title: Defining and Assessing Professional Competence

abstract: CONTEXT ::: Current assessment formats for physicians and trainees reliably test core knowledge and basic skills. However, they may underemphasize some important domains of professional medical practice, including interpersonal skills, lifelong learning, professionalism, and integration of core knowledge into clinical practice. ::: ::: ::: OBJECTIVES ::: To propose a definition of professional competence, to review current means for assessing it, and to suggest new approaches to assessment. ::: ::: ::: DATA SOURCES ::: We searched the MEDLINE database from 1966 to 2001 and reference lists of relevant articles for English-language studies of reliability or validity of measures of competence of physicians, medical students, and residents. ::: ::: ::: STUDY SELECTION ::: We excluded articles of a purely descriptive nature, duplicate reports, reviews, and opinions and position statements, which yielded 195 relevant citations. ::: ::: ::: DATA EXTRACTION ::: Data were abstracted by 1 of us (R.M.E.). Quality criteria for inclusion were broad, given the heterogeneity of interventions, complexity of outcome measures, and paucity of randomized or longitudinal study designs. ::: ::: ::: DATA SYNTHESIS ::: We generated an inclusive definition of competence: the habitual and judicious use of communication, knowledge, technical skills, clinical reasoning, emotions, values, and reflection in daily practice for the benefit of the individual and the community being served. Aside from protecting the public and limiting access to advanced training, assessments should foster habits of learning and self-reflection and drive institutional change. Subjective, multiple-choice, and standardized patient assessments, although reliable, underemphasize important domains of professional competence: integration of knowledge and skills, context of care, information management, teamwork, health systems, and patient-physician relationships. Few assessments observe trainees in real-life situations, incorporate the perspectives of peers and patients, or use measures that predict clinical outcomes. ::: ::: ::: CONCLUSIONS ::: In addition to assessments of basic skills, new formats that assess clinical reasoning, expert judgment, management of ambiguity, professionalism, time management, learning strategies, and teamwork promise a multidimensional assessment while maintaining adequate reliability and validity. Institutional support, reflection, and mentoring must accompany the development of assessment programs.


title: Deep Learning for RFID-Based Activity Recognition

abstract: We present a system for activity recognition from passive RFID data using a deep convolutional neural network. We directly feed the RFID data into a deep convolutional neural network for activity recognition instead of selecting features and using a cascade structure that first detects object use from RFID data followed by predicting the activity. Because our system treats activity recognition as a multi-class classification problem, it is scalable for applications with large number of activity classes. We tested our system using RFID data collected in a trauma room, including 14 hours of RFID data from 16 actual trauma resuscitations. Our system outperformed existing systems developed for activity recognition and achieved similar performance with process-phase detection as systems that require wearable sensors or manually-generated input. We also analyzed the strengths and limitations of our current deep learning architecture for activity recognition from RFID data.


title: PixelTone: a multimodal interface for image editing

abstract: Photo editing can be a challenging task, and it becomes even more difficult on the small, portable screens of mobile devices that are now frequently used to capture and edit images. To address this problem we present PixelTone, a multimodal photo editing interface that combines speech and direct manipulation. We observe existing image editing practices and derive a set of principles that guide our design. In particular, we use natural language for expressing desired changes to an image, and sketching to localize these changes to specific regions. To support the language commonly used in photo-editing we develop a customized natural language interpreter that maps user phrases to specific image processing operations. Finally, we perform a user study that evaluates and demonstrates the effectiveness of our interface.


title: Fabrication of continuous diffractive optical elements using a fast tool servo diamond turning process

abstract: Continuous diffractive optical elements (CDOEs) can be used for laser-beam reshaping, pattern generation and can help reduce large angle scattering. Lithography, the method for the production of binary diffractive surfaces, is not suitable for fabrication of CDOEs. Diamond turning using fast tool servo, on the other hand, is a non-cleanroom method for generating continuous microstructures with high precision and efficiency. In this paper, an algorithm for designing CDOEs is introduced. The moving least-squares (MLS) method is then used to obtain the local fitting equation of the diffractive surface. Based on the MLS fitting equation, the selection of diamond cutting tool geometries (including the tool nose radius, rake angle and clearance angle) is discussed and a tool nose radius compensation algorithm is included. This algorithm is a general method for the diamond turning of complex surfaces that can be represented by a point cloud. Surface measurements and diffractive patterns generated on test samples have shown that continuous diffractive surfaces were successfully machined. In the future, CDOEs can be machined on an optical mold surface for high-volume industrial production using methods such as injection molding.


title: Active Silicon Interposer Design for Interposer-Level Wireless Power Transfer Technology for High-Density 2.5-D and 3-D ICs

abstract: In this paper, we first propose and demonstrate an interposer-level wireless power transfer (WPT) scheme to reduce the number of wired power supply interconnections for high-density silicon interposer-based 2.5-D and 3-D integrated circuits (2.5-D and 3-D ICs). We first suggest the concept of an active silicon interposer, fabricate it with active circuits for WPT, and finally proved its WPT operation. The active circuits are the rectifier and dc–dc converter designed with 0.18- $\mu \text{m}$ SK-Hynix CMOS process to convert wirelessly delivered ac power from a printed circuit board (PCB) package into dc power. We improved the power transfer efficiency by applying magnetic field resonance coupling with proper coil structure selection. The equivalent circuit model of the WPT interconnection scheme is suggested with analytic equations. For experimental demonstration, the fabricated active silicon interposer is attached on PCB package. The average dc output voltage of the designed CMOS rectifier with an MIM smoothing capacitor was measured 2.21 V, and that of the designed dc–dc converter was measured 1 V, which is a suitable voltage level for 1 V devices in 2.5-D and 3-D ICs.


title: Multi-Lane Dection and Tracking using Dual Parabolic Model

abstract: Multi-lane recognition system gives a significant assistance for driver’s safety. In this paper, we try to use a dual parabolic model for more precious and robust lane description. For gathering an efficient feature, we used a parallel RANSAC algorithm. The line model characterization is combined with adjacent point histogram peak analysis and two-axis parabola. Proposed algorithm can detect lane automatically and adaptively in every frame. The designed method is applied using various video images captured from the commercial black box camera and is verified to be robust.


title: Probabilistic Evaluation of Optimal Location of Surge Arresters on EHV and UHV Networks Due to Switching and Lightning Surges

abstract: Switching surges are of primary importance in insulation coordination of extremely high voltage and ultra-high voltage networks. However, in regions of high lightning activity or high ground resistance insulation design, preferably, should be based on the risk of failure caused by lightning and switching surges and the probability of line outage, a combination of lightning and switching flashover rates (SSFOR). This paper describes an effective installation of transmission line arresters (TLAs) to obtain a better protection scheme (i.e., minimizing global risk to the network). As a consequence, protection costs are reduced in accordance with the costs of elements actually protected and the number of TLAs utilized. In order to accomplish this, a probabilistic method for calculating the lightning related failure and an artificial neural network for estimating the SSFOR are presented. A multicriteria optimization method based on a genetic algorithm is also developed to determine the optimum location of TLAs.


title: Globalization and English in Chinese higher education

abstract: ABSTRACT: This paper sets China's education of English majors within the changing global and national context. It examines the impact of accelerating globalisation and the rise of global English, the adjustment of China's English language policy, the growth of the education of English majors and the challenges faced by this sector of education. To adapt to the changes, efforts have been made to change the training models, revise the national curriculum and update textbooks. The introduction of six new training models is significant: “English major plus courses in other specialisms”, “English major plus an orientation towards other disciplines”, “English major plus a minor”, “A major plus English language”, “English language plus another foreign language”, and “Dual degree: BA degree of English language and literature plus another BA degree”. Turning out ‘composite-type’ graduates has become a training objective of the curriculum for English majors, with consequent implications for the future development of this sector of education in China.


title: Semantic Relatedness Using Salient Semantic Analysis

abstract: This paper introduces a novel method for measuring semantic relatedness using semantic profiles constructed from salient encyclopedic features. The model is built on the notion that the meaning of a word can be characterized by the salient concepts found in its immediate context. In addition to being computationally efficient, the new model has superior performance and remarkable consistency when compared to both knowledge-based and corpus-based state-of-the-art semantic relatedness models.


title: Sequential cost-sensitive decision making with reinforcement learning

abstract: Recently, there has been increasing interest in the issues of cost-sensitive learning and decision making in a variety of applications of data mining. A number of approaches have been developed that are effective at optimizing cost-sensitive decisions when each decision is considered in isolation. However, the issue of sequential decision making, with the goal of maximizing total benefits accrued over a period of time instead of immediate benefits, has rarely been addressed. In the present paper, we propose a novel approach to sequential decision making based on the reinforcement learning framework. Our approach attempts to learn decision rules that optimize a sequence of cost-sensitive decisions so as to maximize the total benefits accrued over time. We use the domain of targeted' marketing as a testbed for empirical evaluation of the proposed method. We conducted experiments using approximately two years of monthly promotion data derived from the well-known KDD Cup 1998 donation data set. The experimental results show that the proposed method for optimizing total accrued benefits out performs the usual targeted-marketing methodology of optimizing each promotion in isolation. We also analyze the behavior of the targeting rules that were obtained and discuss their appropriateness to the application domain.


