title: Novel Modified Playfair Cipher using a SquareMatrix

abstract: This paper is about encryption and decryption of text using a secret password provided by the user. The encryption machine takes the password and source message as input and generates acipher text based on Modified Playfair Algorithm using dynamic rectangular matrix. The decryption machine takes the same password and the cipher text generated by the encryption engine as input to produce the original message. It also checks if the input is valid and blocks the user when an invalid input is provided. In this paper the entire program will be simulated by using an PHP application.


Isometric Functional Calculus on the Bidisk and Invariant Subspaces


title: The relation between length of institutionalization and sensory integration in children adopted from Eastern Europe.

abstract: OBJECTIVE. To examine the relation between length of institutionalization and sensory integration in children adopted from Eastern Europe. METHOD. The Sensory Integration and Praxis Tests (SIPT) and the Developmental and Sensory Processing Questionnaire were administered to 60 adopted children, 4 years to 8 years, 11 months of age. Thirty had longer institutionalization histories (mean: 34 months; LIH group) and the other 30 had shorter institutionalization histories (mean: 3 months; SIH group). RESULTS. The LIH group demonstrated significantly lower scores than the SIH group on the SIPT in vestibular-proprioceptive, visual, and praxis areas, and effect sizes ranged from .09 to 1.13. The LIH group also had significantly more frequent behaviors suggestive of sensory modulation dysfunction compared to the SIH group, particularly in touch and movement seeking, vision, and audition. Effect sizes ranged from 0 to 1.39. CONCLUSION. Longer lengths of institutionalization are associated with more atypical sensory discrimination, praxis, and sensory modulation scores in children adopted from Eastern European orphanages. The areas of sensory integration that appear to be more vulnerable to deprived conditions in early childhood are vestibular-proprioceptive, tactile, visual, auditory, and praxis. Adopted children with lengthy periods of institutionalization may benefit from occupational therapy for early sensory integrative and developmental screenings.


title: The influence of moisture on the electrical properties of crosslinked polyethylene/silica nanocomposites

abstract: Crosslinked polyethylene (XLPE)/silica nanocomposites are promising candidates for future cable insulation. While a significant number of studies have demonstrated improved dielectric properties in nanocomposites compared to XLPE, the performance of polyolefin nanocomposites in humid environments has not received much attention. This paper presents and explains the dielectric behavior of XLPE/silica nanocomposites in humid environments such as the decrease in AC breakdown strength, increases in loss and space charge formation, and the significant reduction in water tree aging. XLPE/silica nanocomposites are found to have an increased moisture uptake compared to the XLPE base polymer due to inclusion of silica particles. It is hypothesized that the formation of a concentric shell surrounding the particle with a high concentration of water (water shell), and the change in the inter-particle/cluster distances are two major factors governing the dielectric behavior in wet XLPE/silica nanocomposites. The dispersion and distribution of the nanofillers were quantified using a new tool and a method to reconstruct the 3D structure was used to determine the size of the water shell required for percolation. It was found that a water layer thickness of tens of nanometers could initiate percolation in the XLPE/silica nanocomposites studied. Notwithstanding that, water tree growth was substantially reduced in the XLPE/silica nanocomposites, and some speculative explanations are provided on the basis of the characteristics observed.


title: Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets

abstract: With the rapid advance of the Internet, search engines (e.g., Google, Bing, Yahoo!) are used by billions of users for each day. The main function of a search engine is to locate the most relevant webpages corresponding to what the user requests. This report focuses on the core problem of information retrieval: how to learn the relevance between a document (very often webpage) and a query given by user. Our analysis consists of two parts: 1) we use standard statistical methods to select important features among 137 candidates given by information retrieval researchers from Microsoft. We find that not all the features are useful, and give interpretations on the top-selected features; 2) we give baselines on prediction over the real-world dataset MSLR-WEB by using various learning algorithms. We find that models of boosting trees, random forest in general achieve the best performance of prediction. This agrees with the mainstream opinion in information retrieval community that tree-based algorithms outperform the other candidates for this problem.


title: A Study on Social Network Metrics and Their Application in Trust Networks

abstract: Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networks, micro logging, or customer review sites. Such media often serve as platforms for information dissemination and product placement or promotion. In this environment, influence and trust are becoming essential qualities among user interactions. In this work, we perform an extensive study of various metrics related to the aforementioned elements, and their effect in the process of information propagation in the virtual world. In order to better understand the properties of links and the dynamics of social networks, we distinguish between permanent and transient links and in the latter case, we consider the link freshness. Moreover, we distinguish between local and global influence and compare suggestions provided by locally or globally trusted users.


title: Efficient simplification of point-sampled surfaces

abstract: In this paper we introduce, analyze and quantitatively compare a number of surface simplification methods for point-sampled geometry. We have implemented incremental and hierarchical clustering, iterative simplification, and particle simulation algorithms to create approximations of point-based models with lower sampling density. All these methods work directly on the point cloud, requiring no intermediate tesselation. We show how local variation estimation and quadric error metrics can be employed to diminish the approximation error and concentrate more samples in regions of high curvature. To compare the quality of the simplified surfaces, we have designed a new method for computing numerical and visual error estimates for point-sampled surfaces. Our algorithms are fast, easy to implement, and create high-quality surface approximations, clearly demonstrating the effectiveness of point-based surface simplification.


title: The Impact-on-Family Scale : A Test of Invariance Across Culture

abstract: Objective: To test the cultural invariance of the Impact-on-Family Scale in order to make cross-cultural comparisons. Methods: The Italian version of the scale was administered to mothers of children with chronic illnesses. Factorial invariance was examined to investigate whether the four factors found with the original United States (U.S.) scale could be replicated. Results: The results clearly demonstrate the replicability of the first three factors, Financial Burden, Familial/ Social Impact, and Personal Strain. In addition, internal consistency and the homogeneity of the items of the corresponding scales are satisfactory. However, the fourth factor, Mastery, could not be replicated, and the reliability of the corresponding scale is poor. Italian mothers scored significantly lower on the Financial Burden and on the Familial/Social Impact dimension, compared to the American sample. Conclusions: Three of the four factors of the Impact-on-Family Scale are useful for cross-cultural comparisons between U.S. and Italian samples.


Demo : Fall Detection Using 3 D Head Trajectory Extracted From a Single Camera Video Sequence


title: Secure cloud storage meets with secure network coding

abstract: This paper reveals an intrinsic relationship between secure cloud storage and secure network coding for the first time. Secure cloud storage was proposed only recently while secure network coding has been studied for more than ten years. Although the two areas are quite different in their nature and are studied independently, we show how to construct a secure cloud storage protocol given any secure network coding protocol. This gives rise to a systematic way to construct secure cloud storage protocols. Our construction is secure under a definition which captures the real world usage of the cloud storage. Furthermore, we propose two specific secure cloud storage protocols based on two recent secure network coding protocols. In particular, we obtain the first publicly verifiable secure cloud storage protocol in the standard model. We also enhance the proposed generic construction to support user anonymity and third-party public auditing, which both have received considerable attention recently. Finally, we prototype the newly proposed protocol and evaluate its performance. Experimental results validate the effectiveness of the protocol.


title: Inferring Decision Trees Using the Minimum Description Length Principle *

abstract: Abstract We explore the use of Rissanen's minimum description length principle for the construction of decision trees. Empirical results comparing this approach to other methods are given.


title: Open Mind Common Sense: Knowledge Acquisition from the General Public

abstract: Open Mind Common Sense is a knowledge acquisition system designed to acquire commonsense knowledge from the general public over the web. We describe and evaluate our first fielded system, which enabled the construction of a 450,000 assertion commonsense knowledge base. We then discuss how our second-generation system addresses weaknesses discovered in the first. The new system acquires facts, descriptions, and stories by allowing participants to construct and fill in natural language templates. It employs word-sense disambiguation and methods of clarifying entered knowledge, analogical inference to provide feedback, and allows participants to validate knowledge and in turn each other.


title: ViBe: A Universal Background Subtraction Algorithm for Video Sequences

abstract: This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.


title: Mining reference tables for automatic text segmentation

abstract: Automatically segmenting unstructured text strings into structured records is necessary for importing the information contained in legacy sources and text collections into a data warehouse for subsequent querying, analysis, mining and integration. In this paper, we mine tables present in data warehouses and relational databases to develop an automatic segmentation system. Thus, we overcome limitations of existing supervised text segmentation approaches, which require comprehensive manually labeled training data. Our segmentation system is robust, accurate, and efficient, and requires no additional manual effort. Thorough evaluation on real datasets demonstrates the robustness and accuracy of our system, with segmentation accuracy exceeding state of the art supervised approaches.


