title: Heterogeneous Network Embedding via Deep Architectures

abstract: Data embedding is used in many machine learning applications to create low-dimensional feature representations, which preserves the structure of data points in their original space. In this paper, we examine the scenario of a heterogeneous network with nodes and content of various types. Such networks are notoriously difficult to mine because of the bewildering combination of heterogeneous contents and structures. The creation of a multidimensional embedding of such data opens the door to the use of a wide variety of off-the-shelf mining techniques for multidimensional data. Despite the importance of this problem, limited efforts have been made on embedding a network of scalable, dynamic and heterogeneous data. In such cases, both the content and linkage structure provide important cues for creating a unified feature representation of the underlying network. In this paper, we design a deep embedding algorithm for networked data. A highly nonlinear multi-layered embedding function is used to capture the complex interactions between the heterogeneous data in a network. Our goal is to create a multi-resolution deep embedding function, that reflects both the local and global network structures, and makes the resulting embedding useful for a variety of data mining tasks. In particular, we demonstrate that the rich content and linkage information in a heterogeneous network can be captured by such an approach, so that similarities among cross-modal data can be measured directly in a common embedding space. Once this goal has been achieved, a wide variety of data mining problems can be solved by applying off-the-shelf algorithms designed for handling vector representations. Our experiments on real-world network datasets show the effectiveness and scalability of the proposed algorithm as compared to the state-of-the-art embedding methods.


title: Fast Thermal Calibration of Low-Grade Inertial Sensors and Inertial Measurement Units

abstract: The errors of low-cost inertial sensors, especially Micro-Electro Mechanical Systems (MEMS) ones, are highly dependent on environmental conditions such as the temperature. Thus, there is a need for the development of accurate and reliable thermal compensation models to reduce the impact of such thermal drift of the sensors. Since the conventional thermal calibration methods are typically time-consuming and costly, an efficient thermal calibration method to investigate the thermal drift of a full set of gyroscope and accelerometer errors (i.e., biases, scale factor errors and non-orthogonalities) over the entire temperature range in a few hours is proposed. The proposed method uses the idea of the Ramp method, which removes the time-consuming process of stabilizing the sensor temperature, and addresses its inherent problems with several improvements. We change the temperature linearly for a complete cycle and take a balanced strategy by making comprehensive use of the sensor measurements during both heating and cooling processes. Besides, an efficient 8-step rotate-and-static scheme is designed to further improve the calibration accuracy and efficiency. Real calibration tests showed that the proposed method is suitable for low-grade IMUs and for both lab and factory calibration due to its efficiency and sufficient accuracy.


title: Investigating the Agility Bias in DNS Graph Mining

abstract: The concept of agile domain name system (DNS) refers to dynamic and rapidly changing mappings between domain names and their Internet protocol (IP) addresses. This empirical paper evaluates the bias from this kind of agility for DNS-based graph theoretical data mining applications. By building on two conventional metrics for observing malicious DNS agility, the agility bias is observed by comparing bipartite DNS graphs to different subgraphs from which vertices and edges are removed according to two criteria. According to an empirical experiment with two longitudinal DNS datasets, irrespective of the criterion, the agility bias is observed to be severe particularly regarding the effect of outlying domains hosted and delivered via content delivery networks and cloud computing services. With these observations, the paper contributes to the research domains of cyber security and DNS mining. In a larger context of applied graph mining, the paper further elaborates the practical concerns related to the learning of large and dynamic bipartite graphs.


title: Analysis and Observations From the First Amazon Picking Challenge

abstract: This paper presents an overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team’s background, mechanism design, perception apparatus, planning, and control approach. We identify trends in this data, correlate it with each team’s success in the competition, and discuss observations and lessons learned based on survey results and the authors’ personal experiences during the challenge. Note to Practitioners —Perception, motion planning, grasping, and robotic system engineering have reached a level of maturity that makes it possible to explore automating simple warehouse tasks in semistructured environments that involve high-mix, low-volume picking applications. This survey summarizes lessons learned from the first Amazon Picking Challenge, highlighting mechanism design, perception, and motion planning algorithms, as well as software engineering practices that were most successful in solving a simplified order fulfillment task. While the choice of mechanism mostly affects execution speed, the competition demonstrated the systems challenges of robotics and illustrated the importance of combining reactive control with deliberative planning.


title: Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network

abstract: In remotely located watersheds or large waterbodies, monitoring water quality parameters is often not feasible because of high costs and site inaccessibility. A cost-effective remote sensing-based methodology was developed to predict water quality parameters over a large and logistically difficult area. Landsat spectral data were used as a proxy, and a neural network model was developed to quantify water quality parameters, namely chlorophyll-a, turbidity, and phosphorus before and after ecosystem restoration and during the wet and dry seasons. The results demonstrate that the developed neural network model provided an excellent relationship between the observed and simulated water quality parameters. These correlated for a specific region in the greater Florida Everglades at R 2 > 0.95 in 1998–1999 and in 2009–2010 (dry and wet seasons). Moreover, the root mean square error values for phosphorus, turbidity, and chlorophyll-a were below 0.03 mg L−1, 0.5 NTU, and 0.17 mg m−3, respectively, at the neural network training and validation phases. Using the developed methodology, the trends for temporal and spatial dynamics of the selected water quality parameters were investigated. In addition, the amounts of phosphorus and chlorophyll-a stored in the water column were calculated demonstrating the usefulness of this methodology to predict water quality parameters in complex ecosystems.


Evaluation of Inter-Process Communication Mechanisms


Biochemical composition and physicochemical properties of two red seaweeds (Gracilaria fisheri and G. tenuistipitata) from the Pattani Bay in Southern Thailand


title: Multispectral remote sensing image segmentation using rival penalized controlled competitive learning and fuzzy entropy

abstract: This paper proposes an image segmentation approach for multispectral remote sensing imagery based on rival penalized controlled competitive learning (RPCCL) and fuzzy entropy. In this approach, the clustering center component for each band of the image is first chosen based on the fuzzy entropy histogram of the corresponding band of the image. The initial clustering centers are then formed by combining the obtained clustering center components. The number of clusters and the real clustering centers are then determined by the use of the RPCCL method. The advantages of the proposed approach are the appropriate initial cluster centers and the fact that the number of clusters is determined automatically. The results of the experiments showed that without providing the number of clustering centers before the clustering operation, the proposed method can effectively perform an unsupervised segmentation of remote sensing images.


title: Blockchain-Based Dynamic Key Management for Heterogeneous Intelligent Transportation Systems

abstract: As modern vehicle and communication technologies advanced apace, people begin to believe that the Intelligent Transportation System (ITS) would be achievable in one decade. ITS introduces information technology to the transportation infrastructures and aims to improve road safety and traffic efficiency. However, security is still a main concern in vehicular communication systems (VCSs). This can be addressed through secured group broadcast. Therefore, secure key management schemes are considered as a critical technique for network security. In this paper, we propose a framework for providing secure key management within the heterogeneous network. The security managers (SMs) play a key role in the framework by capturing the vehicle departure information, encapsulating block to transport keys and then executing rekeying to vehicles within the same security domain. The first part of this framework is a novel network topology based on a decentralized blockchain structure. The blockchain concept is proposed to simplify the distributed key management in heterogeneous VCS domains. The second part of the framework uses the dynamic transaction collection period to further reduce the key transfer time during vehicles handover. Extensive simulations and analysis show the effectiveness and efficiency of the proposed framework, in which the blockchain structure performs better in term of key transfer time than the structure with a central manager, while the dynamic scheme allows SMs to flexibly fit various traffic levels.


title: Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression

abstract: We present a novel formulation for providing advice to a reinforcement learner that employs support-vector regression as its function approximator. Our new method extends a recent advice-giving technique, called Knowledge-Based Kernel Regression (KBKR), that accepts advice concerning a single action of a reinforcement learner. In KBKR, users can say that in some set of states, an action's value should be greater than some linear expression of the current state. In our new technique, which we call Preference KBKR (Pref-KBKR), the user can provide advice in a more natural manner by recommending that some action is preferred over another in the specified set of states. Specifying preferences essentially means that users are giving advice about policies rather than Q values, which is a more natural way for humans to present advice. We present the motivation for preference advice and a proof of the correctness of our extension to KBKR. In addition, we show empirical results that our method can make effective use of advice on a novel reinforcement-learning task, based on the RoboCup simulator, which we call Breakaway. Our work demonstrates the significant potential of advice-giving techniques for addressing complex reinforcement learning problems, while further demonstrating the use of support-vector regression for reinforcement learning.


