title: GORDIAN: Efficient and Scalable Discovery of Composite Keys

abstract: Identification of (composite) key attributes is of fundamental importance for many different data management tasks such as data modeling, data integration, anomaly detection, query formulation, query optimization, and indexing. However, information about keys is often missing or incomplete in many real-world database scenarios. Surprisingly, the fundamental problem of automatic key discovery has received little attention in the existing literature. Existing solutions ignore composite keys, due to the complexity associated with their discovery. Even for simple keys, current algorithms take a brute-force approach; the resulting exponential CPU and memory requirements limit the applicability of these methods to small datasets. In this paper, we describe GORDIAN, a scalable algorithm for automatic discovery of keys in large datasets, including composite keys. GORDIAN can provide exact results very efficiently for both real-world and synthetic datasets. GORDIAN can be used to find (composite) key attributes in any collection of entities, e.g., key column-groups in relational data, or key leaf-node sets in a collection of XML documents with a common schema. We show empirically that GORDIAN can be combined with sampling to efficiently obtain high quality sets of approximate keys even in very large datasets.


title: Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCA

abstract: We present a framework for analyzing the exact dynamics of a class of online learning algorithms in the high-dimensional scaling limit. Our results are applied to two concrete examples: online regularized linear regression and principal component analysis. As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical measures of the target feature vector and its estimates provided by the algorithms will converge weakly to a deterministic measured-valued process that can be characterized as the unique solution of a nonlinear PDE. Numerical solutions of this PDE can be efficiently obtained. These solutions lead to precise predictions of the performance of the algorithms, as many practical performance metrics are linear functionals of the joint empirical measures. In addition to characterizing the dynamic performance of online learning algorithms, our asymptotic analysis also provides useful insights. In particular, in the high-dimensional limit, and due to exchangeability, the original coupled dynamics associated with the algorithms will be asymptotically"decoupled", with each coordinate independently solving a 1-D effective minimization problem via stochastic gradient descent. Exploiting this insight for nonconvex optimization problems may prove an interesting line of future research.


title: PET/CT comparing 68Ga-DOTATATE and other radiopharmaceuticals and in comparison with CT/MRI for the localization of sporadic metastatic pheochromocytoma and paraganglioma

abstract: PURPOSE ::: Pheochromocytomas/paragangliomas (PPGLs) and their metastases are tumors that predominantly express somatostatin receptor 2 (SSR2). (68)Ga-DOTA(0)-Tyr(3)-octreotate ((68)Ga-DOTATATE) is a PET radiopharmaceutical with both high and selective affinity for SSRs. The purpose of this study was to evaluate the utility of (68)Ga-DOTATATE in comparison with other specific and nonspecific radiopharmaceuticals recommended in the current guidelines for the localization of metastatic sporadic PPGL by PET/CT. ::: ::: ::: METHODS ::: This prospective study included 22 patients (15 men, 7 women; aged 50.0 ± 13.9 years) with confirmed metastatic PPGL, a negative family history for PPGL, and negative genetic testing, who underwent (68)Ga-DOTATATE, (18)F-fluoro-2-deoxy-D-glucose ((18)F-FDG) PET/CT, and CT/MRI. Only 12 patients underwent an additional (18)F-fluorodihydroxyphenylalanine ((18)F-FDOPA) PET/CT scan and only 11 patients underwent an additional (18)F-fluorodopamine ((18)F-FDA) PET/CT scan. The rates of detection of metastatic lesions were compared among all the imaging studies. A composite of all functional and anatomical imaging studies served as the imaging comparator. ::: ::: ::: RESULTS ::: (68)Ga-DOTATATE PET/CT showed a lesion-based detection rate of 97.6 % (95 % confidence interval, CI, 95.8 - 98.7 %). (18)F-FDG PET/CT, (18)F-FDOPA PET/CT, (18)F-FDA PET/CT, and CT/MRI showed detection rates of 49.2 % (CI 44.5 - 53.6 %; p < 0.01), 74.8 % (CI 69.0 - 79.9 %); p < 0.01), 77.7 % (CI 71.5 - 82.8 %; p < 0.01), and 81.6 % (CI 77.8 - 84.8 %; p < 0.01), respectively. ::: ::: ::: CONCLUSION ::: The results of this study demonstrate the superiority of (68)Ga-DOTATATE PET/CT in the localization of sporadic metastatic PPGLs compared to all other functional and anatomical imaging modalities, and suggest modification of future guidelines towards this new imaging modality.


title: Software review: Netlogo, a multiagent simulation environment

abstract: NetLogo [41, 40] is a freely downloadable, agent-based software package that was created at the Center for Connected Learning and Computer-Based Modeling (CCL) at Northwestern University, directed by Uri Wilensky. It is the latest in a series of multi-agent simulation environments that includes StarLogo [28, 33], which was developed at the MIT Media Lab by Wilensky and Mitchel Resnick. In these environments, agents are represented as ‘‘turtles’’ and are programmed with a variant of Seymour Papert’s LOGO [8, 19]. Designed to be simple enough for children to program, NetLogo illustrates what can happen when populations of turtles are given sets of rules to obey. Despite the entry-level programming interface, NetLogo is capable of quite sophisticated modeling and allows experienced programmers to add their own Java extensions. As a result, NetLogo has been widely used by a broad audience, from elementary school children to academics in the social, computer, and ‘‘hard’’ sciences; and the on-line community page includes models constructed by a wide range of representatives from each of these segments of the population. The NetLogo home page (http://ccl.northwestern.edu/netlogo/) includes a download area, models pages, sample downloadable extensions, user manuals, a FAQ, and links to various resources. The software package comes with a large number of demonstration models, each of which can be executed and modified, since the source for each demo is included in the package. These demos are also available on the ‘‘models’’ portion of the home page, where they can be run in a Web browser. They are contributed by community members and are tested and supported by the development team at CCL. Model application areas range from biology to computer science to art to artificial life and include simulations such as predator-prey, spread of disease, rumor mill, traffic jams, small worlds, fractals, erosion, dining philosophers, cellular automata, crystallization, and radioactivity. In addition, on the ‘‘community models’’ portion of the home page, an even larger and more diverse set of models is available. These, too, have been contributed by community members, but they are not checked by CCL staff; they are supported by their authors, whose contact details are listed on the Web page. NetLogo was first released in 1999, and the most recent release (as of this writing) is version 3.1.2 (August 9, 2006). NetLogo runs on virtually any of today’s most popular platforms—Microsoft Windows, Mac OS-X, and Linux. For each version released, the Web page (URL) enumerates the operating systems on which NetLogo has been successfully installed and executed. NetLogo is written in Java, which aids its portability and compatibility. One of the most convenient features of NetLogo allows programmers to save models as Java applets, permitting seamless publication of simulations from NetLogo’s built-in integrated development environment to a Web page.


Maternal and Child Sexual Abuse History: An Intergenerational Exploration of Children’s Adjustment and Maternal Trauma-Reflective Functioning


title: ASSESSING FACTORS THAT FORM PATIENT’S ATTITUDE TO TREATMENT PRECEDING HOSPITALIZATION FOR ACUTE CORONARY SYNDROM (DATA OF QUESTIONNAIRE WITHIN THE LIS REGISTER)

abstract: Aim. To determine the most significant factors forming patient’s attitude to treatment and factors of adherence to treatment preceding hospitalization in patients with acute coronary syndrome (ACS). Material and methods. A register of patients admitted to Lyubertsy Regional Hospital №2 for ACS from 01.12.2011 to 01.12.2012 was used in the study. A total of 272 patients (men – 157, women – 115) were enrolled into the study. Age range was between 31 and 89 years, mean age was 63.6±12.6 years. Acute myocardial infarction was diagnosed in 181 patients, unstable angina pectoris – in 91 patients. While in hospital all the patients had filled out the questionnaire on therapy adherence. Results. Half of the 272 patients (133 persons) enrolled into the study had regularly been followed-up for chronic diseases in different medical centers; 138 patients (50.7%) had not been followed-up before the reference hospitalization. According to the valid Morisky-Green test results 120 patients (44.1%) were adherent to treatment, in 141 (51.8%) patients the adherence was unsatisfactory, 11 persons (4.1%) did not answer the test questions. Younger patients adhered to doctors’ recommendations better. According to the patients (125 patients – 46%) the most effective strategy to improve treatment adherence was the obtaining of detailed information about their disease and methods of its treatment provided by the doctor. 32 patients (11.8%) pointed out the importance of lowering drug costs. 11 patients of 272 respondents demanded more care from their doctor; 4 persons suggested that better quality of medical care organization would help to ameliorate treatment adherence. Regular follow-up for chronic diseases with detailed informing about the condition by a physician positively influences adherence to recommended therapy. Patients of commercial medical centers and patients without any medical follow-up least of all adhered to treatment. Conclusion. Specially designed questionnaire allowed to determine the most significant factors that form patients’ attitude to therapy preceding the reference hospitalization and factors of adherence to treatment prescribed before the hospitalization. All the significant factors were related to the physician-patient relationships, what emphasizes the important role of both doctors and patients in improvement of treatment adherence.