CS341 Final Report: Towards Real-time Detection and Camera Triggering


title: Towards agile and smooth video adaptation in dynamic HTTP streaming

abstract: Dynamic Adaptive Streaming over HTTP (DASH) is widely deployed on the Internet for live and on-demand video streaming services. Video adaptation algorithms in existing DASH systems are either too sluggish to respond to congestion level shifts or too sensitive to short-term network bandwidth variations. Both degrade user video experience. In this paper, we formally study the responsiveness and smoothness trade-off in DASH through analysis and experiments. We show that client-side buffered video time is a good feedback signal to guide video adaptation. We then propose novel video rate control algorithms that balance the needs for video rate smoothness and high bandwidth utilization. We show that a small video rate margin can lead to much improved smoothness in video rate and buffer size. The proposed DASH designs are also extended to work with multiple CDN servers. We develop a fully-functional DASH system and evaluate its performance through extensive experiments on a network testbed and the Internet. We demonstrate that our DASH designs are highly efficient and robust in realistic network environment.


title: What can we learn from Facebook activity?: using social learning analytics to observe new media literacy skills

abstract: Social media platforms such as Facebook are now a ubiquitous part of everyday life for many people. New media scholars posit that the participatory culture encouraged by social media gives rise to new forms of literacy skills that are vital to learning. However, there have been few attempts to use analytics to understand the new media literacy skills that may be embedded in an individual's participation in social media. In this paper, I collect raw activity data that was shared by an exploratory sample of Facebook users. I then utilize factor analysis and regression models to show how (a) Facebook members' online activity coalesce into distinct categories of social media behavior and (b) how these participatory behaviors correlate with and predict measures of new media literacy skills. The study demonstrates the use of analytics to understand the literacies embedded in people's social media activity. The implications speak to the potential of social learning analytics to identify and predict new media literacy skills from data streams in social media platforms.


title: Latency arbitrage, market fragmentation, and efficiency: a two-market model

abstract: We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We propose a simple model of latency arbitrage in which a single security is traded on two exchanges, with aggregate information available to regular traders only after some delay. An infinitely fast arbitrageur profits from market fragmentation by reaping the surplus when the two markets diverge due to this latency in cross-market communication. We develop a discrete-event simulation system to capture this processing and information transfer delay, and using an agent-based approach, we simulate the interactions between high-frequency and zero-intelligence trading agents at the millisecond level. We then evaluate allocative efficiency and market liquidity arising from the simulated order streams, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods.


title: Mining succinct predicated bug signatures

abstract: A bug signature is a set of program elements highlighting the cause or effect of a bug, and provides contextual information for debugging. In order to mine a signature for a buggy program, two sets of execution profiles of the program, one capturing the correct execution and the other capturing the faulty, are examined to identify the program elements contrasting faulty from correct. Signatures solely consisting of control flow transitions have been investigated via discriminative sequence and graph mining algorithms. These signatures might be handicapped in cases where the effect of a bug is not manifested by any deviation in control flow transitions. In this paper, we introduce the notion of predicated bug signature\/ that aims to enhance the predictive power of bug signatures by utilizing both data predicates and control-flow information. We introduce a novel ``discriminative itemset generator'' mining technique to generate succinct\/ signatures which do not contain redundant or irrelevant program elements. Our case studies demonstrate that predicated signatures can hint at more scenarios of bugs where traditional control-flow signatures fail.


title: Minimally Invasive Vertebral Replacement with Cages in Thoracic and Lumbar Spine

abstract: Patients and Methods: Anterior reconstruction of the thoracic and lumbar spine was performed using Synex™-cages for vertebral body replacement implanted via open but minimally invasive access in 57 patients with trauma (n = 48), metastasis (n = 6), pseudarthrosis (n = 2), and spondylodiscitis (n = 1). Results: No case had to be changed into an open procedure. The upper thoracic spine was approached by a right-sided (n = 10), the thoracolumbar junction by a leftsided (n = 37) mini-thoracotomy, and the lumbar spine by a left-sided mini-retroperitoneal approach (n = 10). The overall mean operating time was 150 min (range 40–325 min) but varied depending on the spine pathology and the magnitude of the intervention to the anterior part of the spine. There were neither visceral/vascular complications nor other intra- or postoperative complications related to the minimal access in particular. No intercostal neuralgias, no post-thoracotomy pain syndromes and no superficial or deep wound infections occurred. One patient with metastatic destruction of the vertebra died intraoperatively due to a thromboembolic complication caused by his underlying disease. Two cases of pseudoobstruction were treated conservatively. Conclusion: In this study, we describe the use of a new vertebral body replacement for reconstruction of the thoracic and lumbar spine which had been implanted by minimal-access technology.


title: A 12-b 10-GS/s Interleaved Pipeline ADC in 28-nm CMOS Technology

abstract: A 12-bit 10-GS/s interleaved (IL) pipeline analog-to-digital converter (ADC) is described in this paper. The ADC achieves a signal to noise and distortion ratio (SNDR) of 55 dB and a spurious free dynamic range (SFDR) of 66 dB with a 4-GHz input signal, is fabricated in the 28-nm CMOS technology, and dissipates 2.9 W. Eight pipeline sub-ADCs are interleaved to achieve 10-GS/s sample rate, and mismatches between sub-ADCs are calibrated in the background. The pipeline sub-ADCs employ a variety of techniques to lower power, like avoiding a dedicated sample-and-hold amplifier (SHA-less), residue scaling, flash background calibration, dithering and inter-stage gain error background calibration. A push–pull input buffer optimized for high-frequency linearity drives the interleaved sub-ADCs to enable >7-GHz bandwidth. A fast turn-ON bootstrapped switch enables 100-ps sampling. The ADC also includes the ability to randomize the sub-ADC selection pattern to further reduce residual interleaving spurs.


title: A Comparative Study on Two Typical Schemes for Securing Spatial-Temporal Top-k Queries in Two-Tiered Mobile Wireless Sensor Networks

abstract: A novel network paradigm of mobile edge computing, namely TMWSNs (two-tiered mobile wireless sensor networks), has just been proposed by researchers in recent years for its high scalability and robustness. However, only a few works have considered the security of TMWSNs. In fact, the storage nodes, which are located at the upper layer of TMWSNs, are prone to being attacked by the adversaries because they play a key role in bridging both the sensor nodes and the sink, which may lead to the disclosure of all data stored on them as well as some other potentially devastating results. In this paper, we make a comparative study on two typical schemes, EVTopk and VTMSN, which have been proposed recently for securing Top-k queries in TMWSNs, through both theoretical analysis and extensive simulations, aiming at finding out their disadvantages and advancements. We find that both schemes unsatisfactorily raise communication costs. Specifically, the extra communication cost brought about by transmitting the proof information uses up more than 40% of the total communication cost between the sensor nodes and the storage nodes, and 80% of that between the storage nodes and the sink. We discuss the corresponding reasons and present our suggestions, hoping that it will inspire the researchers researching this subject.


title: Unifying learning to rank and domain adaptation: enabling cross-task document scoring

abstract: For document scoring, although learning to rank and domain adaptation are treated as two different problems in previous works, we discover that they actually share the same challenge of adapting keyword contribution across different queries or domains. In this paper, we propose to study the cross-task document scoring problem, where a task refers to a query to rank or a domain to adapt to, as the first attempt to unify these two problems. Existing solutions for learning to rank and domain adaptation either leave the heavy burden of adapting keyword contribution to feature designers, or are difficult to be generalized. To resolve such limitations, we abstract the keyword scoring principle, pointing out that the contribution of a keyword essentially depends on, first, its importance to a task and, second, its importance to the document. For determining these two aspects of keyword importance, we further propose the concept of feature decoupling, suggesting using two types of easy-to-design features: meta-features and intra-features. Towards learning a scorer based on the decoupled features, we require that our framework fulfill inferred sparsity to eliminate the interference of noisy keywords, and employ distant supervision to tackle the lack of keyword labels. We propose the Tree-structured Boltzmann Machine (T-RBM), a novel two-stage Markov Network, as our solution. Experiments on three different applications confirm the effectiveness of T-RBM, which achieves significant improvement compared with four state-of-the-art baseline methods.