title: A New Statistical Parser Based On Bigram Lexical Dependencies

abstract: This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street Journal data show that the method performs at least as well as SPATTER (Magerman 95; Jelinek et al. 94), which has the best published results for a statistical parser on this task. The simplicity of the approach means the model trains on 40,000 sentences in under 15 minutes. With a beam search strategy parsing speed can be improved to over 200 sentences a minute with negligible loss in accuracy.


title: Feldenkrais Method balance classes are based on principles of motor learning and postural control retraining: a qualitative research study.

abstract: BACKGROUND ::: Feldenkrais Method balance classes have been found to be effective in improving balance in recent studies, but there has been little research into possible mechanisms behind the effectiveness of these classes. Indeed, there has been little research into the content of any balance training classes. ::: ::: ::: OBJECTIVES ::: To analyse the content of a series of Feldenkrais Method balance classes to gain an understanding of how the results in these studies may have been achieved and the principles through which the method may be effective. ::: ::: ::: DESIGN ::: Qualitative research approach (content analysis). ::: ::: ::: METHOD ::: Feldenkrais Method Awareness Through Movement lessons were transcribed and the contents were analysed. An intercoder reliability study was undertaken. ::: ::: ::: RESULTS ::: The content analysis revealed that the classes used motor skill acquisition elements of internal feedback, repetition and variability of practice using an exploratory learning approach. Postural control skills of intersegmental coordination of ankle/hip/trunk synergies were practised, with control of the centre of mass over the base of support explored in anterior/posterior, medio/lateral, diagonal, rotational and circular directions. Key findings were the extensive involvement of trunk flexibility and control in the balance activities, and also the intensive attention to internal feedback which was linked to body awareness training. ::: ::: ::: CONCLUSION ::: The Awareness Through Movement lessons contained many elements consistent with current theories of motor skill acquisition and postural control, providing a sound theoretical basis for the effectiveness of the Feldenkrais approach in improving balance. The methodology used in this study may provide a useful model for similar investigations into other balance training approaches.


title: An Ensemble CNN2ELM for Age Estimation

abstract: Age estimation is a challenging task, because it can be easily affected by gender, race, and other intrinsic and extrinsic attributes. At the same time, performing age estimation for a narrow age range may lead to better results. In this paper, to achieve robust age estimation, an ensemble structure referred to as CNN2ELM, which includes convolutional neural network (CNN) and extreme learning machine (ELM), is proposed for age estimation. The three-level system includes feature extraction and fusion, age grouping via an ELM classifier, and age estimation via an ELM regressor. Age-Net, Gender-Net, and Race-Net are trained using different targets, such as age class, gender class, and race class, respectively, and the three networks are used to extract features corresponding to age, gender, and race from the same image of a person during validation and test stages. Features related to the age property are enhanced by fusing these of race and gender properties. Then, to achieve a narrow age range, the ELM classifies the fusion results into one of the age groups. Afterward, an age decision is made using an ELM regressor. Our network is pretrained on an ImageNet database and then fine-tuned on the IMDB–WIKI database. The recently released Adience benchmark, ChaLearn Looking at People 2016 (LAP-2016), and MORPH-II are used to verify the performance of “Race-Net + Age-Net + Gender-Net + ELM classifier + ELM regressor (RAGN).” RAGN outperforms the existing state-of-the-art age estimation methods. The mean absolute error of the age estimation of RAGN for MORPH-II is determined to be 2.61 years; the accuracy of the age estimation for the Adience benchmark is 0.6649; and the normal score ( $\epsilon$ ) for the sequestered test set of the LAP-2016 data set is 0.3679.


title: User access mode selection in fog computing based radio access networks

abstract: Fog computing based radio access network is a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. With the help of the new designed fog computing based access points (F-APs), the user-centric objectives can be achieved through the adaptive technique and will relieve the load of fronthaul and alleviate the burden of base band unit pool. In this paper, we derive the coverage probability and ergodic rate for both F-AP users and device-to-device users by taking into account the different nodes locations, cache sizes as well as user access modes. Particularly, the stochastic geometry tool is used to derive expressions for above performance metrics. Simulation results validate the accuracy of our analysis and we obtain interesting trade-offs that depend on the effect of the cache size, user node density, and the quality of service constrains on the different performance metrics.


A Summary of Survey Methodology Best Practices for Security and Privacy Researchers


Intelligent Parking Space Detection System Based on Image Processing


Low Cost, Efficient, High Gain and Wideband Antenna using Parasitic Patches on a Superstrate Layer 1


title: A global model for concept-to-text generation

abstract: Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection ("what to say") and surface realization ("how to say") in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We recast generation as the task of finding the best derivation tree for a set of database records and describe an algorithm for decoding in this framework that allows to intersect the grammar with additional information capturing fluency and syntactic well-formedness constraints. Experimental evaluation on several domains achieves results competitive with state-of-the-art systems that use domain specific constraints, explicit feature engineering or labeled data.


title: SURF-Face: Face Recognition Under Viewpoint Consistency Constraints

abstract: We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated. Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized. A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail. The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.


title: Inflammation-free, gas-permeable, lightweight, stretchable on-skin electronics with nanomeshes

abstract: Thin-film electronic devices can be integrated with skin for health monitoring and/or for interfacing with machines. Minimal invasiveness is highly desirable when applying wearable electronics directly onto human skin. However, manufacturing such on-skin electronics on planar substrates results in limited gas permeability. Therefore, it is necessary to systematically investigate their long-term physiological and psychological effects. As a demonstration of substrate-free electronics, here we show the successful fabrication of inflammation-free, highly gas-permeable, ultrathin, lightweight and stretchable sensors that can be directly laminated onto human skin for long periods of time, realized with a conductive nanomesh structure. A one-week skin patch test revealed that the risk of inflammation caused by on-skin sensors can be significantly suppressed by using the nanomesh sensors. Furthermore, a wireless system that can detect touch, temperature and pressure is successfully demonstrated using a nanomesh with excellent mechanical durability. In addition, electromyogram recordings were successfully taken with minimal discomfort to the user.


title: What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

abstract: BACKGROUND ::: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. ::: ::: ::: OBJECTIVE ::: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). ::: ::: ::: METHODS ::: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. ::: ::: ::: RESULTS ::: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients' concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus. ::: ::: ::: CONCLUSIONS ::: We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life.


title: A Battery-Assisted Passive EPC Gen-2 RFID Sensor Tag IC With Efficient Battery Power Management and RF Energy Harvesting

abstract: Herein, we present a fully integrated electronic product code (EPC) Gen-2 compatible battery-assisted passive (BAP) radio-frequency identification sensor tag integrated circuit (IC) with efficient power management. The key components of the tag IC include an adaptive radio-frequency (RF) energy harvester, a battery access controller (BAC), a storage capacitor charger, and a power-gated sensor block. External RF energy is efficiently harvested using dynamically controlled rectifying stages and a threshold-compensation technique. The BAC tightly controls the power path, cutting off leakage current from the battery. The power-gated sensor block supports multiplexed sensing operations in a power efficient manner. The sensor interface includes a chopper amplifier, a programmable gain amplifier, multiplexers, and an 8-bit ADC. For sensing data logging, custom designed nonvolatile memory is employed via one-time programmable memory. The digital control block is based on an EPCglobal Gen-2 standard that is modified to support the sensing operation. The tag chip was fabricated in a 1-poly 6-metal standard 0.18-μm standard CMOS process. The tag IC consumes ${\mathrm{1\;\mu A}}$ for leakage and active operation (0.1% duty), and the estimated lifespan of the BAP tag IC is about 0.2 year/mA·h.


