title: Patient Subtyping via Time-Aware LSTM Networks

abstract: In the study of various diseases, heterogeneity among patients usually leads to different progression patterns and may require different types of therapeutic intervention. Therefore, it is important to study patient subtyping, which is grouping of patients into disease characterizing subtypes. Subtyping from complex patient data is challenging because of the information heterogeneity and temporal dynamics. Long-Short Term Memory (LSTM) has been successfully used in many domains for processing sequential data, and recently applied for analyzing longitudinal patient records. The LSTM units are designed to handle data with constant elapsed times between consecutive elements of a sequence. Given that time lapse between successive elements in patient records can vary from days to months, the design of traditional LSTM may lead to suboptimal performance. In this paper, we propose a novel LSTM unit called Time-Aware LSTM (T-LSTM) to handle irregular time intervals in longitudinal patient records. We learn a subspace decomposition of the cell memory which enables time decay to discount the memory content according to the elapsed time. We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes. Experiments on synthetic and real world datasets show that the proposed T-LSTM architecture captures the underlying structures in the sequences with time irregularities.


title: Grounded Theory Applications in Reviewing Knowledge Management Literature Patrick E.W. Onions The Knowledge Studio

abstract: Literature reviews are complex, time-consuming undertakings. Most research methodologies are deductive and require the researcher to commence with a review to inform the study and formulate the research question. On the other hand the grounded theory method is inductive, ending with a theory rather than beginning with a hypothesis, and literature in these studies is typically used as a comparator for the emerging theory. This paper proposes that a theory of contemporary thought in one particular subject, knowledge management, may be developed using a grounded theory method of reviewing its literature.


title: Reweighted Anisotropic Total Variation Minimization for Limited-Angle CT Reconstruction

abstract: Limited-angle problems encountered in computed tomography (CT) often necessitate image reconstruction using projection data from a particular angle range. To solve this severely ill-posed problem, prior information is utilized to constrain the problem. As a special case of compressed sensing, a total variation (TV) transform with an $l_{1}$ -norm image gradient is utilized in most cases, and manages to obtain very impressive reconstruction results. However, it is unfit for limited-angle problems owing to its isotropic property. This paper proposes a new iteratively reweighted anisotropic TV (ATV) method, in which a reweighted technique is incorporated into the idea of ATV. Our strategy successfully combines their merits and results in significantly improved performance. By using the reweighted technique, we are able to approximate the most direct measure of sparsity— $l_{0}$ -norm—better than $l_{1}$ -norm. As a result, the property of image sparsity can be utilized more efficiently. Because TV is isotropic, which prevents detection of blurred edges caused by missing angle ranges and may weaken edge-preserving ability along nonblurred directions, we consider the angle range of the data as additional prior information by assigning different weights to different directions; this allows the anisotropic property to be utilized. Therefore, the blurred directions can be prevented from affecting edge detection, and better reconstruction results can be achieved. To demonstrate the advantages of our method, we perform reconstruction using projection data from phantom CT scans and actual CT scans. We conducted comprehensive comparison between our method and many existing TV-based methods. Both qualitative and quantitative results are presented.


title: Phytochemistry of Cannabis sativa L.

abstract: Cannabis (Cannabis sativa, or hemp) and its constituents-in particular the cannabinoids-have been the focus of extensive chemical and biological research for almost half a century since the discovery of the chemical structure of its major active constituent, Δ9-tetrahydrocannabinol (Δ9-THC). The plant's behavioral and psychotropic effects are attributed to its content of this class of compounds, the cannabinoids, primarily Δ9-THC, which is produced mainly in the leaves and flower buds of the plant. Besides Δ9-THC, there are also non-psychoactive cannabinoids with several medicinal functions, such as cannabidiol (CBD), cannabichromene (CBC), and cannabigerol (CBG), along with other non-cannabinoid constituents belonging to diverse classes of natural products. Today, more than 560 constituents have been identified in cannabis. The recent discoveries of the medicinal properties of cannabis and the cannabinoids in addition to their potential applications in the treatment of a number of serious illnesses, such as glaucoma, depression, neuralgia, multiple sclerosis, Alzheimer's, and alleviation of symptoms of HIV/AIDS and cancer, have given momentum to the quest for further understanding the chemistry, biology, and medicinal properties of this plant.This contribution presents an overview of the botany, cultivation aspects, and the phytochemistry of cannabis and its chemical constituents. Particular emphasis is placed on the newly-identified/isolated compounds. In addition, techniques for isolation of cannabis constituents and analytical methods used for qualitative and quantitative analysis of cannabis and its products are also reviewed.


title: Solventless green synthesis of sodalite zeolite using diatomite as silica source by a microwave heating technique

abstract: Abstract Microporous zeolite sodalite can be simply synthesized by mixing, grinding and microwave heating the solid mixture of raw natural clay and pseudoboehmite without solvent. The sample obtained by microwave-assisted solventless route exhibits good crystallinity and high yield. This method is very efficient, rapid, economic, and environment friendly for the synthesis of sodalite.


title: Characterisation of Clostridium difficile Biofilm Formation, a Role for Spo0A

abstract: Clostridium difficile is a Gram-positive anaerobic, spore-forming bacillus that is the leading cause of nosocomial diarrhoea worldwide. We demonstrate that C. difficile aggregates and forms biofilms in vitro on abiotic surfaces. These polymicrobial aggregates are attached to each other and to an abiotic surface by an extracellular polymeric substance (EPS). The EPS matrix provides the scaffold bonding together vegetative cells and spores, as well as forming a protective barrier for vegetative cells against oxygen stress. The master regulator of sporulation, Spo0A, may play a key role in biofilm formation, as genetic inactivation of spo0A in strain R20291 exhibits decreased biofilm formation. Our findings highlight an important attribute of C. difficile pathogenesis, which may have significant implications for infection, treatment and relapse.


title: Optical Characteristics of Solution Processed MoO2/ZnO Quantum Dots Based Thin Film Transitor

abstract: The present work reports the ultra-violet (UV) and electrical characteristics of solution processed MoO2/ZnO Quantum Dot (QD) based thin film transistor, ZnO QDs thin films are deposited using low-cost solution processing over the Al2O3/p-Si substrates. A very thick layer of Al2O3 (250 nm) is deposited using e-beam evaporation (10-6 mbar). MoO2 is deposited using the thermal evaporation over the ZnO thin film and the achieved thickness is of 25nm. Top and bottom Al contacts were deposited using thermal evaporation and of 50nm and 80nm respectively. Device is characterized in the form of back gate ZnO Thin film transistor (TFT), illuminated from the front side. The UV detection property of the device is performed under the illumination of UV lamp (Puv = 650µW) with wavelength of 365nm. The results obtained with the Vds versus Ids plot shows the significant improvement in the drain current with UV exposure.


title: Product allocation to different types of distribution center in retail logistics networks

abstract: We consider the problem of assigning stockkeeping units to distribution centers (DCs) belonging to different DC types of a retail network, e.g., central, regional, and local DCs. The problem is motivated by the real situation of a retail company and solved by an MIP solution approach. The MIP model reflects the interdependencies between inbound transportation, outbound transportation and instore logistics as well as capital tied up in inventories and differences in picking costs between the warehouses. A novel solution approach is developed and applied to a real-life case of a leading European grocery retail chain. The application of the new approach results in cost savings of 6% of total operational costs compared to the present assignment. These savings amount to several million euros per year. In-depth analyses of the results and sensitivity analyses provide insights into the solution structure and the major related issues.


title: LongDistance Quantum Communication With Entangled Photons Using Satellites

abstract: The use of satellites to distribute entangled photon pairs (and single photons) provides a unique solution for long-distance quantum communication networks. This overcomes the principle limitations of Earth-bound technology, i.e., the range of the order of 100 km afforded by both optical fiber and by terrestrial free-space links.


title: An anatomical study of the indirect tendon of the rectus femoris using ultrasonography

abstract: UNLABELLED ::: The aim of this study was to demonstrate that ultrasound can allow a precise assessment of the indirect tendon of the rectus femoris using a new lateral approach. ::: ::: ::: METHODS AND MATERIALS ::: Four hips were dissected for the anatomical study of the proximal rectus femoris insertions. Under ultrasonographic guidance, spinal needles piercing the direct tendon were placed in the indirect tendon, following by dissection. Then, high-resolution ultrasound was performed in 20 volunteers with assessment of the indirect tendon of the rectus femoris. ::: ::: ::: RESULTS ::: At dissection, the spinal needles were located in or immediately adjacent to the indirect tendon, thus confirming that it was correctly depicted by ultrasound. The indirect tendon could be identified in each cadaver and each volunteer with ultrasound. The optimal position of the probe to allow assessment of the indirect tendon could be defined. No significant changes in the appearance or thickness of the tendon could be observed. ::: ::: ::: CONCLUSION ::: The results of our study showed that the indirect tendon of the rectus femoris muscle can be clearly depicted by sonography in healthy adult subjects. The potential applications of this new use of sonography must now be confirmed by clinical studies. ::: ::: ::: KEY POINTS ::: • The anatomy of the proximal rectus femoris is reviewed • Until now, sonography was limited for assessing direct and conjoined tendons • The indirect tendon can be clearly depicted by sonography • A new lateral approach for studying the indirect tendon is described.


