title: A moving target defense mechanism for MANETs based on identity virtualization

abstract: Mechanisms for continuously changing or shifting a system's attack surface are emerging as game-changers in cyber security. In this paper, we propose a novel defense mechanism for protecting the identity of nodes in Mobile Ad Hoc Networks and defeat the attacker's reconnaissance efforts. The proposed mechanism turns a classical attack mechanism - Sybil - into an effective defense mechanism, with legitimate nodes periodically changing their virtual identity in order to increase the uncertainty for the attacker. To preserve communication among legitimate nodes, we modify the network layer by introducing (i) a translation service for mapping virtual identities to real identities; (ii) a protocol for propagating updates of a node's virtual identity to all legitimate nodes; and (iii) a mechanism for legitimate nodes to securely join the network. We show that the proposed approach is robust to different types of attacks, and also show that the overhead introduced by the update protocol can be controlled by tuning the update frequency.


title: DN-ResNet: Efficient Deep Residual Network for Image Denoising

abstract: A deep learning approach to blind denoising of images without complete knowledge of the noise statistics is considered. We propose DN-ResNet, which is a deep convolutional neural network (CNN) consisting of several residual blocks (ResBlocks). With cascade training, DN-ResNet is more accurate and more computationally efficient than the state of art denoising networks. An edge-aware loss function is further utilized in training DN-ResNet, so that the denoising results have better perceptive quality compared to conventional loss function. Next, we introduce the depthwise separable DN-ResNet (DS-DN-ResNet) utilizing the proposed Depthwise Seperable ResBlock (DS-ResBlock) instead of standard ResBlock, which has much less computational cost. DS-DN-ResNet is incrementally evolved by replacing the ResBlocks in DN-ResNet by DS-ResBlocks stage by stage. As a result, high accuracy and good computational efficiency are achieved concurrently. Whereas previous state of art deep learning methods focused on denoising either Gaussian or Poisson corrupted images, we consider denoising images having the more practical Poisson with additive Gaussian noise as well. The results show that DN-ResNets are more efficient, robust, and perform better denoising than current state of art deep learning methods, as well as the popular variants of the BM3D algorithm, in cases of blind and non-blind denoising of images corrupted with Poisson, Gaussian or Poisson-Gaussian noise. Our network also works well for other image enhancement task such as compressed image restoration.


title: A sublinear additive sieve for finding prime number

abstract: A new algorithm is presented for the problem of finding all primes between 2 and N. It is based on Mairson's sieve algorithm which uses θ(N) additions and multiplications. The new algorithm improves on this algorithm by using a dynamic sieve technique that avoids most of the nonprimes in the range 2 to N, and by using a tabulation method to simulate multiplications. It is shown to require θ(N/log log N) additions. A related algorithm is outlined that has the same complexity but a storage requirement of only θ(N/log log N) bits.


title: A SiC-based power converter module for medium-voltage fast charger for plug-in electric vehicles

abstract: This paper presents an isolated power converter module for medium-voltage (2.4 kV), high-power-quality (PF ≥ 0.98, current THD ≤ 2%), 50 kW fast charger for plug-in electric vehicles. The proposed high-efficiency (above 96%), and reduced-footprint converter module utilizes off-the-shelf Silicon Carbide (SiC) devices to step down the rectified single-phase medium-voltage input. The developed module can also serve as a building block for other medium voltage rectifier applications, including power supplies for data centers and other dc power distribution systems. Based on the system requirements, the appropriate unidirectional converter topology was selected, its operation was simulated and validated by experiments on the developed converter prototype.


title: Isolation of fully synthetic promoters for high-level gene expression in Corynebacterium glutamicum.

abstract: Corynebacterium glutamicum is an important industrial organism that is widely used in the production of amino acids, nucleotides and vitamins. To extend its product spectrum and improve productivity, C. glutamicum needs to undergo further engineering, including the development of applicable promoter system. Here, we isolated new promoters from the fully synthetic promoter library consisting of 70-bp random sequences in C. glutamicum. Using green fluorescent protein (GFP) as a reporter, highly fluorescent cells were screened from the library by fluorescent activated cell sorting (FACS). Twenty potential promoters of various strengths were isolated and characterized through extensive analysis of DNA sequences and mRNA transcripts. Among 20 promoters, 6 promoters which have different strengths were selected and their activities were successfully demonstrated using two model proteins (antibody fragment and endoxylanase). Finally, the strongest promoter (P(H36)) was employed for the secretory production of endoxylanase in fed-batch cultivation, achieving production levels of 746 mg/L in culture supernatant. This is the first report of synthetic promoters constructed in C. glutamicum, and our screening strategy together with the use of synthetic promoters of various strengths will contribute to the future engineering of C. glutamicum.


title: A knowledge-driven framework for simulation application integration

abstract: This paper describes the motivations, methods, and solution concepts of a knowledge-driven framework for simulation application integration. First, the solution ideas are motivated by providing a characterization of the challenges associated with simulation integration at the semantic level. Next, opportunities provided by recent advances in knowledge representation and ontology development methods are outlined. The important role of ontologies in simulation integration is then briefly described. Next, a method for knowledge-driven simulation application integration is described in detail. Illustrative application examples are then outlined in order to provide a flavour for the practical value for the research. Finally, we outline the potential benefits of the research.


title: Informedia digital video library

abstract: Vastdigital libraries of information will soon be available on the nation’s Information Superhighway as a result of emerging technologies for multimedia computing. These libraries wiU profoundly impact the conduct of business, professional, andpersonal activity. However, it is not enough to simply store and play back video (ss in currently envisioned commercial video-on-demand services); to be most effective, new technology is needed for searching through these vast data collections and retrieving the most relevant selections.


title: Design of electronic shelf label based on electronic paper display

abstract: The Electronic Shelf Label (ESL) is an electronic display device based on LCD or electronic paper. In this work, we focus on the application of electronic paper display in ESL. We Put forward a kind of driver interface circuit from the electronic shelf label architecture and principle of electronic paper display controller and driver chip; Combined with electronic paper waveforms and SDRAM, we catch the design points of the driver and process; The scheme has been proved to achieve the image display and update with a good reading experience as traditional paper, so it has certain reference significance for electronic shelf label design, and will be beneficial to the popularity of electronic paper application and promotion.


title: LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs

abstract: Being able to access knowledge bases in an intuitive way has been an active area of research over the past years. In particular, several question answering (QA) approaches which allow to query RDF datasets in natural language have been developed as they allow end users to access knowledge without needing to learn the schema of a knowledge base and learn a formal query language. To foster this research area, several training datasets have been created, e.g. in the QALD (Question Answering over Linked Data) initiative. However, existing datasets are insufficient in terms of size, variety or complexity to apply and evaluate a range of machine learning based QA approaches for learning complex SPARQL queries. With the provision of the Large-Scale Complex Question Answering Dataset (LC-QuAD), we close this gap by providing a dataset with 5000 questions and their corresponding SPARQL queries over the DBpedia dataset. In this article, we describe the dataset creation process and how we ensure a high variety of questions, which should enable to assess the robustness and accuracy of the next generation of QA systems for knowledge graphs.


title: Eugene – A Domain Specific Language for Specifying and Constraining Synthetic Biological Parts, Devices, and Systems

abstract: Background ::: Synthetic biological systems are currently created by an ad-hoc, iterative process of specification, design, and assembly. These systems would greatly benefit from a more formalized and rigorous specification of the desired system components as well as constraints on their composition. Therefore, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) that allows for the specification of synthetic biological designs based on biological parts, as well as provides a very expressive constraint system to drive the automatic creation of composite Parts (Devices) from a collection of individual Parts. ::: Results ::: We illustrate Eugene's capabilities in three different areas: Device specification, design space exploration, and assembly and simulation integration. These results highlight Eugene's ability to create combinatorial design spaces and prune these spaces for simulation or physical assembly. Eugene creates functional designs quickly and cost-effectively. ::: Conclusions ::: Eugene is intended for forward engineering of DNA-based devices, and through its data types and execution semantics, reflects the desired abstraction hierarchy in synthetic biology. Eugene provides a powerful constraint system which can be used to drive the creation of new devices at runtime. It accomplishes all of this while being part of a larger tool chain which includes support for design, simulation, and physical device assembly.


Limited knowledge shilling attacks in collaborative filtering systems


title: Creating 360° educational video: a case study

abstract: The application of virtual reality (VR) to education has been documented for over half a century. During this time studies investigating its use have demonstrated positive findings ranging from increased time on task, to enjoyment, motivation and retention. Despite this, VR systems have never achieved widespread adoption in education. This is arguably due to both limitations of the VR technologies themselves, and the overhead incurred by both content developers and users. In this paper we describe a case study of an alternative approach to creating educational VR content. Instead of using computer graphics, we used a spherical camera in conjunction with a VR head-mounted display to provide 360° educational lectures. The content creation process, as well as issues we encountered during this study are explained, before we conclude by discussing the viability of this approach.


