title: Dual-Band Probe-Fed Stacked Patch Antenna for GNSS Applications

abstract: A novel proximity-coupled probe-fed stacked patch antenna is proposed for Global Navigation Satellite Systems (GNSS) applications. The antenna has been designed to operate for the satellite navigation frequencies in L-band including GPS, GLONASS, Galileo, and Compass (1164-1239 MHz and 1559-1610 MHz). A key feature of our design is the proximity-coupled probe feeds to increase impedance bandwidth and the integrated 90deg broadband balun to improve polarization purity. The final antenna exhibits broad pattern coverage, high gain at the low angles (more than -5 dBi), and VSWR <1.5 for all the operating bands. The design procedures and employed tuning techniques to achieve the desired performance are presented.


title: A Classification and Comparison Framework for Software Architecture Description Languages

abstract: Software architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future.


title: Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks

abstract: Fog computing extends the cloud computing paradigm by placing resources close to the edges of the network to deal with the upcoming growth of connected devices. Smart city applications, such as health monitoring and predictive maintenance, will introduce a new set of stringent requirements, such as low latency, since resources can be requested on-demand simultaneously by multiple devices at different locations. It is then necessary to adapt existing network technologies to future needs and design new architectural concepts to help meet these strict requirements. This article proposes a fog computing framework enabling autonomous management and orchestration functionalities in 5G-enabled smart cities. Our approach follows the guidelines of the European Telecommunications Standards Institute (ETSI) NFV MANO architecture extending it with additional software components. The contribution of our work is its fully-integrated fog node management system alongside the foreseen application layer Peer-to-Peer (P2P) fog protocol based on the Open Shortest Path First (OSPF) routing protocol for the exchange of application service provisioning information between fog nodes. Evaluations of an anomaly detection use case based on an air monitoring application are presented. Our results show that the proposed framework achieves a substantial reduction in network bandwidth usage and in latency when compared to centralized cloud solutions.


title: Communication costs of Strassen's matrix multiplication

abstract: Algorithms have historically been evaluated in terms of the number of arithmetic operations they performed. This analysis is no longer sufficient for predicting running times on today's machines. Moving data through memory hierarchies and among processors requires much more time (and energy) than performing computations. Hardware trends suggest that the relative costs of this communication will only increase. Proving lower bounds on the communication of algorithms and finding algorithms that attain these bounds are therefore fundamental goals. We show that the communication cost of an algorithm is closely related to the graph expansion properties of its corresponding computation graph. Matrix multiplication is one of the most fundamental problems in scientific computing and in parallel computing. Applying expansion analysis to Strassen's and other fast matrix multiplication algorithms, we obtain the first lower bounds on their communication costs. These bounds show that the current sequential algorithms are optimal but that previous parallel algorithms communicate more than necessary. Our new parallelization of Strassen's algorithm is communication-optimal and outperforms all previous matrix multiplication algorithms.


title: Spectral Image Analysis For Measuring Ripeness Of Tomatoes

abstract: The present invention provides transport apparatus for transporting and articulating an article into and out of a housing having limited access, and includes a lifting beam having a curved shape to which is attached a lifting assembly. A hoist device is connected to the lifting assembly for raising and lowering the entire transport apparatus and also includes a worm-drive assembly for moving the lifting assembly relative to the lifting beam to thereby articulate an article about a first axis. In addition, one end of the lifting beam includes a rotational mechanism which is connected to a second worm-drive assembly. Operation of the second worm-drive assembly allows the article to be rotated relative to the lifting beam which allows the article to be rotated relative to a second axis. The combined operations of hoisting and movement about the two axes allows the article to be articulated in any direction for insertion or removal from a housing having limited access.


Weighted Self-Incremental Transfer Learning for 3 D-Semantic Segmentation


title: LTE-WLAN aggregation (LWA) in 3GPP Release 13 & Release 14

abstract: Integrating different radio access technologies (RATs) is one effective method to meet the demand to provide ever increasing data rates to the users. For integrating LTE and WLAN in particular, the purpose is to enhance the operators control of when the UE uses WLAN. Another aim, in addition to better network control on which RAT is used, is the possible data aggregation which enables simultaneous utilization of both resources. In this paper, we present LTE-WLAN Aggregation (LWA) as specified in 3GPP and compare it to the LTE-WLAN Radio Level Integration with IPsec tunnel (LWIP), also specified in 3GPP. In addition, we provide simulation results comparing the schemes in an example scenario.


title: Automated classification of mislabeled near-infrared left and right iris images using convolutional neural networks

abstract: In this paper, we propose a Convolutional Neural Network (CNN) with unified architecture (no need to re-design it for each unique iris database used) that operates well in a diverse set of iris databases. The CNN is designed to automatically recognize mislabeled left and right iris images by iris recognition system operators, and thus, extend the capabilities of a conventional iris recognition system. Our proposed approach is composed of three steps. First, for each iris database used as input, a CNN is trained using part of the database. Second, an empirical parameter optimization study is conducted so that classification performance is acceptable. Finally, the proposed classifier is tested on the remaining images of the same database used for training. The performance of the proposed network is evaluated on small- and large-scale iris databases, including the NIST's Iris Challenge Evaluation (ICE), LG ICAM 4000 iris, the CASIA Lamp, and the Pupil Light Reflex (PLR) databases. Experimental results show that independent of the databases used or whether the classification performance is tested on either a left-or right-side dataset, our approach results in a classification performance ranging from 97.5 to 100%.


title: Blurred target tracking by Blur-driven Tracker

abstract: Visual tracking plays an important role in many computer vision tasks. A common assumption in previous methods is that the video frames are blur free. In reality, motion blurs are pervasive in the real videos. In this paper we present a novel BLUr-driven Tracker (BLUT) framework for tracking motion-blurred targets. BLUT actively uses the information from blurs without performing debluring. Specifically, we integrate the tracking problem with the motion-from-blur problem under a unified sparse approximation framework. We further use the motion information inferred by blurs to guide the sampling process in the particle filter based tracking. To evaluate our method, we have collected a large number of video sequences with significatcant motion blurs and compared BLUT with state-of-the-art trackers. Experimental results show that, while many previous methods are sensitive to motion blurs, BLUT can robustly and reliably track severely blurred targets.


title: A Domain Adaptation Regularization for Denoising Autoencoders

abstract: Finding domain invariant features is critical for successful domain adaptation and transfer learning. However, in the case of unsupervised adaptation, there is a significant risk of overfitting on source training data. Recently, a regularization for domain adaptation was proposed for deep models by (Ganin and Lempitsky, 2015). We build on their work by suggesting a more appropriate regularization for denoising autoencoders. Our model remains unsupervised and can be computed in a closed form. On standard text classification adaptation tasks, our approach yields the state of the art results, with an important reduction of the learning cost.


title: LSDA: Large Scale Detection Through Adaptation

abstract: A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. Unfortunately, only a small fraction of those labels are available for the detection task. It is much cheaper and easier to collect large quantities of image-level labels from search engines than it is to collect detection data and label it with precise bounding boxes. In this paper, we propose Large Scale Detection through Adaptation (LSDA), an algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Our method has the potential to enable detection for the tens of thousands of categories that lack bounding box annotations, yet have plenty of classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge demonstrates the efficacy of our approach. This algorithm enables us to produce a >7.6K detector by using available classification data from leaf nodes in the ImageNet tree. We additionally demonstrate how to modify our architecture to produce a fast detector (running at 2fps for the 7.6K detector). Models and software are available at lsda.berkeleyvision.org.


title: Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

abstract: One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.


title: An initial study on the use of execution complexity metrics as indicators of software vulnerabilities

abstract: Allocating code inspection and testing resources to the most problematic code areas is important to reduce development time and cost. While complexity metrics collected statically from software artifacts are known to be helpful in finding vulnerable code locations, some complex code is rarely executed in practice and has less chance of its vulnerabilities being detected. To augment the use of static complexity metrics, this study examines execution complexity metrics that are collected during code execution as indicators of vulnerable code locations. We conducted case studies on two large size, widely-used open source projects, the Mozilla Firefox web browser and the Wireshark network protocol analyzer. Our results indicate that execution complexity metrics are better indicators of vulnerable code locations than the most commonly-used static complexity metric, lines of source code. The ability of execution complexity metrics to discriminate vulnerable code locations from neutral code locations and to predict vulnerable code locations vary depending on projects. However, the vulnerability prediction models using execution complexity metrics are superior to the models using static complexity metrics in reducing inspection effort.


