title: Slip estimation for small-scale robotic tracked vehicles

abstract: A method is presented for using an extended Kalman filter with state noise compensation to estimate the trajectory, orientation, and slip variables for a small-scale robotic tracked vehicle. The principal goal of the method is to enable terrain property estimation. The methodology requires kinematic and dynamic models for skid-steering, as well as tractive force models parameterized by key soil parameters. Simulation studies initially used to verify the model basis are described, and results presented from application of the estimation method to both simulated and experimental study of a 60-kg robotic tracked vehicle. Preliminary results show the method can effectively estimate vehicle trajectory relying only on the model-based estimation and onboard sensor information. Estimates of slip on the left and right track as well as slip angle are essential for ongoing work in vehicle-based soil parameter estimation. The favorable comparison against motion capture data suggests this approach will be useful for laboratory and field-based application.


title: On the Accuracy of Hyper-local Geotagging of Social Media Content

abstract: Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data-driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of n-grams that appear in the text. We explore the trade-off between accuracy and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is preferred to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to assigning location data to short social media texts, and offer implications for all applications that use data-driven approaches to locate content.


title: A cost analysis of RDL-first and mold-first fan-out wafer level packaging

abstract: Industry interest in fan-out wafer level packaging has been increasing steadily. This is partially because of the potential behind panel-based fan-out packaging, and partially because wafer level packaging may be fulfilling needs not met by interposer-based and 3D technologies. As is the case with any technology, there are variations within the fan-out wafer level packaging category. This paper will focus on two of the primary processes: RDL-first and mold-first (also called chip-first). While these process flows have many of the same activities, those activities are carried out in a different order, and there are a few key steps that will differ. Each process has unique challenges and benefits, and these will be explored and analyzed.


title: An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems

abstract: Vehicular ad hoc networks are expected to significantly improve traffic safety and transportation efficiency while providing a comfortable driving experience. However, available communication, storage, and computation resources of the connected vehicles are not well utilized to meet the service requirements of intelligent transportation systems. Vehicular cloud computing (VCC) is a promising approach that makes use of the advantages of cloud computing and applies them to vehicular networks. In this paper, we propose an optimal computation resource allocation scheme to maximize the total long-term expected reward of the VCC system. The system reward is derived by taking into account both the income and cost of the VCC system as well as the variability feature of available resources. Then, the optimization problem is formulated as an infinite horizon semi-Markov decision process (SMDP) with the defined state space, action space, reward model, and transition probability distribution of the VCC system. We utilize the iteration algorithm to develop the optimal scheme that describes which action has to be taken under a certain state. Numerical results demonstrate that the significant performance gain can be obtained by the SMDP-based scheme within the acceptable complexity.


title: Kaempferol attenuates hyperglycemia-induced cardiac injuries by inhibiting inflammatory responses and oxidative stress

abstract: PURPOSE ::: Suppression of inflammation and oxidative stress is an attractive strategy to against diabetic cardiomyopathy (DCM). Kaempferol (KPF) exerts both anti-inflammatory and antioxidant pharmacological properties. However, little is known about the effect of KPF on protecting myocardial injury in diabetes. The present study aimed to investigate the effect of KPF on DCM and underlying mechanism. ::: ::: ::: METHODS ::: Anti-inflammation and anti-oxidative stress activities of KPF were evaluated in H9c2 cells or primary cardiomyocytes by real-time quantitate PCR, immunoblotting, immunofluorescence, ELISA, and FACS. Streptozotocin (STZ)-induced type 1 diabetes mellitus mice were constructed. Corresponding to experiments in vitro, the therapeutic effect of KPF was also assessed using heart tissues from mice. ::: ::: ::: RESULTS ::: KPF significantly inhibited high glocose (HG) induced expression of inflammatory cytokines and generation of ROS, leading to reduced fibrotic responses and cell apoptosis in vitro. KPF mediated DCM protective effects through inhibiting nuclear factor-κB (NF-κB) nucleus translocation and activating nuclear factor-erythroid 2 p45-related factor-2 (Nrf-2). In STZ-induced type 1 diabetic mouse model, KPF prevented diabetes-induced cardiac fibrosis and apoptosis. These changes were also accompanied by reducing inflammation and oxidative stress in diabetic mice hearts. ::: ::: ::: CONCLUSION ::: KPF is a potential therapeutic agent for the treatment of DCM, mechanically linked to inhibition of NF-κB and Nrf-2 activation.


title: Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

abstract: Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. ::: In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an end-to-end fashion. We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.


title: Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

abstract: It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Contextual information outside the region of interest is integrated using spatial recurrent neural networks. Inside, we use skip pooling to extract information at multiple scales and levels of abstraction. Through extensive experiments we evaluate the design space and provide readers with an overview of what tricks of the trade are important. ION improves state-of-the-art on PASCAL VOC 2012 object detection from 73.9% to 77.9% mAP. On the new and more challenging MS COCO dataset, we improve state-of-the-art from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection Challenge, our ION model won "Best Student Entry" and finished 3rd place overall. As intuition suggests, our detection results provide strong evidence that context and multi-scale representations improve small object detection.


title: Fine-grained dynamic voltage scaling on OLED display

abstract: Organic Light Emitting Diode (OLED) has emerged as the new generation display technique for mobile multimedia devices. Compared to existing technologies OLEDs are thinner, brighter, lighter, and cheaper. However, OLED panels are still the biggest contributor to the total power consumption of mobile devices. In this work, we proposed a fine-grained dynamic voltage scaling (FDVS) technique to reduce the OLED power. An OLED panel is partitioned into multiple display areas of which the supply voltage is adaptively adjusted based on the displayed content. A DVS-friendly OLED driver design is also proposed to enhance the color accuracy of the OLED pixels at the scaled supply voltage. Our experimental results show that compared to the existing global DVS technique, FDVS technique can achieve 25.9%∼43.1% more OLED power saving while maintaining a high image quality measured by Structural Similarity Index (SSIM=0.98). The further analysis shows shat FDVS technology can also effectively reduce the color remapping cost when color compensation is required to improve the image quality of an OLED panel working at a scaled voltage.


title: A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement

abstract: Despite noise suppression being a mature area in signal processing, it remains highly dependent on fine tuning of estimator algorithms and parameters. In this paper, we demonstrate a hybrid DSP/deep learning approach to noise suppression. We focus strongly on keeping the complexity as low as possible, while still achieving high-quality enhanced speech. A deep recurrent neural network with four hidden layers is used to estimate ideal critical band gains, while a more traditional pitch filter attenuates noise between pitch harmonics. The approach achieves significantly higher quality than a traditional minimum mean squared error spectral estimator, while keeping the complexity low enough for real-time operation at 48 kHz on a low-power CPU.


title: A pathfinding algorithm in real-time strategy game based on Unity3D

abstract: Pathfinding in the context of video games concerns the way in which a moving entity finds a path around obstacles; the most frequent context is real-time strategy games (in which the player directs units around a play area containing obstacles), but forms of this are found in most modern video games. It is probably the most popular but frustrating game artificial intelligence (AI) problem in game industry. This article introduces A-Star algorithm first, then describes the details of A-Star algorithm. Finally, a real example of how the A-Star pathfinding technique is used in a famous 3D game engine: Unity3D.


title: A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic

abstract: This paper presents a multi-dialect, multi-genre, human annotated corpus of dialectal Arabic with data obtained from both online newspaper commentary and Twitter. Most Arabic corpora are small and focus on Modern Standard Arabic (MSA). There has been recent interest, however, in the construction of dialectal Arabic corpora (Zaidan and Callison-Burch, 2011a; Al-Sabbagh and Girju, 2012). This work differs from previously constructed corpora in two ways. First, we include coverage of five dialects of Arabic: Egyptian, Gulf, Levantine, Maghrebi and Iraqi. This is the most complete coverage of any dialectal corpus known to the authors. In addition to data, we provide results for the Arabic dialect identification task that outperform those reported in Zaidan and Callison-Burch (2011a).