A System for Pedestrian Indoor Localization with Foot Mounted IMU and Additional Sensors


title: Realizing Multimodal Behavior Closing the gap between behavior planning and embodied agent presentation

abstract: Generating coordinated multimodal behavior for an embodied agent (speech, gesture, facial expression...) is challenging. It requires a high degree of animation control, in particular when reactive behaviors are required. We suggest to distinguish realization planning, where gesture and speech are processed symbolically using the behavior markup language (BML), and presentation which is controlled by a lower level animation language (EMBRScript). Reactive behaviors can bypass planning and directly control presentation. In this paper, we show how to define a behavior lexicon, how this lexicon relates to BML and how to resolve timing using formal constraint solvers. We conclude by demonstrating how to integrate reactive emotional behaviors.


title: A Flexible, Planar-Coil-Based Sensor for Through-Shaft Angle Sensing

abstract: In this paper, a simple angle sensor is presented, which is easy to fabricate and is low cost. The sensor is constructed using square shaped flexible planar coils, bent, and arranged in a cylindrical pattern. The sensor design was verified using appropriate analytical design, and an algorithm to identify the quadrants of the measured angle was developed. The output of the sensor is tolerant toward errors due to the axial displacement of the sensing coil. This makes it suitable for applications where the shaft may be subjected to axial motion (even up to 30% of sensing coil length) in addition to angular motion. The radial space required for the existing through-shaft angle sensors is large. Although capacitive sensors can be employed, their output is sensitive to moisture, dust, and oil. The proposed sensor requires very small radial space (about 1.5mm) and its output is not sensitive to such environmental parameters. A prototype sensor was constructed to verify the sensor operation and to evaluate its performance. The prototype sensor had an rms error of 0.58% and the maximum error was within ±1%. The resolution of the measurement system was estimated to be 0.15°. A detailed error analysis has been conducted and the outcome shows that the output of the sensor has very low sensitivity to the source of errors.


title: CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition

abstract: Most of the traditional work on intrinsic image decomposition rely on deriving priors about scene characteristics. On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process. As a consequence, although current deep learning approaches show superior performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving high qualitative results. In this paper, the aim is to exploit the best of the two worlds. A method is proposed that (1) is empowered by deep learning capabilities, (2) considers a physics-based reflection model to steer the learning process, and (3) exploits the traditional approach to obtain intrinsic images by exploiting reflectance and shading gradient information. The proposed model is fast to compute and allows for the integration of all intrinsic components. To train the new model, an object centered large-scale datasets with intrinsic ground-truth images are created. The evaluation results demonstrate that the new model outperforms existing methods. Visual inspection shows that the image formation loss function augments color reproduction and the use of gradient information produces sharper edges.


title: Estimation methods for stochastic volatility models: a survey

abstract: Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been introduced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index. Copyright Blackwell Publishers Ltd, 2004.


title: Context-Aware Hypergraph Construction for Robust Spectral Clustering

abstract: Spectral clustering is a powerful tool for unsupervised data analysis. In this paper, we propose a context-aware hypergraph similarity measure (CAHSM), which leads to robust spectral clustering in the case of noisy data. We construct three types of hypergraphs-the pairwise hypergraph, the k-nearest-neighbor (kNN) hypergraph, and the high-order over-clustering hypergraph. The pairwise hypergraph captures the pairwise similarity of data points; the kNNhypergraph captures the neighborhood of each point; and the clustering hypergraph encodes high-order contexts within the dataset. By combining the affinity information from these three hypergraphs, the CAHSM algorithm is able to explore the intrinsic topological information of the dataset. Therefore, data clustering using CAHSM tends to be more robust. Considering the intra-cluster compactness and the inter-cluster separability of vertices, we further design a discriminative hypergraph partitioning criterion (DHPC). Using both CAHSM and DHPC, a robust spectral clustering algorithm is developed. Theoretical analysis and experimental evaluation demonstrate the effectiveness and robustness of the proposed algorithm.


title: House Price Prediction Using LSTM

abstract: In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time series. Also, stateful LSTM networks and stack LSTM networks are employed to further study the improvement of accuracy of the house prediction model.


title: Expansion Planning of Active Distribution Networks With Centralized and Distributed Energy Storage Systems

abstract: A multistage active distribution network (ADN) planning model that is integrated with the application of energy storage system (ESS) is presented in this paper. Both the long-term investment cost and short-term operation conditions of ADN are considered in the proposed model. The power supply reliability improvement brought by ESS is also analyzed. At each planning stage, the operation conditions are divided into several typical day scenarios and an extreme condition scenario which are based on the load forecast data. The long-term expansion planning decisions including those for replacing and adding circuits, introducing ESS, and considering ADN short-term operation strategies for the ESS charging and discharging are optimized together in the proposed model. The ESS benefits pertaining to peak load shaving and power reliability enhancement are demonstrated using numerical cases, in which the centralized and the distributed ESS are considered as options for the ADN implementation. The effectiveness of the proposed model is demonstrated through various discussions in the paper.


title: Large-scale agile transformation at Ericsson: a case study

abstract: Many large organizations are adopting agile software development as part of their continuous push towards higher flexibility and shorter lead times, yet few reports on large-scale agile transformations are available in the literature. In this paper we report how Ericsson introduced agile in a new R&D product development program developing a XaaS platform and a related set of services, while simultaneously scaling it up aggressively. The overarching goal for the R&D organization, distributed to five sites at two continents, was to achieve continuous feature delivery. This single case study is based on 45 semi-structured interviews during visits at four sites, and five observation sessions at three sites. We describe how the organization experimented with different set-ups for their tens of agile teams aiming for rapid end-to-end development: from component-based virtual teams to totally cross-functional, cross-component, cross-site teams. Moreover, we discuss the challenges the organization faced and how they mitigated them on their journey towards continuous and rapid software engineering. We present four lessons learned for large-scale agile transformations: 1) consider using an experimental approach to transformation, 2) consider implementing the transformation step-wise in complex large-scale settings, 3) team inter-changeability can be limited in a complex large-scale product — specialization might be needed, and 4) not using a common agile framework for the whole organization, in combination with insufficient common trainings and coaching may lead to a lack of common direction in the agile implementation. Further in-depth case studies on large-scale agile transformations, on customizing agile to large-scale settings, as well as on the use of scaling frameworks are needed.


title: Depressive symptoms and suicidal thoughts among former users of finasteride with persistent sexual side effects

abstract: OBJECTIVE ::: Finasteride, a commonly prescribed medication for male pattern hair loss, has recently been associated with persistent sexual side effects. In addition, depression has recently been added to the product labeling of Propecia (finasteride 1 mg). Finasteride reduces the levels of several neuroactive steroids linked to sexual function and depression. This study assesses depressive symptoms and suicidal thoughts in former users of finasteride who developed persistent sexual side effects despite the discontinuation of finasteride. ::: ::: ::: METHOD ::: In 2010-2011, former users of finasteride (n = 61) with persistent sexual side effects for ≥ 3 months were administered standardized interviews that gathered demographic information, medical and psychiatric histories, and information on medication use, sexual function, and alcohol consumption. All former users were otherwise healthy men with no baseline sexual dysfunction, chronic medical conditions, current or past psychiatric conditions, or use of oral prescription medications before or during finasteride use. A control group of men (n = 29), recruited from the community, had male pattern hair loss but had never used finasteride and denied any history of psychiatric conditions or use of psychiatric medications. The primary outcomes were the prevalence of depressive symptoms and the prevalence of suicidal thoughts as determined by the Beck depression inventory II (BDI-II); all subjects self-administered this questionnaire at the time of the interview or up to 10 months later. ::: ::: ::: RESULTS ::: Rates of depressive symptoms (BDI-II score ≥ 14) were significantly higher in the former finasteride users (75%; 46/61) as compared to the controls (10%; 3/29) (P < .0001). Moderate or severe depressive symptoms (BDI-II score ≥ 20) were present in 64% (39/61) of the finasteride group and 0% of the controls. Suicidal thoughts were present in 44% (27/61) of the former finasteride users and in 3% (1/29) of the controls (P < .0001). ::: ::: ::: CONCLUSIONS ::: Clinicians and potential users of finasteride should be aware of the potential risk of depressive symptoms and suicidal thoughts. The preliminary findings of this study warrant further research with controlled studies.


