title: Measuring Problematic Mobile Phone Use: Development and Preliminary Psychometric Properties of the PUMP Scale

abstract: This study aimed to develop and assess the psychometric properties of an English language measure of problematic mobile phone use. Participants were recruited from a university campus, health science center, and other public locations. The sample included 244 individuals (68.4% female) aged 18-75. Results supported a unidimensional factor structure for the 20-item self-report Problematic Use of Mobile Phones (PUMP) Scale. Internal consistency was excellent (α = 0.94). Strong correlations (r = .76, P < .001) were found between the PUMP Scale and an existing scale of cellular phone dependency that was validated in Asia, as well as items assessing frequency and intensity of mobile phone use. Results provide preliminary support for the use of the PUMP Scale to measure problematic use of mobile phones.


title: Disciplining virtue: investigating the discourses of opioid addiction in nursing.

abstract: Two nurses diagnosed with opioid addiction launched legal action after being found guilty of unprofessional conduct due to addiction-related behaviors. When covered by the media, their cases sparked both public and legal controversies. We are curious about the broader discursive framings that led to these strong reactions, and analyze the underlying structures of knowledge and power that shape the issue of opioid addiction in the profession of nursing through a critical discourse analysis of popular media, legal blogs and hearing tribunals. We argue that addiction in nursing is framed as personal choice, as a failure in the moral character of the nurses, as decontextualized from addiction as disease arguments, and as an individualized issue devoid of contextual factors leading to addiction. Our investigation offers a critical case study of a nursing regulatory body that upheld popular assumptions of addiction as an autonomous, rational choice replete with individual-based consequences – a framing that is inconsistent with evidence-based practice in health-care. We put forth this critical interrogation to open up possibilities for counterdiscourses that may promote more nuanced and effective responses to the issue of addiction in nursing.


Propulsion for advanced commercial transports


title: Passive mode locking of ytterbium- and erbium-doped all-fiber lasers using graphene oxide saturable absorbers

abstract: Broadband graphene oxide/PVA films were used as saturable absorbers (SAs) for mode locking erbium-doped fiber laser (EDFL) and ytterbium-doped fiber laser (YDFL) at 1.06 μm and 1.55 μm. They provide modulation depths of 3.15% and 6.2% for EDFL and YDFL, respectively. Stable self-starting mode-locked pulses are obtained for both lasers, confirming that the graphene oxide is cost-effective. We have generated mode-locked pulses with spectral width, repetition rate, and pulse duration of 0.75 nm, 9.5 MHz, and 2.7 ps. This is the shortest pulse duration directly obtained from an all-normal-dispersion YDFL with graphene-oxide saturable absorber.


title: A Novel Multi-Class EEG-Based Sleep Stage Classification System

abstract: Sleep stage classification is one of the most critical steps in effective diagnosis and the treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time-consuming and burdensome task. A computer-assisted sleep stage classification system is thus essential for both sleep-related disorders diagnosis and sleep monitoring. In this paper, we propose a system to classify the wake and sleep stages with high rates of sensitivity and specificity. The EEG signals of 25 subjects with suspected sleep-disordered breathing, and the EEG signals of 20 healthy subjects from three data sets are used. Every EEG epoch is decomposed into eight subband epochs each of which has a frequency band pertaining to one EEG rhythm (i.e., delta, theta, alpha, sigma, beta 1, beta 2, gamma 1, or gamma 2). Thirteen features are extracted from each subband epoch. Therefore, 104 features are totally obtained for every EEG epoch. The Kruskal–Wallis test is used to examine the significance of the features. Non-significant features are discarded. The minimal-redundancy-maximal-relevance feature selection algorithm is then used to eliminate redundant and irrelevant features. The features selected are classified by a random forest classifier. To set the system parameters and to evaluate the system performance, nested 5-fold cross-validation and subject cross-validation are performed. The performance of our proposed system is evaluated for different multi-class classification problems. The minimum overall accuracy rates obtained are 95.31% and 86.64% for nested 5-fold and subject cross-validation, respectively. The system performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art systems. The proposed system can be used in health care applications with the aim of improving sleep stage classification.


title: Critical Analysis of Strategies for Determining Rigor in Qualitative Inquiry

abstract: Criteria for determining the trustworthiness of qualitative research were introduced by Guba and Lincoln in the 1980s when they replaced terminology for achieving rigor, reliability, validity, and generalizability with dependability, credibility, and transferability. Strategies for achieving trustworthiness were also introduced. This landmark contribution to qualitative research remains in use today, with only minor modifications in format. Despite the significance of this contribution over the past four decades, the strategies recommended to achieve trustworthiness have not been critically examined. Recommendations for where, why, and how to use these strategies have not been developed, and how well they achieve their intended goal has not been examined. We do not know, for example, what impact these strategies have on the completed research. In this article, I critique these strategies. I recommend that qualitative researchers return to the terminology of social sciences, using rigor, reliability, valid...


title: Edge-Preserving Depth Map Upsampling by Joint Trilateral Filter

abstract: Compared to the color images, their associated depth images captured by the RGB-D sensors are typically with lower resolution. The task of depth map super-resolution (SR) aims at increasing the resolution of the range data by utilizing the high-resolution (HR) color image, while the details of the depth information are to be properly preserved. In this paper, we present a joint trilateral filtering (JTF) algorithm for depth image SR. The proposed JTF first observes context information from the HR color image. In addition to the extracted spatial and range information of local pixels, our JTF further integrates local gradient information of the depth image, which allows the prediction and refinement of HR depth image outputs without artifacts like textural copies or edge discontinuities. Quantitative and qualitative experimental results demonstrate the effectiveness and robustness of our approach over prior depth map upsampling works.


title: The spine instability neoplastic score (SINS) in the assessment of response to radiotherapy for bone metastases

abstract: BACKGROUND ::: Vertebral metastases are often causing pain and spine instability. Radiotherapy is of significant benefit for painful spine metastases but the response can be very variable. The spine instability neoplastic score (SINS) is a recent classification system for diagnosis of spinal instability caused by vertebral metastases. We analysed the degree of pain relief, the need of drug therapy and the imaging features and the SINS before and after radiotherapy. In particular, we investigated the possible correlation of spine instability defined by pre-treatment SINS with pretreatment pain and with response to radiotherapy. ::: ::: ::: MATERIAL/METHODS ::: This study included 121 patients with spine metastases treated with palliative 3D conformal radiotherapy. Pain "at rest" and "breakthrough pain", need for drug therapy in terms of "anti-inflammatory", "weak opioid", "strong opioid", imaging studies and SINS were assessed before and after radiotherapy. Statistical analysis was performed by the correlation coefficient of Spearman and Kruskal-Wallis test. ::: ::: ::: RESULTS ::: Pain relief after radiotherapy was observed in 50.4 and 57.8% of patients in terms of pain at rest and breakthrough pain, respectively. The correlation between pain before radiotherapy and SINS was not statistically significant for both pain at rest (p = 0.4) and breakthrough pain (p = 0.49). The correlation between pain response after radiotherapy and SINS was statistically significant for both pain at rest (p = 0.007) and breakthrough pain (p = 0.047). ::: ::: ::: DISCUSSION/CONCLUSION ::: The degree of instability, classified according to SINS, resulted to be predictive factor for pain response after radiotherapy. SINS might become a valid tool to identify those patients who can benefit the most from radiotherapy.


title: Football Action Recognition Using Hierarchical LSTM

abstract: We present a hierarchical recurrent network for understanding team sports activity in image and location sequences. In the hierarchical model, we integrate proposed multiple person-centered features over a temporal sequence based on LSTM's outputs. To achieve this scheme, we introduce the Keeping state in LSTM as one of externally controllable states, and extend the Hierarchical LSTMs to include mechanism for the integration. Experimental results demonstrate effectiveness of the proposed framework involving hierarchical LSTM and person-centered feature. In this study, we demonstrate improvement over the reference model. Specifically, by incorporating the person-centered feature with meta-information (e.g., location data) in our proposed late fusion framework, we also demonstrate increased discriminability of action categories and enhanced robustness against fluctuation in the number of observed players.


