title: An approximate analysis of the LRU and FIFO buffer replacement schemes

abstract: In this paper, we develop approximate analytical models for predicting the buffer hit probability under the Least Recently Used (LRU) and First In First Out (FIFO) buffer replacement policies under the independent reference model. In the case of the analysis of the LRU policy, the computational complexity for estimating the buffer hit probability is O ( KB ) where B is the size of the buffer and K denotes the number of items having distinct access probabilities. In the case of the FIFO policy, the solution algorithm is iterative and the computational complexity of each iteration is O ( K ). Results from these models are compared to exact results for models originally developed by King [KING71] for small values of the buffer size, B , and the total number of items sharing the buffer, D . Results are also compared with results from a simulation for large values of B and D . In most cases, the error is extremely small (less than 0.1%) for both LRU and FIFO, and a maximum error of 3% is observed for very small buffer size (less than 5) when the access probabilities are extremely skewed. To demonstrate the usefulness of the model, we consider two applications. In our first application, we compare the LRU and FIFO policies to an optimal static buffer allocation policy for a database consisting of two classes of data items. We observe that the performance of LRU is close to that of the optimal allocation. As the optimal allocation requires knowledge of the access probabilities, the LRU policy is preferred when this information is unavailable. We also observe that the LRU policy always performs better than the FIFO policy in our experiments. In our second application, we show that if multiple independent reference streams on mutually disjoint sets of data compete for the same buffer, it is better to partition the buffer using an optimal allocation policy than to share a common buffer.


title: TGANv2: Efficient Training of Large Models for Video Generation with Multiple Subsampling Layers

abstract: In this paper, we propose a novel method to efficiently train a Generative Adversarial Network (GAN) on high dimensional samples. The key idea is to introduce a differentiable subsampling layer which appropriately reduces the dimensionality of intermediate feature maps in the generator during training. In general, generators require large memory and computational costs in the latter stages of the network as the feature maps become larger, though the latter stages have relatively fewer parameters than the earlier stages. It makes training large models for video generation difficult due to the limited computational resource. We solve this problem by introducing a method that gradually reduces the dimensionality of feature maps in the generator with multiple subsampling layers. We also propose a network (Temporal GAN v2) with such layers and perform video generation experiments. As a consequence, our model trained on the UCF101 dataset at $192 \times 192$ pixels achieves an Inception Score (IS) of 24.34, which shows a significant improvement over the previous state-of-the-art score of 14.56.


title: Cross-layer secure cyber-physical control system design for networked 3D printers

abstract: Due to the high costs of 3D-printing infrastructure, outsourcing the production to third parties specializing in 3D-printing process becomes necessary. The integration of a 3D-printing system with networked communications constitutes a cyber-physical system, bringing new security challenges. Adversaries can explore the vulnerabilities of networks to damage the physical parts of the system. In this paper, we explore the vulnerabilities of 3D-printing systems, and design a cross-layer approach for the system. At the physical layer, we use a Markov jump system to model the system and develop a robust control policy to deal with uncertainties. At the cyber-layer, we apply FlipIt game to model the contention between the defender and attacker for the control of the 3D-printing system. To connect these two layers, we develop a Stackelberg framework to capture the interactions between cyber-layer attacker and defender game and the physical-layer controller and disturbance game, and define a new equilibrium concept that captures interdependence of the zero-sum and FlipIt games. We present numerical examples to better understand the equilibria and design defense strategies for 3D printers as a tradeoff between security and robustness.


title: Online Large-Margin Training Of Dependency Parsers

abstract: We present an effective training algorithm for linearly-scored dependency parsers that implements online large-margin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). The trained parsers achieve a competitive dependency accuracy for both English and Czech with no language specific enhancements.


Envisioning Internet of Things using Fog Computing


title: SNR metric and crosstalk in color image sensor of small size pixel

abstract: According to the high resolution trend in CMOS image sensor, pixel size is shrinking continuously towards and beyond 1.0 μm and reaching technical barrier to get the required performance. To achieve high SNR's in both low and high illumination, while high sensitivity and high full well capacity are essential but limited by pixel size of around 1.0 μm, reduction of crosstalk is more efficient way, which makes `effectively' sensitivity and full well capacity higher. In this paper, successful demonstration of ultralow crosstalk high SNR pixel will be presented.


Design of Movie Recommendation System by Means of Collaborative Filtering


title: Hierarchical Incident Ticket Classification with Minimal Supervision

abstract: In this paper, we introduce a novel approach for incident ticket classification that aims at minimizing the manual labelling effort while achieving good-quality predictions. To accomplish this, we devise a two-stage technique that employs hierarchical clustering using a combination of graph clustering (community finding) and topic modelling as first stage, followed by either another round of hierarchical clustering or an active learning approach as second stage. We evaluate the performance of our method in terms of manual labelling effort, prediction quality and efficiency on three real-world datasets and demonstrate that classical approaches to text classification are not well suited for incident ticket texts.


title: Inverters for single-phase grid connected photovoltaic systems-an overview

abstract: An overview on developments and a summary of the state-of-the-art of inverter technology in Europe for single-phase grid-connected photovoltaic (PV) systems for power levels up to 5 kW is provided in this paper. The information includes details not only on the topologies commercially available but also on the switching devices employed and the associated switching frequencies, efficiency, price trends and market share. Finally, the paper outlines issues associated with the development of relevant international industry standards affecting PV inverter technology.


title: The Paradoxes of Distributed Trust: Peer-to-Peer Architecture and User Confidence in Bitcoin

abstract: The decentralized electronic currency system Bitcoin gives the possibility to execute transactions via direct communication between users, without the need to resort to third parties entrusted with legitimizing the concerned monetary value. In its current state of development - a recent, fast-changing, volatile and highly mediatized technology - the discourses that unfold within spaces of information and discussion related to Bitcoin can be analysed in light of their ability to produce at once the representations of value, the practices according to which it is transformed and evolves, and the devices allowing for its implementation. The literature on the system is a testament to how the Bitcoin debates do not merely spread, communicate and diffuse representation of this currency, but are closely intertwined with the practice of the money itself. By focusing its attention on a specific corpus, that of expert discourse, the article shows how, introducing and discussing a specific device, dynamic or operation as being in some way related to trust, this expert knowledge contributes to the very definition and shaping of this trust within the Bitcoin system - ultimately contributing to perform the shared definition of its value as a currency.


title: IMAGE STITCHING USING MATLAB

abstract: Images are an integral part of our daily lives. Image stitching is the process performed to generate one panoramic image from a series of smaller, overlapping images. Stitched images are used in applications such as interactive panoramic viewing of images, architectural walk-through, multi-node movies and other applications associated with modeling the 3D environment using images acquired from the real world. Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it. Specifically, image stitching presents different stages to render two or more overlapping images into a seamless stitched image, from the detection of features to blending in a final image. In this process, Scale Invariant Feature Transform (SIFT) algorithm[1] can be applied to perform the detection and matching control points step, due to its good properties. The process of create an automatic and effective whole stitching process leads to analyze different methods of the stitching stages. Several commercial and online software tools are available to perform the stitching process, offering diverse options in different situations.


title: Efficient Clustering for Orders

abstract: Lists of ordered objects are widely used as representational forms. Such ordered objects include Web search results or best-seller lists. Clustering is a useful data analysis technique for grouping mutually similar objects. To cluster orders, hierarchical clustering methods have been used together with dissimilarities defined between pairs of orders. However, hierarchical clustering methods cannot be applied to large-scale data due to their computational cost in terms of the number of orders. To avoid this problem, we developed an k-o'means algorithm. This algorithm successfully extracted grouping structures in orders, and was computationally efficient with respect to the number of orders. However, it was not efficient in cases where there are too many possible objects yet. We therefore propose a new method (k-o'means-EBC), grounded on a theory of order statistics. We further propose several techniques to analyze acquired clusters of orders


title: Sexual harassment as a gendered expression of power

abstract: Drawing on recent insights from the study of legal consciousness and gender relations, the authors test the generality of Catharine MacKinnon's theory of the sexual harassment of adult women. Survey and interview data from the Youth Development Study and the General Social Survey are analyzed to identify a behavioral syndrome of sexual harassment for males and females during adolescence and young adulthood and to compare the syndrome against subjective reports of sexual harassment. A clear harassment syndrome is found for all age and sex groups and MacKinnon's predictions about the influence of workplace power and gender relations are generally supported. Financially vulnerable men as well as women are most likely to experience harassing behaviors, and men pursuing more egalitarian gender relationships are most likely to identify such behaviors as sexual harassment. Nevertheless, adult women remain the most frequent targets of classic sexual harassment markers, such as unwanted touching and invasion of pe...


title: Complex Urban LiDAR Data Set

abstract: This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides for maximal coverage. One 2D LiDAR faces backward while the other faces forwards to collect data of roads and buildings, respectively. Raw sensor data from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global Positioning System (GPS) are presented in a file format for vehicle pose estimation. The pose information of the vehicle estimated at 100 Hz is also presented after applying the graph simultaneous localization and mapping (SLAM) algorithm. For the convenience of development, the file player and data viewer in Robot Operating System (ROS) environment were also released via the web page. The full data sets are available at: http://irap.kaist.ac.kr/dataset. In this website, 3D preview of each data set is provided using WebGL.