title: On the Distinction Between Rationality and Intelligence: Implications for Understanding Individual Differences in Reasoning

abstract: A concern for individual differences has been missing from the Great Rationality Debate in cognitive science—the debate about how much irrationality to attribute to human cognition. There are individual differences in rational thinking that are less than perfectly correlated with individual differences in intelligence because intelligence and rationality occupy different conceptual locations in models of cognition. A tripartite extension of currently popular dual-process theories is presented in this chapter that illustrates how intelligence and rationality are theoretically separate concepts. The chapter concludes by showing how this tripartite model of mind, taken in the context of studies of individual differences, can help to resolve the Great Rationality Debate.


title: Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment

abstract: In the midst of today's pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g. rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. These prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation of modeling methodologies, this study explores user-specific features in a social media environment for rumor detection. The new approach hypothesizes that whether a user tends to spread a rumor is dependent upon specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, information propagation patterns of rumors versus those of credible messages in a social media environment are systematically differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation for rumor recognition. The new approach is capable of differentiating rumors from credible messages through observing distinctions in their respective propagation patterns in social media. Experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content.


title: Topic level expertise search over heterogeneous networks

abstract: In this paper, we present a topic level expertise search framework for heterogeneous networks. Different from the traditional Web search engines that perform retrieval and ranking at document level (or at object level), we investigate the problem of expertise search at topic level over heterogeneous networks. In particular, we study this problem in an academic search and mining system, which extracts and integrates the academic data from the distributed Web. We present a unified topic model to simultaneously model topical aspects of different objects in the academic network. Based on the learned topic models, we investigate the expertise search problem from three dimensions: ranking, citation tracing analysis, and topical graph search. Specifically, we propose a topic level random walk method for ranking the different objects. In citation tracing analysis, we aim to uncover how a piece of work influences its follow-up work. Finally, we have developed a topical graph search function, based on the topic modeling and citation tracing analysis. Experimental results show that various expertise search and mining tasks can indeed benefit from the proposed topic level analysis approach.


title: A Multi-interface VR Platform For Rehabilitation Research

abstract: With the introduction of new Virtual Reality (VR) devices such as Microsoft HoloLens, Oculus, HTC Vive etc., one cannot ignore the fact that research related to Human and VR interaction is bound to take a big leap. One of the main areas that has already gain momentum, is the area of VR based stroke rehabilitation research. Although many VR based research studies have been performed for lower and upper extremity training purposes, very few research studies are related to development of VR based research platforms. However, these platforms are usually single device dedicated, and therefore cannot be used to add another device. This study contributes to the field of HCI by achieving three major goals-- 1) explore the design aspects of VR based training research; 2) innovate the field of VR based training research by developing a multi-interface platform; and 3) inspire future researchers by providing an open source SDK so as to expand the horizon for human VR interaction. A pilot study was conducted in order to test the applicability of our platform, using 25 healthy subjects across four different interfaces. From the promising results achieved during the pilot study we are currently in the process of evaluating our platform using stroke patients at the Spain Rehabilitation Center at the University of Alabama, in Birmingham. Our findings and insights could inform future VR based rehabilitation system designs and help researchers and other stakeholders to assess the viability of using VR based scenarios for training purposes.


title: Current Concepts in Validity and Reliability for Psychometric Instruments: Theory and Application

abstract: Validity and reliability relate to the interpretation of scores from psychometric instruments (eg, symptom scales, questionnaires, education tests, and observer ratings) used in clinical practice, research, education, and administration. Emerging paradigms replace prior distinctions of face, content, and criterion validity with the unitary concept “construct validity,” the degree to which a score can be interpreted as representing the intended underlying construct. Evidence to support the validity argument is collected from 5 sources: ● Content: do instrument items completely represent the construct? ● Response process: the relationship between the intended construct and the thought processes of subjects or observers ● Internal structure: acceptable reliability and factor structure ● Relations to other variables: correlation with scores from another instrument assessing the same construct


title: Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length

abstract: 3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112 $\times$ 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion , which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM/CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).


title: A hybrid deep learning model for consumer credit scoring

abstract: Consumer credit scoring is an essential part of credit risk management in the fast-growing consumer finance industry and various data mining techniques have been proposed and used on it. Recently, deep learning techniques have gained significant popularity and shown excellent performance in many fields such as image recognition, computer vision and so on. In this paper, we try to take the advantage of deep learning and introduce it into consumer credit scoring. We propose a hybrid model that combines the well-known convolutional neural network with the feature selection algorithm Relief. Experiments are carried on a real-world dataset from a Chinese consumer finance company, and the results show that the proposed model gets superior performance in comparison with other benchmark models such as logistic regression and random forest.


title: A Low-Flicker-Noise 30-GHz Class-F23 Oscillator in 28-nm CMOS Using Implicit Resonance and Explicit Common-Mode Return Path

abstract: This paper presents a millimeter-wave (mmW) frequency generation stage aimed at minimizing phase noise (PN) via waveform shaping and harmonic extraction while suppressing flicker noise upconversion via proper harmonic terminations. A 2nd-harmonic resonance is assisted by a proposed embedded decoupling capacitor inside a transformer for explicit common-mode current return path. Class-F operation with 3rd-harmonic boosting and extraction techniques allow maintaining high quality factor of a 10-GHz tank at the 30-GHz frequency generation. We further propose a comprehensive quantitative analysis method of flicker noise upconversion mechanism exploiting latest insights into the flicker noise mechanisms in nanoscale short-channel transistors, and it is numerically verified against foundry models. The proposed 27.3- to 31.2-GHz oscillator is implemented in TSMC 28-nm CMOS. It achieves PN of −106 dBc/Hz at 1-MHz offset and figure-of-merit (FoM) of −184 dBc/Hz at 27.3 GHz. Its flicker phase-noise ( $1/f^{3}$ ) corner of 120 kHz is an order-of-magnitude better than currently achievable at mmW.


title: Slicing the next mobile packet core network

abstract: The mobile network plays an important role in the evolution of humanity and society. However, due to the increase of users as well as of mobile applications, the current mobile network architecture faces many challenges. In this paper we describe V-Core, a new architecture for the mobile packet core network which is based on Software Defined Networking and Network Function Virtualization. Then, we introduce a MobileVisor which is a machine to slice the above mobile packet core network into different control platforms according to either different mobile operators or different technologies (e.g. 3G or 4G). With our architecture, the mobile network operators can reduce their costs for deployment and operation as well as use network resources efficiently.


Cultural Influences on Collaborative Work in Software Engineering Teams


title: A Brief Survey on RFID Privacy and Security

abstract: In this survey we take a look at dier- ent approaches proposed in the literature for address- ing the privacy and security issues derived from the Radio-Frequency IDentication (RFID) deployment. We concentrate on the lack of privacy that RFID users can suer from, and we elaborate on how the security in the communications between RFID devices can be assured. The main goal of this brief survey is giving a concise classication of the most relevant privacy pro- tection protocols applied to RFID technology. For the sake of brevity and clarity, only the most relevant ap- proaches are selected and classied according to the computational power of the utilised passive devices (i.e. the tags).


title: A novel filter based on a dual-mode air-filled substrate integrated waveguide cavity resonator

abstract: This paper presents a novel filter based on an air-filled substrate integrated waveguide (SIW) cavity resonator. This structure is inspired by the air-filled SIW, recently proposed to realize integrated interconnects with reduces losses. The proposed resonant cavity allows introducing two poles and two transmission zeros in the frequency response of the filter. The size and position of the air-filled portion of the SIW cavity permits to control the resonance frequency of the first two cavity modes, thus setting the pass-band of the filter. The position of the input/output port allows controlling the position of the transmission zero. A filter operating at 4 GHz, with a relative bandwidth of 1.5% and two transmission zeros, has been designed and experimentally verified.


title: Multipole interactions and macroscopic differences in ortho and para hydrogen

abstract: Abstract Differences in the low temperature viscosity and virial coefficients of ortho and para hydrogen are attributed to centrifugal stretching, exchange symmetry, and, to a lesser extent, molecular anisotorpy. Of the latter, quadrupole interactions are the most important.


title: An improved phase-locked loop method for automatic resonance frequency tracing based on static capacitance broadband compensation for a high-power ultrasonic transducer

abstract: The phase-locked loop (PLL) method is widely used for automatic resonance frequency tracing (ARFT) of high-power ultrasonic transducers, which are usually vibrating systems with high mechanical quality factor (Qm). However, a heavily-loaded transducer usually has a low Qm because the load has a large mechanical loss. In this paper, a series of theoretical analyses is carried out to detail why the traditional PLL method could cause serious frequency tracing problems, including loss of lock, antiresonance frequency tracing, and large tracing errors. The authors propose an improved ARFT method based on static capacitance broadband compensation (SCBC), which is able to address these problems. Experiments using a generator based on the novel method were carried out using crude oil as the transducer load. The results obtained have demonstrated the effectiveness of the novel method, compared with the conventional PLL method, in terms of improved tracing accuracy (±9 Hz) and immunity to antiresonance frequency tracing and loss of lock.