title: Profile based recommendation of code reviewers

abstract: Code reviews consist in proof-reading proposed code changes in order to find their shortcomings such as bugs, insufficient test coverage or misused design patterns. Code reviews are conducted before merging submitted changes into the main development branch. The selection of suitable reviewers is crucial to obtain the high quality of reviews. In this article we present a new method of recommending reviewers for code changes. This method is based on profiles of individual programmers. For each developer we maintain his/her profile. It is the multiset of all file path segments from commits reviewed by him/her. It will get updated when he/she presents a new review. We employ a similarity function between such profiles and change proposals to be reviewed. The programmer whose profile matches the change most is recommended to become the reviewer. We performed an experimental comparison of our method against state-of-the-art techniques using four large open-source projects. We obtained improved results in terms of classification metrics (precision, recall and F-measure) and performance (we have lower time and space complexity).


title: Tunable 1.55 - 2.1 GHz 4-Pole Elliptic Bandpass Filter With Bandwidth Control and ${> 50}~{\hbox {dB}}$ Rejection for Wireless Systems

abstract: This paper presents a four-pole elliptic tunable combline bandpass filter with center frequency and bandwidth control. The filter is built on a Duroid substrate with er=10.2 and h=25 mils, and the tuning is done using packaged Schottky diodes. A frequency range of 1.55-2.1 GHz with a 1-dB bandwidth tuning from 40-120 MHz (2.2-8% fractional bandwidth) is demonstrated. A pair of tunable transmission zeroes are synthesized at both passband edges and significantly improve the filter selectivity. The rejection level at both the lower and upper stopbands is >; 50 dB and no spurious response exists close to the passband. The measured third-order intermodulation intercept point (TOI) and 1-dB power compression point at midband (1.85 GHz) and a bandwidth of 110 MHz are >; 14& dBm and 6 dBm, respectively, and are limited by the Schottky diodes. It is believed that this is the first four-pole combline tunable bandpass filter with an elliptic function response and center frequency and bandwidth control. The application areas are in tunable filters for wireless systems and cognitive radios.


Wasserstein Generative Adversarial Networks


title: A survey on detection and classification of rice plant diseases

abstract: Identifying disease from the images of the plant is one of the interesting research areas in computer and agriculture field. This paper presents a survey of different image processing and machine-learning techniques used in the identification of rice plant diseases based on images of disease infected rice plants. This paper presents not only survey of various techniques but also concisely discusses important concepts of image processing and machine learning applied to plant disease detection and classification. We carry out detailed study of 19 papers, covering the work on rice plant diseases and other different plants and fruits, and present a survey of these papers based on important criteria. These criteria include size of image dataset, no. of classes(diseases), preprocessing, segmentation techniques, types of classifiers, accuracy of classifiers etc. We utilize our survey and study to propose and design our work on detection and classification of rice plant diseases.


title: Smart grid neighborhood area networks: a survey

abstract: Smart grid is an intelligent power network featured by its two-way flows of electricity and information. With an integrated communication infrastructure, smart grid manages the operation of all connected components to provide reliable and sustainable electricity supplies. Many advanced communication technologies have been identified for their applications in different domains of smart grid networks. This paper focuses on wireless communication networking technologies for smart grid neighborhood area networks (NANs). In particular, we aim to offer a comprehensive survey to address various important issues on implementation of smart grid NANs, including network topology, gateway deployment, routing algorithms, and security. We will identify four major challenges for the implementation of NANs, including timeliness management, security assurance, compatibility design, and cognitive spectrum access, based on which the future research directions are suggested.


title: Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso)

abstract: The problem of consistently estimating the sparsity pattern of a vector beta* isin Rp based on observations contaminated by noise arises in various contexts, including signal denoising, sparse approximation, compressed sensing, and model selection. We analyze the behavior of l1-constrained quadratic programming (QP), also referred to as the Lasso, for recovering the sparsity pattern. Our main result is to establish precise conditions on the problem dimension p, the number k of nonzero elements in beta*, and the number of observations n that are necessary and sufficient for sparsity pattern recovery using the Lasso. We first analyze the case of observations made using deterministic design matrices and sub-Gaussian additive noise, and provide sufficient conditions for support recovery and linfin-error bounds, as well as results showing the necessity of incoherence and bounds on the minimum value. We then turn to the case of random designs, in which each row of the design is drawn from a N (0, Sigma) ensemble. For a broad class of Gaussian ensembles satisfying mutual incoherence conditions, we compute explicit values of thresholds 0 0, if n > 2 (thetasu + delta) klog (p- k), then the Lasso succeeds in recovering the sparsity pattern with probability converging to one for large problems, whereas for n < 2 (thetasl - delta)klog (p - k), then the probability of successful recovery converges to zero. For the special case of the uniform Gaussian ensemble (Sigma = Iptimesp), we show that thetasl = thetas


title: Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment

abstract: On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundamental PE ratios are often estimated only by subjective expert opinions. The purpose of this research is to formalize a process of fundamental PE estimation by employing advanced dynamic Bayesian network (DBN) methodology. The estimated PE ratio from our model can be used either as a information support for an expert to make investment decisions, or as an automatic trading system illustrated in experiments. Forward-backward inference and EM parameter estimation algorithms are derived with respect to the proposed DBN structure. Unlike existing works in literatures, the economic interpretation of our DBN model is well-justified by behavioral finance evidences of volatility. A simple but practical trading strategy is invented based on the result of Bayesian inference. Extensive experiments show that our trading strategy equipped with the inferenced PE ratios consistently outperforms standard investment benchmarks.


title: Ensemble classification of colon biopsy images based on information rich hybrid features

abstract: In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we propose a colon biopsy image classification system, called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology. Normal and malignant colon biopsy images differ with each other in terms of the color distribution of different biological constituents. The colors of different constituents are sharp in normal images, whereas the colors diffuse with each other in malignant images. In order to exploit this variation, two feature types, namely color components based statistical moments (CCSM) and Haralick features have been proposed, which are color components based variants of their traditional counterparts. Moreover, in normal colon biopsy images, epithelial cells possess sharp and well-defined edges. Histogram of oriented gradients (HOG) based features have been employed to exploit this information. Different combinations of hybrid features have been constructed from HOG, CCSM, and Haralick features. The minimum Redundancy Maximum Relevance (mRMR) feature selection method has been employed to select meaningful features from individual and hybrid feature sets. Finally, an ensemble classifier based on majority voting has been proposed, which classifies colon biopsy images using the selected features. Linear, RBF, and sigmoid SVM have been employed as base classifiers. The proposed system has been tested on 174 colon biopsy images, and improved performance (=98.85%) has been observed compared to previously reported studies. Additionally, the use of mRMR method has been justified by comparing the performance of CBIC on original and reduced feature sets.


title: Deploying carrier-grade WiFi: offload traffic, not money

abstract: WiFi data offloading provides a promising auxiliary to alleviate network congestion by diverting traffic from the cellular infrastructure onto WiFi access points (APs). Despite the importance and momentum of this method, the current deployment of APs by the carriers follows mostly a heuristic approach. In addition, the prevalent free-of-charge WiFi access approach may result in significant opportunity costs for the carriers as this traffic could yield non-negligible revenues. In this paper, we propose and study the problem of optimizing the deployment of WiFi offloading infrastructure, and pricing the offloading service with the goal of maximizing carrier profits. Addressing this problem is a prerequisite for the efficient integration of WiFi technology to next generation of cellular systems and the development of carrier-grade offloading solutions. Our framework considers a fundamental, intuitive model of carrier costs and revenues, and two demand models that predict how traffic will change in response to alteration in the price and the set of deployed APs. We present both analytical and approximate solutions for this intricate problem, and reveal how key network parameters shape the offloading benefits. Using a dataset of WiFi access patterns collected from real users, we evaluate the impact of offloading for different regional markets around the world. We find that in mature markets WiFi can help carriers reduce their costs, while charging users up to 50% lower than the cellular service. The gains are higher for small "virtual carriers" who resell other's mobile data services (up to a factor of 2). However, in less mature markets where the AP deployment or access costs are higher, deploying APs can actually lead to a net loss for the carrier. Our evaluation code is publicly available for the benefit of research community.