Experiences with Publicly Open Human Activity Data Sets - Studying the Generalizability of the Recognition Models


title: The wadsworth BCI research and development program: at home with BCI

abstract: The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.


title: Expectations and First Experience with a Social Robot

abstract: This paper concerns interaction with social robots and focuses on the evaluation of a robot application that allows users to access interesting information from Wikipedia. The evaluation method compares the users' expectations with their experience with the robot, and takes into account their self-declared previous experience with robots. The results show that most participants had an overall positive experience, even though the averages indicate a slight negative tendency related to expectations of the robot's behavior and being understood by the robot. Interestingly, the most experienced users seem to be the most critical.


title: On Modelling in Memetics

abstract: The field of memetics is characterised and two types of memetic model analysed: the a priori model and the 'black-box' model. These are used to motivate a picture of scientific modelling, where by chains of models are built from abstract models at the 'top' down to data models derived from measurement. These models are linked to each other by validation (where the structure of a model comes from another) and verification (where the results of a model are checked against another). The role of formal models in clearly establishing these links is discussed. This picture is applied to the process of modelling in memetics. The paper argues that stronger selection are required to enable the field to evolve. In particular it argues that validation and verification constitute a minimum such set for memetic models.


title: Dietary fat content alters insulin-mediated glucose metabolism in healthy men

abstract: BACKGROUND: A high dietary fat intake is involved in the pathogenesis of insulin resistance. OBJECTIVE: The aim was to compare the effect of different amounts of dietary fat on hepatic and peripheral insulin sensitivity. DESIGN: Six healthy men were studied on 3 occasions after consuming for 11 d diets with identical energy and protein contents but different percentages of energy as fat and carbohydrate as follows: 0% and 85% [low-fat, high-carbohydrate (LFHC) diet], 41% and 44% [intermediate-fat, intermediate-carbohydrate (IFIC) diet], and 83% and 2% [high-fat, low-carbohydrate (HFLC) diet]. Insulin sensitivity was quantified by using a hyperinsulinemic euglycemic clamp (plasma insulin concentration: approximately 190 pmol/L). RESULTS: During hyperinsulinemia, endogenous glucose production was higher after the HFLC diet (2.5 +/- 0.3 micromol x kg(-1) x min(-1); P <0.05) than after the IFIC and LFHC diets (1.7 +/- 0.3 and 1.2 +/- 0.4 micromol x kg(-1) x min(-1), respectively). The ratio of dietary fat to carbohydrate had no unequivocal effects on insulin-stimulated glucose uptake. In contrast, insulin-stimulated, nonoxidative glucose disposal tended to increase in relation to an increase in the ratio of fat to carbohydrate, from 14.8 +/- 5.1 to 20.6 +/- 1.9 to 26.2 +/- 2.9 micromol x kg(-1) x min(-1) (P <0.074 between the 3 diets). Insulin-stimulated glucose oxidation was significantly lower after the HFLC diet than after the IFIC and LFHC diets: 1.7 +/- 0.8 compared with 13.4 +/- 2.1 and 19.0 +/- 2.1 micromol x kg(-1) x min(-1), respectively (P <0.05). During the clamp study, plasma fatty acid concentrations were higher after the HFLC diet than after the IFIC and LFHC diets: 0.22 +/- 0.02 compared with 0.07 +/- 0.01 and 0.05 +/- 0.01 mmol/L, respectively (P <0.05). CONCLUSION: A high-fat, low-carbohydrate intake reduces the ability of insulin to suppress endogenous glucose production and alters the relation between oxidative and nonoxidative glucose disposal in a way that favors storage of glucose


A Limit‐Point Criterion for Expressions with Intermittently Positive Coefficients


title: Towards a Framework for Health Information Systems Evaluation

abstract: The evaluation of Health Information Systems (HIS) is crucial to ensure their effective implementation and positive impact on healthcare delivery. A review of HIS evaluation studies was carried out which indicated that the improvement of current methods is required. In order to satisfy this requirement, a new framework is introduced for the evaluation of Information Systems (IS) in healthcare settings. The proposed HOT-fit framework (Human, Organization and Technology-fit) was developed after having critically appraised the existing findings of HIS evaluation studies. It also builds on previous models of IS evaluation, in particular, the IS Success Model and the IT-Organization Fit Model. HOT-fit incorporates the concept of fit between technology, human and organization. The paper outlined the proposed methodology of a Fundus Imaging Systems in a primary care in the UK. The framework can be potentially used as a tool to conduct better and comprehensive HIS evaluation.


title: Constructing Bayesian networks for criminal profiling from limited data

abstract: The increased availability of information technologies has enabled law enforcement agencies to compile databases with detailed information about major felonies. Machine learning techniques can utilize these databases to produce decision-aid tools to support police investigations. This paper presents a methodology for obtaining a Bayesian network (BN) model of offender behavior from a database of cleared homicides. The BN can infer the characteristics of an unknown offender from the crime scene evidence, and help narrow the list of suspects in an unsolved homicide. Our research shows that 80% of offender characteristics are predicted correctly on average in new single-victim homicides, and when confidence levels are taken into account this accuracy increases to 95.6%.


Ethical justification of single-blind and double-blind placebo-controlled response tests in neuropathic pain and N-of-1 treatment paradigm in clinical settings


title: The edge-window-decoder representation for tree-based problems

abstract: We describe the "edge-window-decoder" strategy (EWD), a decoder-based redundant encoding strategy for tree-based combinatorial problems, and explore its performance on three well-known tree-based network optimization problems: the degree constrained minimum spanning tree problem (DCMST), the optimum communication spanning tree problem (OCST), and the quadratic minimum spanning tree problem (q-MST). Each is an NP-hard problem, and in each case the best previously published approach (for obtaining fast solutions on large instances) is an evolutionary algorithm (EA), characterized by a specialized encoding. EWD simply encodes a tree as a list of nodes (in which nodes may be repeated), and a tree is built from this list via a "tree construction routine" (TCR). A particular instantiation of EWD involves choosing a specific TCR, and choosing specific designs for the mutation and crossover operators (which work only at the genotype (list of nodes) level). Different combinations of TCRs and operators are described and explored and found to be suited to different classes of problem. We compare these several instantiations of EWD with other encodings (including the previous best reported for each problem) over several benchmark instances, as well as randomly generated instances. We find that instantiations of EWD generally perform favorably, clearly outperforming the best comparative approach in two of the three problem classes, and close to best in the third. Some analyses of EWD in terms of its locality, heritability, and bias are provided. These indicate that EWD shows high locality and heritability, and an intermediate level of bias toward the MST of the problem under consideration. In particular, the basic EWD encoding strategy is unbiased, but its good performance on a range of tree-based problems appears to be explainable in terms of the bias inherent in the associated operators. Further, experiments show that EWD strategies using one particular biased operator are able to apply a consistent intermediate level of bias "pressure" over time, thus allowing the search to range more freely in the early stages over the less represented areas of the landscape, rather than (as is the case with the other biased encoding/operator combinations compared against) quickly becoming committed to MST-like regions.