title: Security and usability: the gap in real-world online banking

abstract: Online banking is one of the most sensitive tasks performed by general Internet users. Most traditional banks now offer online banking services, and strongly encourage customers to do online banking with 'peace of mind.' Although banks heavily advertise an apparent '100% online security guarantee,' typically the fine print makes this conditional on users fulfilling certain security requirements. We examine some of these requirements as set by major Canadian banks, in terms of security and usability. We opened personal checking accounts at the five largest Canadian banks, and one online-only bank. We found that many security requirements are too difficult for regular users to follow, and believe that some marketing-related messages about safety and security actually mislead users. We are also interested in what kind of computer systems people really use for online banking, and whether users satisfy common online banking requirements. Our survey of 123 technically advanced users from a university environment strongly supports our view of an emerging gap between banks' expectations (or at least what their written customer policy agreements imply) and users' actions related to security requirements of online banking. Our participants, being more security-aware than the general population, arguably makes our results best-case regarding what can be expected from regular users. Yet most participants failed to satisfy common security requirements, implying most online banking customers do not (or cannot) follow banks' stated end-user security requirements and guidelines. The survey also sheds light on the security settings of systems used for sensitive online transactions. This work is intended to spur a discussion on real-world system security and user responsibilities, in a scenario where everyday users are heavily encouraged to perform critical tasks over the Internet, despite the continuing absence of appropriate tools to do so.


title: A new method to investigate the CCZ-equivalence between functions with low differential uniformity

abstract: Recently, many new classes of differentially 4-uniform permutations have been constructed. However, it is difficult to decide whether they are CCZ-inequivalent or not. In this paper, we propose a new notion called "Projected Differential Spectrum". By considering the properties of the projected differential spectrum, we find several relations that should be satisfied by CCZ-equivalent functions. Based on these results, we mathematically prove that any differentially 4-uniform permutation constructed in 11 by C. Carlet, D. Tang, X. Tang, et al., is CCZ-inequivalent to the inverse function. We also get two interesting results with the help of computer experiments. The first one is a proof that any permutation constructed in 11 is CCZ-inequivalent to a function which is the summation of the inverse function and any Boolean function on F 2 2 k when 4 ź k ź 7 . The second one is a differentially 4-uniform permutation on F 2 6 which is CCZ-inequivalent to any function in the aforementioned two classes.


title: 4.2 A 6A 40MHz four-phase ZDS hysteretic DC-DC converter with 118mV droop and 230ns response time for a 5A/5ns load transient

abstract: In recent years, the clock frequency, the number of cores, and the power dissipation of application processors (APs) for portable electronics have dramatically increased. As a result, peak processor currents have reached several amperes with slew rates on the order of 1A/ns. These fast large steps incur large output voltage (VOUT) droops, which induce failed paths and cause processor black-outs. Present voltage regulators (VRs) combat these challenges by using bulky output capacitor (COUT) arrays that could add up to over 100μF. However, this practice is untenable for next-generation APs, which have severely limited PCB area and require fast dynamic voltage scaling. These challenges have led to great demand for ultra-fast VRs. PWM control requires a bandwidth 5 to 10× less than the switching frequency, fSW, which results in a slow response [1]. Hysteretic control has been proposed to achieve faster response [2, 3], however, it still suffers from an inherent delay (tdelay) up to the discharge period, (1-D)T, due to realistic hysteretic window size and inductor current (IL) slew limit. Consider, for example, a fast hysteretic VR achieving 10% voltage droop for an instant load step of 5A. For an inductor (L) chosen to have IL ripple <; 200mA, and COUT under a few μF, tdelay cannot exceed a few ns. This requires a fSW from 0.5 to 1GHz, which in turn causes a large switching loss and restricts the feasible power level of the converter. This is against the power demand trend of APs. An interleaved multiphase topology can be the most effective way to improve both the system response and the equivalent IL slew rate by changing the number of phases; however, clock and phase synchronization and current sharing for conventional hysteretic control are challenging.


title: In defense of soft-assignment coding

abstract: In object recognition, soft-assignment coding enjoys computational efficiency and conceptual simplicity. However, its classification performance is inferior to the newly developed sparse or local coding schemes. It would be highly desirable if its classification performance could become comparable to the state-of-the-art, leading to a coding scheme which perfectly combines computational efficiency and classification performance. To achieve this, we revisit soft-assignment coding from two key aspects: classification performance and probabilistic interpretation. For the first aspect, we argue that the inferiority of soft-assignment coding is due to its neglect of the underlying manifold structure of local features. To remedy this, we propose a simple modification to localize the soft-assignment coding, which surprisingly achieves comparable or even better performance than existing sparse or local coding schemes while maintaining its computational advantage. For the second aspect, based on our probabilistic interpretation of the soft-assignment coding, we give a probabilistic explanation to the magic max-pooling operation, which has successfully been used by sparse or local coding schemes but still poorly understood. This probability explanation motivates us to develop a new mix-order max-pooling operation which further improves the classification performance of the proposed coding scheme. As experimentally demonstrated, the localized soft-assignment coding achieves the state-of-the-art classification performance with the highest computational efficiency among the existing coding schemes.


title: Auto-encoder bottleneck features using deep belief networks

abstract: Neural network (NN) bottleneck (BN) features are typically created by training a NN with a middle bottleneck layer. Recently, an alternative structure was proposed which trains a NN with a constant number of hidden units to predict output targets, and then reduces the dimensionality of these output probabilities through an auto-encoder, to create auto-encoder bottleneck (AE-BN) features. The benefit of placing the BN after the posterior estimation network is that it avoids the loss in frame classification accuracy incurred by networks that place the BN before the softmax. In this work, we investigate the use of pre-training when creating AE-BN features. Our experiments indicate that with the AE-BN architecture, pre-trained and deeper NNs produce better AE-BN features. On a 50-hour English Broadcast News task, the AE-BN features provide over a 1% absolute improvement compared to a state-of-the-art GMM/HMM with a WER of 18.8% and pre-trained NN hybrid system with a WER of 18.4%. In addition, on a larger 430-hour Broadcast News task, AE-BN features provide a 0.5% absolute improvement over a strong GMM/HMM baseline with a WER of 16.0%. Finally, system combination with the GMM/HMM baseline and AE-BN systems provides an additional 0.5% absolute on 430 hours over the AE-BN system alone, yielding a final WER of 15.0%.


Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder


title: Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey

abstract: As a promising machine learning tool to handle the accurate pattern recognition from complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to wireless networks with large-scale topology and complex radio conditions. DL uses many neural network layers to achieve a brain-like acute feature extraction from high-dimensional raw data. It can be used to find the network dynamics (such as hotspots, interference distribution, congestion points, traffic bottlenecks, spectrum availability, etc.) based on the analysis of a large amount of network parameters (such as delay, loss rate, link signal-to-noise ratio, etc.). Therefore, DL can analyze extremely complex wireless networks with many nodes and dynamic link quality. This paper performs a comprehensive survey of the applications of DL algorithms for different network layers, including physical layer modulation/coding, data link layer access control/resource allocation, and routing layer path search, and traffic balancing. The use of DL to enhance other network functions, such as network security, sensing data compression, etc., is also discussed. Moreover, the challenging unsolved research issues in this field are discussed in detail, which represent the future research trends of DL-based wireless networks. This paper can help the readers to deeply understand the state-of-the-art of the DL-based wireless network designs, and select interesting unsolved issues to pursue in their research.


title: Learning Hypergraph-regularized Attribute Predictors

abstract: We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem, in which a collection of attribute projections is jointly learnt from the feature space to a hypergraph embedding space aligned with the attributes. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and N-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.


title: Controlling Linguistic Style Aspects in Neural Language Generation

abstract: Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controlling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts. We demonstrate the approach on the movie reviews domain and show that it is successful in generating coherent sentences corresponding to the required linguistic style and content.


title: Multiple Local Curvature Gabor Binary Patterns for Facial Action Recognition

abstract: Curvature Gabor features have recently been shown to be powerful facial texture descriptors with applications on face recognition. In this paper we introduce their use in facial action unit (AU) detection within a novel framework that combines multiple Local Curvature Gabor Binary Patterns (LCGBP) on different filter sizes and curvature degrees. The proposed system uses the distances of LCGBP histograms between neutral faces and AU containing faces combined with an AU-specific feature selection and classification process. We achieve 98.6% overall accuracy in our tests with the extended Cohn-Kanade database, which is higher than achieved previously by any state-of-the-art method.


title: LDX: Causality Inference by Lightweight Dual Execution

abstract: Causality inference, such as dynamic taint anslysis, has many applications (e.g., information leak detection). It determines whether an event e is causally dependent on a preceding event c during execution. We develop a new causality inference engine LDX. Given an execution, it spawns a slave execution, in which it mutates c and observes whether any change is induced at e. To preclude non-determinism, LDX couples the executions by sharing syscall outcomes. To handle path differences induced by the perturbation, we develop a novel on-the-fly execution alignment scheme that maintains a counter to reflect the progress of execution. The scheme relies on program analysis and compiler transformation. LDX can effectively detect information leak and security attacks with an average overhead of 6.08% while running the master and the slave concurrently on separate CPUs, much lower than existing systems that require instruction level monitoring. Furthermore, it has much better accuracy in causality inference.