A Helical Model of Media Competition , Innovation and Performance


title: Privacy-preserving trajectory data publishing by local suppression

abstract: The pervasiveness of location-aware devices has spawned extensive research in trajectory data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing trajectory data among different parties often creates an obstacle for effective data mining. In this paper, we study the challenges of anonymizing trajectory data: high dimensionality, sparseness, and sequentiality. Employing traditional privacy models and anonymization methods often leads to low data utility in the resulting data and ineffective data mining. In addressing these challenges, this is the first paper to introduce local suppression to achieve a tailored privacy model for trajectory data anonymization. The framework allows the adoption of various data utility metrics for different data mining tasks. As an illustration, we aim at preserving both instances of location-time doublets and frequent sequences in a trajectory database, both being the foundation of many trajectory data mining tasks. Our experiments on both synthetic and real-life data sets suggest that the framework is effective and efficient to overcome the challenges in trajectory data anonymization. In particular, compared with the previous works in the literature, our proposed local suppression method can significantly improve the data utility in anonymous trajectory data.


title: Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

abstract: Since the late 1990s, there has been a burst of research on robotic devices for poststroke rehabilitation. Robot-mediated therapy produced improvements on recovery of motor capacity; however, so far, the use of robots has not shown qualitative benefit over classical therapist-led training sessions, performed on the same quantity of movements. Multidegree-of-freedom robots, like the modern upper-limb exoskeletons, enable a distributed interaction on the whole assisted limb and can exploit a large amount of sensory feedback data, potentially providing new capabilities within standard rehabilitation sessions. Surprisingly, most publications in the field of exoskeletons focused only on mechatronic design of the devices, while little details were given to the control aspects. On the contrary, we believe a paramount aspect for robots potentiality lies on the control side. Therefore, the aim of this review is to provide a taxonomy of currently available control strategies for exoskeletons for neurorehabilitation, in order to formulate appropriate questions toward the development of innovative and improved control strategies.


title: Learning Mixtures of Product Distributions Using Correlations and Independence

abstract: We study the problem of learning mixtures of distributions, a natural formalization of clustering. A mixture of distributions is a collection of distributions D = {D1, . . .DT }, and mixing weights, {w1, . . . , wT } such that


High PSRR Low Drop-out Voltage Regulator ( LDO )


title: Lexicon-Based Sentiment Analysis for Reviews of Products in Brazilian Portuguese

abstract: This paper presents some results on lexicon-based classification of sentiment polarity in web reviews of products written in Brazilian Portuguese. They represent a first step towards a robust opinion miner from reviews of technology products. The evaluation shows the performance of 3 different sentiment lexicons combined with simple strategies. It is also discussed the risk of considering the rating provided by the writers for the purpose of evaluating the algorithms. The results show that the better combination is the version of the algorithm that deals also with negation and intensification and uses the sentiment lexicon Sent ilex. The average F-measure achieved 0.73.


title: Measures of impulsivity in Parkinson's disease decrease after DBS in the setting of stable dopamine therapy

abstract: Abstract Introduction Recent evidence suggests deep brain stimulation can alter impulse control. Our objective was to prospectively evaluate the effects of subthalamic nucleus (STN) and globus pallidus internus (GPi) deep brain stimulation on impulse control disorders (ICDs) in the setting of a conservative dopamine reduction strategy. Methods Patients (n = 37) undergoing de novo, unilateral STN or GPi DBS lead implantation were evaluated pre-operatively and 6–12 months post-operatively for the presence of ICDs using the Questionnaire for Impulsivity in Parkinson's disease (QUIP) and by clinical interview. Results Of the patients enrolled, 23 underwent electrode implantation in the globus pallidus internus and 14 were implanted in the subthalamic nucleus. Mean time to long term follow-up was 9.7 ± 2.4 months. Post-operative LEDD was not significantly lower than pre-operative LEDD (pre-op: 1238.53 ± 128.47 vs. post-op: 1178.18 ± 126.43, p = 0.2972, paired t -test). Mean QUIP scores were significantly lower at follow up compared to pre-operative baseline (1.51 ± 0.45 vs. 2.51 ± 0.58, p = 0.0447, paired t -test). Patients with ICDs pre-operatively (n = 14, 37.8%) had significant improvement in QUIP scores at follow-up (6.00 ± 0.94 vs. 2.64 ± 0.98, p = 0.0014, paired t -test). Improvement was not uniform across the cohort: 1 patient with ICD at baseline developed worsening symptoms, and 4 patients with no ICD pre-operatively developed clinically significant ICDs post-operatively. Conclusion When LEDD is relatively unchanged following STN or GPi DBS for PD, ICD symptoms tend toward improvement, although worsening and emergence of new ICDs can occur. In the setting of stable LEDD, these findings suggest that the intrinsic effects of DBS may play a significant role in altering impulsive behavior.


title: Temporal Action Detection with Structured Segment Networks

abstract: Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we present the structured segment network (SSN), a novel framework which models the temporal structure of each action instance via a structured temporal pyramid. On top of the pyramid, we further introduce a decomposed discriminative model comprising two classifiers, respectively for classifying actions and determining completeness. This allows the framework to effectively distinguish positive proposals from background or incomplete ones, thus leading to both accurate recognition and localization. These components are integrated into a unified network that can be efficiently trained in an end-to-end fashion. Additionally, a simple yet effective temporal action proposal scheme, dubbed temporal actionness grouping (TAG) is devised to generate high quality action proposals. On two challenging benchmarks, THUMOS14 and ActivityNet, our method remarkably outperforms previous state-of-the-art methods, demonstrating superior accuracy and strong adaptivity in handling actions with various temporal structures.


title: A Joint Model for Question Answering and Question Generation

abstract: We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework that encodes the document and generates a question (answer) given an answer (question). Significant improvement in model performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the model benefits from jointly learning to perform both tasks. We believe the joint model's novelty offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.


title: Knowledge-guided Semantic Computing Network

abstract: It is very useful to integrate human knowledge and experience into traditional neural networks for faster learning speed, fewer training samples and better interpretability. However, due to the obscured and indescribable black box model of neural networks, it is very difficult to design its architecture, interpret its features and predict its performance. Inspired by human visual cognition process, we propose a knowledge-guided semantic computing network which includes two modules: a knowledge-guided semantic tree and a data-driven neural network. The semantic tree is pre-defined to describe the spatial structural relations of different semantics, which just corresponds to the tree-like description of objects based on human knowledge. The object recognition process through the semantic tree only needs simple forward computing without training. Besides, to enhance the recognition ability of the semantic tree in aspects of the diversity, randomicity and variability, we use the traditional neural network to aid the semantic tree to learn some indescribable features. Only in this case, the training process is needed. The experimental results on MNIST and GTSRB datasets show that compared with the traditional data-driven network, our proposed semantic computing network can achieve better performance with fewer training samples and lower computational complexity. Especially, Our model also has better adversarial robustness than traditional neural network with the help of human knowledge.


title: A Survey on Human Activity Recognition using Wearable Sensors

abstract: Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.


title: Transfer learning for image classification with incomplete multiple sources

abstract: Transfer learning plays a powerful role in mitigating the discrepancy between test data (target) and auxiliary data (source). There is often the case that multiple sources are available in transfer learning. However, naively combining multiple sources does not lead to valid results, since they will introduce negative transfer as well. Furthermore, each single source from multiple sources may not cover all the labels of the target data. In this paper, we consider the problem that how to better utilize multiple incomplete sources for effective knowledge transfer. To this end, we propose a Bi-directional Low-Rank Transfer learning framework (BLRT). First, we adapt the conventional low-rank transfer learning to multiple sources knowledge transfer scenario. Second, an iterative structure learning is proposed to better use prior knowledge for transfer learning coefficients matrix. Third, a cross-source regularizer is added to couple the same labels from multiple incomplete sources, so that they could jointly compensate missing data from other sources. Experimental results on three groups of databases including face and object images have demonstrated that our method can successfully inherit knowledge from incomplete multiple sources and adapt to the target data successfully.


title: Estimating image depth using shape collections

abstract: Images, while easy to acquire, view, publish, and share, they lack critical depth information. This poses a serious bottleneck for many image manipulation, editing, and retrieval tasks. In this paper we consider the problem of adding depth to an image of an object, effectively 'lifting' it back to 3D, by exploiting a collection of aligned 3D models of related objects. Our key insight is that, even when the imaged object is not contained in the shape collection, the network of shapes implicitly characterizes a shape-specific deformation subspace that regularizes the problem and enables robust diffusion of depth information from the shape collection to the input image. We evaluate our fully automatic approach on diverse and challenging input images, validate the results against Kinect depth readings, and demonstrate several imaging applications including depth-enhanced image editing and image relighting.


title: Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation

abstract: Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.


title: Perception as Abduction: Turning Sensor Data into Meaningful Representation

abstract: This article presents a formal theory of robot perception as a form of abduction. The theory pins down the process whereby low-level sensor data is transformed into a symbolic representation of the external world, drawing together aspects such as incompleteness, top-down information flow, active perception, attention, and sensor fusion in a unifying framework. In addition, a number of themes are identified that are common to both the engineer concerned with developing a rigorous theory of perception, such as the one on offer here, and the philosopher of mind who is exercised by questions relating to mental representation and intentionality.