An Event Service Framework for Distributed Real-Time Systems


title: Blur kernel estimation using the radon transform

abstract: Camera shake is a common source of degradation in photographs. Restoring blurred pictures is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely underconstrained. In this work, we estimate camera shake by analyzing edges in the image, effectively constructing the Radon transform of the kernel. Building upon this result, we describe two algorithms for estimating spatially invariant blur kernels. In the first method, we directly invert the transform, which is computationally efficient since it is not necessary to also estimate the latent sharp image. This approach is well suited for scenes with a diversity of edges, such as man-made environments. In the second method, we incorporate the Radon transform within the MAP estimation framework to jointly estimate the kernel and the image. While more expensive, this algorithm performs well on a broader variety of scenes, even when fewer edges can be observed. Our experiments show that our algorithms achieve comparable results to the state of the art in general and produce superior outputs on man-made scenes and photos degraded by a small kernel.


title: Comparative analysis of some prominent MCDM methods: A case of ranking Serbian banks

abstract: In the literature, many multiple criteria decision making methods have been proposed. There are also a number of papers, which are devoted to comparison of their characteristics and performances. However, a definitive answer to questions: which method is most suitable and which method is most effective is still actual. Therefore, in this paper, the use of some prominent multiple criteria decision making methods is considered on the example of ranking Serbian banks. The objective of this paper is not to determine which method is most appropriate for ranking banks. The objective of this paper is to emphasize that the use of various multiple criteria decision making methods sometimes can produce different ranking orders of alternatives, highlighted some reasons which lead to different results, and indicate that different results obtained by different MCDM methods are not just a random event, but rather reality.


title: Multi-finger and whole hand gestural interaction techniques for multi-user tabletop displays

abstract: Recent advances in sensing technology have enabled a new generation of tabletop displays that can sense multiple points of input from several users simultaneously. However, apart from a few demonstration techniques [17], current user interfaces do not take advantage of this increased input bandwidth. We present a variety of multifinger and whole hand gestural interaction techniques for these displays that leverage and extend the types of actions that people perform when interacting on real physical tabletops. Apart from gestural input techniques, we also explore interaction and visualization techniques for supporting shared spaces, awareness, and privacy. These techniques are demonstrated within a prototype room furniture layout application, called RoomPlanner.


title: Feature Pyramid Networks for Object Detection

abstract: Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available.


title: Gene Ontology: tool for the unification of biology

abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.


title: On the meaning and use of kurtosis.

abstract: For symmetric unimodal distributions, positive kurtosis indicates heavy tails and peakedness relative to the normal distribution, whereas negative kurtosis indicates light tails and flatness. Many textbooks, however, describe or illustrate kurtosis incompletely or incorrectly. In this article, kurtosis is illustrated with well-known distributions, and aspects of its interpretation and misinterpretation are discussed. The role of kurtosis in testing univariate and multivariate normality; as a measure of departures from normality; in issues of robustness, outliers, and bimodality; in generalized tests and estimators, as well as limitations of and alternatives to the kurtosis measure [32, are discussed.


title: A Two-Step Similarity Ranking Scheme for Image Retrieval

abstract: similarity ranking is one of the keys of a content-based image retrieval (CBIR) system. Among various methods, manifold ranking (MR) is popular for its application to relevance feedback in CBIR. Most existing MR methods only take the visual features into account in the similarity ranking, however, which is not accurate enough to reflect the intrinsic semantic structure of a given image database. In this paper, we propose a two-step similarity ranking scheme that aims to preserve both visual and semantic resemblance in the similarity ranking. Concretely, in the first step it derives an initial visual-based similarity rank through a self-tuning MR solution. In particular, the Gaussian kernel used in our scheme is refined by using a point-wise bandwidth. In the second step, the rank of each database image is further adjusted to achieve semantic consistency by mining the query log. An empirical study shows that using two-step similarity ranking in CBIR is beneficial, and the proposed scheme is more effective than some existing MR approaches.


title: TinyECC: A Configurable Library for Elliptic Curve Cryptography in Wireless Sensor Networks

abstract: Public key cryptography (PKC) has been the enabling technology underlying many security services and protocols in traditional networks such as the Internet. In the context of wireless sensor networks, elliptic curve cryptography (ECC), one of the most efficient types of PKC, is being investigated to provide PKC support in sensor network applications so that the existing PKC-based solutions can be exploited. This paper presents the design, implementation, and evaluation of TinyECC, a configurable library for ECC operations in wireless sensor networks. The primary objective of TinyECC is to provide a ready-to-use, publicly available software package for ECC-based PKC operations that can be flexibly configured and integrated into sensor network applications. TinyECC provides a number of optimization switches, which can turn specific optimizations on or off based on developers' needs. Different combinations of the optimizations have different execution time and resource consumptions, giving developers great flexibility in integrating TinyECC into sensor network applications. This paper also reports the experimental evaluation of TinyECC on several common sensor platforms, including MICAz, Tmote Sky, and Imotel. The evaluation results show the impacts of individual optimizations on the execution time and resource consumptions, and give the most computationally efficient and the most storage efficient configuration of TinyECC.


title: Designing a Better Data Representation for Deep Neural Networks and Text Classification

abstract: Traditional machine learning requires data to be described by attributes prior to applying a learning algorithm. In text classification tasks, many feature engineering methodologies have been proposed to extract meaningful features, however, no best practice approach has emerged. Traditional methods of feature engineering have inherent limitations due to loss of information and the limits of human design. An alternative is to use deep learning to automatically learn features from raw text data. One promising deep learning approach is to use convolutional neural networks. These networks can learn abstract text concepts from character representations and be trained to perform discriminate tasks, such as classification. In this paper, we propose a new approach to encoding text for use with convolutional neural networks that greatly reduces memory requirements and training time for learning from character-level text representations. Additionally, this approach scales well with alphabet size allowing us to preserve more information from the original text, potentially enhancing classification performance. By training tweet sentiment classifiers, we demonstrate that our approach uses less computational resources, allows faster training for networks and achieves similar, or better performance compared to the previous method of character encoding.


title: An Introductory Study on Time Series Modeling and Forecasting

abstract: Modeling and forecasting of time series data has fundamental importance in various practical domains. The aim of this book is to present a concise description of some popular time series forecasting models with their salient features. Three important classes of time series models, viz. stochastic, neural networks and support vector machines are studied together with their inherent forecasting strengths and weaknesses. The book also meticulously discusses about several basic issues related to time series analysis, such as stationarity, parsimony, overfitting, etc. Our study is enriched by presenting the empirical forecasting results, conducted on six real-world time series datasets. Five performance measures are used to evaluate the forecasting accuracies of different models as well as to compare the models. For each of the six time series datasets, we further show the obtained forecast diagram which graphically depicts the closeness between the original and predicted observations.


title: Cell-based wideband variable-gain amplifier with accurate gain adjustment in 65 nm CMOS technology

abstract: Design of an inductor-less wideband variable-gain amplifier (VGA) using a cell-based strategy is presented. The VGA has a measured gain range of 24 dB, of which 17.3 dB is dB-linear with less than ±0.3 dB gain error. The −3 dB bandwidth is from 2 GHz to 2.2 GHz for the entire dB-linear range. An output 1 dB compression point of 1.8 dBm and a noise figure of 24 dB are measured. Due to the simple structure, the current consumption of the VGA core is only 2.9 mA from a 1.2 V supply, and the size is only 225 μm × 45 μm, excluding pads.


title: What is Intuitive Interaction? Balancing Users’ Performance and Satisfaction with Natural User Interfaces

abstract: Designers of natural user interfaces are faced with several challenges when creating interaction models for controlling applications, including the wide range of possible input actions and the lack of affordances, which they can use to design controls. In order to contribute to the development of design guidelines in this design space, we conducted an exploratory, mixed methods study. We investigated three top-down approaches to designing intuitive interaction mappings for a whole body system implemented with camera vision. These were metaphoric, isomorphic and ‘everyday’or conventional. In order to identify some of the benefits and limitations of each approach, we compared the designs based on measures of usability, intuitiveness and engagement with the material represented in the system. From our study, we found that while the metaphoric design enhanced users’ performance at completing tasks, the lack of discoverability of the interaction model left them feeling incompetent and dissatisfied. We found that the isomorphic design enabled users to focus on tasks rather than learning how to use the system. Conversely, designs based on previous conventions had to be learned, had a time cost for the learning and negatively impacted users’ engagement with content. For tasks and controls that can be designed based on an image schematic input action, users performed most accurately with the metaphoric design. There are benefits and limitations to each approach to designing to support intuitive interaction. We conclude with preliminary design considerations, suggest ways to balance performance with high user satisfaction depending on contextual design goals and question a single definition of intuitive intuition within whole body interface design.


title: The Biceps Femoris Muscle Complex at the Knee Its Anatomy and Injury Patterns Associated with Acute Anterolateral-Anteromedial Rotatory Instability

abstract: We dissected 30 cadaveric knees to provide a detailed anatomic description of the biceps femoris muscle complex at the knee. The main components of the long head of the muscle are a reflected arm, a direct arm, an anterior arm, and a lateral and an anterior aponeurosis. The main components of the short head of the biceps femoris muscle are a proximal attachment to the long head's tendon, a capsular arm, a confluens of the biceps and the capsuloosseous layer of the iliotibial tract, a direct arm, an anterior arm, and a lateral apo neurosis. We examined 82 consecutive, acutely in jured knees with clinical signs of anterolateral-antero medial rotatory instability for the incidence and anatomic location of injuries to the biceps femoris mus cle. Injuries to components of that muscle were iden tified in 59 (72%) of these knees; 29 knees (35.4%) had multiple components injured. There were 3 injuries to the long head of the biceps femoris muscle (all in the reflected arm) and 89 to the short head. A statistically significant correlation (P = 0.01) was found between increased anterior translation with the knee at 25° of flexion as demonstrated by the Lachman test and in jury to the biceps-capsuloosseous iliotibial tract conflu ens. Additionally, adduction laxity at 30° of flexion cor related with a Segond fracture (P = 0.04). These data establish, in part, the relationship of the biceps femoris complex injury to anterior translation instability.