title: YOLO9000: Better, Faster, Stronger

abstract: We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that dont have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. YOLO9000 predicts detections for more than 9000 different object categories, all in real-time.


title: Synthetic aperture photography using a moving camera-IMU system

abstract: Occlusion poses as a critical challenge in computer vision for a long time. Recently, the technique of synthetic aperture photography using a camera array has been regarded as a promising way to address the problem of occluded object imaging. Unfortunately, the expensive cost of a standard camera array system with the required calibration procedure still limits the widespread popularity of this technique. To make synthetic aperture photography practical, in this paper, we propose a novel approach based on a mobile camera-IMU system moving on a planar surface. The contributions of our work include: (1) with geometric property assumptions, we mathematically prove that the transformation matrices for synthetic aperture image generation can be factorized into an infinite homography and a shift; (2) the IMU data are utilized to estimate the homography by selecting the plane at infinity as the reference plane, which is scene independent and does not require any calibration; (3) compared to the conventional camera array based approaches, the proposed method effectively improves the solution for synthetic aperture images capturing and generation by using a more flexible, convenient and inexpensive system. Extensive experimental results with qualitative and quantitative evaluations have demonstrated the see-through-occlusion performance with satisfactory accuracy of the proposed approach in different scenarios. HighlightsThe first method that uses camera and IMU data to generate synthetic aperture images.Analytical factorization of the problem into two parts and solved by separate data.Totally scene independent homography estimation based on IMU data.Avoids complicated optimization and reduces computational complexity.Provides user a more flexible and inexpensive system than conventional methods.


Why Are Consumers Willing to Pay for Privacy? An Application of the Privacy-freemium Model to Media Companies


title: 2.2 GHz LC VCO for RFID on a 0.5-μm digital gate-array designed for ultra-thin silicon substrates

abstract: A new technology provides opportunities in the implementation of radio frequency identification (RFID) tags. A test chip comprising four transmitter circuits is implemented on a 0.5-μm gate array designed for flexible ultra-thin silicon substrates (20 μm). Two transmitters are based on the cross-coupled oscillator topology and two are based on ring oscillators. The main focus of this paper is the cross-coupled voltage controlled oscillator (VCO) core that operates at 2.2 GHz with a 20% tuning ratio. It features a four band frequency tuning characteristic utilizing a binary-weighted switched capacitor bank and metal-oxide semiconductor (MOS) varactors. Power consumption of the VCO core is 20 mW with a tail current of 5 mA and a signal swing equal to 588 mV. Simulated phase noise is −65 dBc/Hz at 100 kHz offset.


title: Video Summarization with Attention-Based Encoder-Decoder Networks

abstract: This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named Attentive encoder-decoder networks for Video Summarization (AVS), in which the encoder uses a Bidirectional Long Short-Term Memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on three video summarization benchmark datasets, i.e., SumMe, and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches,with remarkable improvements from 0.8% to 3% on two datasets,respectively..


title: High Dimensional Spaces, Deep Learning and Adversarial Examples

abstract: In this paper, we analyze deep learning from a mathematical point of view and derive several novel results. The results are based on intriguing mathematical properties of high dimensional spaces. We first look at perturbation based adversarial examples and show how they can be understood using topological arguments in high dimensions. We point out fallacy in an argument presented in a published paper in 2015 by Goodfellow et al., see reference, and present a more rigorous, general and correct mathematical result to explain adversarial examples in terms of image manifolds. Second, we look at optimization landscapes of deep neural networks and examine the number of saddle points relative to that of local minima. Third, we show how multi-resolution nature of images explains perturbation based adversarial examples in form of a stronger result. Our results state that expectation of $L_2$-norm of adversarial perturbations shrinks to 0 as image resolution becomes arbitrarily large. Finally, by incorporating the parts-whole manifold learning hypothesis for natural images, we investigate the working of deep neural networks and root causes of adversarial examples and discuss how future improvements can be made and how adversarial examples can be eliminated.


title: Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers

abstract: The decomposition-based multiobjective evolutionary algorithms (MOEAs) generally make use of aggregation functions to decompose a multiobjective optimization problem into multiple single-objective optimization problems. However, due to the nature of contour lines for the adopted aggregation functions, they usually fail to preserve the diversity in high-dimensional objective space even by using diverse weight vectors. To address this problem, we propose to maintain the desired diversity of solutions in their evolutionary process explicitly by exploiting the perpendicular distance from the solution to the weight vector in the objective space, which achieves better balance between convergence and diversity in many-objective optimization. The idea is implemented to enhance two well-performing decomposition-based algorithms, i.e., MOEA, based on decomposition and ensemble fitness ranking. The two enhanced algorithms are compared to several state-of-the-art algorithms and a series of comparative experiments are conducted on a number of test problems from two well-known test suites. The experimental results show that the two proposed algorithms are generally more effective than their predecessors in balancing convergence and diversity, and they are also very competitive against other existing algorithms for solving many-objective optimization problems.


title: RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles

abstract: Recently, several large-scale RDF knowledge bases have been built and applied in many knowledge-based applications. To further increase the number of facts in RDF knowledge bases, logic rules can be used to predict new facts based on the existing ones. Therefore, how to automatically learn reliable rules from large-scale knowledge bases becomes increasingly important. In this paper, we propose a novel rule learning approach named RDF2Rules for RDF knowledge bases. RDF2Rules first mines frequent predicate cycles (FPCs), a kind of interesting frequent patterns in knowledge bases, and then generates rules from the mined FPCs. Because each FPC can produce multiple rules, and effective pruning strategy is used in the process of mining FPCs, RDF2Rules works very efficiently. Another advantage of RDF2Rules is that it uses the entity type information when generates and evaluates rules, which makes the learned rules more accurate. Experiments show that our approach outperforms the compared approach in terms of both efficiency and accuracy.


title: Generalized Grid Framework for multi sensor data fusion

abstract: This paper presents a methodology for multi sensor data fusion that uses the accumulation grid idea for the representation of data. A generalized grid framework is introduced to represent measurement data and fusion results in a common way. This allows the definition of standardized prediction and fusion operations and includes the variation between Cartesian and polar grids as well as the extension to 3D grids. It is shown that most of the typical object detection and tracking schemas can be represented by the framework. Two application examples including processing results from the field of automotive environment recognition are given to demonstrate special cases of the framework.


title: A Conceptual Framework Based on Activity Theory for Mobile CSCL.

abstract: There is a need for collaborative group activities that promote student social interaction in the classroom. Handheld computers interconnected by a wireless network allow people who work on a common task to interact face to face while maintaining the mediation afforded by a technology-based system. Wirelessly interconnected handhelds open up new opportunities for introducing collaboration and thereby changing classroom pedagogical practices. We present a conceptual framework and a method for the design of a mobile computer-supported collaborative learning system based on Activity Theory. An instance of the framework for teaching basic mathematics skills was evaluated with 24 6- and 7-year-old children in a month-long study. Positive effects were observed on student social interaction, motivation and learning.


LIP ACTIVITY DETECTION FOR TALKING FACES CLASSIFICATION IN TV-CONTENT


title: Examining playfulness in adults: Testing its correlates with personality, positive psychological functioning, goal aspirations, and multi-methodically assessed ingenuity

abstract: The prime aim of this set of studies was to test the disposition to play (playfulness) in adults in its relation with various measures of personality but also ability (self-estimated but also psychometrically measured ingenuity). Study 1 (n = 180) shows that adults playfulness relates primarily to extraversion, lower conscientiousness, and higher endorsements of culture; joy of being laughed at (gelotophilia) and agreeableness were also predictive in a regression analysis; Study 2 (n = 264) shows that playfulness relates primarily to a high expectation of intrinsic and a low expectation of extrinsic goals as well as greater intrinsic and lower extrinsic importance of goals (for expressive and fun-variants of playfulness); Study 3 (n = 212) shows that playfulness relates to greater selfperception of one’s degree of ingenuity and psychometric ingenuity correlated primarily with greater spontaneous and creative variants of playfulness (in about the same range for origence and fluidity of the productions). Overall, the findings were in line with the expectations and could stimulate further studies of playfulness in adults.


title: Corona: Interactivity of Body Electrostatics in Mobile Scenarios Using Wearable High-Voltage Static Charger

abstract: We present Corona, a wearable device that allows constant high-voltage electrostatic charge to be continuously accumulated in the human body. We propose the usages of Corona for three basic functions; generating haptic sensations, generating electric power from body static charge and near-body electric field, and inducing physical force near the body. We describe detailed principle of operation, analysis of produced energy and force, discussion on safety issues, as well as demonstration of proof-of-concept applications for aforementioned basic functions. We conclude with discussion of our experiments using the prototype and applications, which also involves a study to gather user feedbacks. To the best of our knowledge, Corona is the first work to exploit continuous high-voltage static charge on the human body for Human-Computer Interaction purposes.


title: The Effect of Ad Claims and Ad Context on Attitude toward the Advertisement

abstract: Abstract This study examines the effect of one content and two contextual advertising manipulations on several measures of attitude toward the ad (Aad). Results indicate that such antecedent variables, each of which requires some degree of cognitive processing of information, can impact Aad in excess of their effects on perceptions of the value of the deal. Results also show that Aad is a significant predictor of attitude toward the deal after covarying out the experimental manipulations and perceptions of the value of the deal. Findings suggest that separate measures of cognitive and affective dimensions of Aad may be more appropriate than the single composite measures which have typically been employed by Aad researchers.