title: The DCS Theorem

abstract: Blockchain design involves many tradeoffs, and much debate has focused on tradeoffs related to scaling parameters such as blocksize. To address some of the confusion around this subject, we present a probability proof of the DCS Triangle. We use the triangle to show decentralized consensus systems, like blockchains, can have Decentralization, Consensus, or Scale, but not all three properties simultaneously. We then describe two methods for getting around the limitations suggested by the triangle.


title: The synchronous dataflow programming language LUSTRE

abstract: The authors describe LUSTRE, a data flow synchronous language designed for programming reactive systems-such as automatic control and monitoring systems-as well as for describing hardware. The data flow aspect of LUSTRE makes it very close to usual description tools in these domains (block-diagrams, networks of operators, dynamical sample-systems, etc.), and its synchronous interpretation makes it well suited for handling time in programs. Moreover, this synchronous interpretation allows it to be compiled into an efficient sequential program. The LUSTRE formalism is very similar to temporal logics. This allows the language to be used for both writing programs and expressing program properties, which results in an original program verification methodology. >


title: The Tongue-in-Groove Technique in Septorhinoplasty: A 10-Year Experience

abstract: Background Correction of the lower third of the nose is perhaps the most challenging component of performing a rhinoplasty. The tongue-in-groove (TIG) technique provides a method for correcting excess columellar show and maintaining correction of caudal deviation. It is also indicated for controlling nasal tip rotation and projection while preserving the integrity of the lobular cartilaginous complex and may be combined with either the external or endonasal rhinoplasty. It is typically used in combination with other septorhinoplasty maneuvers. The TIG technique consists of a method by which the medial crura are advanced cephaloposteriorly onto the caudal septum into a surgically created space between them. Objective To determine the effectiveness of the TIG technique to aid in correction of columellar show, a deviated caudal septum, and various tip rotation and projection problems. Setting Private practices of 2 authors (R.W.H.K., Houston, Tex, and H.M.T.F., Alexandria, Egypt). Patients The records of 287 patients who underwent TIG rhinoplasties in one private practice from 1989 through early 1999 (n = 203) and in another practice from 1994 through early 1999 (n = 84) were reviewed. Main Outcome Measures Physician judgment of outcome was based on reduction of columellar show, change in tip rotation or projection, narrowing of the columella, and straightening of a deviated caudal septum as indicated in preoperative and postoperative photographs taken of all but 4 of the 287 patients. Patient judgment of outcome was measured by patient requests for revision and patient comments made during follow-up visits. Results Of 287 patients, 278 (97%) had satisfactory to excellent results. Only 9 (3%) required revisions related to the TIG technique. Eight of these 9 did not achieve enough reduction of columellar show or adequate rotation or projection. A repeated TIG technique was used in these 8 patients with satisfactory revision results. One of the 9 was overprojected and required revision. Of the 108 patients who had preoperative caudal septal deviation, none needed further surgery. Conclusions The TIG technique provides a direct, effective solution to columellar show and is a reliable, reproducible method for achieving predictable tip rotation and projection. Furthermore, when used in conjunction with septoplasty techniques, it helps maintain the correction of a deviated caudal septum.


title: A Comparative Study of Methods for Transductive Transfer Learning

abstract: The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While previous work has studied the supervised version of this problem, we study the more challenging case of unsupervised transductive transfer learning, where no labeled data from the target domain are available at training. We describe some current state-of-the-art inductive and transductive approaches and then adapt these models to the problem of transfer learning for protein name extraction. In the process, we introduce a novel maximum entropy based technique, iterative feature transformation (IFT), and show that it achieves comparable performance with state-of-the-art transductive SVMs. We also show how simple relaxations, such as providing additional information like the proportion of positive examples in the test data, can significantly improve the performance of some of the transductive transfer learners.


title: Deep Amortized Inference for Probabilistic Programs

abstract: Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference is often intractable, and existing PPLs mostly rely on expensive, approximate sampling-based methods. To alleviate this problem, one could try to learn from past inferences, so that future inferences run faster. This strategy is known as amortized inference; it has recently been applied to Bayesian networks and deep generative models. This paper proposes a system for amortized inference in PPLs. In our system, amortization comes in the form of a parameterized guide program. Guide programs have similar structure to the original program, but can have richer data flow, including neural network components. These networks can be optimized so that the guide approximately samples from the posterior distribution defined by the original program. We present a flexible interface for defining guide programs and a stochastic gradient-based scheme for optimizing guide parameters, as well as some preliminary results on automatically deriving guide programs. We explore in detail the common machine learning pattern in which a 'local' model is specified by 'global' random values and used to generate independent observed data points; this gives rise to amortized local inference supporting global model learning.


title: Leveraging Moderate User Data for News Recommendation

abstract: It is very crucial for news aggregator websites which are recent in the market to actively engage its existing users. A recommendation system would help to tackle such a problem. However, due to the lack of sufficient amount of data, most of the state-of-the-art methods perform poorly in terms of recommending relevant news items to the users. In this paper, we propose a novel approach for Item-based Collaborative filtering for recommending news items using Markov Decision Process (MDP). Due to the sequential nature of news reading, we choose MDP to model our recommendation system as it is based on a sequence optimization paradigm. Further, we also incorporate factors like article freshness and similarity into our system by extrinsically modelling it in terms of reward for the MDP. We compare it with various other state-of-the-art methods. On a moderately low amount of data we see that our MDP-based approach outperforms the other approaches. One of the reasons for this is that the baselines fail to identify the underlying patterns within the sequence in which the articles are read by the users. Hence, the baselines are not able to generalize well.


title: Survey of MapReduce frame operation in bioinformatics

abstract: Bioinformatics is challenged by the fact that traditional analysis tools have difficulty in processing large-scale data from high-throughput sequencing. The open source Apache Hadoop project, which adopts the MapReduce framework and a distributed file system, has recently given bioinformatics researchers an opportunity to achieve scalable, efficient and reliable computing performance on Linux clusters and on cloud computing services. In this article, we present MapReduce frame-based applications that can be employed in the next-generation sequencing and other biological domains. In addition, we discuss the challenges faced by this field as well as the future works on parallel computing in bioinformatics.


title: Model predictive control-based gait pattern generation for wearable exoskeletons

abstract: This paper introduces a new method for controlling wearable exoskeletons that do not need predefined joint trajectories. Instead, it only needs basic gait descriptors such as step length, swing duration, and walking speed. End point Model Predictive Control (MPC) is used to generate the online joint trajectories based on these gait parameters. Real-time ability and control performance of the method during the swing phase of gait cycle is studied in this paper. Experiments are performed by helping a human subject swing his leg with different patterns in the LOPES gait trainer. Results show that the method is able to assist subjects to make steps with different step length and step duration without predefined joint trajectories and is fast enough for real-time implementation. Future study of the method will focus on controlling the exoskeletons in the entire gait cycle.


title: MM-UAV: Mobile Manipulating Unmanned Aerial Vehicle

abstract: Given significant mobility advantages, UAVs have access to many locations that would be impossible for an unmanned ground vehicle to reach, but UAV research has historically focused on avoiding interactions with the environment. Recent advances in UAV size to payload and manipulator weight to payload ratios suggest the possibility of integration in the near future, opening the door to UAVs that can interact with their environment by manipulating objects. Therefore, we seek to investigate and develop the tools that will be necessary to perform manipulation tasks when this becomes a reality. We present our progress and results toward a design and physical system to emulate mobile manipulation by an unmanned aerial vehicle with dexterous arms and end effectors. To emulate the UAV, we utilize a six degree-of-freedom miniature gantry crane that provides the complete range of motion of a rotorcraft as well as ground truth information without the risk associated with free flight. Two four degree-of-freedom manipulators attached to the gantry system perform grasping tasks. Computer vision techniques and force feedback servoing provide target object and manipulator position feedback to the control hardware. To test and simulate our system, we leverage the OpenRAVE virtual environment and ROS software architecture. Because rotorcraft are inherently unstable, introduce ground effects, and experience changing flight dynamics under external loads, we seek to address the difficult task of maintaining a stable UAV platform while interacting with objects using multiple, dexterous arms. As a first step toward that goal, this paper describes the design of a system to emulate a flying, dexterous mobile manipulator.


title: Prosocial Lies: When Deception Breeds Trust

abstract: Philosophers, psychologists, and economists have long asserted that deception harms trust. We challenge this claim. We break new ground by demonstrating that some types of deception increase trust. Across four studies, we demonstrate that prosocial lying increases both behavioral and attitudinal measures of trust. In Study 1, we find that prosocial lying increases trust in the trust game. In Study 2, we rule out reciprocity as an alternative explanation and examine different types of lies. In our third study, we disentangle deception from selfish and prosocial intentions. We find that deception, per se, has no effect on trusting behavior, but intentions matter immensely. Our results suggest that benevolence is more important than integrity in predicting trust behavior. In Study 4, we explore the relationship between prosocial lies and distinct types of trust. This work expands our understanding of deception and deepens our insight into the mechanics of trust.