title: Accelerator Design with Effective Resource Utilization for Binary Convolutional Neural Networks on an FPGA

abstract: In binary convolutional neural networks (BCNN), arithmetic operations are replaced by bitwise operations and the required memory size is greatly reduced, which is a good opportunity to accelerate training or inference on FPGAs. This paper proposes a BCNN architecture with a single engine that achieves high resource utilization. The proposed design deploys a large number of processing elements in parallel to increase throughput, and a forwarding scheme to increase resource utilization on the existing engine. In addition, we demonstrate a novel reuse scheme to make fully-connected layers exploit the same engine. The proposed design is combined with an inference environment for comparison and implemented on a Xilinx XCVU190 FPGA. The implemented design uses 61k look-up tables (LUTs), 45k flip-flops (FFs), and 13.9Mbit block RAM (BRAM). In addition, it achieves 61.6 GOPS/kLUT at 240MHz, which is 1.16 times higher than that of the best prior BCNN design, even though it uses a single engine without optimal configurations on each layer.


title: Retrieval-Enhanced Adversarial Training for Neural Response Generation

abstract: Dialogue systems are usually built on either generation-based or retrieval-based approaches, yet they do not benefit from the advantages of different models. In this paper, we propose a Retrieval-Enhanced Adversarial Training (REAT) method for neural response generation. Distinct from existing ap- proaches, the REAT method leverages an encoder-decoder framework in terms of an adversarial training paradigm, while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. An empirical study on a large scale public available benchmark dataset shows that the REAT method significantly outper- forms the vanilla Seq2Seq model as well as the conventional adversarial training approach.


title: An Approach to Detecting Distributed Denial of Service Attacks in Software Defined Networks

abstract: Distributed Denial of Service (DDoS) flooding attack continue to be one of the major security concerns as attack volumes are increasing year on year. Many works have shown Slowloris, ACK and SYN flooding attacks to be notorious amongst several other forms of DDoS attacks. This is mainly due to similarity in attack and legitimate traffic, weaknesses in protocols been exploited and anomaly detection mechanism deployed. Conventional approach to detection demands a paradigm shift and the emerging Software Defined Networks (SDN) introduces new opportunities to detect and mitigate attacks based on its central management system. In this work, we assess the severity of TCP-ACK, SYN and Slowloris attack on the server at the data plane layer. We evaluate the detection of DDoS attacks by simulating with Mininet. Our detection mechanism relies on deviation from the confidence interval obtained from normal distribution of throughput polled without attack from the server. Our emulation result shows that using a window size of one minute, flooding attacks can be detected with an accuracy of 99%.


title: Svdfeature: A toolkit for featurebased collaborative filtering

abstract: In this paper we introduce SVDFeature, a machine learning toolkit for feature-based collaborative filtering. SVDFeature is designed to efficiently solve the feature-based matrix factorization. The feature-based setting allows us to build factorization models incorporating side information such as temporal dynamics, neighborhood relationship, and hierarchical information. The toolkit is capable of both rate prediction and collaborative ranking, and is carefully designed for efficient training on large-scale data set. Using this toolkit, we built solutions to win KDD Cup for two consecutive years.


title: Analysing The Governance, Risk And Compliance (Grc) Implementation Process: Primary Insights

abstract: Governance, Risk and Compliance (GRC) as an integrated concept has gained great interest recently ::: among researchers in the Information Systems (IS) field. The need for more effective and efficient ::: business processes in the area of financial controls drives enterprises to successfully implement GRC ::: systems as an overall goal when they are striving for enterprise value of their integrated systems. The ::: GRC implementation process is a significant parameter influencing the success of operational ::: performance and financial governance and supports the practices for competitive advantage within the ::: organisations. However, GRC literature is limited regarding the analysis of their implementation and ::: adoption success. Therefore, there is a need for further research and contribution in the area of GRC ::: systems and more specifically their implementation process. The research at hand recognizes GRC as a ::: fundamental business requirement and focuses on the need to analyse the implementation process of ::: such enterprise solutions. The research includes theoretical and empirical investigation of the GRC ::: implementation within an enterprise and develops a framework for the analysis of the GRC adoption. ::: The approach suggests that the three success factors (integration, optimisation, information) influence ::: the adoption of the GRC and more specifically their implementation process. The proposed framework ::: followed a case study approach to confirm its functionality and is evaluated through interviews with ::: stakeholders involved in GRC implementations. Furthermore, it can be used by the organisations when ::: considering the adoption of GRC solutions and can also suggest a tool for researchers to analyse and ::: explain further the GRC implementation process.


title: Performance Evaluation for Interrupt-Driven Embedded Software Based on EDSPN

abstract: In the design of trustworthy software for real-time embedded systems, the interrupt mechanism plays an important role. Due to the randomness and non-determinism of interrupt handling behaviors, the performance evaluation of embedded software is an important but challenging problem. To solve this problem, we propose a performance model for embedded software based on extended deterministic and stochastic Petri nets (EDSPN), which is a semantic expansion of DSPN that add non-deterministic time transitions. In this manner, the performance model can effectively express interrupt handling behaviors, including random requests, responses according to priority, interrupt nesting, and non-deterministic execution times. In addition, we also provide a performance evaluation method for the EDSPN model based on Markov regenerative theory and demonstrate the analysis process via a specific example. Finally, we analyze the performance of embedded software, taking into account all types of the impact factors of interrupts through experimentation.


grAndiose nArcissism versus vulnerAble nArcissism in threAtening situAtions : emotionAl reActions to Achievement FAilure And interPersonAl rejection


Strengthening Transversus Abdominis in Pregnancy Related Pelvic Pain: The Pressure Biofeedback Stabilization Training


"Cognitive, social, and physiological determinants of emotional state": Erratum


title: Seismic studies in the North Celtic Sea Basin: implications for basin development

abstract: Modelling of seismic refraction data from the North Celtic Sea Basin offshore Ireland demonstrates the presence of a NNW-trending basement flexure. Mesozoic sediments are 4 km thick on the west and 6 km thick on the east of the feature, which is Interpreted as a transfer fault zone lying along a major reactivated basement fracture. Bulk extension is greater in the lower lithosphere than in the upper crust, with the interface being a rheologically regulated mid-crustal detachment surface. The basin developed in response to the interplay between periodic disengagement along the detachment and selective strain transfer into the upper crust.


title: A Belief Rule Based Flood Risk Assessment Expert System using Real Time Sensor Data Streaming

abstract: Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. The integrated BRBES produces reliable results in comparison to other data-driven approaches. Data for the expert system has been collected by considering different case study areas of Bangladesh to validate the system.


title: Review Communicating science to the public: MMR vaccine and autism

abstract: Media attention and consequent public concerns about vaccine safety followed publication of a small case-series of children who developed autism after receipt of the measles-mumps-rubella (MMR) vaccine. Many well-controlled studies performed subsequently found no evidence that MMR vaccine causes autism. However, despite these studies, some parents remain concerned that the MMR vaccine is not safe. We will discuss the origins of the hypothesis that the MMR vaccine causes autism, studies performed to test the hypothesis, how these studies have been communicated to the public, and some suggested strategies for how this communication can be improved.


title: Optimal discriminant plane for a small number of samples and design method of classifier on the plane

abstract: Abstract In a previous work (Zi-Quan Hong and Jing-Yu Yang, Minimum distance classifier on the optimal discriminant plane), we suggested and derived a method for constructing classifier on the optimal discriminant plane using minimum distance criteria. In this paper, the problem of solving optimal discriminant plane for a small number of samples is discussed, which is based on the above paper by the same authors. In the case of a small number of samples, generalized eigenequation AX = λBX established for a large number of samples usually has no solution because within-class scatter matrix is singular. To obtain the solution of the generalized eigenequation, a new method is suggested, in which the Singular Value Perturbation is added to the within-class scatter matrix such that the matrix becomes a nonsingular matrix. Therefore, the generalized eigenequation can be solved using existing algorithms. We proved that the generalized eigenequations on the optimal discriminant plane are stable in respect of eigenvalues and the generalized eigenvectors are indeed the optimal discriminant directions, if the perturbation is subject to some certain conditions. The experimental results have shown that our method works well and the minimum distance classifier on the optimal discriminant plane can be constructed with high performance even in the case of a small number of samples.