A Semi-Supervised Framework for Simultaneous Classification and Regression of Zero-Inflated Time Series Data with Application to Precipitation Prediction


title: Color deflickering for high-speed video in the presence of artificial lighting

abstract: When acquiring high-speed video (more than 100 frames per second), artificial lighting can cause severe non-uniform luminosity and chroma variation between frames, commonly labeled periodic flicker. Non-uniform periodic flicker is not easy to correct in the presence of general motion, since its estimation requires background and object tracking, and most tracking techniques assume consistent illumination. In this paper, we propose a joint tracking/color correction scheme using a block matching technique paired with color variation estimation. We introduce a robust method for stabilizing brightness variations in image sequences. A post-processing step is also proposed in order to deal with blocking artifacts. We demonstrate the efficacy of our method both on simulated and real data.


From Theory to Practice : Frame Semantics and the Design of FrameNet


title: Sparse distance learning for object recognition combining RGB and depth information

abstract: In this work we address joint object category and instance recognition in the context of RGB-D (depth) cameras. Motivated by local distance learning, where a novel view of an object is compared to individual views of previously seen objects, we define a view-to-object distance where a novel view is compared simultaneously to all views of a previous object. This novel distance is based on a weighted combination of feature differences between views. We show, through jointly learning per-view weights, that this measure leads to superior classification performance on object category and instance recognition. More importantly, the proposed distance allows us to find a sparse solution via Group-Lasso regularization, where a small subset of representative views of an object is identified and used, with the rest discarded. This significantly reduces computational cost without compromising recognition accuracy. We evaluate the proposed technique, Instance Distance Learning (IDL), on the RGB-D Object Dataset, which consists of 300 object instances in 51 everyday categories and about 250,000 views of objects with both RGB color and depth. We empirically compare IDL to several alternative state-of-the-art approaches and also validate the use of visual and shape cues and their combination.


title: Prediction of school dropout risk group using Neural Network

abstract: Dropping out of school is one of the most complex and crucial problems in education, causing social, economic, political, academic and financial losses. In order to contribute to solve the situation, this paper presents the potentials of an intelligent, robust and innovative system, developed for the prediction of risk groups of student dropout, using a Fuzzy-ARTMAP Neural Network, one of the techniques of artificial intelligence, with possibility of continued learning. This study was conducted under the Federal Institute of Education, Science and Technology of Mato Grosso, with students of the Colleges of Technology in Automation and Industrial Control, Control Works, Internet Systems, Computer Networks and Executive Secretary. The results showed that the proposed system is satisfactory, with global accuracy superior to 76% and significant degree of reliability, making possible the early identification, even in the first term of the course, the group of students likely to drop out.


title: Multi-robot cooperative stereo for outdoor scenarios

abstract: In this paper, we propose a cooperative perception framework for multi-robot real-time 3D high dynamic target estimation in outdoor scenarios based on monocular camera available on each robot. The relative position and orientation between robots establishes a flexible and dynamic stereo baseline. Overlap views subject to geometric constraints emerged from the stereo formulation, which allowed us to obtain a decentralized cooperative perception layer. Epipolar constraints related to the global frame are applied both in image feature matching and to feature searching and detection optimization in the image processing of robots with low computational capabilities. In contrast to classic stereo, the proposed framework considers all sources of uncertainty (in localization, attitude and image detection from both robots) in the determination of the objects best 3D localization and its uncertainty. The proposed framework can be later integrated in a decentralized data fusion (DDF) multi-target tracking approach where it can contribute to reduce rumor propagation data association and track initialization issues. We demonstrate the advantages of this approach in real outdoor scenario. This is done by comparing a stereo rigid baseline standalone target tracking with the proposed multi-robot cooperative stereo between a micro aerial vehicle (MAV) and an autonomous ground vehicle (AGV).


title: Life detection system using continuous wave Doppler radar and blind source separation

abstract: This paper presents the development of a sensitive high frequency life detection technique which makes use of Blind Source Separation to detect the presence of various human subjects. The use of Blind Source Separation allows the detection of multiple subjects at the same time. The system has been extensively tested under simulated conditions and the results have been presented — this system will be extremely helpful in the development of a MIMO system for the detection of multiple human subjects buried under rubble.


title: Document Summarization in Malayalam with sentence framing

abstract: Document Summarization is a technique of conveying important information in a given document. It is one of the most important chores of Natural Language Processing as the summary produced is helpful for information retrieval systems, question answering systems, medical domain and news domain etc. Most of the summarization works in Indian languages are of extractive nature and not much work is oriented towards the abstractive summarization approaches in Indian languages as they need more linguistic processing. As Indian languages belong to several language families and since they are morphologically rich and agglutinative in nature, a lot of challenges are faced while doing abstractive summarization because it requires natural language generation techniques. The proposed work is an approach for abstractive text summarization that will accept single document as input in Malayalam, processes the input by building a suitable semantic representation and then use sentence framing techniques to generate the final summary. The entire framework is composed of eight modules that mainly deals with constructing a suitable semantic representation called Karaka tree and a sentence framing module to generate the natural summary.


title: Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN

abstract: In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such as encoding, decoding, modulation, and equalization. However, accurate instantaneous channel transfer function, \emph{i.e.}, the channel state information (CSI), is necessary to compute the gradient of the DNN representing. In many communication systems, the channel transfer function is hard to obtain in advance and varies with time and location. In this article, this constraint is released by developing a channel agnostic end-to-end system that does not rely on any prior information about the channel. We use a conditional generative adversarial net (GAN) to represent the channel effects, where the encoded signal of the transmitter will serve as the conditioning information. In addition, in order to deal with the time-varying channel, the received signal corresponding to the pilot data can also be added as a part of the conditioning information. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) and Rayleigh fading channels, which opens a new door for building data-driven communication systems.


title: Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks

abstract: We propose a semantic segmentation model that exploits rotation and reflection symmetries. We demonstrate significant gains in sample efficiency due to increased weight sharing, as well as improvements in robustness to symmetry transformations. The group equivariant CNN framework is extended for segmentation by introducing a new equivariant (G->Z2)-convolution that transforms feature maps on a group to planar feature maps. Also, equivariant transposed convolution is formulated for up-sampling in an encoder-decoder network. To demonstrate improvements in sample efficiency we evaluate on multiple data regimes of a rotation-equivariant segmentation task: cancer metastases detection in histopathology images. We further show the effectiveness of exploiting more symmetries by varying the size of the group.


title: Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

abstract: The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available "apps" that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis. The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject's height or leg length. Absolute measurement errors were least during toe off (3.12 ± 5.44 degrees) compared to heel strike (5.81 ± 5.26 degrees). There were significant (p < 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p > 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle. The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.


title: Field-circuit co-simulation of the Marx generator

abstract: A full wave field-circuit co-simulation method is employed to characterise the time domain behaviour of the Marx generator. Radiation and conduction effects are fully considered. Results from the co-simulation and the circuit simulation are compared; it can be found that the field-circuit co-simulation provides complete information of the system which includes the radiation effect and the mutual coupling in space. The radiated field strength is important when designing the shielding room which is used to protect the measurement instruments in practice.


