title: A literature review on emotion recognition system using various facial expression

abstract: Recognition and extracting various emotions and then validating those emotions from the facial expressions has become important for improving the overall human computer interaction. So this paper also describe about Emotion Recognition Techniques. Emotion Recognition has become a progressive research area and it plays a major role in Human-Computer-Interaction. For any Facial Expression Recognition, it is necessary to extract the features of face that can be possibly used to detect the expression. For Feature Extraction the Principal Component Analysis will be used. A survey of various techniques used in emotion recognition like PCA, LBP etc. is done in this paper and listing their performance. The goal of this paper is to show the comparison with Other Recognition Techniques with different approaches. And also describe the general step of the how to recognize emotion from various facial expression


GENDER AND LEADERSHIP BEHAVIOR AMONG SENIOR PASTORS


title: Grid Integration of Electric Vehicles and Demand Response With Customer Choice

abstract: As electric vehicles (EVs) take a greater share in the personal automobile market, their penetration may bring higher peak demand at the distribution level. This may cause potential transformer overloads, feeder congestions, and undue circuit faults. This paper focuses on the impact of charging EVs on a residential distribution circuit. Different EV penetration levels, EV types, and charging profiles are considered. In order to minimize the impact of charging EVs on a distribution circuit, a demand response strategy is proposed in the context of a smart distribution network. In the proposed DR strategy, consumers will have their own choices to determine which load to control and when. Consumer comfort indices are introduced to measure the impact of demand response on consumers' lifestyle. The proposed indices can provide electric utilities a better estimation of the customer acceptance of a DR program, and the capability of a distribution circuit to accommodate EV penetration.


title: The Effect of the Distance From the Contact Point to the Crest of Bone on the Presence or Absence of the Interproximal Dental Papilla

abstract: This study was designed to determine whether the distance from the base of the contact area to the crest of bone could be correlated with the presence or absence of the interproximal papilla in humans. A total of 288 sites in 30 patients were examined. If a space was visible apical to the contact point, then the papilla was deemed missing; if tissue filled the embrasure space, the papilla was considered to be present. The results showed that when the measurement from the contact point to the crest of bone was 5 mm or less, the papilla was present almost 100% of the time. When the distance was 6 mm, the papilla was present 56% of the time, and when the distance was 7 mm or more, the papilla was present 27% of the time or less. J Periodontol 1992; 63:995–996.


Sound Signal Processing Based on Seq 2 Tree Network


title: Periodicity detection in time series databases

abstract: Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.


title: DNA transfer: review and implications for casework.

abstract: DNA-bearing cellular material can come to be present on a surface by either direct or indirect transfer. Direct transfer includes contact, but also includes activities within the vicinity of an item that may result in the transfer of DNA directly from an individual without any contact, such as speaking, coughing, and sneezing. Indirect transfer of DNA is when DNA from an individual comes to be on an item via an intermediary surface. It is important to consider indirect transfer in the evaluation of trace DNA in casework. The term 'trace DNA' in this review refers solely to DNA that cannot be attributed to an identifiable body fluid. This review presents and considers data from trace DNA experiments to establish whether the quantity of DNA recovered from a crime stain and/or the quality of a DNA profile obtained can be used to infer the likely mechanism of transfer. The data show that varied results are obtained from apparently similar trace DNA samples, presumably due to the many factors that affect the detection of trace DNA. The nature and effect of these varying factors and the application of the data to casework is considered generally and with specific reference to DNA transfer to skin, DNA beneath fingernails, 'wearer DNA', and various contamination considerations.


title: Frequency and Quality of Social Networking Among Young Adults: Associations With Depressive Symptoms, Rumination, and Corumination

abstract: Two studies examined associations between social networking and depressive symptoms among youth. In Study 1, 384 participants (68% female; mean age = 20.22 years, SD = 2.90) were surveyed. In Study 2, 334 participants (62% female; M age = 19.44 years, SD = 2.05) were surveyed initially and 3 weeks later. Results indicated that depressive symptoms were associated with quality of social networking interactions, not quantity. There was some evidence that depressive rumination moderated associations, and both depressive rumination and corumination were associated with aspects of social networking usage and quality. Implications for understanding circumstances that increase social networking, as well as resulting negative interactions and negative affect are discussed.


title: Regulation of energy balance by a gut–brain axis and involvement of the gut microbiota

abstract: Despite significant progress in understanding the homeostatic regulation of energy balance, successful therapeutic options for curbing obesity remain elusive. One potential target for the treatment of obesity is via manipulation of the gut-brain axis, a complex bidirectional communication system that is crucial in maintaining energy homeostasis. Indeed, ingested nutrients induce secretion of gut peptides that act either via paracrine signaling through vagal and non-vagal neuronal relays, or in an endocrine fashion via entry into circulation, to ultimately signal to the central nervous system where appropriate responses are generated. We review here the current hypotheses of nutrient sensing mechanisms of enteroendocrine cells, including the release of gut peptides, mainly cholecystokinin, glucagon-like peptide-1, and peptide YY, and subsequent gut-to-brain signaling pathways promoting a reduction of food intake and an increase in energy expenditure. Furthermore, this review highlights recent research suggesting this energy regulating gut-brain axis can be influenced by gut microbiota, potentially contributing to the development of obesity.


title: An efficient parallel FP-Growth algorithm

abstract: FP-Growth algorithm recursively generates huge amounts of conditional pattern bases and conditional FP-trees when the dataset is huge. In such a case, both the memory usage and computational cost are expensive, such that, the FP-tree can not meet the memory requirement. In this work, we propose a novel parallel FP-Growth algorithm, which is designed to run on the computer cluster. To avoid memory overflow, this algorithm finds all the conditional pattern bases of frequent items by the projection method without constructing an FP-tree. Hereafter, it splits the mining task into number of independent sub-tasks, executes these sub-tasks in parallel on nodes and then aggregates the results back for the final result. Our algorithm works independently at each node. As a result, it can efficiently reduce the inter-node communication cost. Experiments show that this parallel algorithm not only avoids the memory overflow but accelerate the computational speed. In addition, it achieves much better scalability than that of the FP-Growth algorithm.


title: Amygdala–frontal connectivity during emotion regulation

abstract: Successful control of affect partly depends on the capacity to modulate negative emotional responses through the use of cognitive strategies (i.e., reappraisal). Recent studies suggest the involvement of frontal cortical regions in the modulation of amygdala reactivity and the mediation of effective emotion regulation. However, within-subject inter-regional connectivity between amygdala and prefrontal cortex in the context of affect regulation is unknown. Here, using psychophysiological interaction analyses of functional magnetic resonance imaging data, we show that activity in specific areas of the frontal cortex (dorsolateral, dorsal medial, anterior cingulate, orbital) covaries with amygdala activity and that this functional connectivity is dependent on the reappraisal task. Moreover, strength of amygdala coupling with orbitofrontal cortex and dorsal medial prefrontal cortex predicts the extent of attenuation of negative affect following reappraisal. These findings highlight the importance of functional connectivity within limbic-frontal circuitry during emotion regulation.


Computational Intelligence: Principles, Techniques and Applications


title: The surgical anatomy of the blood supply to the femoral head: DESCRIPTION OF THE ANASTOMOSIS BETWEEN THE MEDIAL FEMORAL CIRCUMFLEX AND INFERIOR GLUTEAL ARTERIES AT THE HIP

abstract: The inferior gluteal artery is described in standard anatomy textbooks as contributing to the blood supply of the hip through an anastomosis with the medial femoral circumflex artery. The site(s) of the anastomosis has not been described previously. We undertook an injection study to define the anastomotic connections between these two arteries and to determine whether the inferior gluteal artery could supply the lateral epiphyseal arteries alone. From eight fresh-frozen cadaver pelvic specimens we were able to inject the vessels in 14 hips with latex moulding compound through either the medial femoral circumflex artery or the inferior gluteal artery. Injected vessels around the hip were then carefully exposed and documented photographically. In seven of the eight specimens a clear anastomosis was shown between the two arteries adjacent to the tendon of obturator externus. The terminal vessel arising from this anastomosis was noted to pass directly beneath the posterior capsule of the hip before ascending the superior aspect of the femoral neck and terminating in the lateral epiphyseal vessels. At no point was the terminal vessel found between the capsule and the conjoined tendon. The medial femoral circumflex artery receives a direct supply from the inferior gluteal artery immediately before passing beneath the capsule of the hip. Detailed knowledge of this anatomy may help to explain the development of avascular necrosis after hip trauma, as well as to allow additional safe surgical exposure of the femoral neck and head.


title: Bi-directional PHEV battery charger based on normally-off GaN-on-Si multi-chip module

abstract: A high frequency, high efficiency bi-directional battery charger for Plug-in Hybrid Electric Vehicle (PHEV) is built with high voltage normally-off GaN-on-Si HFETs. The battery charger topology consists of a 500 kHz Full Bridge (FB) AD/DC stage and a 500 kHz Dual Active Bridge (DAB) DC/DC stage. The system functionality is verified and measured efficiency is 97% for the AC/DC stage and 97.2% for the DC/DC stage, which leads to a total efficiency 94.2%. By doing sinusoidal charging, the DC link capacitance can be reduced to much less than one third but efficiency will drop to 92.0%.


