title: ZebraLancer: Private and Anonymous Crowdsourcing System atop Open Blockchain

abstract: We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer, and overcome two fundamental challenges of decentralizing crowdsourcing, i.e. data leakage and identity breach. First, our outsource-then-prove methodology resolves the tension between blockchain transparency and data confidentiality, which is critical in crowdsourcing use-case. ZebraLancer ensures: (i) a requester will not pay more than what data deserve, according to a policy announced when her task is published via the blockchain; (ii) each worker indeed gets a payment based on the policy, if he submits data to the blockchain; (iii) the above properties are realized not only without a central arbiter, but also without leaking the data to the open blockchain. Furthermore, the transparency of blockchain allows one to infer private information about workers and requesters through their participation history. On the other hand, allowing anonymity will enable a malicious worker to submit multiple times to reap rewards. ZebraLancer overcomes this problem by allowing anonymous requests/submissions without sacrificing the accountability. The idea behind is a subtle linkability: if a worker submits twice to a task, anyone can link the submissions, or else he stays anonymous and unlinkable across tasks. To realize this delicate linkability, we put forward a novel cryptographic concept, i.e. the common-prefix-linkable anonymous authentication. We remark the new anonymous authentication scheme might be of independent interest. Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum. The experiment results show the applicability of our protocol with the existing real-world blockchain.


title: A New Target-specific Object Proposal Generation Method for Visual Tracking

abstract: Object proposal generation methods have been widely applied to many computer vision tasks. However, existing object proposal generation methods often suffer from the problems of motion blur, low contrast, deformation, etc., when they are applied to video related tasks. In this paper, we propose an effective and highly accurate target-specific object proposal generation (TOPG) method, which takes full advantage of the context information of a video to alleviate these problems. Specifically, we propose to generate target-specific object proposals by integrating the information of two important objectness cues: colors and edges, which are complementary to each other for different challenging environments in the process of generating object proposals. As a result, the recall of the proposed TOPG method is significantly increased. Furthermore, we propose an object proposal ranking strategy to increase the rank accuracy of the generated object proposals. The proposed TOPG method has yielded significant recall gain (about 20%-60% higher) compared with several state-of-the-art object proposal methods on several challenging visual tracking datasets. Then, we apply the proposed TOPG method to the task of visual tracking and propose a TOPG-based tracker (called as TOPGT), where TOPG is used as a sample selection strategy to select a small number of high-quality target candidates from the generated object proposals. Since the object proposals generated by the proposed TOPG cover many hard negative samples and positive samples, these object proposals can not only be used for training an effective classifier, but also be used as target candidates for visual tracking. Experimental results show the superior performance of TOPGT for visual tracking compared with several other state-of-the-art visual trackers (about 3%-11% higher than the winner of the VOT2015 challenge in term of distance precision).


title: Articulated Human Detection with Flexible Mixtures of Parts

abstract: We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and foreshortened) templates, we use a mixture of small, nonoriented parts. We describe a general, flexible mixture model that jointly captures spatial relations between part locations and co-occurrence relations between part mixtures, augmenting standard pictorial structure models that encode just spatial relations. Our models have several notable properties: 1) They efficiently model articulation by sharing computation across similar warps, 2) they efficiently model an exponentially large set of global mixtures through composition of local mixtures, and 3) they capture the dependency of global geometry on local appearance (parts look different at different locations). When relations are tree structured, our models can be efficiently optimized with dynamic programming. We learn all parameters, including local appearances, spatial relations, and co-occurrence relations (which encode local rigidity) with a structured SVM solver. Because our model is efficient enough to be used as a detector that searches over scales and image locations, we introduce novel criteria for evaluating pose estimation and human detection, both separately and jointly. We show that currently used evaluation criteria may conflate these two issues. Most previous approaches model limbs with rigid and articulated templates that are trained independently of each other, while we present an extensive diagnostic evaluation that suggests that flexible structure and joint training are crucial for strong performance. We present experimental results on standard benchmarks that suggest our approach is the state-of-the-art system for pose estimation, improving past work on the challenging Parse and Buffy datasets while being orders of magnitude faster.


title: Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method

abstract: Alzheimer’s Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD diagnosis is largely based on clinical history and neuropsychological data including magnetic resource imaging (MRI). Increasing research has been reported on applying machine learning to AD recognition in recent years. This paper presents our latest contribution to the advance. It describes an automatic AD recognition algorithm that is based on deep learning on 3D brain MRI. The algorithm uses a convolutional neural network (CNN) to fulfil AD recognition. It is unique in that the three dimensional topology of brain is considered as a whole in AD recognition, resulting in an accurate recognition. The CNN used in this study consists of three consecutive groups of processing layers, two fully connected layers and a classification layer. In the structure, every one of the three groups is made up of three layers, including a convolutional layer, a pooling layer and a normalization layer. The algorithm was trained and tested using the MRI data from Alzheimer’s Disease Neuroimaging Initiative. The data used include the MRI scanning of about 47 AD patients and 34 normal controls. The experiment had shown that the proposed algorithm delivered a high AD recognition accuracy with a sensitivity of 1 and a specificity of 0.93.


title: Increased aggression after rapid eye movement sleep deprivation in Wistar rats is not influenced by reduction of dimensions of enclosure

abstract: Twelve female and twelve male Wistar rats were assigned to home groups, platform-control groups, and rapid eye movement sleep deprivation groups. The deprivation was performed with the cuff—pedestal method. Rats were tested in pairs in a shrinking chamber to increase proximity of the rats and to provoke interaction between them. An increase in boxing behavior was noted in the sleep-deprived groups. The result confirms that deprivation of rapid eye movement sleep increases aggressive behavior in the rat. However, this behavior is not influenced by the area of the testing arena. The sleep-deprived females explored each other more than the controls; the males' behavior did not differ in this respect. The amount of genital exploration decreased with diminishing area of enclosure, whereas boxing was not area dependent.


title: A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress

abstract: Inverse reinforcement learning is the problem of inferring the reward function of an observed agent, given its policy or behavior. Researchers perceive IRL both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researchers and practitioners in machine learning to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges which include accurate inference, generalizability, correctness of prior knowledge, and growth in solution complexity with problem size. The article elaborates how the current methods mitigate these challenges. We further discuss the extensions of traditional IRL methods: (i) inaccurate and incomplete perception, (ii) incomplete model, (iii) multiple rewards, and (iv) non-linear reward functions. This discussion concludes with some broad advances in the research area and currently open research questions.


title: Deep Sequence Learning with Auxiliary Information for Traffic Prediction

abstract: Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.


title: Intergroup Reconciliation: Effects of Adversary's Expressions of Empathy, Responsibility, and Recipients' Trust

abstract: The present study explores the effects of expressions of empathy for the ingroup's conflict-related suffering and assumed responsibility for causing it by a representative of the rival outgroup on recipient's willingness for reconciliation. It is suggested that such positive expressions by an adversary will have positive effects on reconciliation only in the presence of a basic level of trust in the outgroup. In two studies, Israeli-Jewish participants were exposed to a Palestinian leader who either expressed or did not express empathy and/or Palestinian responsibility for Israelis' suffering. After reading the speech, participants completed a questionnaire that measured their attitudes toward reconciliation with Palestinians. Results of both studies show that whereas expression of empathy led to more positive attitudes when trust was high, it tended to have adverse effects when trust was low. Similar effects were not found for assumed responsibility. Implications for research on intergroup conflict and r...


title: Full-mouth adhesive rehabilitation of a severely eroded dentition: the three-step technique. Part 3.

abstract: Dental erosion is a frequently underestimated pathology that nowadays affects an increasing number of younger individuals. Often the advanced tooth destruction is the result of not only a difficult initial diagnosis (e.g. multifactorial etiology of tooth wear), but also a lack of timely intervention. A clinical trial testing a fully adhesive approach for patients affected by severe dental erosion is underway at the School of Dental Medicine of the University of Geneva. All the patients are systematically and exclusively treated with adhesive techniques, using onlays in the posterior region and a combination of facially bonded porcelain restorations and indirect palatal resin composite restorations in the anterior maxillary region. To achieve maximum preservation of tooth structure and predict the most esthetic and functional outcome, an innovative concept has been developed: the three-step technique. Three laboratory steps are alternated with three clinical steps, allowing the clinician and the dental technician to constantly interact during the planning and execution of a full-mouth adhesive rehabilitation. In this article, the third and last step of the three-step technique has been described in detail.


