/TCP as the target network protocol and we used it to launch several attacks on real and simulated Water Distribution. We evaluate our implementation with distributed reply and distributed impersonation attacks on a CPS, and show that malicious control with negligible latency is possible.


title: Fuel cell applications on more electrical aircraft

abstract: In order to meet the increasing environmental and economic requirements, commercial aircraft industries have been challenged to reduce fuel consumption, noise and emissions. As a result, more electrical aircraft (MEA), on which engine driven electrical power replaces other primary powers, is being investigated. However, with the increasing demands of electrical power capacity on MEA, the engines have to be redesigned to supply enough power and space for bigger generators. In order to avoid this problem, fuel cell systems could partially/entirely replace the engine driven generators to supply electric power on board. Compared to the traditional electrical power system which is driven by main engines/auxiliary power unit (APU) on MEA, fuel cell based systems would be more efficient and more environmental friendly. Also, fuel cells could work continuously during the entire flight envelope. This paper introduces fuel cell system concepts on MEA. Characters of solid oxide fuel cell (SOFC) and polymer electrolyte membrane fuel cell (PEMFC) are compared. An SOFC APU application on MEA is introduced. Finally, challenges of fell cells application on MEA are discussed.


title: A Comparative Study of Distributed Tools for Analyzing Streaming Data

abstract: Continuous and rapid moving data known as streaming-data need to be analyzed in a short span of time, as the size of the data may overload the storage system quickly. Traditional tools often don't help significantly in the analysis of streaming data in real time. A large number of distributed tools have been developed to process streaming data in a real-time manner. In this paper, the state of the art of stream processing tools for low-latency Big Data analytics is presented. A qualitative comparison between the most popular tools like Apache Spark, Storm, Samza and Apache S4 is provided at the end the paper.


title: Morpheo: Traceable Machine Learning on Hidden data

abstract: Morpheo is a transparent and secure machine learning platform collecting and analysing large datasets. It aims at building state-of-the art prediction models in various fields where data are sensitive. Indeed, it offers strong privacy of data and algorithm, by preventing anyone to read the data, apart from the owner and the chosen algorithms. Computations in Morpheo are orchestrated by a blockchain infrastructure, thus offering total traceability of operations. Morpheo aims at building an attractive economic ecosystem around data prediction by channelling crypto-money from prediction requests to useful data and algorithms providers. Morpheo is designed to handle multiple data sources in a transfer learning approach in order to mutualize knowledge acquired from large datasets for applications with smaller but similar datasets.


title: Characterization and Modeling of the Capacitive HBC Channel

abstract: The increasing interest in wireless body area networks has created the need for alternative communication schemes. One example of such schemes is the use of the human body as a communication medium. This technology is called human body communication (HBC), and it offers advantages over the most common radiation-based methods, which makes it an interesting alternative to implement body area networks. The aim of this paper is to identify the influence of a fixture on the HBC channel characterization, and an extended model that includes the test fixtures to explain the measured channel response is proposed. The model was tested against the channel measurement results, and a good experiment-model correlation was obtained. The results show that the test fixture has a nonnegligible influence and that an extended model, based on the physical meaning of the phenomena involved, helps to explain the channel frequency profile results and behavior.


title: Time Dependent Vccmin Degradation of SRAM Fabricated with High-k Gate Dielectrics

abstract: The paper presents the results of simulations of voltage-induced V ccmin drift of SRAMs fabricated with high-k gate dielectrics. The authors show that high-k based SRAMs are fundamentally more susceptible to Vccmin stability problems because PMOS NBTI and NMOS PBTI lead to an additive degradation in the bit-cell read voltage. Given the differing time dependences of the NBTI and PBTI phenomena, the drift of Vccmin is enhanced at much earlier times compared to SRAMs that have SiO2 gate dielectric only devices. Maintaining Vccmin stability presents a significantly greater challenge for high-k gate dielectrics compared to SiO2


title: Fast estimation of fractal dimension and correlation integral on stream data

abstract: In this paper we give a very space-efficient, yet fast method for estimating the fractal dimensionality of the points in a data stream. Algorithms to estimate the fractal dimension exist, such as the straightforward quadratic algorithm and the faster O(N log N) or even O(N) box-counting algorithms. However, the sub-quadratic algorithms require Ω(N) space. In this paper, we propose an algorithm that computes the fractal dimension in a single pass, using a constant amount of memory relative to data cardinality. Experimental results on synthetic and real world data sets demonstrate the effectiveness of our algorithm.