title: Mapping users across networks by manifold alignment on hypergraph

abstract: Nowadays many people are members of multiple online social networks simultaneously, such as Facebook, Twitter and some other instant messaging circles. But these networks are usually isolated from each other. Mapping common users across these social networks will benefit many applications. Methods based on user-name comparison perform well on parts of users, however they can not work in the following situations: (a) users choose different usernames in different networks; (b) a unique username corresponds to different individuals. In this paper, we propose to utilize social structures to improve the mapping performance. Specifically, a novel subspace learning algorithm, Manifold Alignment on Hypergraph (MAH), is proposed. Different from traditional semi-supervised manifold alignment methods, we use hypergraph to model high-order relations here. For a target user in one network, the proposed algorithm ranks all users in the other network by their possibilities of being the corresponding user. Moreover, methods based on username comparison can be incorporated into our algorithm easily to further boost the mapping accuracy. Experimental results have demonstrated the effectiveness of our proposed algorithm in mapping users across networks.


title: Concomitant Imperforate Hymen and Transverse Vaginal Septum Complicated with Pyocolpos and Abdominovaginal Fistula

abstract: A 13-year-old patient with a complaint of worsening lower abdominal pain during the past 4 months was admitted to the emergency department. An abdominopelvic ultrasound scan revealed a distended uterocervical cavity suggestive of hematometrocolpos. Imperforate hymen was observed on examination of the external genitalia. MRI scan revealed an air-fluid level representing pyometrocolpos within a distended vagina. Posterior vaginal extraperitoneal leakage as the sign of a fistula between the vagina and the rectovaginal space was detected. Although laparoscopic approach was planned, malodorous pus expelled after the insertion of the Veress needle, it was decided to proceed to laparotomy. Pus with peritoneal microabscess formations was observed at laparotomy. The imperforate hymen and TVS were excised vaginally. A more complex anomaly should be suspected in cases with hematometra and concomitant imperforated hymen without any bulging and thorough evaluation using radiological imaging techniques should be performed before surgical approach.


title: Resolution limit in community detection

abstract: Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. This finding is confirmed through several examples, both in artificial and in real social, biological, and technological networks, where we show that modularity optimization indeed does not resolve a large number of modules. A check of the modules obtained through modularity optimization is thus necessary, and we provide here key elements for the assessment of the reliability of this community detection method.


title: Electrically Small Dual-Band Reconfigurable Complementary Split-Ring Resonator (CSRR)-Loaded Eighth-Mode Substrate Integrated Waveguide (EMSIW) Antenna

abstract: In this paper, we propose a novel electrically small dual-band reconfigurable antenna. By using a compact eighth-mode substrate integrated waveguide (EMSIW) structure, we designed a compact antenna. This antenna is capable of dual-band operation by additionally loading an electrically small complementary split-ring resonator (CSRR) structure. The EMSIW and CSRR structures are designed for satisfactory operations at bandwidths of 1.575 GHz [global positioning system (GPS)] and 2.4 GHz [wireless local area network (WLAN)], respectively. We load the CSRR with a varactor diode to achieve a narrow bandwidth and to enable the resonant frequency to continuously vary from 2.4 GHz to 2.5 GHz. Thus, we realize a channel selection function, which is used according to WLAN standards. Irrespective of the variation in the varactor diode voltage, the resonant frequency of the EMSIW is not varied, such that the antenna maintains a constant frequency at the GPS bandwidth even at different voltages. Consequently, as the DC bias voltage varies from 11.4 V to 30 V, the resonant frequency at the WLAN bandwidth continuously varies between 2.38 GHz and 2.5 GHz. We observe that the simulated and measured s-parameter values and radiation patterns are in good agreement with each other.


title: Disaster Recovery as a Service: A Disaster Recovery Plan in the Cloud for SMEs

abstract: In a world of interdependent economies, the numberof threats imposed on the information system of companies andorganizations is steadily increasing. Companies need more thanever to prepare for major crises and organize to be able toensure continuity of its critical IT activities through theestablishment of a Disaster Recovery Plan. For manycompanies, the establishment of such plan was considered avery difficult task to achieve. The development of cloudcomputing offerings and their adoption within companies opensup new practices. One of them is the rescue of the informationsystem in the cloud. In this paper, we focus on presenting thisnew solution known as Disaster Recovery as a Service as well asgood practices in planning for a company to succeed such aproject. Our work was based on a comparative study betweentraditional IT strategies and DRaaS solution and arguedthrough the enumeration of some research works as well as thereturn to experience of some companies.


title: Performance comparison of TOA and TDOA based location estimation algorithms in LOS environment

abstract: In this paper, various positioning algorithms for range-based TOA and TDOA localization have been analyzed, which include the analytical method, least square method, approximate maximum likelihood method, Taylor series method, two-stage maximum likelihood method and genetic algorithm. The assumed scenario is an overdetermined system in a 3D space under line of sight (LOS) situation and a number of sensor nodes placed arbitrarily across this area. The performance of the algorithms has been compared in the assumed scenario. Both the average error and the failure rate have been investigated in terms of the number of reference nodes and the root mean squared error (RMSE) of the range estimation.


title: Detecting Cancer Metastases on Gigapixel Pathology Images

abstract: Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task. At 8 false positives per image, we detect 92.4% of the tumors, relative to 82.7% by the previous best automated approach. For comparison, a human pathologist attempting exhaustive search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides. In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal. Our approach could considerably reduce false negative rates in metastasis detection.


title: Label, Nudge or Tax? A Review of Health Policies for Risky Behaviours

abstract: This work proposes a critical, non systematic, review of the three main lines of health policy interventions to deal with risky behaviours, such as over-eating, smoking, sedentary lives, and excess alcohol drinking, namely: i) the release of information on health risks and consequences; ii) the use of incentives; and iii) direct policy intervention in markets, through regulation and taxation. First, the health and economic impact of the risky behaviours epidemics are briefly described. Then a critical review follows on the evidence existing on the effectiveness of each type of intervention. The review will also highlight the public health approach staying beyond each type of policy on risky behaviours and critically consider them within the context of more general health and social policy interventions.


title: Mixing--Demixing Transition in Polymer-Grafted Spherical Nanoparticles

abstract: Polymer-grafted nanoparticles (PGNPs) can provide property profiles that cannot be obtained individually by polymers or nanoparticles (NPs). Here, we have studied the mixing–demixing transition of symmetric copolymer melts of polymer-grafted spherical nanoparticles by means of coarse-grained molecular dynamics simulation and a theoretical mean-field model. We find that a larger size of NPs leads to higher stability for a given number of grafted chains and chain lengths, reaching a point where demixing is not possible. Most importantly, the increase in the number of grafted chains, Ng, can initially favour the phase separation of PGNPs, but a further increase can lead to more difficult demixing. The reason is the increasing impact of an effective core that forms as the grafting density of the tethered polymer chains around the NPs increases. The range and exact values of Ng where this change in behaviour takes place depend on the NP size and the chain length of the grafted polymer chains. Our study elucidates the phase behaviour of PGNPs and in particular the influence of the grafting density on the phase behaviour of the systems, anticipating that it will open new doors in the understanding of these systems with implications in materials science and medicine.


title: Current evidence and future directions for research with omega-3 fatty acids and attention deficit hyperactivity disorder

abstract: PURPOSE OF REVIEW ::: Nutritional insufficiencies of nutrients such as omega-3 highly unsaturated fatty acids (HUFAs), vitamins and minerals have been linked to suboptimal developmental outcomes including attention deficit hyperactivity disorder (ADHD). Although the predominant treatment is currently psychostimulant medications, randomized clinical trials with omega-3 HUFAs have reported small-to-modest effects in reducing symptoms of ADHD in children despite arguable individual methodological and design misgivings. ::: ::: ::: RECENT FINDINGS ::: This review presents, discusses and critically evaluates data and findings from meta-analytic and systematic reviews and clinical trials published within the last 12 months. Recent trajectories of this research are discussed, such as comparing eicosapentaenoic acid and docosahexaenoic acid and testing the efficacy of omega-3 HUFAs as an adjunct to methylphenidate. Discussion includes highlighting limitations and potential future directions such as addressing variable findings by accounting for other nutritional deficiencies and behavioural food intolerances. ::: ::: ::: SUMMARY ::: The authors conclude that given the current economic burden of ADHD, estimated in the region of $77 billion in the USA alone, in addition to the fact that a proportion of patients with ADHD are either treatment resistant, nonresponders or withdraw from medication because of adverse side-effects, the investigation of nonpharmacological interventions including omega-3 HUFAs in clinical practice warrants extrapolating.