title: Vitex agnus-castus(Chaste-Tree/Berry) in the Treatment of Menopause-Related Complaints

abstract: Background: The origin of the current practice of administering Vitex agnus-castus in menopause-related complaints is uncertain, but appears to be relatively recent. Here we review the evidence for this application of Vitex based on evidence from pharmacological studies and clinical research. Methods: The mechanisms of potential relevance in the context of menopause are explored with reference to the current understanding of the endocrinology and neuroendocrinology of menopause and associated symptoms. Conclusions: We conclude that, while evidence from rigorous randomized controlled trials is lacking for the individual herb in this context, emerging pharmacological evidence supports a role for V. agnus-castus in the alleviation of menopausal symptoms and suggests that further investigation may be appropriate.


title: Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

abstract: Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.


title: A map of rice genome variation reveals the origin of cultivated rice

abstract: Crop domestications are long-term selection experiments that have greatly advanced human civilization. The domestication of cultivated rice (Oryza sativa L.) ranks as one of the most important developments in history. However, its origins and domestication processes are controversial and have long been debated. Here we generate genome sequences from 446 geographically diverse accessions of the wild rice species Oryza rufipogon, the immediate ancestral progenitor of cultivated rice, and from 1,083 cultivated indica and japonica varieties to construct a comprehensive map of rice genome variation. In the search for signatures of selection, we identify 55 selective sweeps that have occurred during domestication. In-depth analyses of the domestication sweeps and genome-wide patterns reveal that Oryza sativa japonica rice was first domesticated from a specific population of O. rufipogon around the middle area of the Pearl River in southern China, and that Oryza sativa indica rice was subsequently developed from crosses between japonica rice and local wild rice as the initial cultivars spread into South East and South Asia. The domestication-associated traits are analysed through high-resolution genetic mapping. This study provides an important resource for rice breeding and an effective genomics approach for crop domestication research.


title: Personal Networks and Mortality Risk in Older Adults: A Twenty-Year Longitudinal Study

abstract: Background Research on aging has consistently demonstrated an increased chance of survival for older adults who are integrated into rich networks of social relationships. Theoretical explanations state that personal networks offer indirect psychosocial and direct physiological pathways. We investigate whether effects on and pathways to mortality risk differ between functional and structural characteristics of the personal network. The objective is to inquire which personal network characteristics are the best predictors of mortality risk after adjustment for mental, cognitive and physical health. Methods and Findings Empirical tests were carried out by combining official register information on mortality with data from the Longitudinal Aging Study Amsterdam (LASA). The sample included 2,911 Dutch respondents aged 54 to 85 at baseline in 1992 and six follow-ups covering a time span of twenty years. Four functional characteristics (emotional and social loneliness, emotional and instrumental support) and four structural characteristics (living arrangement, contact frequency, number of contacts, number of social roles) of the personal network as well as mental, cognitive and physical health were assessed at all LASA follow-ups. Statistical analyses comprised of Cox proportional hazard regression models. Findings suggest differential effects of personal network characteristics on survival, with only small gender differences. Mortality risk was initially reduced by functional characteristics, but disappeared after full adjustment for the various health variables. Mortality risk was lowest for older adults embedded in large (HR = 0.986, 95% CI 0.979—0.994) and diverse networks (HR = 0.948, 95% CI 0.917—0.981), and this effect continued to show in the fully adjusted models.


title: AEP: An error-bearing neural network accelerator for energy efficiency and model protection

abstract: Neural Networks (NNs) have recently gained popularity in a wide range of modern application domains due to its superior inference accuracy. With growing problem size and complexity, modern NNs, e.g., CNNs (Convolutional NNs) and DNNs (Deep NNs), contain a large number of weights, which require tremendous efforts not only to prepare representative training datasets but also to train the network. There is an increasing demand to protect the NN weight matrices, an emerging Intellectual Property (IP) in NN field. Unfortunately, adopting conventional encryption method faces significant performance and energy consumption overheads. In this paper, we propose AEP, a DianNao based NN accelerator design for IP protection. AEP aggressively reduces DRAM timing to generate a device dependent error mask, i.e., a set of erroneous cells while the distribution of these cells are device dependent due to process variations. AEP incorporates the error mask in the NN training process so that the trained weights are device dependent, which effectively defects IP piracy as exporting the weights to other devices cannot produce satisfactory inference accuracy. In addition, AEP speeds up NN inference and achieves significant energy reduction due to the fact that main memory dominates the energy consumption in DianNao accelerator. Our evaluation results show that by injecting 0.1% to 5% memory errors, AEP has negligible inference accuracy loss on the target device while exhibiting unacceptable accuracy degradation on other devices. In addition, AEP achieves an average of 72% performance improvement and 44% energy reduction over the DianNao baseline.


title: Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications

abstract: An important problem in fiber-optic communications is to invert the nonlinear Schr\"odinger equation in real time to reverse the deterministic effects of the channel. Interestingly, the popular split-step Fourier method (SSFM) leads to a computation graph that is reminiscent of a deep neural network. This observation allows one to leverage tools from machine learning to reduce complexity. In particular, the main disadvantage of the SSFM is that its complexity using M steps is at least M times larger than a linear equalizer. This is because the linear SSFM operator is a dense matrix. In previous work, truncation methods such as frequency sampling, wavelets, or least-squares have been used to obtain "cheaper" operators that can be implemented using filters. However, a large number of filter taps are typically required to limit truncation errors. For example, Ip and Kahn showed that for a 10 Gbaud signal and 2000 km optical link, a truncated SSFM with 25 steps would require 70-tap filters in each step and 100 times more operations than linear equalization. We find that, by jointly optimizing all filters with deep learning, the complexity can be reduced significantly for similar accuracy. Using optimized 5-tap and 3-tap filters in an alternating fashion, one requires only around 2-6 times the complexity of linear equalization, depending on the implementation.


title: Stereolithographic (SLA) 3D printing of oral modified-release dosage forms.

abstract: The aim of this work was to evaluate the suitability of stereolithography (SLA) to fabricate drug-loaded tablets with modified-release characteristics. The SLA printer creates solid objects by using a laser beam to photopolymerise monomers. In this work polyethylene glycol diacrylate (PEGDA) was used as a monomer and diphenyl(2,4,6-trimethylbenzoyl)phosphine oxide was used as a photo-initiator. 4-aminosalicylic acid (4-ASA) and paracetamol (acetaminophen) were selected as model drugs. Tablets were successfully printed and formulations with different properties were fabricated by adding polyethylene glycol 300 (PEG 300) to the printing solution. The loading of paracetamol and 4-ASA in the printed tablets was 5.69% and 5.40% respectively. In a realistic dynamic dissolution simulation of the gastrointestinal tract, drug release from the tablets was dependent on the composition of the formulations, but independent of dissolution pH. In conclusion SLA 3DP technology allows the manufacture of drug loaded tablets with specific extended-release profiles. In the future this technology could become a manufacturing technology for the elaboration of oral dosage forms, for industrial production or even for personalised dose.


title: A simplified IEEE 802.22 PHY layer in Matlab-Simulink and SDR platform

abstract: Due to its ability to provide high data rates and its effectiveness against multi-path delay spread commonly encountered in mobile communications channel, Orthogonal Frequency Division Multiplexing (OFDM) has been the modulation of choice for many current wireless communication standards including IEEE 802.11(WLAN), IEEE 802.16(WiMAX), IEEE 802.22(WRAN) and DVB-T. The IEEE 802.22 standard is now known as the Cognitive radio (CR) standard utilizing white spaces in the TV frequency spectrum and many research efforts are now focused on the use of this TV white space for providing broadband access to rural areas. In this paper a simplified IEEE 802.22 physical layer (PHY) was successfully simulated in Matlab-Simulink and implemented in real time in a Software defined radio (SDR) platform through the combination of GNU Radio and the Universal Software Radio Peripheral (USRP). The SDR platform as a testbed was able to demonstrate the flexibility and adaptability required of an OFDM-based PHY layer of the IEEE 802.22 standard.


title: Diencephalic Asymmetries

abstract: Structural asymmetry in diencephalic regions has been reported in a number of studies since the pioneering observations by Kemali and Braitenberg, Atlas of the frog's brain. Springer Verlag: 1969. Anatomical differences between the left and right habenulae have been identified in many lower vertebrate species. While there are few reports of structural asymmetry in the dorsal thalamus, there is evidence that asymmetrical thalamofugal projections can be induced in the visual system of chicks by lateralized sensory stimulation prior to hatching. Finally, there have been consistent reports of differences between and right sides of the hypothalamus in their sensitivity to the effects of circulating gonadal hormones in rats. In most cases, these asymmetries are sex-linked and correspond to a lateralization of function. Although the significance of these diencephalic asymmetries is still enigmatic, their existence indicates that asymmetry is not a phylogenetically recent feature of the brain, and the left-right differences in the brain may be mediated by a common ontogenetic mechanism and may underlie the development of highly specialized functions.