title: LFTL: A multi-threaded FTL for a Parallel IO Flash Card under Linux

abstract: New PCI-e flash cards and SSDs supporting over 100,000 IOPs are now available, with several usecases in the design of a high performance storage system. By using an array of flash chips, arranged in multiple banks, large capacities are achieved. Such multi-banked architecture allow parallel read, write and erase operations. In a raw PCI-e flash card, such parallelism is directly available to the software layer. In addition, the devices have restrictions such as, pages within a block can only be written sequentially. The devices also have larger minimum write sizes (greater than 4KB). Current flash translation layers (FTLs) in Linux are not well suited for such devices due to the high device speeds, architectural restrictions as well as other factors such as high lock contention. We present a FTL for Linux that takes into account the hardware restrictions, that also exploits the parallelism to achieve high speeds. We also consider leveraging the parallelism for garbage collection by scheduling the garbage collection activities on idle banks. We propose and evaluate an adaptive method to vary the amount of garbage collection according to the current I/O load on the device.


title: Continuous wavelet transform based speech emotion recognition

abstract: Emotion recognition from speech is one of the most interesting topics in research community and has developed to a great extent in the last few years. The real challenge in speech emotion recognition (ER) lies in the extraction of features that efficiently encapsulate the emotional information in speech and also do not depend on the speaker. This paper deals with the challenging task of speaker independent ER based on feature selection and classification algorithms. Features are selected based on continuous wavelet transform (CWT) and prosodic coefficients, and are classified and compared using support vector machine (SVM).


title: A novel long-reach robot with propulsion through water-jet

abstract: Long-reach robots offer good performance developing tasks in areas where the access is difficult or dangerous. Due to their multiple degrees of freedom, they are able to adapt easily to different environments. These robots base their locomotion to two different elements: tendon cables or fluid pressure elements. Normally these robots are divided in sections and each section has its independent degrees of freedom. Therefore, if the length of the robot increases, the number of sections increases as well. This also means an increment in the diameter for each section and a more complex control for the whole system. In this paper we introduce the concept of a novel water-jet long-reach robot, which allows increasing the length of the robot without affect its number of elements, control complexity and diameter. Due to its characteristics, it is possible to use this robot in different environments, confined or opened spaces. We test the performance of the first prototype in different scenarios in order to validate our concept.


title: Affective and motivational factors of learning in online mathematics courses

abstract: We investigated what factors would be related to students' achievement in mathematics courses offered at a virtual high school. This was an attempt to understand why some succeed and some do not as well as to suggest what should be done to help with student success. Seventy-two students responded to a self-report survey on motivation (ie, self-efficacy, intrinsic value), mathematics achievement emotions (ie, anxiety, anger, shame, hopelessness, boredom, enjoyment, pride), and cognitive processes (ie, cognitive strategy use, self-regulation). A three-step hierarchical multivariate regression was employed to examine which of the factors predict student achievement. Results showed that motivation accounted for approximately 13% of the variance in student achievement and self-efficacy was the significant individual predictor of student achievement. However, when achievement emotions were added to the analysis, self-efficacy failed to predict student achievement and emotions accounted for 37% of the variance in student achievement. Cognitive strategy use and self-regulation did not explain any additional variance in the final scores. Findings are discussed and implications for future research and development are also suggested. [ABSTRACT FROM AUTHOR]


title: Temperament, Personality and Developmental Psychopathology: A Review Based on the Conceptual Dimensions Underlying Childhood Traits

abstract: The numerous temperament and personality constructs in childhood impede the systematic integration of findings on how these individual differences relate to developmental psychopathology. This paper reviews the main temperament and personality theories and proposes a theoretical taxonomy representing the common structure of both temperament and personality traditions within one conceptual framework. This integrated lexicon of childhood temperament/personality traits facilitates an overview of the most important research findings on the role of temperament and personality in the development of anxiety, depression, ADHD, proactive and reactive antisocial behavior. Several directions for future research are discussed to further validate and refine these reviewed relationships.


title: Influence Maximization on Social Graphs: A Survey

abstract: Influence Maximization (IM), which selects a set of $k$ users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective , with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research.


title: Security Issues in OAuth 2.0 SSO Implementations

abstract: Many Chinese websites (relying parties) use OAuth 2.0 as the basis of a single sign-on service to ease password management for users. Many sites support five or more different OAuth 2.0 identity providers, giving users choice in their trust point. However, although OAuth 2.0 has been widely implemented (particularly in China), little attention has been paid to security in practice. In this paper we report on a detailed study of OAuth 2.0 implementation security for ten major identity providers and 60 relying parties, all based in China. This study reveals two critical vulnerabilities present in many implementations, both allowing an attacker to control a victim user’s accounts at a relying party without knowing the user’s account name or password. We provide simple, practical recommendations for identity providers and relying parties to enable them to mitigate these vulnerabilities. The vulnerabilities have been reported to the parties concerned.


title: CROWDFUNDING HARDWARE STARTUPS IN GERMANY

abstract: The key question raised in this research in progress paper is whether the development stage of a (hardware) startup can give an indication of the crowdfunding type it decides to choose. Throughout the paper, I empirically investigate the German crowdfunding landscape and link it to startups in the hardware sector, picking up the proposed notion of an emergent hardware renaissance. To identify the potential points of contact between crowdfunds and startups, an evaluation of different startup stage models with regard to funding requirements is provided, as is an overview of currently used crowdfunding typologies. The example of two crowdfunding platforms (donation and non-monetary reward crowdfunding vs. equity-based crowdfunding) and their respective hardware projects and startups is used to highlight the potential of this research in progress.


title: Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

abstract: Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems. Our method addresses two key challenges in inverse optimal control: first, the need for informative features and effective regularization to impose structure on the cost, and second, the difficulty of learning the cost function under unknown dynamics for high-dimensional continuous systems. To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering. To address the latter challenge, we formulate an efficient sample-based approximation for MaxEnt IOC. We evaluate our method on a series of simulated tasks and real-world robotic manipulation problems, demonstrating substantial improvement over prior methods both in terms of task complexity and sample efficiency.


Lower Face Rejuvenation with Injections: Botox, Juvederm, and Kybella for Marionette Lines and Jowls


Design of an Active 1-DOF Lower-Limb Exoskeleton with Inertia Compensation


title: Are Meta-Paths Necessary?: Revisiting Heterogeneous Graph Embeddings

abstract: The graph embedding paradigm projects nodes of a graph into a vector space, which can facilitate various downstream graph analysis tasks such as node classification and clustering. To efficiently learn node embeddings from a graph, graph embedding techniques usually preserve the proximity between node pairs sampled from the graph using random walks. In the context of a heterogeneous graph, which contains nodes from different domains, classical random walks are biased towards highly visible domains where nodes are associated with a dominant number of paths. To overcome this bias, existing heterogeneous graph embedding techniques typically rely on meta-paths (i.e., fixed sequences of node types) to guide random walks. However, using these meta-paths either requires prior knowledge from domain experts for optimal meta-path selection, or requires extended computations to combine all meta-paths shorter than a predefined length. In this paper, we propose an alternative solution that does not involve any meta-path. Specifically, we propose JUST, a heterogeneous graph embedding technique using random walks with JUmp and STay strategies to overcome the aforementioned bias in an more efficient manner. JUST can not only gracefully balance between homogeneous and heterogeneous edges, it can also balance the node distribution over different domains (i.e., node types). By conducting a thorough empirical evaluation of our method on three heterogeneous graph datasets, we show the superiority of our proposed technique. In particular, compared to a state-of-the-art heterogeneous graph embedding technique Hin2vec, which tries to optimally combine all meta-paths shorter than a predefined length, our technique yields better results in most experiments, with a dramatically reduced embedding learning time (about 3x speedup).


title: How to Interpret Implicit User Feedback?

abstract: Our research is focused on interpreting user preference from his/her implicit behavior. There are many types of relevant behavior e.g. time on page, scrolling, clickstream etc. which we will further denote as Relevant Behavior Types (RBT). RBT s varies both in quality and incidence and thus we might need different approaches to process them. In this early work we focus on how to derive user preference from each RBT separately. We selected number of common indicators, design two novel e-commerce specific RBT interpreting methods and conducted series of offline experiments. After the off-line evaluation an A/B test on the real-world users of a travel agency was conducted comparing best off-line method with simple binary feedback. The experiments, although preliminary, showed importance of considering multiple RBTs together.