title: Finger pinch force estimation through muscle activations using a surface EMG sleeve on the forearm

abstract: For prosthetic hand manipulation, the surface Electromyography(sEMG) has been widely applied. Researchers usually focus on the recognition of hand grasps or gestures, but ignore the hand force, which is equally important for robotic hand control. Therefore, this paper concentrates on the methods of finger forces estimation based on multichannel sEMG signal. A custom-made sEMG sleeve system omitting the stage of muscle positioning is utilised to capture the sEMG signal on the forearm. A mathematic model for muscle activation extraction is established to describe the relationship between finger pinch forces and sEMG signal, where the genetic algorithm is employed to optimise the coefficients. The results of experiments in this paper shows three main contributions: 1) There is a systematical relationship between muscle activations and the pinch finger forces. 2) To estimate the finger force, muscle precise positioning for electrodes placement is not inevitable. 3) In a multi-channel EMG system, selecting specific combinations of several channels can improve the estimation accuracy for specific gestures.


title: Weakly-Supervised Neural Text Classification

abstract: Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.


title: Functionality-Oriented Microservice Extraction Based on Execution Trace Clustering

abstract: The main task of microservice extraction is to find which software entities (e.g., methods, classes) should be grouped together from existing monolithic software as candidate microservices, responsible for specific functionalities and evolving independently. Current methods extract microservices by analyzing source code and following the assumption that "classes with strong relation should be in the same service", which originates from software structure analysis. We find that 1) many program behaviors cannot be explicitly reflected in the source code, and 2) the relation at code-level is not equivalent to the same functionality. Thus, we propose a functionality-oriented microservice extraction (FoME) method in this study by monitoring program dynamic behavior and clustering execution traces. Instead of source code analysis, the execution traces of a program are applied to group source code entities that are dedicated to the same functionality. We also construct a systematic measurement of microservice by integrating five complementary metrics of service cohesion and coupling. These metrics measure Functional Independence of microservices. That is, it qualifies whether a microservices can have its own responsibilities independently. In the experiment, our method is compared with three state-of-the-art methods on four open-source projects. The microservice candidates generated using our method present similar functional cohesion to the services produced using the other methods, but have considerably looser coupling measurements (dramatically reducing measurements of IRN and OPN).


title: The Mafia in the Post-Fordist Era

abstract: This chapter explains why we should analyse Cosa Nostra under the structural transformations of economy, combining the ideas of Umberto Santino and Vincenzo Ruggiero. Moreover, it suggests strategies to fight anti-Mafia. If Cosa Nostra developed in relation to the capitalist changes, it is possible by this token to assume that it underwent a further transformation during the passage from a Fordist economy, based on mass production and vertical organisation, to a post-Fordist economy, based on a network structure and on a demand-oriented production.


title: Hydrogen production from solid sodium borohydride with hydrogen peroxide decomposition reaction

abstract: This study investigates hydrogen production from solid sodium borohydride with hydrogen peroxide decomposition reaction for a fuel cell based air-independent propulsion system in space and underwater applications. Sodium borohydride in the solid state was used as a hydrogen source in the present study. Pure hydrogen could be generated by a catalytic hydrolysis reaction in which the water source was obtained from the hydrogen peroxide decomposition. Hydrogen peroxide was selected as an oxidizer, being decomposed catalytically to generate oxygen and water. The pure oxygen was provided to a fuel cell and the water was stored separately for the hydrolysis reaction. A fuel cell system was fabricated to validate the fuel cell based air-independent power system proposed in the present study. Two catalytic reactors were prepared; one for the solid sodium borohydride hydrolysis reaction and the other for the hydrogen peroxide decomposition reaction. The hydrogen and oxygen generation rate were measured based on the various conditions. The performance evaluation of a fuel cell system proposed in the present study was carried out.


title: Downlink Resource Reuse for Device-to-Device Communications Underlaying Cellular Networks

abstract: The full potential of Device-to-device (D2D) communication relies on efficient resource reuse strategies including power control and matching of D2D links and cellular users (CUs). This letter investigates downlink resource reuse between multiple D2D links and multiple CUs. Our goal is to achieve a network utility enhancement for D2D communication while ensuring the QoS of the CUs. Despite the combinatorial nature of the problem and the coupled power constraints, we characterize the optimal D2D-CU matching as well as their power coordination, and propose an efficient algorithm to jointly optimize all D2D links and CUs. The proposed downlink resource reuse strategy shows a superiority over existing D2D schemes.


title: Video Description using Bidirectional Recurrent Neural Networks

abstract: Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.


title: A Coverage Planner for Multi-Robot Systems in Agriculture

abstract: Driving heavy tractors and agricultural machinery on soil for growing plants causes more permanent damage to the soil than was previously believed. Compacting the soil severely deteriorates soil fertility leading to poor crop yields and increased population from agricultural lands. In addition, compacted soils require many years of expensive treatment to recover their fertility. Soil compaction problem can be solved by replacing heavy tractors and machinery with a number of smaller and lighter vehicles that is capable of treating crop fields as well as heavy ones without compacting the soil. However, this scenario is impractical because of the high operating cost to secure a human operator/driver for each vehicle. A practical solution is to replace heavy tractors and machinery by a set of autonomous vehicles that require minimum or no human intervention. In this paper, the technology required to enable a single farmer to supervise and operate a team of automated vehicles, is proposed. This will include the development of a Mission Control Center and Intelligent Coverage Path Planning algorithms to enable team/swarm of autonomous vehicles to communicate and cooperate and solve a range of agricultural tasks in a safe and efficient way.


title: Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation

abstract: There are a number of statistical techniques that downscale coarse climate information from general circula- tion models (GCMs). However, many of them do not repro- duce the small-scale spatial variability of precipitation ex- hibited by the observed meteorological data, which is an im- portant factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias- Correction and Stochastic Analog method; BCSA) was de- veloped to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation se- quences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to re- sults obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggrega- tion and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spa- tial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these char- acteristics, (2) the BCSD and BCCA methods underesti- mated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, tran- sition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial vari- ance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study under- scores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be rea- sonably predicted. For low-relief, rainfall-dominated water- sheds, where reproducing small-scale spatiotemporal precip- itation variability is important, the BCSA method is recom- mended for use over the BCSD, BCCA, or SDBC methods.


title: Functional reactive animation

abstract: Fran (Functional Reactive Animation) is a collection of data types and functions for composing richly interactive, multimedia animations. The key ideas in Fran are its notions of behaviors and events. Behaviors are time-varying, reactive values, while events are sets of arbitrarily complex conditions, carrying possibly rich information. Most traditional values can be treated as behaviors, and when images are thus treated, they become animations. Although these notions are captured as data types rather than a programming language, we provide them with a denotational semantics, including a proper treatment of real time, to guide reasoning and implementation. A method to effectively and efficiently perform event detection using interval analysis is also described, which relies on the partial information structure on the domain of event times. Fran has been implemented in Hugs, yielding surprisingly good performance for an interpreter-based system. Several examples are given, including the ability to describe physical phenomena involving gravity, springs, velocity, acceleration, etc. using ordinary differential equations.


title: ParCube: sparse parallelizable tensor decompositions

abstract: How can we efficiently decompose a tensor into sparse factors, when the data does not fit in memory? Tensor decompositions have gained a steadily increasing popularity in data mining applications, however the current state-of-art decomposition algorithms operate on main memory and do not scale to truly large datasets. In this work, we propose ParCube, a new and highly parallelizable method for speeding up tensor decompositions that is well-suited to producing sparse approximations. Experiments with even moderately large data indicate over 90% sparser outputs and 14 times faster execution, with approximation error close to the current state of the art irrespective of computation and memory requirements. We provide theoretical guarantees for the algorithm's correctness and we experimentally validate our claims through extensive experiments, including four different real world datasets (Enron, Lbnl, Facebook and Nell), demonstrating its effectiveness for data mining practitioners. In particular, we are the first to analyze the very large Nell dataset using a sparse tensor decomposition, demonstrating that ParCube enables us to handle effectively and efficiently very large datasets.