title: CLX: Towards a scalable and comprehensible design of PBE data transformations

abstract: Effective data analytics on data collected from the real world usually begins with a notoriously expensive pre-processing step of data transformation and wrangling. Human-in-the-loop tools have been proposed to speed up the process of data transformation, using the Programming By Example (PBE) approach. However, two important usability issues limit the effective use of such PBE data transformation systems: (1) the cost of user effort grows quickly as volume or heterogeneity of the raw data increases (prohibitive user effort), and (2) the underlying process of transformation is opaque to the user and hence difficult to validate, correct and debug (incomprehensibility). In this project, we propose a new PBE data transformation paradigm design CLX (pronounced"clicks") for data normalization to address these two issues. For the issue of prohibitive user effort, we present a pattern profiling algorithm that hierarchically clusters the input raw data based on format structures that help the user quickly identify both well-formatted and ill-formatted data and specify the desired format. After the desired transformation logic is inferred, CLX explains it as a set of simple regular expression replacement operations to improve comprehensibility. We experimentally compared the CLX prototype with FlashFill, a state-of-the-art data transformation tool. The results show improvements over the state of the art in saving user effort and enhancing comprehensibility, without loss of efficiency or expressive power. In a user effort study on data sets of various sizes, when the data size grew by a factor of 30, the user effort required by the CLX prototype grew 1.2x whereas that required by FlashFill grew 9.1x. In another test assessing the users' understanding of the transformation logic, the CLX users achieved a success rate about twice that of the FlashFill users.


title: DNA Mini-barcodes

abstract: Conventional DNA barcoding uses an approximately 650 bp DNA barcode of the mitochondrial gene COI for species identification in animal groups. Similar size fragments from chloroplast genes have been proposed as barcode markers for plants. While PCR amplification and sequencing of a 650 bp fragment is consistent in freshly collected and well-preserved specimens, it is difficult to obtain a full-length barcode in older museum specimens and samples which have been preserved in formalin or similar DNA-unfriendly preservatives. A comparable issue may prevent effective DNA-based authentication and testing in processed biological materials, such as food products, pharmaceuticals, and nutraceuticals. In these cases, shorter DNA sequences-mini-barcodes-have been robustly recovered and shown to be effective in identifying majority of specimens to a species level. Furthermore, short DNA regions can be utilized via high-throughput sequencing platforms providing an inexpensive and comprehensive means of large-scale species identification. These properties of mini-barcodes, coupled with the availability of standardized and universal primers make mini-barcodes a feasible option for DNA barcode analysis in museum samples and applied diagnostic and environmental biodiversity analysis.


Developing Trust in Virtual Teams


PCA Enhanced Kalman Filter for ECG Denoising


title: Quantitation of GSTP1 methylation in nonneoplastic prostatic tissue and organ-con®ned prostate adenocarcinoma

abstract: Background: Methylation of regulatory sequences near GSTP1, which encodes the π class glutathione S-transferase, is the most common epigenetic alteration associated with prostate cancer. We determined whether the quantitation of GSTP1 methylation in histopathologically distinct prostate tissue samples could improve prostate cancer detection. Methods: We used a fluorogenic real-time methylation-specific polymerase chain reaction (MSP) assay to analyze cytidine methylation in the GSTP1 promoter in prostate tissue samples from 69 patients with early-stage prostatic adenocarcinoma (28 of whom also had prostatic intraepithelial neoplasia lesions) and 31 patients with benign prostatic hyperplasia. The relative level of methylated GSTP1 DNA in each sample was determined as the ratio of MSP-amplified GSTP1 to MYOD1, a reference gene. We also performed a prospective, blinded investigation to quantitate GSTP1 promoter methylation in sextant prostate biopsy specimens from 21 additional patients with elevated serum prostate-specific antigen levels, 11 of whom had histologically identified adenocarcinoma and 10 of whom had no morphologic evidence of adenocarcinoma. All data were analyzed by using nonparametric two-sided statistical tests. Results: The median ratios (and interquartile ranges) of MSP-amplified GSTP1 to MYOD1 in resected benign hyperplastic prostatic tissue, intraepithelial neoplasia, and adenocarcinoma were 0 (range, 0-0.1), 1.4 (range, 0-45.9), and 250.8 (range, 53.5-697.5), respectively; all of these values were statistically significantly different (P<.001). The median ratios of MSP-amplified GSTP1 to MYOD1 in the prospectively collected sextant biopsy samples were 410.6 for the patients with adenocarcinoma and 0.0 for the patients with no evidence of adenocarcinoma (P<.001). Conclusion: Quantitation of GSTP1 methylation accurately discriminates between normal hyperplastic tissue and prostatic carcinoma in small samples of prostate tissue and may augment the standard pathologic/histologic assessment of the prostate.


title: An image analysis framework for the early assessment of hypertensive retinopathy signs

abstract: This paper presents a framework for the detection and measurement of retinal vessels in fundoscopy images. The proposed method segments retinal vessels, enabling the actual measurement of the vessel diameter. A Graphical User Interface (GUI) further supports a number of methods for the automatic and interactive measurement of vessel diameters at any selected point or region of interest. In addition, it provides methods for editing the vessel representation in order to recover from possible segmentation misclassifications. All the above steps are integrated into a clinical application capable of supporting vascular risk stratification in persons with hypertension. The performance of the segmentation is assessed on two publicly available DRIVE and STARE datasets. The experimental results show that the method can achieve a high accuracy on both datasets with very low computational cost, being comparative or slightly better than widely used supervised and unsupervised methods.


Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets


title: Accurate Shared Bottleneck Detection Based On SVD and Outliers Detection

abstract: We present a new mechanism for detecting shared bottlenecks between end-to-end paths in a network. Our mech- anism, which only needs one-way delays from endpoints as an input, is based on the well known linear algebraic approach SVD (Singular Value Decomposition). Clusters of flows which share a bottleneck are extracted from SVD results by applying an outlier detection method. Simulations with varying topologies and different network conditions show the high accuracy of our technique.


title: Modified biconical antenna for ultrawideband applications

abstract: In this paper a compact antenna for ultrawideband applications is presented. The antenna is based on the biconical antenna design and has two identical elements. Each element is composed of a cone extended with a ring and an inner cylinder. The modification of the well-known biconical structure is made in order to reduce the influence of the radiation of the feeding cable. To obtain the optimum parameters leading to a less impact of the cable effect on the antenna performance, during the optimization process the antenna was coupled with a feeding coaxial cable. The proposed antenna covers the frequency range from 1.5 to 41 GHz with voltage standing wave ratio below 2 and has an omnidirectional radiation pattern. The realized total efficiency is above 85 % which indicates a good performance.


Sentiment Analysis for Effective Stock Market Prediction


title: Video analysis of Hammersmith lateral tilting examination using Kalman filter guided multi-path tracking

abstract: Video object tracking plays an important role in many computer vision-aided applications. This paper presents a novel multi-path analysis-based video object tracking algorithm. Trajectory of the moving object is refined using a Kalman filter-based prediction method. The proposed algorithm has been used successfully to analyze one of the complex infant neurological examinations often referred to as Hammersmith lateral tilting test. This is an important test of the infant neurological assessment process, and this test is difficult to grade by visual observation. It has been shown in this paper that the proposed video object tracking algorithm can be used to analyze the videos of fast moving objects by incorporating application-specific information. For example, the proposed tracking algorithm can be used to assess lateral tilting test of the Hammersmith infant neurological examinations. The algorithm has been tested with several video recordings of this test which were captured at the neurodevelopment clinic of the SSKM Hospital, Kolkata, India during the period of the study. It is found that the proposed algorithm is capable of estimating the score for the test with high values of sensitivity and specificity.


