title: Functional Verification of USB Mass Storage

abstract: This paper presents a functional verification of USB2.0 Card Reader, which includes verification environment, functional coverage model design and course of debug. This system not only finds bugs in the DUT, but also verifies the compliance between hosts and device. The methods of the verification and coverage model design facilitate the verification of USB Mass Storage project which need to be accelerated into market. The system of verification has advantage of being portable to other USB Mass Storage devices.


title: A Quantitative Comparison of Semantic Web Page Segmentation Approaches

abstract: We compare three known semantic web page segmentation algorithms, each serving as an example of a particular approach to the problem, and one self-developed algorithm, WebTerrain, that combines two of the approaches. We compare the performance of the four algorithms for a large benchmark of modern websites we have constructed, examining each algorithm for a total of eight configurations. We found that all algorithms performed better on random pages on average than on popular pages, and results are better when running the algorithms on the HTML obtained from the DOM rather than on the plain HTML. Overall there is much room for improvement as we find the best average F-score to be 0.49, indicating that for modern websites currently available algorithms are not yet of practical use.


Fitness inheritance in genetic algorithms


title: Automated Inference System for End-To-End Diagnosis of Network Performance Issues in Client-Terminal Devices

abstract: Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create the Intelligent Automated Client Diagnostic (IACD) system, which only relies on collection of Transmission Control Protocol (TCP) packet traces. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems and (ii) identifies characteristics unique to the specific fault to report the root cause. The modular design of the system enables support for new access link and fault types. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy. The system can perform fault diagnosis independent of the user’s specific TCP implementation, enabling diagnosis of diverse range of client devices.


title: Automated Text Summarization And The Summarist System

abstract: This paper consists of three parts: a preliminary typology of summaries in general; a description of the current and planned modules and performance of the SUMMARIST automated multilingual text summarization system being built sat ISI, and a discussion of three methods to evaluate summaries.


title: Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud

abstract: In recent years ad hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, the processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. Consequently, the allocated compute resources may be inadequate for big parts of the submitted job and unnecessarily increase processing time and cost. In this paper, we discuss the opportunities and challenges for efficient parallel data processing in clouds and present our research project Nephele. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today's IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution. Based on this new framework, we perform extended evaluations of MapReduce-inspired processing jobs on an IaaS cloud system and compare the results to the popular data processing framework Hadoop.


title: Learning force control policies for compliant manipulation

abstract: Developing robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manipulation tasks imply complex contact interactions with the external world, and involve reasoning about the forces and torques to be applied. Planning under contact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learning. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI2) algorithm to learn these force/torque profiles by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.


title: Optical Single-Sideband Modulation With Tunable Optical Carrier to Sideband Ratio in Radio Over Fiber Systems

abstract: We show that an optical modulator that consists of an integrated dual parallel Mach-Zehnder modulator (dMZM) can be used for obtaining not only optical single-side band modulation but also tunability of optical carrier to sideband ratio (OCSR) simultaneously. Such a modulator will be vital for optimizing the performance of radio over fiber links by improving modulation efficiency and receiver sensitivity and by removing fiber chromatic dispersion induced RF power fading. It is shown that a wide range of OCSR tunability can be obtained by altering bias voltage of dMZM, and optimum OCSR, to maximize the output RF power, depends on RF modulation index and extinction ratio of the integrated dMZM. Good agreement between theory, simulation, and experiment is obtained. For typical extinction ratio and low modulation index, it is found that an OCSR of 0 dB is optimum to maximize RF carrier power. However, for multiband orthogonal frequency-division multiplexing (MB-OFDM) ultra-wideband (UWB) radio, the best error vector magnitude (EVM) of -21.8 dB is obtained experimentally at an OCSR of ~5.4 dB due to avoidance of clipping induced nonlinear distortion.


CMOS ISFET device for DNA Sequencing : Device Compensation , Application Requirements and Recommendations


title: Malware classification with recurrent networks

abstract: Attackers often create systems that automatically rewrite and reorder their malware to avoid detection. Typical machine learning approaches, which learn a classifier based on a handcrafted feature vector, are not sufficiently robust to such reorderings. We propose a different approach, which, similar to natural language modeling, learns the language of malware spoken through the executed instructions and extracts robust, time domain features. Echo state networks (ESNs) and recurrent neural networks (RNNs) are used for the projection stage that extracts the features. These models are trained in an unsupervised fashion. A standard classifier uses these features to detect malicious files. We explore a few variants of ESNs and RNNs for the projection stage, including Max-Pooling and Half-Frame models which we propose. The best performing hybrid model uses an ESN for the recurrent model, Max-Pooling for non-linear sampling, and logistic regression for the final classification. Compared to the standard trigram of events model, it improves the true positive rate by 98.3% at a false positive rate of 0.1%.


Collecting fluency corrections for spoken learner English.


title: A Modbus/TCP Fuzzer for testing internetworked industrial systems

abstract: Modbus/TCP is a network protocol for industrial communications encapsulated in TCP/IP network packets. There is an increasing need to test existing Modbus protocol implementations for security vulnerabilities, as devices become accessible even from the Internet. Fuzz testing can be used to discover implementation bugs in a fast and economical way. We present the design and implementation of MTF, a Modbus/TCP Fuzzer. The MTF incorporates a reconnaissance phase in the testing procedure so as to assist mapping the capabilities of the tested device and to adjust the attack vectors towards a more guided and informed testing rather than plain random testing. The MTF was used to test eight implementations of the Modbus protocol and revealed bugs and vulnerabilities that crash the execution, effectively resulting in denial of service attacks using only a few network packets.


title: Adipose tissue-derived stem cells suppress hypertrophic scar fibrosis via the p38/MAPK signaling pathway

abstract: BackgroundHypertrophic scars (HS) generally occur after injury to the deep layers of the dermis, resulting in functional deficiency for patients. Growing evidence has been identified that the supernatant of adipose tissue-derived stem cells (ADSCs) significantly ameliorates fibrosis of different tissues, but limited attention has been paid to its efficacy on attenuating skin fibrosis. In this study, we explored the effect and possible mechanism of ADSC-conditioned medium (ADSC-CM) on HS.MethodReal-time quantitative polymerase chain reaction (qRT-PCR) and Western blotting were used to detect the expression of collagen I (Col1), collagen III (Col3), and α-smooth muscle actin (α-SMA) after fibroblasts and cultured HS tissues were stimulated with ADSC-CM and p38 inhibitor/activator. Immunofluorescence staining was performed to test the expression of α-SMA. Masson’s trichrome staining, hematoxylin and eosin (H&E) staining, and immunohistochemistry staining were carried out to assess the histological and pathological change of collagen in the BALB/c mouse excisional model. All data were analyzed by using SPSS17.0 software. Statistical analysis was performed by Student’s t tests.ResultsThe in vitro and ex vivo study revealed ADSC-CM decreased the expression of Col1, Col3, and α-SMA. Together, thinner and orderly arranged collagen was manifested in HS tissues cultured with ADSC-CM. Dramatically, the assessed morphology showed an accelerated healing rate, less collagen deposition, and col1- and col3-positive cells in the ADSC-CM treated group. Importantly, the protein level of p-p38 was downregulated in a concentration-dependent manner in HS-derived fibroblasts with ADSC-CM treatment, which further decreased the expression of p-p38 after the application of its inhibitor, SB203580. SB203580 led to an obvious decline in the expression of Col1, Col3, and α-SMA in fibroblasts and cultured HS tissues and presented more ordered arrangement and thinner collagen fibers in BALB/c mice. Lastly, anisomycin, an agonist of p38, upregulated the expression of fibrotic proteins and revealed more disordered structure and denser collagen fibers.ConclusionThis study demonstrated that ADSC-CM could decrease collagen deposition and scar formation in in vitro, ex vivo and in vivo experiments. The regulation of the p38/MAPK signaling pathway played an important role in the process. The application of ADSC-CM may provide a novel therapeutic strategy for HS treatment, and the anti-scarring effect can be achieved by inhibition of the p38/MAPK signaling pathway.


title: AntMiner: mining more bugs by reducing noise interference

abstract: Detecting bugs with code mining has proven to be an effective approach. However, the existing methods suffer from reporting serious false positives and false negatives. In this paper, we developed an approach called AntMiner to improve the precision of code mining by carefully preprocessing the source code. Specifically, we employ the program slicing technique to decompose the original source repository into independent sub-repositories, taking critical operations (automatically extracted from source code) as slicing criteria. In this way, the statements irrelevant to a critical operation are excluded from the corresponding sub-repository. Besides, various semantics-equivalent representations are normalized into a canonical form. Eventually, the mining process can be performed on a refined code database, and false positives and false negatives can be significantly pruned. We have implemented AntMiner and applied it to detect bugs in the Linux kernel. It reported 52 violations that have been either confirmed as real bugs by the kernel development community or fixed in new kernel versions. Among them, 41 cannot be detected by a widely used representative analysis tool Coverity. Besides, the result of a comparative analysis shows that our approach can effectively improve the precision of code mining and detect subtle bugs that have previously been missed.