title: Tumor necrosis factor measurement and use of different anticoagulants: Possible interference in plasma samples and supernatants from endotoxin-stimulated monocytes

abstract: OBJECTIVE AND DESIGN ::: Unfractionated heparin is frequently used as an anticoagulant during blood sampling and in cell culture experiments. In the present study we investigated whether heparin and other anticoagulants (citrate and EDTA) interfered with measurements of plasma tumor necrosis factor alpha (TNF alpha) concentrations or with TNF alpha release from endotoxin-stimulated monocytes. ::: ::: ::: MATERIAL AND METHODS ::: TNF alpha was measured by a WEHI 164 bioassay in the plasma of 16 septic patients anticoagulated with heparin, citrate, or EDTA. Anticoagulants were incubated with the bioassay cell line and cell lysis was monitored. To exclude falsely low TNF alpha concentrations, anticoagulants were incubated in increasing amounts with human recombinant TNF alpha/saline solution, and rTNF alpha recovery was measured either with the WEHI 164 bioassay or an ELISA test. Further, anticoagulants were incubated with monocytes isolated from healthy volunteers and stimulated with endotoxin. Supernatants were analyzed for TNF alpha with both test systems. ::: ::: ::: RESULTS ::: No biologically active TNF alpha was detected in the plasma with heparin anticoagulation, whereas with citrate, reproducible, TNF alpha-induced cytotoxicity was detectable in blood samples of 13 of the 16 patients. Anticoagulation with EDTA resulted in fairly high, variable and poorly reproducible TNF alpha values. Only EDTA produced falsely high values by unspecific lysis of WEHI cells. Only heparin at a concentration of 20 I.U./ml or more was found to produce falsely low values by interaction with the TNF alpha bioassay, but also with the ELISA test. In monocyte culture experiments, heparin significantly attenuated the stimulatory effect of endotoxin on TNF alpha release already at the lowest concentration tested (25 I.U./ml). ::: ::: ::: CONCLUSIONS ::: Heparin and EDTA may have significant adverse effects on TNF alpha measurement when used for blood sampling. Citrate does not interfere with the TNF alpha bioassay or ELISA, and seems, therefore, to be the anticoagulant of choice. Due to intrinsic interactions with various cell systems (including the WEHI cell and monocytes), one should be careful in using heparin in cell culture studies in which effects of TNF alpha or of endotoxin are being studied.


title: A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

abstract: Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our system as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.


title: Parabix: Boosting the efficiency of text processing on commodity processors

abstract: Modern applications employ text files widely for providing data storage in a readable format for applications ranging from database systems to mobile phones. Traditional text processing tools are built around a byte-at-a-time sequential processing model that introduces significant branch and cache miss penalties. Recent work has explored an alternative, transposed representation of text, Parabix (Parallel Bit Streams), to accelerate scanning and parsing using SIMD facilities. This paper advocates and develops Parabix as a general framework and toolkit, describing the software toolchain and run-time support that allows applications to exploit modern SIMD instructions for high performance text processing. The goal is to generalize the techniques to ensure that they apply across a wide variety of applications and architectures. The toolchain enables the application developer to write constructs assuming unbounded character streams and Parabix's code translator generates code based on machine specifics (e.g., SIMD register widths). The general argument in support of Parabix technology is made by a detailed performance and energy study of XML parsing across a range of processor architectures. Parabix exploits intra-core SIMD hardware and demonstrates 2×–7× speedup and 4× improvement in energy efficiency when compared with two widely used conventional software parsers, Expat and Apache-Xerces. SIMD implementations across three generations of x86 processors are studied including the new SandyBridge. The 256-bit AVX technology in Intel SandyBridge is compared with the well established 128-bit SSE technology to analyze the benefits and challenges of 3-operand instruction formats and wider SIMD hardware. Finally, the XML program is partitioned into pipeline stages to demonstrate that thread-level parallelism enables the application to exploit SIMD units scattered across the different cores, achieving improved performance (2× on 4 cores) while maintaining single-threaded energy levels.


title: Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks

abstract: Median filtering is used as an anti-forensic technique to erase processing history of some image manipulations such as JPEG, resampling, etc. Thus, various detectors have been proposed to detect median filtered images. To counter these techniques, several anti-forensic methods have been devised as well. However, restoring the median filtered image is a typical ill-posed problem, and thus it is still difficult to reconstruct the image visually close to the original image. Also, it is further hard to make the restored image have the statistical characteristic of the raw image for the anti-forensic purpose. To solve this problem, we present a median filtering anti-forensic method based on deep convolutional neural networks, which can effectively remove traces from median filtered images. We adopt the framework of generative adversarial networks to generate images that follow the underlying statistics of unaltered images, significantly enhancing forensic undetectability. Through extensive experiments, we demonstrate that our method successfully deceives the existing median filtering forensic techniques.


title: Prescriptive Control of Business Processes New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry

abstract: This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.


title: Secure Automotive On-Board Protocols: A Case of Over-the-Air Firmware Updates

abstract: The software running on electronic devices is regularly updated, these days. A vehicle consists of many such devices, but is operated in a completely different manner than consumer devices. Update operations are safety critical in the automotive domain. Thus, they demand for a very well secured process. We propose an on-board security architecture which facilitates such update processes by combining hardware and software modules. In this paper, we present a protocol to show how this security architecture is employed in order to achieve secure firmware updates for automotive control units.


title: Accent conversion through cross-speaker articulatory synthesis

abstract: Accent conversion (AC) seeks to transform second-language (L2) utterances to appear as if produced with a native (L1) accent. In the acoustic domain, AC is difficult due to the complex interaction between linguistic content and voice quality. Alternatively, AC can be performed in the articulatory domain by building a mapping from L2 articulators to L2 acoustics, and then driving the model with L1 articulators. However, collecting articulatory data for each L2 learner is impractical. Here we propose an approach that avoids this expensive step. Our method builds a cross-speaker forward mapping (CSFM) to generate L2 acoustic observations directly from L1 articulatory trajectories. We evaluated the CSFM against a baseline articulatory synthesizer trained with L2 articulators. Subjective listening tests show that both methods perform comparably in terms of accent reduction and ability to preserve the voice quality of the L2 speaker, with only a small impact in acoustic quality. Index terms– Data-driven articulatory synthesis, accent conversion, voice conversion


title: Semantic Web 0 (0) 1 1 IOS Press Watson, more than a Semantic Web search

abstract: In this tool report, we present an overview of the Watson system, a Semantic Web search engine providing various functionalities not only to find and locate ontologies and semantic data online, but also to explore the content of these semantic documents. Beyond the simple facade of a search engine for the Semantic Web, we show that the availability of such a component brings new possibilities in terms of developing semantic applications that exploit the content of the Semantic Web. Indeed, Watson provides a set of APIs containing high level functions for finding, exploring and querying semantic data and ontologies that have been published online. Thanks to these APIs, new applications have emerged that connect activities such as ontology construction, matching, sense disambiguation and question answering to the Semantic Web, developed by our group and others. In addition, we also describe Watson as a unprecedented research platform for the study the Semantic Web, and of formalised knowledge in general.


title: Compiling path queries in software-defined networks

abstract: Monitoring the flow of traffic along network paths is essential for SDN programming and troubleshooting. For example, traffic engineering requires measuring the ingress-egress traffic matrix; debugging a congested link requires determining the set of sources sending traffic through that link; and locating a faulty device might involve detecting how far along a path the traffic makes progress. Past path-based monitoring systems operate by diverting packets to collectors that perform "after-the-fact" analysis, at the expense of large data-collection overhead. In this paper, we show how to do more efficient "during-the-fact" analysis. We introduce a query language that allows each SDN application to specify queries independently of the forwarding state or the queries of other applications. The queries use a regular-expression-based path language that includes SQL-like "groupby" constructs for count aggregation. We track the packet trajectory directly on the data plane by converting the regular expressions into an automaton, and tagging the automaton state (i.e., the path prefix) in each packet as it progresses through the network. The SDN policies that implement the path queries can be combined with arbitrary packet-forwarding policies supplied by other elements of the SDN platform. A preliminary evaluation of our prototype shows that our "during-the-fact" strategy reduces data-collection overhead over "after-the-fact" strategies.


title: A Path Model of Effective Technology-Intensive Inquiry-Based Learning

abstract: Individual aptitude, attitudes, and behavior in inquiry-based learning (IBL) settings may affect work and learning performance outcomes during activities using different technologies. To encourage multifaceted learning, factors in IBL settings must be statistically significant and effective, and not cognitively or psychomotor intensive. We addressed these questions in a study of 421 students from 11 Slovenian middle schools using an experimental design. Learning achievements were measured by pre- and post-test, while IBL experiences and perceptions were surveyed in a one-shot study. IBL and its effects were successfully measured with a reliable technological literacy test. We designed a path model to capture the effects from multiple interferers. Course content was the most decisive influential factor, with strong impacts on learning achievements, satisfaction, and perceived course intensity. Prior knowledge and capacity, which affects IBL and decreases its psychomotor intensity, was a surprisingly strong influence. IBL had a large, positive effect on technological knowledge and the development of problem solving, critical thinking, and decision-making abilities. The study findings showed that the proposed IBL model is an effective teaching approach in technology-intensive education.