title: Towards more effective robotic gait training for stroke rehabilitation: a review

abstract: BackgroundStroke is the most common cause of disability in the developed world and can severely degrade walking function. Robot-driven gait therapy can provide assistance to patients during training and offers a number of advantages over other forms of therapy. These potential benefits do not, however, seem to have been fully realised as of yet in clinical practice.ObjectivesThis review determines ways in which robot-driven gait technology could be improved in order to achieve better outcomes in gait rehabilitation.MethodsThe literature on gait impairments caused by stroke is reviewed, followed by research detailing the different pathways to recovery. The outcomes of clinical trials investigating robot-driven gait therapy are then examined. Finally, an analysis of the literature focused on the technical features of the robot-based devices is presented. This review thus combines both clinical and technical aspects in order to determine the routes by which robot-driven gait therapy could be further developed.ConclusionsActive subject participation in robot-driven gait therapy is vital to many of the potential recovery pathways and is therefore an important feature of gait training. Higher levels of subject participation and challenge could be promoted through designs with a high emphasis on robotic transparency and sufficient degrees of freedom to allow other aspects of gait such as balance to be incorporated.


title: Key Principles of Community-Based Natural Resource Management: A Synthesis and Interpretation of Identified Effective Approaches for Managing the Commons

abstract: This article examines recent research on approaches to community-based environmental and natural resource management and reviews the commonalities and differences between these interdisciplinary and multistakeholder initiatives. To identify the most effective characteristics of Community-based natural resource management (CBNRM), I collected a multiplicity of perspectives from research teams and then grouped findings into a matrix of organizational principles and key characteristics. The matrix was initially vetted (or “field tested”) by applying numerous case studies that were previously submitted to the World Bank International Workshop on CBNRM. These practitioner case studies were then compared and contrasted with the findings of the research teams. It is hoped that the developed matrix may be useful to researchers in further focusing research, understanding core characteristics of effective and sustainable CBNRM, providing practitioners with a framework for developing new CBNRM initiatives for managing the commons, and providing a potential resource for academic institutions during their evaluation of their practitioner-focused environmental management and leadership curriculum.


title: Robot Web Tools: Efficient messaging for cloud robotics

abstract: Since its official introduction in 2012, the Robot Web Tools project has grown tremendously as an open-source community, enabling new levels of interoperability and portability across heterogeneous robot systems, devices, and front-end user interfaces. At the heart of Robot Web Tools is the rosbridge protocol as a general means for messaging ROS topics in a client-server paradigm suitable for wide area networks, and human-robot interaction at a global scale through modern web browsers. Building from rosbridge, this paper describes our efforts with Robot Web Tools to advance: 1) human-robot interaction through usable client and visualization libraries for more efficient development of front-end human-robot interfaces, and 2) cloud robotics through more efficient methods of transporting high-bandwidth topics (e.g., kinematic transforms, image streams, and point clouds). We further discuss the significant impact of Robot Web Tools through a diverse set of use cases that showcase the importance of a generic messaging protocol and front-end development systems for human-robot interaction.


title: DC Pulsed Atmospheric-Pressure Plasma Jet Image Information

abstract: This paper presents optical imaging and optical emission spectroscopy (OES) data of an atmospheric-pressure plasma jet. It is shown how the visual information and OES information of the air discharge are related as the blown arc extends from the nozzle (2-4 mm) with a molecular nitrogen rotational temperature on the order of 1700 K and the flowing afterglow beyond this region is dominated by the cooler (300-K) NO-O chemiluminescent reaction that produces NO2 species.


title: A Broadband Dual-Polarized Dual-OAM-Mode Antenna Array for OAM Communication

abstract: The generation of multimode orbital angular momentum (OAM) carrying beams has attracted more and more attention. A broadband dual-polarized dual-OAM-mode uniform circular array is proposed in this letter. The proposed antenna array, which consists of a broadband dual-polarized bow-tie dipole array and a broadband phase-shifting feeding network, can be used to generate OAM mode −1 and OAM mode 1 beams from 2.1 to 2.7 GHz (a bandwidth of 25%) for each of two polarizations. Four orthogonal channels can be provided by the proposed antenna array. A 2.5-m broadband OAM link is built. The measured crosstalk between the mode matched channels and the mode mismatched channels is less than −12 dB at 2.1, 2.4, and 2.7 GHz. Four different data streams are transmitted simultaneously by the proposed array with a bit error rate less than 4.2×10-3 at 2.1, 2.4, and 2.7 GHz.


title: Pedestrian Detection with Semantic Regions of Interest

abstract: For many pedestrian detectors, background vs. foreground errors heavily influence the detection quality. Our main contribution is to design semantic regions of interest that extract the foreground target roughly to reduce the background vs. foreground errors of detectors. First, we generate a pedestrian heat map from the input image with a full convolutional neural network trained on the Caltech Pedestrian Dataset. Next, semantic regions of interest are extracted from the heat map by morphological image processing. Finally, the semantic regions of interest divide the whole image into foreground and background to assist the decision-making of detectors. We test our approach on the Caltech Pedestrian Detection Benchmark. With the help of our semantic regions of interest, the effects of the detectors have varying degrees of improvement. The best one exceeds the state-of-the-art.


title: Possible selves as roadmaps

abstract: Abstract Possible selves, expectations, and concerns about the coming year, can promote feeling good (“I may not be doing well in school this year, but I will next year.”) or can promote regulating for oneself (“I may not be doing well in school this year, but to make sure I do better next year, I have signed up for summer tutoring.”). We hypothesized that improved academic outcomes were likely only when a possible self could plausibly be a self-regulator. Hierarchical regression analyses supported this conclusion, with more support for the influence of self-regulation on change in behavior and academic outcomes than on affect regulation. N =160 low-income eighth graders improved grades, spent more time doing homework, participated in class more, and were referred less to summer school (controlling for fall grades and the dependent variable of interest) when academic possible selves were plausibly self-regulatory.


title: Fast and robust point cloud matching based on EM-ICP prepositioning

abstract: In this paper a robust point cloud matching algorithm, which is applicable in time critical scenarios, is described. The presented algorithm was developed for 2D point cloud data, but can be extend to higher dimensions. The algorithm itself works in three stages: first the dataset is reduced to contain only significant point, then the reduced point set is prepositioned using a EM-ICP algorithm. This results in robust initial values for use with an ordinary ICP algorithm to fit the full dataset in the third stage. The algorithm is used for real time evaluation of 2D line scanner data, which work with a scan rate of 100 Hz. Therefore the execution time of the proposed algorithm is limited to 10ms at 450 points per scan.


title: Compact GaN MMIC T/R module front-end for X-band pulsed radar

abstract: An X-band Single-Chip monolithic microwave integrated circuit (MMIC) has been developed by using a European GaN HEMT technology. The very compact MMIC die occupying only an area of 3.0 mm × 3.0 mm, integrates a high power amplifier (HPA), a low-noise amplifier (LNA) and a robust asymmetrical absorptive/reflective SPDT switch. At the antenna RF pad in the frequency range from 8.6 to 11.2 GHz, nearly 8 W of output power and 22 dB of linear gain were measured when operated in transmit mode. When operated in receive mode, a noise figure of 2.5 dB with a gain of 15 dB were measured at the Rx RF pad in the same frequency range.


title: Safeguarding Malaysia's critical national information infrastructure (CNII) against cyber terrorism: Towards development of a policy framework

abstract: Critical National Information Infrastructure (CNII) is crucial to the survivability of a nation. The destruction or disruption of these systems and communication networks would significantly affect the economic strength, image, defense and security, government capabilities to function, and public health and safety. CNII would probably become an attractive target for terrorists as the result of cyber attacks could leave the nation with difficult conditions due to the disruption of critical services. This paper provides an overview on the concept and fundamental elements of cyber terrorism. This paper also highlights the cyber security policy initiatives as a guideline for the development of the policy framework. This paper further recommends the need of policy development addressing the protection of CNII from cyber terrorism activities specifically for Malaysia.


title: A Contextual Approach to Privacy Online

abstract: Recent media revelations have demonstrated the extent of third-party tracking and monitoring online, much of it spurred by data aggregation, profiling, and selective targeting. How to protect privacy online is a frequent question in public discourse and has reignited the interest of government actors. In the United States, notice-and-consent remains the fallback approach in online privacy policies, despite its weaknesses. This essay presents an alternative approach, rooted in the theory of contextual integrity. Proposals to improve and fortify notice-and-consent, such as clearer privacy policies and fairer information practices, will not overcome a fundamental flaw in the model, namely, its assumption that individuals can understand all facts relevant to true choice at the moment of pair-wise contracting between individuals and data gatherers. Instead, we must articulate a backdrop of context-specific substantive norms that constrain what information websites can collect, with whom they can share it, and under what conditions it can be shared. In developing this approach, the paper warns that the current bias in conceiving of the Net as a predominantly commercial enterprise seriously limits the privacy agenda.