Technology infusion for complex systems: A framework and case study


title: Contagion of Wishful Thinking in Markets

abstract: Prior research provides only weak and controversial evidence that people overestimate the likelihood of desirable events (wishful thinking), but strong evidence that people bet more heavily on those events (wishful betting). Two experiments show that wishful betting contaminates beliefs in laboratory financial markets because wishful betters appear to possess more favorable information than they actually do. As a consequence, market interaction exacerbates rather than mitigates wishful thinking. This phenomenon, “contagion of wishful thinking,” could be problematic in many settings where people infer others' beliefs from their behavior.


title: ColPack: Software for graph coloring and related problems in scientific computing

abstract: We present a suite of fast and effective algorithms, encapsulated in a software package called ColPack, for a variety of graph coloring and related problems. Many of the coloring problems model partitioning needs arising in compression-based computation of Jacobian and Hessian matrices using Algorithmic Differentiation. Several of the coloring problems also find important applications in many areas outside derivative computation, including frequency assignment in wireless networks, scheduling, facility location, and concurrency discovery and data movement operations in parallel and distributed computing. The presentation in this article includes a high-level description of the various coloring algorithms within a common design framework, a detailed treatment of the theory and efficient implementation of known as well as new vertex ordering techniques upon which the coloring algorithms rely, a discussion of the package's software design, and an illustration of its usage. The article also includes an extensive experimental study of the major algorithms in the package using real-world as well as synthetically generated graphs.


title: Direction of Arrival Estimation Using Directive Antennas in Uniform Circular Arrays

abstract: The effect of directional antenna elements in uniform circular arrays (UCAs) for direction of arrival (DOA) estimation is studied in this paper. While the vast majority of previous work assumes isotropic antenna elements or omnidirectional dipoles, this work demonstrates that improved DOA estimation accuracy and increased bandwidth is achievable with appropriately-designed directional antennas. The Cramer–Rao Lower Bound (CRLB) is derived for UCAs with directional antennas and is compared to isotropic antennas for 4- and 8-element arrays using a theoretical radiation pattern. The directivity that minimizes the CRLB is identified and microstrip patch antennas approximating the optimal theoretical gain pattern are designed to compare the resulting DOA estimation accuracy with a UCA using dipole antenna elements. Simulation results show improved DOA estimation accuracy and robustness using microstrip patch antennas as opposed to conventional dipoles. Additionally, it is shown that the bandwidth of a UCA for DOA estimation is limited only by the broadband characteristics of the directional antenna elements and not by the electrical size of the array as is the case with omnidirectional antennas.


Revisit Long Short-Term Memory : An Optimization Perspective


title: Towards Simultaneous Interpreting: The Timing of Incremental Machine Translation and Speech Synthesis

abstract: In simultaneous interpreting, human experts incrementally construct and extend partial hypotheses about the source speaker’s message, and start to verbalize a corresponding message in the target language, based on a partial translation – which may have to be corrected occasionally. They commence the target utterance in the hope that they will be able to finish understanding the source speaker’s message and determine its translation in time for the unfolding delivery. Of course, both incremental understanding and translation by humans can be garden-pathed, although experts are able to optimize their delivery so as to balance the goals of minimal latency, translation quality and high speech fluency with few corrections. We investigate the temporal properties of both translation input and output to evaluate the tradeoff between low latency and translation quality. In addition, we estimate the improvements that can be gained with a tempo-elastic speech synthesizer.


title: Proposed novel nomenclature of vulvar smooth muscle tumors; a case of Smooth Muscle Tumor of Uncertain Malignant Potential (STUMP) of the vulva

abstract: •Clinical experience with smooth muscle tumors of the vulva is limited.•Some tumors present ambiguous histological features concerning for malignancy.•These include infiltration, mitotic activity, size, atypia and tumor cell necrosis.•A case of smooth muscle tumor of the vulva with cellular atypia is presented.•"Smooth Muscle Tumor of Uncertain Malignant Potential" of the vulva is advocated.


title: Major Determinants of Height Development in Turner Syndrome (TS) Patients Treated With GH: Analysis of 987 Patients From KIGS

abstract: Little is known about factors determining height outcome during GH treatment in Turner syndrome (TS). We investigated 987 TS children within the Kabi International Growth Study (KIGS) who had reached near adult height (NAH) after >4 y GH treatment (including >1 y before puberty). Through multiple regression analysis we developed a model for NAH and total gain. Our results were as follows (median): 1) At start, age 9.7 yrs, height (HT) 118.0 cm (0.0 TS SDS), projected adult height 146.1 cm, GH dose 0.27 mg/kg wk; 2) NAH HT 151.0 cm (1.5 TS SDS); 3) Prepubertal gain 21.2 cm (1.6 TS SDS); 4) Pubertal gain 9.4 cm (0.0 TS SDS). NAH correlated (r = 0.67) with (ranked) HT at GH start (+), 1 year responsiveness to GH (+), MPH (+), age at puberty onset (+), age at GH start (-), and dose (+). The same factors explained (R = 0.90) the total HT gain. However, HT at GH start correlated negatively. Karyotype had no influence on outcome. Evidently, height at GH start (the taller, the better), age at GH start (the younger, the better), the responsiveness to GH (the higher, the better) and age at puberty (the later, the better) determine NAH.


title: Simplification of Information Technology Services at Financial Institutions: A Framework

abstract: Simplification of IT services is an imperative of the times we are in. Large legacy behemoths that exist at financial institutions are a result of years of patch work development on legacy landscapes that have developed in silos at various lines of businesses (LOBs). This increases costs -- for running financial services, changing the services as well as providing services to customers. We present here a basic guide to what constitutes complexity of IT landscape at financial institutions, what simplification means, and opportunities for simplification and how it can be carried out. We also explain a 4-phase approach to planning and executing Simplification of IT services at financial institutions.


title: Optimal Transport for Domain Adaptation

abstract: Domain adaptation from one data space (or domain) to the other is one of the most challenging tasks of modern data analytics. If the adaptation is done cor-rectly, models built on a specific data space become able to process data depicting the same semantic concepts (the classes), but observed by another observation system with its own specificities. In this paper, we propose an optimal transporta-tion model that aligns the representations in the source and target domains. We learn a transportation plan matching both PDFs and constrain labeled samples in the source domain to remain close during transport with a non convex group regularization. This way, we exploit at the same time the labeled information in the source (the same that will be used by the classifier after adaptation) and the unlabeled distributions observed in both domains. We propose an efficient majoration-minimization algorithm to solve the resultaing optimization problem and discuss its convergence. Numerical experiments of real data show the interest of the method, that outperforms state-of-the-art approaches.


title: High-efficiency DC-DC converter with high voltage gain and reduced switch stress

abstract: In this study, a high-efficiency DC-DC converter with high voltage gain and reduced switch stress is proposed. In the proposed topology, a three-winding coupled inductor is used for providing a high voltage gain without extreme switch duty-cycle and enhancing the utility rate of magnetic core. Moreover, the energy in the leakage inductor is released directly to the output terminal for avoiding the phenomenon of circulating current and the production of switch surge voltage. In addition, the delay time formed with the cross of primary and secondary currents of the coupled inductor is manipulated to alleviate the reverse-recovery current of the output diode. It can achieve the aim of high-efficiency power conversion. Furthermore, the closed-loop control methodology is utilized in the proposed scheme to overcome the voltage drift problem of the power source under the variation of loads. Some experimental results via an example of a proton exchange membrane fuel cell (PEMFC) power source with 250 watts nominal rating are given to demonstrate the effectiveness of the proposed power conversion strategy.


title: DESIGN OF A NOVEL SUPER WIDE BAND CIRCULAR- HEXAGONAL FRACTAL ANTENNA

abstract: In this paper, a novel circular-hexagonal Fractal antenna is investigated for super wide band applications. The proposed antenna is made of iterations of a hexagonal slot inside a circular metallic patch with a transmission line. A partial ground plane and asymmetrical patch toward the substrate are used for designing the antenna to achieve a super wide bandwidth ranging from 2.18GHz to 44.5GHz with a bandwidth ratio of 20:4 : 1. The impedance bandwidth and gain of the proposed antenna are much better than the recently reported super wideband antennas which make it appropriate for many wireless communications systems such as ISM, Wi-Fi, GPS, Bluetooth, WLAN and UWB. Details of the proposed antenna design are presented and discussed.