Putting Objects in Perspective


Compliance through Informed Consent: Semantic Based Consent Permission and Data Management Model


title: Bearing fault diagnosis based on improved LMD

abstract: A new time-frequency analysis method named Local mean decomposition (LMD) was studied, which can adaptively decompose complicated component signal into a set of product functions (PF). The PF whose instantaneous frequency has physical significance was the product of an envelope signal and a frequency modulated signal. In order to solve the shortage moving average algorithm in LMD method, the author puts forward to using hermite interpolation instead of moving average algorithm. Through the analysis on simulation signals, we found that the improved LMD method is better than the LMD method both in analysis accuracy and computation time. According to the modulating characteristics of the roller bearing fault vibration signals, a fault diagnosis method for bearings based on improved LMD was proposed. The analysis results show the method can be used in the fault diagnosis.


Cross-lingual Semantic Generalization for the Detection of Metaphor


title: Searcher in a Strange Land: Understanding Web Search from Familiar and Unfamiliar Locations

abstract: With mobile devices, web search is no longer limited to specific locations. People conduct search from practically anywhere, including at home, at work, when traveling and when on vacation. How should this influence search tools and web services? In this paper, we argue that information needs are affected by the familiarity of the environment. To formalize this idea, we propose a new contextualization model for activities on the web. The model distinguishes between a search from a familiar place (F-search) and a search from an unfamiliar place (U-search). We formalize the notion of familiarity, and propose a method to identify familiar places. An analysis of a query log of millions of users, demonstrates the differences between search activities in familiar and in unfamiliar locations. Our novel take on search contextualization has the potential to improve web applications, such as query autocompletion and search personalization.


title: Alternating minimization for hybrid precoding in multiuser OFDM mmWave systems

abstract: Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter wave (mmWave) communications. While existing works on hybrid precoding mainly focus on single-user single-carrier transmission, in practice multicarrier transmission is needed to combat the much increased bandwidth, and multiuser MIMO can provide additional spatial multiplexing gains. In this paper, we propose a new hybrid precoding structure for multiuser OFDM mmWave systems, which greatly simplifies the hybrid precoder design and is able to approach the performance of the fully digital precoder. In particular, two groups of phase shifters are combined to map the signals from radio frequency (RF) chains to antennas. Then an effective hybrid precoding algorithm based on alternating minimization (AltMin) is proposed, which will alternately optimize the digital and analog precoders. A major algorithmic innovation is a LASSO formulation for the analog precoder, which yields computationally efficient algorithms. Simulation results will show the performance gain of the proposed algorithm. Moreover, it will reveal that canceling the interuser interference is critical in multiuser OFDM hybrid precoding systems.


title: Performance evaluation of air-gap-based coaxial RF TSV for 3D NoC

abstract: In this paper, a novel air-gap-based coaxial Through Silicon Via (TSV) was proposed for on-chip RF interconnect, to fill up the increasing bandwidth gap between the demand and supply according for the 2007 International Technology Roadmap for Semiconductors. Based on earlier work, the expressions of characteristic impedance, attenuation and bandwidth have been proposed. Consequently, we assume an 80 multi-core 3D Network-on-Chip (NoC) system with Binary Phase Shift Keying (BPSK) modulation, to evaluate energy per bit and area per Gbps of the proposed air-gap-based coaxial TSV. The result indicates that the energy dissipation and area consumption of the proposed one are 40∼50% and 10% better than the conventional dielectric RF TSV.


title: A spectral algorithm for learning mixture models

abstract: We show that a simple spectral algorithm for learning a mixture of k spherical Gaussians in Rn works remarkably well--it succeeds in identifying the Gaussians assuming essentially the minimum possible separation between their centers that keeps them unique (solving an open problem of Arora and Kannan (Proceedings of the 33rd ACM STOC, 2001). The sample complexity and running time are polynomial in both n and k. The algorithm can be applied to the more general problem of learning a mixture of "weakly isotropic" distributions (e.g. a mixture of uniform distributions on cubes).


GORAM – Group ORAM for Privacy and Access Control in Outsourced Personal Records 1


title: Tree kernel-based semantic relation extraction with rich syntactic and semantic information

abstract: This paper proposes a novel tree kernel-based method with rich syntactic and semantic information for the extraction of semantic relations between named entities. With a parse tree and an entity pair, we first construct a rich semantic relation tree structure to integrate both syntactic and semantic information. And then we propose a context-sensitive convolution tree kernel, which enumerates both context-free and context-sensitive sub-trees by considering the paths of their ancestor nodes as their contexts to capture structural information in the tree structure. An evaluation on the Automatic Content Extraction/Relation Detection and Characterization (ACE RDC) corpora shows that the proposed tree kernel-based method outperforms other state-of-the-art methods.


title: AdaOUBoost: adaptive over-sampling and under-sampling to boost the concept learning in large scale imbalanced data sets

abstract: Automatic concept learning from large scale imbalanced data sets is a key issue in video semantic analysis and retrieval, which means the number of negative examples is far more than that of positive examples for each concept in the training data. The existing methods adopt generally under-sampling for the majority negative examples or over-sampling for the minority positive examples to balance the class distribution on training data. The main drawbacks of these methods are: (1) As a key factor that affects greatly the performance, in most existing methods, the degree of re-sampling needs to be pre-fixed, which is not generally the optimal choice; (2) Many useful negative samples may be discarded in under-sampling. In addition, some works only focus on the improvement of the computational speed, rather than the accuracy. To address the above issues, we propose a new approach and algorithm named AdaOUBoost (Adaptive Over-sampling and Under-sampling Boost). The novelty of AdaOUBoost mainly lies in: adaptively over-sample the minority positive examples and under-sample the majority negative examples to form different sub-classifiers. And combine these sub-classifiers according to their accuracy to create a strong classifier, which aims to use fully the whole training data and improve the performance of the class-imbalance learning classifier. In AdaOUBoost, first, our clustering-based under-sampling method is employed to divide the majority negative examples into some disjoint subsets. Then, for each subset of negative examples, we utilize the borderline-SMOTE (synthetic minority over-sampling technique) algorithm to over-sample the positive examples with different size, train each sub-classifier using each of them, and get the classifier by fusing these sub-classifiers with different weights. Finally, we combine these classifiers in each subset of negative examples to create a strong classifier. We compare the performance between AdaOUBoost and the state-of-the-art methods on TRECVID 2008 benchmark with all 20 concepts, and the results show the AdaOUBoost can achieve the superior performance in large scale imbalanced data sets.


title: A FDR Sensor for Measuring Complex Soil Dielectric Permittivity in the 10–500 MHz Frequency Range

abstract: Mechanical details as well as electrical models of FDR (frequency domain reflectometry) sensors for the measurement of the complex dielectric permittivity of porous materials are presented. The sensors are formed from two stainless steel parallel waveguides of various lengths. Using the data from VNA (vector network analyzer) with the connected FDR sensor and selected models of the applied sensor it was possible obtain the frequency spectrum of dielectric permittivity from 10 to 500 MHz of reference liquids and soil samples of various moisture and salinity. The performance of the analyzed sensors were compared with TDR (time domain reflectometry) ones of similar mechanical construction.


title: Pseudo-arthrosis repair of a posterior cruciate ligament avulsion fracture

abstract: A pseudo-arthrosis repair of a 4-year-old bony avulsion fracture of the PCL using a minimally invasive technique, screw fixation, and bone grafting is reported. The case presented seems to be rather unique due to the fragment size and the approach for pseudo-arthrosis repair. There was a good functional result following minimally invasive pseudo-arthrosis repair of a posterior cruciate ligament avulsion fracture. There are no previous reports of similar pseudo-arthrosis repairs, and other authors report good results of delayed refixation of PCL avulsion fractures. Therefore, refixation and pseudo-arthrosis repair should be considered as a viable treatment.


title: Epistemological View: Data Ethics, Privacy & Trust on Digital Platform

abstract: ‘Data’ had always an important asset of scientific study i.e. measurement, analysis, recording, representation, validation etc. Initially, data generation were increasing gradually along with Computer Generations. As per rapid growth of Computer technologies, ‘Data generation’ have been increased exponentially. Enhancement in data carries responsibility of ‘ethics’, whose fetches individual or distributed ‘morality’ towards its applications; ‘privacy’ towards users & services data; ‘transparencies’ towards scheduled activities or transections among consumers & providers; ‘trust’ towards AI agents and feedback systems. Countries have approached applications of modern technologies with limitations, different adoptions and regulations to prevent, privacy, confidentialities, social culture, transparency, human rights and security. Sometimes, careless attitude and blind faith may lead to data theft, hack and unpleased uses. Advance technologies may invade the individuality with different attributes using your attitude and open data. The objective of this paper is to identify evolution of ‘data ethics’ mind set and its different set of thought-wings, influence and penitration of digital data world(open data) in human life and their believes wrt trust, faith towards individual and group privacy,transperencies.Latest technologies and their ethical issues, training and principals related to ethical assessment have been looked over.