EXPLORING THE MOTIVATIONS AND BARRIERS FOR SECOND HAND PRODUCT CONSUMPTION


title: Nonlinear Analogue of the May-Wigner Instability Transition

abstract: We study a system of [Formula: see text] degrees of freedom coupled via a smooth homogeneous Gaussian vector field with both gradient and divergence-free components. In the absence of coupling, the system is exponentially relaxing to an equilibrium with rate μ We show that, while increasing the ratio of the coupling strength to the relaxation rate, the system experiences an abrupt transition from a topologically trivial phase portrait with a single equilibrium into a topologically nontrivial regime characterized by an exponential number of equilibria, the vast majority of which are expected to be unstable. It is suggested that this picture provides a global view on the nature of the May-Wigner instability transition originally discovered by local linear stability analysis.


title: Exploring many-core architecture design space for parallel discrete event simulation

abstract: As multicore and manycore processor architectures are emerging and the core counts per chip continue to increase, it is important to evaluate and understand the performance and scalability of Parallel Discrete Event Simulation (PDES) on these platforms. Most existing architectures are still limited to a modest number of cores, feature simple designs and do not exhibit heterogeneity, making it impossible to perform comprehensive analysis and evaluations of PDES on these platforms. Instead, in this paper we evaluate PDES using a full-system cycle-accurate simulator of a multicore processor and memory subsystem. With this approach, it is possible to flexibly configure the simulator and perform exploration of the impact of architecture design choices on the performance of PDES. In particular, we answer the following four questions with respect to PDES performance and scalability: (1) For the same total chip area, what is the best design point in terms of the number of cores and the size of the on-chip cache? (2) What is the impact of using in-order vs. out-of-order cores? (3) What is the impact of a heterogeneous system with a mix of in-order and out-of-order cores? (4) What is the impact of object partitioning on PDES performance in heterogeneous systems? To answer these questions, we use MARSSx86 simulator for evaluating performance, and rely on Cacti and McPAT tools to derive the area and latency estimates for cores and caches.


title: Analyzing the Targets of Hate in Online Social Media

abstract: Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.


title: Multi-Antenna Wireless Legitimate Surveillance Systems: Design and Performance Analysis

abstract: To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious communication link between a suspicious pair via proactive jamming. Assuming that the legitimate monitor can successfully overhear the suspicious information only when its achievable data rate is no smaller than that of the suspicious receiver, the key objective is to maximize the eavesdropping non-outage probability by joint design of the jamming power, receive and transmit beamformers at the legitimate monitor. Depending on the number of receive/transmit antennas implemented, i.e., single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output (MIMO), four different scenarios are investigated. For each scenario, the optimal jamming power is derived in a closed form and efficient algorithms are obtained for the optimal transmit/receive beamforming vectors. Moreover, low-complexity suboptimal beamforming schemes are proposed for the MIMO case. Our analytical findings demonstrate that by exploiting multiple antennas at the legitimate monitor, the eavesdropping non-outage probability can be significantly improved compared with the single-antenna case. In addition, the proposed suboptimal transmit zero-forcing scheme yields similar performance as the optimal scheme.


title: Challenge and warpage optimization of thermal compression bonding technology on coreless substrates

abstract: Recently, Flip Chip Packages provide the ideal solution for low to high I/O, high electrical performance demand in handheld devices where high frequency, high speed are required. To approach these requirements, we present an alternative fine bump pitch solution of low cost and high throughput which used thermal compression bonding (TCB) combining with molding underfill (MUF) technology and comparing with traditional mass reflow (MR). As we known MR is the lowest cost and mature process in general. By using coreless substrate with embedded fine-trace substrate (ETS) technology to achieve package miniaturization requirement. Comparing with conventional substrate, coreless substrate technology eliminates the substrate core, and utilize build-up layers to interconnect the chip and the PCB board. It brings about not only low Z-height, lightweight, but also short interconnection distance and good power integrity. The ETS coreless technology is a promising solution for the next generation substrate. A Flip Chip Scale Package (FCCSP) and ETS coreless substrate combination are used to this paper experiment. The major challenge is thinner ETS coreless substrate to induce warpage concern which because of no core material as supporting material, also ETS coreless substrate no rigid product characteristic and easy to bring handling issue during manufacturing and assembly process. Thermal compression bonding could offer less thermal budget and stress to mitigate CTE mismatch between silicon chip and coreless substrate. The study result of warpage control between TCB and mass reflow process, the TCB could be provided less warpage behavior after compared to traditional mass reflow, also this TCB technology is popular used for thin core, coreless and advance silicon node product field. The characterization analysis will utilize simulation methodology & typical reliability testing (Temperature Cycle Test, un-bias HAST and High Temperature Storage Test) results as a verification monitor items for TCB process with ETS coreless structure feasibility evaluation. Finally, this paper will find out the suitable key process index for future product application.


title: Frequency compensation techniques for op-amps and LDOs: A tutorial overview

abstract: Today's op-amp is not just a stand-alone IC, rather it is more custom and complex, catering the needs of highly integrated SoC. Tighter line and load regulation, low quiescent current operation, capacitor-free and wide-range output capacitor specifications are some of the contradicting requirements in an LDO, which drive newer topologies and newer frequency compensation techniques. The objective of this paper is to provide a tutorial treatment of some of the basics and recent advances in frequency compensation. Transistor level implementation of efficient LHP zero techniques and design examples are detailed.


title: Bit-Interleaved Coded Modulation (BICM) for ATSC 3.0

abstract: In this paper, we summarize and expound upon the choices made for the bit-interleaved coded modulation (BICM) part of the next-generation terrestrial broadcast standard known as ATSC 3.0. The structure of the ATSC 3.0 BICM consists of a forward error correcting code, bit interleaver, and constellation mapper. In order to achieve high efficiency over a wide range of reception conditions and carrier-to-noise (C/N) ratio values, several notable new elements have been standardized. First, 24 original low-density parity check (LDPC) codes have been designed, with coding rates from 2/15 (0.13) up to 13/15 (0.87) for two code sizes: 16 200 bits and 64 800 bits. Two different LDPC structures have been adopted; one structure more suited to medium and high coding rates and another structure suited to very low coding rates. Second, in addition to quaternary phase shift keying, non-uniform constellations (NUCs) have been chosen for constellation sizes from 16QAM to 4096QAM to bridge the gap to the Shannon theoretical limit. Two different types of NUCs have been proposed: 1-D NUCs for 1024- and 4096-point constellations, and 2-D-NUCs for 16-, 64-, and 256-point constellations. 2-D-NUCs achieve a better performance than 1-D-NUCs but with a higher complexity since they cannot be separated into two independent I/Q components. NUCs have been optimized for each coding rate for the 64 800 bits LPDCs. The same constellations are used for 16 200 bits LDPCs, although they have been limited up to 256QAM. Finally, a bit interleaver, optimized for each NUC/coding rate combination, has been designed to maximize the performance. The result is a BICM that provides the largest operating range (more than 30 dB, with the most robust mode operating below -5 dB C/N) and the highest spectral efficiency compared to any digital terrestrial broadcast system today, outperforming the current state-of-the-art DVB-T2 standard BICM by as much as 1 dB in some cases. ATSC 3.0 will also provide a considerable increase in the maximum transmission capacity when using the high-order NUCs such as 1024QAM and 4096QAM, which will represent a major milestone for terrestrial broadcasting since the highest order constellation currently available is uniform 256QAM. This paper describes the coding, modulation, and bit interleaving modules of the BICM block of ATSC 3.0 and compares its performance with other DTT standards such as ATSC A/53 and DVB-T2.


title: Comparison of the pharmacokinetics of imipenem after intravenous and intrathecal administration in rabbits.