title: Deep neural network based demand side short term load forecasting

abstract: In smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analysis to recent machine learning approach and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management including individual load forecasting is becoming critical. In this paper, we propose deep neural network (DNN) based load forecasting models, and apply them to demand side empirical load database. DNNs are trained by two different ways: pre-training restricted Boltzmann machine and using rectified linear unit without pre-training. DNN forecasting models are trained by individual customer's electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with shallow neural network (SNN), and double seasonal Holt-Winters (DSHW) model. The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. The results show that DNNs exhibit accurate and robust forecasts compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN, and 9% and 29% compared to DSHW.


title: Head, modifier, and constraint detection in short texts

abstract: Head and modifier detection is an important problem for applications that handle short texts such as search queries, ads keywords, titles, captions, etc. In many cases, short texts such as search queries do not follow grammar rules, and existing approaches for head and modifier detection are coarse-grained, domain specific, and/or require labeling of large amounts of training data. In this paper, we introduce a semantic approach for head and modifier detection. We first obtain a large number of instance level head-modifier pairs from search log. Then, we develop a conceptualization mechanism to generalize the instance level pairs to concept level. Finally, we derive weighted concept patterns that are concise, accurate, and have strong generalization power in head and modifier detection. Furthermore, we identify a subset of modifiers that we call constraints. Constraints are usually specific and not negligible as far as the intent of the short text is concerned, while non-constraint modifiers are more subjective. The mechanism we developed has been used in production for search relevance and ads matching. We use extensive experiment results to demonstrate the effectiveness of our approach.


title: Genre classification for million song dataset using confidence-based classifiers combination

abstract: We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided.


title: Authorship Attribution Based on Specific Vocabulary

abstract: In this article we propose a technique for computing a standardized Z score capable of defining the specific vocabulary found in a text (or part thereof) compared to that of an entire corpus. Assuming that the term occurrence follows a binomial distribution, this method is then applied to weight terms (words and punctuation symbols in the current study), representing the lexical specificity of the underlying text. In a final stage, to define an author profile we suggest averaging these text representations and then applying them along with a distance measure to derive a simple and efficient authorship attribution scheme. To evaluate this algorithm and demonstrate its effectiveness, we develop two experiments, the first based on 5,408 newspaper articles (Glasgow Herald) written in English by 20 distinct authors and the second on 4,326 newspaper articles (La Stampa) written in Italian by 20 distinct authors. These experiments demonstrate that the suggested classification scheme tends to perform better than the Delta rule method based on the most frequent words, better than the chi-square distance based on word profiles and punctuation marks, better than the KLD scheme based on a predefined set of words, and better than the naive Bayes approach.


title: Feature-Rich Part-Of-Speech Tagging With A Cyclic Dependency Network

abstract: We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result.


title: Miniaturized rat-race coupler with bandpass response and good stopband rejection

abstract: Coupled-resonator network is applied to the design of a rat-race coupler with bandpass response in this paper. Net-type resonators are used to reduce the circuit size and control the harmonics. As a result, the proposed coupler exhibits not only a compact size but also good stopband rejection. The proposed coupler occupies only one sixth area of the conventional design, while the rejection level in the stopband is better than 30 dB up to 4.3ƒ 0 . The measured results are in good agreement with the simulated predictions.


title: Personality types in software engineering

abstract: Software engineering is forecast to be among the fastest growing employment field in the next decades. The purpose of this investigation is two-fold: Firstly, empirical studies on the personality types of software professionals are reviewed. Secondly, this work provides an up-to-date personality profile of software engineers according to the Myers-Briggs Type Indicator.


title: A New Coreless Axial Flux Interior Permanent Magnet Synchronous Motor With Sinusoidal Rotor Segments

abstract: Axial flux permanent magnet (AFPM) motors are frequently used in many applications. Axial gap coreless motors with surface-mounted magnets are the most noteworthy ones due to their potentially higher efficiency and smaller axial length. In this paper, a new spoke-type AFPM coreless disk motor with dual rotor and single stator is proposed with sinusoidal rotor segments. This new disk-type motor is an interior PM (IPM) motor with spoke-type rotor and variable air gap to obtain sinusoidal back Electro-Motive Force waveform. It is shown that it is possible to increase the air-gap flux density in the proposed new coreless AF-IPM motor and increase the torque output compared with the conventional surface-mounted AFPM motors.


title: Currency security and forensics: a survey

abstract: By its definition, the word `currency' refers to an agreed medium for exchange, a nation's currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desired.


title: Transfer learning for children's speech recognition

abstract: Children's speech processing is more challenging than that of adults due to lacking of large scale children's speech corpora. With the developing of the physical speech organ, high inter speaker and intra speaker variabilities are observed in children's speech. On the other hand, data collection on children is difficult as children usually have short attention span and their language proficiency is limited. In this paper, we propose to improve children's automatic speech recognition performance with transfer learning technique. We compare two transfer learning approaches in enhancing children's speech recognition performance with adults' data. The first method is to perform acoustic model adaptation on the pre-trained adult model. The second is to train acoustic model with deep neural network based multi-task learning approach: the adults' and children's acoustic characteristics are learnt jointly in the shared hidden layers, while the output layers are optimized with different speaker groups. Our experiment results show that both transfer learning approaches are effective in transferring rich phonetic and acoustic information from adults' model to children model. The multi-task learning approach outperforms the acoustic adaptation approach. We further show that the speakers' acoustic characteristics in languages can also benefit the target language under the multi-task learning framework.


title: GTApprox: surrogate modeling for industrial design

abstract: We describe GTApprox - a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approximation algorithms, several advanced methods of automated model selection, novel options in the form of hints. We demonstrate the efficiency of GTApprox on a large collection of test problems. In addition, we describe several applications of GTApprox to real engineering problems.


title: Light Field Intrinsics with a Deep Encoder-Decoder Network

abstract: We present a fully convolutional autoencoder for light fields, which jointly encodes stacks of horizontal and vertical epipolar plane images through a deep network of residual layers. The complex structure of the light field is thus reduced to a comparatively low-dimensional representation, which can be decoded in a variety of ways. The different pathways of upconvolution we currently support are for disparity estimation and separation of the lightfield into diffuse and specular intrinsic components. The key idea is that we can jointly perform unsupervised training for the autoencoder path of the network, and supervised training for the other decoders. This way, we find features which are both tailored to the respective tasks and generalize well to datasets for which only example light fields are available. We provide an extensive evaluation on synthetic light field data, and show that the network yields good results on previously unseen real world data captured by a Lytro Illum camera and various gantries.


title: CNN vs. SIFT for Image Retrieval: Alternative or Complementary?

abstract: In the past decade, SIFT is widely used in most vision tasks such as image retrieval. While in recent several years, deep convolutional neural networks (CNN) features achieve the state-of-the-art performance in several tasks such as image classification and object detection. Thus a natural question arises: for the image retrieval task, can CNN features substitute for SIFT? In this paper, we experimentally demonstrate that the two kinds of features are highly complementary. Following this fact, we propose an image representation model, complementary CNN and SIFT (CCS), to fuse CNN and SIFT in a multi-level and complementary way. In particular, it can be used to simultaneously describe scene-level, object-level and point-level contents in images. Extensive experiments are conducted on four image retrieval benchmarks, and the experimental results show that our CCS achieves state-of-the-art retrieval results.


Building consumer trust online


title: Lazy theta*: any-angle path planning and path length analysis in 3D

abstract: Grids with blocked and unblocked cells are often used to represent continuous 2D and 3D environments in robotics and video games. The shortest paths formed by the edges of 8-neighbor 2D grids can be up to ≈ 8% longer than the shortest paths in the continuous environment. Theta* typically finds much shorter paths than that by propagating information along graph edges (to achieve short runtimes) without constraining paths to be formed by graph edges (to find short "any-angle" paths). We show in this paper that the shortest paths formed by the edges of 26-neighbor 3D grids can be ≈ 13% longer than the shortest paths in the continuous environment, which highlights the need for smart path planning algorithms in 3D. Theta* can be applied to 3D grids in a straight-forward manner, but it performs a line-of-sight check for each unexpanded visible neighbor of each expanded vertex and thus it performs many more line-of-sight checks per expanded vertex on a 26-neighbor 3D grid than on an 8-neighbor 2D grid. We therefore introduce Lazy Theta*, a variant of Theta* which uses lazy evaluation to perform only one line-of-sight check per expanded vertex (but with slightly more expanded vertices). We show experimentally that Lazy Theta* finds paths faster than Theta* on 26-neighbor 3D grids, with one order of magnitude fewer line-of-sight checks and without an increase in path length.


title: Deep Convolutional Neural Networks and Noisy Images

abstract: The presence of noise represent a relevant issue in image feature extraction and classification. In deep learning, representation is learned directly from the data and, therefore, the classification model is influenced by the quality of the input. However, the ability of deep convolutional neural networks to deal with images that have a different quality when compare to those used to train the network is still to be fully understood. In this paper, we evaluate the generalization of models learned by different networks using noisy images. Our results show that noise cause the classification problem to become harder. However, when image quality is prone to variations after deployment, it might be advantageous to employ models learned using noisy data.