A formalism for the treatment of polysemy of vertical prepositions in Brazilian Portuguese


title: Study on flip chip assembly of high density micro-LED array

abstract: Flip chip assembly technology is an attractive solution for high I/O density and fine-pitch microelectronics packaging. Recently, high efficient GaN-based light-emitting diodes (LEDs) have undergone a rapid development and flip chip bonding has been widely applied to fabricate high-brightness GaN micro-LED arrays [1]. The flip chip GaN LED has some advantages over the traditional top-emission LED, including improved current spreading, higher light extraction efficiency, better thermal dissipation capability and the potential of further optical component integration [2, 3]. With the advantages of flip chip assembly, micro-LED (μLED) arrays with high I/O density can be performed with improved luminous efficiency than conventional p-side-up micro-LED arrays and are suitable for many potential applications, such as micro-displays, bio-photonics and visible light communications (VLC), etc. In particular, μLED array based selif-emissive micro-display has the promising to achieve high brightness and contrast, reliability, long-life and compactness, which conventional micro-displays like LCD, OLED, etc, cannot compete with. In this study, GaN micro-LED array device with flip chip assembly package process was presented. The bonding quality of flip chip high density micro-LED array is tested by daisy chain test. The p-n junction tests of the devices are measured for electrical characteristics. The illumination condition of each micro-diode pixel was examined under a forward bias. Failure mode analysis was performed using cross sectioning and scanning electron microscopy (SEM). Finally, the fully packaged micro-LED array device is demonstrated as a prototype of dice projector system.


title: A.Zelevinsky, Cluster algebras of finite type and positive symmetrizable matrices

abstract: The paper is motivated by an analogy between cluster algebras and Kac-Moody algebras: both theories share the same classification of finite type objects by familiar Cartan-Killing types. However, the underlying combinatorics beyond the two classifications is different: roughly speaking, Kac-Moody algebras are associated with (symmetrizable) Cartan matrices, while cluster algebras correspond to skew-symmetrizable matrices. We study an interplay between the two classes of matrices, in particular, establishing a new criterion for deciding whether a given skew-symmetrizable matrix gives rise to a cluster algebra of finite type.


title: The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana

abstract: In this paper, we compare the utility of modified versions of the unified theory of acceptance and use of technology (UTAUT) model in explaining mobile learning adoption in higher education in a developing country and evaluate the size and direction of the impacts of the UTAUT factors on behavioural intention to adopt mobile learning in higher education. The data were obtained through a web survey of university students and the models are estimated in a structural equations modelling framework. Many of the UTAUT relationships are confirmed, but some are contradicted. The results suggest that culture and country level differences moderate the UTAUT effects, hence, a straightforward application of the model regardless of the context can lead to non-detection of important relationships and to suboptimal mobile learning promotion strategies. Including attitude in the model is also a prudent modification since it increases its explanatory power.


title: Balanced Traffic Routing : Design , Implementation , and Evaluation

abstract: Navigators based on real-time traffic conditions achieve suboptimal results since, in face of congestion, they greedily shift drivers to currently light-traffic roads and cause new traffic jams. This article presents Themis, a participatory system navigating drivers in a balanced way. By analyzing time-stamped position reports and route decisions collected from the Themis mobile app, the Themis server estimates both the current traffic rhythm and the future traffic distribution. According to the estimated travel time and a popularity score computed for each route, Themis coordinates the traffic between alternative routes and proactively alleviates congestion. Themis has been implemented and its performance has been evaluated in both a synthetic experiment using real data from over 26,000 taxis and a field study. Results from both experiments demonstrate that Themis reduces traffic congestion and average travel time at various traffic densities and system penetration rates.


title: How effective is UML modeling ? An empirical perspective on costs and benefits

abstract: Modeling has become a common practice in modern software engineering. Since the mid 1990s the Unified Modeling Language (UML) has become the de facto standard for modeling software systems. The UML is used in all phases of software development: ranging from the requirement phase to the maintenance phase. However, empirical evidence regarding the effectiveness of modeling in software development is few and far apart. This paper aims to synthesize empirical evidence regarding the effectiveness of modeling using UML in software development, with a special focus on the cost and benefits.


title: A comparative study of cyber physical cloud, cloud of sensors and internet of things: Their ideology, similarities and differences

abstract: Internet is an ever changing and rapidly progressing entity. With each passing day, we come across a new technology or concept. Many of these concepts pass as a fad but few become the cornerstone of our future technology. Three of the hottest current research topics which are highly inter-related are cyber physical cloud (CPC), cloud of sensors (CoS) and internet of things (IoT). All these have sensors and cloud as integral part of their architecture. While the first two topics are directly related to cloud, the third one requires cloud computing in backend for huge amounts of data processing and storage. In this paper, we will study these concepts with intent of finding the area of overlap or similarities and their subtle yet important differences.


title: A Novelty Detection Approach to Classification

abstract: Novelty Detection techniques are concept-learning methods that proceed by recognizing positive instances of a concept rather than differentiating between its positive and negative instances. Novelty Detection approaches consequently require very few, if any, negative training instances. This paper presents a particular Novelty Detection approach to classification that uses a Redundancy Compression and Non-Redundancy Differentiation technique based on the [Gluck & Myers, 1993] model of the hippocampus, a part of the brain critically involved in learning and memory. In particular, this approach consists of training an autoencoder to reconstruct positive input instances at the output layer and then using this autoencoder to recognize novel instances. Classification is possible, after training, because positive instances are expected to be reconstructed accurately while negative instances are not. The purpose of this paper is to compare HIPPO, the system that implements this technique, to C4.5 and feedforward neural network classification on several applications.


title: Women's Perceptions of Access to Prenatal Care in the United States: A Literature Review

abstract: Women report many barriers to accessing prenatal care. This article reviews the literature from 1990 to the present on women's perceptions of access to prenatal care within the United States. Barriers can be classified into societal, maternal, and structural dimensions. Women may not be motivated to seek care, especially for unintended pregnancies. Societal and maternal reasons cited for poor motivation include a fear of medical procedures or disclosing the pregnancy to others, depression, and a belief that prenatal care is unnecessary. Structural barriers include long wait times, the location and hours of the clinic, language and attitude of the clinic staff and provider, the cost of services, and a lack of child-friendly facilities. Knowledge of women's views of access can help in development of policies to decrease barriers. Structural barriers could be reduced through changes in clinic policy and prenatal care format, and the creation of child-friendly waiting and examination rooms. Maternal and societal barriers can be addressed through community education. A focus in future research on facilitators of access can assist in creating open pathways to perinatal care for all women.


title: Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

abstract: Importance ::: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. ::: ::: ::: Objective ::: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. ::: ::: ::: Design and Setting ::: A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. ::: ::: ::: Exposure ::: Deep learning-trained algorithm. ::: ::: ::: Main Outcomes and Measures ::: The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. ::: ::: ::: Results ::: The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%. ::: ::: ::: Conclusions and Relevance ::: In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.


title: Body shape and women's attractiveness : The critical role of waist-to-hip ratio.

abstract: This paper examines the role of body fat distribution as measured by waist-to-hip ratio (WHR) on the judgment of women’s physical attractiveness. It presents evidence that WHR is correlated with a woman’s reproductive endocrinological status and long-term health risk. Three studies were conducted to investigate whether humans have perceptual and cognitive mechanisms to utilize the WHR to infer attributes of women’s health, youthfulness, attractiveness, and reproductive capacity. College-age as well as older subjects of both sexes rank female figures with normal weight and low WHR as attractive and assign to them higher reproductive capability. The study concludes that WHR is a reliable and honest signal of a woman’s reproductive potential. The adaptive significance of body fat distribution and its role in mate selection is also discussed.


title: Online farming based on embedded systems and wireless sensor networks

abstract: The agricultural scenario seems to be one of the most promising application areas for WSN due to the necessity of providing the agricultural production chain in terms of precision and quality. This involves a careful system design, since requirements are very strict, battery life-time maximization, robustness, recovery strategies, network flexibility and re-configurability. This paper implements zigbee based wireless sensor network in agriculture such as monitoring of environmental conditions like weather, soil moisture content, soil temperature, soil fertility, weed detection, water level, monitoring growth of the crop, precision agriculture, automated irrigation facility and storage of agricultural products. The aim of this project is to monitor and maintain the farm from any part of the world through internet. Monitoring is done with the help of wireless sensor networks and all the control process is done with the help of the microcontrollers. Wireless cameras are used to feed live video of the farm to any part of the world. With the help of this project, modern technologies and new ideas can be implemented in the farm sector of our country. A farmer can see the real condition of his farm and also control his farm from any part of the world by logging into farming website. This makes the Indian farmers feel comfortable and reduces the risks involved in irrigation. The idea has the potential to attract entrepreneurs to invest in the agricultural sector.


title: 3D User Interfaces: New Directions and Perspectives

abstract: Three-dimensional user interfaces (3D UIs) let users interact with virtual objects, environments, or information using direct 3D input in the physical and/or virtual space. In this article, the founders and organizers of the IEEE Symposium on 3D User Interfaces reflect on the state of the art in several key aspects of 3D UIs and speculate on future research.