title: A Systematic Investigation of Rectangular Patch Antenna Bending Effects for Wearable Applications

abstract: The emerging applications for wearable electronics have experienced enormous growth over the last decade. Antennas, being one of the critical components in modern wireless devices, thus need to be specifically designed to function while being worn and bent. In this paper, we present a systematic investigation of the effects of bending applied on wearable patch antennas over cylindrical surfaces. The resonant frequency variations and radiation pattern variations have been studied by simulating and measuring patch antennas that are bent by different angles. A frequency shift plot with respect to bending radius has been generated to target antennas for various wearable applications. An analytical approach has been presented to study the effect of resonant frequency shifting for both E- and H-planes bending. A lumped element circuit model is used to interpret the E-plane bending from different perspectives. One of the main objectives of this paper is to develop useful design-oriented charts and provide a better understanding of the effects of antenna bending to assist wearable antenna designers.


title: Phoneme visem mapping for Marathi language using linguistic approach

abstract: Visual speech is transcribed using visems. Visem is a set of visibly different phonemes in a language. There is high correlation between Phoneme and visem and phoneme to visem mapping is many to one because of the fact that many phonemes cannot be distinguished visually. Since each language has its own phonetic set, visem classification becomes language dependant process. Most of the phoneme visem maps available in literature are developed for English language. This paper presents phoneme visem mapping of consonants and vowels of Marathi language based on linguistic approach. This is the first attempt towards Marathi phoneme visem mapping and will be useful for visual speech recognition in Marathi language.


title: Learning Character-level Representations for Part-of-Speech Tagging

abstract: Distributed word representations have recently been proven to be an invaluable resource for NLP. These representations are normally learned using neural networks and capture syntactic and semantic information about words. Information about word morphology and shape is normally ignored when learning word representations. However, for tasks like part-of-speech tagging, intra-word information is extremely useful, specially when dealing with morphologically rich languages. In this paper, we propose a deep neural network that learns character-level representation of words and associate them with usual word representations to perform POS tagging. Using the proposed approach, while avoiding the use of any handcrafted feature, we produce state-of-the-art POS taggers for two languages: English, with 97.32% accuracy on the Penn Treebank WSJ corpus; and Portuguese, with 97.47% accuracy on the Mac-Morpho corpus, where the latter represents an error reduction of 12.2% on the best previous known result.


Music ’ s Effect on Mood and Helping Behavior


title: Adaptively Weighted Multi-task Deep Network for Person Attribute Classification

abstract: Multi-task learning aims to boost the performance of multiple prediction tasks by appropriately sharing relevant information among them. However, it always suffers from the negative transfer problem. And due to the diverse learning difficulties and convergence rates of different tasks, jointly optimizing multiple tasks is very challenging. To solve these problems, we present a weighted multi-task deep convolutional neural network for person attribute analysis. A novel validation loss trend algorithm is, for the first time proposed to dynamically and adaptively update the weight for learning each task in the training process. Extensive experiments on CelebA, Market-1501 attribute and Duke attribute datasets clearly show that state-of-the-art performance is obtained; and this validates the effectiveness of our proposed framework.


title: Validity and test-retest reliability of Children's Hand-use Experience Questionnaire in children with unilateral cerebral palsy

abstract: Aim: To investigate the validity of the internet-based version of the Children's Hand-use Experience Questionnaire (CHEQ) by testing the new four-category rating scale, internal structure, and test-retest reliability.Method: Data were collected for 242 children with unilateral cerebral palsy (CP) (137 males and 105 females; mean age 9y 10mo, SD 3y 5mo, range 6-18y). Twenty children from the study sample (mean age 11y 8mo, SD 3y 10mo) participated in a retest within 7 to 14 days. Validity was tested by Rasch analysis based on a rating scale model and test-retest reliability by Kappa analysis and intraclass correlation coefficient (ICC).Results: The four-category rating scale was within recommended criteria for rating scale structure. One item was removed because of misfit. CHEQ showed good scale structure according to the criteria. The effective operational range was >90% for two of the CHEQ scales. Test-retest reliability for the three CHEQ scales was: grasp efficacy, ICC=0.91; time taken, ICC=0.88; and feeling bothered, ICC=0.91.Interpretation: The internet-based CHEQ with a four-category rating scale is valid and reliable for use in children with unilateral CP. Further studies are needed to investigate the validity of the internet-based version of CHEQ for children with upper limb reduction deficiency or obstetric brachial plexus palsy and the validity of the recommended improvements to the current version.


title: A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal

abstract: Removal of muscle artifacts from the ECG signals is crucial for a reliable and accurate measurement of local features of ECG signals. In this paper, we present an automatic method for removal of muscle artifacts from ECG signals, based on four steps: decomposing ECG signal using sparse signal decomposition on mixed dictionaries; obtaining QRS complex signal; determining time-instants of R-peak; and removal of muscle artifacts from ECG signal. The noise reduction performance of the proposed method is tested and validated using ECG signals taken from a standard MIT-BIH Arrhythmia database. The reconstructed signals are assessed using both subjective quality assessment test and objective quality assessment metrics. Performance evaluation results show that the proposed method outperforms other existing ECG denoising methods inadequately removing the muscle artifacts without significantly distorting the morphologies of P-wave, QRS-complex and T-wave of the ECG signals.


title: ClearShot: Eavesdropping on Keyboard Input from Video

abstract: Eavesdropping on electronic communication is usually prevented by using cryptography-based mechanisms. However, these mechanisms do not prevent one from obtaining private information through side channels, such as the electromagnetic emissions of monitors or the sound produced by keyboards. While extracting the same information by watching somebody typing on a keyboard might seem to be an easy task, it becomes extremely challenging if it has to be automated. However, an automated tool is needed in the case of long-lasting surveillance procedures or long user activity, as a human being is able to reconstruct only a few characters per minute. This paper presents a novel approach to automatically recovering the text being typed on a keyboard, based solely on a video of the user typing. As part of the approach, we developed a number of novel techniques for motion tracking, sentence reconstruction, and error correction. The approach has been implemented in a tool, called ClearShot, which has been tested in a number of realistic settings where it was able to reconstruct a substantial part of the typed information.


The Impact of Industry 4 . 0 on Procurement and Supply Management : A Conceptual and Qualitative Analysis


title: An Evaluation of Similarity Coefficients for Software Fault Localization

abstract: Automated diagnosis of software faults can improve the efficiency of the debugging process, and is therefore an important technique for the development of dependable software. In this paper we study different similarity coefficients that are applied in the context of a program spectral approach to software fault localization (single programming mistakes). The coefficients studied are taken from the systems diagnosis/automated debugging tools Pinpoint, Tarantula, and AMPLE, and from the molecular biology domain (the Ochiai coefficient). We evaluate these coefficients on the Siemens Suite of benchmark faults, and assess their effectiveness in terms of the position of the actual fault in the probability ranking of fault candidates produced by the diagnosis technique. Our experiments indicate that the Ochiai coefficient consistently outperforms the coefficients currently used by the tools mentioned. In terms of the amount of code that needs to be inspected, this coefficient improves 5% on average over the next best technique, and up to 30% in specific cases


title: Channel models in the near field

abstract: We present in this work the state of the art of near field communications (NFC) systems channel models. We proceed by proposing a new model and compare the performance with the most frequently used ones.


title: On the diffusion approximation of nonconvex stochastic gradient descent

abstract: We study the Stochastic Gradient Descent (SGD) method in nonconvex optimization problems from the point of view of approximating diffusion processes. We prove rigorously that the diffusion process can approximate the SGD algorithm weakly using the weak form of master equation for probability evolution. In the small step size regime and the presence of omnidirectional noise, our weak approximating diffusion process suggests the following dynamics for the SGD iteration starting from a local minimizer (resp.~saddle point): it escapes in a number of iterations exponentially (resp.~almost linearly) dependent on the inverse stepsize. The results are obtained using the theory for random perturbations of dynamical systems (theory of large deviations for local minimizers and theory of exiting for unstable stationary points). In addition, we discuss the effects of batch size for the deep neural networks, and we find that small batch size is helpful for SGD algorithms to escape unstable stationary points and sharp minimizers. Our theory indicates that one should increase the batch size at later stage for the SGD to be trapped in flat minimizers for better generalization.