title: Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting

abstract: Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks (CRNNs). Inspired by large-scale state-of-the-art speech recognition systems, we combine the strengths of convolutional layers and recurrent layers to exploit local structure and long-range context. We analyze the effect of architecture parameters, and propose training strategies to improve performance. With only ~230k parameters, our CRNN model yields acceptably low latency, and achieves 97.71% accuracy at 0.5 FA/hour for 5 dB signal-to-noise ratio.


title: The role of transparency in recommender systems

abstract: Recommender Systems act as a personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user's perspective. The goal of this research was to examine the role of transprency (user understanding of why a particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. Preliminary results indicate that users like and feel more confident about recommendations that they perceive as transparent.


Personalized, Cross-Lingual TTS Using Phonetic Posteriorgrams.


title: From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites

abstract: A compelling set of links between the composition of the gut microbiota, the host diet, and host physiology has emerged. Do these links reflect cause-and-effect relationships, and what might be their mechanistic basis? A growing body of work implicates microbially produced metabolites as crucial executors of diet-based microbial influence on the host. Here, we will review data supporting the diverse functional roles carried out by a major class of bacterial metabolites, the short-chain fatty acids (SCFAs). SCFAs can directly activate G-coupled-receptors, inhibit histone deacetylases, and serve as energy substrates. They thus affect various physiological processes and may contribute to health and disease.


title: A Joint Model for Answer Sentence Ranking and Answer Extraction

abstract: Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.


title: Medical Speech Recognition: Reaching Parity with Humans

abstract: We present a speech recognition system for the medical domain whose architecture is based on a state-of-the-art stack trained on over 270 h of medical speech data and 30 million tokens of text from clinical episodes. Despite the acoustic challenges and linguistic complexity of the domain, we were able to reduce the system’s word error rate to below 16% in a realistic clinical use case. To further benchmark our system, we determined the human word error rate on a corpus covering a wide variety of speakers, working with multiple medical transcriptionists, and found that our speech recognition system performs on a par with humans.


title: HashBox : Hash Hierarchical Segmentation exploiting Bounding Box Object Detection

abstract: We propose a novel approach to address the problem of Simultaneous Detection and Segmentation introduced in [Hariharan et al 2014]. Using the hierarchical structures first presented in [Arbel\'aez et al 2011] we use an efficient and accurate procedure that exploits the feature information of the hierarchy using Locality Sensitive Hashing. We build on recent work that utilizes convolutional neural networks to detect bounding boxes in an image [Ren et al 2015] and then use the top similar hierarchical region that best fits each bounding box after hashing, we call this approach C&Z Segmentation. We then refine our final segmentation results by automatic hierarchical pruning. C&Z Segmentation introduces a train-free alternative to Hypercolumns [Hariharan et al 2015]. We conduct extensive experiments on PASCAL VOC 2012 segmentation dataset, showing that C&Z gives competitive state-of-the-art segmentations of objects.