title: Counterfactual thinking and emotions: regret and envy learning

abstract: Emotions like regret and envy share a common origin: they are motivated by the counterfactual thinking of what would have happened had we made a different choice. When we contemplate the outcome of a choice we made, we may use the information on the outcome of a choice we did not make. Regret is the purely private comparison between two choices that we could have taken, envy adds to this the information on outcome of choices of others. However, envy has a distinct social component, in that it adds the change in the social ranking that follows a difference in the outcomes. We study the theoretical foundation and the experimental test of this view.


title: PEBIL: Efficient static binary instrumentation for Linux

abstract: Binary instrumentation facilitates the insertion of additional code into an executable in order to observe or modify the executable's behavior. There are two main approaches to binary instrumentation: static and dynamic binary instrumentation. In this paper we present a static binary instrumentation toolkit for Linux on the x86/x86_64 platforms, PEBIL (PMaC's Efficient Binary Instrumentation Toolkit for Linux). PEBIL is similar to other toolkits in terms of how additional code is inserted into the executable. However, it is designed with the primary goal of producing efficient-running instrumented code. To this end, PEBIL uses function level code relocation in order to insert large but fast control structures. Furthermore, the PEBIL API provides tool developers with the means to insert lightweight hand-coded assembly rather than relying solely on the insertion of instrumentation functions. These features enable the implementation of efficient instrumentation tools with PEBIL. The overhead introduced for basic block counting by PEBIL is an average of 65% of the overhead of Dyninst, 41% of the overhead of Pin, 15% of the overhead of DynamoRIO, and 8% of the overhead of Valgrind.


title: Efficient GPU hardware transactional memory through early conflict resolution

abstract: It has been proposed that Transactional Memory be added to Graphics Processing Units (GPUs) in recent years. One proposed hardware design, Warp TM, can scale to 1000s of concurrent transactions. As a programming method that can atomicize an arbitrary number of memory access locations and greatly reduce the efforts to program parallel applications, transactional memory handles the complexity of inter-thread synchronization. However, when thousands of transactions run concurrently on a GPU, conflicts and resource contentions arise, causing performance loss. In this paper, we identify and analyze the cause of conflicts and contentions and propose two enhancements that try to resolve conflicts early: (1) Early-Abort global conflict resolution that allows conflicts to be detected before they reach the Commit Units so that contention in the Commit Units is reduced and (2) Pause-and-Go execution scheme that reduces the chance of conflict and the performance penalty of re-executing long transactions. These two enhancements are enabled by a single hardware modification. Our evaluation shows the combination of the two enhancements greatly improves overall execution speed while reducing energy consumption.


title: Beyond TFIDF Weighting for Text Categorization in the Vector Space Model

abstract: KNN and SVM are two machine learning approaches to Text Categorization (TC) based on the Vector Space Model. In this model, borrowed from Information Retrieval, documents are represented as a vector where each component is associated with a particular word from the vocabulary. Traditionally, each component value is assigned using the information retrieval TFIDF measure. While this weighting method seems very appropriate for IR, it is not clear that it is the best choice for TC problems. Actually, this weighting method does not leverage the information implicitly contained in the categorization task to represent documents. In this paper, we introduce a new weighting method based on statistical estimation of the importance of a word for a specific categorization problem. This method also has the benefit to make feature selection implicit, since useless features for the categorization problem considered get a very small weight. Extensive experiments reported in the paper shows that this new weighting method improves significantly the classification accuracy as measured on many categorization tasks.


title: A discrete event simulation to model passenger flow in the airport terminal

abstract: The Paper describes the analysis of passenger flow in the terminal airport, from entrance to boarding. In particular this study develops a simulation model. Based on the Discrete Event theory, that can be used to build a structure that helps to predict delay and to produce a logical and rational management of check-in and security checkpoint inside the airport terminal. The proposed model, tested in the South Italian reality (Capodichino - Naples International Airport), has a modular architecture and interface, enabling quick and easy model building and providing the capability of being adaptable to the configuration and operational characteristics of a wide range of airport terminals.


title: Context preserving dynamic word cloud visualization

abstract: In this paper, we introduce a visualization method that couples a trend chart with word clouds to illustrate temporal content evolutions in a set of documents. Specifically, we use a trend chart to encode the overall semantic evolution of document content over time. In our work, semantic evolution of a document collection is modeled by varied significance of document content, represented by a set of representative keywords, at different time points. At each time point, we also use a word cloud to depict the representative keywords. Since the words in a word cloud may vary one from another over time (e.g., words with increased importance), we use geometry meshes and an adaptive force-directed model to lay out word clouds to highlight the word differences between any two subsequent word clouds. Our method also ensures semantic coherence and spatial stability of word clouds over time. Our work is embodied in an interactive visual analysis system that helps users to perform text analysis and derive insights from a large collection of documents. Our preliminary evaluation demonstrates the usefulness and usability of our work.


title: Architectural Modeling and Analysis for Safety Engineering

abstract: Architecture description languages such as AADL allow systems engineers to specify the structure of system architectures and perform several analyses over them, including schedulability, resource analysis, and information flow. In addition, they permit system-level requirements to be specified and analyzed early in the development process of airborne and ground-based systems. These tools can also be used to perform safety analysis based on the system architecture and initial functional decomposition.


title: Examining social adjustment to college in the age of social media : Factors influencing successful transitions and persistence Computers & Education

abstract: Social adjustment plays a critical role in student persistence at college. Social media such as Facebook, used widely by this population, have the potential to positively enhance students' transition to college by encouraging connection and interaction among peers. The present study examines the role Facebook plays in students' social adjustment during their first year of college using survey data (N = 338) collected from students at a private, liberal arts college in the Midwest. We develop and test a model that includes both traditional and Facebook-specific predictors of social support and social adjustment, as well as explore the role that these factors play in predicting students' enrollment status the following year. Results indicate positive relationships between two Facebook variables-the number of Facebook Friends students have at the college and their engagement in collaborative behaviors with classmates through the site-and measures of social support and social adjustment, as well as a positive relationship between social adjustment and persistence at the university.


title: Semantic-Based Text Document Clustering Using Cognitive Semantic Learning and Graph Theory

abstract: Semantic-based text document clustering aims to group documents into a set of topic clusters. We propose a new approach for semantically clustering of text documents based on cognitive science and graph theory. We apply a computational cognitive model of semantic association for human semantic memory, known as Incremental Construction of an Associative Network (ICAN). The vector-based model of Latent Semantic Analysis (LSA), has been a leading computational cognitive model for semantic learning and topic modeling, but it has well-known limitations including not considering the original word-order and doing semantic reduction with neither a linguistic nor cognitive basis. These issues have been overcome by the ICAN model. ICAN model is used to generate document-level semantic-graphs. Cognitive and graph-based topic and context identification methods are used for semantic reduction of ICAN graphs. A corpus-graph is generated from ICAN graphs, and then a community-detection graph algorithm is applied for the final step of document clustering. Experiments are conducted on three commonly used datasets. Using the purity and entropy criteria for clustering quality, our results show a notable outperformance over the LSA-based approach.


title: A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier

abstract: Heart Disease (CHD) is a common form of disease affecting the heart and an important cause for premature death. From the point of view of medical sciences, data mining is involved in discovering various sorts of metabolic syndromes. Classification techniques in data mining play a significant role in prediction and data exploration. Classification technique such as Decision Trees has been used in predicting the accuracy and events related to CHD. In this paper, a Data mining model has been developed using Random Forest classifier to improve the prediction accuracy and to investigate various events related to CHD. This model can help the medical practitioners for predicting CHD with its various events and how it might be related with different segments of the population. The events investigated are Angina, Acute Myocardial Infarction (AMI), Percutaneous Coronary Intervention (PCI), and Coronary Artery Bypass Graft surgery (CABG). Experimental results have shown that classification using Random Forest Classification algorithm can be successfully used in predicting the events and risk factors related to CHD.


title: Simulating the effect of workers' mood on the productivity of assembly lines

abstract: Production lines have various components, from workers to loads and machines, each of which can influence the productivity of the entire system either directly or indirectly. One of the most vulnerable parts of an assembly line is the human element. Studies have been conducted on the methods to improve assembly line productivity, both from a worker's ergonomic perspective and from a system simulation perspective, but neither approach has considered the worker's mood. This study uses systems simulation capabilities to address some of the major psychological difficulties that may affect worker efficiency beyond the ergonomic conditions, such as emotional and cognitive factors. This study aims to present feasible solutions for increasing the productivity of an assembly line in a backpack company in Montana, with regard to an employee's mood, and cognitive and physical states.


Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation


title: Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

abstract: ABSTRACT The anomaly detection methods are receiving growing attention in the intrusion detection community. The two main reasons for this are their ability to handle large volumes of unlabeled data and to detect previously unknown attacks. In this contribution we investigate the application of a modern machine learning technique – one-class Support Vector Machines (SVM) – for anomaly detection in unlabeled data. We propose a novel formulation of this technique which is particularly suited for the data typical for intrusion detection systems. Our evaluation on the well-known KDDCup dataset demonstrates a significant improvement over previous formulations of the one-class SVM.


title: Dermoscopy of pigmented skin lesions

abstract: Dermoscopy is an in vivo method for the early diagnosis of malignant melanoma and the differential diagnosis of pigmented lesions of the skin. It has been shown to increase diagnostic accuracy over clinical visual inspection in the hands of experienced physicians. This article is a review of the principles of dermoscopy as well as recent technological developments.


title: BGetting things done: The science behind stress-free productivity,[ Long Range

abstract: In 2001 David Allen proposed ‘Getting Things Done’ (GTD) as a method for enhancing personal productivity and reducing the stress caused by information overload. This paper argues that recent insights in psychology and cognitive science support and extend GTD's recommendations. We first summarize GTD with the help of a flowchart, and then review the theories of situated, embodied and distributed cognition that purport to explain how the brain processes information and plans actions in the real world. The conclusion is that the brain heavily relies on the environment to function as an external memory, a trigger for actions, and a source of ‘affordances’, disturbances and feedback. We show how these principles are practically implemented in GTD, with its focus on organizing tasks into ‘actionable’ external memories, and on opportunistic, situation-dependent execution. Finally, inspired by the concept of stigmergy, we propose an extension of GTD to support collaborative work.


title: A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data

abstract: In the past several years, social media (e.g., Twitter and Facebook) has been experiencing a spectacular rise and popularity, and becoming a ubiquitous discourse for content sharing and social networking. With the widespread of mobile devices and location-based services, social media typically allows users to share whereabouts of daily activities (e.g., check-ins and taking photos), and thus strengthens the roles of social media as a proxy to understand human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time, and interactive visual exploration of movement dynamics in massive location-based social media at multiple scales.


title: Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model

abstract: In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.


title: Na,K-ATPase in isolated nephron segments in rats with experimental heart failure

abstract: To characterize renal transport of Na+ in heart failure, urinary Na+ excretion (UNaV), aldosterone levels, and Na,K-ATPase activity in isolated nephron segments were determined in three groups: control rats, rats with heart failure and moderate sodium retention, and rats with heart failure and severe sodium retention. Heart failure was induced by a fistula between the aorta and vena cava. For the control group, UNaV was 0.66 +/- 0.04 (mean +/- SEM) mueq/min, and aldosterone was 18.4 +/- 3.5 ng%. Na,K-ATPase activity (in 10(-11) mol/mm/min) was 28.4 +/- 1.1 in the proximal convoluted tubule, 23.3 +/- 1.0 in the proximal straight tubule, 37.4 +/- 1.9 in the medullary thick ascending limb, 40.2 +/- 1.9 in the cortical thick ascending limb, 43.2 +/- 2.2 in the distal convoluted tubule, and 20.5 +/- 0.9 in the cortical collecting duct. For the group with moderate heart failure, UNaV was 0.35 +/- 0.02 (p less than 0.001 versus control), and aldosterone was 15.9 +/- 4.4 (p = NS versus control). Na,K-ATPase activity was unchanged in the proximal convoluted tubule, proximal straight tubule, medullary thick ascending limb, and cortical collecting duct, but it increased in the cortical thick ascending limb to 57.7 +/- 3.1 (p less than 0.001 versus control) and decreased in the distal convoluted tubule to 35.3 +/- 1.2 (p less than 0.005 versus control). For the group with severe heart failure, UNaV was 0.029 +/- 0.016 (p less than 0.001 versus control), and aldosterone was 186.0 +/- 14.8 (p less than 0.001 versus control).(ABSTRACT TRUNCATED AT 250 WORDS)


title: Grad-CAM: Why did you say that?

abstract: We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations. Our approach, called Gradient-weighted Class Activation Mapping (Grad-CAM), uses class-specific gradient information to localize important regions. These localizations are combined with existing pixel-space visualizations to create a novel high-resolution and class-discriminative visualization called Guided Grad-CAM. These methods help better understand CNN-based models, including image captioning and visual question answering (VQA) models. We evaluate our visual explanations by measuring their ability to discriminate between classes, to inspire trust in humans, and their correlation with occlusion maps. Grad-CAM provides a new way to understand CNN-based models. ::: We have released code, an online demo hosted on CloudCV, and a full version of this extended abstract.


title: An approximate L0 norm minimization algorithm for compressed sensing

abstract: l0 norm based signal recovery is attractive in compressed sensing as it can facilitate exact recovery of sparse signal with very high probability. Unfortunately, direct l0 norm minimization problem is NP-hard. This paper describes an approximate l0 norm algorithm for sparse representation which preserves most of the advantages of l0 norm. The algorithm shows attractive convergence properties, and provides remarkable performance improvement in noisy environment compared to other popular algorithms. The sparse representation algorithm presented is capable of very fast signal recovery, thereby reducing retrieval latency when handling


title: Deeply-supervised CNN for prostate segmentation

abstract: Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided intervention. However, the lack of clear boundary specifically at the apex and base, and huge variation of shape and texture between the images from different patients make the task very challenging. To overcome these problems, in this paper, we propose a deeply supervised convolutional neural network (CNN) utilizing the convolutional information to accurately segment the prostate from MR images. The proposed model can effectively detect the prostate region with additional deeply supervised layers compared with other approaches. Since some information will be abandoned after convolution, it is necessary to pass the features extracted from early stages to later stages. The experimental results show that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches.


title: Hybrid Resonant and PWM Converter With High Efficiency and Full Soft-Switching Range

abstract: A novel soft-switching converter combining resonant half-bridge and phase-shifted pulsewidth modulation (PWM) full-bridge configuration is proposed to ensure the switches in the leading-leg operating at zero-voltage switching from true zero-load to full-load, and the switches in the lagging leg working at zero-current switching with minimum duty cycle loss and circulating conduction loss by significantly reducing leakage or series inductance. Experimental results of a 3.4-kW hardware prototype show that the circuit achieves true full-range soft switching with 98% peak efficiency. The hybrid resonant and PWM converter is attractive for electrical vehicle battery charger application.


title: Data Mining Approaches for Intrusion Detection

abstract: In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intra-and inter-audit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.


title: A Low-Power Bidirectional Telemetry Device With a Near-Field Charging Feature for a Cardiac Microstimulator

abstract: In this paper, wireless telemetry using the near-field coupling technique with round-wire coils for an implanted cardiac microstimulator is presented. The proposed system possesses an external powering amplifier and an internal bidirectional microstimulator. The energy of the microstimulator is provided by a rectifier that can efficiently charge a rechargeable device. A fully integrated regulator and a charge pump circuit are included to generate a stable, low-voltage, and high-potential supply voltage, respectively. A miniature digital processor includes a phase-shift-keying (PSK) demodulator to decode the transmission data and a self-protective system controller to operate the entire system. To acquire the cardiac signal, a low-voltage and low-power monitoring analog front end (MAFE) performs immediate threshold detection and data conversion. In addition, the pacing circuit, which consists of a pulse generator (PG) and its digital-to-analog (D/A) controller, is responsible for stimulating heart tissue. The chip was fabricated by Taiwan Semiconductor Manufacturing Company (TSMC) with 0.35-μm complementary metal-oxide semiconductor technology to perform the monitoring and pacing functions with inductively powered communication. Using a model with lead and heart tissue on measurement, a -5-V pulse at a stimulating frequency of 60 beats per minute (bpm) is delivered while only consuming 31.5 μW of power.