title: Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis

abstract: Abstract“Engagement” with digital behaviour change interventions (DBCIs) is considered important for their effectiveness. Evaluating engagement is therefore a priority; however, a shared understanding of how to usefully conceptualise engagement is lacking. This review aimed to synthesise literature on engagement to identify key conceptualisations and to develop an integrative conceptual framework involving potential direct and indirect influences on engagement and relationships between engagement and intervention effectiveness. Four electronic databases (Ovid MEDLINE, PsycINFO, ISI Web of Knowledge, ScienceDirect) were searched in November 2015. We identified 117 articles that met the inclusion criteria: studies employing experimental or non-experimental designs with adult participants explicitly or implicitly referring to engagement with DBCIs, digital games or technology. Data were synthesised using principles from critical interpretive synthesis. Engagement with DBCIs is conceptualised in terms of both experiential and behavioural aspects. A conceptual framework is proposed in which engagement with a DBCI is influenced by the DBCI itself (content and delivery), the context (the setting in which the DBCI is used and the population using it) and the behaviour that the DBCI is targeting. The context and “mechanisms of action” may moderate the influence of the DBCI on engagement. Engagement, in turn, moderates the influence of the DBCI on those mechanisms of action. In the research literature, engagement with DBCIs has been conceptualised in terms of both experience and behaviour and sits within a complex system involving the DBCI, the context of use, the mechanisms of action of the DBCI and the target behaviour.


title: Scalable sequential pattern mining for biological sequences

abstract: Biosequences typically have a small alphabet, a long length, and patterns containing gaps (i.e., "don't care") of arbitrary size. Mining frequent patterns in such sequences faces a different type of explosion than in transaction sequences primarily motivated in market-basket analysis. In this paper, we study how this explosion affects the classic sequential pattern mining, and present a scalable two-phase algorithm to deal with this new explosion. The Segment Phase first searches for short patterns containing no gaps, called segments. This phase is efficient. The Pattern Phase searches for long patterns containing multiple segments separated by variable length gaps. This phase is time consuming. The purpose of two phases is to exploit the information obtained from the first phase to speed up the pattern growth and matching and to prune the search space in the second phase. We evaluate this approach on synthetic and real life data sets.


Manipulation of thought content as a determinant of mood and corrugator electromyographic activity in depressed patients.


title: A Review of User Interface Design for Interactive Machine Learning

abstract: Interactive Machine Learning (IML) seeks to complement human perception and intelligence by tightly integrating these strengths with the computational power and speed of computers. The interactive process is designed to involve input from the user but does not require the background knowledge or experience that might be necessary to work with more traditional machine learning techniques. Under the IML process, non-experts can apply their domain knowledge and insight over otherwise unwieldy datasets to find patterns of interest or develop complex data-driven applications. This process is co-adaptive in nature and relies on careful management of the interaction between human and machine. User interface design is fundamental to the success of this approach, yet there is a lack of consolidated principles on how such an interface should be implemented. This article presents a detailed review and characterisation of Interactive Machine Learning from an interactive systems perspective. We propose and describe a structural and behavioural model of a generalised IML system and identify solution principles for building effective interfaces for IML. Where possible, these emergent solution principles are contextualised by reference to the broader human-computer interaction literature. Finally, we identify strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications.


Child murder by mothers : patterns and prevention SPECIAL ARTICLE


title: Web testing made easy

abstract: In this paper we describe WebTest, an Open Source tool for automated testing of web applications. In particular we will show how to quickly create tests that shine with excellent maintainability and runtime performance as well as perfect integration in the application development cycle.


title: Getting to know your Card: Reverse-Engineering the Smart-Card Application Protocol Data Unit

abstract: Smart-cards are considered to be one of the most secure, tamper-resistant, and trusted devices for implementing confidential operations, such as authentication, key management, encryption and decryption for financial, communication, security and data management purposes. The commonly used RSA PKCS#11 standard defines the Application Programming Interface for cryptographic devices such as smart-cards. Though there has been work on formally verifying the correctness of the implementation of PKCS#11 in the API level, little attention has been paid to the low-level cryptographic protocols that implement it. We present REPROVE, the first automated system that reverse-engineers the low-level communication between a smart-card and a reader, deduces the card's functionality and translates PKCS#11 cryptographic functions into communication steps. REPROVE analyzes both standard-conforming and proprietary implementations, and does not require access to the card. To the best of our knowledge, REPROVE is the first system to address proprietary implementations and the only system that maps cryptographic functions to communication steps and on-card operations. We have evaluated REPROVE on five commercially available smart-cards and we show how essential functions to gain access to the card's private objects and perform cryptographic functions can be compromised through reverse-engineering traces of the low-level communication.


title: Teaching introductory artificial intelligence with Pac-man

abstract: The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the Pac- Man environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.


title: Intrusion detection using text processing techniques with a kernel based similarity measure

abstract: This paper focuses on intrusion detection based on system call sequences using text processing techniques. It introduces kernel based similarity measure for the detection of host-based intrusions. The k-nearest neighbour (kNN) classifier is used to classify a process as either normal or abnormal. The proposed technique is evaluated on the DARPA-1998 database and its performance is compared with other existing techniques available in the literature. It is shown that this technique is significantly better than the other techniques in achieving lower false positive rates at 100% detection rate.


title: A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories

abstract: This article introduces a new SAS procedure written by the authors that analyzes longitudinal data (developmental trajectories) by fitting a mixture model. The TRAJ procedure fits semiparametric (discrete) mixtures of censored normal, Poisson, zero-inflated Poisson, and Bernoulli distributions to longitudinal data. Applications to psychometric scale data, offense counts, and a dichotomous prevalence measure in violence research are illustrated. In addition, the use of the Bayesian information criterion to address the problem of model selection, including the estimation of the number of components in the mixture, is demonstrated.


title: D.: On learning hierarchical Classifications

abstract: Many significant real-world classification tasks involve a large number of categories which are arranged in a hierarchical structure; for example, classifying documents into subject categories under the library of congress scheme, or classifying world-wide-web documents into topic hierarchies. We investigate the potential benefits of using a given hierarchy over base classes to learn accurate multi-category classifiers for these domains. First, we consider the possibility of exploiting a class hierarchy as prior knowledge that can help one learn a more accurate classifier. We explore the benefits of learning categorydiscriminants in a “hard” top-down fashion and compare this to a “soft” approach which shares training data among sibling categories. In doing so, we verify that hierarchies have the potential to improve prediction accuracy. But we argue that the reasons for this can be subtle. Sometimes, the improvement is only because using a hierarchy happens to constrain the expressiveness of a hypothesis class in an appropriate manner. However, various controlled experiments show that in other cases the performance advantage associated with using a hierarchy really does seem to be due to the “prior knowledge” it encodes.


title: Analyzing Tennis Tactics from Broadcasting Tennis Video Clips

abstract: This paper attempts to classify tennis games into 58 winning patterns for training purpose. It bases on tracking ball movement from broadcasting tennis video. Trajectory and landing position are used as the basic features for classification. We use an improved Bayesian networks to classify the landing position of different patterns. Intelligent agents are used to combine trajectories and landing positions as two features are in different dimensions. Semantic labels are granted after classification. The aim of the analysis is to provide a browsing tool for coachers or other personnel to retrieve tennis video clips.


Link between enteroviruses and dilated cardiomyopathy: serological and molecular data


title: Track level fusion algorithms for automotive safety applications

abstract: Fusion of information from different sensor systems is vital for automotive safety systems. In a typical automotive sensor fusion setup the fusion can be a measurement fusion or a track level fusion in a centralized fusion center. Track level fusion is desired due to communication, computation and organizational constraints. Track level fusion algorithms have to deal with different correlation issues among the sensor systems and the fusion center that maintains the global track. This correlation problem leads to a degraded fusion estimate which is not desirable in a safety and time critical application. This paper presents an overview of track level fusion algorithms to fuse two homogenous sensor systems and evaluates their performance.


title: Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports

abstract: For a large and evolving software system, the project team could receive a large number of bug reports. Locating the source code files that need to be changed in order to fix the bugs is a challenging task. Once a bug report is received, it is desirable to automatically point out to the files that developers should change in order to fix the bug. In this paper, we propose BugLocator, an information retrieval based method for locating the relevant files for fixing a bug. BugLocator ranks all files based on the textual similarity between the initial bug report and the source code using a revised Vector Space Model (rVSM), taking into consideration information about similar bugs that have been fixed before. We perform large-scale experiments on four open source projects to localize more than 3,000 bugs. The results show that BugLocator can effectively locate the files where the bugs should be fixed. For example, relevant buggy files for 62.60% Eclipse 3.1 bugs are ranked in the top ten among 12,863 files. Our experiments also show that BugLocator outperforms existing state-of-the-art bug localization methods.


title: An ECC-Based Blind Signature Scheme

abstract: Cryptography is increasingly applied to the E-commerce world, especially to the untraceable payment system and the electronic voting system. Protocols for these systems strongly require the anonymous digital signature property, and thus a blind signature strategy is the answer to it. Chaum stated that every blind signature protocol should hold two fundamental properties, blindness and intractableness. All blind signature schemes proposed previously almost are based on the integer factorization problems, discrete logarithm problems, or the quadratic residues, which are shown by Lee et al. that none of the schemes is able to meet the two fundamental properties above. Therefore, an ECC-based blind signature scheme that possesses both the above properties is proposed in this paper.