title: Understanding and predicting interestingness of videos

abstract: The amount of videos available on the Web is growing explosively. While some videos are very interesting and receive high rating from viewers, many of them are less interesting or even boring. This paper conducts a pilot study on the understanding of human perception of video interestingness, and demonstrates a simple computational method to identify more interesting videos. To this end we first construct two datasets of Flickr and YouTube videos respectively. Human judgements of interestingness are collected and used as the ground-truth for training computational models. We evaluate several off-the-shelf visual and audio features that are potentially useful for predicting interestingness on both datasets. Results indicate that audio and visual features are equally important and the combination of both modalities shows very promising results.


title: Taming role mining complexity in RBAC

abstract: In this paper we address the problem of reducing the role mining complexity in RBAC systems. To this aim, we propose a three steps methodology: first, we associate a weight to roles; second, we identify user-permission assignments that cannot belong to roles with a weight exceeding a given threshold; and third, we restrict the role-finding problem to user-permission assignments identified in the second step. We formally show-the proofs of our results are rooted in graph theory-that this methodology allows role engineers for the elicitation of stable candidate roles, by contextually simplifying the role selection task. Efficient algorithms to implement our strategy are also described. Further, we discuss practical applications of our approach. Finally, we tested our methodology on real dataset. Results achieved confirm both the viability of our proposal and the analytical findings.


title: Opinion mining and sentiment analysis on online customer review

abstract: The opinion mining is very much essential in e-commerce websites, furthermore advantageous with individual. An ever increasing amount of results are stored in the web as well as the amount of people would acquiring items from web are increasing. As a result, the users' reviews or posts are increasing day by day. The reviews toward shipper sites express their feeling. Any organization for example, web forums, discourse groups, blogs etc., there will be an extensive add up for information. Records identified with items on the Web, which are functional to both makers and clients. The process of finding user opinion about the topic or product or problem is called as opinion mining. It can also be defined as the process of automatic extraction of knowledge by means of opinions expressed by the user who is currently using the product about some product is called as opinion mining. Analyzing the emotions from the extracted opinions is defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. This work concentrates on mining reviews from the websites like Amazon, which allows user to freely write the view. It automatically extracts the reviews from the website. It also uses algorithm such as Naive Bayes classifier, Logistic Regression and SentiWordNet algorithm to classify the review as positive and negative review. At the end we have used quality metric parameters to measure the performance of each algorithm.


title: Person Re-identification by Articulated Appearance Matching

abstract: Re-identification of pedestrians in video-surveillance settings can be effectively approached by treating each human figure as an articulated body, whose pose is estimated through the framework of Pictorial Structures (PS). In this way, we can focus selectively on similarities between the appearance of body parts to recognize a previously seen individual. In fact, this strategy resembles what humans employ to solve the same task in the absence of facial details or other reliable biometric information. Based on these insights, we show how to perform single image re-identification by matching signatures coming from articulated appearances, and how to strengthen this process in multi-shot re-identification by using Custom Pictorial Structures (CPS) to produce improved body localizations and appearance signatures. Moreover, we provide a complete and detailed breakdown-analysis of the system that surrounds these core procedures, with several novel arrangements devised for efficiency and flexibility. Finally, we test our approach on several public benchmarks, obtaining convincing results.


title: Problematic Smartphone Use: Investigating Contemporary Experiences Using a Convergent Design

abstract: Internet-enabled smartphones are increasingly ubiquitous in the Western world. Research suggests a number of problems can result from mobile phone overuse, including dependence, dangerous and prohibited use. For over a decade, this has been measured by the Problematic Mobile Phone Use Questionnaire (PMPU-Q). Given the rapid developments in mobile technologies, changes of use patterns and possible problematic and addictive use, the aim of the present study was to investigate and validate an updated contemporary version of the PMPU-Q (PMPU-Q-R). A mixed methods convergent design was employed, including a psychometric survey (N = 512) alongside qualitative focus groups (N = 21), to elicit experiences and perceptions of problematic smartphone use. The results suggest the PMPU-Q-R factor structure can be updated to include smartphone dependence, dangerous driving, and antisocial smartphone use factors. Theories of problematic mobile phone use require consideration of the ubiquity and indispensability of smartphones in the present day and age, particularly regarding use whilst driving and in social interactions.


Sentiment Analysis Methodology for Social Web Intelligence Full


title: Processing set expressions over continuous update streams

abstract: There is growing interest in algorithms for processing and querying continuous data streams (i.e., data that is seen only once in a fixed order) with limited memory resources. In its most general form, a data stream is actually an update stream, i.e., comprising data-item deletions as well as insertions. Such massive update streams arise naturally in several application domains (e.g., monitoring of large IP network installations, or processing of retail-chain transactions).Estimating the cardinality of set expressions defined over several (perhaps, distributed) update streams is perhaps one of the most fundamental query classes of interest; as an example, such a query may ask "what is the number of distinct IP source addresses seen in passing packets from both router R1 and R2 but not router R3?". Earlier work has only addressed very restricted forms of this problem, focusing solely on the special case of insert-only streams and specific operators (e.g., union). In this paper, we propose the first space-efficient algorithmic solution for estimating the cardinality of full-fledged set expressions over general update streams. Our estimation algorithms are probabilistic in nature and rely on a novel, hash-based synopsis data structure, termed "2-level hash sketch". We demonstrate how our 2-level hash sketch synopses can be used to provide low-error, high-confidence estimates for the cardinality of set expressions (including operators such as set union, intersection, and difference) over continuous update streams, using only small space and small processing time per update. Furthermore, our estimators never require rescanning or resampling of past stream items, regardless of the number of deletions in the stream. We also present lower bounds for the problem, demonstrating that the space usage of our estimation algorithms is within small factors of the optimal. Preliminary experimental results verify the effectiveness of our approach.


title: Generation of THz wave with orbital angular momentum by graphene patch reflectarray

abstract: A graphene patch reflectarray is proposed for the first time to generate THz wave with orbital angular momentum (OAM). It is shown that by properly assigning the graphene patches with specific reflection coefficients, an OAM-carrying beam can be generated. The graphene reflectarray, which consists of 8 regions and each region contains about 170 patches with uniform chemical potential and size, has successfully produced the OAM-carrying beam of either 1 or −1 mode.


title: Algorithmic Self-Assembly of DNA: Theoretical Motivations and 2D Assembly Experiments

abstract: Biology makes things far smaller and more complex than anything produced by human engineering. The biotechnology revolution has for the first time given us the tools necessary to consider engineering on the molecular level. Research in DNA computation, launched by Len Adleman, has opened the door for experimental study of programmable biochemical reactions. Here we focus on a single biochemical mechanism, the self-assembly of DNA structures, that is theoretically sufficient for Turing-universal computation. The theory combines Hao Wang?s purely mathematical Tiling Problem with the branched DNA constructions of Ned Seeman. In the context of mathematical logic, Wang showed how jigsaw-shaped tiles can be designed to simulate the operation of any Turing Machine. For a biochemical implementation, we will need molecular Wang tiles. DNA molecular structures and intermolecular interactions are particularly amenable to design and are sufficient for the creation of complex molecular objects. The structure of individual molecules can be designed by maximizing desired and minimizing undesired Watson-Crick complementarity. Intermolecular interactions are programmed by the design of sticky ends that determine which molecules associate, and how. The theory has been demonstrated experimentally using a system of synthetic DNA double-crossover molecules that self-assemble into two-dimensional crystals that have been visualized by atomic force microscopy. This experimental system provides an excellent platform for exploring the relationship between computation and molecular self-assembly, and thus represents a first step toward the ability to program molecular reactions and molecular structures.


title: Fast parallel parking for autonomous vehicles using Gompertz curves

abstract: A new method for the planning and autonomous execution of a single-trajectory, velocity-independent, parallel-parking manoeuvre for autonomous vehicles is presented. The procedure commences with the identification and pre-selection of a smooth sigmoid trajectory known as the Gompertz curve in parametric format. Trajectory parameters are determined in real-time during the path-planning phase using an optimization scheme in order to generate a candidate path. The optimization scheme takes into account the maximum steering angles that can be physically realized and checks the generated candidate trajectory for collisions. Thereafter, the trajectory is re-parametrized to arc-length format using the cubic interpolation method and the vehicle orientation at every point of the trajectory is deduced. Following that, values of the steering angle(s) are determined. In the final step, the vehicle uses odometry to follows the arc-length parametrized path in reverse in order to park itself in a single-manoeuvre. The proposed method is substantiated through both extensive simulations and real sensor data.