title: IAP guidelines on rickettsial diseases in children

abstract: OBJECTIVE ::: To formulate practice guidelines on rickettsial diseases in children for pediatricians across India. ::: ::: ::: JUSTIFICATION ::: Rickettsial diseases are increasingly being reported from various parts of India. Due to low index of suspicion, nonspecific clinical features in early course of disease, and absence of easily available, sensitive and specific diagnostic tests, these infections are difficult to diagnose. With timely diagnosis, therapy is easy, affordable and often successful. On the other hand, in endemic areas, where healthcare workers have high index of suspicion for these infections, there is rampant and irrational use of doxycycline as a therapeutic trial in patients of undifferentiated fevers. Thus, there is a need to formulate practice guidelines regarding rickettsial diseases in children in Indian context. ::: ::: ::: PROCESS ::: A committee was formed for preparing guidelines on rickettsial diseases in children in June 2016. A meeting of consultative committee was held in IAP office, Mumbai and scientific content was discussed. Methodology and results were scrutinized by all members and consensus was reached. Textbook references and published guidelines were also used in few instances to make recommendations. Various Indian and international publications pertinent to present study were collated and guidelines were approved by all committee members. Future updates in these guidelines will be dictated by new scientific data in the field of rickettsial diseases in children. ::: ::: ::: RECOMMENDATIONS ::: Indian tick typhus and scrub typhus are commonly seen rickettsial diseases in India. It is recommended that practicing pediatricians should be well conversant with compatible clinical scenario, suggestive epidemiological features, differential diagnoses and suggestive laboratory features to make diagnosis and avoid over diagnosis of these infections, as suggested in these guidelines. Doxycycline is the drug of choice and treatment should begin promptly without waiting for confirmatory laboratory results.


title: Ordinal Common-sense Inference

abstract: Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task. We train a neural sequence-to-sequence model on this dataset, which we use to score and generate possible inferences. Further, we annotate subsets of previously established datasets via our ordinal annotation protocol in order to then analyze the distinctions between these and what we have constructed.


title: A new strategy for adaptively constructing multilayer feedforward neural networks

abstract: In this paper a new strategy for adaptively and autonomously constructing a multi-hidden-layer feedforward neural network (FNN) is introduced. The proposed scheme belongs to a class of structure level adaptation algorithms that adds both new hidden units and new hidden layers one at a time when it is determined to be needed. Using this strategy, a FNN may be constructed having as many hidden layers and hidden units as required by the complexity of the problem being considered. Simulation results applied to regression problems are included to demonstrate the performance capabilities of the proposed scheme.


title: Volunteering and Subjective Well-Being in Midlife and Older Adults: The Role of Supportive Social Networks

abstract: Objective. This study examined the extent to which associations between volunteering and subjective well-being (SWB) could be related to volunteers having more supportive social networks relative to nonvolunteers. Method. The sample consisted of 561 midlife and older adults (aged 55–94 years) from the TRAnsitions In Later Life study. Multiple mediation analyses examined associations between hours spent volunteering per week; availability of social support from friends, relatives, and neighbors; positive and negative social exchanges; and SWB. Results. The results indicated that the higher life satisfaction and positive affect reported by those who volunteer at moderate levels (up to 7 hr per week) are related to their higher levels of positive social exchanges and greater availability of social support from friends and family, relative to nonvolunteers. Those who volunteer at higher levels (7 hr or more per week) also reported greater levels of positive affect in comparison to nonvolunteers, and this was related to their greater availability of social support from friends. Availability of support from friends accounted for the greatest proportion of the volunteering–SWB associations. Discussion. The findings suggest that the positive SWB associated with volunteering is related to volunteers’ more


Implementation and Assessment of a Capstone Course Designed to Achieve Pprogram Learning Objectives


title: Handwritten English Character Recognition Using Neural Network

abstract: Neural Networks are recently being used in various kind of pattern recognition. Handwritings of different person are different; therefore it is very difficult to recognize the handwritten characters. Handwritten Character recognition is an area of pattern recognition that has become the subject of research during the last some decades. Neural network is playing an important role in handwritten character recognition. Many reports of character recognition in English have been published but still high recognition accuracy and minimum training time of handwritten English characters using neural network is an open problem. Therefore, it is a great important to develop an automatic handwritten character recognition system for English language [1]. In this paper, efforts have been made to develop automatic handwritten character recognition system for English language with high recognition accuracy and minimum training and classification time. Experimental result shows that the approach used in this paper for English character recognition is giving high recognition accuracy and minimum training time.


title: CMOS Oscillator Having Stable Frequency with Process, Temperature and Voltage Variation

abstract: Clock generation is an extremely important part of any electronic system. At present Crystal oscillator is the most stable and reliable clock generation technique used as clock generation for any type of SoC. But this solution is very expensive and inconvenient for chip integration. Second option can be PLL based clock generation technique. Again PLL based techniques uses huge silicon area and high on-chip power. Further PLL based clocks need a reference clock for locking system. So we need a solution which can provide a stable clock against process, temperature and supply variations. CMOS Ring Oscillator provides a ready solution, but the biggest challenge with this kind of circuit is to achieve stable clock with temperature, process and supply voltage variations. This paper describes a symmetric oscillator structure, which provides an on-chip compensated clock against process, temperature and supply variations. This architecture is not one to one replacement of crystal oscillator or PLL but is very useful for many applications like on-chip charge-pump or DC-DC converters, clock required for modify pulse in Phase Change Memory etc. The oscillator is designed in BCD9S (110nm) process, to produce a stable frequency of 20 MHz, within a temperature range of -40 to 160oC and supply varying from 3V to 5.5V. The variation of frequency is within ±4.5% range and the circuit overall consumes an average current of 68µA.


title: Estimating Missing Temporal Meta-Information using Knowledge-Based-Trust

abstract: A large number of HTML Tables on the Web contain relational data which can be used to augment knowledge bases such as DBpedia, Yago, or Wikidata. A large part of this data is time-dependent, i.e., the correctness of a fact depends on a specific temporal scope. In order ::: to use this data for knowledge base augmentation, we need temporal meta-information. Existing methods rely on timestamps within the table itself or its context as temporal meta-information. Yet, the relationship between these timestamps and data within a table is often unclear. Additionally, timestamps are rather sparse, and there are many web tables for which no timestamps exist. ::: Knowledge-Based-Trust (KBT) uses the overlap with ground-truth to estimate the trustworthiness of a dataset. This paper introduces TimedKBT, which overcomes the dependence on sparse and possibly misinterpreted ::: timestamps by propagating temporal meta-information from ::: a knowledge base to web table data using KBT. It also derives a trust score that estimates the correctness of the data and the assigned temporal meta-information. We evaluate Timed-KBT on the use case of fusing data from a large corpus of web tables for filling missing facts in a knowledge base. Our evaluation shows that Timed-KBT yields an increase in F0.25-Measure of 19.01 % when compared to KBT and 9.44 % when compared to a method that relies solely on timestamps extracted from the ::: table and its context.


title: Global alignment of multiple protein interaction networks with application to functional orthology detection

abstract: Protein-protein interactions (PPIs) and their networks play a central role in all biological processes. Akin to the complete sequencing of genomes and their comparative analysis, complete descriptions of interactomes and their comparative analysis is fundamental to a deeper understanding of biological processes. A first step in such an analysis is to align two or more PPI networks. Here, we introduce an algorithm, IsoRank, for global alignment of multiple PPI networks. The guiding intuition here is that a protein in one PPI network is a good match for a protein in another network if their respective sequences and neighborhood topologies are a good match. We encode this intuition as an eigenvalue problem in a manner analogous to Google's PageRank method. Using IsoRank, we compute a global alignment of the Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens PPI networks. We demonstrate that incorporating PPI data in ortholog prediction results in improvements over existing sequence-only approaches and over predictions from local alignments of the yeast and fly networks. Previous methods have been effective at identifying conserved, localized network patterns across pairs of networks. This work takes the further step of performing a global alignment of multiple PPI networks. It simultaneously uses sequence similarity and network data and, unlike previous approaches, explicitly models the tradeoff inherent in combining them. We expect IsoRank-with its simultaneous handling of node similarity and network similarity-to be applicable across many scientific domains.


title: Where You Like to Go Next: Successive Point-of-Interest Recommendation

abstract: Personalized point-of-interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help provide better user experience as well as enable third-party services, e.g., launching advertisements. To provide a good recommendation, various research has been conducted in the literature. However, pervious efforts mainly consider the "check-ins" in a whole and omit their temporal relation. They can only recommend POI globally and cannot know where a user would like to go tomorrow or in the next few days. In this paper, we consider the task of successive personalized POI recommendation in LBSNs, which is a much harder task than standard personalized POI recommendation or prediction. To solve this task, we observe two prominent properties in the check-in sequence: personalized Markov chain and region localization. Hence, we propose a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions. Our proposed FPMC-LR not only exploits the personalized Markov chain in the check-in sequence, but also takes into account users' movement constraint, i.e., moving around a localized region. More importantly, utilizing the information of localized regions, we not only reduce the computation cost largely, but also discard the noisy information to boost recommendation. Results on two real-world LBSNs datasets demonstrate the merits of our proposed FPMC-LR.