title: Online Outlier Detection in Sensor Data Using Non-Parametric Models

abstract: Sensor networks have recently found many popular applications in a number of different settings. Sensors at different locations can generate streaming data, which can be analyzed in real-time to identify events of interest. In this paper, we propose a framework that computes in a distributed fashion an approximation of multi-dimensional data distributions in order to enable complex applications in resource-constrained sensor networks.We motivate our technique in the context of the problem of outlier detection. We demonstrate how our framework can be extended in order to identify either distance- or density-based outliers in a single pass over the data, and with limited memory requirements. Experiments with synthetic and real data show that our method is efficient and accurate, and compares favorably to other proposed techniques. We also demonstrate the applicability of our technique to other related problems in sensor networks.


title: Singing Voice Separation with Deep U-Net Convolutional Networks

abstract: The decomposition of a music audio signal into its vocal and backing track components is analogous to image-to-image translation, where a mixed spectrogram is transformed into its constituent sources. We propose a novel application of the U-Net architecture — initially developed for medical imaging — for the task of source separation, given its proven capacity for recreating the fine, low-level detail required for high-quality audio reproduction. Through both quantitative evaluation and subjective assessment, experiments demonstrate that the proposed algorithm achieves state-of-the-art performance.


title: SoftAir: a software defined networking architecture for 5G wireless systems

abstract: Abstract One of the main building blocks and major challenges for 5G cellular systems is the design of flexible network architectures which can be realized by the software defined networking paradigm. Existing commercial cellular systems rely on closed and inflexible hardware-based architectures both at the radio frontend and in the core network. These problems significantly delay the adoption and deployment of new standards, impose significant challenges in implementing and innovation of new techniques to maximize the network capacity and accordingly the coverage, and prevent provisioning of truly- differentiated services which are able to adapt to growing and uneven and highly variable traffic patterns. In this paper, a new software-defined architecture, called SoftAir, for next generation (5G) wireless systems, is introduced. Specifically, the novel ideas of network function cloudification and network virtualization are exploited to provide a scalable, flexible and resilient network architecture. Moreover, the essential enabling technologies to support and manage the proposed architecture are discussed in details, including fine-grained base station decomposition, seamless incorporation of Openflow, mobility- aware control traffic balancing, resource-efficient network virtualization, and distributed and collaborative traffic classification. Furthermore, the major benefits of SoftAir architecture with its enabling technologies are showcased by introducing software- defined traffic engineering solutions. The challenging issues for realizing SoftAir are also discussed in details.


title: Role of hepatic cytochromes P450 in bioactivation of the anticancer drug ellipticine: studies with the hepatic NADPH:cytochrome P450 reductase null mouse.

abstract: Ellipticine is an antineoplastic agent, which forms covalent DNA adducts mediated by cytochromes P450 (CYP) and peroxidases. We evaluated the role of hepatic versus extra-hepatic metabolism of ellipticine, using the HRN (Hepatic Cytochrome P450 Reductase Null) mouse model, in which cytochrome P450 oxidoreductase (POR) is deleted in hepatocytes, resulting in the loss of essentially all hepatic CYP function. HRN and wild-type (WT) mice were treated i.p. with 1 and 10 mg/kg body weight of ellipticine. Multiple ellipticine-DNA adducts detected by (32)P-postlabelling were observed in organs from both mouse strains. Highest total DNA binding levels were found in liver, followed by lung, kidney, urinary bladder, colon and spleen. Ellipticine-DNA adduct levels in the liver of HRN mice were up to 65% lower relative to WT mice, confirming the importance of CYP enzymes for the activation of ellipticine in livers, recently shown in vitro with human and rat hepatic microsomes. When hepatic microsomes of both mouse strains were incubated with ellipticine, ellipticine-DNA adduct levels with WT microsomes were up to 2.9-fold higher than with those from HRN mice. The ratios of ellipticine-DNA adducts in extra-hepatic organs between HRN and WT mice of up to 4.7 suggest that these organs can activate ellipticine and that more ellipticine is available in the circulation. These results and the DNA adduct patterns found in vitro and in vivo demonstrate that both CYP1A or 3A and peroxidases participate in activation of ellipticine to reactive species forming DNA adducts in the mouse model used in this study.


title: Genetic algorithms used to design and optimization of broadband whip antenna

abstract: A design approach combing the genetic algorithm (GA) with method of moments (MOM) is investigated for the optimal design of a high frequency (HF) broadband whip antenna. The load lumped elements and their locations are optimized simultaneously. Both a sample HF broadband whip antenna and its 1/3-scale model antenna are built and tested to verify the optimal designs. Communication experiment indicates that the performance of the broadband whip antenna outperforms that of the narrowband whip antenna in use for frequency hopping (FH) mode.


title: Brain-computer interface in paralysis.

abstract: Purpose of reviewCommunication with patients suffering from locked-in syndrome and other forms of paralysis is an unsolved challenge. Movement restoration for patients with chronic stroke or other brain damage also remains a therapeutic problem and available treatments do not offer significant impro


title: Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems

abstract: This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems .


title: A Comparative Analysis of Deep and Shallow Features for Multimodal Face Recognition in a Novel RGB-D-IR Dataset

abstract: With new trends like 3D and deep learning alternatives for face recognition becoming more popular, it becomes essential to establish a complete benchmark for the evaluation of such algorithms, in a wide variety of data sources and non-ideal scenarios. We propose a new RGB-depth-infrared (RGB-D-IR) dataset, RealFace, acquired with the novel Intel® RealSense™ collection of sensors, and characterized by multiple variations in pose, lighting and disguise. As baseline for future works, we assess the performance of multiple deep and “shallow” feature descriptors. We conclude that our dataset presents some relevant challenges and that deep feature descriptors present both higher robustness in RGB images, as well as an interesting margin for improvement in alternative sources, such as depth and IR.


title: FUN PLEdGE: a FUNny Platformers LEvels GEnerator

abstract: In the present work, we tackle the issue of designing, prototyping and testing a general-purpose automated level editor for platform video games. Beside relieving level designers from the burden of repetitive work, Procedural Content Generation can be exploited for optimizing the development process, increasing re-playability, adapting games to specific audiences, and enabling new games mechanics. The tool proposed in this paper is aimed at producing levels that are both playable and fun. At the same time, it should guarantee maximum freedom to the level designer, and suggest corrections functional to the quality of the player experience.


Directed dynamical influence is more detectable with noise


Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors


title: Efficient query processing in geographic web search engines

abstract: Geographic web search engines allow users to constrain and order search results in an intuitive manner by focusing a query on a particular geographic region. Geographic search technology, also called local search, has recently received significant interest from major search engine companies. Academic research in this area has focused primarily on techniques for extracting geographic knowledge from the web. In this paper, we study the problem of efficient query processing in scalable geographic search engines. Query processing is a major bottleneck in standard web search engines, and the main reason for the thousands of machines used by the major engines. Geographic search engine query processing is different in that it requires a combination of text and spatial data processing techniques. We propose several algorithms for efficient query processing in geographic search engines, integrate them into an existing web search query processor, and evaluate them on large sets of real data and query traces.


title: Multi-level production planning in a petrochemical industry using elitist Teaching-Learning-Based-Optimization

abstract: Efficient strategy to determine optimal production plans in a multi-level scenario.Proposed strategy leads to better results than the existing results in literature.Performance of elitist TLBO on problems with more than 100 variables and constraints.Proposed strategy requires less than 45s for each run. The complex nature of the petrochemical industries necessitates an efficient decision on a large number of factors so as to optimally operate a plant. Production planning is an integral part of the petrochemical industry and requires the optimal selection of processes, production levels and products to maximize its profit. Previously an MILP formulation has been proposed for guiding the petrochemical industry development in Saudi Arabia (Alfares & Al-Amer, 2002). In this article, we state the limitations of this formulation and propose an alternate elitist TLBO algorithm based strategy to overcome them. The benefits of this strategy include the determination of better production plans that lead to higher profits and have been demonstrated on the eight case studies in the literature. The proposed strategy is generic and can be applied to determine production plans of multiple levels in various industries.