title: High Fidelity Data Reduction for Big Data Security Dependency Analyses

abstract: Intrusive multi-step attacks, such as Advanced Persistent Threat (APT) attacks, have plagued enterprises with significant financial losses and are the top reason for enterprises to increase their security budgets. Since these attacks are sophisticated and stealthy, they can remain undetected for years if individual steps are buried in background "noise." Thus, enterprises are seeking solutions to "connect the suspicious dots" across multiple activities. This requires ubiquitous system auditing for long periods of time, which in turn causes overwhelmingly large amount of system audit events. Given a limited system budget, how to efficiently handle ever-increasing system audit logs is a great challenge. This paper proposes a new approach that exploits the dependency among system events to reduce the number of log entries while still supporting high-quality forensic analysis. In particular, we first propose an aggregation algorithm that preserves the dependency of events during data reduction to ensure the high quality of forensic analysis. Then we propose an aggressive reduction algorithm and exploit domain knowledge for further data reduction. To validate the efficacy of our proposed approach, we conduct a comprehensive evaluation on real-world auditing systems using log traces of more than one month. Our evaluation results demonstrate that our approach can significantly reduce the size of system logs and improve the efficiency of forensic analysis without losing accuracy.


title: Analysis and Classification of Shape-Changing Interfaces for Design and Application-based Research

abstract: Shape-changing interfaces are physically tangible, interactive devices, surfaces, or spaces that allow for rich, organic, and novel experiences with computational devices. Over the last 15 years, research has produced functional prototypes over many use applications; reviews have identified themes and possible future directions but have not yet looked at possible design or application-based research. Here, we gather this information together to provide a reference for designers and researchers wishing to build upon existing prototyping work, using synthesis and discussion of existing shape-changing interface reviews and comprehensive analysis and classification of 84 shape-changing interfaces. Eight categories of prototype are identified alongside recommendations for the field.


title: 2-Element slot meander patch antenna system for LTE-WLAN customer premise equipment

abstract: This work presents 2-element Low-profile slot meander patch (SMP) antenna system for customer premises equipment. The first element is a dual band SMP antenna to receive LTE 1.8 GHz and LTE 2.6 GHz bands. The second element is a WLAN 2.4 GHz transmitter SMP antenna. The first SMP antenna matched band widths are 0.3 GHz and 0.25 GHz. The second SMP antenna matched bandwidth is 0.37 GHz with −10 dB matching criterion (VSWR 2.5:1). Isolation better than 15 dB has been obtained between the two slot meander patch antennas. The isolation is achieved as a result of using Coplanar Wave (CPW) guide feeding mechanism and locating the two elements orthogonally. This dual-feed (2-elements) planar antenna is simulated. The result of this small size structured antenna system shows a good radiation characteristics and small mutual coupling.


title: Roles of Macro-Actions in Accelerating Reinforcement Learning

abstract: Several researchers have proposed reinforcement learning methods that obtain advantages in learning by using temporally extended actions, or macro-actions, but none has carefully analyzed what these advantages are. In this paper, we separate and analyze two advantages of using macro-actions in reinforcement learning: the effect on exploratory behavior, independent of learning, and the effect on the speed with which the learning process propagates accurate value information. We empirically measure the separate contributions of these two effects in gridworld and simulated robotic environments. In these environments, both effects were significant, but the effect of value propagation was larger. We also compare the accelerations of value propagation due to macro-actions and eligibility traces in the gridworld environment. Although eligibility traces increased the rate of convergence to the optimal value function compared to learning with macro-actions but without eligibility traces, eligibility traces did not permit the optimal policy to be learned as quickly as it was using macro-actions.


title: PAC-Bayesian Stochastic Model Selection

abstract: PAC-Bayesian learning methods combine the informative priors of Bayesian methods with distribution-free PAC guarantees. Stochastic model selection predicts a class label by stochastically sampling a classifier according to a “posterior distribution” on classifiers. This paper gives a PAC-Bayesian performance guarantee for stochastic model selection that is superior to analogous guarantees for deterministic model selection. The guarantee is stated in terms of the training error of the stochastic classifier and the KL-divergence of the posterior from the prior. It is shown that the posterior optimizing the performance guarantee is a Gibbs distribution. Simpler posterior distributions are also derived that have nearly optimal performance guarantees.


title: A 25 Gb/s 3D-Integrated CMOS/Silicon-Photonic Receiver for Low-Power High-Sensitivity Optical Communication

abstract: Integrating optical receivers based on double-sampling architecture exhibit a low-power alternative to those designed around transimpedance amplifiers (TIA). In this paper, we present a 3D-integrated CMOS/silicon-photonic optical receiver. The receiver features a low-bandwidth TIA integrating front-end double-sampling technique and dynamic offset modulation. The copper-pillar-based 3D-integration technology used here enables ultralow parasitics and 40 μm pitch for interconnection. We study different tradeoffs in designing an optical receiver and how to choose between a full-bandwidth TIA front-end and integrating architecture using a resistive front-end or a low-bandwidth TIA front-end. The design methodology is supported by measurements of two 3D-integrated prototypes based on a conventional TIA and a double-sampling integrating receiver. The proposed receiver architecture achieves −14.9 dBm of sensitivity and energy efficiency of 170 fJ/b at 25 Gb/s, while the conventional receiver achieves a sensitivity of −10.4 dBm and energy efficiency of 260 fJ/b at 21.2 Gb/s.


title: Real-time traffic sign detection with vehicle camera images

abstract: This paper presents a real-time traffic sign detection method using color properties and shape-based features for real-world environment applications. The proposed method has two main steps: color-based region segmentation and shape-based verification of the segmented area. In the first step, region-of-interest (ROI) is roughly segmented by a simple color-based thresholding method and each segment is corrected by a guided filter. Next, each ROI is verified through a shape analysis to decide whether the ROI is a circle or a triangle. For detecting circles, we compare three different methods: RSD, BCT, and STVUE. For triangles, RPD, VBT and STVUT were applied. We evaluated these alternatives with 232 experimental images containing 142 circular signs and 82 triangular signs. We found that RSD in the circle detection and STVUT in the triangle detection provide the best detection rates of 93% and 90% respectively. The main contribution of this paper is to present a novel approach for extracting boundary of traffic sign.


title: 3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks

abstract: In video super-resolution, the spatio-temporal coherence between, and among the frames must be exploited appropriately for accurate prediction of the high resolution frames. Although 2D convolutional neural networks (CNNs) are powerful in modelling images, 3D-CNNs are more suitable for spatio-temporal feature extraction as they can preserve temporal information. To this end, we propose an effective 3D-CNN for video super-resolution, called the 3DSRnet that does not require motion alignment as preprocessing. Our 3DSRnet maintains the temporal depth of spatio-temporal feature maps to maximally capture the temporally nonlinear characteristics between low and high resolution frames, and adopts residual learning in conjunction with the sub-pixel outputs. It outperforms the most state-of-the-art method with average 0.45 and 0.36 dB higher in PSNR for scales 3 and 4, respectively, in the Vidset4 benchmark. Our 3DSRnet first deals with the performance drop due to scene change, which is important in practice but has not been previously considered.


Dosage and scheduling regimens for erythrocyte-sedimenting macromolecules.


title: SiC solid state circuit breaker with an adjustable current-time tripping profile

abstract: This paper reports a SiC-based solid-state circuit breaker (SSCB) with an adjustable current-time (I-t) tripping profile for both ultrafast short circuit protection and overload protection. The tripping time ranges from 0.5 microsecond to 10 seconds for a fault current ranging from 0.8X to 10X of the nominal current. The I-t tripping profile, adjustable by choosing different resistance values in the analog control circuit, can help avoid nuisance tripping of the SSCB due to inrush transient current. The maximum thermal capability of the 1200V SiC JFET static switch in the SSCB is investigated to set a practical thermal limit for the I-t tripping profile. Furthermore, a low fault current ‘blind zone’ limitation of the prior SSCB design is discussed and a new circuit solution is proposed to operate the SSCB even under a low fault current condition. Both simulation and experimental results are reported.


title: Switched tank converter based partial power architecture for voltage regulation applications

abstract: Switched capacitor converters and their resonant alternatives (e.g., switched-tank converters) have been shown to provide high efficiency and high power density. One major problem of these converters is poor voltage regulation capability. This paper proposes a partial power architecture based on the switched-tank converters to realize the voltage regulation. This topology can provide high efficiency, high power density, and can be potentially used in applications with wide voltage and power ranges. A prototype of a 48-to-12 V 500 W converter was designed and built to demonstrate the advantages of this topology, achieving a peak efficiency of 98.4% and a power density of ∼800 W/in3.


title: Haptoglobin: function and polymorphism.