title: A novel watermarking scheme against geometrical distortions based on Contourlet transform

abstract: This paper proposes a design for data hiding in digital image that robust with both geometric and common signal attacks. We combine scale-invariant feature transform with Neighborhood Pixel Ratio domain to embed resynchronized information so watermarked image can be reconstructed under geometrical attacks. The Contourlet-based algorithm is used to hide message into Contourlet transform domain. It can get a high robustness level to against other common attacks. The experiment results show that our proposed scheme is strong robustness when sensible combining above algorithms.


title: Tunable SIW bandpass filters with PIN diodes

abstract: This paper introduces a novel tunable SIW filter implemented using PIN diode switching elements. The two-pole filter provides six states ranging from 1.55 GHz to 2.0 GHz (25% tuning). Fractional bandwidth ranges from 2.3% – 3.0% with insertion loss less than 5.4 dB and return loss greater than 14 dB over the entire tuning range. Each SIW cavity is tuned by perturbing via posts connecting or disconnecting to/from the cavity's top metal layer. In order to separate the biasing network from the SIW filter, a three-layer PCB is fabricated using Rogers RT/duroid substrates.


title: Automating role-based provisioning by learning from examples

abstract: Role-based provisioning has been adopted as a standard component in leading Identity Management products due to its low administration cost. However, the cost of adjusting existing roles to entitlements from newly deployed applications is usually very high. In this paper, a learning-based approach to automate the provisioning process is proposed and its effectiveness is verified by real provisioning data. Specific learning issues related to provisioning are identified and relevant solutions are presented.


Generalized keratosis pilaris rubra


title: Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges

abstract: Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Captured on a continual basis, aggregated, and effectively mined, such information can bring about a positive transformative change in the health care landscape. In particular, the availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing algorithms can: (a) facilitate an evolution in the practice of medicine, from the current post facto diagnose-and-treat reactive paradigm, to a proactive framework for prognosis of diseases at an incipient stage, coupled with prevention, cure, and overall management of health instead of disease, (b) enable personalization of treatment and management options targeted particularly to the specific circumstances and needs of the individual, and (c) help reduce the cost of health care while simultaneously improving outcomes. In this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care.


title: Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

abstract: We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.


title: Towards Customer-Centric Software Development: A Multiple-Case Study

abstract: Customer involvement in software development is essential for building successful software products. Incremental improvements and enhancements of software require an in-depth and continuous understanding of customer needs. Also, mechanisms for managing customer feedback data need to be in place. However, previous research shows that the feedback loops from customers are slow and the process for obtaining timely feedback is challenging. In this study, we investigate customer feedback mechanisms and the ways in which customer data can be used to inform continuous improvement of software products. The contribution of this paper is twofold. First, we present a multiple-case study conducted in five Finnish software companies, where we identify how customer feedback data is collected and used in different product development activities. Second, we provide an explanatory 'customer touch point' (CTP) model which provides an overall understanding of customer feedback data collection and the related challenges in the case companies during software development.


title: Enterprise Architecture Characteristics in Context Enterprise Governance Base On COBIT 5 Framework

abstract: The existence of the enterprise architecture is an attempt of managing and planning over the evolution of information systems in the sphere of an enterprise with model-based. In developing the enterprise architecture, there are several tools definition of components in the system. This tool is known as enterprises architecture (EA) framework. In this paper, we present a method to build a model of enterprise architecture in accordance with the needs of the Organization by Understanding the characteristics of the EA framework such as Zachman, TOGAF, and FEAF. They are selected as the EA framework will be used to determine the characteristics of an EA because the framework is most widely used in corporate or Government. In COBIT 5 framework, there is a process associated with enterprise architecture it is APO03 Manage Enterprise Architecture. At this stage of the research, we describe the link between the characteristics of the EA with one process in COBIT 5 framework. The results contribute to give a recommendation how to design EA for organization based on the characteristic of EA in Context Enterprise Governance using COBIT 5 Framework.


title: DNA sequence determination and biochemical analysis of the immunogenic protein P 36 , the lactate dehydrogenase ( LDH ) of Mycoplasma hyopneumoniae

abstract: Summary: The DNA sequence of the gene encoding the early and specific immunogenic protein P36 of Mycoplasma hyopneumoniae has been determined. Comparison of the DNA sequence and the deduced amino acid sequence of P36 with known genes and proteins in data banks indicated that P36 is a L-lactate dehydrogenase (LDH) (EC 1.1.1.27). Biochemical analysis of protein P36 expressed from the cloned gene in Escherichia coli confirmed that P36 has L-lactate dehydrogenase activity. Protein P36 of M. hyopneumoniae therefore is termed LDH and its gene Idh. M. hyopneumoniae LDH was shown to contain the typical domains of LDH of other bacterial species. Immunologically however, we have shown that polyclonal antibodies against M. hyopneumoniae LDH do not cross-react with related LDH and show high specificity for M. hyopneumoniae. The Idh gene is preceded by several typical −10 sequences found in promoters of prokaryotes, but lacks the −35 sequence. Sequences rich in A + T, however, precede the −10 boxes, suggesting that factors involved in transcription initiation and their regulation may be different in M. hyopneumoniae compared to other bacterial species, but the putative ribosome binding site seems to be conserved.


title: Polarization-angle insensitive metamaterial absorber for wide incident angles

abstract: In this paper, a polarization angle insensitive metamaterial absorber which works for wide incident angles has been proposed for C-band applications. A unit cell of the proposed absorber has an etched Jerusalem Cross (JC) inside a circle at the center of the square patch and four semi circles are rotated around the center circle by 90° each. Absorptivity of the proposed structure at 5.1 GHz is close to 95 % for all the polarization angles under normal incidence. This polarization insensitivity nature is due to the vertically and horizontally symmetric structure of the unit cell. The four semi circles used in the resonator provides incidence angle insensitivity. The angular insensitivity is studied at different radius of four semi circles. When the radius of the semi circles is 3.2 mm, the simulated absorptivity is higher than 90% and the frequency variation is less than 0.95% for incident angles up to 60°.


title: Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models

abstract: Abstract Volatility plays crucial roles in financial markets, such as in derivative pricing, portfolio risk management, and hedging strategies. Therefore, accurate prediction of volatility is critical. We propose a new hybrid long short-term memory (LSTM) model to forecast stock price volatility that combines the LSTM model with various generalized autoregressive conditional heteroscedasticity (GARCH)-type models. We use KOSPI 200 index data to discover proposed hybrid models that combine an LSTM with one to three GARCH-type models. In addition, we compare their performance with existing methodologies by analyzing single models, such as the GARCH, exponential GARCH, exponentially weighted moving average, a deep feedforward neural network (DFN), and the LSTM, as well as the hybrid DFN models combining a DFN with one GARCH-type model. Their performance is compared with that of the proposed hybrid LSTM models. We discover that GEW-LSTM, a proposed hybrid model combining the LSTM model with three GARCH-type models, has the lowest prediction errors in terms of mean absolute error (MAE), mean squared error (MSE), heteroscedasticity adjusted MAE (HMAE), and heteroscedasticity adjusted MSE (HMSE). The MAE of GEW-LSTM is 0.0107, which is 37.2% less than that of the E-DFN (0.017), the model combining EGARCH and DFN and the best model among those existing. In addition, the GEW-LSTM has 57.3%, 24.7%, and 48% smaller MSE, HMAE, and HMSE, respectively. The first contribution of this study is its hybrid LSTM model that combines excellent sequential pattern learning with improved prediction performance in stock market volatility. Second, our proposed model markedly enhances prediction performance of the existing literature by combining a neural network model with multiple econometric models rather than only a single econometric model. Finally, the proposed methodology can be extended to various fields as an integrated model combining time-series and neural network models as well as forecasting stock market volatility.


title: Cyber physical system: Paper survey

abstract: Embedded systems form the basis of all devices that are currently growing. With increasingly sophisticated technology advancements the dimensions of the devices are getting smaller, and the computing capabilities are now almost on par with the PC in the past two to three years. Based on those abilities the device can be paired with other components such as wireless communication technology, sensors and other supporting components to determine the state of the environment, or actuators as well to give response. Because all of these things take place in the digital world, there arose a discourse what if the digital world were to be combined with the physical world. A system that combines the capabilities of computing, communications and data storage, in order to monitor or control the entities that exist in the physical are world called Cyber Physical Systems. This paper will discuss the things that happened in the early progression of CPS topics (2006 – 2013), as the process of declaring the term CPS, reviews by the research group guided by Edward A Lee in the CPS field, CPS development in terms of scientific publications in the International Conference on Cyber-Physical Systems (ICCPS), and further research opportunities based on the direction of the development of research topics in the CPS field.