title: Ways to React: Comparing Reactive Languages and Complex Event Processing

abstract: Reactive applications demand for detecting the changes that occur in a domain of interest and for timely reactions. Examples range from simple interactive applications to complex monitoring tasks involving distributed and heterogeneous systems. Over the last years, different programming paradigms and solutions have been proposed to support such applications. In this paper, we focus on two prominent approaches: event-based programming, specifically Complex Event Processing (CEP), and Reactive Languages (RLs). CEP systems enable the definition of high level situations of interest from low level primitive events detected in the external environment. On the other hand, RLs support time-changing values and their composition as dedicated language abstractions. These research fields have been investigated by different communities, belonging respectively to the database and the distributed systems areas and to the programming language area. It is our belief that a deeper understanding of these research fields, including their benefits and limitations, their similarities and differences, could drive further developments in supporting reactive applications. For this reason, we propose a first comparison of the two fields. Despite huge differences, we believe that such a comparison can trigger an interesting discussion across the communities, favor knowledge sharing, and let new ideas emerge.


title: On the reception and detection of pseudo-profound bullshit

abstract: Although bullshit is common in everyday life and has attracted attention from philosophers, its reception (critical or ingenuous) has not, to our knowledge, been subject to empirical investigation. Here we focus on pseudo-profound bullshit, which consists of seemingly impressive assertions that are presented as true and meaningful but are actually vacuous. We presented participants with bullshit statements consisting of buzzwords randomly organized into statements with syntactic structure but no discernible meaning (e.g., â€œWholeness quiets infinite phenomenaâ€ ). Across multiple studies, the propensity to judge bullshit statements as profound was associated with a variety of conceptually relevant variables (e.g., intuitive cognitive style, supernatural belief). Parallel associations were less evident among profundity judgments for more conventionally profound (e.g., â€œA wet person does not fear the rainâ€ ) or mundane (e.g., â€œNewborn babies require constant attentionâ€ ) statements. These results support the idea that some people are more receptive to this type of bullshit and that detecting it is not merely a matter of indiscriminate skepticism but rather a discernment of deceptive vagueness in otherwise impressive sounding claims. Our results also suggest that a bias toward accepting statements as true may be an important component of pseudo-profound bullshit receptivity.


title: Effectiveness of Random Search in SVM hyper-parameter tuning

abstract: Classification is one of the most common machine learning tasks. SVMs have been frequently applied to this task. In general, the values chosen for the hyper-parameters of SVMs affect the performance of their induced predictive models. Several studies use optimization techniques to find a set of hyper-parameter values that induces classifiers with good predictive performance. This paper investigates the hypothesis that a simple Random Search method is sufficient to adjust the hyper-parameters of SVMs. A set of experiments compared the performance of five tuning techniques: three meta-heuristics commonly used, Random Search and Grid Search. The experimental results show that the predictive performance of models using Random Search is equivalent to those obtained using meta-heuristics and Grid Search, but with a lower computational cost.


title: Image tampering detection using noise histogram features

abstract: With the rapid development of digital image editing tools, the authenticity of digital images becomes questionable in recent years. Image tampering detection is a technology that detects tampered images by using intrinsic image regularities. However, existing intrinsic image regularities are designed for one specific type of tampering operations. When multiple types of tampering operations are used to process a digital image, image tampering detection accuracy is seriously degraded. This paper exploits the re-normalized histogram of noise and noise difference as a new type of intrinsic image regularities, which is computed as the ratio of the histogram to the peak-value. When the image is operated by image tampering, the re-normalized histogram would increase as the peak value of this histogram would decrease. By applying the re-normalized histogram as features, the experimental results show that the proposed method has the ability to detect a wide range of tamper operations, including single type and multiple types of tampering operations without the prior knowledge of tampering operation order, type and parameter.


title: Gland Instance Segmentation Using Deep Multichannel Neural Networks

abstract: Objective: A new image instance segmentation method is proposed to segment individual glands (instances) in colon histology images. This process is challenging since the glands not only need to be segmented from a complex background, they must also be individually identified. Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information—regional, location, and boundary cues—in gland histology images. Our proposed algorithm, a deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirements by altering channels. Results: Compared with methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent instance segmentation methods, we observe state-of-the-art results based on the evaluation metrics. Conclusion: The proposed deep multichannel algorithm is an effective method for gland instance segmentation. Significance: The generalization ability of our model not only enable the algorithm to solve gland instance segmentation problems, but the channel is also alternative that can be replaced for a specific task.


How (un)important is penis size for women with heterosexual experience


title: Development of through silicon via (TSV) interposer technology for large die (21×21mm) fine-pitch Cu/low-k FCBGA package

abstract: Because of Moore's (scaling/integration) law, the Cu/low-k silicon chip is getting bigger, the pin-out is getting higher, and the pitch is getting finer. Thus, the conventional organic buildup substrates cannot support these kinds of silicon chips anymore. To address these needs, Si interposer with TSV has emerged as a good solution to provide high wiring density interconnection, to minimize CTE mismatch to the Cu/low-k chip that is vulnerable to thermal-mechanical stress, and to improve electrical performance due to shorter interconnection from the chip to the substrate. This paper presents the development of TSV interposer technology for a 21×21 mm Cu/low-k test chip on FCBGA package. The Cu/low-k chip is a 65 nm, 9-metal layer chip with 150 µm SnAg bump pitch of total 11,000 I/O, with via chain and daisy chain for interconnect integrity monitoring and reliability testing. The TSV interposer size is 25×25×0.3 mm with CuNiAu as UBM on the top side, and SnAgCu bumps on the underside. The conventional BT substrate size is 45×45 mm with BGA pad pitch of 1 mm and core thickness of 0.8 mm. Mechanical and thermal modeling and simulation for the FCBGA package with TSV interposer have been performed. TSV interposer fabrication processes and assembly process of the large die mounted on TSV interposer with Pb-free micro solder bumps and underfill have been set up. The FCBGA samples have been subjected to moisture sensitivity test and thermal cycling (TC) reliability assessments.


title: An Experiment on Fear of Public Speaking in Virtual Reality

abstract: Can virtual reality exposure therapy be used to treat people with social phobia? To answer this question it is vital to known if people will respond to virtual humans (avatars) in a virtual social setting in the same way they would to real humans. If someone is extremely anxious with real people, will they also be anxious when faced with simulated people, despite knowing that the avatars are computer generated? In [17] we described a small pilot study that placed 10 people before a virtual audience. The purpose was to assess the extent to which social anxiety, specifically fear of public speaking, was induced by the virtual audience and the extent of influence of degree of immersion (head mounted display or desktop monitor. The current paper describes a follow up study conducted with 40 subjects and the results clearly show that not only is social anxiety induced by the audience, but the degree of anxiety experienced is directly related to the type of virtual audience feedback the speaker receives. In particular, a hostile negative audience scenario was found to generate strong affect in speakers, regardless of whether or not they normally suffered from fear of public speaking.


title: Design of microstrip bandpass filters with multiorder spurious-mode suppression

abstract: This paper proposes a bandpass filter design method for suppressing spurious responses in the stopband by choosing the constitutive resonators with the same fundamental frequency, but staggered higher order resonant frequencies. The design concept is demonstrated by a four-pole parallel-coupled Chebyshev bandpass filter and a compact four-pole cross-coupled elliptic-type bandpass filter. Each filter is composed of four different stepped-impedance resonators (SIRs) for which a general design guideline has been provided in order to have the same fundamental frequency and different spurious frequencies by proper adjusting the impedance and length ratios of the SIR. Being based on knowledge of the coupling coefficients and following the traditional design procedure, the resultant filter structures are simple and easy to synthesize. The measured results are in good agreement with the simulated predictions, showing that better than -30-dB rejection levels in the stopband up to 5.4f/sub 0/ and 8.2f/sub 0/ are achieved by the Chebyshev and quasi-elliptic filters, respectively.