abstract: Haptoglobin is an acute phase protein capable of binding haemoglobin, thus preventing iron loss and renal damage. Haptoglobin also acts as an antioxidant, has antibacterial activity and plays a role in modulating many aspects of the acute phase response. There are 3 major haptoglobin phenotypes--Hp(1-1), Hp(2-1) and Hp(2-2). Possession of a particular phenotype has been associated with a variety of common disorders (e.g. cardiovascular disease, autoimmune disorders, malignancy), a fact which can only be explained by the idea that possession of a particular phenotype offers some protection against the development of these disorders. Knowledge of phenotype could therefore aid in the prognosis of disease and allow treatment to be better tailored to suit an individuals' needs.


title: ‘Dreadful things can happen’: Cautionary tales for the safe practitioner

abstract: This paper investigates the repertoires teachers employ when talking about taking children outside of the classroom and keeping them safe. Research data were a series of individual and focus group interviews with primary school teachers. Three repertoires were identified: “safe practitioner,” “adventurous risk-taker” and “fun, pleasure and excitement seeker”. The main body of the paper discusses these repertoires. An interview sequence highlights repertoires ‘in action’. The paper illustrates how, according to the teachers, cautionary tales structure their safe practice in outdoor education.


title: Detecting Hate Speech in Social Media

abstract: In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78% accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.


title: Compounding of Wealth in Proof-of-Stake Cryptocurrencies

abstract: Proof-of-stake (PoS) is a promising approach for designing efficient blockchains, where block proposers are randomly chosen with probability proportional to their stake. A primary concern in PoS systems is the “rich getting richer” effect, whereby wealthier nodes are more likely to get elected, and hence reap the block reward, making them even wealthier. In this paper, we introduce the notion of equitability, which quantifies how much a proposer can amplify her stake compared to her initial investment. Even with everyone following protocol (i.e., honest behavior), we show that existing methods of allocating block rewards lead to poor equitability, as does initializing systems with small stake pools and/or large rewards relative to the stake pool. We identify a geometric reward function, which we prove is maximally equitable over all choices of reward functions under honest behavior and bound the deviation for strategic actions; the proofs involve the study of optimization problems and stochastic dominances of Polya urn processes. These results allow us to provide a systematic framework to choose the parameters of a practical incentive system for PoS cryptocurrencies.


title: Understanding Taxi Service Strategies From Taxi GPS Traces

abstract: Taxi service strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical time-stamped GPS traces. Mining GPS traces to understand the service strategies of skilled taxi drivers can benefit the drivers themselves, passengers, and city planners in a number of ways. This paper intends to uncover the efficient and inefficient taxi service strategies based on a large-scale GPS historical database of approximately 7600 taxis over one year in a city in China. First, we separate the GPS traces of individual taxi drivers and link them with the revenue generated. Second, we investigate the taxi service strategies from three perspectives, namely, passenger-searching strategies, passenger-delivery strategies, and service-region preference. Finally, we represent the taxi service strategies with a feature matrix and evaluate the correlation between service strategies and revenue, informing which strategies are efficient or inefficient. We predict the revenue of taxi drivers based on their strategies and achieve a prediction residual as less as 2.35 RMB/h, The currency unit in China; 1 RMB $\approx$ U.S. $0.17.


title: On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models

abstract: This paper addresses the general problem of reinforcement learning (RL) in partially observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned from scratch to drive simulated cars from high-dimensional video input. However, real brains are more powerful in many ways. In particular, they learn a predictive model of their initially unknown environment, and somehow use it for abstract (e.g., hierarchical) planning and reasoning. Guided by algorithmic information theory, we describe RNN-based AIs (RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending sequences of tasks, some of them provided by the user, others invented by the RNNAI itself in a curious, playful fashion, to improve its RNN-based world model. Unlike our previous model-building RNN-based RL machines dating back to 1990, the RNNAI learns to actively query its model for abstract reasoning and planning and decision making, essentially "learning to think." The basic ideas of this report can be applied to many other cases where one RNN-like system exploits the algorithmic information content of another. They are taken from a grant proposal submitted in Fall 2014, and also explain concepts such as "mirror neurons." Experimental results will be described in separate papers.


title: 6-DoF model-based tracking of arbitrarily shaped 3D objects

abstract: Image-based 6-DoF pose estimation of arbitrarily shaped 3D objects based on their shape is a rarely studied problem. Most existing image-based methods for pose estimation either exploit textural information in form of local features or, if shape-based, rely on the extraction of straight line segments or other primitives. Straight-forward extensions of 2D approaches are potentially more general, but in practice assume a limited range of possible view angles. The general problem is that a 3D object can potentially produce completely different 2D projections depending on its relative pose to the observing camera. One way to reduce the solution space is to exploit temporal information, i.e. perform tracking. Again, existing model-based tracking approaches rely on relatively simple object geometries. In this paper, we propose a particle filter based tracking approach that can deal with arbitrary shapes and arbitrary or even no texture, i.e. it offers a general solution to the rigid object tracking problem. As our approach can deal with occlusions, it is in particular of interest in the context of goal-directed imitation learning involving the observation of object manipulations. Results of simulation experiments as well as real-world experiments with different object types prove the practical applicability of our approach.


title: Detection of ECG characteristic points using wavelet transforms

abstract: An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious base line drift and noise.<<ETX>>


title: Discovering Response-Eliciting Factors in Social Question Answering : A Reddit Inspired Study.

abstract: Questions form an integral part of our everyday communication, both offline and online. Getting responses to our questions from others is fundamental to satisfying our information need and in extending our knowledge boundaries. A question may be represented using various factors such as social, syntactic, semantic, etc. We hypothesize that these factors contribute with varying degrees towards getting responses from others for a given question. We perform a thorough empirical study to measure effects of these factors using a novel question and answer dataset from the website Reddit.com. We also use a sparse non-negative matrix factorization technique to automatically induce interpretable semantic factors from the question dataset. Such interpretable factor-based analysis overcomes limitations faced by prior related research. We also document various patterns on response prediction we observe during our analysis. For instance, we found that preference-probing questions are rarely answered by actors.


title: A Lower-Back Robotic Exoskeleton

abstract: A lower-back exoskeleton prototype designed to provide back support for industrial workers who manually handle heavy materials is presented in this article. Reducing spinal loads during these tasks can reduce the risk of work-related back injuries. Biomechanical studies show that compression of the lumbar spine is a key risk factor for musculoskeletal injuries. To address this issue, we present a wearable exoskeleton designed to provide back support and reduce lumbar spine compression. To provide effective assistance and avoid injury to muscles or tendons, we apply a continuous torque of approximately 40 Nm on both hip joints to actively assist both hip abduction/adduction (HAA) and hip flexion/extension (HFE). Each actuation unit includes a modular and a compact series-elastic actuator (SEA) with a clutch. The SEA provides mechanical compliance at the interface between the exoskeleton and the user, and the clutches can automatically disengage the torque between the exoskeleton and the user. These experimental results show that the exoskeleton can lower lumbar compression by reducing the need for muscular activity in the spine. Furthermore, powering both HFE and HAA can effectively reduce the lumbar spinal loading user experience when lifting and lowering objects while in a twisted posture.


RapidChain: A Fast Blockchain Protocol via Full Sharding.


title: Fast Dynamic Routing Based on Weighted Kernel Density Estimation

abstract: Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks. A capsule groups data into vectors or matrices as poses rather than conventional scalars to represent specific properties of target instance. Besides of pose, a capsule should be attached with a probability (often denoted as activation) for its presence. The dynamic routing helps capsules achieve more generalization capacity with many fewer model parameters. However, the bottleneck that prevents widespread applications of capsule is the expense of computation during routing. To address this problem, we generalize existing routing methods within the framework of weighted kernel density estimation, and propose a fast routing methods. Our method prompts the time efficiency of routing by nearly 40% with negligible performance degradation. By stacking a hybrid of convolutional layers and capsule layers, we construct a network architecture to handle inputs at a resolution of \(64\times {64}\) pixels. The proposed models achieve a parallel performance with other leading methods in multiple benchmarks.