title: Signals in tactile afferents from the fingers eliciting adaptive motor responses during precision grip

abstract: While human subjects lift small objects using the precision grip between the tips of the fingers and thumb the ratio between the grip force and the load force (i.e. the vertical lifting force) is adapted to the friction between the object and the skin. The present report provides direct evidence that signals in tactile afferent units are utilized in this adaptation. Tactile afferent units were readily excited by small but distinct slips between the object and the skin revealed as vibrations in the object. Following such afferent slip responses the force ratio was upgraded to a higher, stable value which provided a safety margin to prevent further slips. The latency between the onset of the a slip and the appearance of the ratio change (74 ±9 ms) was about half the minimum latency for intended grip force changes triggered by cutaneous stimulation of the fingers. This indicated that the motor responses were automatically initiated. If the subjects were asked to very slowly separate their thumb and the opposing finger while the object was held in air, grip force reflexes originating from afferent slip responses appeared to counteract the voluntary command, but the maintained upgrading of the force ratio was suppressed. In experiments with weak electrical cutaneous stimulation delivered through the surfaces of the object it was established that tactile input alone could trigger the upgrading of the force ratio. Although, varying in responsiveness, each of the three types of tactile units which exhibit a pronounced dynamic sensitivity (FA I, FA II and SA I units) could reliably signal these slips. Similar but generally weaker afferent responses, sometimes followed by small force ratio changes, also occurred in the FA I and the SA I units in the absence of detectable vibrations events. In contrast to the responses associated with clear vibratory events, the weaker afferent responses were probably caused by localized frictional slips, i.e. slips limited to small fractions of the skin area in contact with the object. Indications were found that the early adjustment to a new frictional condition, which may appear soon (ca. 0.1–0.2 s) after the object is initially gripped, might depend on the vigorous responses in the FA I units during the initial phase of the lifts (see Westling and Johansson 1987). The role of the tactile input in the adaptation of the force coordination to the frictional condition is discussed.


Monitoring Byzantine Fault Tolerant Systems to Support Dynamic Adaptation Bernardo


title: Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis

abstract: Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient. However, due to the insufficient number of projection views, an analytic reconstruction approach results in severe streaking artifacts and CS-based iterative approach is computationally very expensive. To address this issue, here we propose a novel deep residual learning approach for sparse view CT reconstruction. Specifically, based on a novel persistent homology analysis showing that the manifold of streaking artifacts is topologically simpler than original ones, a deep residual learning architecture that estimates the streaking artifacts is developed. Once a streaking artifact image is estimated, an artifact-free image can be obtained by subtracting the streaking artifacts from the input image. Using extensive experiments with real patient data set, we confirm that the proposed residual learning provides significantly better image reconstruction performance with several orders of magnitude faster computational speed.


title: Bender: a virtual ribbon for deforming 3D shapes in biomedical and styling applications

abstract: In contrast to machined mechanical parts, the 3D shapes encountered in biomedical or styling applications contain many tubular parts, protrusions, engravings, embossings, folds, and smooth bends. It is difficult to design and edit such features using the parameterized operations or even free-form deformations available in CAD or animation systems. The Bender tool proposed here complements previous solutions by allowing a designer holding a 6 DoF 3D tracker in each hand to control the position and orientation of the ends of a stretchable virtual ribbon, which is used to grab the shape in its vicinity and to deform it in realtime, as the designer continues to move, bend, and twist the ribbon. To ensure realtime performance and intuitive control of the ribbon, we model its centerline as a circular biarc and perform adaptive refinement of the triangle-mesh approximation of the surface. To produce a natural and predictable warp, we use the initial and final shapes of the ribbon to define a one-parameter family of screw-motions. The deformation of a surface point is computed by finding its locally closest projection, or projections, on the biarc and by applying the corresponding screws, weighted by a function that decays with the distance to the projection. The combination of these solutions leads to an easy-to-use and effective tool for the direct manipulation of organic or stylized shapes.


title: Modeling and Rendering of Dynamic Grassland Scene in the Wind

abstract: In order to simulate dynamic grassland scenes, this paper views wind field as a stationary stochastic process/field related with time and space adhering to the principles of Wind Engineering .Use Linear auto-regression filter to generate eligible wind speed and use Finite element method to achieve the motion of different part of grass driven by the wind force. In addition, this paper adopt a hybrid method with L-system and image-based to achieve the parametric modeling of grass in close view, then model grass in far view using star structure for rendering efficiently. The result demonstrates that the proposed scheme can realistically simulate the dynamic scenes in real-time.


title: 60-GHz Textile Antenna Array for Body-Centric Communications

abstract: To demonstrate that commercial textiles can be used as antenna substrates at millimeter waves, a microstrip patch antenna array printed on textile is proposed for off-body communications in the 60-GHz band. The textile substrate is characterized in V-band using the open stub technique. A new fabrication process is introduced for the reliable and accurate manufacturing of millimeter-wave microstrip antennas on textiles. The antenna reflection coefficient, radiation patterns and efficiency are studied in free space, with and without bending, and on a homogeneous skin-equivalent phantom. The numerical and experimental results are in a good agreement. To the best of our knowledge, this is the first textile millimeter-wave antenna optimized for off-body communications presented in the literature at millimeter waves.


title: An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing

abstract: This study investigates the sector choices of Industrial Engineering students.Data were derived by web-based survey procedure.The weights of criteria were obtained by using the fuzzy AHP.The ranking of alternatives was obtained by using fuzzy MOORA.The most preferred sectors are respectively; technology, informatics and finance. Industrial engineering is a field of profession that offers various work areas around the world. Since it has a wide variety of work areas, students encounter with the problem of taking a decision on which sector to work in the future. Therefore, a questionnaire has been conducted on 60 students who study at Industrial Engineering department at different universities in Turkey. Fuzzy AHP (Analytic Hierarchy Process) and fuzzy MOORA (Multi-Objective Optimization by Ratio Analysis) methods have been applied to the results of the questionnaire that has been done for 7 sectors (manufacturing, logistics, finance/ banking, health, technology, software/ informatics and academics) and 10 criteria (payment, job satisfaction, career opportunities, being productive, having a goal, status, guidance/ pressure, social opportunities, employment deficit and easiness of job). The significance of criteria has been determined by using the fuzzy AHP method while the sectors that have been preferred mostly within the certain criteria have been determined using the fuzzy MOORA method. Finally, the most commonly preferred sectors have been identified as technology, software/informatics and finance.


title: All-Moves-As-First Heuristics in Monte-Carlo Go

abstract: We present and explore the effectiveness of sev- eral variations on the All-Moves-As-First (AMAF) heuristic in Monte-Carlo Go. Our results show that: Random play-outs provide more information about the goodness of moves made earlier in the play-out. AMAF updates are not just a way to quickly initialize counts, they are useful after every play-out. Updates even more aggressive than AMAF can be even more beneficial.


title: Drug Solubility: Importance and Enhancement Techniques

abstract: Solubility, the phenomenon of dissolution of solute in solvent to give a homogenous system, is one of the important parameters to achieve desired concentration of drug in systemic circulation for desired (anticipated) pharmacological response. Low aqueous solubility is the major problem encountered with formulation development of new chemical entities as well as for the generic development. More than 40% NCEs (new chemical entities) developed in pharmaceutical industry are practically insoluble in water. Solubility is a major challenge for formulation scientist. Any drug to be absorbed must be present in the form of solution at the site of absorption. Various techniques are used for the enhancement of the solubility of poorly soluble drugs which include physical and chemical modifications of drug and other methods like particle size reduction, crystal engineering, salt formation, solid dispersion, use of surfactant, complexation, and so forth. Selection of solubility improving method depends on drug property, site of absorption, and required dosage form characteristics.


title: STATISTICAL PARSING FOR HARMONIC ANALYSIS OF JAZZ CHORD SEQUENCES

abstract: Analysing music resembles natural language parsing in requiring the derivation of structure from an unstructured and highly ambiguous sequence of elements, whether they are notes or words. Such analysis is fundamental to many music processing tasks, such as key identification and score transcription. The focus of the present paper is on harmonic analysis. We use the three-dimensional tonal harmonic space developed by [4, 13, 14] to define a theory of tonal harmonic progression, which plays a role analogous to semantics in language. Our parser applies techniques from natural language processing (NLP) to the problem of analysing harmonic progression. It uses a formal grammar of jazz chord sequences of a kind that is widely used for NLP, together with the statistically based modelling techniques standardly used in wide-coverage parsing, to map music onto underlying harmonic progressions in the tonal space. Using supervised learning over a small corpus of jazz chord sequences annotated with harmonic analyses, we show that grammar-based musical parsing using simple statistical parsing models is more accurate than a baseline Markovian model trained on the same corpus.