title: Fast Matrix-Vector Multiplications for Large-Scale Logistic Regression on Shared-Memory Systems

abstract: Shared-memory systems such as regular desktopsnow possess enough memory to store large data. However, thetraining process for data classification can still be slow if we donot fully utilize the power of multi-core CPUs. Many existingworks proposed parallel machine learning algorithms by modifyingserial ones, but convergence analysis may be complicated. Instead, we do not modify machine learning algorithms, butconsider those that can take the advantage of parallel matrixoperations. We particularly investigate the use of parallel sparsematrix-vector multiplications in a Newton method for largescalelogistic regression. Various implementations from easy tosophisticated ones are analyzed and compared. Results indicatethat under suitable settings excellent speedup can be achieved.


title: A Freely Available Automatically Generated Thesaurus of Related Words

abstract: A freely available English thesaurus of related words is presented that has been automatically compiled by analyzing the distributional similarities of words in the British National Corpus. The quality of the results has been evaluated by comparison with human judgments as obtained from non-native and native speakers of English who were asked to provide rankings of word similarities. According to this measure, the results generated by our system are better than the judgments of the non-native speakers and come close to the native speakers’ performance. An advantage of our approach is that it does not require syntactic parsing and therefore can be more easily adapted to other languages. As an example, a similar thesaurus for German has already been completed.


title: Conversations Gone Awry: Detecting Early Signs of Conversational Failure

abstract: One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very start of a conversation whether it will get out of hand. As opposed to detecting undesirable behavior after the fact, this task aims to enable early, actionable prediction at a time when the conversation might still be salvaged. ::: To this end, we develop a framework for capturing pragmatic devices---such as politeness strategies and rhetorical prompts---used to start a conversation, and analyze their relation to its future trajectory. Applying this framework in a controlled setting, we demonstrate the feasibility of detecting early warning signs of antisocial behavior in online discussions.


title: A Novel 1.2–V 4.5-ppm/°C Curvature-Compensated CMOS Bandgap Reference

abstract: This paper proposes a novel CMOS bandgap reference (BGR) with high-order curvature-compensation by using MOS transistors operating in weak inversion region. The mechanism of the proposed curvature-compensation technique is analyzed thoroughly and the corresponding BGR circuit was implemented in standard CMOS 0.18 μm technology. The experimental results show that the proposed BGR achieves 4.5 ppm/°C over the temperature range of -40°C to 120°C at 1.2 V supply voltage. It consumes only 36 μA. In addition, it achieves line regulation performance of 0.054%/V. It is suitable for low-power applications requiring references with high precision.


title: Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level

abstract: Question Answering (QA) systems over Knowledge Graphs (KG) automatically answer natural language questions using facts contained in a knowledge graph. Simple questions, which can be answered by the extraction of a single fact, constitute a large part of questions asked on the web but still pose challenges to QA systems, especially when asked against a large knowledge resource. Existing QA systems usually rely on various components each specialised in solving different sub-tasks of the problem (such as segmentation, entity recognition, disambiguation, and relation classification etc.). In this work, we follow a quite different approach: We train a neural network for answering simple questions in an end-to-end manner, leaving all decisions to the model. It learns to rank subject-predicate pairs to enable the retrieval of relevant facts given a question. The network contains a nested word/character-level question encoder which allows to handle out-of-vocabulary and rare word problems while still being able to exploit word-level semantics. Our approach achieves results competitive with state-of-the-art end-to-end approaches that rely on an attention mechanism.


title: The Application of the Accelerated Stability Assessment Program (ASAP) to Quality by Design (QbD) for Drug Product Stability

abstract: An isoconversion paradigm, where times in different temperature and humidity-controlled stability chambers are set to provide a fixed degradant level, is shown to compensate for the complex, non-single order kinetics of solid drug products. A humidity-corrected Arrhenius equation provides reliable estimates for temperature and relative humidity effects on degradation rates. A statistical protocol is employed to determine best fits for chemical stability data, which in turn allows for accurate estimations of shelf life (with appropriate confidence intervals) at any storage condition including inside packaging (based on the moisture vapor transmission rate of the packaging and moisture sorption isotherms of the internal components). These methodologies provide both faster results and far better predictions of chemical stability limited shelf life (expiry) than previously possible. Precise shelf-life estimations are generally determined using a 2-week, product-specific protocol. Once the model for a product is developed, it can play a critical role in providing the product understanding necessary for a quality by design (QbD) filing for product approval and enable rational control strategies to assure product stability. Moreover, this Accelerated Stability Assessment Program (ASAP) enables the coupling of product attributes (e.g., moisture content, packaging options) to allow for flexibility in how control strategies are implemented to provide a balance of cost, speed, and other factors while maintaining adequate stability.


title: Dual Quaternion Blending Algorithm and Its Application in Character Animation

abstract: In this paper we generalize established techniques and blending algorithm for quaternions to dual quaternions to include all rigid transformations, and implement real-time character animation with dual quaternions under the platform of OpenGL. Classical quaternions are restricted to the representation of rotations although combination of matrix calculation and quaternions operator has been a popular tool in character animation since 1990s. In character animation and some other applications of 3D computer graphics, we are actually faced with rigid transformation which just includes translation and rotation. Similar to the way quaternions represent rotations, dual quaternions represent rigid transformations. Algorithms based on dual quaternions own better properties than those based on quaternions in practical applications, which include reduced overhead, increased computational efficiency and coordinate invariance.. Finally we demonstrate the effectiveness and superiority of dual quaternions blending algorithm by cartoon male and female mesh models with the animation of walking and waving. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3339 Full Text: PDF


title: Participatory design and "democratizing innovation"

abstract: We sketch an alternative “ddemocratizing innovation”” practice more in line with the original visions of participatory design based on our experience of running Malmo Living Labs - an open innovation milieu where new constellations, issues and ideas evolve from bottom-up long--term collaborations amongst diverse stakeholders..


title: Sort Story: Sorting Jumbled Images and Captions into Stories

abstract: Temporal common sense has applications in AI tasks such as QA, multi-document summarization, and human-AI communication. We propose the task of sequencing -- given a jumbled set of aligned image-caption pairs that belong to a story, the task is to sort them such that the output sequence forms a coherent story. We present multiple approaches, via unary (position) and pairwise (order) predictions, and their ensemble-based combinations, achieving strong results on this task. We use both text-based and image-based features, which depict complementary improvements. Using qualitative examples, we demonstrate that our models have learnt interesting aspects of temporal common sense.


title: Nonlinear Camera Response Functions and Image Deblurring: Theoretical Analysis and Practice

abstract: This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. We present a comprehensive study to analyze the effects of CRFs on motion deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially varying blur. We prove that such nonlinearity can cause large errors around edges when directly applying deconvolution to a motion blurred image without CRF correction. These errors are inevitable even with a known point spread function (PSF) and with state-of-the-art regularization-based deconvolution algorithms. In addition, we show how CRFs can adversely affect PSF estimation algorithms in the case of blind deconvolution. To help counter these effects, we introduce two methods to estimate the CRF directly from one or more blurred images when the PSF is known or unknown. Our experimental results on synthetic and real images validate our analysis and demonstrate the robustness and accuracy of our approaches.


title: Practical user authentication leveraging channel state information (CSI)

abstract: User authentication is the critical first step to detect identity-based attacks and prevent subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply the cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, we explore to use channel state information (CSI), which is available from off-the-shelf WiFi devices, to conduct fine-grained user authentication. We propose an user-authentication framework that has the capability to build the user profile resilient to the presence of the spoofer. Our machine learning based user-authentication techniques can distinguish two users even when they possess similar signal fingerprints and detect the existence of the spoofer. Our experiments in both office building and apartment environments show that our framework can filter out the signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS).