title: Two classes of mutant mammary tumor virus-infected HTC cell with defects in glucocorticoid-regulated gene expression.

abstract: Abstract ::: We have isolated mutant derivatives of M1.54 (a mammary tumor virus [MTV]-infected rat hepatoma [HTC] cell line containing multiple integrated proviruses) that fail to express hormone-inducible cell surface viral glycoproteins. In wild-type M1.54, the synthetic glucocorticoid dexamethasone selectively stimulates the rate of synthesis of MTV RNA. In addition, dexamethasone is essential for posttranslational maturation of three of the four cell surface viral glycoproteins processed from the MTV glycosylated precursor polyprotein; the fourth mature species is produced constitutively. Two mutant phenotypes are described; each contains glucocorticoid receptors that are indistinguishable from the wild-type receptor with respect to hormone affinity, intracellular concentration, nuclear translocation efficiency, DNA-cellulose chromatography, and sedimentation rate. In one class, represented by the mutant line CR1, dexamethasone fails to stimulate the low basal rate of MTV gene transcription; surprisingly, hormonal regulation of tyrosine aminotransferase activity is also defective in CR1, whereas several other cellular responses to dexamethasone are normal. In the second class of mutants, represented by CR4, dexamethasone stimulates synthesis of MTV transcripts indistinguishable from those produced in M1.54, but only the constitutive cell surface viral glycoprotein is expressed. Thus, these mutants define two distinct and novel aspects of glucocorticoid regulated gene expression in HTC cells: CR4 contains a defect in a hormone inducible protein maturation pathway that acts on specific viral (and presumably cellular) precursor polypeptides, whereas the lesion in CR1 appears to affect the expression of a subset of the gene products normally under glucocorticoid control in M1.54.


title: A Decision Tree Algorithm for Distributed Data Mining: Towards Network Intrusion Detection

abstract: This paper presents preliminary works on an agent-based approach for distributed learning of decision trees. The distributed decision tree approach is applied to intrusion detection domain, the interest of which is recently increasing. In the approach, a network profile is built by applying a distributed data analysis method for the collection of data from distributed hosts. The method integrates inductive generalization and agent-based computing, so that classification rules are learned via tree induction from distributed data to be used as intrusion profiles. Agents, in a collaborative fashion, generate partial trees and communicate the temporary results among them in the form of indices to the data records. Experimental results are presented for military network domain data used for the network intrusion detection in KDD cup 1999. Several experimental results show that the performance of distributed version of decision tree is much better than that of non-distributed version with data collected manually from distributed hosts.


title: Mining Educational Data Using Classification to Decrease Dropout Rate of Students

abstract: In the last two decades, number of Higher Education Institutions (HEI) grows rapidly in India. Since most of the institutions are opened in private mode therefore, a cut throat competition rises among these institutions while attracting the student to got admission. This is the reason for institutions to focus on the strength of students not on the quality of education. This paper presents a data mining application to generate predictive models for engineering student's dropout management. Given new records of incoming students, the predictive model can produce short accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.


title: An UHF RFID Energy-Harvesting System Enhanced by a DC-DC Charge Pump in Silicon-on-Insulator Technology

abstract: An RF-DC converter enhanced by a DC-DC voltage booster in silicon-on-insulator technology for UHF radio frequency identification (RFID) energy harvesting is presented in this letter. When the received RF power level is -14 dBm or higher, the system, fabricated on an FR4 substrate using off-the-shelf low-cost discrete components and connected to a flexible dipole antenna, is able to produce 2.4 V DC voltage to power general-purpose electronic devices. As a simple proof of concept, a device comprising microcontroller, temperature sensor, and EEPROM is considered in this work. The experimental results demonstrate the capability of the system to autonomously perform temperature data logging up to a distance of 5 m from a conventional UHF RFID reader used as an RF energy source.


title: BioGRID: a general repository for interaction datasets

abstract: Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at http://www.thebiogrid.org. BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.


title: An Improved Rotor Position Estimation With Vector-Tracking Observer in PMSM Drives With Low-Resolution Hall-Effect Sensors

abstract: This paper presents an improved approach for estimating high-resolution rotor position in permanent-magnet synchronous motor (PMSM) drives with low-resolution Hall-effect sensors. A vector-tracking position observer in conjunction with discrete Hall sensors' output signals has been proposed, which is similar to a phase-locked loop structure. It consists of a position error detector, based on the vector cross product of the unit back-electromotive-force vectors obtained from a stator electrical model, and a proportional-integral-typed controller to make the position error rapidly converge to zero. This structure does not only compensate the misalignment effect of Hall sensors but also enhance their transient operating capability. The effectiveness of the proposed approach has been verified from several experiments in a low-voltage PMSM drive for automotive applications.


title: Efficient clustering of high-dimensional data sets with application to reference matching

abstract: important problems involve clustering large datasets. Although naive implementations of clustering are computa- tionally expensive, there are established ecient techniques for clustering when the dataset has either (1) a limited num- ber of clusters, (2) a low feature dimensionality, or (3) a small number of data points. However, there has been much less work on methods of eciently clustering datasets that are large in all three ways at once|for example, having millions of data points that exist in many thousands of di- mensions representing many thousands of clusters. We present a new technique for clustering these large, high- dimensional datasets. The key idea involves using a cheap, approximate distance measure to eciently divide the data into overlapping subsets we call canopies .T hen cluster- ing is performed by measuring exact distances only between points that occur in a common canopy. Using canopies, large clustering problems that were formerly impossible become practical. Under reasonable assumptions about the cheap distance metric, this reduction in computational cost comes without any loss in clustering accuracy. Canopies can be applied to many domains and used with a variety of cluster- ing approaches, including Greedy Agglomerative Clustering, K-means and Expectation-Maximization. We present ex- perimental results on grouping bibliographic citations from the reference sections of research papers. Here the canopy approach reduces computation time over a traditional clus- tering approach by more than an order of magnitude and decreases error in comparison to a previously used algorithm by 25%.


Neuroeconomics: how neuroscience can inform economics


title: FPGA hardware of the LSB steganography method

abstract: Steganography is one of the most powerful techniques to conceal the existence of hidden secret data inside a cover object. Images are the most popular cover objects for steganography, and thus the importance of image steganography. Embedding secret information inside images requires intensive computations, and therefore, designing steganography in hardware speeds up steganography. This work presents a hardware design of Least Significant Bit (LSB) steganography technique in a cyclone II FPGA of the Altera family. The design utilizes the Nios embedded processor as well as specialized logic to perform the steganography steps. The design balances the tradeoffs such as imperceptibility, quality and capacity.


title: Creating creativity: user interfaces for supporting innovation

abstract: A challenge for human-computer interaction researchers and user interf ace designers is to construct information technologies that support creativity. This ambitious goal can be attained by building on an adequate understanding of creative processes. This article offers a four-phase framework for creativity that might assist designers in providing effective tools for users: (1) Collect : learn from provious works stored in libraries, the Web, etc.; (2) Relate : consult with peers and mentors at early, middle, and late stages, (3) Create : explore, compose, evaluate possible solutions; and (4) Donate : disseminate the results and contribute to the libraries. Within this integrated framework, this article proposes eight activities that require human-computer interaction research and advanced user interface design. A scenario about an architect illustrates the process of creative work within such an environment.


title: Dual enzyme amperometric biosensor for putrescine with interference suppression

abstract: A putrescine biosensor has been developed. Putrescine oxidase, peroxidase, and bovine serum albumin were cross-linked directly on the surface of a graphite electrode by glutaraldehyde. The sensor was used to determine putrescine at 0 mV (vs. Ag/AgCl reference electrode) in a potassium phosphate buffer (pH 7.4) solution containing 4 mM dopamine (3-hydroxytyramine). The response of putrescine was linear up to 600 μM with a standard deviation of 2.8%. The detection limit was 5 μM. The response time was around 30 s. The lifetime was over 30 days when the electrode was used every day. The interferences of cadaverine and spermidine could be eliminated by added dopamine which, apparently, inhibits the enzymatic conversion of these substrates more strongly than that of putrescine.


title: PeerSpace - An Online Collaborative Learning Environment for Computer Science Students

abstract: The aim of Peer Space is to promote peer support and peer learning in introductory Computer Science (CS) courses by providing the students with online collaborative tools for convenient synchronous and asynchronous interactions on course related topics and social matters. This paper presents the development of various social and learning components in Peer Space that are unique in promoting collaborative learning. Analysis of preliminary results is presented.