title: The human brain is intrinsically organized into dynamic, anticorrelated functional networks

abstract: During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related dichotomy is represented intrinsically in the resting human brain through examination of spontaneous fluctuations in the functional MRI blood oxygen level-dependent signal. We identify two diametrically opposed, widely distributed brain networks on the basis of both spontaneous correlations within each network and anticorrelations between networks. One network consists of regions routinely exhibiting task-related activations and the other of regions routinely exhibiting task-related deactivations. This intrinsic organization, featuring the presence of anticorrelated networks in the absence of overt task performance, provides a critical context in which to understand brain function. We suggest that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain.


title: Weakly Supervised Multilabel Clustering and its Applications in Computer Vision

abstract: Clustering is a useful statistical tool in computer vision and machine learning. It is generally accepted that introducing supervised information brings remarkable performance improvement to clustering. However, assigning accurate labels is expensive when the amount of training data is huge. Existing supervised clustering methods handle this problem by transferring the bag-level labels into the instance-level descriptors. However, the assumption that each bag has a single label limits the application scope seriously. In this paper, we propose weakly supervised multilabel clustering, which allows assigning multiple labels to a bag. Based on this, the instance-level descriptors can be clustered with the guidance of bag-level labels. The key technique is a weakly supervised random forest that infers the model parameters. Thereby, a deterministic annealing strategy is developed to optimize the nonconvex objective function. The proposed algorithm is efficient in both the training and the testing stages. We apply it to three popular computer vision tasks: 1) image clustering; 2) semantic image segmentation; and 3) multiple objects localization. Impressive performance on the state-of-the-art image data sets is achieved in our experiments.


title: Data-driven Discovery of Closure Models

abstract: Derivation of reduced order representations of dynamical systems requires the modeling of the truncated dynamics on the retained dynamics. In its most general form, this so-called closure model has to account for memory effects. In this work, we present a framework of operator inference to extract the governing dynamics of closure from data in a compact, non-Markovian form. We employ sparse polynomial regression and artificial neural networks to extract the underlying operator. For a special class of non-linear systems, observability of the closure in terms of the resolved dynamics is analyzed and theoretical results are presented on the compactness of the memory. The proposed framework is evaluated on examples consisting of linear to nonlinear systems with and without chaotic dynamics, with an emphasis on predictive performance on unseen data.


title: Multidisciplinary approach to defining public e-procurement and evaluating its impact on procurement efficiency

abstract: Utilising the methodology of content analysis, this study uses a multidisciplinary approach to define public e-procurement. Various aspects of e-procurement have been discussed from information systems, supply chain management, electronic commerce/electronic government, and public procurement to come up with an integrated definition of public e-procurement. Following this, e-procurement assimilation has been defined and its impact on procurement efficiency has been evaluated. Following the confirmatory factor analysis in structural equation modelling, dimensional level analysis in ANOVA has been undertaken for the three forms of e-procurement technologies namely e-tendering, e-catalogue management systems, and e-marketplace. The results show the positive and significant impact of the assimilation process on procurement efficiency.


title: Budgeted Online Collective Inference

abstract: Updating inference in response to new evidence is a fundamental challenge in artificial intelligence. Many real problems require large probabilistic graphical models, containing millions of interdependent variables. For such large models, jointly updating the most likely (i.e., MAP) configuration of the variables each time new evidence is encountered can be infeasible, even if inference is tractable. In this paper, we introduce budgeted online collective inference, in which the MAP configuration of a graphical model is updated efficiently by revising the assignments to a subset of the variables while holding others fixed. The goal is to selectively update certain variables without sacrificing quality with respect to full inference. To formalize the consequences of partially updating inference, we introduce the concept of inference regret. We derive inference regret bounds for a class of graphical models with strongly-convex free energies. These theoretical insights, combined with a thorough analysis of the optimization solver, motivate new approximate methods for efficiently updating the variable assignments under a budget constraint. In experiments, we demonstrate that our algorithms can reduce inference time by 65% with accuracy comparable to full inference.


title: Limiting the Spread of Misinformation in Social Networks

abstract: In this work, we study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the notion of limiting campaigns to counteract the effect of misinformation. The problem can be summarized as identifying a subset of individuals that need to be convinced to adopt the competing (or "good") campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. We show that this optimization problem is NP-hard and provide approximation guarantees for a greedy solution for various definitions of this problem by proving that they are submodular. We experimentally compare the performance of the greedy method to various heuristics. The experiments reveal that in most cases inexpensive heuristics such as degree centrality compare well with the greedy approach. We also study the influence limitation problem in the presence of missing data where the current states of nodes in the network are only known with a certain probability and show that prediction in this setting is a supermodular problem. We propose a prediction algorithm that is based on generating random spanning trees and evaluate the performance of this approach. The experiments reveal that using the prediction algorithm, we are able to tolerate about 90% missing data before the performance of the algorithm starts degrading and even with large amounts of missing data the performance degrades only to 75% of the performance that would be achieved with complete data.


title: Prenatal maternal depression and child serotonin transporter linked polymorphic region (5-HTTLPR) and dopamine receptor D4 (DRD4) genotype predict negative emotionality from 3 to 36 months.

abstract: Prenatal maternal depression and a multilocus genetic profile of two susceptibility genes implicated in the stress response were examined in an interaction model predicting negative emotionality in the first 3 years. In 179 mother–infant dyads from the Maternal Adversity, Vulnerability, and Neurodevelopment cohort, prenatal depression (Center for Epidemiologic Studies Depressions Scale) was assessed at 24 to 36 weeks. The multilocus genetic profile score consisted of the number of susceptibility alleles from the serotonin transporter linked polymorphic region gene ( 5-HTTLPR ): no long-rs25531(A) (L A : short/short, short/long-rs25531(G) [L G ], or L G /L G ] vs. any L A ) and the dopamine receptor D4 gene (six to eight repeats vs. two to five repeats). Negative emotionality was extracted from the Infant Behaviour Questionnaire—Revised at 3 and 6 months and the Early Child Behavior Questionnaire at 18 and 36 months. Mixed and confirmatory regression analyses indicated that prenatal depression and the multilocus genetic profile interacted to predict negative emotionality from 3 to 36 months. The results were characterized by a differential susceptibility model at 3 and 6 months and by a diathesis–stress model at 36 months.


title: Hip Capsular Reconstruction Using Dermal Allograft

abstract: Because hip arthroscopic procedures are increasing in number, complications related to the operation itself are starting to emerge. Whereas the capsule has been recognized as an important static stabilizer for the hip, it has not been until recently that surgeons have realized the importance of its preservation and restoration. Disruption of the capsule during arthroscopic procedures is a potential contributor to postoperative iatrogenic hip instability. In cases of a symptomatic deficient capsule, a capsular reconstruction is mandatory because instability may lead to detrimental chondral and labral changes. The purpose of this report was to describe our technique for arthroscopic hip capsular reconstruction using dermal allograft.


title: Classification of text documents

abstract: The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have made this classification problem very difficult. We investigate four different methods for document classification: the naive Bayes classifier, the nearest neighbour classifier, decision trees and a subspace method. These were applied to seven-class Yahoo news groups (business, entertainment, health, international, politics, sports and technology) individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classifiers on our data sets. Combinations of multiple classifiers did not always improve the classification accuracy compared to the best individual classifier. Among the three different combination approaches, our adaptive classifier combination method introduced here performed the best. The best classification accuracy that we are able to achieve on this seven-class problem is approximately 83%, which is comparable to the performance of other similar studies. However, the classification problem considered here is more difficult because the pattern classes used in our experiments have a large overlap of words in their corresponding documents.


title: Discovering scientific influence using cross-domain dynamic topic modeling

abstract: We describe an approach using dynamic topic modeling to model influence and predict future trends in a scientific discipline. Our study focuses on climate change and uses assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, which comprise a correlated dataset of temporally grounded documents. We use a custom dynamic topic modeling algorithm to generate topics for both datasets and apply cross-domain analytics to identify the correlations between the IPCC chapters and their cited documents. The approach reveals both the influence of the cited research on the reports and how previous research citations have evolved over time. For the IPCC use case, the report topic model used 410 documents and a vocabulary of 5911 terms while the citations topic model was based on 200K research papers and a vocabulary more than 25K terms. We show that our approach can predict the importance of its extracted topics on future IPCC assessments through the use of cross domain correlations, Jensen-Shannon divergences and cluster analytics.