title: Community based educational data repositories and analysis tools

abstract: This workshop will explore community based repositories for educational data and analytic tools that are used to connect researchers and reduce the barriers to data sharing. Leading innovators in the field, as well as attendees, will identify and report on bottlenecks that remain toward our goal of a unified repository. We will discuss these as well as possible solutions. We will present LearnSphere, an NSF funded system that supports collaborating on and sharing a wide variety of educational data, learning analytics methods, and visualizations while maintaining confidentiality. We will then have hands-on sessions in which attendees have the opportunity to apply existing learning analytics workflows to their choice of educational datasets in the repository (using a simple drag-and-drop interface), add their own learning analytics workflows (requires very basic coding experience), or both. Leaders and attendees will then jointly discuss the unique benefits as well as the limitations of these solutions. Our goal is to create building blocks to allow researchers to integrate their data and analysis methods with others, in order to advance the future of learning science.


title: ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature.

abstract: The emergence of “big data” initiatives has led to the need for tools that can automatically extract valuable chemical information from large volumes of unstructured data, such as the scientific literature. Since chemical information can be present in figures, tables, and textual paragraphs, successful information extraction often depends on the ability to interpret all of these domains simultaneously. We present a complete toolkit for the automated extraction of chemical entities and their associated properties, measurements, and relationships from scientific documents that can be used to populate structured chemical databases. Our system provides an extensible, chemistry-aware, natural language processing pipeline for tokenization, part-of-speech tagging, named entity recognition, and phrase parsing. Within this scope, we report improved performance for chemical named entity recognition through the use of unsupervised word clustering based on a massive corpus of chemistry articles. For phrase parsing an...


Is Gun Crime Learned? Social Learning Theory and Guns


title: Effects of a whatsapp-delivered physical activity intervention to enhance health-related physical fitness components and cardiovascular disease risk factors in older adults.

abstract: BACKGROUND ::: This pilot study evaluated the feasibility and preliminary effectiveness of a 10-week WhatsApp-based intervention aimed at enhancing health-related physical fitness components and cardiovascular disease (CVD) risk factors compared with a face-to-face condition. ::: ::: ::: METHODS ::: Participants (N.=32) were assigned to one of three groups: training group (N.=16), mobile group (N.=7) and control group (N.=9). Training group and mobile group performed the same training program, based on strength training with elastics bands and aerobic exercise, during 10 weeks; only the delivery mode differed. ::: ::: ::: RESULTS ::: The mobile group increased handgrip strength, aerobic capacity and decreased systolic blood pressure and heart rate after exercise though there were no significant differences respect to control group. The training group decreased significantly systolic blood pressure (P=0.038), diastolic blood pressure (P=0.005), mean arterial pressure (P=0.006) and heart rate after exercise (P=0.002), respect to control group. ::: ::: ::: CONCLUSIONS ::: Comparison between training and mobile group showed that WhatsApp-based physical activity intervention was less effective than face-to-face condition. The results indicate that the use of an online social network produced slight changes in some health-related physical fitness components and CVD risk factors.


The building blocks of a working digital culture: The case of some Finnish schools


title: Temporal coding of local spectrogram features for robust sound recognition

abstract: There is much evidence to suggest that the human auditory system uses localised time-frequency information for the robust recognition of sounds. Despite this, conventional systems typically rely on features extracted from short windowed frames over time, covering the whole frequency spectrum. Such approaches are not inherently robust to noise, as each frame will contain a mixture of the spectral information from noise and signal. Here, we propose a novel approach based on the temporal coding of Local Spectrogram Features (LSFs), which generate spikes that are used to train a Spiking Neural Network (SNN) with temporal learning. LSFs represent robust location information in the spectrogram surrounding keypoints, which are detected in a signal-driven manner such that the effect of noise on the temporal coding is reduced. Our experiments demonstrate the robust performance of our approach across a variety of noise conditions, such that it is able to outperform the conventional frame-based baseline methods.


title: The Architecture of a Churn Prediction System Based on Stream Mining.

abstract: Churning is the movement of customers from a company to another. For any company, being able to predict with some time which of their customers will churn is essential to take actions in order to retain them, and for this reason most sectors invest substantial effort in techniques for (semi)automatically predicting churning, and data mining and machine learning are among the techniques successfully used to this effect. In this paper we describe a prototype for churn prediction using stream mining methods, which offer the additional promise of detecting new patterns of churn in real-time streams of high-speed data, and adapting quickly to a changing reality. The prototype is implemented on top of the MOA (Massive Online Analysis) framework for stream mining. The application implicit in the prototype is the telecommunication operator (mobile phone) sector. A shorter version of this paper, omitting Section 5, was presented at CCIA’13 (http://mon.uvic.cat/ccia2013/en/).


title: Explainable Artificial Intelligence for Training and Tutoring

abstract: This paper describes an Explainable Artificial Intelligence (XAI) tool that allows entities to answer questions about their activities within a tactical simulation. We show how XAI can be used to provide more meaningful after-action reviews and discuss ongoing work to integrate an intelligent tutor into the XAI framework.


title: Approximate dynamic programming modeling for a typical blood platelet bank

abstract: It addresses some of the literature gaps and overcome curse of dimension problem.The model considers eight blood types with stochastic demand, stochastic supply.It uses news vendor model and the current inventory to control the supply uncertainty.It uses linear programing to solve the single-period inventory model optimally.It uses ADP to solve the multi-period model without any downsizing. This paper introduces a workable model for the establishment of an inventory bank holding perishable blood platelets with a short shelf life. The model considers a blood platelet bank with eight blood types, stochastic demand, stochastic supply, and deterministic lead time. The model is formulated using approximate dynamic programming. The model is evaluated in terms of four measures of effectiveness: blood platelet shortage, outdating, inventory level, and reward gained. Moreover, several alternative inventory control policies are analyzed. The order quantity decision is taken using a news-vendor model. In addition, the variation of the O- percentage is studied. This study confirms that the blood platelet bank reward can be maximized by operating at the optimal inventory level, thereby minimizing the number of outdated units as well as shortages. In addition, the suitable O- percentage within the blood platelet bank inventory was studied. As the O- blood type inventory levels increase to 40%, shortages drop from 3.9% to 1.5%. Outdated units drop from 4.6% to 1.8%. Furthermore, when the order quantity is received twice a day, shortages drop to 1.8% and outdated units drop to 2.1%.


title: Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation

abstract: An intrusion detection evaluation test bed was developed which generated normal traffic similar to that on a government site containing 100's of users on 1000's of hosts. More than 300 instances of 38 different automated attacks were launched against victim UNIX hosts in seven weeks of training data and two weeks of test data. Six research groups participated in a blind evaluation and results were analyzed for probe, denial-of-service (DoS) remote-to-local (R2L), and user to root (U2R) attacks. The best systems detected old attacks included in the training data, at moderate detection rates ranging from 63% to 93% at a false alarm rate of 10 false alarms per day. Detection rates were much worse for new and novel R2L and DoS attacks included only in the test data. The best systems failed to detect roughly half these new attacks which included damaging access to root-level privileges by remote users. These results suggest that further research should focus on developing techniques to find new attacks instead of extending existing rule-based approaches.