title: Pro-inflammatory effects of e-cigarette vapour condensate on human alveolar macrophages

abstract: Objective Vaping may increase the cytotoxic effects of e-cigarette liquid (ECL). We compared the effect of unvaped ECL to e-cigarette vapour condensate (ECVC) on alveolar macrophage (AM) function. Methods AMs were treated with ECVC and nicotine-free ECVC (nfECVC). AM viability, apoptosis, necrosis, cytokine, chemokine and protease release, reactive oxygen species (ROS) release and bacterial phagocytosis were assessed. Results Macrophage culture with ECL or ECVC resulted in a dose-dependent reduction in cell viability. ECVC was cytotoxic at lower concentrations than ECL and resulted in increased apoptosis and necrosis. nfECVC resulted in less cytotoxicity and apoptosis. Exposure of AMs to a sub-lethal 0.5% ECVC/nfECVC increased ROS production approximately 50-fold and significantly inhibited phagocytosis. Pan and class one isoform phosphoinositide 3 kinase inhibitors partially inhibited the effects of ECVC/nfECVC on macrophage viability and apoptosis. Secretion of interleukin 6, tumour necrosis factor &agr;, CXCL-8, monocyte chemoattractant protein 1 and matrix metalloproteinase 9 was significantly increased following ECVC challenge. Treatment with the anti-oxidant N-acetyl-cysteine (NAC) ameliorated the cytotoxic effects of ECVC/nfECVC to levels not significantly different from baseline and restored phagocytic function. Conclusions ECVC is significantly more toxic to AMs than non-vaped ECL. Excessive production of ROS, inflammatory cytokines and chemokines induced by e-cigarette vapour may induce an inflammatory state in AMs within the lung that is partly dependent on nicotine. Inhibition of phagocytosis also suggests users may suffer from impaired bacterial clearance. While further research is needed to fully understand the effects of e-cigarette exposure in humans in vivo, we caution against the widely held opinion that e-cigarettes are safe.


title: WicLoc: An indoor localization system based on WiFi fingerprints and crowdsourcing

abstract: WiFi fingerprint-based indoor localization techniques have been proposed and widely used in recent years. Most solutions need a site survey to collect fingerprints from interested locations to construct the fingerprint database. However, the site survey is labor-intensive and time-consuming. To overcome this shortcoming, we record user motions as well as WiFi signals without the active participation of the users to construct the fingerprint database, in place of the previous site survey. In this paper, we develop an indoor localization system called WicLoc, which is based on WiFi fingerprinting and crowdsourcing. We design a fingerprint model to form fingerprints of each location of interest after fingerprint collection. We propose a weighted KNN (K-Nearest Neighbor) algorithm to assign different weights to APs and achieve room-level localization. To obtain the absolute coordinate of users, we design a novel MDS (Multi-Dimensional Scaling) algorithm called MDS-C (Multi-Dimensional Scaling with Calibrations) to calculate coordinates of interested locations in the corridor and rooms, where anchor points are used to calibrate absolute coordinates of users. Experimental results show that our system can achieve a competitive localization accuracy compared with state-of-the-art WiFi fingerprint-based methods while avoiding the labor-intensive site survey.


title: Going Cashless at the Point of Sale: Hits and Misses in Developed Countries

abstract: There may be factors specific to Asia at play in explaining the more positive experience in Asia customers' fascination with new technology, the importance of conveying innovation in the branding strategy of mobile operators, the low penetration of credit cards. Some of the Asian formulas are now finding their way back to Europe. Hong Kong's success with transit cards (Octopus) is also proving its mettle in London (Oyster). Japanese and Korean operators are slowly proving the benefits of mobile phones with very short-range communications technologies for use with in-person mobile payments, and many European operators are now anxiously awaiting the spread in Europe of phones with embedded near-field communications (NFC) capabilities. This paper reviews some of the bigger failures and some of the more promising experiences in the use of smartcards and mobile phones as payment platforms in developed countries. The authors selected just a few examples from dozens of possibilities and did not delve into much detail on any given scheme. Beyond telling the stories of these ventures, our objective is to extract some lessons behind the failures and the successes. Although these developed country experiences may not directly translate into lessons that can be used in developing countries, this paper better informs us about what may or may not be possible and may or may not be different in the developing world context.


title: A survey of video datasets for anomaly detection in automated surveillance

abstract: Automated anomaly detection in surveillance system demands to address various problems of real-time monitoring. Due to these challenges, the researchers from computer vision community focused on automation of the surveillance system. The research carried out in this domain has been proposed many datasets which are suitable for different applications. Though many prominent datasets are available, there is lack of description and categorization of these datasets according to the application demand. A survey on anomaly detection video datasets is necessary for researchers to understand and progress in this domain. This paper presents various datasets description briefly.


title: Full-bridge series resonant multi-inverter featuring new 900-V SiC devices for improved induction heating appliances

abstract: Induction heating is a key technology due to its advantages in terms of performance and efficiency. Recent developments in domestic induction heating including flexible cooking surfaces with multiple output power converters. Besides, advances in wide bandgap devices allow new design possibilities such as cost-effective and high efficiency power converters. In this paper, an improved full-bridge series resonant multi-inverter with SiC devices is presented. The proposed converter and the modulation strategies to obtain proper output power control are detailed. Finally, a two-load experimental prototype is built, showing the experimental results and the advantages of the converter.


title: Text Simplification Tools: Using Machine Learning to Discover Features that Identify Difficult Text

abstract: Although providing understandable information is a critical component in healthcare, few tools exist to help clinicians identify difficult sections in text. We systematically examine sixteen features for predicting the difficulty of health texts using six different machine learning algorithms. Three represent new features not previously examined: medical concept density, specificity (calculated using word-level depth in MeSH); and ambiguity (calculated using the number of UMLS Metathesaurus concepts associated with a word). We examine these features for a binary prediction task on 118,000 simple and difficult sentences from a sentence-aligned corpus. Using all features, random forests is the most accurate with 84% accuracy. Model analysis of the six models and a complementary ablation study shows that the specificity and ambiguity features are the strongest predictors (24% combined impact on accuracy). Notably, a training size study showed that even with a 1% sample (1,062 sentences) an accuracy of 80% can be achieved.


title: Research Robots for Applications in Artificial Intelligence, Teleoperation and Entertainment

abstract: Sarcos Research Corporation, and the Center for Engineering Design at the University of Utah, have long been interested in both the fundamental and the applied aspects of robots and other computationally driven machines. We have produced substantial numbers of systems that function as products for commercial applications, and as advanced research tools specifically designed for experimental use.This paper reviews various aspects of the design and control of a number of robot-like machines ranging from our first projects, the Utah Arm and the Utah/MIT Dextrous Hand, to present work on humanoid robots and the Wearable Energetically Autonomous Robot (WEAR). Our systems have been used in: entertainment, operator remotization from hazardous environments, R&D, and medicine.In addition to the robots and their subsystems, extensive work has been devoted to command systems that drive the robots. Command systems have been: playback supervisors, teleoperation masters, and various higher level approaches based on wor...


title: AFSCN Scheduling: How the Problem and Solution Have Evolved ∗

abstract: The Air Force Satellite Control Network (AFSCN) coordinates communications to more than 100 satellites via nine ground stations positioned around the globe. Customers request an antenna at a ground station for a specific time window along with possible alternative slots. Typically, 500 requests per day result in more than 100 conflicts, which are requests that cannot be satisfied because other tasks need the same slot. Scheduling access requests is referred to as the Satellite Range Scheduling Problem (SRSP). This paper presents an overview of three key issues: (1) how has the problem changed over the last 10 years, (2) what algorithms work best and (3) what objective function is appropriate for AFSCN. We compared data sets from 1992 and from 2002/2003 and found significant differences in the problems. Our evaluation of solutions focuses on three algorithms: local search, Gooley's algorithm from AFIT, and the Genitor genetic algorithm. It can be shown that local search (and therefore metaheuristics based on local search) fail to compete with Gooley's algorithm and Genitor. Finally, while all prior work on AFSCN minimizes request conflicts, we explore an alternative objective function. Because human schedulers must eventually schedule all requests, it might be better to optimize schedules for ''repairability''. Our results suggest that minimizing schedule overlaps makes it easier to fit larger requests into the schedule.


title: Sparse component analysis and blind source separation of underdetermined mixtures

abstract: In this letter, we solve the problem of identifying matrices S /spl isin/ /spl Ropf//sup n/spl times/N/ and A /spl isin/ /spl Ropf//sup m/spl times/n/ knowing only their multiplication X = AS, under some conditions, expressed either in terms of A and sparsity of S (identifiability conditions), or in terms of X (sparse component analysis (SCA) conditions). We present algorithms for such identification and illustrate them by examples.