Effective Master-Slave Communication On A Multi-Agent Deep Reinforcement Learning System


title: Towards unsupervised physical activity recognition using smartphone accelerometers

abstract: The development of smartphones equipped with accelerometers gives a promising way for researchers to accurately recognize an individual's physical activity in order to better understand the relationship between physical activity and health. However, a huge challenge for such sensor-based activity recognition task is the collection of annotated or labelled training data. In this work, we employ an unsupervised method for recognizing physical activities using smartphone accelerometers. Features are extracted from the raw acceleration data collected by smartphones, then an unsupervised classification method called MCODE is used for activity recognition. We evaluate the effectiveness of our method on three real-world datasets, i.e., a public dataset of daily living activities and two datasets of sports activities of race walking and basketball playing collected by ourselves, and we find our method outperforms other existing methods. The results show that our method is viable to recognize physical activities using smartphone accelerometers.


title: The ecology of the microbiome: Networks, competition, and stability

abstract: The human gut harbors a large and complex community of beneficial microbes that remain stable over long periods. This stability is considered critical for good health but is poorly understood. Here we develop a body of ecological theory to help us understand microbiome stability. Although cooperating networks of microbes can be efficient, we find that they are often unstable. Counterintuitively, this finding indicates that hosts can benefit from microbial competition when this competition dampens cooperative networks and increases stability. More generally, stability is promoted by limiting positive feedbacks and weakening ecological interactions. We have analyzed host mechanisms for maintaining stability—including immune suppression, spatial structuring, and feeding of community members—and support our key predictions with recent data.


title: Towards fully autonomous driving: Systems and algorithms

abstract: In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential.


Proactive error prediction to improve storage system reliability


title: Temporal-Difference Learning With Sampling Baseline for Image Captioning

abstract: The existing methods for image captioning usually train the language model under the cross entropy loss, which results in the exposure bias and inconsistency of evaluation metric. Recent research has shown these two issues can be well addressed by policy gradient method in reinforcement learning domain attributable to its unique capability of directly optimizing the discrete and non-differentiable evaluation metric. In this paper, we utilize reinforcement learning method to train the image captioning model. Specifically, we train our image captioning model to maximize the overall reward of the sentences by adopting the temporal-difference (TD) learning method, which takes the correlation between temporally successive actions into account. In this way, we assign different values to different words in one sampled sentence by a discounted coefficient when back-propagating the gradient with the REINFORCE algorithm, enabling the correlation between actions to be learned. Besides, instead of estimating a "baseline" to normalize the rewards with another network, we utilize the reward of another Monte-Carlo sample as the "baseline" to avoid high variance. We show that our proposed method can improve the quality of generated captions and outperforms the state-of-the-art methods on the benchmark dataset MS COCO in terms of seven evaluation metrics.


title: Evaluation of background subtraction techniques for video surveillance

abstract: Background subtraction is one of the key techniques for automatic video analysis, especially in the domain of video surveillance. Although its importance, evaluations of recent background subtraction methods with respect to the challenges of video surveillance suffer from various shortcomings. To address this issue, we first identify the main challenges of background subtraction in the field of video surveillance. We then compare the performance of nine background subtraction methods with post-processing according to their ability to meet those challenges. Therefore, we introduce a new evaluation data set with accurate ground truth annotations and shadow masks. This enables us to provide precise in-depth evaluation of the strengths and drawbacks of background subtraction methods.


title: An improved deep learning architecture for person re-identification

abstract: In this work, we propose a method for simultaneously learning features and a corresponding similarity metric for person re-identification. We present a deep convolutional architecture with layers specially designed to address the problem of re-identification. Given a pair of images as input, our network outputs a similarity value indicating whether the two input images depict the same person. Novel elements of our architecture include a layer that computes cross-input neighborhood differences, which capture local relationships between the two input images based on mid-level features from each input image. A high-level summary of the outputs of this layer is computed by a layer of patch summary features, which are then spatially integrated in subsequent layers. Our method significantly outperforms the state of the art on both a large data set (CUHK03) and a medium-sized data set (CUHK01), and is resistant to over-fitting. We also demonstrate that by initially training on an unrelated large data set before fine-tuning on a small target data set, our network can achieve results comparable to the state of the art even on a small data set (VIPeR).


title: Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images

abstract: We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We introduce Deep Sliding Shapes, a 3D ConvNet formulation that takes a 3D volumetric scene from a RGB-D image as input and outputs 3D object bounding boxes. In our approach, we propose the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D. In particular, we handle objects of various sizes by training an amodal RPN at two different scales and an ORN to regress 3D bounding boxes. Experiments show that our algorithm outperforms the state-of-the-art by 13.8 in mAP and is 200× faster than the original Sliding Shapes.


title: Selective activation of ceruloplasmin promoter in ovarian tumors: potential use for gene therapy.

abstract: Gene therapy provides a novel treatment approach to cancer patients. Ideally, expression of therapeutic genes driven by cancer-specific promoters would only target tumors resulting in minimal toxicity to normal tissues. While there is a need of more effective and tolerable treatments for ovarian cancer patients, we aimed to identify gene promoters with high activity in ovarian tumors that can be potentially used in gene therapy to drive the expression of a therapeutic gene in tumors. To identify such promoters, a literature search was performed to reveal genes that are preferentially expressed in ovarian cancer compared with normal ovarian tissue. We found that the ceruloplasmin promoter drove up to 30-fold higher luciferase expression in ovarian cancer cells compared with immortalized normal cells. Furthermore, deletion studies revealed an activator protein-1 (AP-1) site in the ceruloplasmin promoter to be critical for optimal ceruloplasmin promoter activity. Ceruloplasmin promoter activity was significantly activated by 1-O-tetradecanoyl phorbol-13-acetate, a c-jun activator, and conversely suppressed by SP600125, a c-jun inhibitor. Consistently, the ceruloplasmin AP-1 site was specifically recognized by c-jun both in vitro and in vivo. Immunohistochemical analyses of human ovarian cancer specimens showed a direct correlation (r = 0.7, P = 0.007) between expression levels of c-jun and ceruloplasmin. In nude mice carrying SKOV3.ip1 xenografts, the ceruloplasmin promoter demonstrated significantly higher activities in tumors compared with normal organs. Together, these results suggest that the ceruloplasmin promoter activity is significantly enhanced in ovarian cancer and therefore may be exploited as a promising cancer-specific promoter in developing new gene therapy strategies for ovarian cancer.


E-Commerce Security using PKI approach


title: Real time implementation of FMCW radar for target detection using GNU radio and USRP

abstract: Nowadays, FMCW (Frequency Modulated Continuous Wave) radar is widely adapted due to the use of solid state microwave amplifier to generate signal source. The FMCW radar can be implemented and analyzed at low cost and less complexity by using Software Defined Radio (SDR). In this paper, SDR based FMCW radar for target detection and air traffic control radar application is implemented in real time. The FMCW radar model is implemented using open source software and hardware. GNU Radio is utilized for software part of the radar and USRP (Universal Software Radio Peripheral) N210 for hardware part. Log-periodic antenna operating at 1GHZ frequency is used for transmission and reception of radar signals. From the beat signal obtained at receiver end and range resolution of signal, target is detected. Further low pass filtering followed by Fast Fourier Transform (FFT) is performed to reduce computational complexity.


title: Optical Character Recognition

abstract: Optical character recognition (OCR) is very popular research field since 1950's. Character recognition techniques associate a symbolic identity with the image of character. In a typical OCR systems input characters are digitized by an optical scanner. Each character is then located and segmented, and the resulting character image is fed into a pre-processor for noise reduction and normalization. Certain characteristics are the extracted from the character for classification. The feature extraction is critical and many different techniques exist, each having its strengths and weaknesses. After classification the identified characters are grouped to reconstruct the original symbol strings, and context may then be applied to detect and correct errors.


title: Image visualization based malware detection

abstract: Malware detection is one of the challenging tasks in Cyber security. The advent of code obfuscation, metamorphic malware, packers and zero day attacks has made malware detection a challenging task. In this paper we present a visualization based approach for malware detection. First the executable is converted to a gray-scale image called byteplot. Later we extract low level features like intensity based and texture based features. We apply computationally intelligent techniques for malware detection using these features. In this work we used Support Vector Machines (SVMs) and obtained an accuracy of 95% on a dataset containing 25000 malware and 12000 benign samples.


title: Tracking and Visualizing Turbulent 3D Features

abstract: Visualizing 3D time-varying fluid datasets is difficult because of the immense amount of data to be processed and understood. These datasets contain many evolving amorphous regions, and it is difficult to observe patterns and visually follow regions of interest. In this paper, we present a technique which isolates and tracks full-volume representations of regions of interest from 3D regular and curvilinear computational fluid dynamics datasets. Connected voxel regions ("features") are extracted from each time step and matched to features in subsequent time steps. Spatial overlap is used to determine the matching. The features from each time step are stored in octree forests to speed up the matching process. Once the features have been identified and tracked, the properties of the features and their evolutionary history can be computed. This information can be used to enhance isosurface visualization and volume rendering by color coding individual regions. We demonstrate the algorithm on four 3D time-varying simulations from ongoing research in computational fluid dynamics and show how tracking can significantly improve and facilitate the processing of massive datasets.