title: Predictors of Postpartum Depression in Partnered Mothers and Fathers from a Longitudinal Cohort

abstract: Postpartum depression (PPD) is a growing mental health concern in new mothers and fathers. The purpose of this study was to determine the predictors of depression at 3 months postpartum, comparing depressed couples to couples with only one depressed partner or no depressed partner, using data from the Alberta Pregnancy Outcomes and Nutrition study. Data from mothers and fathers were collected at second trimester and 3 months postpartum. Results showed predictors of PPD in mothers to be low household income, high prenatal depressive symptoms, and postnatally, low social support and higher number of stressful life events. Fathers had similar predictors, including low household income, high prenatal depressive symptoms, and postnatally low social support and smoking. Compared with non-depressed couples, factors that predicted PPD in both mothers and fathers in couples included low income, high prenatal depressive symptoms in mothers and low prenatal social support reported by fathers.


title: Variational inference for crowdsourcing

abstract: Crowdsourcing has become a popular paradigm for labeling large datasets. However, it has given rise to the computational task of aggregating the crowdsourced labels provided by a collection of unreliable annotators. We approach this problem by transforming it into a standard inference problem in graphical models, and applying approximate variational methods, including belief propagation (BP) and mean field (MF). We show that our BP algorithm generalizes both majority voting and a recent algorithm by Karger et al. [1], while our MF method is closely related to a commonly used EM algorithm. In both cases, we find that the performance of the algorithms critically depends on the choice of a prior distribution on the workers' reliability; by choosing the prior properly, both BP and MF (and EM) perform surprisingly well on both simulated and real-world datasets, competitive with state-of-the-art algorithms based on more complicated modeling assumptions.


title: A Bioinspired Soft Actuated Material

abstract: E. T. Roche, J. T.B. Overvelde, Prof. D. J. Mooney, Prof. K. Bertoldi, Prof. C. J. Walsh School of Engineering and Applied Sciences Harvard University Pierce Hall, 29 Oxford Street Cambridge , MA , 02138 , USA E-mail: walsh@seas.harvard.edu E. T. Roche, Prof. D. J. Mooney, Prof. C. J. Walsh Wyss Institute for Biologically Inspired Engineering Harvard University 3 Blackfan circle , Boston , MA , 02155 , USA R. Wohlfarth Technical University of Munich Germany , Arcisstr. 21 , D-80333 , Munich , Germany J. T. B. Overvelde, Prof. K. Bertoldi Kavli Institute for Bionano Science and Technology Harvard University 29 Oxford Street , Cambridge , MA , 02138 , USA Dr. N. V. Vasilyev, Dr. F. A. Pigula Department of Cardiac Surgery Boston Children’s Hospital 300 Longwood Ave , Boston , MA , 02115 , USA


title: Exploring of clustering algorithm on class-imbalanced data

abstract: Imbalanced data distribution still remains an unsolved problem in data mining and machine learning. This paper introduces the problem of the class-imbalanced data in classification learning and naturally introduces it into the clustering learning since data clustering is an important and frequently used unsupervised learning method. In this paper, two verification methods based on two different aspects of original data are proposed to test and verify the influence of class-imbalanced data on clustering. Furthermore, we also conduct some experiments on different imbalanced-ratios to exploring its importance in clustering algorithm since is a very important factor for the performance in classification learning. Experimental results indicate that the class-imbalance of the dataset can seriously influence the final performance and efficiency of the clustering algorithm, and the higher the ratio, the higher the adverse effects of the clustering performance based on class-imbalanced data.


Volatools : Integrating Volatile Memory Forensics into the Digital Investigation Process


title: Analysis and design of broadband U-slot cut rectangular microstrip antennas

abstract: Broadband microstrip antenna using variations of U-slot has been widely reported. However, in most of the reported work, an in-depth explanation about the mode introduced by U-slot and procedure to design U-slot cut antennas at any given frequency is not explained. In this paper, first an extensive analysis to study the broadband response in symmetrical and a new configuration of asymmetrical U-slot cut rectangular microstrip antennas is presented. The U-slot tunes higher-order orthogonal mode resonance frequency of the patch with respect to fundamental mode to realise wider bandwidth. Further formulation in resonant length at modified patch modes in symmetrical U-slot cut antenna is proposed. Frequencies calculated using these formulations show closer agreement with simulated and measured results. Using proposed formulations, a procedure to design U-slot cut antenna at different frequencies over 800–4000 MHz range which shows broadband response is explained. Thus, the proposed work gives an insight into the functioning of widely used U-slot cut antennas and the formulations will be helpful for designing at any given frequency.


Knocking the door to the deep Web: integrating Web query interfaces


title: Online Grocery Order Fulfillment Tradeoffs

abstract: This study analyzes tradeoffs online grocers face in their fulfillment strategy. Brick and click grocers initially favor fulfillment from stores rather than distribution centers. However, store fulfillment is vulnerable to congestion and "trolley rage" when pickers of online orders get in the way of traditional shoppers. Although fulfillment from distribution centers relieves store congestion, and improves picking productivity and product selection, online grocers show concern for the high cost and uncertain demand. We propose a model for minimizing congestion of online grocery orders. Practitioners can use the model to prevent customer dissatisfaction while researchers will find this study provides a basis for future model variants.


title: Instructional control of reinforcement learning : A behavioral and neurocomputational investigation

abstract: Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is “overridden” at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract “Q-learning” and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a “confirmation bias” in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.


A DIRT-T A PPROACH TO U NSUPERVISED D OMAIN


title: The social engineering attack spiral (SEAS)

abstract: Cybercrime is on the increase and attacks are becoming ever more sophisticated. Organisations are investing huge sums of money and vast resources in trying to establish effective and timely countermeasures. This is still a game of catch up, where hackers have the upper hand and potential victims are trying to produce secure systems hardened against what feels like are inevitable future attacks. The focus so far has been on technology and not people and the amount of resource allocated to countermeasures and research into cyber security attacks follows the same trend. This paper adds to the growing body of work looking at social engineering attacks and therefore seeks to redress this imbalance to some extent. The objective is to produce a model for social engineering that provides a better understanding of the attack process such that improved and timely countermeasures can be applied and early interventions implemented.


title: The use of predictive analytics technology to detect credit card fraud in Canada

abstract: Credit card fraud losses in Canada continue to increase despite the availability of various prevention technologies. To reduce financial loss and reputational risks, credit card companies and financial institutions in Canada have adopted fraud monitoring solutions such as Predictive Analytics Technologies (PAT) as part of fraud prevention programs to mitigate credit card fraud. This research paper focuses on the creation of a scorecard from relevant evaluation criteria, features, and capabilities of predictive analytics vendor solutions currently being used to detect credit card fraud. The scorecard provides a side-by-side comparison of five credit card predictive analytics vendor solutions adopted in Canada. From the ensuing research findings, a list of credit card fraud PAT vendor solution challenges, risks, and limitations was outlined.


title: Dynamic duty cycle and adaptive contention window based QoS-MAC protocol for wireless multimedia sensor networks

abstract: Rapid penetration of small, customized wireless devices and enormous growth of wireless communication technologies has already set the stage for large-scale deployment of wireless sensor networks. While the need to minimize the energy consumption has driven significant researches in wireless sensor networks, offering some precise quality of service (QoS) for multimedia transmission over sensor networks has not received significant attention. However, the emerging new applications like video surveillance, telemedicine and traffic monitoring needs transmission of wireless multimedia over sensor networks. Naturally, offering some better QoS for wireless multimedia over sensor networks raises significant challenges. The network needs to cope up with battery-constraints, while providing improved QoS (end-to-end delay and bandwidth requirement). This calls for a suitable sensory MAC protocol capable of achieving application-specific QoS. In this paper, we have proposed a new QoS-based sensory MAC protocol, which not only adapts to application-oriented QoS, but also attempts to conserve energy without violating QoS-constraints. Performance modeling, analysis and simulation results demonstrate that the proposed protocol is capable of providing lower delay and better throughput, at the cost of reasonable energy consumption, in comparison to other existing sensory MAC protocols.