title: An evaluation of statistical spam filtering techniques

abstract: This paper evaluates five supervised learning methods in the context of statistical spam filtering. We study the impact of different feature pruning methods and feature set sizes on each learner's performance using cost-sensitive measures. It is observed that the significance of feature selection varies greatly from classifier to classifier. In particular, we found support vector machine, AdaBoost, and maximum entropy model are top performers in this evaluation, sharing similar characteristics: not sensitive to feature selection strategy, easily scalable to very high feature dimension, and good performances across different datasets. In contrast, naive Bayes, a commonly used classifier in spam filtering, is found to be sensitive to feature selection methods on small feature set, and fails to function well in scenarios where false positives are penalized heavily. The experiments also suggest that aggressive feature pruning should be avoided when building filters to be used in applications where legitimate mails are assigned a cost much higher than spams (such as λ = 999), so as to maintain a better-than-baseline performance. An interesting finding is the effect of mail headers on spam filtering, which is often ignored in previous studies. Experiments show that classifiers using features from message header alone can achieve comparable or better performance than filters utilizing body features only. This implies that message headers can be reliable and powerfully discriminative feature sources for spam filtering.


title: TF-IDF uncovered: a study of theories and probabilities

abstract: Interpretations of TF-IDF are based on binary independence retrieval, Poisson, information theory, and language modelling. This paper contributes a review of existing interpretations, and then, TF-IDF is systematically related to the probabilities P(q|d) and P(d|q). Two approaches are explored: a space of independent, and a space of disjoint terms. For independent terms, an "extreme" query/non-query term assumption uncovers TF-IDF, and an analogy of P(d|q) and the probabilistic odds O(r|d, q) mirrors relevance feedback. For disjoint terms, a relationship between probability theory and TF-IDF is established through the integral + 1/x dx = log x. This study uncovers components such as divergence from randomness and pivoted document length to be inherent parts of a document-query independence (DQI) measure, and interestingly, an integral of the DQI over the term occurrence probability leads to TF-IDF.


title: SSD: Single Shot MultiBox Detector

abstract: We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For \(300 \times 300\) input, SSD achieves 74.3 % mAP on VOC2007 test at 59 FPS on a Nvidia Titan X and for \(512 \times 512\) input, SSD achieves 76.9 % mAP, outperforming a comparable state of the art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at https://github.com/weiliu89/caffe/tree/ssd.


Monolingual Data Improves Low-Resource Neural Machine Translation


title: Design and simulation of a Sierpinski carpet fractal antenna for 5G commercial applications

abstract: This article presents the design and simulation of a multi-band microstrip patch antenna, using Sierpinski carpet fractal concept. The proposed antenna is designed on a FR-4 substrate with a relative permittivity of 4.4 and excited using a microstrip feed line at the edge of the patch. Sierpinski carpet fractal with iteration-3 is applied to the radiating patch. After the application of fractal, this patch antenna resonates at 14.80 GHz, 17.92 GHz, 23.12 GHz, and 27.92 GHz with impedance bandwidth of 5800 MHz, 3040 MHz, 2960 MHz, and 3840 MHz respectively. With these four bands, this antenna finds application in commercial 5G wireless communications. To illustrate the effectiveness of the fractal concept, four patch antennas are designed independently at those four resonating frequencies and compared with the single fractal antenna in terms of their size and other performance parameters. All the antennas proposed here, are designed using HFSS15 and results are analyzed in terms of return loss, VSWR, gain, and bandwidth.


title: SStreaMWare: a service oriented middleware for heterogeneous sensor data management

abstract: Smart sensors are already being used in various application domains such as medical, environmental, urban, domestic and industrial. These applications mostly need data from sensors of different types (temperature, pressure, location, camera, etc.) that may be managed by different software, e.g., proprietary software from manufacturers. Heterogeneous distributed sensor data should then be aggregated in order to obtain more accurate and complete information on real world events. This paper proposes SStreaMWare, a service-oriented middleware for heterogeneous sensor data management. SStreaMWare's simple data schema allows data representation of various types of sensors in a common generic way. Declarative queries can then be formulated according to this schema. Thanks to the service-oriented approach of SStreaMWare, heterogeneity of sensor software is hidden by generic query services, which can be discovered and used dynamically.


title: Privacy-Preserving Search Over Encrypted Personal Health Record In Multi-Source Cloud

abstract: Cloud-based Personal Health Record systems (CB-PHR) have great potential in facilitating the management of individual health records. Security and privacy concerns are among the main obstacles for the wide adoption of CB-PHR systems. In this paper, we consider a multi-source CB-PHR system in which multiple data providers, such as hospitals and physicians are authorized by individual data owners to upload their personal health data to an untrusted public cloud. The health data are submitted in an encrypted form to ensure data security, and each data provider also submits encrypted data indexes to enable queries over the encrypted data. We propose a novel Multi-Source Order-Preserving Symmetric Encryption (MOPSE) scheme whereby the cloud can merge the encrypted data indexes from multiple data providers without knowing the index content. MOPSE enables efficient and privacy-preserving query processing in that a data user can submit a single data query, the cloud can process over the encrypted data from all related data providers without knowing the query content. We also propose an enhanced scheme, MOPSE+, to more efficiently support the data queries by hierarchical data providers. Extensive analysis and experiments over real data sets demonstrate the efficacy and efficiency of MOPSE and MOPSE+.


title: Software-Defined Next-Generation Satellite Networks: Architecture, Challenges, and Solutions

abstract: Traditional satellite networks depend on the closed and planned architecture. Thus, there are many challenges such as configuration update, new communication and networking technologies introduction, truly-differentiated services provision, satellite network device interoperability, and the integration of satellite and terrestrial networks. Software-defined networking (SDN) has the features of flexibility, programmability, and logical centralization, which increases network resource utilization, simplifies network management, reduces operating cost, and promotes the evolution and innovation. In this paper, a new software-defined architecture for next-generation satellite networks, called SoftSpace, is presented. The concepts of network function virtualization, network virtualization, and software-defined radio are exploited in the SoftSpace to facilitate the incorporation of new applications, services, and satellite communication technologies. This can not only reduce the capital expenditures and operational expenditures but also integrate satellite networks with terrestrial networks seamlessly, as well as can improve the interoperability of satellite network devices. In addition, we discuss the challenges and solutions for network management. The necessary network management instruments including multi-layer controller architecture, cooperative traffic classification, and utility-optimal network virtualization are presented. Finally, we discuss the challenges and solutions for space networking. The software-defined space networking solutions including quality of experience-aware space routing, SDN-enabled hybrid fault recovery mechanism, and software-defined space mobility management are developed.


title: Derivation of Pasteurella multocida-free rabbit litters by enrofloxacin treatment.

abstract: Pasteurella multocida is an important bacterial pathogen of rabbits that is easily transmitted from infected does to their kits prior to weaning. Enrofloxacin, a flouroquinolone antibiotic, is effective at limiting nasal carriage of P. multocida in rabbits. To determine if enrofloxacin treatment of pregnant does infected with P. multocida can be used to produce P. multocida-free litters, groups of 3 rabbits were inoculated intranasally on day 10 of gestation with 1.0 x 10(6) P. multocida CFUs. Beginning on day 14, one group received enrofloxacin IM (5 mg kg-1, BID), and a second group received enrofloxacin in the drinking water (200 mgl-1). IM treatment continued until kindling, while PO treatment continued 1 week after kindling. A third group was infected but received only IM saline, and a fourth group was infected but not treated. In addition, a fifth group was neither infected nor treated. Culture of nasal lavage samples and tissues from does and kits showed that both routes of enrofloxacin treatment failed to completely eliminate P. multocida from does, but all kits from enrofloxacin-treated does were free from P. multocida. These results suggest that treatment does with enrofloxacin during the periparturient period may interrupt transmission of P. multocida from infected does to their kits and that this treatment may be useful for deriving Pasteurella-free rabbits from infected does.


title: Simulation of three-phase cascaded H-bridge Multilevel Inverter with THIPWM

abstract: This paper presents the simulation of a Three Phase Five Level Cascaded H-bridge Multilevel Inverter. Third Harmonics Injection Pulse Width Modulation (THIPWM) technique was employed through s-function approach in SIMULINK environment to minimize the complexity of circuit design. The advantages of THIPWM are revealed in term of voltage and current THD reduction compared to Sinusoidal Pulse Width Modulation (SPWM) in three phase system. Simulation results reveal that voltage and current THD of THIPWM improved as much as 36% and 28% respectively compared to SPWM's.