title: Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration.

abstract: We present a novel CNN-based image editing method that allows the user to change the semantic information of an image over a user-specified region. Our method makes this possible by combining the idea of manifold projection with spatial conditional batch normalization (sCBN), a version of conditional batch normalization with user-specifiable spatial weight maps. With sCBN and manifold projection, our method lets the user perform (1) spatial class translation that changes the class of an object over an arbitrary region of user's choice, and (2) semantic transplantation that transplants semantic information contained in an arbitrary region of the reference image to an arbitrary region in the target image. These two transformations can be used simultaneously, and can realize a complex composite image-editing task like "change the nose of a beagle to that of a bulldog, and open her mouth." The user can also use our method with intuitive copy-paste-style manipulations. We demonstrate the power of our method on various images. Code will be available at this https URL.


title: Energy-Aware Service Function Placement for Service Function Chaining in Data Centers

abstract: Nowadays, data centers deploy a large number of servers and network devices to provide various service functions (e.g. firewalls, deep packet inspection, content caches, WAN optimizers, etc.) and sufficient network connection. Most traffic in data centers is subject to treatment by multiple service functions (SFs) which form an ordered service function chain (SFC). In this paper, we study the energy-aware service function placement problem for SFC in data centers. By considering the computing, bandwidth resources constrains and the power models both in servers and switches, a new service function placement algorithm Merge-RD is proposed to save the energy consumption in data centers. The simulation results indicate that the Merge-RD can effectively reduce the energy consumption and average transmission delay in data center compared with some existing service function placement algorithms while guaranteeing the packet delivery ratio at a high level.


title: Traditional Birth Attendants and Maternal Mortality

abstract: The paper explored the consequences involved with the practice of traditional birth attendants and maternal mortality. In developing countries, most childbirth occurs at home and is not assisted by skilled attendants. This situation increases the risk of mortality for both mother and child with severe maternal and neonatal health complications. Maternal mortality is high in most African countries, particularly in rural areas where access to formal health care is limited. Physical distance and financial limitations are two major constraints that prevent community members from accessing, using trained attendants and institutional deliveries. Traditional Birth Attendants (TBAs) provide basic health care, support and advice during and after pregnancy/childbirth in rural communities. Traditional birth attendants are predominantly in rural, remote and other medically underserved areas. TBAs may not receive formal education and training in health care provision, and there are no specific professional requisites such as certification or licensing. Due to lack of education with some TBAs, the way they attend to delivery is risky for women and their babies, leading to poor health outcomes and even death. The paper suggests that since TBAs have made great impact in the rural communities, their roles cannot be abolished or overlooked therefore effective measures to train and improve their skills and define their roles is necessary. There should be integration and collaboration with the health sector to supervise and monitor their activities.


title: Open Category Detection with PAC Guarantees

abstract: Open category detection is the problem of detecting"alien"test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a"clean"training set that contains only the target categories of interest and an unlabeled"contaminated"training set that contains a fraction $\alpha$ of alien examples. Under the assumption that we know an upper bound on $\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements.


title: A Data-Driven Approach for Spatio-Temporal Crime Predictions in Smart Cities

abstract: The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.


title: Agile estimation using people and project related factors

abstract: The key measures of any project about its perfectness are ability to complete in the scheduled time and within the estimated cost. Cost and effort are important factors whenever a project is developed. The estimation of cost and effort is a difficult task in Agile environment. It has been observed that the current Agile methods mostly depends on an expert opinion, planning poker and historical data of project for estimation of cost, size, effort and duration. In absence of the historical data and experts the previous methods are not efficient. So there is need of an algorithmic method, which can calculate cost and effort of the project. In this direction S.Bhalero, Maya Ingel [2] has considered some project-related factors that calculate the size as well as duration of the project. However, several other project as well as people related factors may affect the Agile environment. In this work an Algorithmic estimation method is being proposed considering various factors thereby estimating the more accurate release date, cost, effort and duration for the project. The effectiveness and feasibility of the proposed algorithm has been shown by considering three cases in which different levels of factors are taken and compared.


title: Enterprise resource planning systems’ impact on accounting processes

abstract: The advent of the IT‐led era and the increased competition have forced companies to react to the new changes in order to remain competitive. Enterprise resource planning (ERP) systems offer distinct advantages in this new business environment as they lower operating costs, reduce cycle times and (arguably) increase customer satisfaction. This study examines, via an exploratory survey of 26 companies, the underlying reasons why companies choose to convert from conventional information systems (IS) to ERP systems and the changes brought in, particularly in the accounting process. The aim is not only to understand the changes and the benefits involved in adopting ERP systems compared with conventional IS, but also to establish the best way forward in future ERP applications. The empirical evidence confirms a number of changes in the accounting process introduced with the adoption of ERP systems.


title: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants

abstract: We study optimization algorithms based on variance reduction for stochastic gradient descent (SGD). Remarkable recent progress has been made in this direction through development of algorithms like SAG, SVRG, SAGA. These algorithms have been shown to outperform SGD, both theoretically and empirically. However, asynchronous versions of these algorithms---a crucial requirement for modern large-scale applications---have not been studied. We bridge this gap by presenting a unifying framework for many variance reduction techniques. Subsequently, we propose an asynchronous algorithm grounded in our framework, and prove its fast convergence. An important consequence of our general approach is that it yields asynchronous versions of variance reduction algorithms such as SVRG and SAGA as a byproduct. Our method achieves near linear speedup in sparse settings common to machine learning. We demonstrate the empirical performance of our method through a concrete realization of asynchronous SVRG.


title: Embedding Semantic Relations into Word Representations

abstract: Learning representations for semantic relations is important for various tasks such as analogy detection, relational search, and relation classification. Although there have been several proposals for learning representations for individual words, learning word representations that explicitly capture the semantic relations between words remains under developed. We propose an unsupervised method for learning vector representations for words such that the learnt representations are sensitive to the semantic relations that exist between two words. First, we extract lexical patterns from the co-occurrence contexts of two words in a corpus to represent the semantic relations that exist between those two words. Second, we represent a lexical pattern as the weighted sum of the representations of the words that co-occur with that lexical pattern. Third, we train a binary classifier to detect relationally similar vs. non-similar lexical pattern pairs. The proposed method is unsupervised in the sense that the lexical pattern pairs we use as train data are automatically sampled from a corpus, without requiring any manual intervention. Our proposed method statistically significantly outperforms the current state-of-the-art word representations on three benchmark datasets for proportional analogy detection, demonstrating its ability to accurately capture the semantic relations among words.


title: Small planar UWB antennas in proximity of human head

abstract: The effects of a human head on the performance of small planar ultra-wideband (UWB) antennas in proximity of the head are investigated numerically and experimentally. In simulation, a numerical head model is used in the XFDTD software package. The head model developed by REMCOM is with the frequency-dependent dielectric constant and conductivity obtained from the average data of anatomical human heads. Two types of planar antennas printed on printed circuit board (PCB) are designed to cover the UWB band. The impedance and radiation performance of the antennas are examined when the antennas are placed very close to the human head. The study shows that the human head slightly affects the impedance performance of the antennas. The radiated field distributions and the gain of the antennas demonstrate that the human head significantly blocks and absorbs the radiation from the antennas so that the radiation patterns are directional in the horizontal planes and the average gain greatly decreases. The information derived from the study is helpful to engineers who are applying UWB devices around/on human heads.


title: The analysis of facial beauty: an emerging area of research in pattern analysis

abstract: Much research presented recently supports the idea that the human perception of attractiveness is data-driven and largely irrespective of the perceiver. This suggests using pattern analysis techniques for beauty analysis. Several scientific papers on this subject are appearing in image processing, computer vision and pattern analysis contexts, or use techniques of these areas. In this paper, we will survey the recent studies on automatic analysis of facial beauty, and discuss research lines and practical applications.