title: Sudden cardiac death prediction using ECG signal derivative (Heart Rate Variability): A review

abstract: Sudden cardiac death (SCD) prediction using Electrocardiogram (ECG) signal is a popular area of research because of the seriousness of the matter. There are tons of papers published in this research which available online. Severe importance for prediction and recent development of new algorithms fuels this research further. There are numerous methods to detect or predict SCD based on Heart Rate Variability (HRV), T-Wave Alternans (TWA), Heart Rate Turbulence (HRT), Signal Averaged Electrocardiogram (SA-ECG), Data Mining, and Non Linear Analysis to state few. Researchers favors statistical analysis over other classifier (Neural network, KNN etc.,) based methods since it gives more perspective to the research findings. Various statistical methods like Kaplan-Meier method, t-test, Mann-Whitney U test, Wilk's Lambda test were carried out to describe correlation between ECG derived parameters (HRV, TWA, HRT etc.,) and SCD. Even though there are many papers published, none of them are clinically practicable because of its own limitations. In this review, we would see methods and techniques based on HRV used by researchers in order to detect and predict sudden cardiac death.


title: Multi-objective genetic algorithm approach to feature subset optimization

abstract: The presence of unimportant and superfluous features in datasets motivates researchers to devise novel feature selection strategies. The problem of feature selection is multi-objective in nature and hence optimizing feature subsets with respect to any single evaluation criteria is not sufficient [1]. Moreover, discovering a single best subset of features is not of much interest. In fact, finding several feature subsets reflecting a trade off among several objective criteria is more beneficial as it provides the users a broad choice for feature subset selection. Thus, in order to combine several feature selection criteria, we propose multi-objective optimization of feature subsets using Multi-Objective Genetic Algorithm. This work is an attempt to discover non-dominated feature subsets of smaller cardinality with high predictive power and least redundancy. To meet this purpose we have used NSGA II, a well known Multi-objective Genetic Algorithm (MOGA), for discovering non-dominated feature subsets for the task of classification. The main contribution of this paper is the design of a novel multi-objective fitness function consisting of information gain, mutual correlation and size of the feature subset as the multi-optimization criteria. The suggested approach is validated on seven datasets from the UCI machine learning repository. Support Vector Machine, a well tested classification algorithm is used to measure the classification accuracy. The results confirm that the proposed system is able to discover diverse optimal feature subsets that are well spread in the overall feature space and the classification accuracy of the resulting feature subsets is reasonably high.


title: Making graphene visible

abstract: Microfabrication of graphene devices used in many experimental studies currently relies on the fact that graphene crystallites can be visualized using optical microscopy if prepared on top of silicon wafers with a certain thickness of silicon dioxide. We study graphene's visibility and show that it depends strongly on both thickness of silicon dioxide and light wavelength. We have found that by using monochromatic illumination, graphene can be isolated for any silicon dioxide thickness, albeit 300 nm (the current standard) and, especially, approx. 100 nm are most suitable for its visual detection. By using a Fresnel-law-based model, we quantitatively describe the experimental data without any fitting parameters.


title: Analysis of users and non-users of smartphone applications

abstract: Purpose: Smartphones facilitate the potential adoption of new mobile applications. The purpose of this research is to study users and non-users of three selected mobile applications, and find out what really drives the intention to use these applications across users and non-users. Design/methodology/approach: The authors measured actual usage of mobile applications in a panel study of 579 Finnish smartphone users, using in-device measurements as an objective way to identify users and non-users. A web-based survey was used in collecting data to test an extended TAM model in explaining intention to use. Findings: Perceived technological barriers negatively affect behavioural control, reflecting people's assessment of themselves being capable of using the services without trouble. Behavioural control is directly linked to perceived usefulness (except for games) and perceived enjoyment, as hypothesized. Perceived enjoyment and usefulness were generically found to explain intention to use applications for both users and for non-users. Research limitations/implications: With regards to the impact of social norms, the study finds that further research needs to be done in exploring its impact more thoroughly. The dataset of the research, consisting purely of male-dominated, young smartphone users, make the generalization of results difficult. Practical implications: There are differences regarding what drives the usage of different kinds of mobile applications. In this study, map applications and mobile Internet, are driven by more utilitarian motivations, whereas games are more hedonic. It is also clear that not everybody are using applications facilitated by smartphones, and therefore the presented approach of studying users and non-users separately provides a new approach to analyze adoption on a practical level. Originality/value: This research proves that models like TAM should not treat mobile services as a generic concept, but instead to specifically address individual mobile services. The research also demonstrates the unique value of combining objective usage measurements (reflecting actual behaviour) with traditional survey data in more comprehensively modelling service adoption.


title: Joint Dictionary Learning for Example-based Image Super-resolution

abstract: In this paper, we propose a new joint dictionary learning method for example-based image super-resolution (SR), using sparse representation. The low-resolution (LR) dictionary is trained from a set of LR sample image patches. Using the sparse representation coefficients of these LR patches over the LR dictionary, the high-resolution (HR) dictionary is trained by minimizing the reconstruction error of HR sample patches. The error criterion used here is the mean square error. In this way we guarantee that the HR patches have the same sparse representation over HR dictionary as the LR patches over the LR dictionary, and at the same time, these sparse representations can well reconstruct the HR patches. Simulation results show the effectiveness of our method compared to the state-of-art SR algorithms.


title: TOLDI: An effective and robust approach for 3D local shape description

abstract: Abstract Feature description for the 3D local shape in the presence of noise, varying mesh resolutions, clutter and occlusion is a quite challenging task in 3D computer vision. This paper tackles the problem by proposing a new local reference frame (LRF) together with a novel triple orthogonal local depth images (TOLDI) representation, forming the TOLDI method for local shape description. Compared with previous methods, TOLDI manages to perform efficient, distinctive and robust description for the 3D local surface simultaneously under various feature matching contexts. The proposed LRF differs from many prior ones in its calculation of the z -axis and x -axis, the z -axis is calculated using the normal of the keypoint and the x -axis is computed by aggregating the weighted projection vectors of the radius neighbors. TOLDI feature descriptors are then obtained by concatenating three local depth images (LDI) captured from three orthogonal view planes in the LRF into feature vectors. The performance of our TOLDI approach is rigorously evaluated on several public datasets, which contain three major surface matching scenarios, namely shape retrieval, object recognition and 3D registration. Experimental results and comparisons with the state-of-the-arts validate the effectiveness, robustness, high efficiency, and overall superiority of our method. Our method is also applied to aligning 3D object and indoor scene point clouds obtained by different devices (i.e., LiDAR and Kinect), the accurate outcomes further confirm the effectiveness of our method.


title: Software engineering projects may fail before they are started: Post-mortem analysis of five cancelled projects

abstract: Context: Software project cancellations are often caused by mistakes made during the project, and such cancellations make a strong economic impact. We analyzed five cancelled software engineering projects. One case was an internal product development project of a company that sells products to its customers. The other four cases were different software engineering projects, and outcomes of these projects were planned to be delivered to external customers. Objective: This study reports a post-mortem analysis of five software engineering projects with the aim of providing more knowledge about the reasons for cancellation decisions and the causes behind those reasons. Methods: The research method is case study. A method for a document-based post-mortem analysis was developed and post-mortem analysis was performed. All project documentation was available for analysis. Results: The reasons for the cancellation decisions were well-known ones. In four cases of five, the outcome of the project was to be delivered to an external customer, but in these cases the causes of the cancellation reasons were not found from the normal project documentation. In these cases the cause of the cancellation originated in a phase before the start of the project and therefore the project was doomed before it was started. Conclusion: It is reasonable to suggest that a remarkable portion of project cancellations are due to mistakes made before the project is started in the case of contract-based software engineering projects.


title: Automatic License Plate Recognition System using SURF Features and RBF Neural Network

abstract: Automated License plate recognition is technology used in traffic management techniques allowing us to recognize identity of a vehicle through its registration plate information usually by videos or still images. It can be used for Parking assistance, automatic toll collection, vehicle theft management etc for improving manual traffic management error rate .In this paper we are proposing a method for implementing Automatic License Plate Recognition using Speed up robust feature matching SURF technique for plate detection and Advanced Radial basis function for matching characters. Simulation through popular tool Matlab ver 2011 shows 95% correctly detected plates and over 90% of character matching..


title: Data center network architecture in cloud computing: review, taxonomy, and open research issues

abstract: The data center network (DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.


title: How Not to Be Frustrated with Neural Networks

abstract: Neural networks are the topic of this paper. Neural networks are very powerful as nonlinear signal processors, but obtained results are often far from satisfactory. The purpose of this article is to evaluate the reasons for these frustrations and show how to make these neural networks successful. The following are the main challenges of neural network applications: (1) Which neural network architectures should be used? (2) How large should a neural network be? (3) Which learning algorithms are most suitable? The multilayer perceptron (MLP) architecture is unfortunately the preferred neural network topology of most researchers. It is the oldest neural network architecture, and it is compatible with all training softwares. However, the MLP topology is less powerful than other topologies such as bridged multilayer perceptron (BMLP), where connections across layers are allowed. The error-back propagation (EBP) algorithm is the most popular learning algorithm, but it is very slow and seldom gives adequate results. The EBP training process requires 100-1,000 times more iterations than the more advanced algorithms such as Levenberg-Marquardt (LM) or neuron by neuron (NBN) algorithms. What is most important is that the EBP algorithm is not only slow but often it is not able to find solutions for close-to-optimum neural networks. The paper describes and compares several learning algorithms.