abstract: BACKGROUND ::: Intrathecal administration of antibiotics has potentially high effectiveness for the treatment for severe intracranial infections, particularly nosocomial meningitis. The use of intrathecal injection of antibiotics has been reported mostly in case reports. However, there is sparse data regarding the pharmacokinetics of antibiotics after intrathecal administration. ::: ::: ::: AIM ::: This study investigated whether intrathecal injection is an effective method for the administration of imipenem. ::: ::: ::: MATERIALS AND METHODS ::: The pharmacokinetics of imipenem after intrathecal and intravenous administration of 1:1 imipenem: cilastatin (IMI/CIL) to rabbits were compared. ::: ::: ::: RESULTS ::: The AUC0-t in the cerebrospinal fluid for intrathecal administration was approximately twice that of an equal dose of intravenous administration at doses of 0.35, 0.7, and 1.4 mg/kg. Brain concentrations of imipenem after intrathecal injection were three times greater than observed after intravenous injection and remained high for at least 8 hours post-injection. Elimination of imipenem after administration by either route was primarily via urine, but a transient surge of imipenem in bile and intestinal tissue was observed. ::: ::: ::: CONCLUSIONS ::: Results indicate that there is a clinical potential for intrathecally administered IMI/CIL. Further studies are warranted to investigate the potential for seizure and to assess the translatability of the rabbit model to human treatment.


title: Gesture recognition in smart home using passive RFID technology

abstract: Gesture recognition is a well-establish topic of research that is widely adopted for a broad range of applications. For instance, it can be exploited for the command of a smart environment without any remote control unit or even for the recognition of human activities from a set of video cameras deployed in strategic position. Many researchers working on assistive smart home, such as our team, believe that the intrusiveness of that technology will prevent the future adoption and commercialization of smart homes. In this paper, we propose a novel gesture recognition algorithm that is solely based on passive RFID technology. This technology enables the localization of small tags that can be embedded in everyday life objects (a cup or a book, for instance) while remaining non intrusive. However, until now, this technology has been largely ignored by researchers on gesture recognition, mostly because it is easily disturbed by noise (metal, human, etc.) and offer limited precision. Despite these issues, the localization algorithms have improved over the years, and our recent efforts resulted in a real-time tracking algorithm with a precision approaching 14cm. With this, we developed a gesture recognition algorithm able to perform segmentation of gestures and prediction on a spatio-temporal data series. Our new model, exploiting works on qualitative spatial reasoning, achieves recognition of 91%. Our goal is to ultimately use that knowledge for both human activity recognition and errors detection.


title: Power Electronics Technologies for Railway Vehicles

abstract: This paper describes the current range of advanced IGBT propulsion inverters used in railways from light rail vehicles to high speed trains & locomotives. The second part of the paper describes future trends in IGBT railway propulsion drives. One such trend is the concept of power integration which leads to weight, volume and cost reduction . Finally, for systems with an AC input supply, a concept for reducing the weight of the main input transformer is also described. This uses a configuration of resonant converter and a medium frequency transformer , the so called e-transformer, and could be particularly targeted for 15 kV, 16.7 Hz supplied systems.


title: Multimodal Face-Pose Estimation With Multitask Manifold Deep Learning

abstract: Human face pose estimation aims at estimating the gazing direction or head postures with 2D images. It gives some very important information such as communicative gestures, saliency detection and so on, which attracts plenty of attention recently. However, it is challenging because of complex background, various orientations and face appearance visibility. Therefore, a descriptive representation of face images and mapping it to poses are critical. In this paper, we make use of multi-modal data and propose a novel face pose estimation method that uses a novel deep learning framework named Multi-task Manifold Deep Learning $M^2DL$. It is based on feature extraction with improved deep neural networks and multi-modal mapping relationship with multi-task learning. In the proposed deep learning based framework, Manifold Regularized Convolutional Layers (MRCL) improve traditional convolutional layers by learning the relationship among outputs of neurons. Besides, in the proposed mapping relationship learning method, different modals of face representations are naturally combined to learn the mapping function from face images to poses. In this way, the computed mapping model with multiple tasks is improved. Experimental results on three challenging benchmark datasets DPOSE, HPID and BKHPD demonstrate the outstanding performance of $M^2DL$.


title: Machine Learning Approach for Intrusion Detection on Cloud Virtual Machines

abstract: Development of the cloud computing in recent years is increasing rapidly and gained great success, its security issues have got more and more attention. Many challenges in cloud computation increase the threat of data and service availability. There is need of many security services in order to improve cloud security for users as well as providers. In this paper, we propose a Anomaly Intrusion Detection System using machine learning approach for virtual machines on cloud computing. Our proposal is feature selection over events from Virtual Machine Monitor to detect anomaly in parallel to training the system so it will learn new threats and update the model. The experiment has been carried out on NSL-KDD’99 datasets using Naive Bayes Tree (NB Tree) Classifier and hybrid approach of NB Tree and Random Forest.


title: A Practical Roadside Camera Calibration Method Based on Least Squares Optimization

abstract: In this paper, we propose a more practical and accurate method for calibrating the roadside camera used in traffic surveillance systems. Considering the characteristics of the traffic scenes, we propose a minimum calibration condition that consists of two vanishing points and a vanishing line, which can be easily satisfied in most traffic scenes. Based on the minimum calibration condition, we provide a calibration method to estimate camera intrinsic parameters and rotation angles, which employs least squares optimization instead of closed-form computation. Compared with the existing calibration methods, our method is suitable for more traffic scenes and is able to accurately determine more camera parameters including the principal point. By making full use of video information, multiple observations of the vanishing points are available from different objects. For more accurate calibration, we present a dynamic calibration method using these observations to correct camera parameters. As for the estimation of the camera translation vector, known lengths in the road or known heights above the road are exploited. The experimental results on synthetic data and real traffic images demonstrate the accuracy, robustness, and practicability of the proposed calibration method.


title: Evolutionary self-adaptation: a survey of operators and strategy parameters

abstract: The success of evolutionary search depends on adequate parameter settings. Ill conditioned strategy parameters decrease the success probabilities of genetic operators. Proper settings may change during the optimization process. The question arises if adequate settings can be found automatically during the optimization process. Evolution strategies gave an answer to the online parameter control problem decades ago: self-adaptation. Self-adaptation is the implicit search in the space of strategy parameters. The self-adaptive control of mutation strengths in evolution strategies turned out to be exceptionally successful. Nevertheless, for years self-adaptation has not achieved the attention it deserves. This paper is a survey of self-adaptive parameter control in evolutionary computation. It classifies self-adaptation in the taxonomy of parameter setting techniques, gives an overview of automatic online-controllable evolutionary operators and provides a coherent view on search techniques in the space of strategy parameters. Beyer and Sendhoff’s covariance matrix self-adaptation evolution strategy is reviewed as a successful example for self-adaptation and exemplarily tested for various concepts that are discussed.


title: A Computational Model of Graded Cueing: Robots Encouraging Behavior Change

abstract: This work presents a model of the occupational therapy technique of graded cueing for teaching and practicing desirable health and social behaviors adapted for use in socially assistive human-machine interaction. Graded cueing is represented as a probabilistic model of first prompt choice based on the perceived user ability level. The model is used to increase imitation proficiency of children with autism spectrum disorders through a “Copy Cat” imitation game.


title: A 65 nm CMOS 4-Element Sub-34 mW/Element 60 GHz Phased-Array Transceiver

abstract: The 60GHz band has received significant attention as an enabler for multi-Gb/s wireless communication. Practical mm-Wave systems will require relatively large phased arrays in order to robustly overcome path-loss and fading issues. Despite significant progress [1,2], CMOS implementations of 60GHz phased arrays have so far been area and power hungry. This paper therefore presents a 60GHz 4-element 65nm CMOS phased-array transceiver consuming <34mW/element (including LO synthesis and distribution) that utilizes baseband (BB) phase shifting and holistic impedance optimization.


title: Combining MIC feature selection and feature-based MSPCA for network traffic anomaly detection

abstract: In this paper, we propose a network anomaly detection system which consists of a Maximal Information Coefficient based feature selection algorithm and a feature-based MSPCA detection algorithm, which can separate the anomalous information more efficiently. Maximal Information Coefficient can provide a good information measurement of any dependency between two random variables. MSPCA combines the benefit of PCA and wavelet analysis to reduce the effect of normal subspace contamination, which is the main challenge of PCA-based anomaly detection algorithm. We utilize multiple network flow features to describe the network traffic instead of using only volumes. To evaluate our proposed system, we test it on the DARPA 1999 dataset. The results indicate a large improvement when using our method compared to PCA-based anomaly detection algorithms.