title: The Browsemaps: Collaborative Filtering at LinkedIn

abstract: Many web properties make extensive use of item-based collaborative filtering, which showcases relationships between pairs of items based on the wisdom of the crowd. This paper presents LinkedIn’s horizontal collaborative filtering infrastructure, known as browsemaps. The platform enables rapid development, deployment, and computation of collaborative filtering recommendations for almost any use case on LinkedIn. In addition, it provides centralized management of scaling, monitoring, and other operational tasks for online serving. We also present case studies on how LinkedIn uses this platform in various recommendation products, as well as lessons learned in the field over the several years this system has been in production.


title: Packet Classification Algorithms: From Theory to Practice

abstract: During the past decade, the packet classification problem has been widely studied to accelerate network applications such as access control, traffic engineering and intrusion detection. In our research, we found that although a great number of packet classification algorithms have been proposed in recent years, unfortunately most of them stagnate in mathematical analysis or software simulation stages and few of them have been implemented in commercial products as a generic solution. To fill the gap between theory and practice, in this paper, we propose a novel packet classification algorithm named HyperSplit. Compared to the well-known HiCuts and HSM algorithms, HyperSplit achieves superior performance in terms of classification speed, memory usage and preprocessing time. The practicability of the proposed algorithm is manifested by two facts in our test: HyperSplit is the only algorithm that can successfully handle all the rule sets; HyperSplit is also the only algorithm that reaches more than 6Gbps throughput on the Octeon3860 multi-core platform when tested with 64-byte Ethernet packets against 10K ACL rules.


title: A universal asynchronous receiver transmitter design

abstract: In this paper, a UART design is proposed. The design has an auto-tuning baud rate generator. For achieving the speed matching of the processor and UART interface, it takes asynchronous FIFOs as buffers to realize data exchange between UART and external devices. The whole design is functionally verified using ModelSim SE 6.0, and synthesized and optimized by Synplicity's Synplify Premier 9.6.2.


title: Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy

abstract: In this perspective, the authors present the basic features of lens-free computational imaging tools and report performance comparisons with conventional microscopy methods. They also discuss the challenges that these computational on-chip microscopes face for their wide-scale biomedical application.


title: Dermoscopy of congenital melanocytic nevi: a ten-year follow-up study and comparative analysis with acquired melanocytic nevi arising in prepubertal age

abstract: Dermoscopic characteristics of congenital melanocytic nevi (CMN) have been reported, however, dermoscopic variation during long-term follow-up and direct comparative analyses with acquired melanocytic nevi (AMN) are poorly documented. To assess dermoscopic changes of CMN (including lesions present at birth or appearing within the first two years of age) after a long-term period and evaluate possible dermoscopic differences with AMN arising during prepubertal age. We re-analysed clinical and dermoscopic features of CMN, investigated ten years earlier. New findings were compared with those previously recorded, as well as with those of AMN appearing before puberty in the same group of patients. In total, 493 lesions (86 CMN and 407 AMN) from 71 patients were examined. Except for a greater size (median area: 73.9 vs 22.8 mm2; p<0.001) and higher prevalence of hair (17.4% vs 4.7; p<0.001) in CMN, no significant difference was observed between the two cohorts, including global/local dermoscopic features (p>0.05). The follow-up of CMN revealed that dermoscopic pattern changed in only four lesions (4.7%) (from globular to globular-reticular or reticular) after ten years, though lesions with a globular architecture presented several "local" changes, namely an increase in circumscribed reticular areas (from 20.0% to 41.5%; p = 0.030), irregularly distributed globules (from 15.6% to 34.1%; p = 0.045), and large globules (from 46.7% to 68.3%; p = 0.043). The dermoscopic appearance of CMN is significantly stable during childhood and is similar to that of AMN arising before puberty, thus supporting a possible link between such types of nevi.


title: Pre-processing online financial text for sentiment classification: A natural language processing approach

abstract: Online financial textual information contains a large amount of investor sentiment, i.e. subjective assessment and discussion with respect to financial instruments. An effective solution to automate the sentiment analysis of such large amounts of online financial texts would be extremely beneficial. This paper presents a natural language processing (NLP) based pre-processing approach both for noise removal from raw online financial texts and for organizing such texts into an enhanced format that is more usable for feature extraction. The proposed approach integrates six NLP processing steps, including a developed syntactic and semantic combined negation handling algorithm, to reduce noise in the online informal text. Three-class sentiment classification is also introduced in each system implementation. Experimental results show that the proposed pre-processing approach outperforms other pre-processing methods. The combined negation handling algorithm is also evaluated against three standard negation handling approaches.


title: Markerless Pose Tracking for Augmented Reality

abstract: In this paper a new approach is presented for markerless pose tracking in augmented reality. Using a tracking by detection approach, we estimate the 3D camera pose by detecting natural feature points in each input frame and building correspondences between 2D feature points. Instead of modeling the 3D environment, which is changing constantly and dynamically, we use a virtual square to define a 3D reference coordinate system. Camera pose can hence be estimated relative to it and the calculated 3D pose parameters can be used to render virtual objects into the real world. We propose and implement several strategies for robust matching, pose estimation and refinement. Experimental evaluation has shown that the approach is capable of online pose tracking and augmentation.


title: From geometric optimization and nonsmooth analysis to distributed coordination algorithms

abstract: We investigate the coordination of groups of autonomous robots performing spatially-distributed sensing tasks. We present facility location functions from geometric optimization and study their differentiable properties. We then design distributed coordination algorithms and analyze them as nonsmooth gradient flows. The resulting control laws correspond to basic interaction behaviors between the robots. The technical approach relies on concepts from computational geometry, nonsmooth analysis, and the dynamical system approach to algorithms.


Neuroprotective effects of lixisenatide and liraglutide in the MPTP mouse model of Parkinson's disease.


title: ClusterNet: Instance Segmentation in RGB-D Images.

abstract: We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of individual objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresponding object center and for the object's size and pose. The final instance segmentation is achieved through clustering in the space of moments. The object-centric training loss is defined on the output of the clustering. Our method outperforms the state-of-the-art instance segmentation method on our synthesized dataset. We show that our method generalizes well on real-world data achieving visually better segmentation results.


Closing the Loop for Edge Detection and Object Proposals.


Investigating the Usability of the Leap Motion Controller : Gesture-Based Interaction with a 3 D Virtual Environment Final Report


title: Six Sigma: concepts, tools, and applications

abstract: Purpose – To provide a sound discussion on the Six Sigma methodology and see how it fits in with other quality initiatives.Design/methodology/approach – A conceptual paper that takes at in‐depth look at the origins, pros and cons of Six Sigma and how it relates to some of the other quality initiatives in industry.Findings – The immediate goal of Six Sigma is defect reduction. Reduced defects lead to yield improvement; higher yields improve customer satisfaction. Six Sigma defect reduction is intended to lead to cost reduction. It has a process focus and aims to highlight process improvement opportunities through systematic measurement. Six Sigma implementation can have negative consequences if applied in the wrong project. Six Sigma is a toolset, not a management system and is best used in conjunction with other more comprehensive quality standards such as the Baldrige Criteria for Performance Excellence or the European Quality Award.Research limitations/implications – This is a conceptual study and hence...


title: Machine learning for predicting thermal power consumption of the Mars Express Spacecraft

abstract: The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its predefined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The remaining power can then be allocated for scientific purposes. We present a machine learning pipeline for efficiently constructing accurate predictive models for predicting the power of the thermal subsystem on board MEX. In particular, we employ state-of-the-art feature engineering approaches for transforming raw telemetry data, in turn used for constructing accurate models with different state-of-the-art machine learning methods. We show that the proposed pipeline considerably improve our previous (competition-winning) work in terms of time efficiency and predictive performance. Moreover, while achieving superior predictive performance, the constructed models also provide important insight into the spacecraft's behavior, allowing for further analyses and optimal planning of MEX's operation.