title: Heartbeat Classification Using Abstract Features From the Abductive Interpretation of the ECG

abstract: Objective: This paper aims to prove that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal. Methods: A set of qualitative morphological and rhythm features are obtained for each heartbeat as a result of the abductive interpretation of the ECG. Then, a QRS clustering algorithm is applied in order to reduce the effect of possible errors in the interpretation. Finally, a rule-based classifier assigns a tag to each cluster. Results: The method has been tested with the MIT-BIH Arrhythmia Database records, showing a significantly better performance than any other automatic approach in the state-of-the-art, and even improving most of the assisted approaches that require the intervention of an expert in the process. Conclusion: The most relevant issues in ECG classification, related to a large extent to the variability of the signal patterns between different subjects and even in the same subject over time, will be overcome by changing the reasoning paradigm. Significance: This paper demonstrates the power of an abductive framework for time-series interpretation to make a qualitative leap in the significance of the information extracted from the ECG by automatic methods.


title: Teaching acceptance and mindfulness to improve the lives of the obese: a preliminary test of a theoretical model.

abstract: Background ::: Obesity is a growing epidemic. Weight control interventions can achieve weight loss, but most is regained over time. Stigma and low quality of life are significant problems that are rarely targeted.


title: Improving Collocated Robot Teleoperation with Augmented Reality

abstract: Robot teleoperation can be a challenging task, often requiring a great deal of user training and expertise, especially for platforms with high degrees-of-freedom (e.g., industrial manipulators and aerial robots). Users often struggle to synthesize information robots collect (e.g., a camera stream) with contextual knowledge of how the robot is moving in the environment. We explore how advances in augmented reality (AR) technologies are creating a new design space for mediating robot teleoperation by enabling novel forms of intuitive, visual feedback. We prototype several aerial robot teleoperation interfaces using AR, which we evaluate in a 48-participant user study where participants completed an environmental inspection task. Our new interface designs provided several objective and subjective performance benefits over existing systems, which often force users into an undesirable paradigm that divides user attention between monitoring the robot and monitoring the robot»s camera feed(s).


title: Learning Policy Representations in Multiagent Systems

abstract: Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by hand-engineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data. Our framework casts agent modeling as a representation learning problem. Consequently, we construct a novel objective inspired by imitation learning and agent identification and design an algorithm for unsupervised learning of representations of agent policies. We demonstrate empirically the utility of the proposed framework in (i) a challenging high-dimensional competitive environment for continuous control and (ii) a cooperative environment for communication, on supervised predictive tasks, unsupervised clustering, and policy optimization using deep reinforcement learning.


title: Exact Recovery of Hard Thresholding Pursuit

abstract: The Hard Thresholding Pursuit (HTP) is a class of truncated gradient descent methods for finding sparse solutions of $\ell_0$-constrained loss minimization problems. The HTP-style methods have been shown to have strong approximation guarantee and impressive numerical performance in high dimensional statistical learning applications. However, the current theoretical treatment of these methods has traditionally been restricted to the analysis of parameter estimation consistency. It remains an open problem to analyze the support recovery performance (a.k.a., sparsistency) of this type of methods for recovering the global minimizer of the original NP-hard problem. In this paper, we bridge this gap by showing, for the first time, that exact recovery of the global sparse minimizer is possible for HTP-style methods under restricted strong condition number bounding conditions. We further show that HTP-style methods are able to recover the support of certain relaxed sparse solutions without assuming bounded restricted strong condition number. Numerical results on simulated data confirms our theoretical predictions.


title: Text String Detection From Natural Scenes by Structure-Based Partition and Grouping

abstract: Text information in natural scene images serves as important clues for many image-based applications such as scene understanding, content-based image retrieval, assistive navigation, and automatic geocoding. However, locating text from a complex background with multiple colors is a challenging task. In this paper, we explore a new framework to detect text strings with arbitrary orientations in complex natural scene images. Our proposed framework of text string detection consists of two steps: 1) image partition to find text character candidates based on local gradient features and color uniformity of character components and 2) character candidate grouping to detect text strings based on joint structural features of text characters in each text string such as character size differences, distances between neighboring characters, and character alignment. By assuming that a text string has at least three characters, we propose two algorithms of text string detection: 1) adjacent character grouping method and 2) text line grouping method. The adjacent character grouping method calculates the sibling groups of each character candidate as string segments and then merges the intersecting sibling groups into text string. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitted text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region covering all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi-scales. The proposed methods outperform the state-of-the-art results on the public Robust Reading Dataset, which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in nonhorizontal orientations.


title: ‘The computer is not for you to be looking around, it is for schoolwork’: Challenges for digital inclusion as Latino immigrant families negotiate children’s access to the internet

abstract: While studies have addressed the role of the internet in the family, the perspectives of Latino immigrant families are largely missing from the research. This article draws primarily on interview data with first-generation Latino immigrant families living in urban Los Angeles to analyze how parents and their middle school-aged children negotiate access to and use of the internet. Parents in the study were torn between a belief in the educational importance of the internet and a strong sense of anxiety about online risks. Their parenting strategies reflected these anxieties and inadvertently contributed to limiting children’s online opportunities. Outside of school, young people only had periodic access to the internet, which was used primarily for doing homework. While young people also found ways to pursue their own interests and motivations online, they had limited opportunities for more open-ended exploration and self-directed learning.


title: The evolving concept of subjective well-being: the multifaceted nature of happiness

abstract: Subjective well-being, or what is popularly often called “happiness,” has been of intense interest throughout human history. We review research showing that it is not a single factor, but that subjective well-being is composed of a number of separable although somewhat related variables. For example, positive feelings, negative feelings, and life satisfaction are clearly separable. In understanding the various types of subjective well-being, it is important to remember that appraisals move from immediate situations to a later recall of feelings, and then to global evaluations of life. At each stage, from momentary feelings to large global life eval-uations, somewhat different processes are involved in what is called “happiness.” In order to understand how to measure subjective well-being, one must understand the time course and components of the phenomenon in question, and be clear about what is most important to assess. On-line feelings are very different from global evaluations of life, although both have been studied under the rubric of subjective well-being. Although debate has focused on which type of subjective well-being should be called “true happiness,” the goal of scientists is to understand each type, their relations with each other, and their causes. The future of the field depends on understanding the differences between various types of well-being, and the different and similar causes of each.


title: Exponentially Weighted Moving Average Charts for Detecting Concept Drift

abstract: Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted Moving Average (EWMA) chart to monitor the misclassification rate of an streaming classifier. Our approach is modular and can hence be run in parallel with any underlying classifier to provide an additional layer of concept drift detection. Moreover our method is computationally efficient with overhead O(1) and works in a fully online manner with no need to store data points in memory. Unlike many existing approaches to concept drift detection, our method allows the rate of false positive detections to be controlled and kept constant over time.


title: End-fire phased array 5G antenna design using leaf-shaped bow-tie elements for 28/38 GHz MIMO applications

abstract: In this paper, a new design of mm-Wave phased array 5G antenna for multiple-input multiple-output (MIMO) applications has been introduced. Two identical linear phased arrays with eight leaf-shaped bow-tie antenna elements have been used at different sides of the mobile-phone PCB. An Arlon AR 350 dielectric with properties of h=0.5 mm, e=3.5, and δ=0.0026 has been used as a substrate of the proposed design. The antenna is working in the frequency range of 25 to 40 GHz (more than 45% FBW) and can be easily fit into current handheld devices. The proposed MIMO antenna has good radiation performances at 28 and 38 GHz which both are powerful candidates to be the carrier frequency of the future 5G cellular networks.


title: How do we engage with activity trackers?: a longitudinal study of Habito

abstract: We report on a 10-month in-the-wild study of the adoption, engagement and discontinuation of an activity tracker called Habito, by a sample of 256 users who installed the tracker on their own volition. We found 'readiness' to behavior change to be a strong predictor of adoption (which ranged from 56% to 20%). Among adopters, only a third updated their daily goal, which in turn impacted their physical activity levels. The use of the tracker was dominated by glances -- brief, 5-sec sessions where users called the app to check their current activity levels with no further interaction, while users displayed true lack of interest in historical data. Textual feedback proved highly effective in fueling further engagement with the tracker as well as inducing physical activity. We propose three directions for design: designing for different levels of 'readiness', designing for multilayered and playful goal setting, and designing for sustained engagement.


title: Design and analysis of enumeration attacks on finding friends with phone numbers

abstract: Users' phone numbers are popularly used for finding friends in instant messaging (IM) services. In this paper, we present a new security concern about this search feature through a case study with KakaoTalk which is the most widely used IM in Korea. We demonstrate that there are multiple ways of collecting victims' personal information such as their (display) names, phone numbers and photos, which can be potentially misused for a variety of cyber-criminal activities. Our experimental results show that a user's personal data can be obtained automatically (0.26źs on average). The results also indicate that a large portion of KakaoTalk users (72.8%) have used real or real-like names in their profiles, which means that our discovered enumeration attacks seem to be practically dangerous. To mitigate these attacks, we present three countermeasures including a misuse detection system that can discover abnormal application activities within a certain time-window.