DESTINATION SOUTH AFRICA: ANALYSIS OF DESTINATION AWARENESS AND IMAGE BY INTERNATIONAL VISITORS


title: Application of Near-Optimal Tower Control and Free Cooling on the Condenser Water Side for Optimization of Central Cooling Systems

abstract: This paper presents an application of tower fan control for optimization of the performance of chiller plants combined with free cooling on the condenser water side. Mathematical models including all the main components of an existing cooling plant were developed and implemented in MATLAB. Simulation results include a mapping of the performance of the plant working in free cooling mode which was used to select control parameters for free cooling operation. Then a mapping of the plant operating with chillers was developed to find the correlation between load and near-optimal air flow, which is the basis of the near-optimal tower control (NOTC) strategy. Finally, simulations were carried out using three consecutive years of historical data to predict the performance of the plant under three different control strategies: 1) tower fan control aiming to keep the temperature of the water supplied to chiller condensers at a constant set point (current control strategy), 2) NOTC and 3) NOTC and free cooling combined. Comparison of the performance of the plant with the baseline (constant condenser water temperature) shows that significant savings can be achieved through the implementation of NOTC along with free cooling. It is expected that the methodology and results of this study provide a useful framework for optimization of cooling plants.


title: Capacity and Trainability in Recurrent Neural Networks

abstract: Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and per-unit capacity bounds with careful training, for a variety of tasks and stacking depths. They can store an amount of task information which is linear in the number of parameters, and is approximately 5 bits per parameter. They can additionally store approximately one real number from their input history per hidden unit. We further find that for several tasks it is the per-task parameter capacity bound that determines performance. These results suggest that many previous results comparing RNN architectures are driven primarily by differences in training effectiveness, rather than differences in capacity. Supporting this observation, we compare training difficulty for several architectures, and show that vanilla RNNs are far more difficult to train, yet have slightly higher capacity. Finally, we propose two novel RNN architectures, one of which is easier to train than the LSTM or GRU for deeply stacked architectures.


title: Characterization and modeling of frequency dispersion in RF LDMOS transistors

abstract: This paper characterises the effects of frequency dispersion in laterally diffused metal-oxide semiconductor (LDMOS) from pulsed-S-parameter measurements. Using the calculated high-frequency drain current from measured data, we demonstrate that length of the lightly doped drain extension is directly proportional to the amount of current collapse at high frequency. To capture the frequency dispersion in a nonlinear model, a frequency-domain mapping is proposed tto augment quasi-static nonlinear electrothermal models. The importance of including the dispersiveness is demonstrated through comparing a standard quasi-static model, the newly augmented model, and on-wafer load-pull measurements. For a 5-mm, 500 μm unit-gate-width transistor, we demonstrate that the quasi-static model over predicts the drain efficiency during large signal drive at P 3dB by 9%. The new augmented model is able to predict the efficiency within 2% of the measured value.


title: Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle

abstract: Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content (C ab), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.


title: Avoiding monotony: improving the diversity of recommendation lists

abstract: The primary premise upon which top-N recommender systems operate is that similar users are likely to have similar tastes with regard to their product choices. For this reason, recommender algorithms depend deeply on similarity metrics to build the recommendation lists for end-users. However, it has been noted that the products offered on recommendation lists are often too similar to each other and attention has been paid towards the goal of improving diversity to avoid monotonous recommendations. Noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem. We explore a solution strategy to this optimization problem by relaxing it to a trust-region problem.This leads to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution. We apply this approach to the top-N prediction problem, evaluate the system performance on the Movielens dataset and compare it with a standard item-based top-N algorithm. A new evaluation metric ItemNovelty is proposed in this work. Improvements on both diversity and accuracy are obtained compared to the benchmark algorithm.


title: Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking

abstract: The human mind possesses the most remarkable ability to perform multiple tasks with apparent simultaneity. In fact, with the present-day explosion in the variety and volume of incoming information streams that must be absorbed and appropriately processed, the opportunity, tendency, and (even) the need to multitask are unprecedented. Thus, it comes as little surprise that the pursuit of intelligent systems and algorithms that are capable of efficient multitasking is rapidly gaining importance among contemporary scientists who are faced with the increasing complexity of real-world problems. To this end, the present paper is dedicated to a detailed exposition on a so-far underexplored characteristic of population-based search algorithms, i.e., their inherent ability (much like the human mind) to handle multiple optimization tasks at once. We present a simple evolutionary methodology capable of cross-domain multitask optimization in a unified genotype space and show that there exist many potential benefits of its application in practical domains. Most notably, it is revealed that multitasking enables one to automatically leverage upon the underlying commonalities between distinct optimization tasks, thereby providing the scope for considerably improved performance in real-world problem solving.


title: Estimation of the Mathematical Parameters of Double-Exponential Pulses Using the Nelder–Mead Algorithm

abstract: Transient pulses for electromagnetic compatibility problems, such as the high-altitude electromagnetic pulse and ultrawideband pulses, are often described by a double-exponential pulse. Such a pulse shape is specified physically by the three characteristic parameters rise time tr, pulsewidth tfwhm (full-width at half-maximum), and maximum amplitude Emax. The mathematical description is a double-exponential function with the parameters α, β, and E0. In practice, it is often necessary to transform the two groups of parameters into each other. This paper shows a novel relationship between the physical parameters tr and tfwhm on the one hand and the mathematical parameters α and β on the other. It is shown that the least-squares method in combination with the Nelder-Mead simplex algorithm is appropriate to determine an approximate closed-form formula between these parameters. Therefore, the extensive analysis of double-exponential pulses is possible in a considerably shorter computation time. The overall approximation error is less than 3.8%.


title: Isointense infant brain MRI segmentation with a dilated convolutional neural network

abstract: Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.


title: Bridging Languages through Etymology: The case of cross language text categorization

abstract: We propose the hypothesis that word etymology is useful for NLP applications as a bridge between languages. We support this hypothesis with experiments in crosslanguage (English-Italian) document categorization. In a straightforward bag-ofwords experimental set-up we add etymological ancestors of the words in the documents, and investigate the performance of a model built on English data, on Italian test data (and viceversa). The results show not only statistically significant, but a large improvement ‐ a jump of almost 40 points in F1-score ‐ over the raw (vanilla bag-ofwords) representation.


title: Real-time Color Correction for Marker-based Augmented Reality Applications !

abstract: Exploiting color information in an augmented reality (AR) application running on a mobile device becomes problematic due to the unknown lighting condition of the environment. In this paper, we propose a real-time color correction method for AR applications that use markers with color patterns. We identify the regions of color patterns in a marker image and estimate the black and white color points using the marker image itself. Then, the pixel colors in the pattern regions are corrected using the scaling vector. The proposed color correction method requires no assumptions on lighting conditions and is fast enough to run in real-time on a ultra mobile PC platform. We demonstrate the performance of our method with the real world data sets.


title: Atrous Convolutional Neural Network (ACNN) for Semantic Image Segmentation with full-scale Feature Maps

abstract: Deep Convolutional Neural Networks (DCNNs) are used extensively in biomedical image segmentation. However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to semantic image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, different atrous blocks, shortcut connections and normalization methods are explored to select the optimal network structure setting. These lead to a new and full-scale DCNN - Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Fine Group Normalization (FGN). Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve comparable segmentation Dice Similarity Coefficients (DSCs) to U-Net, optimized U-Net and hybrid network, but with significantly reduced trainable parameters due to the use of full-scale feature maps and therefore computationally is much more efficient for both the training and inference.


title: Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions

abstract: Vehicular networking has significant potential to enable diverse applications associated with traffic safety, traffic efficiency and infotainment. In this survey and tutorial paper we introduce the basic characteristics of vehicular networks, provide an overview of applications and associated requirements, along with challenges and their proposed solutions. In addition, we provide an overview of the current and past major ITS programs and projects in the USA, Japan and Europe. Moreover, vehicular networking architectures and protocol suites employed in such programs and projects in USA, Japan and Europe are discussed.