title: Hybrid Beamforming Design and Performance with Imperfect Phase Shifters in Multiuser Millimeter Wave Systems

abstract: Hybrid beamforming (HBF) includes analog beamforming with phase shifted array in RF domain and digital beamforming in baseband domain. Phase shifted array is usually made up with a large amount of phase shifters. Limited to the manufacturing techniques, phase shifters are unavoidable to have phaseshifting error and gain error. In the paper we study the influence of imperfect phase shifters upon the performance of multiuser mmWave massive MIMO systems1 with the wireless channel modeled by Rice fading. We derive the upper bound of the achievable sum rate with imperfect phase shifters, which can be very tight when the number of the antennas at the base station is much larger than the number of users. Results show that there is a performance ceiling due to phase-shifting error and gain error. Then we propose a novel channel estimation and hybrid beamforming method to settle this problem with low training overhead. It performs well when the channel paths are few and the channel Rician K-factor is large, which can be easily satisfied in outdoor mmWave communication environment. We further make a tradeoff between the performance and the training overhead so that the algorithm can work well in more kinds of propagation environment.


title: A 10-bit 400-MS/s 160-mW 0.13-/spl mu/m CMOS dual-channel pipeline ADC without channel mismatch calibration

abstract: This paper describes a 10-bit 400-MS/s dual-channel analog-to-digital converter (ADC) insensitive to offset, gain, and sampling-time mismatches between channels. An adaptive closed-loop sampling technique based on a multi-stage amplifier eliminates the channel offset effectively. Multi-stage amplifiers with high DC gain reduce the gain mismatch between channels and guarantee a large signal swing at low supply voltages. A single clock-edge sampling scheme for clock-skew reduction minimizes the sampling-time mismatch. The proposed prototype ADC in a 0.13-mum CMOS process occupies an active area of 4.2mm2, dissipates 160mW from 1.2 V and 400 MS/s, and shows a signal-to-noise-and-distortion ratio of 54.8 dB with a 29-MHz sinusoidal input at 400 MS/s without any channel-mismatch calibration technique. The measured maximum offset and gain mismatches are less than 0.1% and 0.2%, respectively


title: Retrospective comparison between the effects of propofol and inhalation anesthetic agent on postoperative recurrence rate of early- and intermediate-stage hepatocellular carcinoma

abstract: OBJECTIVE ::: Previous studies have reported that propofol has anti-tumor, anti-inflammatory, and anti-oxidative effects in addition to its anesthetic properties. To confirm this, a retrospective investigation was conducted to determine whether different anesthetic agents-in particular, propofol and inhalation anesthetic agent-have an effect on the recurrence of hepatocellular carcinoma (HCC) in patients who were diagnosed with primary HCC and underwent laparoscopic hepatectomy. ::: ::: ::: SUBJECTS AND METHODS ::: Patients with Barcelona Clinic Liver Cancer stages 0, A, and B HCC, who underwent laparoscopic hepatic resection, were enrolled in this study. Postoperative HCC recurrence, which was determined from postoperative liver computed tomography, was evaluated for 24 months postoperatively according to the main anesthetic agents. The characteristics of HCC and other patient- or surgery-related variables were evaluated together. ::: ::: ::: RESULTS AND CONCLUSION ::: During a 24-month period after hepatic resection, there was less recurrence of HCC in the propofol group than in the inhalation group (P = 0.046). The mean time to recurrence was 20.8 months (95% CI, 19.7-22.0) and 19.1 months (95% CI, 17.8-20.4) in the propofol and the inhalation group, respectively. In addition, multivariable Cox proportional regression analysis revealed that the propofol group showed significantly decreased recurrence rate than did the inhalation group (hazard ratio, 0.57; 95% CI, 0.47-0.69; P = 0.029). When propofol had been used as the main general anesthetic agent for laparoscopic hepatic resection, the postoperative two-year recurrence rate had decreased in early- and intermediate-stage HCC.


title: A new actuation approach for human friendly robot design

abstract: Many successful robotic manipulator designs have been introduced. However, there remains the challenge of designing a manipulator that possesses the inherent safety characteristics necessary for human-centered robotics. In this paper, we present a new actuation approach that has the requisite characteristics for inherent safety while maintaining the performance expected of modern designs. By drastically reducing the effective impedance of the manipulator while maintaining high frequency torque capability, we show that the competing design requirements of performance and safety can be successfully integrated into a single manipulation system.


title: ECF - EVENT CORRELATION FOR FORENSICS

abstract: The focus of the research described in this paper is on the nature of the event information provided in commonly available computer and other logs and the extent to which it is possible to correlate such event information despite its heterogeneous nature and origins. The strategic purpose of the research has been to develop a means by which a consolidated repository of such information can be constituted and then queried in order to provide an investigator with post hoc event correlation for forensics purposes (ECF). The paper provides an account of the log processing techniques utilized, and the nature of the database and query engine that have been developed in our current prototype and two examples of scenarios investigated and identified by the current prototype.


title: A general approach to connected-component labeling for arbitrary image representations

abstract: An improved and general approach to connected-component labeling of images is presented. The algorithm presented in this paper processes images in predetermined order , which means that the processing order depends only on the image representation scheme and not on specific properties of the image. The algorithm handles a wide variety of image representation schemes (rasters, run lengths, quadrees, bintrees, etc.). How to adapt the standard UNION-FIND algorithm to permit reuse of temporary labels is shown. This is done using a technique called age balancing , in which, when two labels are merged, the older label becomes the father of the younger label. This technique can be made to coexist with the more conventional rule of weight balancing , in which the label with more descendants becomes the father of the label with fewer descendants. Various image scanning orders are examined and classified. It is also shown that when the algorithm is specialized to a pixel array scanned in raster order, the total processing time is linear in the number of pixels. The linear-time processing time follows from a special property of the UNION-FIND algorithm, which may be of independent interest. This property states that under certain restrictions on the input, UNION-FIND runs in time linear in the number of FIND and UNION operations. Under these restrictions, linear-time performance can be achieved without resorting to the more complicated Gabow-Tarjan algorithm for disjoint set union.


title: Differentiating Cyberbullies and Internet Trolls by Personality Characteristics and Self-Esteem

abstract: Cyberbullying and internet trolling are both forms of online aggression or cyberharassment; however, research has yet to assess the prevalence of these behaviors in relationship to one another. In addition, the current study was the first to investigate whether individual differences and self-esteem discerned between self-reported cyberbullies and/or internet trolls (i.e., Never engaged in either, Cyberbully-only, Troll-only, Both Cyberbully and Troll). Of 308 respondents solicited from Mechanical Turk, 70 engaged in cyberbullying behaviors, 20 engaged in only trolling behaviors, 129 self-reported both behaviors, and 89 self-reported neither behavior. Results yielded low self-esteem, low conscientiousness, and low internal moral values for both cyberbullying and trolling behaviors. However, there were differentiating factors between individuals who only engaged in cyberbullying behaviors (high on neuroticism) vs. trolling-only behaviors (high on openness to experience). Individuals who engaged in both behaviors scored higher on extraversion, lower on agreeableness, and lower on self-esteem compared to individuals who engaged in neither behavior.


title: Learning Activity Patterns Performed With Emotion

abstract: This paper is a preliminary work towards the design of a model able to generate realistic motion sequences conditioned on a number of contextual variables like age, morphology, emotion etc. We focus in a first step on the design of contextual markovian models able to perform recognition of activities performed under various emotions even in the case no training samples are available for a particular (activity, emotion) pair, a zero shot learning setting.


title: Cybersickness and migraine triggers: exploring common ground

abstract: Cybersickness is a challenge for the implementation and enjoyment of virtual reality. There are many similarities in symptoms between cybersickness and migraine, therefore migraine research may help us better understand cybersickness. This paper investigates if many common migraine triggers affect cybersickness in virtual reality, by reviewing existing literature and research. Out of 22 identified common migraine triggers, 11 were found to increase cybersickness, 1 was found to have no effect and 6 had only limited research and the effects could not be determined. The other 4 triggers were found to be not relevant to a virtual reality situation. Based on these results, future research is suggested to determine the effects of these triggers as they may have a currently unknown negative affect on cybersickness.1


title: Applying learning theories and instructional design models for effective instruction.

abstract: Faculty members in higher education are involved in many instructional design activities without formal training in learning theories and the science of instruction. Learning theories provide the foundation for the selection of instructional strategies and allow for reliable prediction of their effectiveness. To achieve effective learning outcomes, the science of instruction and instructional design models are used to guide the development of instructional design strategies that elicit appropriate cognitive processes. Here, the major learning theories are discussed and selected examples of instructional design models are explained. The main objective of this article is to present the science of learning and instruction as theoretical evidence for the design and delivery of instructional materials. In addition, this article provides a practical framework for implementing those theories in the classroom and laboratory.