title: A framework of sensor-cloud integration opportunities and challenges

abstract: In the past few years, wireless sensor networks (WSNs) have been gaining increasing attention because of their potential of enabling of novel and attractive solutions in areas such as industrial automation, environmental monitoring, transportation business, health-care etc. If we add this collection of sensor derived data to various Web-based social networks or virtual communities, blogs etc., we can have a remarkable transformation in our ability to "see" ourselves and our planet. Our primary goal is to facilitate connecting sensors, people and software objects to build community-centric sensing applications. However, the computational tools needed to launch this exploration may be more appropriately built from the data center "Cloud" computing model than the traditional HPC approaches. In this paper, we propose a framework to enable this exploration by integrating sensor networks to the emerging data center "cloud" model of computing. But there are many challenges to enable this framework. We propose a pub-sub based model which simplifies the integration of sensor networks with cloud based community-centric applications. Also there is a need for internetworking cloud providers in case of violation of service level agreement with users. We discussed these issues and proposed reasonable solutions.


title: Terahertz imaging with CMOS/BiCMOS process technologies

abstract: Contrary to the common belief, silicon devices may very well operate beyond their cut-off frequency. The push towards terahertz frequencies, though, presents both challenges and opportunities for emerging applications. This paper summarizes recent attempts to use foundry-level silicon process technologies for the realization of terahertz electronic systems.


title: 23.2 A 5Gb/s/pin 8Gb LPDDR4X SDRAM with power-isolated LVSTL and split-die architecture with 2-die ZQ calibration scheme

abstract: With growing demand for low-power mobile applications, such as wearable devices, smart phones and tablet PCs, low-power mobile DRAM has been identified as a mandatory requirement for low-power system designs. The recently developed LPDDR4 [1] is still a power efficient solution because of its architectural approaches and low-voltage-swing terminated logic (LVSTL). However, demand for enhanced power-efficiency beyond LPDDR4 is still increasing for mobile applications. In this work, a 5.0Gbp/s/pin 8Gb LPDDR4X memory with power-isolated low-voltage-swing terminated logic (PI-LVSTL) and a split-die architecture is proposed to enhance power-efficiency and mass production yield.


title: Graph-Parallel Entity Resolution using LSH & IMM

abstract: In this paper we describe graph-based parallel algorithms for entity resolution that improve over the map-reduce approach. We compare two approaches to parallelize a Locality Sensitive Hashing (LSH) accelerated,IterativeMatch-Merge (IMM) entity resolution technique: BCP, where records hashed together are compared at a single node/reducer, vs an alternative mechanism (RCP) where comparison load is better distributed across processors especially in the presence of severely skewed bucket sizes. We analyze the BCP and RCP approaches analytically as well as empirically using a large synthetically generated dataset. We generalize the lessons learned from our experience and submit that the RCP approach is also applicable in many similar applications that rely on LSH or related grouping strategies to minimize pair-wise comparisons.


title: Linear Coordinate-Descent Message Passing for Quadratic Optimization

abstract: In this letter, we propose a new message-passing algorithm for quadratic optimization. The design of the new algorithm is based on linear coordinate descent between neighboring nodes. The updating messages are in a form of linear functions as compared to the min-sum algorithm of which the messages are in a form of quadratic functions. As a result, the linear coordinate-descent (LiCD) algorithm transmits only one parameter per message as opposed to the min-sum algorithm, which transmits two parameters per message. We show that when the quadratic matrix is walk-summable, the LiCD algorithm converges. By taking the LiCD algorithm as a subroutine, we also fix the convergence issue for a general quadratic matrix. The LiCD algorithm works in either a synchronous or asynchronous message-passing manner. Experimental results show that for a general graph with multiple cycles, the LiCD algorithm has comparable convergence speed to the min-sum algorithm, thereby reducing the number of parameters to be transmitted and the computational complexity.


title: Improving fast_align by Reordering

abstract: fast align is a simple, fast, and efficient approach for word alignment based on the IBM model 2. fast align performs well for language pairs with relatively similar word orders; however, it does not perform well for language pairs with drastically different word orders. We propose a segmenting-reversing reordering process to solve this problem by alternately applying fast align and reordering source sentences during training. Experimental results with JapaneseEnglish translation demonstrate that the proposed approach improves the performance of fast align significantly without the loss of efficiency. Experiments using other languages are also reported.


title: Perturbation analysis of an M/M/1 queue in a diffusion random environment, preprint

abstract: We study in this paper an M/M/1 queue whose server rate depends upon the state of an independent Ornstein---Uhlenbeck diffusion process (X(t)) so that its value at time t is μ ?(X(t)), where ?(x) is some bounded function and μ>0. We first establish the differential system for the conditional probability density functions of the couple (L(t),X(t)) in the stationary regime, where L(t) is the number of customers in the system at time t. By assuming that ?(x) is defined by ?(x)=1??((x ? a/?)?(?b/?)) for some positive real numbers a, b and ?, we show that the above differential system has a unique solution under some condition on a and b. We then show that this solution is close, in some appropriate sense, to the solution to the differential system obtained when ? is replaced with ?(x)=1?? x for sufficiently small ?. We finally perform a perturbation analysis of this latter solution for small ?. This allows us to check at the first order the validity of the so-called reduced service rate approximation, stating that everything happens as if the server rate were constant and equal to $\mu(1-\varepsilon {\mathbb{E}}(X(t)))$ .


title: Towards an Applied Gamification Model for Tracking, Managing, & Encouraging Sustainable Travel Behaviours

abstract: In this paper we introduce a gamification model for encouraging sustainable multi-modal urban travel in modern European cities. Our aim is to provide a mechanism that encourages users to reflect on their current travel behaviours and to engage in more environmentally friendly activities that lead to the formation of sus- tainable, long-term travel behaviours. To achieve this our users track their own be- haviours, set goals, manage their progress towards those goals, and respond to chal- lenges. Our approach uses a point accumulation and level achievement metaphor to abstract from the underlying specifics of individual behaviours and goals to allow an extensible and flexible platform for behaviour management. We present our model within the context of the SUPERHUB project and platform.


title: Approximating the Held-Karp Bound for Metric TSP in Nearly-Linear Time

abstract: We give a nearly linear time randomized approximation scheme for the Held-Karp bound [Held and Karp, 1970] for metric TSP. Formally, given an undirected edge-weighted graph $G$ on $m$ edges and $\epsilon > 0$, the algorithm outputs in $O(m \log^4n /\epsilon^2)$ time, with high probability, a $(1+\epsilon)$-approximation to the Held-Karp bound on the metric TSP instance induced by the shortest path metric on $G$. The algorithm can also be used to output a corresponding solution to the Subtour Elimination LP. We substantially improve upon the $O(m^2 \log^2(m)/\epsilon^2)$ running time achieved previously by Garg and Khandekar. The LP solution can be used to obtain a fast randomized $\big(\frac{3}{2} + \epsilon\big)$-approximation for metric TSP which improves upon the running time of previous implementations of Christofides' algorithm.


title: Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks

abstract: A formalization of the transducer learning problem and an effective and efficient method for the inductive learning of an important class of transducers, the class of subsequential transducers, are presented. The capabilities of subsequential transductions are illustrated through a series of experiments that also show the high effectiveness of the proposed learning method in obtaining very accurate and compact transducers for the corresponding tasks. >


title: Nonvolatile Memory and Computing Using Emerging Ferroelectric Transistors

abstract: Ferroelectric FETs (FeFETs) are emerging as a promising nano device candidate for the next-generation energy-efficient embedded nonvolatile memory (NVM). This promise comes from not only the CMOS-scaling compatibility, but also the compact fusion of logic and non-volatility in a single device that provides opportunities for efficient memory access and in-memory computing. This talk investigates circuit opportunities that harness these intriguing FeFET device features, providing insights into new computation paradigms beyond existing solutions.


title: Electrochemical and optical biosensors based on nanomaterials and nanostructures A Review

abstract: Nanomaterials and nanostructures exhibit unique size-tunable and shape-dependent physicochemical properties that are different from those of bulk materials. Advances of nanomaterials and nanostructures open a new door to develop various novel biosensors. The present work has reviewed the recent progress in electrochemical, surface plasmon resonance (SPR), surface-enhanced Raman scattering (SERS) and fluorescent biosensors based on nanomaterials and nanostructures. An emphasis is put on the research that demonstrates how the performance of biosensors such as the limit of detection, sensitivity and selectivity is improved by the use of nanomaterials and nanostructures.