title: Real-Time Computer Vision/DGPS-Aided Inertial Navigation System for Lane-Level Vehicle Navigation

abstract: Many intelligent transportation system (ITS) applications will increasingly rely on lane-level vehicle positioning that requires high accuracy, bandwidth, availability, and integrity. Lane-level positioning methods must reliably work in real time in a wide range of environments, spanning rural to urban areas. Traditional positioning sensors such as the Global Navigation Satellite Systems may have poor performance in dense urban areas, where obstacles block satellite signals. This paper presents a sensor fusion technique that uses computer vision and differential pseudorange Global Positioning System (DGPS) measurements to aid an inertial navigation system (INS) in challenging environments where GPS signals are limited and/or unreliable. To supplement limited DGPS measurements, this method uses mapped landmarks that were measured through a priori observations (e.g., traffic light location data), taking advantage of existing infrastructure that is abundant within suburban/urban environments. For example, traffic lights are easily detected by color vision sensors in both day and night conditions. A tightly coupled estimation process is employed to use observables from satellite signals and known feature observables from a camera to correct an INS that is formulated as an extended Kalman filter. A traffic light detection method is also outlined, where the projected feature uncertainty ellipse is utilized to perform data association between a predicted feature and a set of detected features. Real-time experimental results from real-world settings are presented to validate the proposed localization method.


title: Small Changes, Big Changes: An Updated View on the Android Permission System

abstract: Since the appearance of Android, its permission system was central to many studies of Android security. For a long time, the description of the architecture provided by Enck et al. in [31] was immutably used in various research papers. The introduction of highly anticipated runtime permissions in Android 6.0 forced us to reconsider this model. To our surprise, the permission system evolved with almost every release. After analysis of 16 Android versions, we can confirm that the modifications, especially introduced in Android 6.0, considerably impact the aptness of old conclusions and tools for newer releases. For instance, since Android 6.0 some signature permissions, previously granted only to apps signed with a platform certificate, can be granted to third-party apps even if they are signed with a non-platform certificate; many permissions considered before as threatening are now granted by default. In this paper, we review in detail the updated system, introduced changes, and their security implications. We highlight some bizarre behaviors, which may be of interest for developers and security researchers. We also found a number of bugs during our analysis, and provided patches to AOSP where possible.


title: Towards reproducibility in recommender-systems research

abstract: Numerous recommendation approaches are in use today. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. In this article, we examine the challenge of reproducibility in recommender-system research. We conduct experiments using Plista's news recommender system, and Docear's research-paper recommender system. The experiments show that there are large discrepancies in the effectiveness of identical recommendation approaches in only slightly different scenarios, as well as large discrepancies for slightly different approaches in identical scenarios. For example, in one news-recommendation scenario, the performance of a content-based filtering approach was twice as high as the second-best approach, while in another scenario the same content-based filtering approach was the worst performing approach. We found several determinants that may contribute to the large discrepancies observed in recommendation effectiveness. Determinants we examined include user characteristics (gender and age), datasets, weighting schemes, the time at which recommendations were shown, and user-model size. Some of the determinants have interdependencies. For instance, the optimal size of an algorithms' user model depended on users' age. Since minor variations in approaches and scenarios can lead to significant changes in a recommendation approach's performance, ensuring reproducibility of experimental results is difficult. We discuss these findings and conclude that to ensure reproducibility, the recommender-system community needs to (1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments, (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research.


title: Dependence language model for information retrieval

abstract: This paper presents a new dependence language modeling approach to information retrieval. The approach extends the basic language modeling approach based on unigram by relaxing the independence assumption. We integrate the linkage of a query as a hidden variable, which expresses the term dependencies within the query as an acyclic, planar, undirected graph. We then assume that a query is generated from a document in two stages: the linkage is generated first, and then each term is generated in turn depending on other related terms according to the linkage. We also present a smoothing method for model parameter estimation and an approach to learning the linkage of a sentence in an unsupervised manner. The new approach is compared to the classical probabilistic retrieval model and the previously proposed language models with and without taking into account term dependencies. Results show that our model achieves substantial and significant improvements on TREC collections.


title: Video-audio driven real-time facial animation

abstract: We present a real-time facial tracking and animation system based on a Kinect sensor with video and audio input. Our method requires no user-specific training and is robust to occlusions, large head rotations, and background noise. Given the color, depth and speech audio frames captured from an actor, our system first reconstructs 3D facial expressions and 3D mouth shapes from color and depth input with a multi-linear model. Concurrently a speaker-independent DNN acoustic model is applied to extract phoneme state posterior probabilities (PSPP) from the audio frames. After that, a lip motion regressor refines the 3D mouth shape based on both PSPP and expression weights of the 3D mouth shapes, as well as their confidences. Finally, the refined 3D mouth shape is combined with other parts of the 3D face to generate the final result. The whole process is fully automatic and executed in real time. The key component of our system is a data-driven regresor for modeling the correlation between speech data and mouth shapes. Based on a precaptured database of accurate 3D mouth shapes and associated speech audio from one speaker, the regressor jointly uses the input speech and visual features to refine the mouth shape of a new actor. We also present an improved DNN acoustic model. It not only preserves accuracy but also achieves real-time performance. Our method efficiently fuses visual and acoustic information for 3D facial performance capture. It generates more accurate 3D mouth motions than other approaches that are based on audio or video input only. It also supports video or audio only input for real-time facial animation. We evaluate the performance of our system with speech and facial expressions captured from different actors. Results demonstrate the efficiency and robustness of our method.


title: MPIOV: Scaling hardware-based I/O virtualization for mixed-criticality embedded real-time systems using non transparent bridges to (Multi-Core) multi-processor systems

abstract: Safety-critical systems consolidating multiple functionalities of different criticality (so-called mixed-criticality systems) require separation between these functionalities to assure safety and security properties. Performance-hungry and safety-critical applications (like a radar processing system steering an autonomous flying aircraft) may demand an embedded high-performance computing cluster of more than one (multi-core) processor. This paper presents the Multi-Processor I/O Virtualization (MPIOV) concept to enable hardware-based Input/Output (I/O) virtualization or sharing with separation among multiple (multi-core) processors in (mixed-criticality) embedded real-time systems, which usually do not have means for separation like an Input/Output Memory Management Unit (IOMMU). The concept uses a Non-Transparent Bridge (NTB) to connect each processing host to the management host, while checking the target address and source / origin ID to decide whether or not to block a transaction. It is a standardized, portable and non-proprietary platform-independent spatial separation solution that does not require an IOMMU in the processor. Furthermore, the concept sketches an approach for PCI Express (PCIe)-based systems to enable sharing of up to 2048 (virtual) functions per end-point, while still being compatible to the plain PCIe standard. A practical evaluation demonstrates that the impact to performance degradation (transfer time, transfer rate) is negligible (about 0.01%) compared to a system without separation.


title: Evaluation of advanced techniques for multi-party privacy-preserving record linkage on real-world health databases

abstract: ABSTRACT ::: ObjectiveThe linking of multiple (three or more) health databases is challenging because of the increasing sizes of databases, the number of parties among which they are to be linked, and privacy concerns related to the use of personal data such as names, addresses, or dates of birth. This entails a need to develop advanced scalable techniques for linking multiple databases while preserving the privacy of the individuals they contain. In this study we empirically evaluate several state-of-the-art multi-party privacy-preserving record linkage (MP-PPRL) techniques with large real-world health databases from Australia. ::: ApproachMP-PPRL is conducted such that no sensitive information is revealed about database records that can be used to infer knowledge about individuals or groups of individuals. Current state-of-the-art methods used in this evaluation use Bloom filters to encode personal identifying information. The empirical evaluation comprises of different multi-party private blocking and matching techniques that are evaluated for different numbers of parties. Each database contains more than 700,000 records extracted from ten years of New South Wales (NSW) emergency presentation data. Each technique is evaluated with regard to scalability, quality and privacy. Scalability and quality are measured using the metrics of reduction ratio, pairs completeness, precision, recall, and F-measure. Privacy is measured using disclosure risk metrics that are based on the probability of suspicion, defined as the likelihood that a record in an encoded database matches to one or more record(s) in a publicly available database such as a telephone directory. MP-PPRL techniques that either utilize a trusted linkage unit, and those that do not, are evaluated. ::: ResultsExperimental results showed MP-PPRL methods are practical for linking large-scale real world data. Private blocking techniques achieved significantly higher privacy than standard hashing-based techniques with a maximum disclosure risk of 0.0003 and 1, respectively, at a small cost to linkage quality and efficiency. Similarly, private matching techniques provided a similar acceptable reduction in linkage quality compared to standard non-private matching while providing high privacy protection. ::: ConclusionThe adoption of privacy-preserving linkage methods has the ability to significantly reduce privacy risks associated with linking large health databases, and enable the data linkage community to offer operational linkage services not previously possible. The evaluation results show that these state-of-the-art MP-PPRL techniques are scalable in terms of database sizes and number of parties, while providing significantly improved privacy with an associated trade-off in linkage quality compared to standard linkage techniques.