Wind, phosphorus release and division rate of Peridinium in Lake Kinneret


title: Psycholinguistic Markers for Specific Language Impairment (SLI)

abstract: In this study 160 children, aged 11 years with a definite history of specific language impairment (SLI), completed four tasks that could be potential positive psycholinguistic markers for this impairment: a third person singular task, a past tense task, a nonword repetition task, and a sentence repetition task. This allowed examination of more than one type of marker simultaneously, facilitating both comparisons between markers and also evaluation of combinations of markers in relation to identifying SLI. The study also provided data regarding the markers in relation to nonverbal IQ, made use of new normative data on all tasks, and examined marker accuracy in relation to current language status. The results show that markers vary in accuracy, with sentence repetition (a previously unused marker) proving to be the most useful. This psycholinguistic marker shows high levels of sensitivity (90%), specificity (85%), and overall accuracy (88%), as well as being able to identify the majority of children whose current language status falls in the normal range despite a history of SLI.


title: Stock Prediction Using Functional Link Artificial Neural Network (FLANN)

abstract: Stock exchange that is, buying and selling of stock is considered to be an important factor in the economy sector. The Stockbrokers typically use time series or technical analysis in predicting the stock price. These techniques are based on trends and not the actual stock value. Therefore a method of prediction which takes into account the historical values of stock is desired. Neural Networks once again have become famous for prediction of stock. This is due to their ability to deal with non-linear data. The use of Artificial Neural Networks to for predicting the stock prices is proposed in this paper. The input features to the model sometimes can be non-related to the output. Hence, Functional Link Artificial Neural Networks is used here to increase the number of related features in the form of inputs. The data is taken from NSE and is converted into a suitable form for FLANN and then prediction is carried out using Multi-layer feed forward Perceptron model.


title: A family of 45nm IA processors

abstract: Nehalem is a family of next-generation IA processors for mobile, desktop and server segments implemented in 45nm high-κ metal-gate CMOS [1]. The family features a new system architecture, significantly enhanced Core architecture [2], innovations in power management and modular design. The 4-core 8MB-L3-cache die has 731M transistors. We introduce a coherent point-to-point link called QuickPath Interconnect that is the foundation for coherent communication between IA processors, chipsets, I/O hubs and coprocessors/ accelerators for multiple generations. It features advanced power management, RAS capabilities and reduced hops/latency. At the physical level, it uses unidirectional variable-width differential signaling. The 45nm implementation features current-mode signaling with cascode driver, clock forwarding, source and sink termination, per-bit skew compensation and 2-tap driver equalization, as showin in Fig. 3.2.1. Signaling speed is up to 6.4GT/s in 45nm but the basic approach scales to 20GT/s using conventional copper interconnect [3].


title: Automatic allocation of identifiers in linear wireless sensor networks using link-level processes

abstract: Wireless sensor networks, which are used for monitoring large-scale linear structures, are having a significant importance due to its implementation and low cost maintenance. The main challenges that this technology faces are the location and automatic identification of thousands of nodes that are part of a linear large-scale infrastructure, with minimum processing and low power consumption of the nodes. This paper proposes an automatic identification and location mechanism of the nodes that are part of a linear structure using link-level processes and the IEEE 802.15.4 protocol. This mechanism improves the reliability in the allocation process of identifiers without using routing protocols to minimize the number of sent messages, also the computation process is done at the nodes, and therefore it reduces energy consumption. The proposed algorithm is evaluated using a prototype, for which we present initial performance analysis results focusing on delay. The implementation results confirm that with the proposed algorithm we obtain automatic allocations of identifiers.


title: Coupling Dimensionality Reduction with Generative Model for Non-Interactive Private Data Release

abstract: A key challenge facing the design of differentially private systems in the non-interactive setting is to maintain the utility of the released data for general analytics applications. To overcome this challenge, we propose the PCA-Gauss system that leverages the novel combination of dimensionality reduction and generative model for synthesizing differentially private data. ::: We present multiple algorithms that combine dimensionality reduction and generative models for a range of machine learning applications, including both unsupervised and supervised learning. Our key theoretical results prove that (a) our algorithms satisfy the strong epsilon-differential privacy guarantee, and (b) dimensionality reduction can quadratically lower the level of perturbation required for differential privacy, with minimal cost of information loss. ::: Finally, we illustrate the effectiveness of PCA-Gauss under three common machine learning applications -- clustering, classification, and regression -- on three large real-world datasets. Our empirical results show that (a) PCA-Gauss outperforms previous approaches by an order of magnitude, and (b) loss in utility compared to the non-private real data is small. Thus, PCA-Gauss can serve as a key enabler for real-world deployment of privacy-preserving data release.


title: Adversarial Examples on Discrete Sequences for Beating Whole-Binary Malware Detection

abstract: In recent years, deep learning has shown performance breakthroughs in many applications, such as image detection, image segmentation, pose estimation, and speech recognition. It was also applied successfully to malware detection. However, this comes with a major concern: deep networks have been found to be vulnerable to adversarial examples. So far successful attacks have been proved to be very effective especially in the domains of images and speech, where small perturbations to the input signal do not change how it is perceived by humans but greatly affect the classification of the model under attack. Our goal is to modify a malicious binary so it would be detected as benign while preserving its original functionality. In contrast to images or speech, small modifications to bytes of the binary lead to significant changes in the functionality. We introduce a novel approach to generating adversarial example for attacking a whole-binary malware detector. We append to the binary file a small section, which contains a selected sequence of bytes that steers the prediction of the network from malicious to be benign with high confidence. We applied this approach to a CNN-based malware detection model and showed extremely high rates of success in the attack.


title: A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations

abstract: Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations.


title: An efficient GPU-based approach for interactive global illumination

abstract: This paper presents a GPU-based method for interactive global illumination that integrates complex effects such as multi-bounce indirect lighting, glossy reflections, caustics, and arbitrary specular paths. Our method builds upon scattered data sampling and interpolation on the GPU. We start with raytraced shading points and partition them into coherent shading clusters using adaptive seeding followed by k-means. At each cluster center we apply final gather to evaluate its incident irradiance using GPU-based photon mapping. We approximate the entire photon tree as a compact illumination cut, thus reducing the final gather cost for each ray. The sampled irradiance values are then interpolated at all shading points to produce rendering. Our method exploits the spatial coherence of illumination to reduce sampling cost. We sample sparsely and the distribution of sample points conforms with the underlying illumination changes. Therefore our method is both fast and preserves high rendering quality. Although the same property has been exploited by previous caching and adaptive sampling methods, these methods typically require sequential computation of sample points, making them ill-suited for the GPU. In contrast, we select sample points adaptively in a single pass, enabling parallel computation. As a result, our algorithm runs entirely on the GPU, achieving interactive rates for scenes with complex illumination effects.


title: Deconstructing the Cortical Column in the Barrel Cortex

abstract: The question of what function is served by the cortical column has occupied neuroscientists since its original description some 60years ago. The answer seems tractable in the somatosensory cortex when considering the inputs to the cortical column and the early stages of information processing, but quickly breaks down once the multiplicity of output streams and their sub-circuits are brought into consideration. This article describes the early stages of information processing in the barrel cortex, through generation of the center and surround receptive field components of neurons that subserve integration of multi whisker information, before going on to consider the diversity of properties exhibited by the layer 5 output neurons. The layer 5 regular spiking (RS) neurons differ from intrinsic bursting (IB) neurons in having different input connections, plasticity mechanisms and corticofugal projections. In particular, layer 5 RS cells employ noise reduction and homeostatic plasticity mechanism to preserve and even increase information transfer, while IB cells use more conventional Hebbian mechanisms to achieve a similar outcome. It is proposed that the rodent analog of the dorsal and ventral streams, a division reasonably well established in primate cortex, might provide a further level of organization for RS cell function and hence sub-circuit specialization.