title: Minimum-time control of robotic manipulators with geometric path constraints

abstract: Conventionally, robot control algorithms are divided into two stages, namely, path or trajectory planning and path tracking (or path control). This division has been adopted mainly as a means of alleviating difficulties in dealing with complex, coupled manipulator dynamics. Trajectory planning usually determines the timing of manipulator position and velocity without considering its dynamics. Consequently, the simplicity obtained from the division comes at the expense of efficiency in utilizing robot's capabilities. To remove at least partially this inefficiency, this paper considers a solution to the problem of moving a manipulator in minimum time along a specified geometric path subject to input torque/force constraints. We first describe the manipulator dynamics using parametric functions which represent geometric path constraints to be honored for collision avoidance as well as task requirements. Second, constraints on input torques/ forces are converted to those on the parameters. Third, the minimum-time solution is deduced in an algorithm form using phase-plane techniques. Finally, numerical examples are presented to demonstrate utility of the trajectory planning method developed.


title: Multimodal presentation method for a dance training system

abstract: This paper presents a multimodal information presentation method for a basic dance training system. The system targets on beginners and enables them to learn basics of dances easily. One of the most effective ways of learning dances is to watch a video showing the performance of dance masters. However, some information cannot be conveyed well through video. One is the translational motion, especially that in the depth direction. We cannot tell exactly how far does the dancers move forward or backward. Another is the timing information. Although we can tell how to move our arms or legs from video, it is difficult to know when to start moving them. We solve the first issue by introducing an image display on a mobile robot. We can learn the amount of translation just by following the robot. We introduce active devices for the second issue. The active devices are composed of some vibro-motors and are developed to direct action-starting cues with vibration. Experimental results show the effectiveness of our multimodal information presentation method.


title: Online community search using thread structure

abstract: Online communities are valuable information sources where knowledge is accumulated by interactions between people. Search services provided by online community sites such as forums are often, however, quite poor. To address this, we investigate retrieval techniques that exploit the hierarchical thread structures in community sites. Since these structures are sometimes not explicit or accurately annotated, we use structure discovery techniques. We then make use of thread structures in retrieval experiments. Our results show that using thread structures that have been accurately annotated can lead to significant improvements in retrieval performance compared to strong baselines.


title: Efficient packet classification for network intrusion detection using FPGA

abstract: Using FPGA technology for real-time network intrusion detection has gained many research efforts recently. In this paper, a novel packet classification architecture called BV-TCAM is presented, which is implemented for an FPGA-based Network Intrusion Detection System (NIDS). The classifier can report multiple matches at gigabit per second network link rates. The BV-TCAM architecture combines the Ternary Content Addressable Memory (TCAM) and the Bit Vector (BV) algorithm to effectively compress the data representations and boost throughput. A tree-bitmap implementation of the BV algorithm is used for source and destination port lookup while a TCAM performs the lookup of the other header fields, which can be represented as a prefix or exact value. The architecture eliminates the requirement for prefix expansion of port ranges. With the aid of a small embedded TCAM, packet classification can be implemented in a relatively small part of the available logic of an FPGA. The design is prototyped and evaluated in a Xilinx FPGA XCV2000E on the FPX platform. Even with the most difficult set of rules and packet inputs, the circuit is fast enough to sustain OC48 traffic throughput. Using larger and faster FPGAs, the system can work at speeds greater than OC192.


title: Chicanos, Anglos, and Cholos: Subverting Constructions of Border Subjects in Crosthwaite’s Estrella de la calle Sexta

abstract: Abstract:This article approaches Luis Humberto Crosthwaite’s Estrella de la calle Sexta not as an anthology, as many scholars do, but as a complete work presenting the reader with diverse perspectives of the border and Tijuana, which collectively subvert traditional Anglo and Mexican representations of the border and border subjects. But it also challenges theoretical constructs of the region that ignore the materiality of Mexico’s northern borderlands. The study centers on how the text’s fragmentary structure, use of narrative voice(s), and representation of border subjects, particularly Chicanos, Anglos, and cholos, contribute to its dynamic representation of the border space of Tijuana.


title: Image Decomposition and Classification Through a Generative Model

abstract: We demonstrate in this paper that a generative model can be designed to perform classification tasks under challenging settings, including adversarial attacks and input distribution shifts. Specifically, we propose a conditional variational autoencoder that learns both the decomposition of inputs and the distributions of the resulting components. During test, we jointly optimize the latent variables of the generator and the relaxed component labels to find the best match between the given input and the output of the generator. The model demonstrates promising performance at recognizing overlapping components from the multiMNIST dataset, and novel component combinations from a traffic sign dataset. Experiments also show that the proposed model achieves high robustness on MNIST and NORB datasets, in particular for high-strength gradient attacks and non-gradient attacks.


title: Speed Error Mitigation for a DSP-Based Resolver-to-Digital Converter Using Autotuning Filters

abstract: Modern resolver-to-digital converters (RDCs) are typically implemented using DSP techniques to reduce hardware footprint and enhance system accuracy. However, in such implementations, both resolver sensor and ADC channel unbalances introduce significant errors, particularly in the speed output of the tracking loop. The frequency spectrum of the output error is variable depending on the resolver mechanical velocity. This paper presents the design of an autotuning output filter based on the interpolation of precomputed filters for a DSP-based RDC with a type-II tracking loop. A fourth-order peak and a second-order high-pass filter are designed and tested for an experimental RDC. The experimental results demonstrate significant reduction of the peak-to-peak error in the estimated speed.


Implementation of Low Power Digital Multipliers Using 10 Transistor Adder Blocks


title: Developing predictive models using electronic medical records: challenges and pitfalls.

abstract: While Electronic Medical Records (EMR) contain detailed records of the patient-clinician encounter - vital signs, laboratory tests, symptoms, caregivers' notes, interventions prescribed and outcomes - developing predictive models from this data is not straightforward. These data contain systematic biases that violate assumptions made by off-the-shelf machine learning algorithms, commonly used in the literature to train predictive models. In this paper, we discuss key issues and subtle pitfalls specific to building predictive models from EMR. We highlight the importance of carefully considering both the special characteristics of EMR as well as the intended clinical use of the predictive model and show that failure to do so could lead to developing models that are less useful in practice. Finally, we describe approaches for training and evaluating models on EMR using early prediction of septic shock as our example application.


title: FlowBender: Flow-level Adaptive Routing for Improved Latency and Throughput in Datacenter Networks

abstract: Datacenter networks provide high path diversity for traffic between machines. Load balancing traffic across these paths is important for both, latency- and throughput-sensitive applications. The standard load balancing techniques used today obliviously hash a flow to a random path. When long flows collide on the same path, this might lead to long lasting congestion while other paths could be underutilized, degrading performance of other flows as well. Recent proposals to address this shortcoming incur significant implementation complexity at the host that would actually slow down short flows (MPTCP), depend on relatively slow centralized controllers for rerouting large congesting flows (Hedera), or require custom switch hardware, hindering near-term deployment (DeTail). We propose FlowBender, a novel technique that: (1) Load balances distributively at the granularity of flows instead of packets, avoiding excessive packet reordering. (2) Uses end-host-driven rehashing to trigger dynamic flow-to-path assignment. (3) Recovers from link failures within a Retransmit Timeout (RTO). (4) Amounts to less than 50 lines of critical kernel code and is readily deployable in commodity data centers today. (5) Is very robust and simple to tune. We evaluate FlowBender using both simulations and a real testbed implementation, and show that it improves average and tail latencies significantly compared to state of the art techniques without incurring the significant overhead and complexity of other load balancing schemes.


title: Molecular Pathophysiology of Parkinson’s Disease

abstract: Parkinson’s disease (PD) is a progressive neurodegenerative movement disorder that results primarily from the death of dopaminergic neurons in the substantia nigra. Although the etiology of PD is incompletely understood, the recent discovery of genes associated with rare monogenic forms of the disease, together with earlier studies and new experimental animal models, has provided important and novel insight into the molecular pathways involved in disease pathogenesis. Increasing evidence indicates that deficits in mitochondrial function, oxidative and nitrosative stress, the accumulation of aberrant or misfolded proteins, and ubiquitin-proteasome system dysfunction may represent the principal molecular pathways or events that commonly underlie the pathogenesis of sporadic and familial forms of PD.