Convolutional Neural Network Based Chart Image Classification


title: Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness

abstract: Users make lasting judgments about a website's appeal within a split second of seeing it for the first time. This first impression is influential enough to later affect their opinions of a site's usability and trustworthiness. In this paper, we demonstrate a means to predict the initial impression of aesthetics based on perceptual models of a website's colorfulness and visual complexity. In an online study, we collected ratings of colorfulness, visual complexity, and visual appeal of a set of 450 websites from 548 volunteers. Based on these data, we developed computational models that accurately measure the perceived visual complexity and colorfulness of website screenshots. In combination with demographic variables such as a user's education level and age, these models explain approximately half of the variance in the ratings of aesthetic appeal given after viewing a website for 500ms only.


title: A 1.6%/V 124.2 pW 9.3 Hz relaxation oscillator featuring a 49.7 pW voltage and current reference generator

abstract: This paper presents a relaxation oscillator that utilizes a supply-stabilized pico-powered voltage and current reference (VCRG) to charge and reset a chopped pair of MIM capacitors at sub-nW power levels. Specifically, a temperature- and line-stabilized reference voltage is generated via a 4-transistor (4T) self-regulated structure, the output of which is used to bias a temperature-compensated gate-leakage transistor to generate a stabilized current reference. The reference current is then used to charge a swapping pair of MIM capacitors to compare to the voltage generated by the same VCRG in a relaxation topology. The design is fabricated in 65 nm CMOS, and 14 measured samples yield a reference voltage of 147.1 mV achieving a temperature coefficient of 364 ppm/°C and a line regulation of 0.21%/V, and a reference current of 10.2 pA achieving a temperature coefficient of 1077.3 ppm/°C and a line regulation of 1.79%/V (all numbers averaged across all samples). The proposed VCRG-based relaxation oscillator achieves an average temperature coefficient of 999.9 ppm/°C from −40 to 120° C and a line regulation of 1.6%/V from 0.6 to 1.1 V, all at a system power consumption of 124.2 pW at 20° C.


title: Solving a Reconfigurable Maze using Hybrid Wall Follower Algorithm

abstract: A key feature of an autonomous vehicle is the ability to get to a target location while traversing through a previously unknown environment. Mapping the environment will allow the vehicle to find an optimum path. This paper explores this issue by programming a mobile robot to find the shortest route in a reconfigurable maze. A wall follower algorithm with combined left-hand and right-hand rules is implemented upon several different maze configurations. It is found that the hybrid algorithm has improved the maze solving capabilities of the maze robot significantly. General Terms Autonomous, navigation, robotics, localization, mapping.


title: Learning Document Embeddings With CNNs

abstract: This paper proposes a new model for document embedding. Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize. We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model. This allows us to train deeper architectures that are fully parallelizable. Stacking layers together increases the receptive filed allowing each successive layer to model increasingly longer range semantic dependences within the document. Empirically we demonstrate superior results on two publicly available benchmarks. Full code will be released with the final version of this paper.


title: Electronically Tunable Doherty Power Amplifier for Multi-Mode Multi-Band Base Stations

abstract: This paper proposes an electronically reconfigurable Doherty amplifier capable of efficiently amplifying multi-standard multi-band wireless signals centered at widely spaced frequencies. The paper outlines closed form equations for an effective design methodology of frequency agile Doherty amplifiers driven with multi-mode signals using a small number of electronically tunable devices. As a proof of concept, a reconfigurable Doherty prototype is designed and fabricated to operate at 1.9, 2.14, and 2.6 GHz meant to efficiently amplify signals with peak-to-average power ratio equal to 6, 9 and 12 dB. The measurement results obtained using continuous wave signals reveal drain efficiencies of about 67% and 42% at the peak power and 12 dB output back off power respectively for the three operating frequencies. In addition, the reconfigurable Doherty amplifier is successfully linearized when driven with 20 MHz wideband code-division multiple access and 20 MHz long term evolution signals, using a Volterra based digital predistrtion algorithm which exploits a pruned Volterra series.


title: Homogenization of material properties in additively manufactured structures

abstract: Additive manufacturing transforms material into three-dimensional parts incrementally, layer by layer or path by path. Subject to the build direction and machine resolution, an additively manufactured part deviates from its design model in terms of both geometry and mechanical performance. In particular, the material inside the fabricated part often exhibits spatially varying material distribution (heterogeneity) and direction dependent behavior (anisotropy), indicating that the design model is no longer a suitable surrogate to consistently estimate the mechanical performance of the printed component.We propose a new two-stage approach to modeling and estimating effective elastic properties of parts fabricated by fused deposition modeling (FDM) process. First, we construct an implicit representation of an effective mesoscale geometry-material model of the printed structure that captures the details of the particular process and published material information. This representation of mesoscale geometry and material of the printed structure is then homogenized at macro scale through a solution of an integral equation formulated using Green's function. We show that the integral equation can be converted into a system of linear equations that is symmetric and positive definite and can be solved efficiently using conjugate gradient method and Fourier transform. The computed homogenized properties are validated by both finite element method and experiment results. The proposed two-stage approach can be used to estimate other effective material properties in a variety of additive manufacturing processes, whenever a similar effective mesoscale geometry-material model can be constructed. A two-stage approach estimates effective elastic properties in additive manufacturing.Implicit geometric model is combined with measured material property information.Mesoscale geometry-material model is computationally homogenized at macroscale.Efficient homogenization relies on integral equation formulated using Green function.Conjugate gradient is used to solve the symmetric and positive definite linear system.


title: Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

abstract: Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations. In this paper, we investigate the robustness and resilience of deep RL to training-time and test-time attacks. Through experimental results, we demonstrate that under noncontiguous training-time attacks, Deep Q-Network (DQN) agents can recover and adapt to the adversarial conditions by reactively adjusting the policy. Our results also show that policies learned under adversarial perturbations are more robust to test-time attacks. Furthermore, we compare the performance of $\epsilon$-greedy and parameter-space noise exploration methods in terms of robustness and resilience against adversarial perturbations.


title: Fingerprint authentication using Raspberry Pi based on IoT

abstract: User authentication is getting inflexible in our Internet of Things (IoT) world. Current approaches for authentication, such as passwords abetted by a second factor, are being reduced moot due to human fault as well as the improved sophistication of further malware attacks. We proposed a way of Fingerprint authentication is centered on minutiae matching where bifurcation, ridge ending are calculated as points and matched. This method is more vigorous than matching based on pattern and singularity points. The fingerprint is collected by optical sensor, which relays to cloud via Raspberry Pi. The fingerprints to be authenticated are stowed in a file server and webserver performs 1:N verification once it obtains the data through POST request. It sends out a retort based on match score along with fingerprint index ID. Our process is secure as fingerprints are stored on server rather local platform.


Assembling Furniture by Asking for Help from a Human Partner


title: FdConfig: A Constraint-Based Interactive Product Configurator

abstract: We present a constraint-based approach to interactive product configuration with arithmetic constraints and support for optimization. Our configurator tool FdConfig is based on feature models (from software product line engineering) for the representation of the valid product variants. Such models can be directly mapped into constraint satisfaction problems and dealt with by appropriate constraint solvers. During the interactive configuration process the user generates new constraints as a result of his configuration decisions and even may retract constraints posted earlier. We discuss the configuration process, explain the underlying techniques and show optimizations.


title: AlGaN/GaN HEMTs-an overview of device operation and applications

abstract: Wide bandgap semiconductors are extremely attractive for the gamut of power electronics applications from power conditioning to microwave transmitters for communications and radar. Of the various materials and device technologies, the AlGaN/GaN high-electron mobility transistor seems the most promising. This paper attempts to present the status of the technology and the market with a view of highlighting both the progress and the remaining problems.


title: Fuzzy clustering with artificial bee colony algorithm

abstract: In this work, performance of the Artificial Bee Colony Algorithm which is a recently proposed algorithm, has been tested on fuzzy clustering. We applied the Artificial Bee Colony (ABC) Algorithm fuzzy clustering to classify different data sets; Cancer, Diabetes and Heart from UCI database, a collection of classification benchmark problems. The results indicate that the performance of Artificial Bee Colony Optimization Algorithm is successful in fuzzy clustering.


title: A Differential Topological View of Challenges in Learning with Feedforward Neural Networks

abstract: Among many unsolved puzzles in theories of Deep Neural Networks (DNNs), there are three most fundamental challenges that highly demand solutions, namely, expressibility, optimisability, and generalisability. Although there have been significant progresses in seeking answers using various theories, e.g. information bottleneck theory, sparse representation, statistical inference, Riemannian geometry, etc., so far there is no single theory that is able to provide solutions to all these challenges. In this work, we propose to engage the theory of differential topology to address the three problems. By modelling the dataset of interest as a smooth manifold, DNNs can be considered as compositions of smooth maps between smooth manifolds. Specifically, our work offers a differential topological view of loss landscape of DNNs, interplay between width and depth in expressibility, and regularisations for generalisability. Finally, in the setting of deep representation learning, we further apply the quotient topology to investigate the architecture of DNNs, which enables to capture nuisance factors in data with respect to a specific learning task.


title: Magnetoresistive Random Access Memory

abstract: In this paper, a review of the developments in MRAM technology over the past 20 years is presented. The various MRAM generations are described with a particular focus on spin-transfer torque MRAM (STT-MRAM) which is currently receiving the greatest attention. The working principles of these various MRAM generations, the status of their developments, and demonstrations of working circuits, including already commercialized MRAM products, are discussed.


title: An Intelligent Teaching Assistant System for Logic

abstract: This paper presents the Logic-ITA, an Intelligent Teaching Assistant system for the teaching/learning of propositional logic. Intelligent Teaching Assistant Systems are dedicated both to learners and teachers. The system embeds three tools: the Logic Tutor, the LT-Configurator and the LT-Analyser. The Logic Tutor is an intelligent tutoring system destined to the students. The other two tools are dedicated to the teacher. The LT-Configurator manages teaching configuration settings and material. The LT-Analyser is for monitoring the class' progress and collect data.