Efficient Sequence Learning with Group Recurrent Networks


title: No Peek: A Survey of private distributed deep learning

abstract: We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gradient descent are compared in addition to private and secure approaches of differential privacy, homomorphic encryption, oblivious transfer and garbled circuits in the context of neural networks. We study their benefits, limitations and trade-offs with regards to computational resources, data leakage and communication efficiency and also share our anticipated future trends.


title: A 1mV voltage ripple 0.97mm2 fully integrated low-power hybrid buck converter

abstract: The output voltage ripple is one of the most significant system parameters in switch-mode power supplies. This ripple degrades the performance of application specific integrated circuits (ASICs). The most common way to reduce it is to use additional integrated low drop-out regulators (LDO) on the ASIC. This technique usually suffers from high system efficiency as it is required for portable electronic systems. It also increases the design challenges of on-chip power management circuits and area required for the LDOs. This work presents a low-power fully integrated 0.97mm2 DC-DC Buck converter with a tuned series LDO with 1mV voltage ripple in a 0.25μm BiCMOS process. The converter prodives a power supply rejection ratio of more than 60 dB from 1 to 6MHz and a load current range of 0...400 mA. A peak efficiency of 93.7% has been measured. For high light load efficiency, automatic mode operation is implemented. To decrease the form factor and costs, the external components count has been reduced to a single inductor of 1 μH and two external capacitors of 2 μF each.


title: Pressure Welding of Silver Nanowires Networks at Room Temperature as Transparent Electrodes for Efficient Organic Light‐Emitting Diodes

abstract: In this work, polymethylmethacrylate (PMMA) as a superior mediate for the pressure welding of silver nanowires (Ag NWs) networks as transparent electrodes without any thermal treatment is demonstrated. After a pressing of 200 kg cm-2 , not only the sheet resistance but also the surface roughness of the PMMA-mediated Ag NWs networks decreases from 2.6 kΩ sq-1 to 34.3 Ω sq-1 and from 76.1 to 12.6 nm, respectively. On the other hand, high transparency of an average transmittance in the visible wavelengths of 93.5% together with a low haze value of 2.58% can be achieved. In terms of optoelectronic applications, the promising potential of the PMMA-mediated pressure-welded Ag NWs networks used as a transparent electrode in a green organic light-emitting diode (OLED) device is also demonstrated. In comparison with the OLED based on commercial tin-doped indium oxide electrode, the increments of power efficiency and external quantum efficiency (EQE) from 80.1 to 85.9 lm w-1 and 19.2% to 19.9% are demonstrated. In addition, the PMMA-mediated pressure welding succeeds in transferring Ag NWs networks to flexible polyethylene naphthalate and polyimide substrates with the sheet resistance of 42 and 91 Ω sq-1 after 10 000 times of bending, respectively.


title: The effect of athletic expertise and trait emotional intelligence on decision-making

abstract: The quality of decision and assessment of risk are key determinants of successful sport performance. Athletes differ fundamentally in their decision-making ability according to their athletic expertise level. Moreover, given the influence of emotions on decision-making, it is likely that a trait reflecting emotional functioning, trait emotional intelligence, may also influence decision-making. Therefore, the aim of this research was to investigate the respective contribution of athletic expertise and trait emotional intelligence to non-athletic decision-making. In total, 269 participants aged between 18 and 26 years with a range of athletic experience i.e., none (n = 71), novice (n = 54), amateur (n = 55), elite (n = 45) and super-elite (n = 44), completed the Emotional Intelligence Scale and the Cambridge Gambling Task. Regression modelling indicated a significant positive relationship of athletic expertise and trait emotional intelligence with the quality of decision-making, and a negative relationship with deliberation time and risk-taking. Cognitive skills transfer may explain the higher decision-making scores associated with higher athletic expertise, while individuals with higher trait emotional intelligence may anticipate better the emotional consequences linked with a gambling task, which may help individuals make better decisions and take less risks.


Face Recognition: A Survey


title: A High-Quality Denoising Dataset for Smartphone Cameras

abstract: The last decade has seen an astronomical shift from imaging with DSLR and point-and-shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor size, smartphone images have notably more noise than their DSLR counterparts. While denoising for smartphone images is an active research area, the research community currently lacks a denoising image dataset representative of real noisy images from smartphone cameras with high-quality ground truth. We address this issue in this paper with the following contributions. We propose a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras. Using this procedure, we have captured a dataset - the Smartphone Image Denoising Dataset (SIDD) - of ~30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras and generated their ground truth images. We used this dataset to benchmark a number of denoising algorithms. We show that CNN-based methods perform better when trained on our high-quality dataset than when trained using alternative strategies, such as low-ISO images used as a proxy for ground truth data.


title: Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets

abstract: The detection and identification of extreme weather events in large scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. ::: Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, there are many different types of spatially localized climate patterns of interest (including hurricanes, extra-tropical cyclones, weather fronts, blocking events, etc.) ::: found in simulation data for which labeled data is not available at large scale for all simulations of interest. ::: We present a multichannel spatiotemporal encoder-decoder CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. ::: This architecture is designed to fully model multi-channel simulation data, temporal dynamics and unlabelled data within a reconstruction and prediction framework so as to improve the detection of a wide range of extreme weather events. ::: Our architecture can be viewed as a 3D convolutional autoencoder with an additional modified one-pass bounding box regression loss. ::: We demonstrate that our approach is able to leverage temporal information and unlabelled data to improve localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data, and facilitate further work in understanding and mitigating the effects of climate change.


title: The effects of animated characters on anxiety, task performance, and evaluations of user interfaces

abstract: Animated characters are common in user interfaces, but important questions remain about whether characters work in all situations and for all users. This experiment tested the effects of different character presentations on user anxiety, task performance, and subjective evaluations of two commerce websites. There were three character conditions (no character, a character that ignored the user, and a character that closely monitored work on the website). Users were separated into two groups that had different attitudes about accepting help from others: people with control orientations that were external (users thought that other people controlled their success) and those with internal orientations (users thought they were in control). Results showed that the effects of monitoring and individual differences in thoughts about control worked as they do in real life. Users felt more anxious when characters monitored their website work and this effect was strongest for users with an external control orientation. Monitoring characters also decreased task performance, but increased trust in website content. Results are discussed in terms of design considerations that maximize the positive influence of animated agents.


title: Gabor-based kernel PCA with fractional power polynomial models for face recognition

abstract: This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.


title: Study on SVM Compared with the other Text Classification Methods

abstract: Based on the text information processing, we have made a study on the application of support vector machine in text categorization. Through introducing the basic principle of SVM, we described the process of text classification and further proposed a SVM-based classification model. Finally, experimental data show that F1 value of SVM classifier has reached more than 86.26%, and the classification results comparing to other classification methods have greatly improved, and it also proves that SVM is an effective machine learning method.


title: BELM: Bayesian Extreme Learning Machine

abstract: The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a Bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.


title: Frame-Based Medium Access Control for 5G Wireless Networks

abstract: Millimeter wave (mmWave) communications is one of the key technologies for future 5th generation (5G) wireless networks. In this paper, we investigate the problem of medium access control (MAC) in mmWave networks. We develop a frame-based scheduling directional MAC protocol, termed FDMAC, to achieve the goal of leveraging collision-free concurrent transmissions to fully exploit spatial reuse in mmWave networks. The high efficiency of FDMAC is achieved by amortizing the scheduling overhead over multiple concurrent, back-to-back transmissions in a row. The core of FDMAC is a graph coloring-based scheduling algorithm, termed greedy coloring (GC) algorithm, that can compute near-optimal schedules with respect to the total transmission time with low complexity. FDMAC is analyzed and evaluated with simulations.


title: Lessons Learned in Exploring the Leap Motion(TM) Sensor for Gesture-based Instrument Design

abstract: The Leap Motion™ sensor offers fine-grained gesture-recognition and hand tracking. Since its release, there have been several uses of the device for instrument design, musical interaction and expression control, documented through online video. However, there has been little formal documented investigation of the potential and challenges of the platform in this context. This paper presents lessons learned from work-in-progress on the development of musical instruments and control applications using the Leap Motion™ sensor. Two instruments are presented: Air-Keys and Air-Pads and the potential for augmentation of a traditional keyboard is explored. The results show that the platform is promising in this context but requires various challenges, both physical and logical, to be overcome.