title: Cyclin-dependent kinases regulate the antiproliferative function of Smads

abstract: Transforming growth factor-β (TGF-β) potently inhibits cell cycle progression at the G1 phase1,2. Smad3 has a key function in mediating the TGF-β growth-inhibitory response. Here we show that Smad3 is a major physiological substrate of the G1 cyclin-dependent kinases CDK4 and CDK2. Except for the retinoblastoma protein family3,4, Smad3 is the only CDK4 substrate demonstrated so far. We have mapped CDK4 and CDK2 phosphorylation sites to Thr 8, Thr 178 and Ser 212 in Smad3. Mutation of the CDK phosphorylation sites increases Smad3 transcriptional activity, leading to higher expression of the CDK inhibitor p15. Mutation of the CDK phosphorylation sites of Smad3 also increases its ability to downregulate the expression of c-myc. Using Smad3-/- mouse embryonic fibroblasts and other epithelial cell lines, we further show that Smad3 inhibits cell cycle progression from G1 to S phase and that mutation of the CDK phosphorylation sites in Smad3 increases this ability. Taken together, these findings indicate that CDK phosphorylation of Smad3 inhibits its transcriptional activity and antiproliferative function. Because cancer cells often contain high levels of CDK activity5,6, diminishing Smad3 activity by CDK phosphorylation may contribute to tumorigenesis and TGF-β resistance in cancers.


title: Remote Memory References at Block Granularity.

abstract: The cost of accessing shared objects that are stored in remote memory, while neglecting accesses to shared objects that are cached in the local memory, can be evaluated by the number of remote memory references (RMRs) in an execution. Two flavours of this measure—cache-coherent (CC) and distributed shared memory (DSM)—model two popular shared-memory architectures. The number of RMRs, however, does not take into account the granularity of memory accesses, namely, the fact that accesses to the shared memory are performed in blocks. ::: This paper proposes a new measure, called block RMRs, counting the number of remote memory references while taking into account the fact that shared objects can be grouped into blocks. On the one hand, this measure reflects the fact that the RMR incurred for bringing a shared object to the local memory might save another RMR for bringing another object placed at the same block. On the other hand, this measure accounts for false sharing: the fact that an RMR may be incurred when accessing an object due to a concurrent access to another object in the same block. ::: This paper proves that in both the CC and the DSM models, finding an optimal placement is NP-hard when objects have different sizes, even for two processes. In the CC model, finding an optimal placement, i.e., grouping of objects into blocks, is NP-hard when a block can store three objects or more; the result holds even if the sequence of accesses is known in advance. In the DSM model, the answer depends on whether there is an efficient mechanism to inform processes that the data in their local memory is no longer valid, i.e., cache coherence is supported. If coherence is supported with cheap invalidation, then finding an optimal solution is NP-hard. If coherence is not supported, an optimal placement can be achieved by placing each object in the memory of the process that accesses it most often, if the sequence of accesses is known in advance.


title: A Secure Biometrics-based Authentication Scheme for Telecare Medicine Information Systems

abstract: The telecare medicine information system (TMIS) allows patients and doctors to access medical services or medical information at remote sites. Therefore, it could bring us very big convenient. To safeguard patients' privacy, authentication schemes for the TMIS attracted wide attention. Recently, Tan proposed an efficient biometrics-based authentication scheme for the TMIS and claimed their scheme could withstand various attacks. However, in this paper, we point out that Tan's scheme is vulnerable to the Denial-of-Service attack. To enhance security, we also propose an improved scheme based on Tan's work. Security and performance analysis shows our scheme not only could overcome weakness in Tan's scheme but also has better performance.


PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings


title: RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse

abstract: Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As ar esult, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data.


title: Decoding Optimization for 5G LDPC Codes by Machine Learning

abstract: In this paper, we propose a generalized minimum-sum decoding algorithm using a linear approximation (LAMS) for protograph-based low-density parity-check (PB-LDPC) codes with quasi-cyclic (QC) structures. The linear approximation introduces some factors in each decoding iteration, which linearly adjust the check node updating and channel output. These factors are optimized iteratively using machine learning, where the optimization can be efficiently solved by a small and shallow neural network with training data produced by the LAMS decoder. The neural network is built according to the parity check matrix of a PB-LDPC code with a QC structure which can greatly reduce the size of the neural network. Since, we optimize the factors once per decoding iteration, the optimization is not limited by the number of the iterations. Then, we give the optimized results of the factors in the LAMS decoder and perform decoding simulations for PB-LDPC codes in fifth generation mobile networks (5G). In the simulations, the LAMS algorithm shows noticeable improvement over the normalized and the offset minimum-sum algorithms and even better performance than the belief propagation algorithm in some high signal-to-noise ratio regions.


title: A billion keys, but few locks: the crisis of web single sign-on

abstract: OpenID and InfoCard are two mainstream Web single sign-on (SSO) solutions intended for Internet-scale adoption. While they are technically sound, the business model of these solutions does not provide content-hosting and service providers (CSPs) with sufficient incentives to become relying parties (RPs). In addition, the pressure from users and identity providers (IdPs) is not strong enough to drive CSPs toward adopting Web SSO. As a result, there are currently over one billion OpenID-enabled user accounts provided by major CSPs, but only a few relying parties. In this paper, we discuss the problem of Web SSO adoption for RPs and argue that solutions in this space must offer RPs sufficient business incentives and trustworthy identity services in order to succeed. We suggest future Web SSO development should investigate and fulfill RPs' business needs, identify IdP business models, and build trust frameworks. Moreover, we propose that Web SSO technology should build identity support into browsers in order to facilitate RPs' adoption.


title: Knowledge Visualization for Self-Regulated Learning

abstract: The Web allows self-regulated learning through interaction with large amounts of learning resources. While enjoying the flexibility of learning, learners may suffer from cognitive overload and conceptual and navigational disorientation when faced with various information resources under disparate topics and complex knowledge structures. This study proposed a knowledge visualization (KV) approach to this problem in an online course. The investigation involved the design, development, and evaluation of an enhanced learning system for the course using the proposed approach. The focus was on visualization of domain knowledge structure and integrating the structure with curriculum design, learning resources, learning assessment, intellectual process, and social learning. Survey and interviews with students demonstrated high user satisfaction and acceptance with the developed system and its functions for KV. These findings lay the foundation for further exploration with the system to determine its impact on reducing cognitive load and improving the learning process.


title: Optimized over-current relay coordination using Flower Pollination Algorithm

abstract: The main purpose of optimization of relay coordination in an extensively large electrical network is to enhance the selectivity and at the same time reducing the fault clearing time to improve reliability of the system. The relays provided are set to function properly in normal as well as abnormal condition. In this paper; the main focus is to find out minimum Time Dial Setting (TDS) for the relays connected in-whatever configuration using Flower Pollination Algorithm. The said algorithm is compared with Linear Programming Technique. The algorithm has been implemented in MATLAB and tested on radial feeder fed from one end as well as on parallel Feeder system. The innovative feature of the paper is application of nature inspired algorithm to one of the major problem of optimization in the field of power system protection.


title: Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks

abstract: Network lifetime is a crucial performance metric to evaluate data-gathering wireless sensor networks (WSNs) where battery-powered sensor nodes periodically sense the environment and forward collected samples to a sink node. In this paper, we propose an analytic model to estimate the entire network lifetime from network initialization until it is completely disabled, and determine the boundary of energy hole in a data-gathering WSN. Specifically, we theoretically estimate the traffic load, energy consumption, and lifetime of sensor nodes during the entire network lifetime. Furthermore, we investigate the temporal and spatial evolution of energy hole and apply our analytical results to WSN routing in order to balance the energy consumption and improve the network lifetime. Extensive simulation results are provided to demonstrate the validity of the proposed analytic model in estimating the network lifetime and energy hole evolution process.


title: QoS Aware Geographic Opportunistic Routing in Wireless Sensor Networks

abstract: QoS routing is an important research issue in wireless sensor networks (WSNs), especially for mission-critical monitoring and surveillance systems which requires timely and reliable data delivery. Existing work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant energy cost. In this work, we exploit the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in WSNs. Existing GOR protocols are not efficient for QoS provisioning in WSNs, in terms of the energy efficiency and computation delay at each hop. To improve the efficiency of QoS routing in WSNs, we define the problem of efficient GOR for multiconstrained QoS provisioning in WSNs, which can be formulated as a multiobjective multiconstraint optimization problem. Based on the analysis and observations of different routing metrics in GOR, we then propose an Efficient QoS-aware GOR (EQGOR) protocol for QoS provisioning in WSNs. EQGOR selects and prioritizes the forwarding candidate set in an efficient manner, which is suitable for WSNs in respect of energy efficiency, latency, and time complexity. We comprehensively evaluate EQGOR by comparing it with the multipath routing approach and other baseline protocols through ns-2 simulation and evaluate its time complexity through measurement on the MicaZ node. Evaluation results demonstrate the effectiveness of the GOR approach for QoS provisioning in WSNs. EQGOR significantly improves both the end-to-end energy efficiency and latency, and it is characterized by the low time complexity.