title: On Content-Based Recommendation and User Privacy in Social-Tagging Systems

abstract: Recommendation systems and content-filtering approaches based on annotations and ratings essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively, has been named social tagging, and it is one of the most popular opportunities for users to engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. In fact, it consists in describing online or offline resources by using free-text labels, i.e., tags, thereby exposing a user's profile and activity to privacy attacks. As a result, users may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy-enhancing technology consisting in generating tags for categories or resources that do not reflect the user's actual preferences too accurately. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation isconsequently investigated in a real-world application scenario where different forgery strategies are evaluated, and the resulting loss in utility is measured and compared. We investigate the effects of different privacy enhancing technologies in content-based recommendation systems.We study the interplay between the degree of privacy and the potential degradation of the quality of the recommendation.We evaluate three different tag forgery strategies: optimised tag forgery, uniform tag forgery and TrackMeNot.We carry out an experimental evaluation on a real dataset extracted from Delicious.


title: Using Debriefing to Foster Task Management among Anesthesiologist Assistant Students

abstract: Purpose: This study evaluated the effect of video-aided oral debriefing on task management of anesthesiologist assistant students through the use of high-fidelity anesthesia crisis simulation scenarios. This information describes an evaluation of student learning that is valuable in student educational and workplace competency. Method: A total of 18 graduate students in an anesthesiologist assistant program voluntarily participated in two anesthesia simulation scenarios separated by one of two debriefing methods (oral debriefing versus video-aided oral debriefing). Task management scores of the scenarios were assessed through the Anesthetists’ Nontechnical Skills system. Task management scores after the pre-intervention scenario were compared to the scores after the post-intervention scenario . The effectiveness of video-aided oral debriefing was studied using a within-subjects analysis. The comparison of video-aided oral debriefing to oral debriefing was examined with a betweensubjects analysis. Results: The results showed a significant improvement in the intervention group from the initial assessment to the subsequent assessment in task management (p<0.001). However, results did not show a statistically significant difference between the two debriefing methods after a second anesthesia crisis scenario (p=0.21). Conclusion: The research study found that task management during anesthesia crisis scenarios improved after video-aided oral debriefing. No difference was found when comparing the improvement in task management between debriefing methods. Therefore, video-aided oral debriefing was comparable to oral debriefing in improving task management in this study.


title: Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms

abstract: Many commercial products and academic research activities are embracing behavior analysis as a technique for improving detection of attacks of many sorts—from retweet boosting, hashtag hijacking to link advertising. Traditional approaches focus on detecting dense blocks in the adjacency matrix of graph data, and recently, the tensors of multimodal data. No method gives a principled way to score the suspiciousness of dense blocks with different numbers of modes and rank them to draw human attention accordingly. In this paper, we first give a list of axioms that any metric of suspiciousness should satisfy; we propose an intuitive, principled metric that satisfies the axioms, and is fast to compute; moreover, we propose CrossSpot , an algorithm to spot dense blocks that are worth inspecting, typically indicating fraud or some other noteworthy deviation from the usual, and sort them in the order of importance (“suspiciousness”). Finally, we apply CrossSpot to the real data, where it improves the F1 score over previous techniques by 68 percent and finds suspicious behavioral patterns in social datasets spanning 0.3 billion posts.


title: Line-based extrinsic calibration of range and image sensors

abstract: Creating rich representations of environments requires integration of multiple sensing modalities with complementary characteristics such as range and imaging sensors. To precisely combine multisensory information, the rigid transformation between different sensor coordinate systems (i.e., extrinsic parameters) must be estimated. The majority of existing extrinsic calibration techniques require one or multiple planar calibration patterns (such as checkerboards) to be observed simultaneously from the range and imaging sensors. The main limitation of these approaches is that they require modifying the scene with artificial targets. In this paper, we present a novel algorithm for extrinsically calibrating a range sensor with respect to an image sensor with no requirement of external artificial targets. The proposed method exploits natural linear features in the scene to precisely determine the rigid transformation between the coordinate frames. First, a set of 3D lines (plane intersection and boundary line segments) are extracted from the point cloud, and a set of 2D line segments are extracted from the image. Correspondences between the 3D and 2D line segments are used as inputs to an optimization problem which requires jointly estimating the relative translation and rotation between the coordinate frames. The proposed method is not limited to any particular types or configurations of sensors. To demonstrate robustness, efficiency and generality of the presented algorithm, we include results using various sensor configurations.


title: Characteristics of Androgenetic Alopecia in Asian

abstract: Androgenetic alopecia (AGA), or pattern hair loss, is a common disorder in Asian men and women, with a reported incidence of up to 73% among general population. There are several descriptions regarding the characteristics of AGA in patients of European descent. Asian patients with AGA have different types of hair loss and family histories from Europeans, which may affect treatment response. Therefore, in this review, prevalence, hair loss patterns, familial factors, androgen receptor gene polymorphisms of Asian AGA patients, and management based on algorithmic guidelines for AGA are discussed. This review may be useful for dermatologists in clinical practice for diagnosing and designing management approaches for Asian patients with AGA.


title: Object Detection for Comics using Manga109 Annotations

abstract: With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image understanding. Although convolutional neural networks (CNN)-based methods archived good performance in object detection for naturalistic images, there are two problems in applying these methods to the comic object detection task. First, there is no large-scale annotated comics dataset. The CNN-based methods require large-scale annotations for training. Secondly, the objects in comics are highly overlapped compared to naturalistic images. This overlap causes the assignment problem in the existing CNN-based methods. To solve these problems, we proposed a new annotation dataset and a new CNN model. We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations. For the assignment problem, we proposed a new CNN-based detector, SSD300-fork. We compared SSD300-fork with other detection methods using Manga109-annotations and confirmed that our model outperformed them based on the mAP score.


title: Learning Environmental Sounds with Multi-scale Convolutional Neural Network

abstract: Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce two-phase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.


title: Semi-Supervised Learning for Neural Machine Translation

abstract: While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems.


title: A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking

abstract: This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.


title: Image Forensic using Block-based Copy-move Forgery Detection

abstract: Copy-move forgery is the image tampering procedure where a region or more regions of the image is copied and pasted into one or more areas of the same image. Detection of image copy-move forgery is essential because, images are used as the primary element of communication and security in many places. In place of law and order, used images must be original or authentic and it is necessary to ensure its authenticity. Therefore, the detection of copy-move forgery has become a prominent and active research area. This paper highlight the different block-based approaches used in copy-move forgery detection with their important results. This paper also critically discusses the advantages and limitations of each approach and enlists the different image datasets used for image forgery detection, different unsolved issues and challenges in the field of forgery detection.


title: Evidence-based medicine: clinicians are taught to say it but not taught to think it

abstract: Preparing for my decennial recertification exam in internal medicine opened my eyes to a startling disconnect in how evidence-based medicine (EBM) is communicated to practising clinicians compared with how it is implemented by expert panels and EBM authorities. Indeed, sources of continuing clinical education often omit basic tenets of EBM, in particular how to integrate the best available evidence with clinical expertise.1–3 This disconnect is mystifying: why should education experts well versed in EBM use communication templates disjointed from EBM to communicate evidence for practising medicine? For example, a common source of preparation for the American Board of Internal Medicine recertification exam is the Medical Knowledge Self-Assessment Program (MKSAP) published by the American College of Physicians.4 Even though the American College of Physicians prioritises the mission of teaching and implementing EBM,5 two advisements from the 2017 MKSAP illustrate how the curriculum they sponsor deviates from this mission. ::: ::: First, ::: ::: > Advisement 1: ‘Routine screening for skin cancer using a total body skin examination is not recommended. The United States Preventive Services Task Force (USPSTF) found insufficient evidence that routine skin examination was effective at reducing the morbidity and mortality from cutaneous melanoma, basal cell carcinoma, or squamous cell carcinoma. Patients with high cumulative levels of sun exposure should be encouraged to wear sunscreen and protective clothing, although the benefit of such counselling is unknown.’ ::: ::: Here, clinicians are not told whether the advisement’s basis in EBM is because there is no direct evidence or because there is direct …