Introduction: Hybrid intelligent adaptive systems


title: In-Vehicle CAN Message Authentication: An Evaluation Based on Industrial Criteria

abstract: Vehicles have evolved from mostly mechanical machines into devices controlled by an internal computer network consisting of more than 100 interconnected Electronic Control Units (ECUs). Moreover, modern vehicles communicate with external devices to enable new features, but these new communication facilities also expose safety-critical functions to security threats. As the most prevalent automotive bus, the Controller Area Network (CAN) bus is a prime target for attacks. Even though the computer security community has proposed several message authentication solutions to alleviate those threats, such solutions have not yet been widely adopted by the automotive industry. We have identified the most promising CAN message authentication solutions and provide a comprehensive overview of them. In order to investigate the lack of adoption of such solutions, we, together with industry experts, have identified five general requirements they must fulfill in order to be considered viable in industry. Based on those requirements, we analyze and evaluate the identified authentication solutions. We find that none of them meet all the requirements, and that backward compatibility and acceptable overhead are the biggest obstacles.


title: Where, When, and How mmWave is Used in 5G and Beyon

abstract: Wireless engineers and business planners commonly raise the question on where, when, and how millimeter-wave (mmWave) will be used in 5G and beyond. Since the next generation network is not just a new radio access standard, but instead an integration of networks for vertical markets with diverse applications, answers to the question depend on scenarios and use cases to be deployed. This paper gives four 5G mmWave deployment examples and describes in chronological order the scenarios and use cases of their probable deployment, including expected system architectures and hardware prototypes. The paper starts with 28 GHz outdoor backhauling for fixed wireless access and moving hotspots, which will be demonstrated at the PyeongChang winter Olympic games in 2018. The second deployment example is a 60 GHz unlicensed indoor access system at the Tokyo-Narita airport, which is combined with Mobile Edge Computing (MEC) to enable ultra-high speed content download with low latency. The third example is mmWave mesh network to be used as a micro Radio Access Network ({\mu}-RAN), for cost-effective backhauling of small-cell Base Stations (BSs) in dense urban scenarios. The last example is mmWave based Vehicular-to-Vehicular (V2V) and Vehicular-to-Everything (V2X) communications system, which enables automated driving by exchanging High Definition (HD) dynamic map information between cars and Roadside Units (RSUs). For 5G and beyond, mmWave and MEC will play important roles for a diverse set of applications that require both ultra-high data rate and low latency communications.


title: A Survey on Handwritten Character Recognition Techniques for Various Indian Languages

abstract: Handwritten character recognition is always an interesting area of pattern recognition for research in the field of image processing. Many researchers have presented their work in this area and still research is undergoing to achieve high accuracy. This paper is mainly concerned for the people who are working on the character recognition and review of work to recognize handwritten character for various Indian languages. The objective of this paper is to describe the set of preprocessing, segmentation, feature extraction and classification techniques.


title: WearIA: Wearable device implicit authentication based on activity information

abstract: Privacy and authenticity of data pushed by or into wearable devices are of important concerns. Wearable devices equipped with various sensors can capture user's activity in fine-grained level. In this work, we investigate the possibility of using user's activity information to develop an implicit authentication approach for wearable devices. We design and implement a framework that does continuous and implicit authentication based on ambulatory activities performed by the user. The system is validated using data collected from 30 participants with wearable devices worn across various regions of the body. The evaluation results show that the proposed approach can achieve as high as 97% accuracy rate with less than 1% false positive rate to authenticate a user using a single wearable device. And the accuracy rate can go up to 99.6% when we use the fusion of multiple wearable devices.


title: ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering

abstract: The number of microblog posts published daily has reached a level that hampers the effective retrieval of relevant messages, and the amount of information conveyed through services such as Twitter is still increasing. Analysts require new methods for monitoring their topic of interest, dealing with the data volume and its dynamic nature. It is of particular importance to provide situational awareness for decision making in time-critical tasks. Current tools for monitoring microblogs typically filter messages based on user-defined keyword queries and metadata restrictions. Used on their own, such methods can have drawbacks with respect to filter accuracy and adaptability to changes in trends and topic structure. We suggest ScatterBlogs2, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events. These message filters include supervised classification and query creation backed by the statistical distribution of terms and their co-occurrences. The created filter methods can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds. We demonstrate the feasibility of our approach for analyzing the Twitter stream in emergency management scenarios.


title: Analysis of Emergency Medical Transport Datasets using Machine Learning

abstract: The selection of hospital once an ambulance has picked up its patient is today decided by the ambulance staff. This report describes a supervised machinelearning approach for predicting hospital se ...


title: Multi-label learning with weak label

abstract: Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" label set associated with each training instance is given by users. In many applications, however, to get the full label set for each instance is difficult and only a "partial" set of labels is available. In such cases, the appearance of a label means that the instance is associated with this label, while the absence of a label does not imply that this label is not proper for the instance. We call this kind of problem "weak label" problem. In this paper, we propose the WELL (WEak Label Learning) method to solve the weak label problem. We consider that the classification boundary for each label should go across low density regions, and that each label generally has much smaller number of positive examples than negative examples. The objective is formulated as a convex optimization problem which can be solved efficiently. Moreover, we exploit the correlation between labels by assuming that there is a group of low-rank base similarities, and the appropriate similarities between instances for different labels can be derived from these base similarities. Experiments validate the performance of WELL.


title: Beyond the Producer-driven / Buyer-driven Dichotomy The Evolution of Global Value Chains in the Internet Era

abstract: Infomediaries are information intermediaries in the Internet that play an important part in reducing online customers’ search costs for finding the most suitable vendors and products. This research explores the process of Web customers’ trust building using infomediaries. Specifically, we identify four sets of trust-related beliefs that impact web customers’ trust attitude and intended behavior, as well as the antecedents of trust-related beliefs. This conceptualization is built on a number of theories, mainly theories of trust, reasoned action, and actor-network. Our empirical results in testing the model indicate that web customers’ trust attitude toward web infomediaries is formed based on their beliefs regarding risk, content quality, system quality, and trustbuilding beliefs. We also found that initial trust is the antecedent of trust-forming beliefs, whereas individuals’ propensity to trust influences their calculative risk beliefs. The implications of these findings are also discussed.


title: Closed-world tracking of multiple interacting targets for indoor-sports applications

abstract: In this paper we present an efficient algorithm for tracking multiple players during indoor sports matches. A sports match can be considered as a semi-controlled environment for which a set of closed-world assumptions regarding the visual as well as the dynamical properties of the players and the court can be derived. These assumptions are then used in the context of particle filtering to arrive at a computationally fast, closed-world, multi-player tracker. The proposed tracker is based on multiple, single-player trackers, which are combined using a closed-world assumption about the interactions among players. With regard to the visual properties, the robustness of the tracker is achieved by deriving a novel sports-domain-specific likelihood function and employing a novel background-elimination scheme. The restrictions on the player's dynamics are enforced by employing a novel form of local smoothing. This smoothing renders the tracking more robust and reduces the computational complexity of the tracker. We evaluated the proposed closed-world, multi-player tracker on a challenging data set. In comparison with several similar trackers that did not utilize all of the closed-world assumptions, the proposed tracker produced better estimates of position and prediction as well as reducing the number of failures.


title: Zoomorphic design

abstract: Zoomorphic shapes are man-made shapes that possess the form or appearance of an animal. They have desirable aesthetic properties, but are difficult to create using conventional modeling tools. We present a method for creating zoomorphic shapes by merging a man-made shape and an animal shape. To identify a pair of shapes that are suitable for merging, we use an efficient graph kernel based technique. We formulate the merging process as a continuous optimization problem where the two shapes are deformed jointly to minimize an energy function combining several design factors. The modeler can adjust the weighting between these factors to attain high-level control over the final shape produced. A novel technique ensures that the zoomorphic shape does not violate the design restrictions of the man-made shape. We demonstrate the versatility and effectiveness of our approach by generating a wide variety of zoomorphic shapes.


title: ALPS: A Bluetooth and Ultrasound Platform for Mapping and Localization

abstract: The proliferation of Bluetooth Low-Energy (BLE) chipsets on mobile devices has lead to a wide variety of user-installable tags and beacons designed for location-aware applications. In this paper, we present the Acoustic Location Processing System (ALPS), a platform that augments BLE transmitters with ultrasound in a manner that improves ranging accuracy and can help users configure indoor localization systems with minimal effort. A user places three or more beacons in an environment and then walks through a calibration sequence with their mobile device where they touch key points in the environment like the floor and the corners of the room. This process automatically computes the room geometry as well as the precise beacon locations without needing auxiliary measurements. Once configured, the system can track a user's location referenced to a map. The platform consists of time-synchronized ultrasonic transmitters that utilize the bandwidth just above the human hearing limit, where mobile devices are still sensitive and can detect ranging signals. To aid in the mapping process, the beacons perform inter-beacon ranging during setup. Each beacon includes a BLE radio that can identify and trigger the ultrasonic signals. By using differences in propagation characteristics between ultrasound and radio, the system can classify if beacons are within Line-Of-Sight (LOS) to the mobile phone. In cases where beacons are blocked, we show how the phone's inertial measurement sensors can be used to supplement localization data. We experimentally evaluate that our system can estimate three-dimensional beacon location with a Euclidean distance error of 16.1cm, and can generate maps with room measurements with a two-dimensional Euclidean distance error of 19.8cm. When tested in six different environments, we saw that the system can identify Non-Line-Of-Sight (NLOS) signals with over 80% accuracy and track a user's location to within less than 100cm.