title: Literature and medicine: narratives of mental illness

abstract: illness as demon possession continued to appear in the 18th and early 19th centuries, even as cultural beliefs about the causes of madness were moving away from a religious model to a secular one that considered mental illness as a defect or disorder of the faculty of reason. This secular way of understanding madness led to the development of both private and public asylums for the confinement of the mentally ill. The goal of the earliest asylums was simply to provide custodial care and to separate the mentally ill from the rest of society. As new philosophies of humane care emerged in Europe at the end of the 18th century, conditions improved in some institutions, and by the late 19th century there was cautious optimism about potentially effective treatments. Even so, the harsh conditions, cruelty, and abuses that persisted in many institutions evoked narratives of protest from patients who recovered well enough to be released and to write about their asylum experiences. These accounts began to appear in the 18th century, and their incidence and urgency increased as the numbers of asylums and their inmates grew during the 19th and early 20th centuries. Wellknown examples include Alexander Cruden’s The London-Citizen Exceedingly Injured (1739), John Perceval’s A Narrative of the Treatment Experienced by a Gentleman, During a State of Mental Derangement (1838, 1840), Clifford Beers’ A Mind That Found Itself (1908), Mary Jane Ward’s autobiographical novel The Snake Pit (1946), and Kate Millett’s relatively recent The Loony-Bin Trip (1990) about her involuntary commitment to an asylum in Ireland. Occasional narratives by those who were not mad but who were nonetheless confined to mental asylums are valuable for the corroborating accounts they provide: for example, William Seabrook’s Asylum (1935) and Janet Frame’s An Angel at My Table (1984), as well as her autobiographical novel Faces in the Water (1961), report much the same conditions and abuses as chronicled in the other works. Many therapies once believed to be efficacious have been abandoned, sometimes in response to narratives of protest by former patients. Charlotte Perkins Gilman, for example, who was subjected in 1887 to S Weir Mitchell’s “rest cure”, during which she was forbidden to write or engage in any intellectual activity, wrote the fictional story “The Yellow Wallpaper” (1892) based on her experience. Although she originally had difficulty finding a publisher for the work, it has since become a feminist classic. In her commentary (1913) about why she wrote the story, Gilman reports that “many years later [she] was told that the great specialist [S Weir Mitchell] had admitted to friends of his that he had Autobiographical accounts of mental illness have for centuries provided a fascinating window on the world of madness for those fortunate enough never to have sojourned there themselves. Even with all the advanced brain-imaging and other technologies of medicine, the subjective experience of mental illness can be conveyed only by those who have lived it. Yet the nature of the experience poses immense challenges for any author, for the very faculties required to construct a narrative— perception, memory, and reason— can be profoundly altered by illnesses such as depression, bipolar disorder, and schizophrenia, as well as by treatments such as electroconvulsive therapy (ECT) and psychotropic drugs. Perhaps as a result—or perhaps just to avoid the stigma of being identified as a mental patient—contemporary authors have sometimes chosen to present their accounts as fiction. Whatever their choice in this regard, the desire to make sense of what has happened to them, the wish to reform abuses in the treatment of the mentally ill, and the hope of helping other patients and their families have been powerful motivations for the hundreds of patients who have written about their experiences of mental illness. How these authors make sense of what happened during their episodes of mental illness has changed substantially from one age to another. In earlier centuries, mental illness was often understood and portrayed as demon possession, to be treated by exorcism or other religious interventions. If these remedies failed, trials and executions for heresy and witchcraft sometimes followed. Indeed, early autobiographical accounts of mental illness have been compared to spiritual autobiographies in their concern with the religious dimensions of the inner life. In The Book of Margery Kempe (c 1436), which many regard as the first such autobiographical account in the English language, Kempe describes her first experience of mental illness, which today might be called postpartum psychosis, as visions of devils tempting her to commit wicked deeds and to forsake her faith. More than two centuries later, the Bavarian artist Christoph Haizmann recorded in his diaries and in a series of paintings his story of the devils that he believed were responsible for his eight episodes of madness (figure). Autobiographical accounts representing mental