Accelerating Neural Networks using Channel Gating


Review of The π-calculus: A Theory of Mobile Processes by D. Sangiorgi and D. Walker


title: Power control techniques used in high frequency induction heating applications

abstract: Resonant inverters are widely used in induction heating applications. Series Resonant Inverter (SRI) fed with voltage source is very much reliable, cost-effective and provides various power control techniques. Different power control schemes like phase shift control (PSC), asymmetrical duty cycle (ADC) control, asymmetrical voltage cancellation (AVC) control, pulse density modulation (PDM) control are normally used at SRI for high frequency induction heating. In this paper a new hybrid control technique by combining PSC and PDM is analyzed. The new control technique overcomes the drawback of individual scheme and provides a wide range of power control. The analyses of all the control techniques are verified through MATLAB simulation results


title: Operations in Financial Services—An Overview

abstract: We provide an overview of the state of the art in research on operations in financial services. We start by highlighting a number of specific operational features that differentiate financial services from other service industries, and discuss how these features affect the modeling of financial services. We then consider in more detail the various different research areas in financial services, namely systems design, performance analysis and productivity, forecasting, inventory and cash management, waiting line analysis for capacity planning, personnel scheduling, operational risk management, and pricing and revenue management. In the last section, we describe the most promising research directions for the near future.


title: Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems

abstract: This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing substrate for wide-area peer-to-peer ap- plications. Pastry performs application-level routing and object location in a po- tentially very large overlay network of nodes connected via the Internet. It can be used to support a variety of peer-to-peer applications, including global data storage, data sharing, group communication and naming. Each node in the Pastry network has a unique identifier (nodeId). When presented with a message and a key, a Pastry node efficiently routes the message to the node with a nodeId that is numerically closest to the key, among all currently live Pastry nodes. Each Pastry node keeps track of its immediate neighbors in the nodeId space, and notifies applications of new node arrivals, node failures and recoveries. Pastry takes into account network locality; it seeks to minimize the distance messages travel, according to a to scalar proximity metric like the number of IP routing hops. Pastry is completely decentralized, scalable, and self-organizing; it automatically adapts to the arrival, departure and failure of nodes. Experimental results obtained with a prototype implementation on an emulated network of up to 100,000 nodes confirm Pastry's scalability and efficiency, its ability to self-organize and adapt to node failures, and its good network locality properties.


Design-Space Exploration in MDE: An Initial Pattern Catalogue


title: Prediction and Validation of Disease Genes Using HeteSim Scores

abstract: Deciphering the gene disease association is an important goal in biomedical research. In this paper, we use a novel relevance measure, called HeteSim, to prioritize candidate disease genes. Two methods based on heterogeneous networks constructed using protein-protein interaction, gene-phenotype associations, and phenotype-phenotype similarity, are presented. In HeteSim_MultiPath (HSMP), HeteSim scores of different paths are combined with a constant that dampens the contributions of longer paths. In HeteSim_SVM (HSSVM), HeteSim scores are combined with a machine learning method. The 3-fold experiments show that our non-machine learning method HSMP performs better than the existing non-machine learning methods, our machine learning method HSSVM obtains similar accuracy with the best existing machine learning method CATAPULT. From the analysis of the top 10 predicted genes for different diseases, we found that HSSVM avoid the disadvantage of the existing machine learning based methods, which always predict similar genes for different diseases. The data sets and Matlab code for the two methods are freely available for download at http://lab.malab.cn/data/HeteSim/index.jsp .


title: Using Voice Coils to Actuate Modular Soft Robots: Wormbot, an Example

abstract: In this study, we present a modular worm-like robot, which utilizes voice coils as a new paradigm in soft robot actuation. Drive electronics are incorporated into the actuators, providing a significant improvement in self-sufficiency when compared with existing soft robot actuation modes such as pneumatics or hydraulics. The body plan of this robot is inspired by the phylum Annelida and consists of three-dimensional printed voice coil actuators, which are connected by flexible silicone membranes. Each electromagnetic actuator engages with its neighbor to compress or extend the membrane of each segment, and the sequence in which they are actuated results in an earthworm-inspired peristaltic motion. We find that a minimum of three segments is required for locomotion, but due to our modular design, robots of any length can be quickly and easily assembled. In addition to actuation, voice coils provide audio input and output capabilities. We demonstrate transmission of data between segments by high-frequency carrier waves and, using a similar mechanism, we note that the passing of power between coupled coils in neighboring modules-or from an external power source-is also possible. Voice coils are a convenient multifunctional alternative to existing soft robot actuators. Their self-contained nature and ability to communicate with each other are ideal for modular robotics, and the additional functionality of sound input/output and power transfer will become increasingly useful as soft robots begin the transition from early proof-of-concept systems toward fully functional and highly integrated robotic systems.


title: Hyperspectral remote sensing of plant pigments

abstract: The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.


title: 3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed

abstract: Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the ‘landscape’ using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method.


Automotive radar: Status and trends


title: Eyes-free interaction with free-hand gestures and auditory menus

abstract: Auditory interfaces can overcome visual interfaces when a primary task, such as driving, competes for the attention of a user controlling a device, such as radio. In emerging interfaces enabled by camera tracking, auditory displays may also provide viable alternatives to visual displays. This paper presents a user study of interoperable auditory and visual menus, in which control gestures remain the same in the visual and the auditory domain. Tested control methods included a novel free-hand gesture interaction with camera-based tracking, and touch screen interaction with a tablet. The task of the participants was to select numbers from a visual or an auditory menu including a circular layout and a numeric keypad layout. Results show, that even with participant's full attention to the task, the performance and accuracy of the auditory interface are the same or even slightly better than the visual when controlled with free-hand gestures. The auditory menu was measured to be slower in touch screen interaction, but questionnaire revealed that over half of the participants felt that the circular auditory menu was faster than the visual menu. Furthermore, visual and auditory feedback in touch screen interaction with numeric layout was measured fastest, touch screen with circular menu second fastest, and the free-hand gesture interface was slowest. The results suggest that auditory menus can potentially provide a fast and desirable interface to control devices with free-hand gestures.


title: M 2 AP: A Minimalist Mutual-Authentication Protocol for Low-cost RFID Tags

abstract: Low-cost Radio Frequency Identification (RFID) tags affixed to consumer items as smart labels are emerging as one of the most pervasive computing technologies in history. This presents a number of advantages, but also opens a huge number of security problems that need to be addressed before its successful deployment. Many proposals have recently appeared, but all of them are based on RFID tags using classical cryptographic primitives such as Pseudorandom Number Generators (PRNGs), hash functions, or block ciphers. We believe this assumption to be fairly unrealistic, as classical cryptographic constructions lie well beyond the computational reach of very low-cost RFID tags. A new approach is necessary to tackle the problem, so we propose a minimalist lightweight mutual authentication protocol for low-cost RFID tags that offers an adequate security level for certain applications, which could be implemented even in the most limited low-cost tags as it only needs around 300 gates.


title: Media coding for the next generation mobile system LTE

abstract: Introduction of LTE (Long Term Evolution) brings enhanced quality for 3GPP multimedia services. The high throughput and low latency of LTE enable higher quality media coding than what is possible in UMTS. LTE-specific codecs have not yet been defined but work on them is ongoing in 3GPP. The LTE codecs are expected to improve the basic signal quality, but also to offer new capabilities such as extended audio bandwidth, stereo and multi-channels for voice and higher temporal and spatial resolutions for video. Due to the wide range of functionalities in media coding, LTE gives more flexibility for service provision to cope with heterogeneous terminal capabilities and transmission over heterogeneous network conditions. By adjusting the bit-rate, the computational complexity, and the spatial and temporal resolution of audio and video, transport and rendering can be optimised throughout the media path hence guaranteeing the best possible quality of service.


title: Combining provenance with trust in social networks for Semantic Web content filtering

abstract: Social networks are a popular movement on the web. On the Semantic Web, it is simple to make trust annotations to social relationships. In this paper, we present a two level approach to integrating trust, provenance, and annotations in Semantic Web systems. We describe an algorithm for inferring trust relationships using provenance information and trust annotations in Semantic Web-based social networks. Then, we present an application, FilmTrust, that combines the computed trust values with the provenance of other annotations to personalize the website. The FilmTrust system uses trust to compute personalized recommended movie ratings and to order reviews. We believe that the results obtained with FilmTrust illustrate the success that can be achieved using this method of combining trust and provenance on the Semantic Web.