title: Merkle-Damg˚ard revisited: How to construct a hash function

abstract: The most common way of constructing a hash function (e.g., SHA-1) is to iterate a compression function on the input message. The compression function is usually designed from scratch or made out of a block-cipher. In this paper, we introduce a new security notion for hash-functions, stronger than collision-resistance. Under this notion, the arbitrary length hash function H must behave as a random oracle when the fixed-length building block is viewed as a random oracle or an ideal block-cipher. The key property is that if a particular construction meets this definition, then any cryptosystem proven secure assuming H is a random oracle remains secure if one plugs in this construction (still assuming that the underlying fixed-length primitive is ideal). In this paper, we show that the current design principle behind hash functions such as SHA-1 and MD5 — the (strengthened) Merkle-Damgard transformation — does not satisfy this security notion. We provide several constructions that provably satisfy this notion; those new constructions introduce minimal changes to the plain Merkle-Damgard construction and are easily implementable in practice.


title: AffectiveSpace 2: enabling affective intuition for concept-level sentiment analysis

abstract: Predicting the affective valence of unknown multi-word expressions is key for concept-level sentiment analysis. AffectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language concepts. By reducing the dimensionality of affective common-sense knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intuitively clustered according to their semantic and affective relatedness. Such an affective intuition (so called because it does not rely on explicit features, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multiword expressions, thus achieving efficient concept-level sentiment analysis.


title: Detailed, Accurate, Human Shape Estimation from Clothed 3D Scan Sequences

abstract: We address the problem of estimating human pose and body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this problem by estimating body shape under clothing from a sequence of 3D scans. Previous methods that have exploited body models produce smooth shapes lacking personalized details. We contribute a new approach to recover a personalized shape of the person. The estimated shape deviates from a parametric model to fit the 3D scans. We demonstrate the method using high quality 4D data as well as sequences of visual hulls extracted from multi-view images. We also make available BUFF, a new 4D dataset that enables quantitative evaluation (http://buff.is.tue.mpg.de). Our method outperforms the state of the art in both pose estimation and shape estimation, qualitatively and quantitatively.


title: Paralinguistics in speech and language-State-of-the-art and the challenge

abstract: Paralinguistic analysis is increasingly turning into a mainstream topic in speech and language processing. This article aims to provide a broad overview of the constantly growing field by defining the field, introducing typical applications, presenting exemplary resources, and sharing a unified view of the chain of processing. It then presents the first broader Paralinguistic Challenge organised at INTERSPEECH 2010 by the authors including a historical overview of the Challenge tasks of recognising age, gender, and affect, a summary of methods used by the participants, and their results. In addition, we present the new benchmark obtained by fusion of participants' predictions and conclude by discussing ten recent and emerging trends in the analysis of paralinguistics in speech and language.


title: A Similarity Measure for Text Classification and Clustering

abstract: Measuring the similarity between documents is an important operation in the text processing field. In this paper, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature, the proposed measure takes the following three cases into account: a) The feature appears in both documents, b) the feature appears in only one document, and c) the feature appears in none of the documents. For the first case, the similarity increases as the difference between the two involved feature values decreases. Furthermore, the contribution of the difference is normally scaled. For the second case, a fixed value is contributed to the similarity. For the last case, the feature has no contribution to the similarity. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems. The results show that the performance obtained by the proposed measure is better than that achieved by other measures.


title: Near optimum error correcting coding and decoding: turbo-codes

abstract: This paper presents a new family of convolutional codes, nicknamed turbo-codes, built from a particular concatenation of two recursive systematic codes, linked together by nonuniform interleaving. Decoding calls on iterative processing in which each component decoder takes advantage of the work of the other at the previous step, with the aid of the original concept of extrinsic information. For sufficiently large interleaving sizes, the correcting performance of turbo-codes, investigated by simulation, appears to be close to the theoretical limit predicted by Shannon.


title: Big data analytics of geosocial media for planning and real-time decisions

abstract: Geosocial Network data can be served as an asset for the authorities to make real-time decisions and future planning by analyzing geosocial media posts. However, there are millions of Geosocial Network users who are producing overwhelming of data, called “Big Data” that is challenging to be analyzed and make real-time decisions. Therefore, in this paper, we proposed an efficient system for exploring Geosocial Networks while harvesting data as well as user's location information. A system architecture is proposed that processes an abundant amount of various social networks' data to monitor Earth events, incidents, medical diseases, user trends, and views to make future real-time decisions and facilitate future planning. The proposed system consists of five layers, i.e., data collection, data processing, application, communication, and data storage. The system deploys Spark at the top of the Hadoop ecosystem in order to run real-time analyses. Twitter and Flickr are analyzed using the proposed architecture in order to identify current events or disasters, such as earthquakes, fires, Ebola virus, and snow. The system is evaluated with respect to efficiency while considering system throughput. We proved that the system has higher throughput and is capable of analyzing massive Geosocial Network data at real-time.


title: Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN

abstract: Learning the spatial-temporal representation of motion information is crucial to human action recognition. Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion. To address this problem, this paper proposes a long-term motion descriptor called sequential deep trajectory descriptor (sDTD). Specifically, we project dense trajectories into two-dimensional planes, and subsequently a CNN-RNN network is employed to learn an effective representation for long-term motion. Unlike the popular two-stream ConvNets, the sDTD stream is introduced into a three-stream framework so as to identify actions from a video sequence. Consequently, this three-stream framework can simultaneously capture static spatial features, short-term motion, and long-term motion in the video. Extensive experiments were conducted on three challenging datasets: KTH, HMDB51, and UCF101. Experimental results show that our method achieves state-of-the-art performance on the KTH and UCF101 datasets, and is comparable to the state-of-the-art methods on the HMDB51 dataset.


title: A simple way to broaden objects search area for tracking methods based on discriminative correlation filters

abstract: We propose a simple way to broaden objects search area for tracking methods based on discriminative correlation filter using two window functions of different bandwidth. Testing at the VOT-2016 Challenge confirmed that this approach provided higher robustness and accuracy of tracking, comparing to standard realizations, where only one window function was used. We created a software implementation of tracking system based on the proposed approach and estimated its performance. The proposed software realization for PC platform and modern ARM platforms can track objects in real-time.


title: Deep Learning of Representations: Looking Forward

abstract: Deep learning research aims at discovering learning algorithms that discover multiple levels of distributed representations, with higher levels representing more abstract concepts. Although the study of deep learning has already led to impressive theoretical results, learning algorithms and breakthrough experiments, several challenges lie ahead. This paper proposes to examine some of these challenges, centering on the questions of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data. It also proposes a few forward-looking research directions aimed at overcoming these challenges.


title: Germanium dioxide whiskers synthesized by laser ablation

abstract: We obtained germanium dioxide (GeO2) whiskers in bulk quantity by ablating a germanium target at 820 °C with a pulsed KrF excimer laser in an argon atmosphere. Most of the GeO2 whiskers were smooth and straight with hexagonal or triangular, or quadrilateral cross sections while some of them had a bamboo-shoot-shaped form. Results of scanning electron microscopy, transmission electron microscopy, and x-ray diffraction showed that the whiskers are hexagonal crystalline GeO2.


From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis


title: EventJava: An Extension of Java for Event Correlation

abstract: Event correlation has become the cornerstone of many reactive applications, particularly in distributed systems. However, support for programming with complex events is still rather specific and rudimentary. This paper presents EventJava, an extension of Java with generic support for event-based distributed programming. EventJava seamlessly integrates events with methods, and broadcasting with unicasting of events; it supports reactions to combinations of events, and predicates guarding those reactions. EventJava is implemented as a framework to allow for customization of event semantics, matching, and dispatching. We present its implementation, based on a compiler transforming specific primitives to Java, along with a reference implementation of the framework. We discuss ordering properties of EventJava through a formalization of its core as an extension of Featherweight Java. In a performance evaluation, we show that EventJava compares favorably to a highly tuned database-backed event correlation engine as well as to a comparably lightweight concurrency mechanism.