title: Appropriation of Information Systems: Using Cognitive Mapping for Eliciting Users' Sensemaking

abstract: This paper explores the use of cognitive mapping for eliciting users' sensemaking during information system (IS) appropriation. Despite the potential usefulness of sensemaking, few studies in IS research use it as a theoretical lens to address IS appropriation. A possible reason for this may be that sensemaking does not easily lend itself to be used in practice. We introduce cognitive mapping as a way to elicit users' sensemaking and illustrate its value by reporting on findings from an empirical study of the introduction of an Electronic Patient Record (EPR) system. The contribution of the paper is threefold: first, our findings demonstrate cognitive mapping's use for eliciting users' sensemaking during IS appropriation. Second, our findings illustrate how cognitive mapping can be used as a dynamic approach facilitating collective negotiation of meaning. Third, we contribute with a thorough discussion of the epistemological and methodological assumptions underlying cognitive mapping to ensure its validity and trustworthiness.


title: Holistic facial expression classification

abstract: This paper details a procedure for classifying facial expressions. This is a growing and relatively new type of problem within computer vision. One of the fundamental problems when classifying facial expressions in previous approaches is the lack of a consistent method of measuring expression. ::: ::: This paper solves this problem by the computation of the Facial Expression Shape Model (FESM). This statistical model of facial expression is based on an anatomical analysis of facial expression called the Facial Action Coding System (FACS). We use the term Action Unit (AU) to describe a movement of one or more muscles of the face and all expressions can be described using the AU's described by FACS. ::: ::: The shape model is calculated by marking the face with 122 landmark points. We use Principal Component Analysis (PCA) to analyse how the landmark points move with respect to each other and to lower the dimensionality of the problem. Using the FESM in conjunction with Support Vector Machines (SVM) we classify facial expressions. SVMs are a powerful machine learning technique based on optimisation theory. ::: ::: This project is largely concerned with statistical models, machine learning techniques and psychological tools used in the classification of facial expression. This holistic approach to expression classification provides a means for a level of interaction with a computer that is a significant step forward in human-computer interaction.


title: Multi-subject Manifold Alignment of Functional Network Structures via Joint Diagonalization.

abstract: Functional magnetic resonance imaging group studies rely on the ability to establish correspondence across individuals. This enables location specific comparison of functional brain characteristics. Registration is often based on morphology and does not take variability of functional localization into account. This can lead to a loss of specificity, or confounds when studying diseases. In this paper we propose multi-subject functional registration by manifold alignment via coupled joint diagonalization. The functional network structure of each subject is encoded in a diffusion map, where functional relationships are decoupled from spatial position. Two-step manifold alignment estimates initial correspondences between functionally equivalent regions. Then, coupled joint diagonalization establishes common eigenbases across all individuals, and refines the functional correspondences. We evaluate our approach on fMRI data acquired during a language paradigm. Experiments demonstrate the benefits in matching accuracy achieved by coupled joint diagonalization compared to previously proposed functional alignment approaches, or alignment based on structural correspondences.


title: Remyelination is extensive in a subset of

abstract: Although spontaneous remyelination does occur in multiple sclerosis lesions, its extent within the global population with this disease is presently unknown. We have systematically analysed the incidence and distribution of completely remyelinated lesions (so-called shadow plaques) or partially remyelinated lesions (shadow plaque areas) in 51 autopsies of patients with different clinical courses and disease durations. The extent of remyelination was variable between cases. In 20% of the patients, the extent of remyelination was extensive with 60–96% of the global lesion area remyelinated. Extensive remyelination was found not only in patients with relapsing multiple sclerosis, but also in a subset of patients with progressive disease. Older age at death and longer disease duration were associated with significantly more remyelinated lesions or lesion areas. No correlation was found between the extent of remyelination and either gender or age at disease onset. These results suggest that the variable and patient-dependent extent of remyelination must be considered in the design of future clinical trials aimed at promoting CNS repair.


title: The premature infant pain profile-revised (PIPP-R): initial validation and feasibility.

abstract: OBJECTIVES ::: To describe revisions to the Premature Infant Pain Profile (PIPP) and initial construct validation and feasibility of the Premature Infant Pain Profile-Revised (PIPP-R). ::: ::: ::: METHODS ::: The PIPP was revised to enhance validity and feasibility. To validate the PIPP-R, data from 2 randomized cross-over studies were utilized to: (1) calculate and compare PIPP and PIPP-R scores in extremely low gestational age infants undergoing a painful and nonpainful event (N=52; dataset #1) and (2) calculate and compare PIPP and PIPP-R scores in assessing the effectiveness of (a) sucrose, (b) non-nutritive sucking (NNS)+sucrose, and (c) facilitated tucking+NNS+sucrose during heel lance (N = 85; dataset #2). Pearson correlations between PIPP and PIPP-R scores were calculated, and Student t tests and 1-way analysis of variance were used to determine construct validity during painful and nonpainful events. To establish feasibility, a survey of 31 Neonatal Intensive Care Unit nurses was conducted. ::: ::: ::: RESULTS ::: PIPP-R scores were significantly lower during nonpainful (mean, 8.3; SD = 2.9) compared with painful (mean, 9.9; SD=3.1; t95 = 4.51, P = 0.036) events in extremely low gestational age infants in dataset #1. In dataset #2, PIPP-R scores were significantly lower in infants 25 to 41 weeks gestation in the group receiving NNS+sucrose compared with the other 2 groups (F2,79 = 2.9, P<0.05). Overall, nurses rated the PIPP-R as feasible. ::: ::: ::: DISCUSSION ::: Initial construct validation and feasibility of the PIPP-R was demonstrated. Further testing with infants of varying gestational ages, diagnoses, and pain conditions is required; as is exploration of PIPP-R in relation to other types of physiological and cognitive responses.


title: The revised illness perception questionnaire (IPQ-R)

abstract: This paper presents a revised version of the Illness Perception Questionnaire (IPQ-R), a recently developed and widely used quantitative measure of the five components of illness representations in Leventhal's self-regulatory model. The revised version stemmed from a need to deal with minor psychometric problems with two subscales, and to include additional subscales, assessing cyclical timeline perceptions, illness coherence, and emotional representations. Item selection was determined by principal components analyses which verified the factorial structure of the questionnaire in a sample of 711 patients from 8 different illness groups. Further analysis provided good evidence for both the internal reliability of the subscales and the short (3 week) and longer term (6 month) retest reliability. The IPQ-R also demonstrated sound discriminant, known group and predictive validity. While it is possible that the new subscales will vary in their applicability in different patient groups, the IPQ-R provides a more comprehensive and psychometrically acceptable assessment of the key components of patients' perceptions of illness.


title: Tensorial approach to the geometry of brain function: Cerebellar coordination via a metric tensor

abstract: Abstract Based on our previous assumption that the brain must be considered a tensorial entity ( Pellionisz & Llinas, 1979 a), a concise model for cerebellar function is proposed: The cerebellum acts as a metric tensor establishing a geometry for the CNS motor hyperspace. This view assumes that the brain is a ‘geometrical object’, that is to say, (1) activity in the neuronal network is vectorial, and (2) the networks are organized tensorially: i.e. activity vectors remain invariant to changes in reference-frames. Understanding brain functions becomes, then, the establishment of the inherent geometrical properties of the activity vectors and, more fundamentally, the determination of the properties of the multi-dimensional internal space (a frequency hyperspace) in which the vectorial transformations occur. A basic question is the form in which vectors are expressed. Are the components orthogonal projections to an oblique set of coordinate-axes or are they the parallelogram vectorial components? Beyond this, the question arises as to whether the CNS hyperspace is endowed with a geometry determined by a metric tensor. Thus the problem of motor coordination is approached geometrically: in these terms intended movements, i.e. intended movement vectors, are generated in the CNS in reference to the three-dimensional space. These movements are executed, however, by the multiparameter system representing the musculo-skeletal apparatus. Given this increase in dimensionality the question of uniqueness needs to be answered: how are the particular components of a motor vector established if they only represent one choice out of the infinite set of possible vectorial solutions? The tensorial treatment of motor coordination suggests that the problem is solved by the embedding of the external three-space into the multidimensional CNS space and that this hyperspace is endowed with a metric tensor represented, for movements, by the cerebellar neuronal network. Thus, a unique implementation of the three-dimensional intended movement is possible by a proposed two-step scheme: First, an intended movement vector is specified by an overcomplete number of CNS covariant vector-components. Second, since covariant components cannot be used directly to execute a movement vector (they yield ‘dysmetric’ movements), they are transformed into contravariant, physical, components of the final motor output. This transformation is the proposed role of the cerebellar metric tensor, resulting in a coordinated unique implementation of movements. While in this paper the vectorial components transformed by the metric are treated as space coordinates, it has not escaped our attention that the movement space-time is actually four dimensional. This point will be elaborated in a forthcoming paper in which the above concept of metric tensor is expanded to include space-time coordinates. Thus, the predictive feature of cerebellar action together with the metric concept provide a unified approach to the dynamic properties of motor control. Beyond providing a geometrical model of motor coordination by the cerebellum, the tensorial approach suggests that the ‘covariant analysis’ and ‘contravariant synthesis’, via metric tensor, may be a general principle of the organization of the CNS.


title: Design and Evaluation of a Short Version of the User Experience Questionnaire (UEQ-S)

abstract: The user experience questionnaire (UEQ) is a widely used questionnaire to measure the subjective impression of users towards the user experience of products. The UEQ is a semantic differential with 26 items. Filling out the UEQ takes approximately 3-5 minutes, i.e. the UEQ is already reasonably efficient concerning the time required to answer all items. However, there exist several valid application scenarios, where filling out the entire UEQ appears impractical. This paper deals with the creation of an 8 item short version of the UEQ, which is optimized for these specific application scenarios. First validations of this short version are also described.


title: Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment

abstract: This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points, corresponding to matches with low temporal flow disparity in the images. Comparative results based on the reference KITTI evaluation framework show that SSLAM is effective and can be implemented efficiently, as it does not require any loop closure or bundle adjustment.