title: Volunteering predicts happiness among older Māori and non-Māori in the New Zealand health, work, and retirement longitudinal study

abstract: Objectives: This study sought to understand the relationship between volunteer activity and happiness among a sample of older adult New Zealanders. It specifically sought to determine if ethnicity (Māori vs. non-Māori) and economic living standards (ELS) functioned as moderators of the relationship between volunteering and happiness. Method: Data were garnered from the 2008 administration of the New Zealand Health, Work, and Retirement Longitudinal Study. Correlational and multiple regression procedures were employed to examine study hypotheses. Results: Results from multiple regression analyses showed that the amount of volunteering per week was a unique predictor of the overall level of happiness. Moderation analyses indicated that ethnicity did not function as a moderator of the relationship between volunteering and happiness, but ELS did. Those with low ELS evidenced a stronger relationship between volunteering and happiness than those with high ELS. Results also indicated that Māori and those with low ELS volunteered more frequently than non-Māori and those with high ELS. Conclusion: This study provides evidence that volunteering is related to increased happiness, irrespective of ethnicity. It also provides further evidence that the relationship between volunteering and happiness is moderated by economic resources. Older individuals at the low end of the economic spectrum are likely to benefit more from volunteering than those at the high end.


title: 3D object class detection in the wild

abstract: Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations. Only recently, there has been a growing interest in revisiting the promise of computer vision from the early days: to precisely delineate the contents of a visual scene, object by object, in 3D. In this paper, we draw from recent advances in object detection and 2D-3D object lifting in order to design an object class detector that is particularly tailored towards 3D object class detection. Our 3D object class detection method consists of several stages gradually enriching the object detection output with object viewpoint, keypoints and 3D shape estimates. Following careful design, in each stage it constantly improves the performance and achieves state-of-the-art performance in simultaneous 2D bounding box and viewpoint estimation on the challenging Pascal3D+ [50] dataset.


title: How do negative self-perceptions of aging become a self-fulfilling prophecy?

abstract: Recent studies have provided considerable evidence on long-term effects of self-perceptions of aging (SPA) on indicators of successful aging such as health or life satisfaction. To date, little is known about the mechanisms underlying these effects. This study therefore examined whether negative SPA impair the use of self-regulation strategies that include selection, optimization, and compensation (SOC) in case of a serious health event and thus turn into self-fulfilling prophecies for health and life satisfaction. Based on a longitudinal nationwide study with 2 measurement points over a 6-month period in 309 older people (65+ years of age) with multiple illnesses, 2 major findings emerged: First, the occurrence of a serious health event predicted increased use of SOC strategies, which in turn predicted higher self-rated health and life satisfaction. Second, this effect was moderated by negative SPA, that is, in case of a serious health event, the perception that aging is associated with physical losses led to lower use of SOC strategies promoting a healthy lifestyle (B = -0.43, SE = 0.15, p < .01). These findings contribute to a better understanding of the underlying mechanisms of SPA on health by showing that negative SPA as associated with physical losses might impair health-related strategies that are important for maintaining a healthy lifestyle. Future intervention studies could attempt to challenge negative SPA to support effective strategy use in older adults with serious illnesses.


title: Study of liquefaction characteristics of saturated stratified sands by dynamic triaxial test

abstract: Liquefaction strength tests for saturated stratified sands with different silty layer thicknesses were carried out by dynamic triaxial testing machine under constant stress amplitude cyclic loads.The deformation and mechanical properties for clean sand and stratified sand with different silty layer thickness were analyzed.The experimental results show that the development law of pore water pressure for stratified silty sands are different from clean sands due to the special soil structure;the liquefaction resistance strength of saturated stratified silty sands does not increase monotonously with the thickness of silty soil layer,but there is a critical thickness.However,the liquefaction resistance strength increases monotonously with the thickness increasing above the critical point;the critical shear strains are different under different liquefaction criteria and different cycle numbers,such as the critical shear strain under the criterion of =1.0 is larger than that under the criterion of =0.9.Besides,the critical shear strain should not be a constant under different earthquake magnitude and intensity.The silty interlayers influence the liquefaction characteristics of stratified silty sands greatly and can simulate the liquefaction characteristics of stratified sands in natural environment better.


title: Critical Discourse Analysis as a Review Methodology: An Empirical Example

abstract: Research disciplines and subdisciplines are steeped in epistemological beliefs and theoretical assumptions that guide and constrain research. These beliefs and assumptions both enable scientific inquiry and limit scientific progress. Theory and review papers tend to be a means for reproducing ideological assumptions. However, review papers can also challenge ideological assumptions by critically assessing taken-for-granted assumptions. Critical review methods are underdeveloped in the management disciplines. The information systems (IS) discipline must do more to improve the critical examination of its scientific discourse. In this paper, we present a method with guiding principles and steps for systematically conducting critical reviews of IS literature based on Habermasian strains of critical discourse analysis. We provide an empirical example of the method. The empirical example offers a critical review of behavioral information security research with a focus on employees’ security behaviors.


title: Knowledge flow‐based business process redesign: applying a knowledge map to redesign a business process

abstract: Purpose – The aim of this paper is to suggest a method to redesign business processes from the viewpoint of knowledge flows using a knowledge map.Design/methodology/approach – Knowledge flows and business processes cannot be separated because knowledge is inputted and outputted through business processes. Knowledge flows inherit the feature and appearance of corresponding business processes; therefore, one can identify problems within business processes by analyzing corresponding knowledge flows. The methodology is composed of the following sections: knowledge mapping, knowledge profiling, knowledge flow identification, knowledge flow optimization and TO‐BE process visualization.Findings – This paper provides a methodology for knowledge flow‐based business process redesign and ten guidelines for knowledge flow optimization. The case study demonstrates that the proposed ideas constitute knowledge‐intensified business processes.Research limitations/implications – A more formal validation method that is base...


title: Parallel training of DNNs with Natural Gradient and Parameter Averaging

abstract: We describe the neural-network training framework used in the Kaldi speech recognition toolkit, which is geared towards training DNNs with large amounts of training data using multiple GPU-equipped or multicore machines. In order to be as hardwareagnostic as possible, we needed a way to use multiple machines without generating excessive network traffic. Our method is to average the neural network parameters periodically (typically every minute or two), and redistribute the averaged parameters to the machines for further training. Each machine sees different data. By itself, this method does not work very well. However, we have another method, an approximate and efficient implementation of Natural Gradient for Stochastic Gradient Descent (NG-SGD), which seems to allow our periodic-averaging method to work well, as well as substantially improving the convergence of SGD on a single machine.


title: A taxonomy of network centric warfare architectures

abstract: Abstract : The Australian Defence Force (ADF) is committed to transitioning, over time, to Network Centric Warfare (NCW). NCW sees the elements of the ADF as "nodes" in a network. The capability of such a force is determined not so much by the individual capabilities of each node, but by the systems properties of the network as a whole. In this paper, we provide a taxonomy of possible NCW architectures, in order to illuminate the possible options and to foster debate and experimentation regarding the appropriate NCW architectures for use by the ADF. The taxonomy is based on the fundamental concepts of value symmetry and homogeneity/heterogeneity, and distinguishes Centralised, Request-Based, and Swarming architectures, as well as combinations of these. We provide several examples of each architecture, and a list of key questions.


title: [Outpatient external dacryocystorhinostomy under regional anesthesia and sedation].

abstract: OBJECTIVE ::: To assess the rate of early complications of outpatient external dacryocystorhinostomy (DCR) and patient satisfaction with the anesthetic technique. ::: ::: ::: MATERIAL AND METHODS ::: This prospective study enrolled 58 patients undergoing external DCR. We analyzed demographic variables, ASA physical status, level of sedation achieved, postoperative pain, systemic complications, intraoperative bleeding, duration of surgery, time until discharge home, and patient and surgeon satisfaction with the anesthetic technique. ::: ::: ::: RESULTS ::: The mean (SD) level of satisfaction was 4.85 (0.80) points on the Iowa Satisfaction With Anesthesia Scale (ISAS). A positive association was found between postoperative pain and a lower ISAS score. There was also a positive association between use of rescue analgesia in the early postoperative period and a lower ISAS score. Mean blood loss per procedure was 178.9 (108.2) mL. The rate of minor systemic complications was 15.5%. The surgeon's rating of conditions in the surgical field was excellent or good in 89.6% of the cases. ::: ::: ::: CONCLUSIONS ::: External DCR can be performed on an outpatient basis within a reasonable safety margin and with a low early postoperative complication rate. Patient satisfaction with anesthesia was high. Provision of preoperative information about the meaning of sedation, postoperative analgesia, and surgical bleeding are aspects to improve in this practice setting.