title: A Universal Turing Machine in Conway's Game of Life

abstract: In this paper we present a Universal Turing Machine build in the Cellular Automaton Conway's Game of Life. This is an extension of the Turing Machine built previously by the author [10]. It is example of spatio-temporal collision based computation and has infinite tape provided by two stack structures which grow continuously using collision based construction. Two patterns the fanout and takeout are described which are key in solving the routing and synchronization problems. The procedure used to find a viable order of synthesis of the parts in the stack construction is described.


title: Decision Support for Handling Mismatches between COTS Products and System Requirements

abstract: In the process of selecting commercial off-the-shelf (COTS) products, it is inevitable to encounter mismatches between COTS products and system requirements. Mismatches occur when COTS attributes do not exactly match our requirements. Many of these mismatches are resolved after selecting a COTS product in order to improve its fitness with the requirements. This paper proposes a decision support approach that aims at addressing COTS mismatches during and after the selection process. Our approach can be integrated with existing COTS selection methods at two stages: (I) When evaluating COTS candidates: our approach is used to estimate the anticipated fitness of the candidates if their mismatches are resolved. This helps to base our COTS selection decisions on the fitness that the COTS candidates will eventually have if selected. (2) After selecting a COTS product: the approach suggests alternative plans for resolving the most appropriate mismatches using suitable actions, such that the most important risk, technical, and resource constraints are met. A case study from the e-services domain is used to illustrate the method and to discuss its added value


title: An inquiry into the nature and causes of the wealth of internet miscreants

abstract: This paper studies an active underground economy which specializes in the commoditization of activities such as credit car d fraud, identity theft, spamming, phishing, online credential the ft, and the sale of compromised hosts. Using a seven month trace of logs collected from an active underground market operating on public Internet chat networks, we measure how the shift from “hacking for fun” to “hacking for profit” has given birth to a societal subs trate mature enough to steal wealth into the millions of dollars in less than one year.


title: A Market in Your Social Network: The Effects of Extrinsic Rewards on Friendsourcing and Relationships

abstract: Friendsourcing consists of broadcasting questions and help requests to friends on social networking sites. Despite its potential value, friendsourcing requests often fall on deaf ears. One way to improve response rates and motivate friends to undertake more effortful tasks may be to offer extrinsic rewards, such as money or a gift, for responding to friendsourcing requests. However, past research suggests that these extrinsic rewards can have unintended consequences, including undermining intrinsic motivations and undercutting the relationship between people. To explore the effects of extrinsic reward on friends' response rate and perceived relationship, we conducted an experiment on a new friendsourcing platform - Mobilyzr. Results indicate that large extrinsic rewards increase friends' response rates without reducing the relationship strength between friends. Additionally, the extrinsic rewards allow requesters to explain away the failure of friendsourcing requests and thus preserve their perceptions of relationship ties with friends.


title: A new criterion for automatic multilevel thresholding

abstract: A new criterion for multilevel thresholding is proposed. The criterion is based on the consideration of two factors. The first one is the discrepancy between the thresholded and original images and the second one is the number of bits required to represent the thresholded image. Based on a new maximum correlation criterion for bilevel thresholding, the discrepancy is defined and then a cost function that takes both factors into account is proposed for multilevel thresholding. By minimizing the cost function, the classification number that the gray-levels should be classified and the threshold values can be determined automatically. In addition, the cost function is proven to possess a unique minimum under very mild conditions. Computational analyses indicate that the number of required mathematical operations in the implementation of our algorithm is much less than that of maximum entropy criterion. Finally, simulation results are included to demonstrate their effectiveness. >


title: Value of Information Systems and Products: Understanding the Users' Perspective and Values

abstract: ABSTRACT Developers aim at providing value through their systems and products. However, value is not financial only, but depends on usage and users' perceptions of value. In this paper, we clarify the concept of value from the users' perspective and the role of user involvement in providing value. First, theories and approaches of psychology, marketing and human-computer interaction are reviewed. Secondly, the concept of 'user values' is suggested to clarify the concept of value from the user's point of view and a category framework of user values is presented to make them more concrete and easier to identify. Thirdly, the activities and methods for adopting user values in development work are discussed. The analysis of the literature shows that value has been considered in multiple ways in development. However, users' perspectives have received less attention. As a conclusion, we draw future research directions for value-centered design and propose that user involvement is essential in identifying user values, interpreting the practical meaning of the values and implementing them in development work. INTRODUCTION As Hirschheim and Klein (1989) point out, all system developers approach the development task with a number of explicit and implicit assumptions about the nature of human organizations, the nature of design task, and what is expected of the developers. One of the most critical assumptions relates to what is seen as valuable in information systems. Companies are most often interested in measuring the business value of the investments, but also other perspectives on value have become popular. For example, Cronk and Fitzgerald (1999) describe how the research focusing on business value has proceeded from using only a financial perspective towards organizational performance and other perspectives. This has happened after the recognition of the difficulty of isolating IS.s contribution from other organizational and external confounding factors. Thus, Cronk and Fitzgerald (1999) propose the three dimensions of IS business value to be: 1) system-dependent dimension, 2) user-dependent dimension, and 3) business-dependent dimension. The user-dependent dimension describes the value that is added to the organization as a result of user characteristics. User characteristics include skills and attitudes that may result in effective or ineffective use of the system. Thus, users are recognized as an essential dimension in value creation. In addition, the value of the information system can be evaluated from different stakeholders. point of views. Usually, the manager.s and system provider.s points of views are the most influential. For example, Bannister (2001) observes that the perspectives of the provider and the consumer or citizen are different in the evaluation of the public sector information systems, but that the consumers. perspective is easily overlooked and replaced with the provider.s perspective of the value. Value-based software engineering strives towards making value considerations explicit throughout software engineering and optimizing decisions to meet explicit objectives of the involved stakeholders, from marketing staff and business analysts to developers, architects, and quality experts, and from process and measurements experts to project managers and executives (Biffl, Aurum, Boehm, Erdogmus, and Grunbacher 2006, p. IX). Users are seen as one stakeholder group and user involvement and negotiations are seen as approaches for identifying their win conditions. There is also a clear and growing focus on value-centered design in a fairly recent development in human-computer interaction. Cockton (2004ab, 2005) introduces a development framework for value-centered design where the focus is on the value of the product for users. Later, Cockton (2006) selects ?worth. as being a less loaded term than ?value. and starts to refer to ?value-centred design. as ?worth-centred development. …


title: Comparison of temperature limits for Trench silicon IGBT technologies for medium power applications

abstract: Abstract This work focuses on determining the switching limits in temperature for Reverse Conducting IGBTs and compares them to “conventional” IGBTs based on Trench technologies, all them belonging to 600 V–50 A application scenario. After, their leakage current under blocking state is tested at several working temperatures, in order to study the leakage current that limits the blocking safe operating area with temperature. Next, overcurrent tests have been performed. This investigation deals with understanding the overcurrent induced failures due to thermal effects using short circuit tests adapted to our needs. As a result, we observe that RC-IGBTs integrating a free-wheeling diode show the highest leakage losses in blocking state, whereas it presents the best ruggedness under short circuit tests.


title: Command&Control: Understanding, Denying and Detecting

abstract: In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.


title: Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition

abstract: A key challenge in fine-grained recognition is how to find and represent discriminative local regions. Recent attention models are capable of learning discriminative region localizers only from category labels with reinforcement learning. However, not utilizing any explicit part information, they are not able to accurately find multiple distinctive regions. In this work, we introduce an attribute-guided attention localization scheme where the local region localizers are learned under the guidance of part attribute descriptions. By designing a novel reward strategy, we are able to learn to locate regions that are spatially and semantically distinctive with reinforcement learning algorithm. The attribute labeling requirement of the scheme is more amenable than the accurate part location annotation required by traditional part-based fine-grained recognition methods. Experimental results on the CUB-200-2011 dataset demonstrate the superiority of the proposed scheme on both fine-grained recognition and attribute recognition.


title: IoT smart parking system for reducing green house gas emission

abstract: Rapid climate change results natural calamity and severe economic impact and threats to the life. Burning fossil fuels by the medium of transportation contributes 1/3 of portion in increasing greenhouse emission and leading to raise surface temperate. Commuters in and around the developed cities faces difficulties in finding parking lot due to lack of notification process and autonomous parking systems. This causes commuters to take multiple rounds trips to get the parking slot which causes burning additional fuel and ultimately producing excessive CO 2 emission. This paper describes solution to smart parking system using Internet of Things (IoT) to override parking hazards and explains how does it helps to minimize emitting greenhouse gases. IoT enables smart parking system using the system of interconnected Raspberry Pi, Distance Sensor, Pi Camera devices together. This hardware reacts to one another collects data and transmits to cloud storage.


title: Novel 77 GHz Low-Cost Automotive Radar Module with Fractional-PLL Frequency Linearizer

abstract: Radar is the key technology for future automotive distance warning systems. This paper reports on a new 77 GHz FMCW sensor module based on state-of-the-art MMICs and flip-chip technology. The concept includes a fractional phase locked loop used for frequency stabilization of the VCO.