On the Impact of Seed Words on Sentiment Polarity Lexicon Induction


title: A Many-core Architecture for In-Memory Data Processing

abstract: For many years, the highest energy cost in processing has been data movement rather than computation, and energy is the limiting factor in processor design [21]. As the data needed for a single application grows to exabytes [56], there is clearly an opportunity to design a bandwidth-optimized architecture for big data computation by specializing hardware for data movement. We present the Data Processing Unit or DPU, a shared memory many-core that is specifically designed for high bandwidth analytics workloads. The DPU contains a unique Data Movement System (DMS), which provides hardware acceleration for data movement and partitioning operations at the memory controller that is sufficient to keep up with DDR bandwidth. The DPU also provides acceleration for core to core communication via a unique hardware RPC mechanism called the Atomic Transaction Engine. Comparison of a DPU chip fabricated in 40nm with a Xeon processor on a variety of data processing applications shows a 3× - 15× performance per watt advantage.CCS CONCEPTS• Computer systems organization $\rightarrow$ Multicore architectures; Special purpose systems;


title: Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

abstract: This paper proposed a retinal image segmentation method based on conditional Generative Adversarial Network (cGAN) to segment optic disc. The proposed model consists of two successive networks: generator and discriminator. The generator learns to map information from the observing input (i.e., retinal fundus color image), to the output (i.e., binary mask). Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing the input image as a condition.Experiments were performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE. The proposed model outperformed state-of-the-art-methods by achieving around 0.96% and 0.98% of Jaccard and Dice coefficients, respectively. Moreover, an image segmentation is performed in less than a second on recent GPU.


title: A FOLDED PRINTED QUADRIFILAR HELICAL ANTENNA EMPLOYING A COMPACT FEEDING NETWORK

abstract: A folded printed quadrifllar helical antenna (FPQHA) with an integrated and compact feeding network is proposed. The axial length of the proposed antenna is miniaturized of about 54.7% than the conventional PQHA by folding the helix arms into several elements. A T-junction power divider combining with two Wilkinson power dividers is utilized to feed the antenna. A fabricated prototype of the FPQHA using the compact feeding network is presented. The measured positive gain bandwidth covers 380{430MHz with the re∞ection coe-cient below i20dB, axial ratio below 1.5dB and a half-power beamwidth of about 120 - . Details of the proposed antenna design and experimental results are presented and discussed.


title: Simultaneous Wireless Information and Power Transfer (SWIPT): Recent Advances and Future Challenges

abstract: Initial efforts on wireless power transfer (WPT) have concentrated toward long-distance transmission and high power applications. Nonetheless, the lower achievable transmission efficiency and potential health concerns arising due to high power applications, have caused limitations in their further developments. Due to tremendous energy consumption growth with ever-increasing connected devices, alternative wireless information and power transfer techniques have been important not only for theoretical research but also for the operational costs saving and for the sustainable growth of wireless communications. In this regard, radio frequency energy harvesting (RF-EH) for a wireless communications system presents a new paradigm that allows wireless nodes to recharge their batteries from the RF signals instead of fixed power grids and the traditional energy sources. In this approach, the RF energy is harvested from ambient electromagnetic sources or from the sources that directionally transmit RF energy for EH purposes. Notable research activities and major advances have occurred over the last decade in this direction. Thus, this paper provides a comprehensive survey of the state-of-art techniques, based on advances and open issues presented by simultaneous wireless information and power transfer (SWIPT) and WPT assisted technologies. More specifically, in contrast to the existing works, this paper identifies and provides a detailed description of various potential emerging technologies for the fifth generation communications with SWIPT/WPT. Moreover, we provide some interesting research challenges and recommendations with the objective of stimulating future research in this emerging domain.


title: Indoor location tracking using RSSI readings from a single Wi -

abstract: This paper describes research towards a system for locating wireless nodes in a home environment requiring merely a single access point. The only sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface, e.g., Wi-Fi. Wireless signal strength maps for the positioning filter are obtained by a two-step parametric and measurement driven ray-tracing approach to account for absorption and reflection characteristics of various obstacles. Location estimates are then computed using Bayesian filtering on sample sets derived by Monte Carlo sampling. We outline the research leading to the system and provide location performance metrics using trace-driven simulations and real-life experiments. Our results and real-life walk-troughs indicate that RSSI readings from a single access point in an indoor environment are sufficient to derive good location estimates of users with sub-room precision.


title: Smartwatch in vivo

abstract: In recent years, the smartwatch has returned as a form factor for mobile computing with some success. Yet it is not clear how smartwatches are used and integrated into everyday life differently from mobile phones. For this paper, we used wearable cameras to record twelve participants' daily use of smartwatches, collecting and analysing incidents where watches were used from over 34 days of user recording. This allows us to analyse in detail 1009 watch uses. Using the watch as a timepiece was the most common use, making up 50% of interactions, but only 14% of total watch usage time. The videos also let us examine why and how smartwatches are used for activity tracking, notifications, and in combination with smartphones. In discussion, we return to a key question in the study of mobile devices: how are smartwatches integrated into everyday life, in both the actions that we take and the social interactions we are part of?


title: A multiple model PHD approach to tracking of cars under an assumed rectangular shape

abstract: This paper presents an extended target tracking method for tracking cars in urban traffic using data from laser range sensors. Results are presented for three real world datasets that contain multiple cars, occlusions, and maneuver changes. The car's shape is approximated by a rectangle, and single track steering models are used for the target kinematics. A multiple model approach is taken for both the dynamics modeling and the measurement modeling. A comparison to ground truth shows that the estimation errors are generally very small: on average the absolute error is less than half a degree for the heading. Multiple cars are handled using a multiple model PHD filter, where a variable probability of detection is integrated to enable tracking of occluded cars.


title: Training Region-Based Object Detectors with Online Hard Example Mining

abstract: The field of object detection has made significant advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune. We present a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region-based ConvNet detectors. Our motivation is the same as it has always been – detection datasets contain an overwhelming number of easy examples and a small number of hard examples. Automatic selection of these hard examples can make training more effective and efficient. OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use. But more importantly, it yields consistent and significant boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012. Its effectiveness increases as datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. Moreover, combined with complementary advances in the field, OHEM leads to state-of-the-art results of 78.9% and 76.3% mAP on PASCAL VOC 2007 and 2012 respectively.


title: Point-to-Multipoint Ultrasonic Communication Modem for Ubiquitous Underwater Wireless Sensor Networks

abstract: Recently, researches on underwater sensor networks (USN) for ocean development and disaster prevention have been emerged as one of interesting topics. Since low-power, high-speed and inexpensive communication modem is a prerequisite for deployment of USN, we design and implement an underwater modem by utilizing general-purpose waterproof ultrasonic sensors in this paper. Also, we make an experiment in a water tank containing sensor nodes and a sink node, a gateway, and show that point-to-multipoint communication is possible at a data rate of 1 kbps.


title: Birth Preparedness and Complication Readiness among Pregnant Women in Southern Ethiopia

abstract: Background ::: Birth preparedness and complication preparedness (BPACR) is a key component of globally accepted safe motherhood programs, which helps ensure women to reach professional delivery care when labor begins and to reduce delays that occur when mothers in labor experience obstetric complications. ::: ::: ::: Objective ::: This study was conducted to assess practice and factors associated with BPACR among pregnant women in Aleta Wondo district in Sidama Zone, South Ethiopia. ::: ::: ::: Methods ::: A community based cross sectional study was conducted in 2007, on a sample of 812 pregnant women. Data were collected using pre-tested and structured questionnaire. The collected data were analyzed by SPSS for windows version 12.0.1. The women were asked whether they followed the desired five steps while pregnant: identified a trained birth attendant, identified a health facility, arranged for transport, identified blood donor and saved money for emergency. Taking at least two steps was considered being well-prepared. ::: ::: ::: Results ::: Among 743 pregnant women only a quarter (20.5%) of pregnant women identified skilled provider. Only 8.1% identified health facility for delivery and/or for obstetric emergencies. Preparedness for transportation was found to be very low (7.7%). Considerable (34.5%) number of families saved money for incurred costs of delivery and emergency if needed. Only few (2.3%) identified potential blood donor in case of emergency. Majority (87.9%) of the respondents reported that they intended to deliver at home, and only 60(8%) planned to deliver at health facilities. Overall only 17% of pregnant women were well prepared. The adjusted multivariate model showed that significant predictors for being well-prepared were maternal availing of antenatal services (OR = 1.91 95% CI; 1.21–3.01) and being pregnant for the first time (OR = 6.82, 95% CI; 1.27–36.55). ::: ::: ::: Conclusion ::: BPACR practice in the study area was found to be low. Effort to increase BPACR should focus on availing antenatal care services.