Recovering Question Answering Errors via Query Revision


title: A Semantic Scraping Model for Web Resources - Applying Linked Data to Web Page Screen Scraping.

abstract: In spite of the increasing presence of Semantic Web Facilities, only a limited amount of the available resources in the Internet provide a semantic access. Recent initiatives such as the emerging Linked Data Web are providing semantic access to available data by porting existing resources to the semantic web using different technologies, such as database-semantic mapping and scraping. Nevertheless, existing scraping solutions are based on ad-hoc solutions complemented with graphical interfaces for speeding up the scraper development. This article proposes a generic framework for web scraping based on semantic technologies. This framework is structured in three levels: scraping services, semantic scraping model and syntactic scraping. The first level provides an interface to generic applications or intelligent agents for gathering information from the web at a high level. The second level defines a semantic RDF model of the scraping process, in order to provide a declarative approach to the scraping task. Finally, the third level provides an implementation of the RDF scraping model for specific technologies. The work has been validated in a scenario that illustrates its application to mashup technologies.


title: Strategy first, execution second: teaching design strategy in technical communication

abstract: In technical communication education, design is often narrowly and essentially framed as execution of features. This approach fails to account for the innovative phase of user research, the iterative design process, and contextual factors such as workflow and governance. Inspired by Alan Cooper's Goal-Directed Design (2014), this paper advocates for a "design strategy" approach to the practice and pedagogy of design in technical communication. In particular, it calls for treating design as a process of research, discovery, prototyping, execution, and evaluation. This design process must strategically serve organizational objectives and user goals.


title: New trends in digital humanities

abstract: The field of research in digital humanities is undergoing a rapid transformation in recent years. A deep reflection on the current needs of the agents involved that takes into account key issues such as the inclusion of citizens in the creation and consumption of the cultural resources offered, the volume and complexity of datasets, available infrastructures, etcetera, is necessary. Present technologies make it possible to achieve projects that were impossible until recently, but the field is currently facing the challenge of proposing frameworks and systems to generalize and reproduce these proposals in other knowledge domains with similar but heterogeneous data sets. The track "New trends in digital humanities" of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2016), tries to set the basis of good practice in digital humanities by reflecting on models, technologies and methods to carry the transformation out.


title: Winding Function Approach for Winding Analysis

abstract: The winding factor is an operand in order to consider the effect of winding distribution and chording on the spatial distribution of the magnetic field in the air gap of synchronous and induction machines. The sinusoidal functions for winding factor calculation presented in literature are not defined and valid for every irregular winding, e.g., single-layer fractional-slot, combined star-delta, multilayer (greater than two), and asymmetrical windings. Although the summation of induced voltage phasors (star of slots) is the most accurate method, asymmetrical windings require to be decomposed in symmetrical components. In this paper, in addition to deriving the symmetrical components for asymmetrical multiphase windings, the analytical formulation is presented to relate the harmonic content of winding functions to winding factors. The harmonic leakage factor is accurately formulated from the winding function instead of the Gorges diagram without the need for summing up an infinite number of normalized winding factors quadratically. Without restriction of the number of layers and the distribution of the winding, including full-pitch, chorded and fractional-slot symmetrical and asymmetrical windings, the suggested analysis method is validated with the star of slots and sinusoidal functions of distribution and pitch factors, where applicable.


title: A Literature Review of Poly(Lactic Acid)

abstract: A literature review is presented regarding the synthesis, and physicochemical, chemical, and mechanical properties of poly(lactic acid)(PLA). Poly(lactic acid) exists as a polymeric helix, with an orthorhombic unit cell. The tensile properties of PLA can vary widely, depending on whether or not it is annealed or oriented or what its degree of crystallinity is. Also discussed are the effects of processing on PLA. Crystallization and crystallization kinetics of PLA are also investigated. Solution and melt rheology of PLA is also discussed. Four different power-law equations and 14 different Mark–Houwink equations are presented for PLA. Nuclear magnetic resonance, UV–VIS, and FTIR spectroscopy of PLA are briefly discussed. Finally, research conducted on starch–PLA composites is introduced.


title: Machine Learning Algorithms in Heavy Process Manufacturing

abstract: In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.


title: Phytobioactive compound-based nanodelivery systems for the treatment of type 2 diabetes mellitus – current status

abstract: Type 2 diabetes mellitus (T2DM) is a major chronic disease that is prevalent worldwide, and it is characterized by an increase in blood glucose, disturbances in the metabolism, and alteration in insulin secretion. Nowadays, food-based therapy has become an important treatment mode for type 2 diabetes, and phytobioactive compounds have gained an increasing amount of attention to this end because they have an effect on multiple biological functions, including the sustained secretion of insulin and regeneration of pancreatic islets cells. However, the poor solubility and lower permeability of these phyto products results in a loss of bioactivity during processing and oral delivery, leading to a significant reduction in the bioavailability of phytobioactive compounds to treat T2DM. Recently, nanotechnological systems have been developed for use as various types of carrier systems to improve the delivery of bioactive compounds and thus obtain a greater bioavailability. Furthermore, carrier systems in most nanodelivery systems are highly biocompatible, with nonimmunologic behavior, a high degree of biodegradability, and greater mucoadhesive strength. Therefore, this review focuses on the various types of nanodelivery systems that can be used for phytobioactive compounds in treating T2DM with greater antidiabetic effects. There is also additional focus on improving the effects of various phytobioactive compounds through nanotechnological delivery to ensure a highly efficient treatment of type 2 diabetes.


title: Sparsity Measure of a Network Graph: Gini Index

abstract: This article examines the application of a popular measure of sparsity, Gini Index, on network graphs. A wide variety of network graphs happen to be sparse. But the index with which sparsity is commonly measured in network graphs is edge density, reflecting the proportion of the sum of the degrees of all nodes in the graph compared to the total possible degrees in the corresponding fully connected graph. Thus edge density is a simple ratio and carries limitations, primarily in terms of the amount of information it takes into account in its definition. In this paper, we have provided a formulation for defining sparsity of a network graph by generalizing the concept of Gini Index and call it sparsity index. A majority of the six properties (viz., Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates and Babies) with which sparsity measures are commonly compared are seen to be satisfied by the proposed index. A comparison between edge density and the sparsity index has been drawn with appropriate examples to highlight the efficacy of the proposed index. It has also been shown theoretically that the two measures follow similar trend for a changing graph, i.e., as the edge density of a graph increases its sparsity index decreases. Additionally, the paper draws a relationship, analytically, between the sparsity index and the exponent term of a power law distribution, a distribution which is known to approximate the degree distribution of a wide variety of network graphs. Finally, the article highlights how the proposed index together with Gini index can reveal important properties of a network graph.


title: Non-convex hull surfaces

abstract: We present a new algorithm to reconstruct approximating watertight surfaces from finite oriented point clouds. The Convex Hull (CH) of an arbitrary set of points, constructed as the intersection of all the supporting linear half spaces, is a piecewise linear watertight surface, but usually a poor approximation of the sampled surface. We introduce the Non-Convex Hull (NCH) of an oriented point cloud as the intersection of complementary supporting spherical half spaces; one per point. The boundary surface of this set is a piecewise quadratic interpolating surface, which can also be described as the zero level set of the NCH Signed Distance function. We evaluate the NCH Signed Distance function on the vertices of a volumetric mesh, regular or adaptive, and generate an approximating polygonal mesh for the NCH Surface using an isosurface algorithm. Despite its simplicity, this simple algorithm produces high quality polygon meshes competitive with those generated by state-of-the-art algorithms. The relation to the Medial Axis Transform is described.


title: Malware Guard Extension: Using SGX to Conceal Cache Attacks

abstract: In modern computer systems, user processes are isolated from each other by the operating system and the hardware. Additionally, in a cloud scenario it is crucial that the hypervisor isolates tenants from other tenants that are co-located on the same physical machine. However, the hypervisor does not protect tenants against the cloud provider and thus the supplied operating system and hardware. Intel SGX provides a mechanism that addresses this scenario. It aims at protecting user-level software from attacks from other processes, the operating system, and even physical attackers.