title: Supervised hashing with kernels

abstract: Recent years have witnessed the growing popularity of hashing in large-scale vision problems. It has been shown that the hashing quality could be boosted by leveraging supervised information into hash function learning. However, the existing supervised methods either lack adequate performance or often incur cumbersome model training. In this paper, we propose a novel kernel-based supervised hashing model which requires a limited amount of supervised information, i.e., similar and dissimilar data pairs, and a feasible training cost in achieving high quality hashing. The idea is to map the data to compact binary codes whose Hamming distances are minimized on similar pairs and simultaneously maximized on dissimilar pairs. Our approach is distinct from prior works by utilizing the equivalence between optimizing the code inner products and the Hamming distances. This enables us to sequentially and efficiently train the hash functions one bit at a time, yielding very short yet discriminative codes. We carry out extensive experiments on two image benchmarks with up to one million samples, demonstrating that our approach significantly outperforms the state-of-the-arts in searching both metric distance neighbors and semantically similar neighbors, with accuracy gains ranging from 13% to 46%.


title: Smart-wire: A 0.5V 44uW 0°C to 100°C power-line energy harvesting sensor node

abstract: A 44 μW self-powered power line monitoring sensor node is implemented in 65 nm CMOS. A 450 kHz 30 kbps BPSK-modulated transceiver allows for 1.5-meter node-to-node powerline communication at 10E-6 BER. The node has a 3.354 ENOB 50 kSps SAR ADC for current measurement and a 440 Sps time-to-digital converter capable of measuring temperature from 0-100 °C in 1.12 °C steps. All components operate at a nominal supply voltage of 0.5 V, and are powered by dedicated regulators enabling fine-grained power management.


title: Dual-polarized patch array antenna package for 5G communication systems

abstract: For applications of 5G (5th generation mobile networks) communication systems, dual-polarized patch array antenna operating at 28.5 GHz is designed on the package substrate. To verify the radiation performance of designed antenna itself, a test package including two patch antennas is also design and its scattering parameters were measured. Using a large height of dielectric materials, 1.5 ∼ 2.0 GHz of antenna bandwidth is achieved which is wide enough. Besides, the dielectric constants are reduced to reflect variances of material properties in the higher frequency region. Measured results of the test package show a good performance at the operating frequency, indicating that the fabricated antenna package will perform well, either. In the future work, manufacturing variances will be investigated further.


title: Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance

abstract: Human capital is of a high concern for companies' management where their most interest is in hiring the highly qualified personnel which are expected to perform highly as well. Recently, there has been a growing interest in the data mining area, where the objective is the discovery of knowledge that is correct and of high benefit for users. In this paper, data mining techniques were utilized to build a classification model to predict the performance of employees. To build the classification model the CRISP-DM data mining methodology was adopted. Decision tree was the main data mining tool used to build the classification model, where several classification rules were generated. To validate the generated model, several experiments were conducted using real data collected from several companies. The model is intended to be used for predicting new applicants' performance.


title: Relevance Subject Machine: A Novel Person Re-identification Framework

abstract: We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem. RSM falls under the category of Bayesian sparse recovery algorithms and uses the sparse representation of the input video under a pre-defined dictionary to identify the subject in the video. Our approach focuses on the multi-shot re-id problem, which is the prevalent problem in many video analytics applications. RSM captures the essence of the multi-shot re-id problem by constraining the support of the sparse codes for each input video frame to be the same. Our proposed approach is also robust enough to deal with time varying outliers and occlusions by introducing a sparse, non-stationary noise term in the model error. We provide a novel Variational Bayesian based inference procedure along with an intuitive interpretation of the proposed update rules. We evaluate our approach over several commonly used re-id datasets and show superior performance over current state-of-the-art algorithms. Specifically, for ILIDS-VID, a recent large scale re-id dataset, RSM shows significant improvement over all published approaches, achieving an 11.5% (absolute) improvement in rank 1 accuracy over the closest competing algorithm considered.


title: Canonical labelling of graphs in linear average time

abstract: Canonical labelling of graphs (CL, for short) can be used, e.g., to test isomorphism. We prove that a simple vertex classification procedure results after only two refinement steps in a CL of random graphs with probability 1 - exp(-cn). With a slight modification we obtain a linear time CL algorithm with only exp(-cn log n/log log n) probability of failure. An additional depth-first search yields a CL of all graphs in linear average time.


title: Risk analysis on groundwater contamination at the megasite Port of Rotterdam

abstract: Groundwater contamination at the Port of Rotterdam is studied using a regional scale model. Reactive transport is calculated along pathlines accounting for redox-dependent biodegradation. The contaminant flux is calculated on so-called planes of compliance. These are the main receptors where there is potential danger of receiving contaminant. In this case the receptors are surface water, the first main aquifer and the border of the port area. Due to the comparatively high degree of uncertainty in the input parameters, a Monte Carlo analysis is performed. The input parameters cause the highest degree of uncertainty in the model. An uncertainty distribution was derived based on all information available. These parameters are: contaminant concentration at the source, degradation rates, redox conditions, and sand-filled vertical drain parameters such as depth and permeability. The Monte Carlo analysis uses the combined uncertainty of the individual input parameters to obtain an uncertainty distribution for the entire megasite. A number of realizations of the pathline analysis obtain different modelling outcomes which can be interpreted as an uncertainty distribution itself. In this case, we have chosen to express the outcome as the chance of exceeding the intervention value on a plane of compliance.


title: Adding Traits to (Statically Typed) Languages

abstract: Traits offer a fine-grained mechanism for composing classes in object-oriented languages from reusable components, while avoiding the fragility problems introduced by multiple inheritance and mixins. Although traits were developed in the context of dynamically typed languages, they would also offer clear benefits for statically typed languages like Java and C#. This report summarizes the issues raised when integrating traits into such languages. We examine traits in the context of the statically typed languages FeatherweightJava, C# and C++.


title: On the ability of the 802.11p MAC method and STDMA to support real-time vehicle-to-vehicle communication

abstract: Traffic safety applications using vehicle-to-vehicle (V2V) communication is an emerging and promising area within the intelligent transportation systems (ITS) sphere. Many of these new applications require real-time communication with high reliability, meaning that packets must be successfully delivered before a certain deadline. Applications with early deadlines are expected to require direct V2V communications, and the only standard currently supporting this is the upcoming IEEE 802.11p, included in the wireless access in vehicular environment (WAVE) stack. To meet a real-time deadline, timely and predictable access to the channel is paramount. However, the medium access method used in 802.11p, carrier sense multiple access with collision avoidance (CSMA/CA), does not guarantee channel access before a finite deadline. In this paper, we analyze the communication requirements introduced by traffic safety applications, namely, low delay, reliable, real-time communications. We show by simulation of a simple, but realistic, highway scenario, that vehicles using CSMA/CA can experience unacceptable channel access delays and, therefore, 802.11p does not support real-time communications. In addition, we present a potential remedy for this problem, namely, the use of self-organizing time division multiple access (STDMA). The real-time properties of STDMA are investigated by means of the same highway simulation scenario, with promising results.


title: Gut Microbiota Modification: Another Piece in the Puzzle of the Benefits of Physical Exercise in Health?

abstract: Regular physical exercise provides many health benefits, protecting against the development of chronic diseases, and improving quality of life. Some of the mechanisms by which exercise provides these effects are the promotion of an anti-inflammatory state, reinforcement of the neuromuscular function, and activation of the hypothalamic–pituitary–adrenal axis. Recently, it has been proposed that physical exercise is able to modify gut microbiota, and thus this could be another factor by which exercise promotes well-being, since gut microbiota appears to be closely related to health and disease. The purpose of this paper is to review the recent findings on gut microbiota modification by exercise, proposing several mechanisms by which physical exercise might cause changes in gut microbiota.


title: FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test

abstract: The maximum mean discrepancy (MMD) is a recently proposed test statistic for two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner's theorem and Fourier transform \cite{Rahimi07}. Taking advantage of sampling of Fourier transform, FastMMD decreases the time complexity for MMD calculation from $O(N^2 d)$ to $O(N d)$, where $N$ and $d$ are the size and dimension of the sample set, respectively. For kernels that are spherically invariant, the computation can be further accelerated to $O(N \log d)$ by using the Fastfood technique \cite{LeQ13}. The uniform convergence of our method has also been theoretically proved in both unbiased and biased estimates. We have further provided a geometric explanation for our method, namely ensemble of circular discrepancy, which facilitates us to understand the insight of MMD, and is hopeful to help arouse more extensive metrics for assessing two-sample test. Experimental results substantiate that FastMMD is with similar accuracy as exact MMD, while with faster computation speed and lower variance than the existing MMD approximation methods.