High-Efficiency Low-Voltage DC-DC Conversion for Portable Applications


title: A Variational Perspective on Accelerated Methods in Optimization

abstract: Accelerated gradient methods play a central role in optimization, achieving optimal rates in many settings. Although many generalizations and extensions of Nesterov's original acceleration method have been proposed, it is not yet clear what is the natural scope of the acceleration concept. In this paper, we study accelerated methods from a continuous-time perspective. We show that there is a Lagrangian functional that we call the Bregman Lagrangian, which generates a large class of accelerated methods in continuous time, including (but not limited to) accelerated gradient descent, its non-Euclidean extension, and accelerated higher-order gradient methods. We show that the continuous-time limit of all of these methods corresponds to traveling the same curve in spacetime at different speeds. From this perspective, Nesterov's technique and many of its generalizations can be viewed as a systematic way to go from the continuous-time curves generated by the Bregman Lagrangian to a family of discrete-time accelerated algorithms.


title: DC-Link Capacitor-Current Ripple Reduction in DPWM-Based Back-to-Back Converters

abstract: This paper proposes an improved offset selection method for discontinuous-pulse-width-modulation (DPWM)-based back-to-back converters to reduce dc-link current ripple. DPWM is introduced to power converters to diminish the stress on power transistors and prolong their lifespan. However, when using the DPWM method, the dc-link current ripple is increased in nonswitching regions of the power transistors. Moreover, in DPWM-based back-to-back converters, the dc-link current ripple reaches its maximum when the two transistors of both inverters are clamped in opposite directions. Therefore, the dc-link capacitors endure more stress, resulting in decreased life duration. To overcome this issue, the switching method should consider the clamping periods, when the current ripple increases. This can be achieved by modifying the DPWM offset, so that the clamping states of both converters are matched. The effectiveness of the proposed method is confirmed by both simulation and experimental results.


title: GeniePath: Graph Neural Networks with Adaptive Receptive Paths

abstract: We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works in both transductive and inductive settings, and extensive experiments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs.


title: Self directed learning.

abstract: The ability to acquire skills in self directed learning may be the key link between undergraduate education, postgraduate training, and continuing professional development. If future and current practitioners are to adopt an ongoing reflective and critical approach to practice, we should aim to provide learning opportunities that promote self confidence, question asking and reflection, openness and risk taking, uncertainty and surprise. Teaching techniques that encourage these skills are being introduced widely and have been shown to be at least as effective as traditional methods of education while promoting more enjoyment and enthusiasm among both staff and students.


title: Knowledge sharing in interest online communities: A comparison of posters and lurkers

abstract: Abstract Online communities have become increasingly popular in recent years. However, despite a significant growth in the number of online communities, few have been successful in retaining members and motivating members to contribute knowledge. Few researchers have investigated the differences between posters and lurkers in terms of knowledge-sharing behavior. We propose that the driving factors of posters and lurkers are contextually different, and as such, a distinction between them is needed. By incorporating extrinsic and intrinsic motivation, as well as intra-community factors, we modeled and examined the differences in the driving factors of the two types of community members – posters and lurkers – and provide comparisons and discussions, thus advancing our understanding of knowledge-sharing behavior in interest communities. A multi-group analysis revealed that the poster group and the lurker group differ somewhat in the factors that motivate the knowledge-sharing intentions of the two groups. The most influential factors affecting the knowledge-sharing intentions of posters are intrinsic motivational factors (i.e., enjoyment in helping others and knowledge self-efficacy). On the other hand, the knowledge-sharing intentions of lurkers are affected by extrinsic motivational factors (i.e., reciprocity). Intra-community factors, such as perceived moderator’s enthusiasm, offline activities, and enjoyability, also affect the knowledge-sharing intentions of posters and lurkers. The theoretical and practical implications of the findings are discussed.


title: Automatic TV Advertisement Detection from MPEG Bitstream

abstract: The Centre for Digital Video Processing at Dublin City University conducts concentrated research and development in the area of digital video management. The current stage of development is demonstrated on our Web-based digital video system called Fischlar (Proceedings of the Content based Multimedia Information Access, RIAO 2000, Vol. 2, Paris, France, 12–14 April 2000, p. 1390), which provides for efficient recording, analysing, browsing and viewing of digitally captured television programmes. ::: ::: Advertisement breaks during or between television programmes are typically recognised by a series of ‘black’ video frames simultaneously accompanying a depression in audio volume which separate each advertisement from one another by recurrently occurring before and after each individual advertisement. It is the regular prevalence of these flags that enables automatic differentiation between what is programme and what is a commercial break. This paper reports on the progress made in the development of this idea into an advertisement detector system that automatically detects the commercial breaks from the bitstream of digitally captured television broadcasts.


title: Consistent shape maps via semidefinite programming

abstract: Recent advances in shape matching have shown that jointly optimizing the maps among the shapes in a collection can lead to significant improvements when compared to estimating maps between pairs of shapes in isolation. These methods typically invoke a cycle-consistency criterion --- the fact that compositions of maps along a cycle of shapes should approximate the identity map. This condition regularizes the network and allows for the correction of errors and imperfections in individual maps. In particular, it encourages the estimation of maps between dissimilar shapes by compositions of maps along a path of more similar shapes. In this paper, we introduce a novel approach for obtaining consistent shape maps in a collection that formulates the cycle-consistency constraint as the solution to a semidefinite program (SDP). The proposed approach is based on the observation that, if the ground truth maps between the shapes are cycle-consistent, then the matrix that stores all pair-wise maps in blocks is low-rank and positive semidefinite. Motivated by recent advances in techniques for low-rank matrix recovery via semidefinite programming, we formulate the problem of estimating cycle-consistent maps as finding the closest positive semidefinite matrix to an input matrix that stores all the initial maps. By analyzing the Karush-Kuhn-Tucker (KKT) optimality condition of this program, we derive theoretical guarantees for the proposed algorithm, ensuring the correctness of the recovery when the errors in the inputs maps do not exceed certain thresholds. Besides this theoretical guarantee, experimental results on benchmark datasets show that the proposed approach outperforms state-of-the-art multiple shape matching methods.


title: Simple LR(k) grammars

abstract: A class of context-free grammars, called the “Simple LR ( k )” or SLR ( k ) grammars is defined. This class has been shown to include weak precedence and simple precedence grammars as proper subsets. How to construct parsers for the SLR ( k ) grammars is also shown. These parser-construction techniques are extendible to cover all of the LR ( k ) grammars of Knuth; they have been implemented and by direct comparison proved to be superior to precedence techniques, not only in the range of grammars covered, but also in the speed of parser construction and in the size and speed of the resulting parsers.


title: Selecting Green Supplier of Thermal Power Equipment by Using a Hybrid MCDM Method for Sustainability

abstract: With the growing worldwide awareness of environmental protection and sustainable development, green purchasing has become an important issue for companies to gain environmental and developmental sustainability. Thermal power is the main power generation form in China, and the green supplier selection is essential to the smooth and sustainable construction of thermal power plants. Therefore, selecting the proper green supplier of thermal power equipment is very important to the company’s sustainable development and the sustainability of China’s electric power industry. In this paper, a hybrid fuzzy multi-attribute decision making approach (fuzzy entropy-TOPSIS) is proposed for selecting the best green supplier. The fuzzy set theory is applied to translate the linguistic preferences into triangular fuzzy numbers. The subjective criteria weights are determined by using decision makers’ superiority linguistic ratings and the objective ones are determined by combining the superiority linguistic ratings and fuzzy-entropy weighting method. The fuzzy TOPSIS is employed to generate an overall performance score for each green supplier. An empirical green supplier selection is conducted to illustrate the effectiveness of this proposed fuzzy entropy-TOPSIS approach. This proposed fuzzy entropy-TOPSIS approach can select the proper green supplier of thermal power equipment, which contributes to promoting the company’s sustainable development and the sustainability of China’s electric power industry to some extent.


Lessons Learned from an Information Security Incident: A Practical Recommendation to Involve Employees in Information Security


title: Fast Fashion Guided Clothing Image Retrieval: Delving Deeper into What Feature Makes Fashion

abstract: Clothing fashion represents human’s aesthetic appreciation towards their outfits and reflects the development status of society, humanitarian and economics. Modelling fashion via machine is extremely difficult due to the fact that fashion is too abstract to be efficiently described by machine. In this paper, we delve into two fashion related problems: what type of image feature best describes fashion and how can we fast retrieve the fashionably similar images with any given query fashion image. To address these two problems, we first conduct extensive experiments on various image features, ranging from traditional low-level hand-crafted features, mid-level style aware features to current high-level powerful deep learning based features, to find the feature best describes clothing fashion. To test each candidate feature’s performance, we further design a fast fashion guided clothing image retrieval framework by efficiently converting float formatted features into binary codes, with which we can achieve much faster image retrieval without much accuracy reduction. Finally, we validate our proposed framework on two publicly available datasets. Experimental results on both intra-domain and cross-domain fashion clothing image retrieval show that deep learning based image features with explicit fashion prior knowledge guidance best describe fashion, and feature binarization scheme also achieves comparable results in terms of various fashion clothing image retrieval tasks.


Hardware for Machine Learning : Challenges and Opportunities ( Invited Paper )


title: Designer Worlds: Procedural Generation of Infinite Terrain from Real-World Elevation Data

abstract: The standard way to procedurally generate random terrain for video games and other applications is to post-process the output of a fast noise generator such as Perlin noise. Tuning the post-processing to achieve particular types of terrain requires game designers to be reasonably well-trained in mathematics. A well-known variant of Perlin noise called value noise is used in a process accessible to designers trained in geography to generate geotypical terrain based on elevation statistics drawn from widely available sources such as the United States Geographical Service. A step-by-step process for downloading and creating terrain from realworld USGS elevation data is described, and an implementation in C++ is given.


title: Modeling behavioral RESTful web service interfaces in UML

abstract: A web service interface contains information about the names of the operations that can be invoked on the service and the input and output parameters of these operations. The Web Application Description Language (WADL) is a language to describe the interface of a web service that follows the Representation State Transfer (REST) architectural style. Currently, WADL descriptions do not describe the behavioral semantics of the operations neither ensure that the published interfaces follow the REST style, that is they are RESTful. In this paper, we present an approach to model the structural and behavioral interface of a RESTful web service using UML class and UML protocol diagrams. These models lead to RESTful interfaces that describe the behavior of operations in terms of preconditions and postconditions. The contracts can then be published in an extended version of the WADL language and used for documentation, stub generation, testing and monitoring purposes.


title: Anytime Motion Planning using the RRT*

abstract: The Rapidly-exploring Random Tree (RRT) algorithm, based on incremental sampling, efficiently computes motion plans. Although the RRT algorithm quickly produces candidate feasible solutions, it tends to converge to a solution that is far from optimal. Practical applications favor “anytime” algorithms that quickly identify an initial feasible plan, then, given more computation time available during plan execution, improve the plan toward an optimal solution. This paper describes an anytime algorithm based on the RRT* which (like the RRT) finds an initial feasible solution quickly, but (unlike the RRT) almost surely converges to an optimal solution. We present two key extensions to the RRT*, committed trajectories and branch-and-bound tree adaptation, that together enable the algorithm to make more efficient use of computation time online, resulting in an anytime algorithm for real-time implementation. We evaluate the method using a series of Monte Carlo runs in a high-fidelity simulation environment, and compare the operation of the RRT and RRT* methods. We also demonstrate experimental results for an outdoor wheeled


title: Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros.

abstract: Mediation analysis is a useful and widely employed approach to studies in the field of psychology and in the social and biomedical sciences. The contributions of this paper are several-fold. First we seek to bring the developments in mediation analysis for non linear models within the counterfactual framework to the psychology audience in an accessible format and compare the sorts of inferences about mediation that are possible in the presence of exposure-mediator interaction when using a counterfactual versus the standard statistical approach. Second, the work by VanderWeele and Vansteelandt (2009, 2010) is extended here to allow for dichotomous mediators and count outcomes. Third, we provide SAS and SPSS macros to implement all of these mediation analysis techniques automatically and we compare the types of inferences about mediation that are allowed by a variety of software macros.


title: Identifying hedonic factors in long-term user experience

abstract: User experience (UX) arises from the user's interaction with a product and its pragmatic and hedonic (pleasure) qualities. Until recently, UX evaluation has focused mainly on examining short-term experiences. However, as the user-product relationship evolves over time, the hedonic aspects of UX eventually seem to gain more weight over the pragmatic aspects. To this end, we have developed a UX Curve method for evaluating long-term user experience, particularly the hedonic quality. In this paper, we present a study in which the UX Curve was used to retrospectively evaluate the UX of Facebook and mobile phones. The results show that compared to a questionnaire, the UX Curve method is more effective for identifying the hedonic aspects of UX. This method can be used by practitioners and researchers who want to understand evolving UX and to design better products. This straightforward method is especially suited for industrial contexts where resources are limited.


title: Oriental Crown-Shaped Differentially Fed Dual-Polarized Multidipole Antenna

abstract: A differentially fed dual-polarized multidipole antenna is proposed in this paper. Two pairs of long dipoles and two pairs of short dipoles are printed on an oriental crown-shaped substrate. Through a rational design of positions of dipoles, stable radiation patterns are achieved at the horizontal plane within a relative bandwidth of 45%. Because of the symmetry of the antenna, its differential port-to-port isolation is theoretically infinite. The antenna is designed, fabricated, and measured. The measurement results are in good agreement with the simulated ones in terms of gain, radiation patterns, and voltage standing wave ratio. Wideband impedance matching and stable radiation patterns are achieved by the proposed antenna. In particular, the half power beamwidth (HPBW) at the horizontal plane maintains a mean value 65° with only 4° variations across the desired band, which is suitable for the requirement of a three-sector base-station antenna. Interestingly, the proposed antenna looks like the famous building: the Oriental Crown-China Pavilion in the World Expo held in Shanghai.


title: Theory of the backpropagation neural network

abstract: The author presents a survey of the basic theory of the backpropagation neural network architecture covering architectural design, performance measurement, function approximation capability, and learning. The survey includes previously known material, as well as some new results, namely, a formulation of the backpropagation neural network architecture to make it a valid neural network (past formulations violated the locality of processing restriction) and a proof that the backpropagation mean-squared-error function exists and is differentiable. Also included is a theorem showing that any L/sub 2/ function from (0, 1)/sup n/ to R/sup m/ can be implemented to any desired degree of accuracy with a three-layer backpropagation neural network. The author presents a speculative neurophysiological model illustrating how the backpropagation neural network architecture might plausibly be implemented in the mammalian brain for corticocortical learning between nearby regions of the cerebral cortex.<<ETX>>


title: Genetic Algorithm Performance with Different Selection Methods in Solving Multi-Objective Network Design Problem

abstract: Selection is one of the key operations of genetic algorithm (GA). This paper presents a comparative analysis of GA performance in solving multi-objective network design problem (MONDP) using different parent selection methods. Three problem instances were tested and results show that on the average tournament selection is the most effective and most efficient for 10-node network design problem, while Ranking & Scaling is the least effective and least efficient. For 21-node and 36-node network problems, Roulette Wheel is the least effective but most efficient while Ranking & Scaling equals and outperformed tournament in effectiveness and efficiency respectively.


title: Pandora: Description of a painting database for art movement recognition with baselines and perspectives

abstract: To facilitate computer analysis of visual art, in the form of paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art movement) database, a collection of digitized paintings labelled with respect to the artistic movement. Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. Our experimental results suggest that accurate recognition is achievable by a combination of various categories.To facilitate computer analysis of visual art, in the form of paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art movement) database, a collection of digitized paintings labelled with respect to the artistic movement. Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. Our experimental results suggest that accurate recognition is achievable by a combination of various categories.


title: Key Technique of Assembly System in an Augmented Reality Environment

abstract: Assembly technology aims to develop fast, reliable and low cost method to improve product design in the manufacturing industry. Augmented Reality (AR) is a promising technology which can enhance the sense of reality by combining virtual objects with real ones. When AR technique is applied in assembly to develop an AR-based assembly system, it can provide more intuitive and natural interactions and make assembly task more reliable and effective than traditional assembly techniques. This paper describes several key aspects in AR-based assembly system: occlusion handling, collision detection and human-computer interaction. A prototype system is implemented to illustrate how to apply these techniques.


title: The production economics of a very large civil aircraft

abstract: A number of studies are taking place to see if there is a viable market for a very large civil aircraft capable of carrying in excess of 600 passengers. The development and launch costs of such an aircraft are very high. Based on indicative costs reported in the press, estimates are derived of how unit costs might vary with the length of the production run. These estimates are then combined with an estimate of market size to judge to what extent dividing production between rival manufacturers would add to unit costs. The indications are that unit costs would increase significantly.


title: Survey of Pedestrian Detection for Advanced Driver Assistance Systems

abstract: Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.


title: Optimizing Strategic Safety Stock Placement in Supply Chains

abstract: Manufacturing managers face increasing pressure to reduce inventories across the supply chain. However, in complex supply chains, it is not always obvious where to hold safety stock to minimize inventory costs and provide a high level of service to the final customer. In this paper we develop a framework for modeling strategic safety stock in a supply chain that is subject to demand or forecast uncertainty. Key assumptions are that we can model the supply chain as a network, that each stage in the supply chain operates with a periodic-review base-stock policy, that demand is bounded, and that there is a guaranteed service time between every stage and its customers. We develop an optimization algorithm for the placement of strategic safety stock for supply chains that can be modeled as spanning trees. Our assumptions allow us to capture the stochastic nature of the problem and formulate it as a deterministic optimization. As a partial validation of the model, we describe its successful application by product flow teams at Eastman Kodak. We discuss how these flow teams have used the model to reduce finished goods inventory, target cycle time reduction efforts, and determine component inventories. We conclude with a list of needs to enhance the utility of the model.


title: Improving RSS-Based Indoor Positioning Algorithm via K-Means Clustering

abstract: Advances in mobile technologies and devices has changed the way users interact with devices and other users. These new interaction methods and services are offered by the help of intelligent sensing capabilities, using context, location and motion sensors. However, indoor location sensing is mostly achieved by utilizing radio signal (Wi-Fi, Bluetooth, GSM etc.) and nearest neighbor identification. The most common algorithm adopted for Received Signal Strength (RSS)-based location sensing is K Nearest Neighbor (KNN), which calculates K nearest neighboring points to mobile users (MUs). Accordingly, in this paper, we aim to improve the KNN algorithm by enhancing the neighboring point selection by applying k-means clustering approach. In the proposed method, k-means clustering algorithm groups nearest neighbors according to their distance to mobile user. Then the closest group to the mobile user is used to calculate the MU's location. The evaluation results indicate that the performance of clustered KNN is closely tied to the number of clusters, number of neighbors to be clustered and the initiation of the center points in k-mean algorithm.


title: ECG Recording on a Bed During Sleep Without Direct Skin-Contact

abstract: A new indirect contact (IDC) electrocardiogram (ECG) measurement method (IDC-ECG) for monitoring ECG during sleep that is adequate for long-term use is provided. The provided method did not require any direct conductive contact between the instrument and bare skin. This method utilizes an array of high-input-impedance active electrodes fixed on the mattress and an indirect-skin-contact ground made of a large conductive textile sheet. A thin cotton bedcover covered the mattress, electrodes, and conductive textile, and the participants were positioned on the mattress over the bedcover. An ECG was successfully obtained, although the signal quality was lower and the motion artifact was larger than in conventional direct-contact measurements (DC-ECG). The results showed that further studies are required to apply the provided method to an ECG diagnosis of cardiovascular diseases. However, currently the method can be used for HRV assessment with easy discrimination of R-peaks


title: A Tabu Search/Path Relinking Algorithm to Solve the Job Shop Scheduling Problem

abstract: We present an algorithm that incorporates a tabu search procedure into the framework of path relinking to generate solutions to the job shop scheduling problem (JSP). This tabu search/path relinking (TS/PR) algorithm comprises several distinguishing features, such as a specific relinking procedure to effectively construct a path linking the initiating solution and the guiding solution, and a reference solution determination mechanism based on two kinds of improvement methods. We evaluate the performance of TS/PR on almost all of the benchmark JSP instances available in the literature. The test results show that TS/PR obtains competitive results compared with state-of-the-art algorithms for JSP in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, TS/PR is able to improve the upper bounds for 49 out of the 205 tested instances and it solves a challenging instance that has remained unsolved for over 20years.


title: Treatment of the Tear Trough Deformity With Hyaluronic Acid

abstract: BACKGROUND ::: Volume loss and muscular hyperactivity are two major components of the aging process that contribute to the formation of the folds and wrinkles. Tear trough deformity is one of the most difficult depressions to correct surgically. ::: ::: ::: OBJECTIVE ::: The authors evaluate the results of periorbital filling with hyaluronic acid (HA) in a small series of patients. ::: ::: ::: METHODS ::: Between June 2008 and December 2009, 25 patients were treated with HA to correct tear trough deformities. The HA was administered into the preperiosteal tissues with a serial puncture technique and approximately 0.1 mL was injected at each pass. Each patient's before and after photographs were reviewed by three surgeons; to objectively assess the outcomes, a quantitative scale was used to grade the pre- and postinjection results. The significance of subjective aesthetic evaluation of the photographs was evaluated with the Mann-Whitney U-test. Differences were regarded as significant if probabilities were less than 0.05. ::: ::: ::: RESULTS ::: The mean (SD) volume per side needed to achieve correction was 0.54 (0.27) mL on the right and 0.61 (0.30) mL on the left. Complications included some degree of bruising, erythema, and local swelling. Most patients (88%) had cosmetic improvement according to the independent evaluation. ::: ::: ::: CONCLUSIONS ::: All patients were very satisfied with their results. During the course of the study, the authors determined that the ideal candidates for this treatment are young, with thick skin and a definite hollow.


title: Fall Detection from Depth Map Video Sequences

abstract: Falls are one of the major risks for seniors living alone at home. Computer vision systems, which do not require to wear sensors, offer a new and promising solution for fall detection. In this work, an occlusion robust method is presented based on two features: human centroid height relative to the ground and body velocity. Indeed, the first feature is an efficient solution to detect falls as the vast majority of falls ends on the ground or near the ground. However, this method can fail if the end of the fall is completely occluded behind furniture. Fortunately, these cases can be managed by using the 3D person velocity computed just before the occlusion.


title: Adaptive insertion policies for high performance caching

abstract: The commonly used LRU replacement policy is susceptible to thrashing for memory-intensive workloads that have a working set greater than the available cache size. For such applications, the majority of lines traverse from the MRU position to the LRU position without receiving any cache hits, resulting in inefficient use of cache space. Cache performance can be improved if some fraction of the working set is retained in the cache so that at least that fraction of the working set can contribute to cache hits. We show that simple changes to the insertion policy can significantly reduce cache misses for memory-intensive workloads. We propose the LRU Insertion Policy (LIP) which places the incoming line in the LRU position instead of the MRU position. LIP protects the cache from thrashing and results in close to optimal hitrate for applications that have a cyclic reference pattern. We also propose the Bimodal Insertion Policy (BIP) as an enhancement of LIP that adapts to changes in the working set while maintaining the thrashing protection of LIP. We finally propose a Dynamic Insertion Policy (DIP) to choose between BIP and the traditional LRU policy depending on which policy incurs fewer misses. The proposed insertion policies do not require any change to the existing cache structure, are trivial to implement, and have a storage requirement of less than two bytes. We show that DIP reduces the average MPKI of the baseline 1MB 16-way L2 cache by 21%, bridging two-thirds of the gap between LRU and OPT.


title: Feature and model space speaker adaptation with full covariance Gaussians.

abstract: Full covariance models can give better results for speech recognition than diagonal models, yet they introduce complications for standard speaker adaptation techniques such as MLLR and fMLLR. Here we introduce efficient update methods to train adaptation matrices for the full covariance case. We also experiment with a simplified technique in which we pretend that the full covariance Gaussians are diagonal and obtain adaptation matrices under that assumption. We show that this approximate method works almost as well as the exact method.


title: A Survey of Optimization Techniques Targeting Low Power VLSI Circuits

abstract: We survey state-of-the-art optimization methods that target low power dissipation in VLSI circuits. Optimizations at the circuit, logic, architectural and system levels are considered.


title: The Modified Checklist for Autism in Toddlers: An Initial Study Investigating the Early Detection of Autism and Pervasive Developmental Disorders

abstract: Autism, a severe disorder of development, is difficult to detect in very young children. However, children who receive early intervention have improved long-term prognoses. The Modified Checklist for Autism in Toddlers (M-CHAT), consisting of 23 yes/no items, was used to screen 1,293 children. Of the 58 children given a diagnostic/developmental evaluation, 39 were diagnosed with a disorder on the autism spectrum. Six items pertaining to social relatedness and communication were found to have the best discriminability between children diagnosed with and without autism/PDD. Cutoff scores were created for the best items and the total checklist. Results indicate that the M-CHAT is a promising instrument for the early detection of autism.


title: Tweedr: Mining twitter to inform disaster response

abstract: In this paper, we introduce Tweedr, a Twitter-mining tool that extracts actionable information for disaster relief workers during natural disasters. The Tweedr pipeline consists of three main parts: classification, clustering and extraction. In the classification phase, we use a variety of classification methods (sLDA, SVM, and logistic regression) to identify tweets reporting damage or casualties. In the clustering phase, we use filters to merge tweets that are similar to one another; and finally, in the extraction phase, we extract tokens and phrases that report specific information about different classes of infrastructure damage, damage types, and casualties. We empirically validate our approach with tweets collected from 12 different crises in the United States since 2006.