title: Related Key Chosen IV Attack on Grain-128a Stream Cipher

abstract: The well-known stream cipher Grain-128 is a variant version of Grain v1 with 128-bit secret key. Grain v1 is a stream cipher which has successfully been chosen as one of seven finalists by European eSTREAM project. Yet Grain-128 is vulnerable against some recently introduced attacks. A new version of Grain-128 with authentication, named Grain-128a, is proposed by Agren, Hell, Johansson, and Meier. The designers claimed that Grain-128a is strengthened against all known attacks and observations on the original Grain-128. So far there exists no attack on Grain-128a except a differential fault attack by Banik, Maitra, and Sarkar. In this paper, we give some observations on Grain-128a, and then propose a related key chosen IV attack on Grain-128a based on these observations. Our attack can recover the 128-bit secret key of Grain-128a with a computational complexity of $2^{96.322} $ , requiring $2^{96} $ chosen IVs and $2^{103.613} $ keystream bits. The success probability of our attack is 0.632. This related key attack is “minimal” in the sense that it only requires two related keys. The result shows that our attack is much better than an exhaustive key search in the related key setting.


title: Agreement, the F-Measure, and Reliability in Information Retrieval

abstract: Information retrieval studies that involve searching the Internet or marking phrases usually lack a well-defined number of negative cases. This prevents the use of traditional interrater reliability metrics like the κ statistic to assess the quality of expert-generated gold standards. Such studies often quantify system performance as precision, recall, and F-measure, or as agreement. It can be shown that the average F-measure among pairs of experts is numerically identical to the average positive specific agreement among experts and that κ approaches these measures as the number of negative cases grows large. Positive specific agreement—or the equivalent F-measure—may be an appropriate way to quantify interrater reliability and therefore to assess the reliability of a gold standard in these studies.


title: The sil Locus in Streptococcus Anginosus Group: Interspecies Competition and a Hotspot of Genetic Diversity

abstract: The Streptococcus Invasion Locus (Sil) was first described in Streptococcus pyogenes and S. pneumoniae, where it has been implicated in virulence. The two-component peptide signaling system consists of the SilA response regulator and SilB histidine kinase along with the SilCR signalling peptide and SilD/E export/processing proteins. The presence of an associated bacteriocin region suggests this system may play a role in competitive interactions with other microbes. Comparative analysis of 42 Streptococcus Anginosus/Milleri Group (SAG) genomes reveals this to be a hot spot for genomic variability. A cluster of bacteriocin/immunity genes is found adjacent to the sil system in most SAG isolates (typically 6-10 per strain). In addition, there were two distinct SilCR peptides identified in this group, denoted here as SilCRSAG-A and SilCRSAG-B, with corresponding alleles in silB. Our analysis of the 42 sil loci showed that SilCRSAG-A is only found in S. intermedius while all three species can carry SilCRSAG-B. In S. intermedius B196, a putative SilA operator is located upstream of bacteriocin gene clusters, implicating the sil system in regulation of microbe-microbe interactions at mucosal surfaces where the group resides. We demonstrate that the S. intermedius B196 responds to its cognate SilCRSAG-A, and, less effectively, to SilCRSAG-B released by other Anginosus group members, to produce putative bacteriocins and inhibit the growth of a sensitive strain of S. constellatus.


title: Parallel K-Medoids++ Spatial Clustering Algorithm Based on MapReduce

abstract: Clustering analysis has received considerable attention in spatial data mining for several years. With the rapid development of the geospatial information technologies, the size of spatial information data is growing exponentially which makes clustering massive spatial data a challenging task. In order to improve the efficiency of spatial clustering for large scale data, many researchers proposed several efficient clustering algorithms in parallel. In this paper, a new K-Medoids++ spatial clustering algorithm based on MapReduce for clustering massive spatial data is proposed. The initialization algorithm to decrease the number of iterations is combined with the MapReduce framework. Comparative Experiments conducted over different dataset and different number of nodes indicate that the proposed K-Medoids spatial clustering algorithm provides better efficiency than traditional K-Medoids and scales well while processing massive spatial data on commodity hardware.


title: The Internet of Things for Health Care: A Comprehensive Survey

abstract: The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.


title: Prediction of indoor temperature and relative humidity using neural network models: model comparison

abstract: The use of neural networks grows great popularity in various building applications such as prediction of indoor temperature, heating load and ventilation rate. But few papers detail indoor relative humidity prediction which is an important indicator of indoor air quality, service life and energy efficiency of buildings. In this paper, the design of indoor temperature and relative humidity predictive neural networks in our test house was developed. The test house presented complicated physical features which are difficult to simulate with physical models. The work presented in this paper aimed to show the suitability of neural networks to perform predictions. Nonlinear AutoRegressive with eXternal input (NNARX) model and genetic algorithm were employed to construct networks and were detailed. The comparison between the two methods was also made. Applicability of some important mathematical validation criteria to practical reality was examined. Satisfactory results with correlation coefficients 0.998 and 0.997 for indoor temperature and relative humidity were obtained in the testing stage.


title: A Low-Power Broad-Bandwidth Noise Cancellation VLSI Circuit Design for In-Ear Headphones

abstract: Conventional active noise cancelling (ANC) headphones often perform well in reducing the low-frequency noise and isolating the high-frequency noise by earmuffs passively. The existing ANC systems often use high-speed digital signal processors to cancel out disturbing noise, which results in high power consumption for a commercial ANC headphone. The contribution of this paper can be classified into: 1) proper filter length selection; 2) low-power storage mechanism for convolution operation; and 3) high-throughput pipelining architecture. With these novel techniques, we develop an area-/power-efficient ANC circuit by using the TSMC 90-nm CMOS technology for in-ear headphone applications. The proposed feedforward filtered-x least mean square ANC circuit design provides the features of using lower operating frequency and consuming much less power that facilitate better performance than the conventional ANC headphones. To verify the effectiveness of the proposed design, a series of physical measurements is executed in an anechoic chamber. Measurement results show that the proposed high-performance/low-power circuit design can reduce disturbing noise of various frequency bands very well, and outperforms the existing works. The proposed design can attenuate 15 dB for broadband pink noise between 50 and 1500 Hz when operated at 20-MHz clock frequency at the costs of 84.2 k gates and power consumption of 6.59 mW only. Compared with the existing designs, the proposed work achieves higher noise cancellation performance in terms of 3 dB further and saves 97% power consumption.


title: Locality Reconstruction Models for Book Representation

abstract: Books, as a representative of lengthy documents, convey rich semantics. Traditional document modeling methods, such as bag-of-words models, have difficulty capturing such rich semantics when only considering term-frequency features. In order to explore term spatial distributions over a book, a tree-structured book representation is investigated in this paper. Moreover, an efficient learning framework, Tree2Vector, is introduced for mapping tree-structured book data into vectorial space. In particular, we present two types of locality reconstruction (LR) models: Euclidean-type and cosine-type, during the transformation process of tree structures into vectorial representations. The LR is used for modeling the reconstruction process, in which each parent node in a tree is supposed to be reconstructed by its child nodes. The prominent advantage of this Tree2Vector framework is that it solely utilizes the local information within a single book tree. In addition, extensive experimental results demonstrate that Tree2Vector is able to deliver comparable or better performance in comparison to methods that consider the information of all trees in a database globally. Experimental results also suggest that cosine-type LR consistently performs better than Euclidean-type LR in applications of book and author recommendations.


title: How Color Enhances Visual Memory for Natural Scenes

abstract: We offer a framework for understanding how color operates to improve visual memory for images of the natural environment, and we present an extensive data set that quantifies the contribution of color in the encoding and recognition phases. Using a continuous recognition task with colored and monochrome gray-scale images of natural scenes at short exposure durations, we found that color enhances recognition memory by conferring an advantage during encoding and by strengthening the encoding-specificity effect. Furthermore, because the pattern of performance was similar at all exposure durations, and because form and color are processed in different areas of cortex, the results imply that color must be bound as an integral part of the representation at the earliest stages of processing.


title: Privacy preserving data mining

abstract: In this paper we introduce the concept of privacy preserving data mining. In our model, two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. This problem has many practical and important applications, such as in medical research with confidential patient records. Data mining algorithms are usually complex, especially as the size of the input is measured in megabytes, if not gigabytes. A generic secure multi-party computation solution, based on evaluation of a circuit computing the algorithm on the entire input, is therefore of no practical use. We focus on the problem of decision tree learning and use ID3, a popular and widely used algorithm for this problem. We present a solution that is considerably more efficient than generic solutions. It demands very few rounds of communication and reasonable bandwidth. In our solution, each party performs by itself a computation of the same order as computing the ID3 algorithm for its own database. The results are then combined using efficient cryptographic protocols, whose overhead is only logarithmic in the number of transactions in the databases. We feel that our result is a substantial contribution, demonstrating that secure multi-party computation can be made practical, even for complex problems and large inputs.