title: Catalytic surface modification of alumina membrane for oxygen separation

abstract: Two types of alumina ceramic membrane in hollow fibre shape was used in this study. Both alumina hollow fibres (AHF) were sintered at different temperatures; (a) 1350 o C and (b) 1450 o C. In order to improve the catalytic activity of the alumina membrane for oxygen separation purposes, surface modification of the membranes was carried out using La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ (LSCF) perovskite catalyst. LSCF was synthesised using simple Pechini sol-gel method. The evaporation time and temperature of the LSCF-sol were varied to obtain various viscosity of catalytic sol. From XRD analysis, pure LSCF perovskite structure formed at temperature at 850 o C. The morphological of unmodified and surface-modified alumina hollow fibre membranes (AHF) were studied using FESEM. The effect of LSCF catalytic sol viscosity was studied and it was found that as the viscosity of the sol increases, the amount of catalyst deposited on the alumina hollow fibre were increased. Besides, the amount of catalyst deposited on 1350 AHF was found to be higher than 1450 AHF. This result is supported by the result of pore distribution data whereby 1350 AHF was observed to be more porous than 1450 AHF, with porosity percentage of 40.38% and 28.80%, respectively. Although higher viscosity of catalytic sol could lead to a high amount of catalyst deposited on the AHF substrate, there is a tendency for micro-cracks to develop. Thus, the viscosity of the catalytic sol is important to control in order to have higher oxygen permeation flux.


title: Community Structure Detection for Overlapping Modules through Mathematical Programming in Protein Interaction Networks

abstract: Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non linear programming (MINLP) model, known as OverMod, which transforms disjoint communities to overlapping. The procedure is evaluated through its application to protein-protein interaction (PPI) networks of the rat, E. coli, yeast and human organisms. Connector nodes of hard partitions exhibit topological and functional properties indicative of their suitability as candidates for multiple module membership. OverMod identifies two types of connector nodes, inter and intra-connector, each with their own particular characteristics pertaining to their topological and functional role in the organisation of the network. Inter-connector proteins are shown to be highly conserved proteins participating in pathways that control essential cellular processes, such as proliferation, differentiation and apoptosis and their differences with intra-connectors is highlighted. Many of these proteins are shown to possess multiple roles of distinct nature through their participation in different network modules, setting them apart from proteins that are simply 'hubs', i.e. proteins with many interaction partners but with a more specific biochemical role.


title: A high-PSR LDO using a feedforward supply-noise cancellation technique

abstract: A feed-forward noise cancellation (FFNC) technique to improve the power supply noise rejection (PSR) of a low dropout regulator (LDO) is presented. The proposed FFNC operates in conjunction with a conventional LDO and extends the noise rejection bandwidth by nearly an order of magnitude. Fabricated in 0.18µm CMOS, at 10mA load current, the prototype achieves a PSR of −50dB and −25dB at 1MHz and 10MHz supply noise frequencies, respectively. Compared to a conventional LDO, this represents an improvement of at least 30dB at 1MHz and 15dB at 10MHz. The prototype uses only 20pF load capacitance and occupies an active area of 0.04mm2.


title: High gain 60 GHz band printed quasi-Yagi antenna using a novel microstrip-slotline transition feed

abstract: In this paper, a high gain printed quasi-Yagi antenna is introduced for 60 GHz wireless applications. The antenna is fed by a novel microstrip-slotline transition consisting of a curved microstripline and circular slot in order to allow wideband characteristics. A corrugated ground plane is employed as a reflector for improving gain and small gain variation. The proposed antenna has an impedance bandwidth of 49.8–68.5 GHz for a −10 dB reflection coefficient and a gain of 10.50–12.8 dBi within this bandwidth. In addition, the antenna exhibits a stable radiation pattern with half power beamwidths of 36°–50° and 41°–52° in the E- and H-planes, respectively.


title: Slotline Stepped Circular Rings for Low-Loss Microstrip-to-Slotline Transitions

abstract: We propose a low-loss slotline (SL) stepped circular ring termination for use in a microstrip-to-slotline (MS-to-SL) transition. Not only does this termination achieve broadband characteristics, but it also reduces SL radiation. The experimental results show that, using the proposed technique, the radiation loss around the pass-band frequency is lower than that of the conventional transitions using radial or circular pad terminations


title: Customized Regression Testing Using Telemetry Usage Patterns

abstract: Pervasive telemetry in modern applications is providing new possibilities in the application of regression testing techniques. Similar to how research in bioinformatics is leading to personalized medicine, tailored to individuals, usage telemetry in modern software allows for custom regression testing, tailored to the usage patterns of an installation. By customizing regression testing based on software usage, the effectiveness of regression testing techniques can be greatly improved, leading to reduced testing costs and enhanced detection of defects that are most important to that customer. In this research, we introduce the concept of fingerprinting software usage patterns through telemetry. Weprovide various algorithms tocompute fingerprints and conduct an empirical study that shows that fingerprints are effective in identifying distinct usage patterns. Further, we discuss how usage fingerprints can be used to improve regression test prioritization run time by over 30 percent compared to traditional prioritization techniques.


title: A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0

abstract: Abstract This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study.


title: Artificial immune systems for the detection of credit card fraud: an architecture, prototype and preliminary results

abstract: Some biological phenomena offer clues to solving real-life, complex problems. Researchers have been studying techniques such as neural networks and genetic algorithms for computational intelligence and their applications to such complex problems. The problem of security management is one of the major concerns in the development of eBusiness services and networks. Recent incidents have shown that the perpetrators of cybercrimes are using increasingly sophisticated methods. Hence, it is necessary to investigate non-traditional mechanisms, such as biological techniques, to manage the security of evolving eBusiness networks and services. Towards this end, this paper investigates the use of an Artificial Immune System (AIS). The AIS emulates the mechanism of human immune systems that save human bodies from complex natural biological attacks. The paper discusses the use of AIS on one aspect of security management, viz. the detection of credit card fraud. The solution is illustrated with a case study on the management of frauds in credit card transactions, although this technique may be used in a range of security management applications in eBusiness.


title: Low numeracy and dyscalculia : identification and intervention

abstract: One important factor in the failure to learn arithmetic in the normal way is an endogenous core deficit in the sense of number. This has been associated with low numeracy in general (e.g. Halberda et al. in Nature 455:665-668, 2008) and with dyscalculia more specifically (e.g. Landerl et al. in Cognition 93:99-125, 2004). Here, we describe straightforward ways of identifying this deficit, and offer some new ways of strengthening the sense of number using learning technologies.


title: A low-power and low-noise X-band receiver MMIC in 90nm CMOS

abstract: This paper presents the design and analysis of a high performance receiver front-end for various X-band applications. The proposed circuits employ transformer feedback and resonant-coupled networks (RCNs) for the low-noise amplifier (LNA). The mixer design uses a double-balanced topology to achieve low power and also low noise characteristics. The IF post-amplifier adopts the Cherry-Hooper based variable gain amplifier (VGA) with 3D inductors for wideband and high linearity considerations. Implemented in a 90-nm CMOS process, the whole receiver front-end dissipates only 22.8 mA from a 1.2-V supply voltage. The measured NF is below 3.8 dB with a maximum conversion gain of 65.1 dB within the X-band of 8–12 GHz. The average output P1dB over this frequency range is measured as 1.12 dBm at the maximum gain.


title: New insights into consumer-led food product development

abstract: This paper builds upon a review of relevant marketing, consumer science and innovation management literature to introduce the concept of consumer-led new product development and describe its main implementation stages. The potential shortcomings of this concept's application in European food industry are described. Contrary to previous optimistic views, it is put forward that without significant changes taking place in the mindset of the organizations involved in Europe's food R&D, the way forward for consumer-led innovation strategies in the agri-business sector will be long and hard.


Earlier Identification of Children with Autism Spectrum Disorder: An Automatic Vocalisation-Based Approach