title: COMPARISON OF DEM GENERATION AND COMBINATION METHODS USING HIGH RESOLUTION OPTICAL STEREO IMAGERY AND INTERFEROMETRIC SAR DATA

abstract: Digital elevation models (DEM) from satellite data are generated mainly from two types of datasets using completely different methods: photogrammetry for optical stereo images (e.g. SPOT5, IKONOS) and interferometry for Synthetic Aperture Radar data (InSAR, e.g. ERS-Tandem, SRTM). Both generation methods show advantages and disadvantages but have similar accuracy values in comparison to a reference DEM. The paper aims at showing the potential for combined usage of several DSM (derived with different sensors and methods) to improve the overall accuracy. Some results are given for DEM fusion utilizing height error maps for each DSM and for DEM integration, where single point information from another DSM is inserted during the triangulation process. The quality of the DSM derived from one source and of the combined DSM depends on the steepness of the terrain and on the land cover type. For flat terrain or moderate hilly landscapes, a height accuracy in the order of 5 meters or better can be achieved for the mentioned sensors. Two test areas are chosen, where many different data sets are available and much knowledge exists from previous studies. The first test area is a region in the south-eastern part of Bavaria comprising a mostly hilly, post-glacial landscape including lakes and also mountains of the German Alps. The second test area is located in Catalonia, Spain, and includes the city of ::: Barcelona as well as a mostly hilly terrain with some steep slopes and additionally the Mediterranean coast. The received DSM are compared qualitatively and quantitatively to the reference DEM with superior quality by looking at profiles and sub-area statistics. The results show that an improvement of the fused DSM and the integrated DSM can be quantitatively measured. Although the overall statistics for a larger region does show only a slight improvement, local errors can be reduced significantly so that the overall accuracy of the combined DSM is higher.


title: A Performance-Aware Low-Quiescent Headphone Amplifier in 65-nm CMOS

abstract: A low-quiescent high-performance class-AB headphone amplifier is presented. The effect of the input-referred offset on the quiescent power of audio power amplifier (APA) that drives very small resistive load is described and analyzed. A digitally assisted offset calibration-based APA topology together with improved frequency compensation is proposed. This permits the use of low quiescent power and small compensation capacitance. Implemented in 65-nm CMOS technology, the amplifier occupies an area of 0.15 mm2 and consumes 0.4 mW at ±1 V supplies. At the load of 16 $\Omega $ //200 pF, the gain-bandwidth product is 1.78 MHz using a total compensation capacitance of 8.5 pF. Other performance metrics are measured with −89-dB total harmonic distortion (THD+N), 93.5-dB signal-to-noise ratio, 84-dB power supply rejection ratio (PSRR+), and 105-dB PSRR– @217 Hz. It can deliver a peak power of 35 mW (1.5 $V_{\mathrm{ pp}}$ swing) to the above load, yielding the best figure-of-merit (FOM) = (peak load power/quiescent power) and the best FOM2 = [peak load power/quiescent power $\ast $ (THD+N)%] with respect to other reported representative works.


title: FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension

abstract: This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of "History of Word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation. Second, it identifies an attention scoring function that better utilizes the "history of word" concept. Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer. We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.


title: Training Selection for Tuning Entity Matching

abstract: Entity matching is a crucial and difficult task for data integration. An effective solution strategy typically has to combine several techniques and to find suitable settings for critical configuration parameters such as similarity thresholds. Supervised (training- based) approaches promise to reduce the manual work for determining (learning) effective strategies for entity matching. However, they critically depend on training data selection which is a difficult problem that has so far mostly been addressed manually by human experts. In this paper we propose a training- based framework called STEM for entity matching and present different generic methods for automatically selecting training data to combine and configure several matching techniques. We evaluate the proposed methods for different match tasks and small- and medium-sized training sets.


From Patching Delays to Infection Symptoms: Using Risk Profiles for an Early Discovery of Vulnerabilities Exploited in the Wild.


title: Topic hierarchy construction for the organization of multi-source user generated contents

abstract: User generated contents (UGCs) carry a huge amount of high quality information. However, the information overload and diversity of UGC sources limit their potential uses. In this research, we propose a framework to organize information from multiple UGC sources by a topic hierarchy which is automatically generated and updated using the UGCs. We explore the unique characteristics of UGCs like blogs, cQAs, microblogs, etc., and introduce a novel scheme to combine them. We also propose a graph-based method to enable incremental update of the generated topic hierarchy. Using the hierarchy, users can easily obtain a comprehensive, in-depth and up-to-date picture of their topics of interests. The experiment results demonstrate how information from multiple heterogeneous sources improves the resultant topic hierarchies. It also shows that the proposed method achieves better F1 scores in hierarchy generation as compared to the state-of-the-art methods.


title: CO2: Inferring Personal Interests From Raw Footprints by Connecting the Offline World with the Online World

abstract: User-generated trajectories (UGTs), such as travel records from bus companies, capture rich information of human mobility in the offline world. However, some interesting applications of these raw footprints have not been exploited well due to the lack of textual information to infer the subject’s personal interests. Although there is rich semantic information contained in the spatial- and temporal-aware user-generated contents (STUGC) published in the online world, such as Twitter, less effort has been made to utilize this information to facilitate the interest discovery process. In this article, we design an effective probabilistic framework named CO2 to c onnect the o ffline world with the o nline world in order to discover users’ interests directly from their raw footprints in UGT. CO2 first infers trip intentions by utilizing the semantic information in STUGC and then discovers user interests by aggregating the intentions. To evaluate the effectiveness of CO2, we use two large-scale real-world datasets as a case study and further conduct a questionnaire survey to show the superior performance of CO2.


Future of Mobile Communication 5G: Perspectives, Challenges and Services


title: Global Structure-from-Motion by Similarity Averaging

abstract: Global structure-from-motion (SfM) methods solve all cameras simultaneously from all available relative motions. It has better potential in both reconstruction accuracy and computation efficiency than incremental methods. However, global SfM is challenging, mainly because of two reasons. Firstly, translation averaging is difficult, since an essential matrix only tells the direction of relative translation. Secondly, it is also hard to filter out bad essential matrices due to feature matching failures. We propose to compute a sparse depth image at each camera to solve both problems. Depth images help to upgrade an essential matrix to a similarity transformation, which can determine the scale of relative translation. Thus, camera registration is formulated as a well-posed similarity averaging problem. Depth images also make the filtering of essential matrices simple and effective. In this way, translation averaging can be solved robustly in two convex L1 optimization problems, which reach the global optimum rapidly. We demonstrate this method in various examples including sequential data, Internet data, and ambiguous data with repetitive scene structures.


title: A natural language query interface for searching personal information on smartwatches

abstract: Currently, personal assistant systems, run on smartphones and use natural language interfaces. However, these systems rely mostly on the web for finding information. Mobile and wearable devices can collect an enormous amount of contextual personal data such as sleep and physical activities. These information objects and their applications are known as quantified-self, mobile health or personal informatics, and they can be used to provide a deeper insight into our behavior. To our knowledge, existing personal assistant systems do not support all types of quantified-self queries. In response to this, we have undertaken a user study to analyze a set of “textual questions/queries” that users have used to search their quantified-self or mobile health data. Through analyzing these questions, we have constructed a light-weight natural language based query interface - including a text parser algorithm and a user interface - to process the users' queries that have been used for searching quantified-self information. This query interface has been designed to operate on small devices, i.e. smartwatches, as well as augmenting the personal assistant systems by allowing them to process end users' natural language queries about their quantified-self data.


title: Extending The BLEU MT Evaluation Method With Frequency Weightings

abstract: We present the results of an experiment on extending the automatic method of Machine Translation evaluation BLUE with statistical weights for lexical items, such as tf.idf scores. We show that this extension gives additional information about evaluated texts; in particular it allows us to measure translation Adequacy, which, for statistical MT systems, is often overestimated by the baseline BLEU method. The proposed model uses a single human reference translation, which increases the usability of the proposed method for practical purposes. The model suggests a linguistic interpretation which relates frequency weights and human intuition about translation Adequacy and Fluency.


title: Action Outcomes Are Represented in Human Inferior Frontoparietal Cortex

abstract: The simple action of pressing a switch has many possible interpretations--the actor could be turning on a light, deleting critical files from a computer, or even turning off a life-support system. In each of these cases, the motor parameters of the action are the same but the physical outcome differs. We report evidence of suppressed responses in right inferior parietal and right inferior frontal cortex when participants saw repeated movies showing the same action outcome, but these regions did not distinguish the kinematic parameters by which the action was accomplished. Thus, these brain areas encode the physical outcomes of human actions in the world. These results are compatible with a hierarchical model of human action understanding in which a cascade of specialized processes from occipital to parietal and frontal regions allow humans to understand the physical consequences of actions in the world and the intentions underlying those actions.


title: A 665μW silicon photomultiplier-based NIRS/EEG/EIT monitoring asic for wearable functional brain imaging

abstract: Functional brain imaging is considered a powerful and practical solution for understanding the brain and neurological diseases. While EEG is an established method for non-invasive electrical activity, electrical-impedance tomography (EIT) and near-infrared spectroscopy (NIRS) can additionally measure impedance changes and hemodynamic processes. To facilitate long-term multi-channel brain imaging in a wearable form factor without cabling overhead, there is a need for low-power local amplifiers [1] to support all these modalities. The main principle of optical hemodynamic measurements is to send light pulses into the tissue and measure the reflected light, which is modulated by the oxygen levels in the blood (Fig. 17.8.1). State-of-the-art NIRS ICs typically consume a few mW, primarily for the LEDs to meet the required light sensitivity at the photodiodes (PDs). Silicon photomultipliers (SiPMs) are promising alternatives because they have excellent low-light detection capabilities, speed of response and higher detection efficiency in both visible and near infrared range [2]. Hence, SiPMs allow deeper brain sensing depth and the possibility to sample consistent cerebral regions with larger inter-optode distance. This benefit would significantly reduce the number of NIRS channels and the associated power for a wearable NIRS device. Although SiPMs require a higher bias voltage (∼30V) than PDs, they achieve similar NIRS responses with a few hundred times less LED current. This results in a low-power NIRS ASIC and an overall power-efficient system. Existing optical sensing ICs are not suitable for a SiPM because of its large and variable output current. Trimming-based calibration methods [3] suffer from drift over time. Auto-zeroing by swapping an integrator capacitor [4][5] compensates ambient light at the cost of the integrator's headroom. Apart from ambient light, the dynamic range (DR) of the amplifier is also limited by a large NIRS signal, leading to a power-hungry readout.


Image Denoising with Deep Convolutional Neural Networks


title: Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles

abstract: In this paper, an ANFIS (adaptive neuro-fuzzy inference system) based autonomous flight controller for UAVs (unmanned aerial vehicles) is described. To control the position of the UAV in three dimensional space as altitude and longitude-latitude location, three fuzzy logic modules are developed. These adjust the pitch angle, the roll angle and the throttle position of the UAV so that its altitude, the heading and the speed are controlled together. The implementation framework utilizes MATLAB's standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed six degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. To demonstrate the performance and potential of the controllers, the Aerosonde UAV model is used. Flight Gear open source flight simulator and Gauges Block Set are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Steep turn maneuvers which are used for basic training of pilots are applied to test the performance of the fuzzy logic controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.


title: A fast local laplacian filter for high quality suggestive contours generation

abstract: Line drawings play an important role in shape description due to they can convey meaningful information by outstanding the key component and distracting details or ignoring less important. Suggestive contours are a type of lines to produce high quality line drawings. To generate those contours, we can generally start from two aspects: from image space or object space. The image space strategies can not only extract suggestive contours much faster than object space methods, but also don't require the information of the 3D objects. However they are sensitive to small noise, which is ubiquitous in the digital image. In this paper, before extracting lines, we apply an accelerated structure-preserving local Laplacian filter to smooth the shaded image. Through our experiments, we draw the conclusion that our method can effectively suppress the redundant details, generating a cleaner, higher quality line drawing by image space methods, and can compare with the result by object space ones.


title: SmartTouch: electric skin to touch the untouchable

abstract: Augmented haptics lets users touch surface information of any modality. SmartTouch uses optical sensors to gather information and electrical stimulation to translate it into tactile display. Augmented reality is an engineer's approach to this dream. In AR, sensors capture artificial information from the world, and existing sensing channels display it. Hence, we virtually acquire the sensor's physical ability as our own. Augmented haptics, the result of applying AR to haptics, would allow a person to touch the untouchable. Our system, SmartTouch, uses a tactile display and a sensor. When the sensor contacts an object, an electrical stimulation translates the acquired information into a tactile sensation, such as a vibration or pressure, through the tactile display. Thus, an individual not only makes physical contact with an object, but also touches the surface information of any modality, even those that are typically untouchable.


Matrix sparsification via the Khintchine inequality


title: Chain Replication for Supporting High Throughput and Availability

abstract: Chain replication is a new approach to coordinating clusters of fail-stop storage servers. The approach is intended for supporting large-scale storage services that exhibit high throughput and availability without sacrificing strong consistency guarantees. Besides outlining the chain replication protocols themselves, simulation experiments explore the performance characteristics of a prototype implementation. Throughput, availability, and several object-placement strategies (including schemes based on distributed hash table routing) are discussed.


title: Cooperative Object Transport in Multi-Robot Systems: A Review of the State-of-the-Art

abstract: In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increase the visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectively.


title: A Taxi Gap Prediction Method via Double Ensemble Gradient Boosting Decision Tree

abstract: Predicting the gap between taxi demand and supply in taxi booking apps is completely new and important but challenging. However, manually mining gap rule for different conditions may become impractical because of massive and sparse taxi data. Existing works unilaterally consider demand or supply, used only few simple features and verified by little data, but not predict the gap value. Meanwhile, none of them dealing with missing values. In this paper, we introduce a Double Ensemble Gradient Boosting Decision Tree Model(DEGBDT) to predict taxi gap. (1) Our approach specifically considers demand and supply to predict the gap between them. (2) Also, our method provides a greedy feature ranking and selecting method to exploit most reliable feature. (3) To deal with missing value, our model takes the lead in proposing a double ensemble method, which secondarily integrates different Gradient Boosting Decision Tree(GBDT) model at the different data sparse situation. Experiments on real large-scale dataset demonstrate that our approach can effectively predict the taxi gap than state-of-the-art methods, and shows that double ensemble method is efficacious for sparse data.


title: ClassBench: A Packet Classification Benchmark

abstract: Packet classification is an enabling technology for next generation network services and often a performance bottleneck in high-performance routers. The performance and capacity of many classification algorithms and devices, including TCAMs, depend upon properties of filter sets and query patterns. Despite the pressing need, no standard performance evaluation tools or filter sets are publicly available. In response to this problem, we present ClassBench, a suite of tools for benchmarking packet classification algorithms and devices. ClassBench includes a filter set generator that produces synthetic filter sets that accurately model the characteristics of real filter sets. Along with varying the size of the filter sets, we provide high-level control over the composition of the filters in the resulting filter set. The tool suite also includes a trace generator that produces a sequence of packet headers to exercise packet classification algorithms with respect to a given filter set. Along with specifying the relative size of the trace, we provide a simple mechanism for controlling locality of reference. While we have already found ClassBench to be very useful in our own research, we seek to eliminate the significant access barriers to realistic test vectors for researchers and initiate a broader discussion to guide the refinement of the tools and codification of a formal benchmarking methodology. (The ClassBench tools are publicly available at the following site: http://www.arl.wustl.edu/~det3/ClassBench/.)


title: Skinfold thickness measurements are better predictors of body fat percentage than body mass index in male Spanish children and adolescents

abstract: OBJECTIVE ::: To develop equations, from some simple anthropometric measurements, for the prediction of body density from underwater weighing in male spanish children and adolescents. ::: ::: ::: SUBJECTS ::: One hundred and seventy-five males, aged 7.0-16.9 y, participated in this study, they were recruited from primary and secondary schools. ::: ::: ::: MEASUREMENTS ::: Body weight and height and skinfold thicknesses by anthropometry, body density by underwater weighing. ::: ::: ::: RESULTS ::: Correlations between body density and body mass index (BMI) were high until 14.0-16.9y. Correlations between body density and log sigma 4 skinfolds were higher than those with BMI at all ages. Log sigma 4 skinfolds explained between 61% (14.0-16.9 y) and 68% (11.0-13.9 y) of the body density variance. Regression equations for body density from BMI and triceps skinfold thickness explained between 51% (14.0-16.9y) and 68% (7.0-10.9 y) of the body density variance. ::: ::: ::: CONCLUSIONS ::: The best estimators of body density in the children and adolescents studied were log sigma 4 skinfolds and a combination of BMI and triceps skinfold.


title: Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances

abstract: The aim of broth and agar dilution methods is to determine the lowest concentration of the assayed antimicrobial agent (minimal inhibitory concentration, MIC) that, under defined test conditions, inhibits the visible growth of the bacterium being investigated. MIC values are used to determine susceptibilities of bacteria to drugs and also to evaluate the activity of new antimicrobial agents. Agar dilution involves the incorporation of different concentrations of the antimicrobial substance into a nutrient agar medium followed by the application of a standardized number of cells to the surface of the agar plate. For broth dilution, often determined in 96-well microtiter plate format, bacteria are inoculated into a liquid growth medium in the presence of different concentrations of an antimicrobial agent. Growth is assessed after incubation for a defined period of time (16-20 h) and the MIC value is read. This protocol applies only to aerobic bacteria and can be completed in 3 d.


title: Speaker-Independent Silent Speech Recognition From Flesh-Point Articulatory Movements Using an LSTM Neural Network

abstract: Silent speech recognition (SSR) converts nonaudio information such as articulatory movements into text. SSR has the potential to enable persons with laryngectomy to communicate through natural spoken expression. Current SSR systems have largely relied on speaker-dependent recognition models. The high degree of variability in articulatory patterns across different speakers has been a barrier for developing effective speaker-independent SSR approaches. Speaker-independent SSR approaches, however, are critical for reducing the amount of training data required from each speaker. In this paper, we investigate speaker-independent SSR from the movements of flesh points on tongue and lips with articulatory normalization methods that reduce the interspeaker variation. To minimize the across-speaker physiological differences of the articulators, we propose Procrustes matching-based articulatory normalization by removing locational, rotational, and scaling differences. To further normalize the articulatory data, we apply feature-space maximum likelihood linear regression and i-vector. In this paper, we adopt a bidirectional long short-term memory recurrent neural network (BLSTM) as an articulatory model to effectively model the articulatory movements with long-range articulatory history. A silent speech dataset with flesh-point articulatory movements was collected using an electromagnetic articulograph from 12 healthy and two laryngectomized English speakers. Experimental results showed the effectiveness of our speaker-independent SSR approaches on healthy as well as laryngectomy speakers. In addition, BLSTM outperformed the standard deep neural network. The best performance was obtained by the BLSTM with all the three normalization approaches combined.


title: A general method for visualizing and explaining black-box regression models

abstract: We propose a method for explaining regression models and their predictions for individual instances. The method successfully reveals how individual features influence the model and can be used with any type of regression model in a uniform way. We used different types of models and data sets to demonstrate that the method is a useful tool for explaining, comparing, and identifying errors in regression models.


title: Keyword extracting using auto-associative neural networks

abstract: The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information.


title: Claw-Wheel: A transformable robot for search and investigation in amphibious environment

abstract: This research develops an amphibious transformable robot, Claw-Wheel IV, which could move on flat ground, uneven terrain, and aquatic environment. Compared to the previous version, Claw-Wheel III, the new generation has waterproof enclosure to adapt to watery environments and paddles are installed to produce underwater propulsion force, while the transformable idea and claw locomotion mechanism design are retained. Different kinds of gaits are designed and compared to reduce the vertical fluctuation of COM of the robot and run more smoothly. Advancing in aquatic environments relies on the paddles driven along with the rotating claws while steering is achieved with velocity differential between claws on both sides. Velocities of individual claws are computed and regulated based on gyration radius and angular velocity parameters obtained in experiments, in order to follow ideal trajectories.


title: Cache performance analysis of traversals and random accesses

abstract: analyze them. This paper describes a model for studying the cache performance of algorithms in a direct-mapped cache. Using this model, we analyze the cache performance of several commonly occurring memory access patterns: (i) sequential and random memory traversals, (ii) systems of random accesses, and (iii) combinations of each. For each of these, we give exact expressions for the number of cache misses per memory access in our model. We illustrate the application of these analyses by determining the cache performance of two algorithms: the traversal of a binary search tree and the counting of items in a large array. Trace driven cache simulations validate that our analyses accurately predict cache performance. The key application of cache performance analysis is towards the cache conscious design of data structures and algorithms. In our previous work we studied the cache conscious design of priority queues [13] and sorting algorithms [14], and were able to make significant performance improvements over traditional implementations by considering cache effects.


title: Spatio-temporal characteristics of bursty words in Twitter streams

abstract: Social networking and microblogging services such as Twitter provide a continuous source of data from which useful information can be extracted. The detection and characterization of bursty words play an important role in processing such data, as bursty words might hint to events or trending topics of social importance upon which actions can be triggered. While there are several approaches to extract bursty words from the content of messages, there is only little work that deals with the dynamics of continuous streams of messages, in particular messages that are geo-tagged. In this paper, we present a framework to identify bursty words from Twitter text streams and to describe such words in terms of their spatio-temporal characteristics. Using a time-aware word usage baseline, a sliding window approach over incoming tweets is proposed to identify words that satisfy some burstiness threshold. For these words then a time-varying, spatial signature is determined, which primarily relies on geo-tagged tweets. In order to deal with the noise and the sparsity of geo-tagged tweets, we propose a novel graph-based regularization procedure that uses spatial cooccurrences of bursty words and allows for computing sound spatial signatures. We evaluate the functionality of our online processing framework using two real-world Twitter datasets. The results show that our framework can efficiently and reliably extract bursty words and describe their spatio-temporal evolution over time.


title: Image-to-Image Translation with Conditional Adversarial Networks

abstract: We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.


title: Wireless batteryless less-invasive blood pressure sensing microsystem for advanced biomedical application

abstract: Two novel, wireless, batteryless, less-invasive blood pressure sensing microsystems based on circular cuff and flat cuff configurations are developed for advanced biomedical application. Both systems employ an instrumented silicone cuff, wrapped around a blood vessel, for real-time blood pressure monitoring. The cuff is filled by silicone gel with an immersed MEMS pressure sensor and integrated electronic system to detect a down-scaled vessel blood pressure waveform. The proposed sensing technique avoids vessel penetration and substantially minimizes vessel restriction due to the soft cuff elasticity. Implant experiments show that the circular sensing cuff is suitable for laboratory animals with an artery diameter around 1 mm, and the flat sensing cuff is adequate for laboratory mice exhibiting an artery size around 0.2 mm.


title: Segmentation-free optical character recognition for printed Urdu text

abstract: This paper presents a segmentation-free optical character recognition system for printed Urdu Nastaliq font using ligatures as units of recognition. The proposed technique relies on statistical features and employs Hidden Markov Models for classification. A total of 1525 unique high-frequency Urdu ligatures from the standard Urdu Printed Text Images (UPTI) database are considered in our study. Ligatures extracted from text lines are first split into primary (main body) and secondary (dots and diacritics) ligatures and multiple instances of the same ligature are grouped into clusters using a sequential clustering algorithm. Hidden Markov Models are trained separately for each ligature using the examples in the respective cluster by sliding right-to-left the overlapped windows and extracting a set of statistical features. Given the query text, the primary and secondary ligatures are separately recognized and later associated together using a set of heuristics to recognize the complete ligature. The system evaluated on the standard UPTI Urdu database reported a ligature recognition rate of 92% on more than 6000 query ligatures.


title: Recent developments in openSMILE, the munich open-source multimedia feature extractor

abstract: We present recent developments in the openSMILE feature extraction toolkit. Version 2.0 now unites feature extraction paradigms from speech, music, and general sound events with basic video features for multi-modal processing. Descriptors from audio and video can be processed jointly in a single framework allowing for time synchronization of parameters, on-line incremental processing as well as off-line and batch processing, and the extraction of statistical functionals (feature summaries), such as moments, peaks, regression parameters, etc. Postprocessing of the features includes statistical classifiers such as support vector machine models or file export for popular toolkits such as Weka or HTK. Available low-level descriptors include popular speech, music and video features including Mel-frequency and similar cepstral and spectral coefficients, Chroma, CENS, auditory model based loudness, voice quality, local binary pattern, color, and optical flow histograms. Besides, voice activity detection, pitch tracking and face detection are supported. openSMILE is implemented in C++, using standard open source libraries for on-line audio and video input. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. openSMILE 2.0 is distributed under a research license and can be downloaded from http://opensmile.sourceforge.net/.


title: Vibrations, Quanta and Biology

abstract: Quantum biology is an emerging field of research that concerns itself with the experimental and theoretical exploration of non-trivial quantum phenomena in biological systems. In this tutorial overview we aim to bring out fundamental assumptions and questions in the field, identify basic design principles and develop a key underlying theme – the dynamics of quantum dynamical networks in the presence of an environment and the fruitful interplay that the two may enter. At the hand of three biological phenomena whose understanding is held to require quantum mechanical processes, namely excitation and charge transfer in photosynthetic complexes, magneto-reception in birds and the olfactory sense, we demonstrate that this underlying theme encompasses them all, thus suggesting its wider relevance as an archetypical framework for quantum biology.


title: Text mining and natural language processing: introduction for the special issue

abstract: This paper provides an introduction to this special issue of SIGKDD Explorations devoted to Natural Language Processing and Text Mining.


title: A Review of Smart Homes—Past, Present, and Future

abstract: A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home.


title: Extreme learning machine-based device displacement free activity recognition model

abstract: Activity recognition based on mobile device is an important aspect in developing human-centric pervasive applications like gaming, industrial maintenance and health monitoring. However, the data distribution of accelerometer is heavily affected by varying device locations and orientations, which will degrade the performance of recognition model. To solve this problem, we propose a fast, robust and device displacement free activity recognition model in this paper, which integrates principal component analysis (PCA) and extreme learning machine (ELM) to realize location-adaptive activity recognition. On the one hand, PCA is employed to reduce the dimensionality of feature space and extract robust features for recognition. On the other hand, in the online phase ELM is applied to classify the activity and adapt the recognition model to new device locations based on high confident recognition results in real time. Experimental results show that, with robust features and fast adaptation capability, the proposed model can adapt the classifier to new device locations quickly and obtain good recognition performance.


title: Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach

abstract: This paper presents a wavelet characteristic based approach for the automated visual inspection of ripple defects in the surface barrier layer (SBL) chips of ceramic capacitors. Difficulties exist in automatically inspecting ripple defects because of their semi-opaque and unstructured appearances, the gradual changes of their intensity levels, and the low intensity contrast between their surfaces and the rough exterior of a SBL chip. To overcome these difficulties, we first utilize wavelet transform to decompose an image and use wavelet characteristics as texture features to describe surface texture properties. Then, we apply multivariate statistics of Hotelling T^2, Mahalanobis distance D^2, and Chi-square X^2, respectively, to integrate the multiple texture features and judge the existence of defects. Finally, we compare the defect detection performance of the three wavelet-based multivariate statistical models. Experimental results show that the proposed approach (Hotelling T^2) achieves a 93.75% probability of accurately detecting the existence of ripple defects and an approximate 90% probability of correctly segmenting their regions.


title: Two Causal Analyses of the Black Hawk Shootdown during Operation Provide Comfort

abstract: We perform a Why-Because Analysis (WBA) starting from the information in the Executive Summary of the U.S. DoD Aircraft Accident Investigation Board report on the shootdown of two U.S. Army Black Hawk transport helicopters by U.S.A.F. F-15 fighter aircraft over Northern Iraq on 14 April 1994, during Operation Provide Comfort. We compare with the effort of Snook to perform a similar analysis.


title: Discourse Structure and Computation: Past, Present and Future

abstract: The discourse properties of text have long been recognized as critical to language technology, and over the past 40 years, our understanding of and ability to exploit the discourse properties of text has grown in many ways. This essay briefly recounts these developments, the technology they employ, the applications they support, and the new challenges that each subsequent development has raised. We conclude with the challenges faced by our current understanding of discourse, and the applications that meeting these challenges will promote.


title: The Focussed D* Algorithm for Real-Time Replanning

abstract: Finding the lowest-cost path through a graph is central to many problems including route planning for a mobile robot If arc costs change during the traverse then the remainder of the path may need to be replanned. This is the case for a sensor-equipped mobile robot with imperfect information about its environment. As the robot acquires additional information via its sensors it can revise its plan to reduce the total cost of the traverse. If the prior information is grossly incomplete the robot may discover useful information in every piece of sensor data. During replanning, the robot must either wait for the new path to be computed or move in the wrong direction therefore rapid replanning is essential The D* algorithm (Dynamic A*) plans optimal traverses ID real-time by incrementally repairing paths to the robots state as new information is discovered. This paper describes an extension to D* that focusses the repairs to significantly reduce the total time required for the initial path calculation and subsequent replanning operations. This extension completes the development of the D* algorithm as a full generalizatin of A* for dynamic environments where arc costs can change during the traverse of the solution path.


title: Resiliency of Deep Neural Networks under Quantization

abstract: The complexity of deep neural network algorithms for hardware implementation can be much lowered by optimizing the word-length of weights and signals. Direct quantization of floating-point weights, however, does not show good performance when the number of bits assigned is small. Retraining of quantized networks has been developed to relieve this problem. In this work, the effects of retraining are analyzed for a feedforward deep neural network (FFDNN) and a convolutional neural network (CNN). The network complexity is controlled to know their effects on the resiliency of quantized networks by retraining. The complexity of the FFDNN is controlled by varying the unit size in each hidden layer and the number of layers, while that of the CNN is done by modifying the feature map configuration. We find that the performance gap between the floating-point and the retrain-based ternary (+1, 0, -1) weight neural networks exists with a fair amount in 'complexity limited' networks, but the discrepancy almost vanishes in fully complex networks whose capability is limited by the training data, rather than by the number of connections. This research shows that highly complex DNNs have the capability of absorbing the effects of severe weight quantization through retraining, but connection limited networks are less resilient. This paper also presents the effective compression ratio to guide the trade-off between the network size and the precision when the hardware resource is limited.


title: Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments

abstract: Current ubiquitous computing applications for smart homes aim to enhance people’s daily living respecting age span. Among the target groups of people, elderly are a population eager for “choices for living arrangements”, which would allow them to continue living in their homes but at the same time provide the health care they need. Given the growing elderly population, there is a need for statistical models able to capture the recurring patterns of daily activity life and reason based on this information. We present an analysis of real-life sensor data collected from 40 different households of elderly people, using motion, door and pressure sensors. Our objective is to automatically observe and model the daily behavior of the elderly and detect anomalies that could occur in the sensor data. For this purpose, we first introduce an abstraction layer to create a common ground for home sensor configurations. Next, we build a probabilistic spatio-temporal model to summarize daily behavior. Anomalies are then defined as significant changes from the learned behavioral model and detected using a cross-entropy measure. We have compared the detected anomalies with manually collected annotations and the results show that the presented approach is able to detect significant behavioral changes of the elderly.


title: Strategic Discovery and Sharing of Vulnerabilities in Competitive Environments

abstract: We investigate the incentives behind investments by competing companies in discovery of their security vulnerabilities and sharing of their findings. Specifically, we consider a game between competing firms that utilise a common platform in their systems. The game consists of two stages: firms must decide how much to invest in researching vulnerabilities, and thereafter, how much of their findings to share with their competitors. We fully characterise the Perfect Bayesian Equilibria (PBE) of this game, and translate them into realistic insights about firms’ strategies. Further, we develop a monetary-free sharing mechanism that encourages both investment and sharing, a missing feature when sharing is arbitrary or opportunistic. This is achieved via a light-handed mediator: it receives a set of discovered bugs from each firm and moderate the sharing in a way that eliminates firms’ concerns on losing competitive advantages. This research provides an understanding of the origins of inefficiency and paves the path towards more efficient sharing of cyber-intelligence among competing entities.


title: Availability Improvement for WLAN-Based Train-Ground Communication Systems in Communication-Based Train Control (CBTC)

abstract: Train-ground communication technology is one of the key technologies for communication-based train control (CBTC), which is an automated train control system for railways that ensures safe operation of rail vehicles using data communications. In CBTC systems, less service availability could cause train derailment, collision or even catastrophic loss of lives or assets. Therefore, the availability of train-ground communication should be carefully considered in designing CBTC systems. In this paper, we propose two WLAN-based train-ground communication schemes with redundancy to improve the availability in CBTC systems. The availability is analyzed using continuous time Markov chain (CTMC) model. Numerical examples illustrate that the proposed schemes with redundancy can significantly improve the availability of WLAN-based train-ground communication in CBTC systems.


title: Full length article: Empirical time and frequency domain models of spectrum use

abstract: Dynamic spectrum access (DSA) has been proposed as a solution to the spectrum scarcity problem. However, the models for spectrum use, that are commonly used in DSA research, are either limited in scope or have not been validated against real-life measurement data. In this paper we introduce a flexible spectrum use model based on extensive measurement results that can be configured to represent various wireless systems. We show that spectrum use is clustered in the frequency domain and should be modelled in the time domain using geometric or lognormal distributions. In the latter case the probability of missed detection is significantly higher due to the heavy-tailed behaviour of the lognormal distribution. The listed model parameters enable accurate modelling of primary user spectrum use in time and frequency domain for future DSA studies. Additionally, they also provide a more empirical basis to develop regulatory or business models.


title: Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors

abstract: This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with all the available attributes, but this sometimes causes misclassification. This is because an attribute that is effective for classifying instances of one class is not always effective for another class. In this case, a metric of classifying the latter class can be undesirably influenced by the irrelevant attribute. This paper solves this problem by taking the importance of each attribute for each class into account when calculating the metric. In addition to the proposal of this new method, this paper also contributes by providing a dataset for pose classification based on wearable sensors, named HDPoseDS. It contains 22 classes of poses performed by 10 subjects with 31 IMU sensors across full body. To the best of our knowledge, it is the richest wearable-sensor dataset especially in terms of sensor density, and thus it is suitable for studying zero-shot pose/action recognition. The presented method was evaluated on HDPoseDS and outperformed relative improvement of 5.9% in comparison to the best baseline method.


title: Pore-based ridge reconstruction for fingerprint recognition

abstract: The use of sweat pores in fingerprint recognition is becoming increasingly popular, mostly because of the wide availability of pores, which provides complementary information for matching distorted or incomplete images. In this work we present a fully automatic pore-based fingerprint recognition framework that combines both pores and ridges to measure the similarity of two images. To obtain the ridge structure, we propose a novel pore-based ridge reconstruction approach by considering a connect-the-dots strategy. To this end, Kruskal's minimum spanning tree algorithm is employed to connect consecutive pores and form a graph representing the ridge skeleton. We evaluate our framework on the PolyU HRF database, and the obtained results are favorably compared to previous results in the literature.


title: The inverted multi-index

abstract: A new data structure for efficient similarity search in very large dataseis of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and preprocessing time, inverted multi-indices achieve a much denser subdivision of the search space compared to inverted indices, while retaining their memory efficiency. Our experiments with large dataseis of SIFT and GIST vectors demonstrate that because of the denser subdivision, inverted multi-indices are able to return much shorter candidate lists with higher recall. Augmented with a suitable reranking procedure, multi-indices were able to improve the speed of approximate nearest neighbor search on the dataset of 1 billion SIFT vectors by an order of magnitude compared to the best previously published systems, while achieving better recall and incurring only few percent of memory overhead.


title: A novel circularly polarized dsrc band square microstrip antenna using minkowski fractal structure for vehicular communication

abstract: This paper proposes the design of a novel Minkowski circularly polarized square microstrip fractal antenna to operate within the full range of 75MHz DSRC band (Dedicated Short-Range Communications- IEEE 802.11p) with the center frequency at 5.9GHz for vehicle to vehicle communication purpose in an Intelligent Transport System (ITS). Compared to the conventional square patch, the use of Minkowski fractal reduces the overall area of the patch considerably at 5.9GHz with 7.4dBi Gain. More than 3dB Gain is maintained throughout a Bandwidth of 600MHz (5.6GHz to 6.2GHz). Axial Ratio of the antenna is also achieved below 3dB for the entire 267MHz Bandwidth from 5.73GHz to 6GHz. I. INTRODUCTION The progressive growth in technology resulted in complex transportation system and smarter vehicles. As a result, traffic safety applications need to be more reliable, low-cost, high performance and low-delay based. But the high speed of vehicles and the harsh, dynamic communication environment offers plenty of novel technical challenges in this area. Most of the vehicular communication equipments like Side View Camera Model (1), Shadow or Edge Features Detector (2) that have been proposed over time for vehicular safety, show performance limitations in the presence of fog, mist and bad weather. Solid State Infrared Detectors (3) are high on cost. Apart from that, mechanically controlled devices like Dynamic Angling Side View Mirror (4) has high response time and is more prone to constant wear and tear. In order to overcome these technical challenges, the application of radiofrequency method in this field has been suggested in this paper, for an effective implementation of vehicular communication methodology. The detailed design of the circularly polarized DSRC band (5) square microstrip antenna using Minkowski Fractal Structure (6) is discussed in Section II. In Section III, the placement of the antenna on the body of a vehicle is illustrated. The results and discussions


title: Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy

abstract: Abstract Diabetes is a chronic metabolic disorder which can lead to severe complications and affect all vital organs. The occurrence of complications due to diabetes can be prevented by regular monitoring and maintaining the blood glucose level in the normal range. Most of the commercially available devices for glucose measurement are invasive or minimally invasive. Invasive devices used for blood glucose monitoring are inconvenient and painful whereas minimal invasive devices have limited time span and stability. Thus, there is a need of an economic, compact, painless and convenient non-invasive device which can promote frequent blood testing which help in control of blood glucose level. In this paper various methods of glucose monitoring are reviewed and overall emphasis is laid on the development of NIRS (near-infrared spectroscopy) based non-invasive glucose monitoring. The motivation of this review is to demonstrate the prospects, limitations and technical challenges for development of NIRS based non-invasive blood glucose measurement system.


title: Weighing the Pros and Cons of Engaging in Open Innovation

abstract: The advantages of open innovation projects are widely discussed in innovation management research and practice (e.g., Man & Duysters, 2005). Particularly, small and medium-sized enterprises (SMEs) are expected to gain most from open innovation collaborations due to their inherently limited capabilities (Lee et al., 2010; van de Vrande 2009). However, these enterprises also face manifold challenges in open innovation practice, leading to uncertainty and even renunciation of open innovation project participation. Thus, SMEs often deal with the decision dilemma of having to cooperate with external partners in order to improve their own innovation capacity, regardless of their ability to cope with the correlated risks. Although it is essential for SMEs to find the right balance between positive effects and possible negative consequences (i.e., the “dark sides” of open innovation, cf. Huizingh, 2011) of open innovation project participation, appropriate methods of finding this balance are still lacking. The research project “Open Darkness” was initiated with the goal of enabling SMEs to weigh the risks and benefits of open innovation participation by developing: i) a weighing and decision process framework and ii) a software tool that automatically applies this framework and allows self-assessment for SMEs. Both solutions aim to structure and support the decision process regarding potential engagement in open innovation projects. In order to tackle these targeted outcomes, an interdisciplinary consortium facilitates a multi-perspective and an integrated holistic research approach. Besides several SMEs, which function as requirement authority and implementer, the consortium consists of three German research institutions: the Chair of Economic Law (University of Paderborn), the Chair of Technology and Innovation Management (University of Aachen), and the Chair of Business Informatics (University of Potsdam).


title: The temporal logic of programs

abstract: A unified approach to program verification is suggested, which applies to both sequential and parallel programs. The main proof method suggested is that of temporal reasoning in which the time dependence of events is the basic concept. Two formal systems are presented for providing a basis for temporal reasoning. One forms a formalization of the method of intermittent assertions, while the other is an adaptation of the tense logic system Kb, and is particularly suitable for reasoning about concurrent programs.


title: Fire Smoke Detection in Video Images Using Kalman Filter and Gaussian Mixture Color Model

abstract: Fire smoke detections are crucial for forest resource protections and public security in surveillance systems. A novel approach for smoke detections with combined Kalman filter and a Gaussian color model is proposed in the paper in open areas. Moving objects are firstly generated by image subtractions from adaptive background of a scene through Kalman filter and MHI(Moving History Image) analysis. Then a Gaussian color model, trained from samples offline by an EM algorithm, is performed to detect candidate fire smoke regions. Final validation is carried out by temporal analysis of dynamic features of suspected smoke areas where higher frequency energies in wavelet domains and color blending coefficients are utilized as smoke features. Experimental results show the proposed method is capable of detecting fire smoke reliably.


title: A wearable microstrip patch antenna for detecting brain cancer

abstract: This paper represents a novel design of wearable pentagon shaped microstrip patch antenna which operates at Industrial, Scientific and Medical (ISM) band (2.4–2.4835GHz) for detecting brain cancer. ISM band is selected for its flexibility toward larger bandwidth. The antenna is designed with a feature that its thickness and dimension is small which makes it practically wearable on human head. This antenna is designed on FR-4 substrate with the thickness of 0.8 mm and the relative permittivity of 4.3 F/m. The real size of the antenna is (33×23×1). Taking into consideration of the patient safety the antenna is surfaced on the human head phantom model which consists of six homogeneous layers that are skin, fat, skull, Dura, cerebrospinal fluid (CSF) and brain respectively using CST microwave studio. The designed wearable antenna observes the return loss, voltage standing wave ratio (VSWR), Radiation pattern in human head phantom model and the results are compared with cancerous tumor consisting head model that carries a 5 mm tumor inside human brain. It is prevised that the proposed antenna is to be functioned in ISM band under IEEE standard safety regulation.


title: Accurate Orientation Estimation Using AHRS under Conditions of Magnetic Distortion

abstract: Low cost, compact attitude heading reference systems (AHRS) are now being used to track human body movements in indoor environments by estimation of the 3D orientation of body segments. In many of these systems, heading estimation is achieved by monitoring the strength of the Earth's magnetic field. However, the Earth's magnetic field can be locally distorted due to the proximity of ferrous and/or magnetic objects. Herein, we propose a novel method for accurate 3D orientation estimation using an AHRS, comprised of an accelerometer, gyroscope and magnetometer, under conditions of magnetic field distortion. The system performs online detection and compensation for magnetic disturbances, due to, for example, the presence of ferrous objects. The magnetic distortions are detected by exploiting variations in magnetic dip angle, relative to the gravity vector, and in magnetic strength. We investigate and show the advantages of using both magnetic strength and magnetic dip angle for detecting the presence of magnetic distortions. The correction method is based on a particle filter, which performs the correction using an adaptive cost function and by adapting the variance during particle resampling, so as to place more emphasis on the results of dead reckoning of the gyroscope measurements and less on the magnetometer readings. The proposed method was tested in an indoor environment in the presence of various magnetic distortions and under various accelerations (up to 3 g). In the experiments, the proposed algorithm achieves <2° static peak-to-peak error and <5° dynamic peak-to-peak error, significantly outperforming previous methods.


title: 3D active and passive geolocation and tracking of Unmanned Aerial Systems

abstract: Defending an area of interest against small, inexpensive Unmanned Aerial Systems (UASs) poses a particuler set of challenges requiring novel methods of detecting, geolocating and tracking these potential threats in highly dynamic environments. These targets of interest tend to operate in two distinctive modes: RF active and passive. In RF active mode the target is typically communicating with a ground-station or some other system. In RF passive mode, the target has usually been pre-programmed to fly to specific waypoints, guided by on-board GPS, before or during its particular mission, which may include the capture of surveillance video and storing it locally and returning to the handler for data retrieval. Certainly, there are many other perturbations of these two modes but these capture the two ends of the geo-location space. In this paper we present a unified signal processing methodology for geo-locating and tracking which addresses both these extreme target types. The algorithm has been demonstrated to be used quite effectively in a suite of inexpensive distributed RF sensors. The general approach for RF active targets, includes using multiple sensors to receive the emitted RF signals directly from the target while for RF passive targets, a separate transmitter is used to illuminate the target, creating a multistatic radar capture. For both target types the distributed set of sensors jointly generate the Time Difference of Arrival (TDOA), Frequency Difference of Arrival (FDOA), and Frequency Rate Difference of Arrival (FRDOA) from directly received signals from RF Active targets or from reflected energy from RF Passive targets. From the TDOA, FDOA, and FRDOA, whether generated from a direct emitter or from a reflected target we demonstrate a distributed algorithm for geo-locating and tracking. This paper focusing on presenting the unified signal processing approach for accurately detecting, geolocating and tracking both active and passive targets as well as presenting simulation results demonstrating the performance of the algorithm for both target types.


title: SloMo: Downclocking WiFi Communication

abstract: As manufacturers continue to improve the energy efficiency of battery-powered wireless devices, WiFi has become one of—if not the—most significant power draws. Hence, modern devices fastidiously manage their radios, shifting into low-power listening or sleep states whenever possible. The fundamental limitation with this approach, however, is that the radio is incapable of transmitting or receiving unless it is fully powered. Unfortunately, applications found on today’s wireless devices often require frequent access to the channel. We observe, however, that many of these same applications have relatively low bandwidth requirements. Leveraging the inherent sparsity in Direct Sequence Spread Spectrum (DSSS) modulation, we propose a transceiver design based on compressive sensing that allows WiFi devices to operate their radios at lower clock rates when receiving and transmitting at low bit rates, thus consuming less power. We have implemented our 802.11b-based design in a software radio platform, and show that it seamlessly interacts with existing WiFi deployments. Our prototype remains fully functional when the clock rate is reduced by a factor of five, potentially reducing power consumption by over 30%.


title: Molecular and clinical studies in 8 patients with Temple syndrome

abstract: Temple syndrome (TS14, #616222) is a rare imprinting disorder characterised by phenotypic features including pre- and postnatal growth retardation, muscular hypotonia and feeding difficulties in infancy, early puberty and short stature with small hands and feet and often truncal obesity. It is caused by maternal uniparental disomies, paternal deletions and primary imprinting defects that affect the chromosomal region 14q32 and lead to a disturbed expression of imprinted genes in this region. Here, we present detailed clinical data of 8 patients with Temple syndrome, 4 with an imprinting defect, 2 with an imprinting defect in a mosaic state as well as 1 complete and 1 segmental maternal uniparental disomy of chromosome 14.


title: A 650 V, 3 A three-phase fully-integrated BLDC motor driver with charge pump and level shifters

abstract: A 650 V three-phase IGBT motor driver is presented in this article. Apart from the power transistors and predrivers, also freewheeling diodes are present in the output power stage to drive inductive loads. The 15 V overdrive voltage as well as all other floating supplies are generated on-chip by means of a charge pump and cascoded current mirrors. The input signals for all switches are at ground level and level-shifted internally. Each half-bridge is able to switch at 20 kHz a 1.5 A current at 600 V and a 3 A current at 300 V. The ground connection contains a current shunt and a sense-amplifier, which can be used as a feedback signal in the BLDC control loop. The system is implemented in a 1 µm SoI technology with 650 V IGBT transistors.


title: Bayesian inference for psychology. Part II: Example applications with JASP

abstract: Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.


title: Automated timetabling for small colleges and high schools using huge integer programs

abstract: We formulate an integer program to solve a highly constrained academic timetabling problem at the United States Merchant Marine Academy. The IP instance that results from our real case study has approximately both 170,000 rows and columns and solves to optimality in 4--24 hours using a commercial solver on a portable computer (near optimal feasible solutions were often found in 4--12 hours). Our model is applicable to both high schools and small colleges who wish to deviate from group scheduling. We also solve a necessary preprocessing student subgrouping problem, which breaks up big groups of students into small groups so they can optimally fit into small capacity classes.


title: Software defined RADAR a state of the art

abstract: A software-defined radar is a versatile radar system, where most of the processing, like signal generation, filtering, up-and down conversion etc. is performed by a software. This paper presents a state of the art of software-defined radar technology. It describes the design concept of software-defined radars and the two possible implementations. A global assessment is presented, and the link with the Cognitive Radar is explained.


title: A low dropout, CMOS regulator with high PSR over wideband frequencies

abstract: Modern system-on-chip (SoC) environments are swamped in high frequency noise that is generated by RF and digital circuits and propagated onto supply rails through capacitive coupling. In these systems, linear regulators are used to shield noise-sensitive analog blocks from high frequency fluctuations in the power supply. The paper presents a low dropout regulator that achieves power supply rejection (PSR) better than -40 dB over the entire frequency spectrum. The system has an output voltage of 1.0 V and a maximum current capability of 10 mA. It consists of operational amplifiers (op amps), a bandgap reference, a clock generator, and a charge pump and has been designed and simulated using BSIM3 models of a 0.5 /spl mu/m CMOS process obtained from MOSIS.


title: Manipulator and object tracking for in-hand 3D object modeling

abstract: Recognizing and manipulating objects is an important task for mobile robots performing useful services in everyday environments. While existing techniques for object recognition related to manipulation provide very good results even for noisy and incomplete data, they are typically trained using data generated in an offline process. As a result, they do not enable a robot to acquire new object models as it operates in an environment. In this paper we develop an approach to building 3D models of unknown objects based on a depth camera observing the robot's hand while moving an object. The approach integrates both shape and appearance information into an articulated Iterative Closest Point approach to track the robot's manipulator and the object. Objects are modeled by sets of surfels, which are small patches providing occlusion and appearance information. Experiments show that our approach provides very good 3D models even when the object is highly symmetric and lacks visual features and the manipulator motion is noisy. Autonomous object modeling represents a step toward improved semantic understanding, which will eventually enable robots to reason about their environments in terms of objects and their relations rather than through raw sensor data.


title: HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors

abstract: In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due to the recent improvements in local descriptors obtained by learning them from large annotated datasets. Therefore, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and classification. This allows for more realistic, and thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation.


title: Twitter: who gets caught? observed trends in social micro-blogging spam

abstract: Spam in Online Social Networks (OSNs) is a systemic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors, as well as negatively affecting users' engagement. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics (i.e., profile properties and social interactions). Our analysis includes over 100 million tweets collected over the course of one month, generated by approximately 30 million distinct user accounts, of which over 7% are suspended or removed due to abusive behaviors and other violations. We show that there exist two behaviorally distinct categories of twitter spammers and that they employ different spamming strategies. The users in these two categories demonstrate different individual properties as well as social interaction patterns. As the Twitter spammers continuously keep creating newer accounts upon being caught, a behavioral understanding of their spamming behavior will be vital in the design of future social media defense mechanisms.


title: Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

abstract: This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [5], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets. Finally, we establish the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks [13].