title: A Level-Crossing Based QRS-Detection Algorithm for Wearable ECG Sensors

abstract: In this paper, an asynchronous analog-to-information conversion system is introduced for measuring the RR intervals of the electrocardiogram (ECG) signals. The system contains a modified level-crossing analog-to-digital converter and a novel algorithm for detecting the R-peaks from the level-crossing sampled data in a compressed volume of data. Simulated with MIT-BIH Arrhythmia Database, the proposed system delivers an average detection accuracy of 98.3%, a sensitivity of 98.89%, and a positive prediction of 99.4%. Synthesized in 0.13 μm CMOS technology with a 1.2 V supply voltage, the overall system consumes 622 nW with core area of 0.136 mm2 which make it suitable for wearable wireless ECG sensors in body-sensor networks.


abstract: POPE, R. P., R. D. HERBERT, J. D. KIRWAN, and B. J. GRAHAM. A randomized trial of preexercise stretching for prevention of lower-limb injury. Med. Sci. Sports Exerc.,Vol. 32, No. 2, pp. 271‐277, 2000. Purpose: This study investigated the effect of muscle stretching during warm-up on the risk of exercise-related injury. Methods: 1538 male army recruits were randomly allocated to stretch or control groups. During the ensuing 12 wk of training, both groups performed active warm-up exercises before physical training sessions. In addition, the stretch group performed one 20-s static stretch under supervision for each of six major leg muscle groups during every warm-up. The control group did not stretch. Results: 333 lower-limb injuries were recorded during the training period, including 214 soft-tissue injuries. There were 158 injuries in the stretch group and 175 in the control group. There was no significant effect of preexercise stretching on all-injuries risk (hazard ratio [HR] 5 0.95, 95% CI 0.77‐1.18), soft-tissue injury risk (HR 5 0.83, 95% CI 0.63‐1.09), or bone injury risk (HR 5 1.22, 95% CI 0.86 ‐1.76). Fitness (20-m progressive shuttle run test score), age, and enlistment date all significantly predicted injury risk ( P , 0.01 for each), but height, weight, and body mass index did not. Conclusion: A typical muscle stretching protocol performed during preexercise warm-ups does not produce clinically meaningful reductions in risk


title: Resource allocation for spectrum underlay in cognitive radio networks

abstract: A resource allocation framework is presented for spectrum underlay in cognitive wireless networks. We consider both interference constraints for primary users and quality of service (QoS) constraints for secondary users. Specifically, interference from secondary users to primary users is constrained to be below a tolerable limit. Also, signal to interference plus noise ratio (SINR) of each secondary user is maintained higher than a desired level for QoS insurance. We propose admission control algorithms to be used during high network load conditions which are performed jointly with power control so that QoS requirements of all admitted secondary users are satisfied while keeping the interference to primary users below the tolerable limit. If all secondary users can be supported at minimum rates, we allow them to increase their transmission rates and share the spectrum in a fair manner. We formulate the joint power/rate allocation with proportional and max-min fairness criteria as optimization problems. We show how to transform these optimization problems into a convex form so that their globally optimal solutions can be obtained. Numerical results show that the proposed admission control algorithms achieve performance very close to that of the optimal solution. Also, impacts of different system and QoS parameters on the network performance are investigated for the admission control, and rate/power allocation algorithms under different fairness criteria.


title: A Quadrotor Test Bench for Six Degree of Freedom Flight

abstract: In this paper, a quadrotor test bench that can test and verify the 6 DOF flight controller is presented. The development of controller for aerial vehicle is usually a long and dangerous procedure. It needs series of tests from simulation to real flight. However, there are differences between simulation and real time flight due to the limit of the current simulation technique. The quadrotor test bench presented in the paper aims to fill the gap between simulation and real time flight. The test bench contains a quadrotor attached on the base through a sphere joint which let the quadrotor be able to rotate around 3 axes. A 6 axes force/torque sensor is used to simulate the position of the aerial vehicle. The paper presents the detailed system design and implementation of the test bench. Furthermore, the modeling and the parameter identification of the quadrotor on the test bench are described. A 6 DOF controller that consists of both guidance controller and attitude controller is designed using a nonlinear control technique named trajectory linearization control (TLC). Finally, the flight tests on the quadrotor test bench are demonstrated. The results indicate the feasibility and the value of the test bench.


title: PAST, PRESENT, AND FUTURE OF EMERGENCY DISPATCH RESEARCH: A SYSTEMATIC LITERATURE REVIEW

abstract: Introduction: Emergency dispatch is a relatively new field, but the growth of dispatching as a profession, along with raised expectations for help before responders arrive, has led to increased production of and interest in emergency dispatch research. As yet, no systematic review of dispatch research has been conducted. Objective: This study reviewed the existing literature and indicated gaps in the research as well as potentially fruitful extensions of current lines of study. Methods: Dispatch-related terms were used to search for papers in research databases (including PubMed, MEDLINE, EMBASE, EMCARE, SciSearch, PsychInfo, and SCOPUS). All research papers with dispatching as the core focus were included. Results: A total 149 papers (114 original research, and 35 seminal concept papers) were identified. A vast majority dealt with medical dispatching (as opposed to police or fire dispatching). Four major issues emerged from the early history of emergency dispatch that continue to dominate dispatch studies: dispatch as first point of care, standardization of the dispatching process, resource allocation, and best practices for dispatching. Conclusion: Substantial peer-reviewed research does exist in dispatch studies. However, a lack of consistent metrics, the near-nonexistence of research in fire and police dispatching, and a relative lack of studies in many areas of interest indicate a need for increased participation in research by communication center administrators and others “on the ground” in emergency dispatch, as well as increased collaboration between research organizations and operations personnel.


The Morphology and Taxonomy of South African Kallymeniaceae


title: Detecting Adversarial Samples Using Density Ratio Estimates

abstract: Machine learning models, especially based on deep learning are used in everyday applications ranging from self driving cars to medical diagnostics. However, it is easy to trick such models using adversarial samples, indistinguishable from real samples to human eye, such samples can lead to incorrect classifications. Impact of adversarial samples is far-reaching and efficient detection of adversarial samples remains an open problem. In this paper we propose to use direct density ratio estimation as a model agnostic measure to detect adversarial samples, we empirically show that adversarial samples have different underlying probability densities compared to real samples. Our proposed method works well with colored and grayscale images, and with different adversarial sample generation methods.


title: Circulating current minimization and current sharing control of parallel boost converters based on Droop Index

abstract: This paper investigates the relationship between current sharing difference and circulating current for two parallel connected dc-dc converters. In the proposed algorithm a new figure-of-merit called Droop Index is introduced, which is a function of normalized current sharing difference and losses in the output side of the converters. This algorithm minimizes the circulating current and current sharing difference between the converters. Although there may exist a trade-off between current sharing difference and voltage regulation, the proposed droop index algorithm gives better performance and low voltage regulation. The detailed analysis and design procedure are explained for two dc-dc boost converters connected in parallel. The effectiveness of proposed method is verified using MATLAB simulation.


title: Survey on Chatbot Design Techniques in Speech Conversation Systems

abstract: Human-Computer Speech is gaining momentum as a technique of computer interaction. There has been a recent upsurge in speech based search engines and assistants such as Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like responses. This type of programme is called a Chatbot, which is the focus of this study. This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the last decade. The paper discusses the similarities and differences in the techniques and examines in particular the Loebner prize-winning Chatbots.


title: A Dynamic Tree-Based Data Structure for Access Privacy in the Cloud

abstract: We present a novel approach for guaranteeing access privacy to data stored at an external cloud provider. Our solution relies on the grouping of resources into buckets then organized with a binary search tree. The tree is built on an index computed in a non-invertible non-order preserving way, and supports efficient key-based retrieval. Our approach to provide access privacy builds on this data organization providing uniform observability to the server in access execution and dynamically changing not only the physical storage allocation, but also the logical structure itself. Our analysis and experimental evaluation show the effectiveness of our approach.


title: uLoRa: Ultra Low-Power, Low-Cost and Open Platform for the LoRa Networks

abstract: Long-Range (LoRa) is an emerging technology that is proposed to realize the Internet of Things (IoT). It enables very long range (up to 15 km) wireless communications with very low-power consumption. Many applications are being enabled by LoRa, such as water flow monitoring, animal tracking, smart parking, among many others. Although LoRa has attracted significant attentions from both industry and academia, its current cost and energy consumption are bringing inconveniences for the academia to exploit and improve LoRa's performance, e.g., when massive deployment of LoRa nodes are required in order to study the scalability performance of LoRa networks. In this paper, we design and implement uLoRa, an ultra low-power, low cost, and open-source platform for LoRa networks. uLoRa is built around an ultra low-power ARM processor. Each uLoRa node only costs ≈10 euros. To reduce the energy consumption of uLoRa, we optimize its design and implementation in both the hardware and software. We evaluate the performance of uLoRa with experiments, in terms of energy consumption and communication coverage. Compared to state-of-the-art LoRa platform, uLoRa can reduce the cost by two thirds and can double the battery life of the nodes. We also demonstrate the flexibility of uLoRa through the application of moisture monitoring.