Applications of RFID in Supply Chains


title: Intelligent Agents - Summary of an Agent Infrastructure

abstract: Intelligent Agents are being used for modelling simple rational behaviours in a wide range of distributed applications. In particular, multi-agent architectures based on the Belief-DesireIntention model have been used successfully in situations where modelling of human reasoning and team behaviour are needed, such as simulating tactical decision-making in defence operations and command and control structures. Other applications include intelligent web applications, manufacturing control, telephone call centres, and air traffic management. The JACK Intelligent Agents framework by Agent Oriented Software brings the concept of intelligent agents into the mainstream of commercial software engineering and Java. JACK Intelligent AgentsTM is a third generation agent framework, designed as a set of lightweight components with high performance and strong data typing. We present the design approach and major technical characteristics of JACK Intelligent Agents, with a focus on some of the more recent developments – modular structuring elements called Capabilities and the team-based reasoning model called SimpleTeam. Two example applications, in decision support and defence simulation are described. Also, we discuss the benefits of the component-based approach, both for the software engineer developing sophisticated distributed applications, and for the researcher exploring agent models and architectures.


title: Human face aging with guided prediction and detail synthesis

abstract: In this paper, we present an example-based method to estimate the aging process of a human face. To tackle the difficulty of collecting considerable chronological photos of individuals, we utilize a two-layer strategy. Based on a sparse aging database, an EM-PCA-based algorithm with the personal guidance vector is first applied to conjecture the temporal variations of a target face. Since the subspace-based prediction may not preserve detailed creases, we propose synthesizing facial details with a separate texture dataset. Besides automatic simulation, the proposed framework can also include further guidance, e.g., parents' impact vector or users' indication of wrinkles. Our estimated results can improve feature point positions and user evaluation demonstrates that the two-layer approach provides more reasonable aging prediction.


title: Software Product Line Engineering with Personas

abstract: In this paper, a user-centered and mass-customized design process is proposed that unifies Personas and Software Product Line Engineering (SPLE). The key to this proposal is the relationship between persona- weighted feature matrices and product-feature matri- ces. We propose a development method that (1) creates personas before the product portfolio scoping stage, and (2) adds extra personas during the application engineering stage. Applicable domains are discussed, and methods for evaluating the approach are reported.


title: Design and experimental demonstration of 1.35 kV SiC super junction Schottky diode

abstract: This paper presents the first functional SiC super junction devices, SiC SJ Schottky diodes, which substantially improve the trade-off between the breakdown voltage and specific on-resistance in SiC power devices. Processes for fabricating SJ structures by using a trench-etching-and-sidewall-implant method has been developed and a functional SJ Schottky diode has been demonstrated based on this processing method. The measured cell blocking voltage was 1350V, which achieves 95% of the simulated blocking voltage for the ideally-charge-balanced SJ structure. The measured device specific on-resistance was 0.92mΩ·cm2. The SJ drift region specific on-resistance as low as 0.32 mΩ·cm2 was obtained after subtracting the substrate resistance.


title: American Clinical Neurophysiology Society Guideline 6 : Minimum Technical Standards for EEG Recording in Suspected Cerebral Death

abstract: This revision to the EEG Guidelines is an update incorporating current EEG technology and practice. The role of the EEG in making the determination of brain death is discussed as are suggested technical criteria for making the diagnosis of electrocerebral inactivity.


title: Dynamic Calibration and Occlusion Handling Algorithms for Lane Tracking

abstract: An approach of rapidly computing the projective width of lanes is presented to predict the projective positions and widths of lanes. The Lane Marking Extraction Finite State Machine is designed to extract points with features of lane markings in the image, and a cubic B-spline is adopted to conduct curve fitting to reconstruct road geometry. A statistical search algorithm is also proposed to correctly and adaptively determine thresholds under various kinds of illumination conditions. Furthermore, the parameters of the camera in a moving car may change with the vibration, so a dynamic calibration algorithm is applied to calibrate camera parameters and lane widths with the information of lane projection. Moreover, a fuzzy logic is applied to determine the situation of occlusion. Finally, a region-of-interest determination strategy is developed to reduce the search region and to make the detection more robust with respect to the occlusion on the lane markings or complicated changes of curves and road boundaries.


title: Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator

abstract: Applications of perceptual image quality assessment (IQA) in image and video processing, such as image acquisition, image compression, image restoration, and multimedia communication, have led to the development of many IQA metrics. In this paper, a reliable full reference IQA model is proposed that utilize gradient similarity (GS), chromaticity similarity (CS), and deviation pooling (DP). By considering the shortcomings of the commonly used GS to model the human visual system (HVS), a new GS is proposed through a fusion technique that is more likely to follow HVS. We propose an efficient and effective formulation to calculate the joint similarity map of two chromatic channels for the purpose of measuring color changes. In comparison with a commonly used formulation in the literature, the proposed CS map is shown to be more efficient and provide comparable or better quality predictions. Motivated by a recent work that utilizes the standard DP, a general formulation of the DP is presented in this paper and used to compute a final score from the proposed GS and CS maps. This proposed formulation of DP benefits from the Minkowski pooling and a proposed power pooling as well. The experimental results on six data sets of natural images, a synthetic data set, and a digitally retouched dataset show that the proposed index provides comparable or better quality predictions than the most recent and competing state-of-the-art IQA metrics in the literature, it is reliable and has low complexity. The MATLAB source code of the proposed metric is available at https://dl.dropboxusercontent.com/u/74505502/MDSI.m .


title: Fact, Fiction, and Value Investing

abstract: Value investing has been a part of the investment lexicon for at least the better part of a century. In particular the diversified systematic “value factor” or “value effect” has been studied extensively since at least the 1980s. Yet, there are still many areas of confusion about value investing. In this article we aim to clarify many of these matters, focusing in particular on the diversified systematic value strategy, but also exploring how this strategy relates to its more concentrated implementation. We highlight many points about value investing and attempt to prove or disprove each of them, referencing an extensive academic literature and performing simple tests based on easily accessible, industry-standard public data.


title: An automotive on-board 3.3 kW battery charger for PHEV application

abstract: An on-board charger is responsible for charging the battery pack in a plug-in hybrid electric vehicle (PHEV). In this paper, a 3.3kW two stage battery charger design is presented for a PHEV application. The objective of the design is to achieve high efficiency, which is critical to minimize the charger size, charging time and the amount and cost of electricity drawn from the utility. The operation of the charger power converter configuration is provided in addition to a detailed design procedure. The mechanical packaging design and key experimental results are provided to verify the suitability of the proposed charger power architecture.


title: Using the particle swarm optimization algorithm to calibrate the parameters relating to the turbulent flux in the surface layer in the source region of the Yellow River

abstract: Accurately determining the fluxes of mass and energy between land and the atmosphere is important for understanding regional climates and hydrological cycles. In numerical modeling, the parameterization of a turbulent flux is usually based on Monin-Obukhov similarity theory (MOST). According to this theory, it is necessary to simultaneously calculate the empirical similarity parameters βm, βh, γm, and γh, the aerodynamic roughness (z0m) and the thermal roughness (zT). However, it is difficult to solve a simultaneous set of nonlinear equations for these six parameters. In this study, a new method was introduced to solving this problem. Using measurements from Maqu Station in the source region of the Yellow River, this study employed the artificial intelligence particle swarm optimization (PSO) algorithm to calibrate the parameters relating to the turbulent flux in the surface layer. We concluded that the differences in the sensible heat and momentum fluxes between the calculations that used the calibrated parameters and the measurements were rather small and that their correlation coefficients were relatively high. The results suggested that PSO algorithm is a feasible approach which can be applied in MOST parameter estimation.