title: Signal bandwidth estimation with energy detector based on windowed FFT for cognitive radio system

abstract: We evaluate an estimation scheme of unknown signal's bandwidth estimation with an energy detector based on a windowed fast Fourier transform (FFT) for cognitive radio system. When the cognitive node obtains the spectrum sensing results after a received time domain signal is processed with FFT, the detection results have a detection error at the adjacent FFT bins of the received signal frequency with high probability because of an energy leakage. Therefore, a window function is used for reducing the effect of the energy leakage. In this paper, we evaluate the spectrum sensing performance considering influences of the energy leakage and windowed waveform.


title: Automatic Cloud I/O Configurator for I/O Intensive Parallel Applications

abstract: As the cloud platform becomes a promising alternative to traditional HPC (high performance computing) centers or in-house clusters, the I/O bottleneck problem is highlighted in this new environment, typically with top-of-the-line compute instances but sub-par communication and I/O facilities. It has been observed that changing the cloud I/O system configurations, such as choices of file systems, number of I/O servers and their placement strategies, etc., will lead to a considerable variation in the performance and cost efficiency of I/O intensive parallel applications. However, storage system configuration is tedious and error-prone to do manually, even for expert users, leading to solutions that are grossly over-provisioned (low cost inefficiency), substantially under-performing (poor performance) or, in the worst case, both. This paper proposes ACIC, a system which automatically searches for optimized I/O system configurations from many candidates for each individual application running on a given cloud platform. ACIC takes advantage of machine learning models to perform performance/cost predictions. To tackle the high-dimensional parameter exploration space, we enable affordable, reusable, and incremental training on cloud platforms, guided by the Plackett and Burman Matrices for experiment design. Our evaluation results with four representative parallel applications indicate that ACIC consistently identifies optimal or near-optimal configurations among a large group of candidate settings. The top ACIC-recommended configuration is capable of improving the applications’ performance by a factor of up to 10.5 (3.1 on average), and cost saving of up to 89 percent (51 percent on average), compared with a commonly used baseline I/O configuration. In addition, we carried out a small-scale user study for one of the test applications, which found that ACIC consistently beat the user and even the application’s developer, often by a significant margin, in selecting optimized configurations.


title: Automated learning of muscle-actuated locomotion through control abstraction

abstract: We present a learning technique that automatically syn- thesizes realistic locomotion for the animation of physics-based models of animals. The method is especially suitable for animals with highly flexible, many-degree-of-freedom bodies and a consid- erable number of internal muscle actuators, such as snakes and fish. The multilevel learning process first performs repeated loco- motion trials in search of actuator control functions that produce efficient locomotion, presuming virtually nothing about the form of these functions. Applying a short-time Fourier analysis, the learn- ing process then abstracts control functions that produce effective locomotion into a compact representation which makes explicit the natural quasi-periodicities and coordination of the muscle actions. The artificial animals can finally put into practice the compact, efficient controllers that they have learned. Their locomotion learn- ing abilities enable them to accomplish higher-level tasks specified by the animator while guided by sensory perception of their vir- tual world; e.g., locomotion to a visible target. We demonstrate physics-based animation of learned locomotion in dynamic models of land snakes, fishes, and even marine mammals that have trained themselves to perform "SeaWorld" stunts.


title: Fuzzy Failure Mode and Effects Analysis Using Fuzzy Evidential Reasoning and Belief Rule-Based Methodology

abstract: The main objective of this paper is to propose a new risk priority model for prioritizing failures in failure mode and effects analysis (FMEA) on the basis of fuzzy evidential reasoning (FER) and belief rule-based (BRB) methodology. The technique is particularly intended to resolve some of the shortcomings in fuzzy FMEA (i.e., fuzzy rule-based) approaches. In the proposed approach, risk factors like occurrence (O), severity (S), and detection (D), along with their relative importance weights, are described using fuzzy belief structures. The FER approach is used to capture and aggregate the diversified, uncertain assessment information given by the FMEA team members; the BRB methodology is used to model the uncertainty, and nonlinear relationships between risk factors and corresponding risk level; and the inference of the rule-based system is implemented using the weighted average-maximum composition algorithm. The Dempster rule of combination is then used to aggregate all relevant rules for assessing and prioritizing the failure modes that have been identified in FMEA. A case study concerning an ocean going fishing vessel in a marine industry is provided and conducted using the proposed model to illustrate its potential applications and benefits.


title: Automated testing of mixed-signal integrated circuits by topology modification

abstract: A general method is proposed to automatically generate a DfT solution aiming at the detection of catastrophic faults in analog and mixed-signal integrated circuits. The approach consists in modifying the topology of the circuit by pulling up (down) nodes and then probing differentiating node voltages. The method generates a set of optimal hardware implementations addressing the multi-objective problem such that the fault coverage is maximized and the silicon overhead is minimized. The new method was applied to a real-case industrial circuit, demonstrating a nearly 100 percent coverage at the expense of an area increase of about 5 percent.


title: Evaluation and measurement of Learning Management System based on user experience

abstract: Learning Management System (LMS) becomes more accessible to provide a virtual learning environment. The growth of pervasive and ubiquitous computing promotes development and improvement LMS rapidly. Shifting paradigm in designing LMS from technology-centered to user-centered design drives the developer to consider user experience as the primary concern to improve LMS. Therefore, evaluation and measurement of user experience for LMS become crucial. There were some studies have been conducted to evaluate and measure an LMS product in to gain feedback from the user. However, the studies implement a partial view of LMS product. They have conducted researches that limited to comparing LMS and getting perception rather than observing the quality of the LMS. This paper proposed an evaluation and measurement of user experience (UX) for LMS by implementing user experience questionnaire tools. The study aims to get feedback, benchmark and identify UX while interacting with an LMS. The data are taken from 38 students who have experience using two LMS in a different semester. The first LMS based on Moodle was implemented in the first semester (LMS 1), on the other hand, the LMS using Schoology was applied in the second semester (LMS 2). As the results, LMS 1 and LMS 2 got the relative different impression in user perception. Similarly, they also achieved a different level of relative quality. Generally, evaluation and measurement LMS using user experience questionnaire not only provide comprehensive user perception but also provide relative quality level about LMS. The results give the feedback and support when implementing the LMS.


title: Nutritional Recommendations for Cardiovascular Disease Prevention

abstract: Lifestyle factors, including nutrition, play an important role in the etiology of Cardiovascular Disease (CVD). This position paper, written by collaboration between the Israel Heart Association and the Israel Dietetic Association, summarizes the current, preferably latest, literature on the association of nutrition and CVD with emphasis on the level of evidence and practical recommendations. The nutritional information is divided into three main sections: dietary patterns, individual food items, and nutritional supplements. The dietary patterns reviewed include low carbohydrate diet, low-fat diet, Mediterranean diet, and the DASH diet. Foods reviewed in the second section include: whole grains and dietary fiber, vegetables and fruits, nuts, soy, dairy products, alcoholic drinks, coffee and caffeine, tea, chocolate, garlic, and eggs. Supplements reviewed in the third section include salt and sodium, omega-3 and fish oil, phytosterols, antioxidants, vitamin D, magnesium, homocysteine-reducing agents, and coenzyme Q10.


title: The NLTK FrameNet API: Designing for Discoverability with a Rich Linguistic Resource

abstract: A new Python API, integrated within the NLTK suite, offers access to the FrameNet 1.7 lexical database. The lexicon (structured in terms of frames) as well as annotated sentences can be processed programatically, or browsed with human-readable displays via the interactive Python prompt.


title: Learning to classify short text from scientific documents using topic models with various types of knowledge

abstract: An efficient framework to classify short text from scientific documents is proposed.Topic models from various types of knowledge were used for enhancing features in documents.Two methods were presented to optimize external features that enhance relatedness in documents.The performances were evaluated by using real-world scientific documents from online publisher.Proposed methods are shown to outperform related work. Classification of short text is challenging due to data sparseness, which is a typical characteristic of short text. In this paper, we propose methods for enhancing features using topic models, which make short text seem less sparse and more topic-oriented for classification. We exploited topic model analysis based on Latent Dirichlet Allocation for enriched datasets, and then we presented new methods for enhancing features by combining external texts from topic models that make documents more effective for classification. In experiments, we utilized the title contents of scientific articles as short text documents, and then enriched these documents using topic models from various types of universal datasets for classification in order to show that our approach performs efficiently.


title: Fast And Space Efficient Trie Searches

abstract: Searching and traversing m-way trees using tries is a well known and broadly used technique. Three algorithms are presented, two have constant insert, search and delete cost, are faster than Hash Trees and can be searched twice as quickly cas Ternary Search Trees (TST). The third has a lg(N) byte compare cost like a TST, but is faster. All require 60% less memory space per node than TST and, unlike Hash Trees, are smoothly extensible and support sorted order functions. The new algorithms defining Array Compacted Trees (ACT), Array Mapped Trees (AMT), Unary Search Trees (UST) and their variants are discussed. These new search trees are applicable in many diverse areas such as high performance IP routers, symbol tables, lexicon scanners and FSA state tables.