title: Empty or Infinitely Full

abstract: The concept of infinity and the inability to interchange two or more mathematical operations in the limit situation intrigue and baffle students of mathematics. One example that nicely illustrates these two concepts is found in the second chapter of Sheldon Ross's popular book, A First Course in Probability [1], under the subheading "Probability and a Paradox." Students of engineering and physics generally disavow the paradox quickly and say that the answer (in the second case) is simply wrong; mathematics students are more cautious in coming to a conclusion as they see a valid argument in the answers. In this note we will look into the nature of the problem and give a mathematical explanation of the paradox. Quoting directly from the book, the paradox is as follows.


title: StockAssIstant: A Stock AI Assistant for Reliability Modeling of Stock Comments

abstract: Stock comments from analysts contain important consulting information for investors to foresee stock volatility and market trends. Existing studies on stock comments usually focused on capturing coarse-grained opinion polarities or understanding market fundamentals. However, investors are often overwhelmed and confused by massive comments with huge noises and ambiguous opinions. Therefore, it is an emerging need to have a fine-grained stock comment analysis tool to identify more reliable stock comments. To this end, this paper provides a solution called StockAssIstant for modeling the reliability of stock comments by considering multiple factors, such as stock price trends, comment content, and the performances of analysts, in a holistic manner. Specifically, we first analyze the pattern of analysts' opinion dynamics from historical comments. Then, we extract key features from the time-series constructed by using the semantic information in comment text, stock prices and the historical behaviors of analysts. Based on these features, we propose an ensemble learning based approach for measuring the reliability of comments. Finally, we conduct extensive experiments and provide a trading simulation on real-world stock data. The experimental results and the profit achieved by the simulated trading in 12-month period clearly validate the effectiveness of our approach for modeling the reliability of stock comments.


title: Compact UWB Printed Slot Antenna With Extra Bluetooth, GSM, and GPS Bands

abstract: A novel compact ultrawideband (UWB) printed slot antenna with three extra bands for various wireless applications is presented. The low-profile antenna consists of an octagonal-shaped slot fed by a beveled and stepped rectangular patch for covering the UWB band (3.1-10.6 GHz). By attaching three inverted U-shaped strips at the upper part of the slot in the ground, additional triple linear polarized bands can be realized covering GPS (1520-1590 MHz), part of GSM (1770-1840 MHz), and Bluetooth (2385-2490 MHz). Simulated and measured results are presented and compared, which shows that the antenna has a stable radiation pattern both at the triple and the whole of the UWB bands.


title: Complex Traumatic Facial Degloving Injury.

abstract: By definition, degloving is detachment of skin and subcutaneous tissue, most often affecting the limbs and extremities and occasionally the scalp. Degloving generally occurs from high-energy trauma. This paper describes a patient of traumatic facial degloving injury. This is an extremely rare condition, as the patient survived despite the risk of imminent death. This patient report addresses the decisions made regarding the emergency management, prevention of necrosis and infection by surgical debridement, and timely repair of the vital soft tissue structures that guided the management.


title: Indonesian Morphology Tool (MorphInd): Towards an Indonesian Corpus

abstract: This paper describes a robust finite state morphology tool for Indonesian (MorphInd), which handles both morphological analysis and lemmatization for a given surface word form so that it is suitable for further language processing. MorphInd has wider coverage on handling Indonesian derivational and inflectional morphology compared to an existing Indonesian morphological analyzer [1], along with a more detailed tagset. MorphInd outputs the analysis in the form of segmented morphemes along with the morphological tags. The implementation was done using finite state technology by adopting the two-level morphology approach implemented in Foma. It achieved 84.6% of coverage on a preliminary stage Indonesian corpus where it mostly fails to capture the proper nouns and foreign words as expected initially.


title: A New Improved Firefly Algorithm for Global Numerical Optimization

abstract: A novel robust meta-heuristic optimization algorithm, which can be considered as an improvement of the recently developed firefly algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of information exchange between the top fireflies, or the optimal solutions during the process of the light intensity updating. The detailed implementation procedure for this improved meta-heuristic method is also described. Standard benchmarking functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of this improved firefly algorithm (IFA) is superior to or at least highly competitive with the standard firefly algorithm, a differential evolution method, a particle swarm optimizer, and a biogeography-based optimizer. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic FA.


title: A Design and Theoretical Analysis of a 145 mV to 1.2 V Single-Ended Level Converter Circuit for Ultra-Low Power Low Voltage ICs

abstract: This paper presents an ultra-low swing level converter with integrated charge pumps that shows measured conversion in a 130-nm CMOS test chip from an input at a 145-mV swing to a 1.2-V output. Lowering the input allowable for a single-ended level converter supports energy harvesting systems that need to use very low voltages.


title: Road Type Recognition Using Neural Networks for Vehicle Seat Vibration Damping

abstract: In a modern vehicle systems one of the main goals to achieve is driver's safety, and many sophisticated systems are made for that purpose. Vibration isolation for the vehicle seats, and at the same time for the driver, is one of the challenging problems. Parameters of the controller used for the isolation can be tuned for a different road types, making the isolation better (specially for the vehicles like dampers, tractors, field machinery, bulldozers, etc.). In this paper we propose the method where neural networks are used for road type recognition. The main goal is to obtain a good road recognition for the purpose of better vibration damping of a driver's semi active controllable seat. The recognition of a specific road type will be based on the measurable parameters of a vehicle. Discrete Fourier Transform of measurable parameters is obtained and used for the neural network learning. The dimension of the input vector, as the main parameter that decides the speed of road recognition, is varied.


title: Unsupervised Object Discovery and Tracking in Video Collections

abstract: This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision. We formulate the problem as a combination of two complementary processes: discovery and tracking. The first one establishes correspondences between prominent regions across videos, and the second one associates similar object regions within the same video. Interestingly, our algorithm also discovers the implicit topology of frames associated with instances of the same object class across different videos, a role normally left to supervisory information in the form of class labels in conventional image and video understanding methods. Indeed, as demonstrated by our experiments, our method can handle video collections featuring multiple object classes, and substantially outperforms the state of the art in colocalization, even though it tackles a broader problem with much less supervision.


title: A miniature force sensor for prosthetic hands

abstract: Tactile sensing is an important part of the development of new prosthetic hands. A number of approaches to establishing an afferent pathway back to the patient for tactile information are becoming available including tactors and direct stimulation of the afferent nerves [1]. Tactile information can also be used by low-level control systems that perform simple tasks for the patient such as establishing a stable grasp and maintaining the grasping forces needed to hold an object. This abstract reports on the design of a small fingertip load cell based on semi-conductor strain gauges. Since this load cell is so small (measuring only 8.5mm in diameter and 6.25 mm in height), it easily fits into the tip of an anthropomorphic mechatronic hand. This load cell is tested by comparing a time series of force and moment data with reference data acquired from a much larger high-precision commercial load cell. Several sensor technologies exist that have been applied to tactile sensing. One common technology is the force sensing resistor (FSR). These are made of a resistive polymer film that responds in a non-linear way to pressure. Another technology is quantum tunneling composite (QTC). When deformed, QTC material turns from a good insulator into a conductive material. Resistance drops exponentially as a function of pressure. Other force-sensing technologies are based on semiconductor straingauges. These have been used to measure pressure and to create multi-dimensional load cells. Multi-dimensional load cells are particularly good for tactile sensing because they can be very sensitive and they can be used to localize a contact point on a fingertip [2]. However, a drawback to using a load cell to sense forces in a robot hand has been size constraints: typical six-axis load cells are too large to fit into the tip or phalange of a human-sized robot finger. The load cell proposed in this paper is comprised of three main components: a spring element, a strain gauge interface board, and an A/D electronics board. The sensor works by sensing loads applied to the outer shell shown in Figure 1. These loads are transmitted to through the spring element (the post in Figure 1) to the rest of the finger, the hand, and eventually to the ground. Six strain gauges are mounted on the spring element. The strain gauges are positioned so as to measure the six independent elements of strain. In order to protect the spring element, the outer shell was designed to make contact with the fingertip base under extreme loads. This secondary load path reduces the strain experienced by the spring element under these conditions. Because of the small load cell size, electrical wire routing from the gauge down the finger and through the hand was Fig. 1. Mechanical design of the outer shell (left), the spring element (middle), and the base of the fingertip load cell.


How LEGO Built the Foundations and Enterprise Capabilities for Digital Leadership