title: Finding objects for blind people based on SURF features

abstract: Nowadays computer vision technology is helping the visually impaired by recognizing objects in their surroundings. Unlike research of navigation and wayfinding, there are no camera-based systems available in the market to find personal items for the blind. This paper proposes an object recognition method to help blind people find missing items using Speeded-Up Robust Features (SURF). SURF features can extract distinctive invariant features that can be utilized to perform reliable matching between different images in multiple scenarios. These features are invariant to image scale, translation, rotation, illumination, and partial occlusion. The proposed recognition process begins by matching individual features of the user queried object to a database of features with different personal items which are saved in advance. Experiment results demonstrate the effectiveness and efficiency of the proposed method.


title: Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

abstract: A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.


title: Domino Temporal Data Prefetcher

abstract: Big-data server applications frequently encounter data misses, and hence, lose significant performance potential. One way to reduce the number of data misses or their effect is data prefetching. As data accesses have high temporal correlations, temporal prefetching techniques are promising for them. While state-of-the-art temporal prefetching techniques are effective at reducing the number of data misses, we observe that there is a significant gap between what they offer and the opportunity. This work aims to improve the effectiveness of temporal prefetching techniques. We identify the lookup mechanism of existing temporal prefetchers responsible for the large gap between what they offer and the opportunity. Existing lookup mechanisms either not choose the right stream in the history, or unnecessarily delay the stream selection, and hence, miss the opportunity at the beginning of every stream. In this work, we introduce Domino prefetching to address the limitations of existing temporal prefetchers. Domino prefetcher is a temporal data prefetching technique that logically looks up the history with both one and two last miss addresses to find a match for prefetching. We propose a practical design for Domino prefetcher that employs an Enhanced Index Table that is indexed by just a single miss address. We show that Domino prefetcher captures more than 90% of the temporal opportunity. Through detailed evaluation targeting a quad-core processor and a set of server workloads, we show that Domino prefetcher improves system performance by 16% over the baseline with no data prefetcher and 6% over the state-of- the-art temporal data prefetcher.


title: Commercial Smartphone-Based Devices and Smart Applications for Personalized Healthcare Monitoring and Management

abstract: Smartphone-based devices and applications (SBDAs) with cost effectiveness and remote sensing are the most promising and effective means of delivering mobile healthcare (mHealthcare). Several SBDAs have been commercialized for the personalized monitoring and/or management of basic physiological parameters, such as blood pressure, weight, body analysis, pulse rate, electrocardiograph, blood glucose, blood glucose saturation, sleeping and physical activity. With advances in Bluetooth technology, software, cloud computing and remote sensing, SBDAs provide real-time on-site analysis and telemedicine opportunities in remote areas. This scenario is of utmost importance for developing countries, where the number of smartphone users is about 70% of 6.8 billion cell phone subscribers worldwide with limited access to basic healthcare service. The technology platform facilitates patient-doctor communication and the patients to effectively manage and keep track of their medical conditions. Besides tremendous healthcare cost savings, SBDAs are very critical for the monitoring and effective management of emerging epidemics and food contamination outbreaks. The next decade will witness pioneering advances and increasing applications of SBDAs in this exponentially growing field of mHealthcare. This article provides a critical review of commercial SBDAs that are being widely used for personalized healthcare monitoring and management.


Nonlinear Image Enhancement to Improve Face Detection in Complex Lighting Environment


title: Estimating defectiveness of source code: A predictive model using GitHub content

abstract: Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for estimating defectiveness of a given source code. These artifacts can be useful for building tools and techniques pertaining to several automated software engineering areas such as bug localization, code review, and recommendation and program repair. ::: In order to achieve our goal, we first extract coding style information (e.g. related to programming language constructs used in the source code) for source code files present on GitHub. Then the information available in bug reports (if any) associated with these source code files are extracted. Thus fetched un(/ semi)-structured information is then transformed into a structured knowledge base. We considered more than 30400 source code files from 20 different GitHub repositories with about 14950 associated bug reports across 4 bug tracking portals. The source code files considered are written in four programming languages (viz., C, C++, Java, and Python) and belong to different types of applications. ::: A machine learning (ML) model for estimating the defectiveness of a given input source code is then trained using the knowledge base. In order to pick the best ML model, we evaluated 8 different ML algorithms such as Random Forest, K Nearest Neighbour and SVM with around 50 parameter configurations to compare their performance on our tasks. One of our findings shows that best K-fold (with k=5) cross-validation results are obtained with the NuSVM technique that gives a mean F1 score of 0.914.


Letter to the editors regarding the article entitled: “paradoxical undressing associated with subarachnoid hemorrhage in a non-hypothermia case?”


title: Current concepts in procedural consolidation

abstract: Practice is vital to the acquisition of new skills, but the brain does not stop processing information when practice stops. After practice, changes take place that strengthen and modify the new skill. These changes, described under the umbrella term 'consolidation', take two distinct forms: the enhancement of skills and the stabilization of memories. Here we describe and evaluate the evidence for these types of consolidation.


title: Design on plantar pressure monitoring device of diabetic patients based on FSR

abstract: Sort by the condition of neuropathy,diabetes can be divided into diabetes with peripheral neuropathy(T2DM)and diabetes without peripheral neuropathy(T1DM).The infection will cause abnormal distribution of plantar pressure and make the T2DM patients' feet become lesion.Now,force plate is major tool of quantitative research of plantar pressure.it is uncommon to apply wearable plantar pressure monitoring system to clinic medicine.A plantar pressure monitoring system called smart insole to detect the plantar pressure in real-time is designed.In the hardware aspect,FSR thin type pressure sensor is applied to fabricate smart insole.The threshold judgement method using sample distance is used to distinguish stable walk pressure value from unstable walk pressure value.And the peaks of pressure are taken as the characteristic value.The SSDA is used as a quick judgement for abnormal condition of diabetes patients' plantar pressure.By experiment,it is proved that using smart insole in hardware and SSDA for algorithm have good effects on real-time detection of T2DM patients' distribution of plantar pressure.


title: A cross-platform analysis of bugs and bug-fixing in open source projects: desktop vs. Android vs. iOS

abstract: As smartphones continue to increase in popularity, understanding how software processes associated with the smartphone platform differ from the traditional desktop platform is critical for improving user experience and facilitating software development and maintenance. In this paper we focus specifically on differences in bugs and bug-fixing processes between desktop and smartphone software. Our study covers 444,129 bug reports in 88 open source projects on desktop, Android, and iOS. The study has two main thrusts: a quantitative analysis to discover similarities and differences between desktop and smartphone bug reports/processes; and a qualitative analysis where we extract topics from bug reports to understand bugs' nature, categories, and differences between platforms. Our findings include: during 2011--2013, iOS bugs were fixed three times faster compared to Android and desktop; top smartphone bug fixers are more involved in reporting bugs than top desktop bug fixers; and most frequent high-severity bugs are due to build issues on desktop, concurrency on Android, and application logic on iOS. Our study, findings, and recommendations are potentially useful to smartphone researchers and practitioners.


title: Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction

abstract: This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we introduce a fixed viewpoint approach to dense surface reconstruction of transparent objects based on refraction of light. We present a simple setup that allows us to alter the incident light paths before light rays enter the object by immersing the object partially in a liquid, and develop a method for recovering the object surface through reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the object shape nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a relatively complex shape and structure, with unknown and inhomogeneous refractive index. We also show that for thin transparent objects, our proposed acquisition setup can be further simplified by adopting a single refraction approximation. Experimental results on both synthetic and real data demonstrate the feasibility and accuracy of our proposed approach.


title: Consistent Subtyping for All

abstract: Consistent subtyping is employed in some gradual type systems to validate type conversions. The original definition by Siek and Taha serves as a guideline for designing gradual type systems with subtyping. Polymorphic types a la System F also induce a subtyping relation that relates polymorphic types to their instantiations. However Siek and Taha’s definition is not adequate for polymorphic subtyping. The first goal of this paper is to propose a generalization of consistent subtyping that is adequate for polymorphic subtyping, and subsumes the original definition by Siek and Taha. The new definition of consistent subtyping provides novel insights with respect to previous polymorphic gradual type systems, which did not employ consistent subtyping. The second goal of this paper is to present a gradually typed calculus for implicit (higher-rank) polymorphism that uses our new notion of consistent subtyping. We develop both declarative and (bidirectional) algorithmic versions for the type system. We prove that the new calculus satisfies all static aspects of the refined criteria for gradual typing, which are mechanically formalized using the Coq proof assistant.