title: Lecture Notes on Network Information Theory

abstract: These lecture notes have been converted to a book titled Network Information Theory published recently by Cambridge University Press. This book provides a significantly expanded exposition of the material in the lecture notes as well as problems and bibliographic notes at the end of each chapter. The authors are currently preparing a set of slides based on the book that will be posted in the second half of 2012. More information about the book can be found at this http URL The previous (and obsolete) version of the lecture notes can be found at this http URL


title: Robust Online Visual Tracking with a Single Convolutional Neural Network

abstract: Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they require very long training time and a large number of training samples. In this work, we present an efficient and very robust online tracking algorithm using a single Convolutional Neural Network (CNN) for learning effective feature representations of the target object over time. Our contributions are multifold: First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation, thus drift, by accommodating the uncertainty of the model output. Second, we enhance the ordinary Stochastic Gradient Descent approach in CNN training with a temporal selection mechanism, which generates positive and negative samples within different time periods. Finally, we propose to update the CNN model in a “lazy” style to speed-up the training stage, where the network is updated only when a significant appearance change occurs on the object, without sacrificing tracking accuracy. The CNN tracker outperforms all compared state-of-the-art methods in our extensive evaluations that involve 18 well-known benchmark video sequences.


title: S-MATCH: AN OPEN SOURCE FRAMEWORK FOR MATCHING

abstract: Achieving automatic interoperability among systems with diverse data structures and languages expressing different viewpoints is a goal that has been difficult to accomplish. This paper describes S-Match, an open source semantic matching framework that tackles the semantic interoperability problem by transforming several data structures such as business catalogs, web directories, conceptual models and web services descriptions into lightweight ontologies and establishing semantic correspondences between them. The framework is the first open source semantic matching project that includes three different algorithms tailored for specific domains and provides an extensible API for developing new algorithms, including possibility to plug-in specific background knowledge according to the characteristics of each application domain.


title: Statistical significance versus clinical relevance: Clinical Epidemiology in Nephrology

abstract: In March this year, the American Statistical Association (ASA) posted a statement on the correct use of P-values, in response to a growing concern that the P-value is commonly misused and misinterpreted. We aim to translate these warnings given by the ASA into a language more easily understood by clinicians and researchers without a deep background in statistics. Moreover, we intend to illustrate the limitations of P-values, even when used and interpreted correctly, and bring more attention to the clinical relevance of study findings using two recently reported studies as examples. We argue that P-values are often misinterpreted. A common mistake is saying that P < 0.05 means that the null hypothesis is false, and P ≥0.05 means that the null hypothesis is true. The correct interpretation of a P-value of 0.05 is that if the null hypothesis were indeed true, a similar or more extreme result would occur 5% of the times upon repeating the study in a similar sample. In other words, the P-value informs about the likelihood of the data given the null hypothesis and not the other way around. A possible alternative related to the P-value is the confidence interval (CI). It provides more information on the magnitude of an effect and the imprecision with which that effect was estimated. However, there is no magic bullet to replace P-values and stop erroneous interpretation of scientific results. Scientists and readers alike should make themselves familiar with the correct, nuanced interpretation of statistical tests, P-values and CIs.


title: Simulated annealing Vs. Genetic Simulated Annealing for automatic transistor sizing

abstract: Transistor size optimization is an important aspect of circuit design. Small and non-complex circuits can be designed easily using manual calculations and circuit simulations. But, as the complexity of circuits increases, manual design becomes too difficult and time consuming. Therefore, tools and techniques for automatic transistor sizing are of great importance in the area of circuit design. The goal of this paper is to implement Genetic Simulated Annealing algorithm as a tool for transistor sizing, and compare its performance with Simulated Annealing, one of the most popular optimization algorithm in use today. The algorithms have been tested on four different digital circuits and the results collated and compared in this paper.


title: Stereo-based 6D object localization for grasping with humanoid robot systems

abstract: Robust vision-based grasping is still a hard problem for humanoid robot systems. When being restricted to using the camera system built-in into the robot's head for object localization, the scenarios get often very simplified in order to allow the robot to grasp autonomously. Within the computer vision community, many object recognition and localization systems exist, but in general, they are not tailored to the application on a humanoid robot. In particular, accurate 6D object localization in the camera coordinate system with respect to a 3D rigid model is crucial for a general framework for grasping. While many approaches try to avoid the use of stereo calibration, we will present a system that makes explicit use of the stereo camera system in order to achieve maximum depth accuracy. Our system can deal with textured objects as well as objects that can be segmented globally and are defined by their shape. Thus, it covers the cases of objects with complex texture and complex shape. Our work is directly linked to a grasping framework being implemented on the humanoid robot ARM AR and serves as its perception module for various grasping and manipulation experiments in a kitchen scenario.


title: Improved spoken term detection by template-based confidence measure

abstract: The confidence measure (CM) in spoken term detection is addressed in this paper. We present a template-based approach in CM procedure. The template speeches are first selected from the development corpus. Then the Dynamic Time Warping (DTW) algorithm is adopted to calculate the distances between the candidate and template speeches. These DTW distances are transformed to the confidence score through some algebra processing. Furthermore, the DTW scores and posterior probability from the speech recognition lattice can be combined to improve the term detection. Experimental results show that the proposed approach offers significant performance improvement. A 22.3% relative increase in Average Term Weighted Value (ATWV) can be obtained in the evaluation set.


title: Parking space detection using ultrasonic sensor in parking assistance system

abstract: This paper deals with parking space detection by using ultrasonic sensor. Using the multiple echo function, the accuracy of edge detection was increased. After inspecting effect on the multiple echo function in indoor experiment, we applied to 11 types of vehicles in real parking environment and made experiments on edge detection with various values of resolution. We can scan parking space more accurately in real parking environment. We propose the diagonal sensor to get information about the side of parking space. Our proposed method has benefit calculation and implementation is very simple.


title: Early predictors of job burnout and engagement.

abstract: A longitudinal study predicted changes in burnout or engagement a year later by identifying 2 types of early indicators at the initial assessment. Organizational employees (N 466) completed measures of burnout and 6 areas of worklife at 2 times with a 1-year interval. Those people who showed an inconsistent pattern at Time 1 were more likely to change over the year than were those who did not. Among this group, those who also displayed a workplace incongruity in the area of fairness moved to burnout at Time 2, while those without this incongruity moved toward engagement. The implications of these 2 predictive indicators are discussed in terms of the enhanced ability to customize interventions for targeted groups within the workplace. Job stress has been recognized as a significant occupational hazard that can impair physical health, psychological well-being, and work performance (see Kahn & Byosiere, 1992; Sauter & Murphy, 1995). The worker's internal experience of strain is assumed to play a mediating role between the impact of external job demands (stressors) and work-related outcomes (such as ab- senteeism or illness). This basic mediation model characterizes the job stress phenomenon known as burnout as well as its positive opposite of engagement with work (Leiter & Maslach, 2005). Burnout is an unpleasant and dysfunctional condition that both individuals and organizations would like to change; indeed, much of the major interest in burnout has been not simply to understand what it is but to figure out what to do about it (Maslach & Goldberg, 1998). Many studies have tried to identify the primary causes or correlates of burnout, with the goal of developing generic intervention strategies to change these factors. The current research takes a different, but complementary, ap- proach by trying to identify early signs of burnout development. If such early indicators were indeed valid predictors of future prob- lems with burnout, then they could be used to identify "high risk" people who could be targeted for early, preventive interventions. This approach is a purely pragmatic one, which simply focuses on people's experiences at particular points in time rather than mak- ing other assumptions or including other variables. The basic premise is that if an individual is experiencing some early signs of burnout, then that information is sufficient for consideration of actions to prevent burnout and build engagement.


title: Learning Structured Semantic Embeddings for Visual Recognition

abstract: Numerous embedding models have been recently explored to incorporate semantic knowledge into visual recognition. Existing methods typically focus on minimizing the distance between the corresponding images and texts in the embedding space but do not explicitly optimize the underlying structure. Our key observation is that modeling the pairwise image-image relationship improves the discrimination ability of the embedding model. In this paper, we propose the structured discriminative and difference constraints to learn visual-semantic embeddings. First, we exploit the discriminative constraints to capture the intra- and inter-class relationships of image embeddings. The discriminative constraints encourage separability for image instances of different classes. Second, we align the difference vector between a pair of image embeddings with that of the corresponding word embeddings. The difference constraints help regularize image embeddings to preserve the semantic relationships among word embeddings. Extensive evaluations demonstrate the effectiveness of the proposed structured embeddings for single-label classification, multi-label classification, and zero-shot recognition.