title: An Acoustic emission event determination method for acoustic emission testing of tank bottom based on cluster analysis

abstract: Acoustic emission (AE) testing method has been widely used for examining tank bottoms. As there is no need to empty the tanks, this method can save a lot of time and money. The evaluation of tank bottom corrosion based on AE testing method is realized by analyzing the number of effective AE events. An effective AE event is an AE event located on the tank bottom. Each AE event is composed of more than three AE signals received by different AE sensors, and the signals should be generated by the same AE source. However, the traditional AE event determination process is carried out without considering the situation where multiple AE sources generate signals simultaneously. So the traditional AE event determination method may identify the AE signals coming from different AE sources as one AE event, which would produce incorrect test result. Also the traditional AE event determination method would be affected by fake hits due to noise. In order to improve the measurement accuracy, a new AE event determination method based on the cluster analysis is proposed. Experiments showed that the AE events extracted by using cluster analysis method are more accurate. In addition, the cluster analysis method exhibits good noise resistance so that it can increase the accuracy of tank bottom integrity evaluation.


title: KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition

abstract: This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in "double discriminant subspaces." The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.


title: Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics.

abstract: The wealth of information provided by real-time streams of data has paved the way for life-changing technological advancements, improving the quality of life of people in many ways, from facilitating knowledge exchange to self-understanding and self-monitoring. Moreover, the analysis of anonymized and aggregated large-scale human behavioral data offers new possibilities to understand global patterns of human behavior and helps decision makers tackle problems of societal importance. In this article, we highlight the potential societal benefits derived from big data applications with a focus on citizen safety and crime prevention. First, we introduce the emergent new research area of big data for social good. Next, we detail a case study tackling the problem of crime hotspot classification, that is, the classification of which areas in a city are more likely to witness crimes based on past data. In the proposed approach we use demographic information along with human mobility characteristics as derived from anonymized and aggregated mobile network data. The hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime is supported by our findings. Our models, built on and evaluated against real crime data from London, obtain accuracy of almost 70% when classifying whether a specific area in the city will be a crime hotspot or not in the following month.


title: Random walks for interactive alpha-matting

abstract: Interactive, efficient, methods of foreground extraction and alpha-matting are of increasing practical importance for digital image editing. Although several new approaches to this problem have recently been developed, many challenges remain. We propose a new technique based on random walks that has the following advantages: First, by leveraging a recent technique from manifold learning theory, we effectively use RGB values to set boundaries for the random walker, even in fuzzy or low-contrast images. Second, the algorithm is straightforward to implement, requires specification of only a single free parameter (set the same for all images), and performs the segmentation and alpha-matting in a single step. Third, the user may locally fine tune the results by interactively manipulating the foreground/background maps. Finally, the algorithm has an inherit parallelism that leads to a particularly efficient implementation via the graphics processing unit (GPU). Our method processes a 1024× 1024 image at the interactive speed of 0.5 seconds and, most importantly, produces highquality results. We show that our algorithm can generate good segmentation and matting results at an interactive rate with minimal user interaction.


title: Building models of animals from video

abstract: This paper argues that tracking, object detection, and model building are all similar activities. We describe a fully automatic system that builds 2D articulated models known as pictorial structures from videos of animals. The learned model can be used to detect the animal in the original video - in this sense, the system can be viewed as a generalized tracker (one that is capable of modeling objects while tracking them). The learned model can be matched to a visual library; here, the system can be viewed as a video recognition algorithm. The learned model can also be used to detect the animal in novel images - in this case, the system can be seen as a method for learning models for object recognition. We find that we can significantly improve the pictorial structures by augmenting them with a discriminative texture model learned from a texture library. We develop a novel texture descriptor that outperforms the state-of-the-art for animal textures. We demonstrate the entire system on real video sequences of three different animals. We show that we can automatically track and identify the given animal. We use the learned models to recognize animals from two data sets; images taken by professional photographers from the Corel collection, and assorted images from the Web returned by Google. We demonstrate quite good performance on both data sets. Comparing our results with simple baselines, we show that, for the Google set, we can detect, localize, and recover part articulations from a collection demonstrably hard for object recognition


Mining Personal Data Using Smartphones and Wearable Devices: A Survey


title: Risk factor profile for bereaved spouses

abstract: Abstract Health care professionals employed in diverse settings are in an ideal situation to identify those bereaved spouses who may be at higher risk for developing physical and/or emotional problems related to the bereavement process. This study examined demographic variables and their relationship to behaviors associated with grief. Significant findings revealed that age, sex, socioeconomic status, and quality of relationship to the deceased are related to specific high-risk bereavement behaviors. The results of this study can be used to screen high-risk spouses.


title: BIOINFORMATICS Striped Smith-Waterman speeds database searches six times over other SIMD implementations.

abstract: Motivation: The only algorithm guaranteed to find the optimal local alignment is the Smith--Waterman. It is also one of the slowest due to the number of computations required for the search. To speed up the algorithm, Single-Instruction Multiple-Data (SIMD) instructions have been used to parallelize the algorithm at the instruction level. ::: ::: Results: A faster implementation of the Smith--Waterman algorithm is presented. This algorithm achieved 2--8 times performance improvement over other SIMD based Smith--Waterman implementations. On a 2.0 GHz Xeon Core 2 Duo processor, speeds of >3.0 billion cell updates/s were achieved. ::: ::: Availability: http://farrar.michael.googlepages.com/Smith-waterman ::: ::: Contact: farrar.michael@gmail.com


title: A Pilot Study of the KIBO Robot in Children with Severe ASD

abstract: This pilot study explores the feasibility of using the KIBO Robot as an engaging platform to positively impact social and emotional development in children with ASD. KIBO is a programmable toy robot originally designed to teach coding and sequencing to neuro-typical children between 4 and 7 years of age. To assess its use in children with severe ASD, twelve participants were introduced to KIBO and engaged in a variety of activities with the robot over four consecutive days. Their interactions were observed on site by raters and simultaneously videotaped for later analysis. We performed a detailed quantitative and qualitative analysis in two subjects who completed six or more of the eight planned KIBO play sessions. We observed that most of the participants showed sustained interest in the KIBO robot and increased the frequency of their interactions with adults across play sessions. Although the participants demonstrated only a limited understanding of programming principles during the study, they managed to manipulate the KIBO appropriately, engaged socially with the adults in the room and interacted positively with the robot during individual play. The findings suggest that the KIBO robot warrants further study as an engaging educational platform for children with ASD.


Multiclass Capped ℓp-Norm SVM for Robust Classifications.


title: Treatment of Hyaluronic Acid Filler-Induced Impending Necrosis With Hyaluronidase: Consensus Recommendations.

abstract: Injection-induced necrosis is a rare but dreaded consequence of soft tissue augmentation with filler agents. It usually occurs as a result of injection of filler directly into an artery, but can also result from compression or injury. We provide recommendations on the use of hyaluronidase when vascular compromise is suspected. Consensus recommendations were developed by thorough discussion and debate amongst the authors at a roundtable meeting on Wednesday June 18, 2014 in Las Vegas, NV as well as significant ongoing written and verbal communications amongst the authors in the months prior to journal submission. All authors are experienced tertiary care providers. A prompt diagnosis and immediate treatment with high doses of hyaluronidase (at least 200 U) are critically important. It is not felt necessary to do a skin test in cases of impending necrosis. Some experts recommend dilution with saline to increase dispersion or lidocaine to aid vasodilation. Additional hyaluronidase should be injected if improvement is not seen within 60 minutes. A warm compress also aids vasodilation, and massage has been shown to help. Some experts advocate the use of nitroglycerin paste, although this area is controversial. Introducing an oral aspirin regimen should help prevent further clot formation due to vascular compromise. In our experience, patients who are diagnosed promptly and treated within 24 hours will usually have the best outcomes.


title: A Unifying Framework of Mining Trajectory Patterns of Various Temporal Tightness

abstract: Discovering trajectory patterns is shown to be very useful in learning interactions between moving objects. Many types of trajectory patterns have been proposed in the literature, but previous methods were developed for only a specific type of trajectory patterns. This limitation could make pattern discovery tedious and inefficient since users typically do not know which types of trajectory patterns are hidden in their data sets. Our main observation is that many trajectory patterns can be arranged according to the strength of temporal constraints. In this paper, we propose a unifying framework of mining trajectory patterns of various temporal tightness, which we call unifying trajectory patterns ( UT-patterns ). This framework consists of two phases: initial pattern discovery and granularity adjustment . A set of initial patterns are discovered in the first phase, and their granularities (i.e., levels of detail) are adjusted by split and merge to detect other types in the second phase. As a result, the structure called a pattern forest is constructed to show various patterns. Both phases are guided by an information-theoretic formula without user intervention. Experimental results demonstrate that our framework facilitates easy discovery of various patterns from real-world trajectory data.


title: Liability and Computer Security: Nine Principles

abstract: The conventional wisdom is that security priorities should be set by risk analysis. However, reality is subtly different: many computer security systems are at least as much about shedding liability as about minimising risk. Banks use computer security mechanisms to transfer liability to their customers; companies use them to transfer liability to their insurers, or (via the public prosecutor) to the taxpayer; and they are also used to shift the blame to other departments (“we did everything that GCHQ/the internal auditors told us to”). We derive nine principles which might help designers avoid the most common pitfalls.


title: Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs

abstract: We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called superpoint graph (SPG), derived from a partition of the scanned scene into geometrically homogeneous elements. SPGs offer a compact yet rich representation of contextual relationships between object parts, which is then exploited by a graph convolutional network. Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset).


title: A DLL With Dual Edge Triggered Phase Detector for Fast Lock and Low Jitter Clock Generator

abstract: A DLL based on a dual edge triggered phase detector (DET-PD) is proposed for a clock generator in low-power systems. The proposed DLL has a faster lock speed with the same loop dynamics compared to the conventional DLL based on a single-edge triggered phase detector (SET-PD). The proposed DET-PD solves the problem of a narrow capture range or low phase detector gain associated with the conventional DET-PD. In addition, the proposed duty cycle difference compensation circuit (DDC) prevents the increase in the phase offset when the two inputs to the DET-PD have different duty cycle. It also controls the DLL bandwidth to maintain the DLL jitter by controlling the negative edge delay difference tracking. Finally, the proposed duty cycle keeper (DCK) enlarges the duty cycle keeping range of the DLL output. The proposed DLL is fabricated using 0.18-μm process technology. It has an area of 0.035 mm2 and a power consumption of 19 mW at 800 MHz operation. Its lock speed is over 1.9 times faster than that of the DLL based on the SET-PD without degrading the jitter.


title: Investigating People’s Rapport Building and Hindering Behaviors When Working with a Collaborative Robot

abstract: Modern industrial robots are increasingly moving toward collaborating with people on complex tasks as team members, and away from working in isolated cages that are separated from people. Collaborative robots are programmed to use social communication techniques with people, enabling human team members to use their existing inter-personal skills to work with robots, such as speech, gestures, or gaze. Research is increasingly investigating how robots can use higher-level social structures such as team dynamics or conflict resolution. One particularly important aspect of human–human teamwork is rapport building: these are everyday social interactions between people that help to develop professional relationships by establishing trust, confidence, and collegiality, but which are formally peripheral to a task at hand. In this paper, we report on our investigations of how and if people apply similar rapport-building behaviors to robot collaborators. First, we synthesized existing human–human rapport knowledge into an initial human–robot interaction framework; this framework includes verbal and non-verbal behaviors, both for rapport building and rapport hindering, that people can be expected to exhibit. We developed a novel mock industrial task scenario that emphasizes ecological validity, and creates a range of social interactions necessary for investigating rapport. Finally, we report on a qualitative study that investigates how people use rapport hindering or building behaviors in our industrial scenario, which reflects how people may interact with robots in industrial settings.


title: Using a Live-In Laboratory for Ubiquitous Computing Research

abstract: Ubiquitous computing researchers are increasingly turning to sensor-enabled living laboratories for the study of people and technologies in settings more natural than a typical laboratory. We describe the design and operation of the PlaceLab, a new live-in laboratory for the study of ubiquitous technologies in home settings. Volunteer research participants individually live in the PlaceLab for days or weeks at a time, treating it as a temporary home. Meanwhile, sensing devices integrated into the fabric of the architecture record a detailed description of their activities. The facility generates sensor and observational datasets that can be used for research in ubiquitous computing and other fields where domestic contexts impact behavior. We describe some of our experiences constructing and operating the living laboratory, and we detail a recently generated sample dataset, available online to researchers.


title: Self-Organization in Biological Systems

abstract: From the Publisher: ::: "Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology - a field of study at the forefront of life sciences research."--BOOK JACKET.


title: IoT-MAP: IoT mashup application platform for the flexible IoT ecosystem

abstract: Recent mobile environment has various types of smart things which are opportunistically adjacent to the surrounding areas of mobile phones. However most of applications in the market are just statically bound to specific model of designated manufacturer which were chosen in development phase, so they are not interoperable with heterogeneous things even though they have similar functionalities. To optimize utilization of smart things residing in each user's personal pervasive environment, IoT mashup application is required which dynamically discovers heterogeneous things, downloads needed software module at runtime, and provides mash-up application to the user. In this paper, we designed and implemented IoT Mashup Application Platform that supports smartphone-centric discovery, identification, installation, mashup and composition of the pervasive smart things. Furthermore, by decoupling the roles of actors in IoT ecosystem utilizing functionally abstracted thing interfaces, the platform provides benefits to each actor — thing manufacturers can maximize compatibility of their products, application developers can concentrate on their own business logic, and the end-users can select which smart things would be actually used for the composition.


title: Removing obstacles to the pedagogical changes required by Jonassen's vision of authentic technology-enabled learning

abstract: Educators have been striving to achieve meaningful technology use in our K-12 classrooms for over 30 years. Yet, despite significant investments of time and money in infrastructure, training, and support ''we have few assurances that [educators] are able to use technology for teaching and learning'' (NEA, 2008, p. 1). In this article, we call for a shift in focus from technology integration (and the tools used to achieve it), to technology-enabled learning (and the pedagogy used to support it). Building on the early work of Jonassen (1996) in which he proposed using technology as cognitive tools, we suggest that future technology integration efforts should focus on helping teachers engage students in authentic technology-enabled learning environments. As such, technology integration is no longer an isolated goal to be achieved separately from pedagogical goals, but simply the means by which students engage in relevant and meaningful interdisciplinary work. Implications for supporting teacher pedagogical change, from a system's perspective, are discussed.


title: Issues and strategies for the physical design of system-on-a-chip ASICs

abstract: The density and performance of advanced silicon technologies have made system-on-a-chip ASICs possible. SoCs bring together a diverse set of functions and technology features on a single die of enormous complexity. The physical design of these complex ASICs requires a rich set of functional elements that integrate efficiently with a set of design flows and tools productive enough to meet product requirements successfully, without consuming more time or design resources than a simpler design. The architecture described, including functional libraries and physical design conventions, enables the creation of multiple SoC ASIC designs from a common infrastructure that addresses silicon integration, electrical robustness, and packaging challenges. An implementation strategy follows from this design infrastructure that includes hierarchical design concepts, placement, routing, and verification processes.


title: A Hybrid Hierarchical Model for Multi-Document Summarization

abstract: Scoring sentences in documents given abstract summaries created by humans is important in extractive multi-document summarization. In this paper, we formulate extractive summarization as a two step learning problem building a generative model for pattern discovery and a regression model for inference. We calculate scores for sentences in document clusters based on their latent characteristics using a hierarchical topic model. Then, using these scores, we train a regression model based on the lexical and structural characteristics of the sentences, and use the model to score sentences of new documents to form a summary. Our system advances current state-of-the-art improving ROUGE scores by ~7%. Generated summaries are less redundant and more coherent based upon manual quality evaluations.


title: STABILIZATION OF MEAT LIPIDS WITH GROUND SPICES

abstract: The antioxidant efficacy of ground clove, ginger, oregano, rosemary, sage, and thyme was investigated in comminuted pork systems. Spices at 200–2000 ppm levels of addition inhibited the formation of the 2-thiobarbituric acid (TBA) reactive substances (TBARS) by 12–96% over 21-days of storage at 4C. The anti-oxidative effect of spices was concentration-dependent; but close followed by sage and then rosemary was most effective. Ginger and thyme exerted the least effect in prevention of oxidation of meat lipids.


title: Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques

abstract: Smartphones in general and Android in particular are increasingly shifting into the focus of cyber criminals. For understanding the threat to security and privacy, it is important for security researchers to analyze malicious software written for these systems. The exploding number of Android malware calls for automation in the analysis. In this paper, we present Mobile-Sandbox, a system designed to automatically analyze Android applications in novel ways: First, it combines static and dynamic analysis, i.e., results of static analysis are used to guide dynamic analysis and extend coverage of executed code. Additionally, it uses specific techniques to log calls to native (i.e., "non-Java") APIs, and last but not least it combines these results with machine-learning techniques to cluster the analyzed samples into benign and malicious ones. We evaluated the system on more than 69,000 applications from Asian third-party mobile markets and found that about 21 % of them actually use native calls in their code.


title: Fingerprint Matching

abstract: Fingerprint matching has been successfully used by law enforcement for more than a century. The technology is now finding many other applications such as identity management and access control. The authors describe an automated fingerprint recognition system and identify key challenges and research opportunities in the field.


title: Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique

abstract: At present, a number of computer vision-based crack detection techniques have been developed to efficiently inspect and manage a large number of structures. However, these techniques have not replaced visual inspection, as they have been developed under near-ideal conditions and not in an on-site environment. This article proposes an automated detection technique for crack morphology on concrete surface under an on-site environment based on convolutional neural networks (CNNs). A well-known CNN, AlexNet is trained for crack detection with images scraped from the Internet. The training set is divided into five classes involving cracks, intact surfaces, two types of similar patterns of cracks, and plants. A comparative study evaluates the successfulness of the detailed surface categorization. A probability map is developed using a softmax layer value to add robustness to sliding window detection and a parametric study was carried out to determine its threshold. The applicability of the proposed method is evaluated on images taken from the field and real-time video frames taken using an unmanned aerial vehicle. The evaluation results confirm the high adoptability of the proposed method for crack inspection in an on-site environment.


title: Interval Type-2 TSK+ Fuzzy Inference System

abstract: Type-2 fuzzy sets and systems can better handle uncertainties compared to its type-1 counterpart, and the widely applied Mamdani and TSK fuzzy inference approaches have been both extended to support interval type-2 fuzzy sets. Fuzzy interpolation enhances the conventional Mamdani and TKS fuzzy inference systems, which not only enables inferences when inputs are not covered by an incomplete or sparse rule base but also helps in system simplification for very complex problems. This paper extends the recently proposed fuzzy interpolation approach TSK+ to allow the utilization of interval type-2 TSK fuzzy rule bases. One illustrative case based on an example problem from the literature demonstrates the working of the proposed system, and the application on the cart centering problem reveals the power of the proposed system. The experimental investigation confirmed that the proposed approach is able to perform fuzzy inferences using either dense or sparse interval type-2 TSK rule bases with promising results generated.


title: Adversarial Balancing for Causal Inference

abstract: Biases in observational data of treatments pose a major challenge to estimating expected treatment outcomes in different populations. An important technique that accounts for these biases is reweighting samples to minimize the discrepancy between treatment groups. We present a novel reweighting approach that uses bi-level optimization to alternately train a discriminator to minimize classification error, and a balancing weights generator that uses exponentiated gradient descent to maximize this error. This approach borrows principles from generative adversarial networks (GANs) to exploit the power of classifiers for measuring two-sample divergence. We provide theoretical results for conditions in which the estimation error is bounded by two factors: (i) the discrepancy measure induced by the discriminator; and (ii) the weights variability. Experimental results on several benchmarks comparing to previous state-of-the-art reweighting methods demonstrate the effectiveness of this approach in estimating causal effects.


A systematic review of social networks research in information systems: Building a foundation for exciting future research


title: An Interactive 4D Vision Augmentation of Rapid Motion

abstract: We propose an interactive 4D visualization (3D space with an additional time dimension) for the purpose of understanding rapid motion in dynamic 3D information. With an interactive 4D visualization, the user can not only observe dynamic 3D motion from different viewpoints, but also freely adapt the speed of visualization on each viewpoint. We developed a system by reconstructing 3D data from an RGBD camera into a VR environment so that the user can visualize the 3D information via an HMD. The experiment results show that our proposed system outperforms conventional 2D and 3D visualizations in terms of both the user's recognition accuracy and view counts when observing rapid motion.


title: Semantic Analysis of Military Relevant Texts for Intelligence Purposes

abstract: Abstract : The current deployments of the German Federal Armed Forces make it necessary to analyze large quantities of intelligence reports and other documents written in different languages. Natural language processing techniques (NLP) can be applied to efficiently handle these tasks. The ZENON project makes use of an information extraction approach for the (partial) content analysis of English HUMINT reports. It has been extended to do multilingual information extraction (i.e., processing Dari and Tajik texts). This paper focuses on the improvement of ZENON's English semantic analysis. To extend the system's coverage when performing content analysis the authors used a semantic role labeling approach. The paper describes the ZENON system and its information extraction functions, the semantic role labeling approach, and the architecture of the implemented application. The presentation includes briefing charts.


title: Business Process Design by Reusing Business Process Fragments from the Cloud

abstract: The constant development of technologies forces companies to be more innovative in order to stay competitive. In fact, designing a process from scratch is time consuming, error prone and costly. In this context, companies are heading to reuse process fragments when designing a new process to ensure a high degree of efficiency, with respect to delivery deadlines. However, reusing these fragments may disclose sensitive business activities, especially if these latter are deployed in an untrusted environment. In addition, companies are concerned about their user's privacy. To address these issues, we investigate how to build a new business process by reusing the safest existing fragments coming from various cloud servers, i.e. The ones that comply at best with company's preferences and policies, and offer an appropriate level of safety.


title: The RAST Server: Rapid Annotations using Subsystems Technology

abstract: BackgroundThe number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them.DescriptionWe describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment.The service normally makes the annotated genome available within 12–24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service.ConclusionBy providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.


title: {Fully-Automatic Bayesian Piecewise Sparse Linear Models}

abstract: Piecewise linear models (PLMs) have been widely used in many enterprise machine learning problems, which assign linear experts to individual partitions on feature spaces and express whole models as patches of local experts. This paper addresses simultaneous model selection issues of PLMs; partition structure determination and feature selection of individual experts. Our contributions are mainly three-fold. First, we extend factorized asymptotic Bayesian (F AB) inference for hierarchical mixture


title: Fully Convolutional Network for Liver Segmentation and Lesions Detection

abstract: In this work we explore a fully convolutional network (FCN) for the task of liver segmentation and liver metastases detection in computed tomography (CT) examinations. FCN has proven to be a very powerful tool for semantic segmentation. We explore the FCN performance on a relatively small dataset and compare it to patch based CNN and sparsity based classification schemes. Our data contains CT examinations from 20 patients with overall 68 lesions and 43 livers marked in one slice and 20 different patients with a full 3D liver segmentation. We ran 3-fold cross-validation and results indicate superiority of the FCN over all other methods tested. Using our fully automatic algorithm we achieved true positive rate of 0.86 and 0.6 false positive per case which are very promising and clinically relevant results.


title: Optimisation based path planning for car parking in narrow environments

abstract: In the last decades, a large variety of robot motion planners emerged. However, manoeuvring in very narrow environments, e.g., for common parking scenarios, cannot be reliably handled with existing path planners at low computing costs. This is why, this paper presents a fast optimisation based path planner which specialises on narrow environments. In the proposed approach, the kinematic differential equations are discretised. For the resulting discrete path segments, a static optimisation problem is formulated to determine the path independently of the considered scenario. In each iteration step, the path length is also optimised to handle close distances to the obstacles as well as longer driving distances. Due to the local nature heuristic rules for driving direction changes are formulated which intend to imitate the behaviour of a human driver. The drawback of the local nature is the lack of global information to handle scenarios with obstacles blocking the path. To overcome this problem, a tree-based guidance for the local planner is introduced. The landmarks for the tree are implicitly created by the local planner allowing an efficient exploration of the free configuration space. The performance of the algorithm is evaluated utilising Monte-Carlo simulations and compared to state-of-the-art path planning algorithms. Path is calculated by solving several local static optimisation problems iteratively.Driving direction changes are locally determined based on simple rules.Obtained paths are similar to manoeuvres of a human driver.Tree-based extension guides the local planner efficiently.High rate of success in narrow environments with low computing costs.