title: Comparative evaluation of soft-switching concepts for bi-directional buck+boost dc-dc converters

abstract: Soft-switching techniques are an enabling technology to further reduce the losses and the volume of automotive dc-dc converters, utilized to interconnect the high voltage battery or ultra-capacitor to the dc-link of a Hybrid Electrical Vehicle (HEV) or a Fuel Cell Vehicle (FCV). However, as the performance indices of a power electronics converter, such as efficiency and power density, are competing and moreover dependent on the underlying specifications and technology node, a comparison of different converter topologies naturally demands detailed analytical models. Therefore, to investigate the performance of the ARCP, CF-ZVS-M, SAZZ and ZCT-QZVT soft-switching converters, the paper discusses in detail the advantages and drawbacks of each concept, and the impact of the utilized semiconductor technology and silicon area on the converter efficiency. The proposed analytical models that correlate semiconductor, capacitor and inductor losses with the component volume furthermore allow for a comparison of power density and to find the η-ρ-Pareto-Front of the CF-ZVS-M converter.


title: A comparison between grounded and floating shield inductors for mmW VCOs

abstract: A floating-shield inductor implemented in CMOS process is compared with a conventional patterned ground shield inductor for the implementation of a LC voltage controlled oscillator (VCO) operating at mmW frequencies. In this work it is shown how the floating-shield inductor achieves higher quality factor and provides a better isolation for substrate-coupled high-frequency interferences.


title: Towards workflow scheduling in cloud computing

abstract: Workflow scheduling is one of the prominent issues in cloud computing which is aimed at complete execution of workflows by considering their QoS requirements such as deadline and budget constraints. Numerous state of the art workflow scheduling schemes have been proposed in the literature for scheduling simple and scientific workflows in the cloud computing and this paper presents a comprehensive survey and analysis of these schemes. It illuminates the objectives of scheduling schemes in the cloud computing and provides a classification of the proposed schemes based on the type of scheduling algorithm applied in each scheme. Beside, each scheme is illustrated and a complete comparison of them is presented to highlight their objectives, properties and limitations. Finally, the concluding remarks and future research directions are provided.


title: Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning

abstract: In this paper, we tackle the problem of common object (multiple classes) discovery from a set of input images, where we assume the presence of one object class in each image. This problem is, loosely speaking, unsupervised since we do not know a priori about the object type, location, and scale in each image. We observe that the general task of object class discovery in a fully unsupervised manner is intrinsically ambiguous; here we adopt saliency detection to propose candidate image windows/patches to turn an unsupervised learning problem into a weakly-supervised learning problem. In the paper, we propose an algorithm for simultaneously localizing objects and discovering object classes via bottom-up (saliency-guided) multiple class learning (bMCL). Our contributions are three-fold: (1) we adopt saliency detection to convert unsupervised learning into multiple instance learning, formulated as bottom-up multiple class learning (bMCL); (2) we propose an integrated framework that simultaneously performs object localization, object class discovery, and object detector training; (3) we demonstrate that our framework yields significant improvements over existing methods for multi-class object discovery and possess evident advantages over competing methods in computer vision. In addition, although saliency detection has recently attracted much attention, its practical usage for high-level vision tasks has yet to be justified. Our method validates the usefulness of saliency detection to output “noisy input” for a top-down method to extract common patterns.


title: Multi-instance Multi-label Learning for Relation Extraction

abstract: Distant supervision for relation extraction (RE) -- gathering training data by aligning a database of facts with text -- is an efficient approach to scale RE to thousands of different relations. However, this introduces a challenging learning scenario where the relation expressed by a pair of entities found in a sentence is unknown. For example, a sentence containing Balzac and France may express BornIn or Died, an unknown relation, or no relation at all. Because of this, traditional supervised learning, which assumes that each example is explicitly mapped to a label, is not appropriate. We propose a novel approach to multi-instance multi-label learning for RE, which jointly models all the instances of a pair of entities in text and all their labels using a graphical model with latent variables. Our model performs competitively on two difficult domains.


title: Supplementary Material for Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

abstract: Learning domain-invariant features is of vital importance to unsupervised domain adaptation, where classifiers trained on the source domain need to be adapted to a different target domain for which no labeled examples are available. In this paper, we propose a novel approach for learning such features. The central idea is to exploit the existence of landmarks, which are a subset of labeled data instances in the source domain that are distributed most similarly to the target domain. Our approach automatically discovers the landmarks and use them to bridge the source to the target by constructing provably easier auxiliary domain adaptation tasks. The solutions of those auxiliary tasks form the basis to compose invariant features for the original task. We show how this composition can be optimized discriminatively without requiring labels from the target domain. We validate the method on standard benchmark datasets for visual object recognition and sentiment analysis of text. Empirical results show the proposed method outperforms the state-of-the-art significantly.


title: Multiobjective Path Planning: Localization Constraints and Collision Probability

abstract: We present a novel path planning algorithm that, starting from a probabilistic roadmap, efficiently constructs a product graph used to search for a near optimal solution of a multiobjective optimization problem. The goal is to find paths that minimize a primary cost, such as the path length from start to goal, subject to a bound on a secondary cost such as the state estimation error covariance. The proposed algorithm is efficient as it relies on a scalar metric, related to the largest eigenvalue of the error covariance, and adaptively quantizes the secondary cost, yielding a product graph whose number of vertices and edges provides a good tradeoff between optimality and computational complexity. We further show how our approach can be extended to handle constraints on the probability of collision avoidance specified at every vertex along the path. Numerical examples show 1) how the computed paths change as a function of the specified bound on the secondary costs, and 2) the tradeoff between accuracy and computational efficiency of the proposed approach compared with methods where the product graph is built by quantizing the secondary cost uniformly.


title: Coping Resources, Coping Processes, and Mental Health

abstract: Coping, defined as action-oriented and intrapsychic efforts to manage the demands created by stressful events, is coming to be recognized both for its significant impact on stress-related mental and physical health outcomes and for its intervention potential. We review coping resources that aid in this process, including individual differences in optimism, mastery, self-esteem, and social support, and examine appraisal and coping processes, especially those marked by approach or avoidance. We address the origins of coping resources and processes in genes, early life experience, and gene-environment interactions, and address neural underpinnings of coping that may shed light on evaluating coping interventions. We conclude by outlining possible intervention strategies for improving coping processes.


title: A/B test tools of native mobile application

abstract: When an application developer wants to introduce a new idea to his application, the idea needs to be validated with hypothesis testing in order to prove the treatment variant (application with new idea) will become a better alternative compared with the control variant (base application). In web application development, controlled experiment (known as A/B test) is commonly used as tools for testing a hypothesis. Controlled experiment for native mobile application (hereinafter, mobile application) in this research is created based on controlled experiment in web. We identified three main difficulties in mobile application controlled experiment compared to experiment of web application: element composition, variant delivery, and network connectivity. We propose techniques to overcome the difficulties in controlled experiment for mobile application: splitter generator and plugin architecture + custom build system. This technique is implemented as set of tools for creating variant easily and conducting controlled experiment of mobile application, consists of variant generator, library for tracking experiment execution, a build system, and a monitoring web application. Tools have been tested using two cases. The experiment shows that our tools are able to help developer produce a controlled variant with clearer separation of concern and to conduct controlled experiment easier.


title: Application of artificial neural networks and related techniques to intrusion detection

abstract: The increasing complexity of today's information technology (IT) together with our dependency upon it, has led to a situation in which a security breach not only has effects for individuals but can also affect the availability of critical services (power supply, communication) or result in significant financial loss. Criminals and terrorists want to exploit system vulnerabilities to capitalise on modern society's interwovenness with IT. To counter this, organisations try to secure their IT assets to enforce security policies, to be compliant with legal and regulatory requirements and ultimately to deter unauthorised intruders from gaining access to them. At the core, the goal of intrusion detection systems is the identification of suspicious traffic flowing within, leaving or entering an organisation. To identify such traffic, intrusion detection systems may focus on data within a single host or on integrated information from various network segments. Identified traffic can then be reported to responsible authorities to take an appropriate course of action. This report is concerned with the state-of-the-art in intrusion detection systems. Systems leveraging information gathered from a single host, i.e. host-based intrusion detection systems, are presented as well as approaches observing and analysing information flowing across networks, i.e. network-based intrusion detection systems. Specific focus is placed on systems that make use of artificial neural networks and variations thereof to separate suspicious and potentially malicious traffic from ordinary traffic.