Primer3 on the WWW for general users and for biologist programmers


title: Predicting occupation via human clothing and contexts

abstract: Predicting human occupations in photos has great application potentials in intelligent services and systems. However, using traditional classification methods cannot reliably distinguish different occupations due to the complex relations between occupations and the low-level image features. In this paper, we investigate the human occupation prediction problem by modeling the appearances of human clothing as well as surrounding context. The human clothing, regarding its complex details and variant appearances, is described via part-based modeling on the automatically aligned patches of human body parts. The image patches are represented with semantic-level patterns such as clothes and haircut styles using methods based on sparse coding towards informative and noise-tolerant capacities. This description of human clothing is proved to be more effective than traditional methods. Different kinds of surrounding context are also investigated as a complementarity of human clothing features in the cases that the background information is available. Experiments are conducted on a well labeled image database that contains more than 5; 000 images from 20 representative occupation categories. The preliminary study shows the human occupation is reasonably predictable using the proposed clothing features and possible context.


title: Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

abstract: Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. This paper presents hyperspectral image classification mechanism using genetic algorithm with empirical mode decomposition and image fusion used in preprocessing stage. 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, image fusion is performed on the hyperspectral bands to selectively merge the maximum possible features from the source images to form a single image. This fused image is classified using genetic algorithm. Different indices, such as K-means (KMI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) are used as objective functions. This method increases classification accuracy of hyperspectral image. Full Text: PDF DOI: http://dx.doi.org/10.11591/ijeecs.v2.i3.pp703-711


title: Multiple Systems in Decision Making

abstract: Neuroeconomics seeks to gain a greater understanding of decision making by combining theoretical and methodological principles from the fields of psychology, economics, and neuroscience. Initial studies using this multidisciplinary approach have found evidence suggesting that the brain may be employing multiple levels of processing when making decisions, and this notion is consistent with dual-processing theories that have received extensive theoretical consideration in the field of cognitive psychology, with these theories arguing for the dissociation between automatic and controlled components of processing. While behavioral studies provide compelling support for the distinction between automatic and controlled processing in judgment and decision making, less is known if these components have a corresponding neural substrate, with some researchers arguing that there is no evidence suggesting a distinct neural basis. This chapter will discuss the behavioral evidence supporting the dissociation between automatic and controlled processing in decision making and review recent literature suggesting potential neural systems that may underlie these processes.


title: Achievement of localization system for humanoid robots with virtual horizontal scan relative to improved odometry fusing internal sensors and visual information

abstract: To achieve tasks in unknown environments with high reliability, highly accurate localization during task execution is necessary for humanoid robots. In this paper, we discuss a localization system which can be applied to a humanoid robot when executing tasks in the real world. During such tasks, humanoid robots typically do not possess a referential to a constant horizontal plane which can in turn be used as part of fast and cost efficient localization methods. We solve this problem by first computing an improved odometry estimate through fusing visual odometry, feedforward commands from gait generator and orientation from inertia sensors. This estimate is used to generate a 3D point cloud from the accumulation of successive laser scans and such point cloud is then properly sliced to create a constant height horizontal virtual scan. Finally, this slice is used as an observation base and fed to a 2D SLAM method. The fusion process uses a velocity error model to achieve greater accuracy, which parameters are measured on the real robot. We evaluate our localization system in a real world task execution experiment using the JAXON robot and show how our system can be used as a practical solution for humanoid robots localization during complex tasks execution processes.


title: Thou shalt not hate: Countering Online Hate Speech

abstract: Hate content in social media is ever increasing. While Facebook, Twitter, Google have attempted to take several steps to tackle the hateful content, they have mostly been unsuccessful. Counterspeech is seen as an effective way of tackling the online hate without any harm to the freedom of speech. Thus, an alternative strategy for these platforms could be to promote counterspeech as a defense against hate content. However, in order to have a successful promotion of such counterspeech, one has to have a deep understanding of its dynamics in the online world. Lack of carefully curated data largely inhibits such understanding. In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube. The data contains 13,924 manually annotated comments where the labels indicate whether a comment is a counterspeech or not. This data allows us to perform a rigorous measurement study characterizing the linguistic structure of counterspeech for the first time. This analysis results in various interesting insights such as: the counterspeech comments receive much more likes as compared to the noncounterspeech comments, for certain communities majority of the non-counterspeech comments tend to be hate speech, the different types of counterspeech are not all equally effective and the language choice of users posting counterspeech is largely different from those posting non-counterspeech as revealed by a detailed psycholinguistic analysis. Finally, we build a set of machine learning models that are able to automatically detect counterspeech in YouTube videos with an F1-score of 0.71. We also build multilabel models that can detect different types of counterspeech in a comment with an F1-score of 0.60.


title: Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

abstract: Identifying regions of interest in an image has long been of great importance in a wide range of tasks, including place recognition. In this letter, we propose a novel attention mechanism with flexible context, which can be incorporated into existing feedforward network architecture to learn image representations for long-term place recognition. In particular, in order to focus on regions that contribute positively to place recognition, we introduce a multiscale context-flexible network to estimate the importance of each spatial region in the feature map. Our model is trained end-to-end for place recognition and can detect regions of interest of arbitrary shape. Extensive experiments have been conducted to verify the effectiveness of our approach and the results demonstrate that our model can achieve consistently better performance than the state of the art on standard benchmark datasets. Finally, we visualize the learned attention maps to generate insights into what attention the network has learned.


title: Appraisal of the Effectiveness and Efficiency of an Information Security Management System Based on ISO 27001

abstract: The ISO27001:2005, as an information security management system (ISMS), is establishing itself more and more as the security standard in enterprises. In 2008 more than 4457 certified enterprises could be registered worldwide. Nevertheless, the registering an ISMS still says nothing about the quality and performance of its implementation. Therefore, in this article, a method for measuring the performance of the implementation and operation of an ISMS is presented.


title: Problems and Prospects of Rice Production in Central District of Edo State, Nigeria

abstract: This paper examined the problems and prospects of rice production in the Central District of Edo State, Nigeria. The methods of study involved the administration of questionnaire and oral interview. Ageing and illiterate people dominate Rice forming. The farming system is characterized by the use of crude implements, small farm holdings and subsistency. There are no extension workers and farmers are unaware of new varieties of rice seedlings. Fertilizers are very expensive and unaffordable t o the local farmers. Rice production is plagued by myriad of problems including; attack from pests like birds, rodents and diseases. Yield per farm is low and financial return is correspondingly low, the prospect of rice production is low as many farmers are shifting to cassava farming. It is recommended that government should subsidize farm inputs and send extension worker to the farming communities. Also, credit facilities with minimum collateral security as well as improving socio-economic facilities wi ll encourage the local farmers to continue his business.


title: A Solitary Feature-Based Lung Nodule Detection Approach for Chest X-Ray Radiographs

abstract: Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in CXR as “solitary white nodule-like blob.” However, the solitary feature has not been employed for lung nodule detection before. In this paper, a solitary feature-based lung nodule detection method was proposed. We employed stationary wavelet transform and convergence index filter to extract the texture features and used AdaBoost to generate white nodule-likeness map. A solitary feature was defined to evaluate the isolation degree of candidates. Both the isolation degree and the white nodule likeness were used as final evaluation of lung nodule candidates. The proposed method shows better performance and robustness than those reported in previous research. More than 80% and 93% of lung nodules in the lung field in the Japanese Society of Radiological Technology (JSRT) database were detected when the false positives per image were two and five, respectively. The proposed approach has the potential of being used in clinical practice.


Effectiveness of PSO Based Neural Network for Seasonal Time Series Forecasting.


title: Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases

abstract: Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way. Here, we develop an end-to-end differentially private method for solving regression problems with convex penalty functions and selecting the penalty parameters by cross-validation. In particular, we focus on penalized logistic regression with elastic-net regularization, a method widely used to in GWAS analyses to identify disease-causing genes. We show how a differentially private procedure for penalized logistic regression with elastic-net regularization can be applied to the analysis of GWAS data and evaluate our method’s performance.


title: Development and initial validation of the EDIN scale, a new tool for assessing prolonged pain in preterm infants

abstract: OBJECTIVE ::: To develop and validate a scale suitable for use in clinical practice as a tool for assessing prolonged pain in premature infants. ::: ::: ::: METHODS ::: Pain indicators identified by observation of preterm infants and selected by a panel of experts were used to develop the EDIN scale (Echelle Douleur Inconfort Nouveau-Né, neonatal pain and discomfort scale). A cohort of preterm infants was studied prospectively to determine construct validity, inter-rater reliability, and internal consistency of the scale. ::: ::: ::: RESULTS ::: The EDIN scale uses five behavioural indicators of prolonged pain: facial activity, body movements, quality of sleep, quality of contact with nurses, and consolability. The validation study included 76 preterm infants with a mean gestational age of 31.5 weeks. Inter-rater reliability was acceptable, with a kappa coefficient range of 0.59-0.74. Internal consistency was high: Cronbach's alpha coefficients calculated after deleting each item ranged from 0.86 to 0.94. To establish construct validity, EDIN scores in two extreme situations (pain and no pain) were compared, and a significant difference was observed. ::: ::: ::: CONCLUSIONS ::: The validation data suggest that the EDIN is appropriate for assessing prolonged pain in preterm infants. Further studies are warranted to obtain further evidence of construct validity by comparing scores in less extreme situations.


title: CloudCmp: Shopping for a Cloud Made Easy

abstract: Cloud computing has gained much popularity recently, and many companies now offer a variety of public cloud computing services, such as Google Ap-pEngine, Amazon AWS, and Microsoft Azure. These services differ in service models and pricing schemes, making it challenging for customers to choose the best suited cloud provider for their applications. This paper proposes a framework called CloudCmp to help a customer select a cloud provider. We outline the design of CloudCmp and highlight the main technical challenges. CloudCmp includes a set of benchmarking tools that compare the common services offered by cloud providers, and uses the benchmarking results to predict the performance and costs of a customer's application when deployed on a cloud provider. We present preliminary benchmarking results on three representative cloud providers. These results show that the performance and costs of various cloud providers differ significantly, suggesting that CloudCmp, if implemented, will have practical relevance.


title: EE-LEACH: development of energy-efficient LEACH Protocol for data gathering in WSN

abstract: A wireless sensor network (WSN) consists of a huge number of sensor nodes that are inadequate in energy, storage and processing power. One of the major tasks of the sensor nodes is the collection of data and forwarding the gathered data to the base station (BS). Hence, the network lifetime becomes the major criteria for effective design of the data gathering schemes in WSN. In this paper, an energy-efficient LEACH (EE-LEACH) Protocol for data gathering is introduced. It offers an energy-efficient routing in WSN based on the effective data ensemble and optimal clustering. In this system, a cluster head is elected for each clusters to minimize the energy dissipation of the sensor nodes and to optimize the resource utilization. The energy-efficient routing can be obtained by nodes which have the maximum residual energy. Hence, the highest residual energy nodes are selected to forward the data to BS. It helps to provide better packet delivery ratio with lesser energy utilization. The experimental results shows that the proposed EE-LEACH yields better performance than the existing energy-balanced routing protocol (EBRP) and LEACH Protocol in terms of better packet delivery ratio, lesser end-to-end delay and energy consumption. It is obviously proves that the proposed EE-LEACH can improve the network lifetime.


title: The majority of skin lesions in pediatric primary care attention could be managed by Teledermatology

abstract: Background ::: Teledermatology is a tool that provides accurate diagnosis and has been gaining more emphasis over time. It can be used for triage in primary care attention to address skin conditions improving access and reducing time to treatment for surgical, severe or even lethal diseases.


title: User-centered perspectives for automotive augmented reality

abstract: Augmented reality (AR) in automobiles has the potential to significantly alter the driver's user experience. Prototypes developed in academia and industry demonstrate a range of applications from advanced driver assist systems to location-based information services. A user-centered process for creating and evaluating designs for AR displays in automobiles helps to explore what collaborative role AR should serve between the technologies of the automobile and the driver. In particular, we consider the nature of this role along three important perspectives: understanding human perception, understanding distraction and understanding human behavior. We argue that AR applications should focus solely on tasks that involve the immediate local driving environment and not secondary task spaces to minimize driver distraction. Consistent depth cues should be supported by the technology to aid proper distance judgement. Driving aids supporting situation awareness should be designed with knowledge of current and future states of road users, while focusing on specific problems. Designs must also take into account behavioral phenomena such as risk compensation, inattentional blindness and an over-reliance on augmented technology in driving decisions.


title: Interaction Proxies for Runtime Repair and Enhancement of Mobile Application Accessibility

abstract: We introduce interaction proxies as a strategy for runtime repair and enhancement of the accessibility of mobile applications. Conceptually, interaction proxies are inserted between an application's original interface and the manifest interface that a person uses to perceive and manipulate the application. This strategy allows third-party developers and researchers to modify an interaction without an application's source code, without rooting the phone, without otherwise modifying an application, while retaining all capabilities of the system (e.g., Android's full implementation of the TalkBack screen reader). This paper introduces interaction proxies, defines a design space of interaction re-mappings, identifies necessary implementation abstractions, presents details of implementing those abstractions in Android, and demonstrates a set of Android implementations of interaction proxies from throughout our design space. We then present a set of interviews with blind and low-vision people interacting with our prototype interaction proxies, using these interviews to explore the seamlessness of interaction, the perceived usefulness and potential of interaction proxies, and visions of how such enhancements could gain broad usage. By allowing third-party developers and researchers to improve an interaction, interaction proxies offer a new approach to personalizing mobile application accessibility and a new approach to catalyzing development, deployment, and evaluation of mobile accessibility enhancements.


title: Distributed Agile Development: Using Scrum in a Large Project

abstract: While seemingly incompatible, combining large-scale global software development and agile practices is a challenge undertaken by many companies. Case study reports on the successful use of agile practices in small distributed projects already exist. How these practices could be applied to larger projects, however, remains unstudied. This paper reports a case study on agile practices in a 40- person development organization distributed between Norway and Malaysia. Based on seven interviews in the development organization, we describe how Scrum practices were successfully applied, e.g., using teleconference and Web cameras for daily scrum meetings, synchronized 4- week sprints and weekly scrum-of-scrums. Additional agility supporting practices for distributed projects were identified, e.g., frequent visits, unofficial distributed meetings and annual gatherings are described.


title: Predict Closed Questions on StackOverflow

abstract: ”Millions of programmers use StackOverflow to get high quality answers to their programming questions every day. There has evolved an effective culture of moderation to safe-guard it. More than six thousand new questions is asked on StackOverflow1 every weekday. Currently about 6% of all new questions end up ”closed”. The goal of this paper is to build a classifier that predicts whether or not a question will be closed given the question as submitted, along with the reason that the question was closed.


title: Natural language reporting for ETL processes

abstract: The conceptual design of the Extract -- Transform -- Load (ETL) processes is a crucial, burdensome, and challenging procedure that takes places at the early phases of a Data Warehouse project. Several models have been proposed for the conceptual design and representation of ETL processes, but all share two inconveniences: they require intensive human effort from the designers to create them, as well as technical knowledge from the business people to understand them. In a previous work, we have relaxed the former difficulty by working on the automation of the conceptual design leveraging Semantic Web technology. In this paper, we built upon our previous results and we tackle the second issue by investigating the application of natural language generation techniques to the ETL environment. In particular, we provide a method for the representation of a conceptual ETL design as a narrative, which is the most natural means of communication and does not require knowledge of any specific model. We discuss how linguistic techniques can be used for the establishment of a common application vocabulary. Finally, we present a flexible and customizable template-based mechanism for generating natural language representations for the ETL process requirements and operations.


title: Examine before You Answer: Multi-task Learning with Adaptive-attentions for Multiple-choice VQA

abstract: Multiple-choice (MC) Visual Question Answering (VQA) is a similar but essentially different task to open-ended VQA because the answer options are provided. Most of existing works tackle them in a unified pipeline by solving a multi-class problem to infer the best answer from a predefined answer set. The option that matches the best answer is selected for MC VQA. Nevertheless, this violates human thinking logics. Normally, people examine the questions, answer options and the reference image before inferring a MC VQA. For MC VQA, human either rely on the question and answer options to directly deduce a correct answer if the question is not image-related, or read the question and answer options and then purposefully search for answers in a reference image. Therefore, we propose a novel approach, namely Multi-task Learning with Adaptive-attention (MTA), to simulate human logics for MC VQA. Specifically, we first fuse the answer options and question features, and then adaptively attend to the visual features for inferring a MC VQA. Furthermore, we design our model as a multi-task learning architecture by integrating the open-ended VQA task to further boost the performance of MC VQA. We evaluate our approach on two standard benchmark datasets: VQA and Visual7W and our approach sets new records on both datasets for MC VQA task, reaching 73.5% and 65.9% average accuracy respectively.


TABLES OF LINEAR CONGRUENTIAL GENERATORS OF DIFFERENT SIZES AND GOOD LATTICE STRUCTURE


title: LETTER Experiments with Computational Creativity

abstract: Neurocognitive model inspired by the putative processes in the brain has been applied to invention of novel words. This domain is proposed as the simplest way to understand creativity using experimental and computational means. Three factors are essential for creativity in this domain: knowledge of the statistical language properties, imagination constrained by this knowledge, and filtering of results that selects most interesting novel words. These principles are implemented using a simple correlation-based algorithm for auto-associative memory that learns the statistical properties of language. Results are surprisingly similar to those created by humans. Perspectives on computational models of creativity are discussed. Keywords—Creativity, brain, language processing, higher cognitive functions, neural modeling.


title: A hybrid PSO-GA method for composing heterogeneous groups in collaborative learning

abstract: The importance and benefits of collaborative learning have been widely recognized. The initial stage of this instructional strategy is assigning students into groups, which is a key process in collaborative learning. One major approach of the group formation is to form heterogeneous groups based on student characteristics. In previous studies, several approaches have been presented to compose the heterogeneous groups. However, they only consider a limited number of particular student characteristics. To solve this problem, in this paper, a method based on a hybrid PSO-GA approach is proposed, which allows for considering an arbitrary number of student characteristics. With respect to the evaluation of the proposed method, we conduct a series of computational experiments, which are developed on eight data sets with different levels of complexity. The simulation results show that the proposed approach is effective and stable for composing heterogeneous groups.


title: Radar waveform design and multi-target detection in vehicular applications

abstract: In traffic environment, conventional FMCW radar with triangular transmit waveform may bring out many false targets in multi-target situations and result in a high false alarm rate. An improved FMCW waveform and multi-target detection algorithm for vehicular applications is presented. The designed waveform in each small cycle is composed of two-segment: LFM section and constant frequency section. They have the same duration, yet in two adjacent small cycles the two LFM slopes are opposite sign and different size. Then the two adjacent LFM bandwidths are unequal. Within a determinate frequency range, the constant frequencies are modulated by a unique PN code sequence for different automotive radar in a big period. Corresponding to the improved waveform, which combines the advantages of both FSK and FMCW formats, a judgment algorithm is used in the continuous small cycle to further eliminate the false targets. The combination of unambiguous ranges and relative velocities can confirm and cancel most false targets in two adjacent small cycles.


title: Online Influence Maximization

abstract: Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or seed nodes), with the hope that they will convince their friends to buy it. One way to formalize this objective is through the problem of influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM), since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) user feedback is used to update influence information. We adopt Explore-Exploit strategies, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling user feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.