title: Rectangular spiral microstrip antenna for WLAN application

abstract: Microstrip patch antennas represent one family of compact antennas that offer a conformal nature and the capability of ready integration with communication system's printed circuitry. This paper highlights a constructed of 2.4 GHz of a coaxial - fed rectangular spiral microstrip patch antenna for WLAN application where it's mounted on a high frequency laminates Rogers 5880 Duroid with 2.2 dielectric constant and 0.508 mm of thickness. The radiation pattern of this antenna illustrates an omni-directional pattern with voltage standing wave ratio of less than two, return loss less than −10 dB, the line impedance of 50 Ohm. The results show the consistency in the simulated and fabricated antenna.


title: Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID)

abstract: Detection of suspicious regions in mammogram images and the subsequent diagnosis of these regions remains a challenging problem in the medical world. There still exists an alarming rate of misdiagnosis of breast cancer. This results in both over treatment through incorrect positive diagnosis of cancer and under treatment through overlooked cancerous masses. Convolutional neural networks have shown strong applicability to various image datasets, enabling detailed features to be learned from the data and, as a result, the ability to classify these images at extremely low error rates. In order to overcome the difficulty in diagnosing breast cancer from mammogram images, we propose our framework for automated breast cancer detection and diagnosis, called BC-DROID, which provides automated region of interest detection and diagnosis using convolutional neural networks. BC-DROID first pretrains based on physician-defined regions of interest in mammogram images. It then trains based on the full mammogram image. The resulting network is able to detect and classify regions of interest as cancerous or benign in one step. We demonstrate the accuracy of our framework's ability to both locate the regions of interest as well as diagnose them. Our framework achieves a detection accuracy of up to 90% and a classification accuracy of 93.5% (AUC of 92.315%). To the best of our knowledge, this is the first work enabling both automated detection and diagnosis of these areas in one step from full mammogram images. Using our framework's website, a user can upload a single mammogram image, visualize suspicious regions, and receive the automated diagnoses of these regions.


title: Application of Numenta® Hierarchical Temporal Memory for land-use classification

abstract: rays are projected upon the retina, and the brain tries to make sense of the world by means of interpreting the visual input pattern. The sensitivity and specificity with which the brain solves this computationally complex problem cannot yet be replicated on a computer. The most imposing of these problems is that of invariant visual pattern recognition. Recently it has been said that the prediction of future sensory input from salient features of current input is the keystone of intelligence. 2 The neocortex is the structure in the brain which is assumed to be responsible for the evolution of intelligence. Current sensory input patterns activate stored traces of previous inputs which then generate top-down expectations, which are verified against the bottom-up input signals. If the verification succeeds, the predicted pattern is recognised. This theory explains how humans, and mammals in general, can recognise images despite changes in location, size and lighting conditions, and in the presence of deformations and large amounts of noise. Parts of this theory, known as the memory-prediction theory (MPT), are modelled in the Hierarchical Temporal Memory or HTM technology developed by a company called Numenta ® ; 3 the model is an attempt to replicate the structural and algorithmic properties of the neocortex. 3 Spatial and temporal relations between features of the sensory signals are formed in an hierarchical memory architecture during a learning process. When a new pattern arrives, the recognition process can be viewed as choosing the stored representation that best predicts the pattern. Hierarchical Temporal Memory has been successfully applied to the recognition of relatively simple images, 4


title: Wide-Coverage Efficient Statistical Parsing with CCG and Log-Linear Models

abstract: This article describes a number of log-linear parsing models for an automatically extracted lexicalized grammar. The models are “full” parsing models in the sense that probabilities are defined for complete parses, rather than for independent events derived by decomposing the parse tree. Discriminative training is used to estimate the models, which requires incorrect parses for each sentence in the training data as well as the correct parse. The lexicalized grammar formalism used is Combinatory Categorial Grammar (CCG), and the grammar is automatically extracted from CCGbank, a CCG version of the Penn Treebank. The combination of discriminative training and an automatically extracted grammar leads to a significant memory requirement (up to 25 GB), which is satisfied using a parallel implementation of the BFGS optimization algorithm running on a Beowulf cluster. Dynamic programming over a packed chart, in combination with the parallel implementation, allows us to solve one of the largest-scale estimation problems in the statistical parsing literature in under three hours. ::: ::: A key component of the parsing system, for both training and testing, is a Maximum Entropy supertagger which assigns CCG lexical categories to words in a sentence. The supertagger makes the discriminative training feasible, and also leads to a highly efficient parser. Surprisingly, given CCG's “spurious ambiguity,” the parsing speeds are significantly higher than those reported for comparable parsers in the literature. We also extend the existing parsing techniques for CCG by developing a new model and efficient parsing algorithm which exploits all derivations, including CCG's nonstandard derivations. This model and parsing algorithm, when combined with normal-form constraints, give state-of-the-art accuracy for the recovery of predicate-argument dependencies from CCGbank. The parser is also evaluated on DepBank and compared against the RASP parser, outperforming RASP overall and on the majority of relation types. The evaluation on DepBank raises a number of issues regarding parser evaluation. ::: ::: This article provides a comprehensive blueprint for building a wide-coverage CCG parser. We demonstrate that both accurate and highly efficient parsing is possible with CCG.


title: The survey of GSM wireless communication system

abstract: In the past decade, wireless communications experienced an explosive growth period and became an integral part of modern society. The convenience and flexibility offered by mobile communications have made it one of the fastest growing areas of telecommunications. Mobile communication systems have experienced rapid growth in the number of users as well as the range of services provided during the last two decades. The Global System for Mobile communications, GSM, is a pan-European Mobile communication system in the 900 MHz band which was first introduced in the early years of this decade. An increasing demand for data transmission services over the GSM system has been driven by the wide use of the internet applications. In this thesis, we focus on the survey of GSM for wireless communication, which is one of the most widely deployed second generation wireless cellular systems in the world. While voice was the primary service provided early communication systems, current systems offer other transmission services. One of the most commonly used cellular systems is based on the Global System for Mobile communication, GSM, standard. We briefly introduce the GSM System configuration and major properties. It included five parts. It is about service and features, architecture of GSM system, channel and frame structure of GSM, GSM security features, data in the GSM System.


title: Modeling and Analysis of Harmonic Stability in an AC Power-Electronics-Based Power System

abstract: This paper addresses the harmonic stability caused by the interactions among the wideband control of power converters and passive components in an ac power-electronics-based power system. The impedance-based analytical approach is employed and expanded to a meshed and balanced three-phase network which is dominated by multiple current- and voltage-controlled inverters with LCL- and LC-filters. A method of deriving the impedance ratios for the different inverters is proposed by means of the nodal admittance matrix. Thus, the contribution of each inverter to the harmonic stability of the power system can be readily predicted through Nyquist diagrams. Time-domain simulations and experimental tests on a three-inverter-based power system are presented. The results validate the effectiveness of the theoretical approach.


title: Fast nonnegative matrix factorization and its application for protein fold recognition

abstract: Linear and unsupervised dimensionality reduction via matrix factorization with nonnegativity constraints is studied. Because of these constraints, it stands apart from other linear dimensionality reduction methods. Here we explore nonnegative matrix factorization in combination with three nearest-neighbor classifiers for protein fold recognition. Since typically matrix factorization is iteratively done, convergence, can be slow. To speed up convergence, we perform feature scaling (normalization) prior to the beginning of iterations. This results in a significantly (more than 11 times) faster algorithm. Justification of why it happens is provided. Another modification of the standard nonnegative matrix factorization algorithm is concerned with combining two known techniques for mapping unseen data. This operation is typically necessary before classifying the data in low-dimensional space. Combining two mapping techniques can yield better accuracy than using either technique alone. The gains, however, depend on the state of the random number generator used for initialization of iterations, a classifier, and its parameters. In particular, when employing the best out of three classifiers and reducing the original dimensionality by around 30%, these gains can reach more than 4%, compared to the classification in the original, high-dimensional space.


title: Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors

abstract: This paper presents a stand-alone multisensor wireless system for continuous condition monitoring of induction motors. The proposed wireless system provides a low-cost alternative to expensive condition monitoring technology available through dedicated current signature analysis or vibration monitoring equipment. The system employs multiple sensors (acoustic, vibration, and current) mounted on a common wireless platform. The faults of interest are static and dynamic air-gap eccentricity, bearing damage, and their combinations. The Hilbert-Huang transform of vibration data and power spectral density of current and acoustic signals are used as the features in a hierarchical classifier. The proposed wireless system can distinguish a faulty motor from a healthy motor with a probability of 99.9% of correct detection and less than 0.1% likelihood of false alarm. It can also discriminate between different fault categories and severity with an average accuracy of 95%.


title: An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City

abstract: The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems.