title: Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie

abstract: Abstract Algorithms were developed from LANDSAT 7 ETM+ data for the July 1, 2000 overpass and LANDSAT 5 Thematic Mapper (TM) data for the September 27, 2000 overpass for Path 20 Row 31 (including Toledo, OH) to measure relative phycocyanin content (PC) and turbidity in the western basin of Lake Erie. Water samples were collected from discrete hydrographic stations arranged in a 20×4 km grid adjacent to the Ohio shoreline during a 6-h period spanning each of the two LANDSAT overpasses. The samples were analyzed for chlorophyll (chl) a content and turbidity. In addition, the concentration of phycocyanin, a light-harvesting pigment associated with cyanobacteria, was estimated from the ratio of phycocyanin/chl a in vivo fluorescence (IVPF/IVCF). A dark-object-subtracted, spectral ratio model derived from the July 1, 2000 data was found to be the most robust, when applied to the September 27, 2000 data. The same July 1, 2000 model (or algorithm) for PC was then applied to LANDSAT 7 ETM+ frames for July 16 and August 1, 2002 of the Path 19 Row 31 frame (including Cleveland, OH) and to the August 8, 2002 frame of Path 20 Row 31. Moderate, very low, and high PC values were detected in the western basin of Lake Erie on July 16, August, 1, and August 8, 2002, respectively. On September 17, 2002, local media reported a large Microcystis bloom in the western basin. The high PC values on August 8, 2002 may have represented early stage detection of the large Microcystis bloom that was reported 5 weeks later. The PC algorithm derived in this study will improve our understanding of the temporal and spatial dynamics of cyanobacterial bloom formation in Lake Erie and other systems. It may also serve to alert municipalities to the presence of potentially toxic bloom events.


title: Integrating Open and Closed Information Extraction: Challenges and First Steps

abstract: Over the past years, state-of-the-art information extraction (IE) systems such as NELL [5] and ReVerb [9] have achieved impressive results by producing very large knowledge resources at web scale with minimal supervision. However, these resources lack the schema information, exhibit a high degree of ambiguity, and are difficult even for humans to interpret. Working with such resources becomes easier if there is a structured information base to which the resources can be linked. In this paper, we introduce the integration of open information extraction projects with Wikipedia-based IE projects that maintain a logical schema, as an important challenge for the NLP, semantic web, and machine learning communities. We describe the problem, present a gold-standard benchmark, and take the first steps towards a data-driven solution to the problem. This is especially promising, since NELL and ReVerb typically achieve a very large coverage, but still still lack a fullfl edged clean ontological structure which, on the other hand, could be provided by large-scale ontologies like DBpedia [2] or YAGO [13].


title: Data Warehouse Design for E-Commerce Environments

abstract: Data warehousing and electronic commerce are two of the most rapidly expanding fields in recent information technologies. In this paper, we discuss the design of data warehouses for e-commerce environments. We discuss requirement analysis, logical design, and aggregation in e-commerce environments. We have collected an extensive set of interesting OLAP queries for e-commerce environments, and classified them into categories. Based on these OLAP queries, we illustrate our design with data warehouse bus architecture, dimension table structures, a base star schema, and an aggregation star schema. We finally present various physical design considerations for implementing the dimensional models. We believe that our collection of OLAP queries and dimensional models would be very useful in developing any real-world data warehouses in e-commerce environments.


Collaborative Filtering with the Simple Bayesian Classifier


title: The single channel interferometer using a pseudo-Doppler direction finding system

abstract: A new technique for obtaining high performance, low power, radio direction finding, (RDF) using a single receiver is presented. For man-portable applications, multichannel systems consume too much power, are too expensive, and are too heavy to easily be carried by a single individual. Most single channel systems are not accurate enough or do not provide the capability to listen while direction finding (DF) is being performed. By employing feedback in a pseudo-Doppler system via a vector modulator in the IF of a single receiver and an adaptive algorithm to control it, the accuracy of a pseudo-Doppler system can be enhanced to the accuracy of an interferometer based system without the expense of a multichannel receiver. I will also maintain audio listen-through while direction finding is being performed-all with a single inexpensive low power receiver. The use of these techniques provides performance not attainable by other single channel methods.


title: A survey on scheduling and load balancing techniques in cloud computing environment

abstract: Now-a-days cloud computing is the most emerging technology due to its elasticity of resource provisioning and the pay-as-you-go pricing model which enables users to pay only according to their need. As cloud can be accessed anytime and anywhere through commodity hardware only its demand is increasing day by day. So it must provide high performance gain to the user and at the same time must be beneficial for the Cloud Service Provider (CSP). To achieve this goal many challenges have to be faced. Load balancing is one of them which helps the CSP to meet the QoS requirements of the users and at the same time maximize his profit by optimum use of the resources. To balance the load in cloud the resources and workloads must be scheduled in an efficient manner. A variety of scheduling algorithms are used by load balancers to determine which backend server to send a request to. The selected server allocates resources and schedules the job dynamically on some virtual machine (VM) located on the same physical machine. It is also the responsibility of the provider to dynamically reallocate or migrate the VM across physical machines for workload consolidation and to avoid over utilization or under utilization of resources. In this paper, we have discussed different algorithms proposed to resolve the issue of load balancing and task scheduling in Cloud Computing. We have mentioned some of their shortcomings for further development. VM migration issues involved in load balancing are also described briefly.


AzureML: Anatomy of a machine learning service.


title: Transfer learning for cross-company software defect prediction

abstract: Context: Software defect prediction studies usually built models using within-company data, but very few focused on the prediction models trained with cross-company data. It is difficult to employ these models which are built on the within-company data in practice, because of the lack of these local data repositories. Recently, transfer learning has attracted more and more attention for building classifier in target domain using the data from related source domain. It is very useful in cases when distributions of training and test instances differ, but is it appropriate for cross-company software defect prediction? Objective: In this paper, we consider the cross-company defect prediction scenario where source and target data are drawn from different companies. In order to harness cross company data, we try to exploit the transfer learning method to build faster and highly effective prediction model. Method: Unlike the prior works selecting training data which are similar from the test data, we proposed a novel algorithm called Transfer Naive Bayes (TNB), by using the information of all the proper features in training data. Our solution estimates the distribution of the test data, and transfers cross-company data information into the weights of the training data. On these weighted data, the defect prediction model is built. Results: This article presents a theoretical analysis for the comparative methods, and shows the experiment results on the data sets from different organizations. It indicates that TNB is more accurate in terms of AUC (The area under the receiver operating characteristic curve), within less runtime than the state of the art methods. Conclusion: It is concluded that when there are too few local training data to train good classifiers, the useful knowledge from different-distribution training data on feature level may help. We are optimistic that our transfer learning method can guide optimal resource allocation strategies, which may reduce software testing cost and increase effectiveness of software testing process.


title: Changes in bursting caused by learning

abstract: The localization of learning in dissociated cultures to the stimulation-evaluation electrode pair was studied. The cultures were trained using the Conditional Repetitive Stimulation (CRS) algorithm, in which repetitive focal stimulation is ended when a preset ratio of desired responses is achieved. We found that CRS can be used to strengthen an initially weak stimulus-response relationship. We used estimations of the instantaneous firing frequencies per electrode during spontaneous network bursts (NBs), called phase profiles, to determine the spatial extent of the changes required to establish a new stimulus-response relationship in the network. We found significant changes in the profiles, both on the stimulated and observed electrode pair, but also at numerous other sites. The results indicate that most of the changes are uncontrolled and that the whole network is involved during learning.


title: User Centric Walk: An Integrated Approach for Modeling the Browsing Behavior of Users on the Web

abstract: The performance evaluation of Web applications usually requires the analysis of sequences of user requests for specific Web pages. These sequences can be obtained, for example, by applying empirical methods (recording the real sequence of requests), or by applying a formal model for generating synthetic results. In this paper, we present our Web Browsing Model and and its implementation as part of our novel User Centric Walk algorithm. By taking into account the hyperlink structure as well as the different user behavior on the Web, User Centric Walk allows us to generate accurate synthetic data that can be used instead of empirically obtained requests. Additionally, in this paper we show using empirical data that the probability of choosing some hyperlink from a given page as well as the probability of a user leaving a page without following a hyperlink is best characterized by a power-law. Finally, we show the flexibility and applicability of our model by performing the required correlations to empirical data, in order to validate our approach.


title: Temporal Action Localization by Structured Maximal Sums

abstract: We address the problem of temporal action localization in videos. We pose action localization as a structured prediction over arbitrary-length temporal windows, where each window is scored as the sum of frame-wise classification scores. Additionally, our model classifies the start, middle, and end of each action as separate components, allowing our system to explicitly model each actions temporal evolution and take advantage of informative temporal dependencies present in that structure. In this framework, we localize actions by searching for the structured maximal sum, a problem for which we develop a novel, provably-efficient algorithmic solution. The frame-wise classification scores are computed using features from a deep Convolutional Neural Network (CNN), which are trained end-to-end to directly optimize for a novel structured objective. We evaluate our system on the THUMOS 14 action detection benchmark and achieve competitive performance.


title: Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art

abstract: The fuel economy and all-electric range (AER) of hybrid electric vehicles (HEVs) are highly dependent on the onboard energy-storage system (ESS) of the vehicle. Energy-storage devices charge during low power demands and discharge during high power demands, acting as catalysts to provide energy boost. Batteries are the primary energy-storage devices in ground vehicles. Increasing the AER of vehicles by 15% almost doubles the incremental cost of the ESS. This is due to the fact that the ESS of HEVs requires higher peak power while preserving high energy density. Ultracapacitors (UCs) are the options with higher power densities in comparison with batteries. A hybrid ESS composed of batteries, UCs, and/or fuel cells (FCs) could be a more appropriate option for advanced hybrid vehicular ESSs. This paper presents state-of-the-art energy-storage topologies for HEVs and plug-in HEVs (PHEVs). Battery, UC, and FC technologies are discussed and compared in this paper. In addition, various hybrid ESSs that combine two or more storage devices are addressed.


title: Algorithms for digital image processing in diabetic retinopathy

abstract: This work examined recent literature on digital image processing in the field of diabetic retinopathy. Algorithms were categorized into 5 steps (preprocessing; localization and segmentation of the optic disk; segmentation of the retinal vasculature; localization of the macula and fovea; localization and segmentation of retinopathy). The variety of outcome measures, use of a gold standard or ground truth, data sample sizes and the use of image databases is discussed. It is intended that our classification of algorithms into a small number of categories, definition of terms and discussion of evolving techniques will provide guidance to algorithm designers for diabetic retinopathy.


title: Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting

abstract: Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear least-squares (pseudo-inverse and eigen analysis); orthogonal least-squares; gradient-weighted least-squares; bias-corrected renormalization; Kalman filtering; and robust techniques (clustering, regression diagnostics, M-estimators, least median of squares). Particular attention has been devoted to discussions about the choice of appropriate minimization criteria and the robustness of the different techniques. Their application to conic fitting is described.


title: Delivery technologies for genome editing

abstract: Genome editing has emerged as an attractive approach to therapeutically manipulate gene expression. Here, Anderson and colleagues provide an overview of genome-editing platforms, focusing on the methods and challenges of intracellular biomacromolecule delivery. Preclinical and clinical trials involving genome-editing technologies are also discussed.


title: High-Efficiency Angled-Dipole Antennas for Millimeter-Wave Phased Array Applications

abstract: A high-efficiency microstrip-fed endfire angled-dipole antenna has been developed for millimeter-wave phased array applications. The antenna is built on both sides of a Teflon substrate (epsivr = 2.2) and this allows a wideband feed from the single-ended microstrip line to the differential dipole. The design results in wide radiation patterns for scanning purposes with a gain of around 2.5 dB at 20-26 GHz and a cross-polarization level of 6 dB at 23.2-24.6 GHz and very low mutual coupling between elements ( 93% when referenced to the microstrip line feed (including mismatch loss). The usefulness of these antennas as phased array radiators is demonstrated by several eight-element linear arrays at 22-24 GHz with scan angle up to 50 degrees. The application areas are in automotive radars and high data-rate communication systems.


title: Pyrolysis of biomass to produce fuels and chemical feedstocks

abstract: This review presents the summary of new studies on pyrolysis of biomass to produce fuels and chemical feedstocks. A number of biomass species, varying from woody and herbaceous biomass to municipal solid waste, food processing residues and industrial wastes, were subjected to different pyrolysis conditions to obtain liquid, gas and solid products. The results of various biomass pyrolysis investigations connected with the chemical composition and some properties of the pyrolysis products as a result of the applied pyrolysis conditions were combined. The characteristics of the liquid products from pyrolysis were examined, and some methods, such as catalytic upgrading or steam reforming, were considered to improve the physical and chemical properties of the liquids to convert them to economic and environmentally acceptable liquid fuels or chemical feedstocks. Outcomes from the kinetic studies performed by applying thermogravimetric analysis were also presented.


title: Automatic gaze analysis in multiparty conversations based on Collective First-Person Vision

abstract: This paper extends the affective computing research field by introducing first-person vision to automatic conversation analysis. We target medium-sized-party face-to-face conversations where each person wears inward-looking and outward-looking cameras. We demonstrate that the fundamental techniques required for group gaze analysis, i.e. speaker detection, face tracking, and gaze estimation, can be accurately and effectively performed via self-training in a unified framework by gathering captured audio-visual signals to a centralized system and using a general conversation rule, i.e. listeners look mainly at the speaker. We visualize the characteristics of participants' gaze behavior as a gazee-centered heat map, which quantitatively reveals what parts of the gazee's body and for how long the participant looked at it while the gazer speaks or listens. An experiment involving two groups of six-person conversations demonstrates the potential of the proposed framework.


title: Internet of Things Business Models

abstract: Almost all businesses are aware of the potential gains that the Internet of Things (IoT) has to offer, they are unsure of how to approach it. This article proposes a business model that builds on Holler et al., (2014) [1]. The model consists of three dimensions: “Who, Where, and Why”. “Who” describes collaborating partners, which builds the “Value Network”. “Where” describes sources of value co-creation rooted in the layer model of digitized objects, and “Why” describes how partners benefit from collaborating within the value network. With the intention of addressing “How”, the proposed framework has integrated the IoT strategy category, tactics, and value chain elements. The framework is then validated through the case studies of some successful players who are either the Awardees of the IoT Award 2014 or the ICT Award 2015 of Hong Kong.


title: One-Sided Unsupervised Domain Mapping

abstract: In unsupervised domain mapping, the learner is given two unmatched datasets $A$ and $B$. The goal is to learn a mapping $G_{AB}$ that translates a sample in $A$ to the analog sample in $B$. Recent approaches have shown that when learning simultaneously both $G_{AB}$ and the inverse mapping $G_{BA}$, convincing mappings are obtained. In this work, we present a method of learning $G_{AB}$ without learning $G_{BA}$. This is done by learning a mapping that maintains the distance between a pair of samples. Moreover, good mappings are obtained, even by maintaining the distance between different parts of the same sample before and after mapping. We present experimental results that the new method not only allows for one sided mapping learning, but also leads to preferable numerical results over the existing circularity-based constraint. Our entire code is made publicly available at this https URL .


title: Software Defect Prediction using Bayesian Networks and Kernel Methods

abstract: There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. In this book, we use Bayesian modeling to determine the influential relationships among software metrics and defect proneness. Furthermore, we propose a novel technique for defect prediction that uses plagiarism detection tools. We use kernel programming to model the relationship between source code similarity and defectiveness and suggest that source code similarity is a good means of predicting both defectiveness and the number of defects in software systems.


title: Workshop on Applications of Pattern Analysis A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases

abstract: This paper developed a diagnosis model based on Support Vector Machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our hybrid feature selection method, named IFSFFS (ImprovedF -score and Sequential Forward Floating Search), combines the advantages of lters and wrappers to select the optimal feature subset from the original feature set. In our IFSFFS, we rstly generalized the originalF -score to the improvedF -score measuring the discrimination of more than two sets of real numbers. Then we proposed to combine Sequential Forward Floating Search (SFFS) and our improved F -score to accomplish the optimal feature subset selection. Where, our improved F -score is an evaluation criterion for lters, while SFFS and SVM compose an evaluation system of wrappers. The best parameters of kernel function of SVM are found out by grid search technique with ten-fold cross validation. Experiments have been conducted on ve random training-test partitions of the erythemato-squamous diseases dataset from UCI machine learning database. The experimental results show that our SVM-based model with IFSFFS achieved the optimal classication accuracy with no more than 14 features as well.


title: Cost and effort estimation in agile software development

abstract: Projects that are over-budget, delivered late, and fall short of user's expectations have been a common problem are a for software development efforts for years. Agile methods, which represent an emerging set of software development methodologies based on the concepts of adaptability and flexibility, are currently touted as a way to alleviate these reoccurring problems and pave the way for the future of development. The estimation in Agile Software Development methods depends on an expert opinion and historical data of project for estimation of cost, size, effort and duration. In absence of the historical data and experts the previous method like analogy and planning poker are not useful. This paper focuses on the research work in Agile Software development and estimation in Agile. It also focuses the problems in current Agile practices thereby proposed a method for accurate cost and effort estimation.


title: Asymmetric T-coil, resistive feedback cascode low noise amplifier for ultrawideband receiver

abstract: This paper presents a wideband low-noise amplifier (LNA) using 0.18-µm CMOS process which is combination of resistive feedback and T-coil techniques. The cascode stage with a resistive feedback technique to ensure the stability of the amplifier is proposed. The T-coil topology is adopted to achieve wideband output response that improves the bandwidth extension of the CMOS differential amplifier. The proposed topology exhibits a bandwidth of 3.1–7.0 GHz, a power gain of 12 dB, a minimum noise figure of 4.00 dB while drawing 1.4 mW from 1.8 V power supply. The simulated IIP3 of the amplifier is −9.5 dBm at 5.5 GHz. Hence, the T-coil network and cascode topology with resistive feedback can be implemented for mode 1, UWB standard.


Causal Inference on Event Sequences


title: Real- Time Video Denoising on Mobile Phones

abstract: We present an algorithm for real-time video denoising on mobile platforms. Based on Gaussian-Laplacian pyramid decomposition, our solution's main contributions are fast alignment and a new interpolation function that fuses noisy frames into a denoised result. The interpolation function is adaptive to local and global properties of the input frame, robust to motion alignment errors, and can be computed efficiently. We show that the proposed algorithm has comparable quality to offline high-quality video denoising methods, but is orders of magnitude faster. On a modern mobile platform, our work takes less than 20ms to process one HD frame, and it achieves the highest score on a public benchmark.


title: Non-intrusive tongue machine interface

abstract: There has been recent interest in designing systems that use the tongue as an input interface. Prior work however either require surgical procedures or in-mouth sensor placements. In this paper, we introduce TongueSee, a non-intrusive tongue machine interface that can recognize a rich set of tongue gestures using electromyography (EMG) signals from the surface of the skin. We demonstrate the feasibility and robustness of TongueSee with experimental studies to classify six tongue gestures across eight participants. TongueSee achieves a classification accuracy of 94.17% and a false positive probability of 0.000358 per second using three-protrusion preamble design.


Higher education in Egypt and needed employability skills in the domestic labor market: case study from Ain Shams University, faculty of engineering


title: Resonance Attacks on Load Frequency Control of Smart Grids

abstract: Load frequency control (LFC) is widely employed to regulate power plants in modern power generation systems of smart grids. This paper presents a simple and yet powerful type of attacks, referred to as resonance attacks, on LFC power generation systems. Specifically, in a resonance attack, an adversary craftily modifies the input of a power plant according to a resonance source (e.g., rate of change of frequency) to produce a feedback on LFC power generation system, such that the state of the power plant quickly becomes instable. Extensive computer simulations on popular LFC power generation system models which consist of linear, non-linear, and/or high-order items clearly demonstrate the effectiveness of the proposed attacks. As the attack has very low computational cost and communication overhead, it is easy to launch in resource-limited devices such as intelligent electronic devices. In our simulations, the attacker keeps modified input within the normal operating range so as to invade plausibility and consistency based attack detection methods and yet the modifications can quickly drive the system beyond the admissible boundary. Another interesting finding is that by maliciously modifying the input such as power load and tie-line signal over multi-area interconnection channels, a multi-area LFC power generation system could become unreliable more quickly than a single-area system. Finally, we propose countermeasures on the proposed attacks.


title: Fisher Vector Coding for Covariance Matrix Descriptors Based on the Log-Euclidean and Affine Invariant Riemannian Metrics

abstract: This paper presents an overview of coding methods used to encode a set of covariance matrices. Starting from a Gaussian mixture model (GMM) adapted to the Log-Euclidean (LE) or affine invariant Riemannian metric, we propose a Fisher Vector (FV) descriptor adapted to each of these metrics: the Log-Euclidean Fisher Vectors (LE FV) and the Riemannian Fisher Vectors (RFV). Some experiments on texture and head pose image classification are conducted to compare these two metrics and to illustrate the potential of these FV-based descriptors compared to state-of-the-art BoW and VLAD-based descriptors. A focus is also applied to illustrate the advantage of using the Fisher information matrix during the derivation of the FV. In addition, finally, some experiments are conducted in order to provide fairer comparison between the different coding strategies. This includes some comparisons between anisotropic and isotropic models, and a estimation performance analysis of the GMM dispersion parameter for covariance matrices of large dimension.


title: A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis

abstract: Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue–medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.


Developing an open-source, rule-based proofreading tool


title: On the Convergence Rate of Training Recurrent Neural Networks

abstract: How can local-search methods such as stochastic gradient descent (SGD) avoid bad local minima in training multi-layer neural networks? Why can they fit random labels even given non-convex and non-smooth architectures? Most existing theory only covers networks with one hidden layer, so can we go deeper? In this paper, we focus on recurrent neural networks (RNNs) which are multi-layer networks widely used in natural language processing. They are harder to analyze than feedforward neural networks, because the \emph{same} recurrent unit is repeatedly applied across the entire time horizon of length $L$, which is analogous to feedforward networks of depth $L$. We show when the number of neurons is sufficiently large, meaning polynomial in the training data size and in $L$, then SGD is capable of minimizing the regression loss in the linear convergence rate. This gives theoretical evidence of how RNNs can memorize data. More importantly, in this paper we build general toolkits to analyze multi-layer networks with ReLU activations. For instance, we prove why ReLU activations can prevent exponential gradient explosion or vanishing, and build a perturbation theory to analyze first-order approximation of multi-layer networks.


title: Hawkeye: Towards a Desired Directed Grey-box Fuzzer

abstract: Grey-box fuzzing is a practically effective approach to test real-world programs. However, most existing grey-box fuzzers lack directedness, i.e. the capability of executing towards user-specified target sites in the program. To emphasize existing challenges in directed fuzzing, we propose Hawkeye to feature four desired properties of directed grey-box fuzzers. Owing to a novel static analysis on the program under test and the target sites, Hawkeye precisely collects the information such as the call graph, function and basic block level distances to the targets. During fuzzing, Hawkeye evaluates exercised seeds based on both static information and the execution traces to generate the dynamic metrics, which are then used for seed prioritization, power scheduling and adaptive mutating. These strategies help Hawkeye to achieve better directedness and gravitate towards the target sites. We implemented Hawkeye as a fuzzing framework and evaluated it on various real-world programs under different scenarios. The experimental results showed that Hawkeye can reach the target sites and reproduce the crashes much faster than state-of-the-art grey-box fuzzers such as AFL and AFLGo. Specially, Hawkeye can reduce the time to exposure for certain vulnerabilities from about 3.5 hours to 0.5 hour. By now, Hawkeye has detected more than 41 previously unknown crashes in projects such as Oniguruma, MJS with the target sites provided by vulnerability prediction tools; all these crashes are confirmed and 15 of them have been assigned CVE IDs.


title: Community Design and Access to Recreational Facilities as Correlates of Adolescent Physical Activity and Body-Mass Index

abstract: BACKGROUND: Neighborhood-level environmental features have been associated with adult physical activity and weight status, but this link has not been estab- lished for adolescents. METHODS: Community design and access to recreational facilities variables were derived using geographic information systems (GIS) for 799 adolescents (age 11 to 15 y, mean = 12.8 y, 53% girls, 43% ethnic minority). Environment variables were calculated for a 1-mile buffer around each participant's residence. Accelerometers measured min/d of physical activity. RESULTS: Number of nearby recreation facilities and number of nearby parks correlated positively with girls' physical activity, and intersection density inversely related to girls' physical activity. Retail floor area ratio correlated positively with boys' physical activity. No community design or access to recreation variables were related to BMI-percentile. CONCLUSIONS: There was limited evidence that both community design and access to recreation facilities variables were associated with adolescent physical activity, but additional built environment variables need to be studied that have particular relevance for youth. Key Words: children, exercise, obesity, built environment, policy, safe routes to school programs


title: Sources of Customer Satisfaction and Dissatisfaction with Information Technology Help Desks

abstract: As the use, development, and control of information systems diffuses through organizations and society, the importance of the information technology help desk in effective management and use of information resources increases. This study examines service encounters with help desks to understand the events and behaviors that cause customers to distinguish very satisfactory from very dissatisfactory service. While considerable research has investigated the determinants of customer satisfaction with service encounters in retail industries such as airlines, restaurants, and hotels, there has been little work investigating the dimensions of professional service encounters. Using the critical incident technique, the authors analyzed over 500 incidents from customers of information services. Help desk critical incidents were classified into three broad categories which were further subdivided into 12 categories which lead to satisfaction and dissatisfaction. The three broad categories, core service delivery failures, customer situation, and helper characteristics and behavior, were consistent with categories found in previous service encounter research, while the subcategories were different in several ways. This suggests that some dimensions of service encounter satisfaction are consistent across service industries, while others are context specific.


title: Sulphur-nitrogen chelating agents. II. Metal complexes of 2-(2-Methylthioethyl) pyridine

abstract: 2-(2-Methylthioethyl)pyridine (N-SMe) has been prepared and its structure has been confirmed by N.M.R. measurements. Complexes with palladium(II), platinum(II), copper(II), copper(I), silver(I), and mercury(II) were obtained but attempts to prepare complexes of cobalt(II) and nickel(II) were unsuccessful. The mono-chelated complexes MX2(N-SMe) (M = Pd, Pt; X = Cl, Br, I, SCN), CuX2(N-SMe) (X = Cl, Br), AgClO4(N-SMe), and HgCl2(N-SMe), and the bis-chelated complexes [M(N-SMe)2](ClO4)2 (M = Pd, Pt, Cu, Hg) and [Cu(N-SNe)2]ClO4 were characterized. Conductimetric titrations of [Pd(N-SMe)2](ClO4)2 in nitrobenzene gave end-points after the addition of two equivalents of halide ion due to the loss of one molecule of ligand to yield PdX2(N-SMe). The platinum complex [Pt(N-SMe)2](ClO4)2 gives two end-points, the first corresponding to the formation of [Pt(N-SMe)2X]+ (X = Br, I). The solid reflectance spectra of CuX2(N-SMe) differ from the absorption spectra in nitromethane solution, indicating a polymeric tetragonal structure in the solid state. The ligand molecules in [Cu(N- SMe)2](ClO4)2 are readily replaced by pyridine. This complex can be reduced to the copper(I) complex [Cu(N-SNe)2]ClO4. The infrared spectrum of AgClO4(N-SMe) seems to indicate that the perchlorato groups are coordinated and the insolubility of the compound suggests a polymeric structure. The mercury complexes apparently contain tetrahedral mercury(II).


title: An IoT based system for remote patient monitoring

abstract: Following a surgical procedure, patients are monitored in an ICU until physically stable, after which are discharged to a ward for further evaluation and recovery. Usually, ward evaluation does not imply continuous physiological parameters monitoring and therefore patient relapse is not uncommon. The present paper describes the steps taken to design and build a low-cost modular monitoring system prototype. This system aims to offer mobile support in order to facilitate faster and better medical interventions in emergency cases and has been developed using low-power dedicated sensor arrays for EKG, SpO2, temperature and movement. The interfaces for these sensors have been developed according to the IoT model: a central control unit exposes a RESTful based Web interface that ensures a platform agnostic behaviour and provides a flexible mechanism to integrate new components.


title: A Provably Efficient Algorithm for Separable Topic Discovery

abstract: We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in which each shared latent factor contains a novel word that is unique to that factor, a property that has come to be known as separability. Our algorithm is based on the key insight that the novel words correspond to the extreme points of the convex hull formed by the row-vectors of a suitably normalized word co-occurrence matrix. We leverage this geometric insight to establish polynomial computational and sample complexity bounds based on a few isotropic random projections of the rows of the normalized word co-occurrence matrix. Our proposed random-projections-based algorithm is naturally amenable to an efficient distributed implementation and is attractive for modern web-scale distributed data mining applications.


title: F430 model chemistry. Evidence for alkyl- and hydrido-nickel intermediates in the reactions of the nickel(I) octaethylisobacteriochlorin anion

abstract: The reactions of Ni I (OEiBC)- with alkyl (isomeric propyl, butyl, and hexyl) and aryl halides were investigated in acetonitrile, dimethylformamide, and tetrahydrofuran solutions. The principal products result from reduction or dehydrohalogenation of the alkyl or aryl halide. Alkene formation is favored by more polar solvents and by secondary vs primary substitution of the alkyl halide. Isomerization of alkenes occurs during the reaction, which strongly implies the intermediacy of hydrido-nickel intermediates. Coupling processes, which produce significant quantities of ethane from methyl iodide, become insignificant as the length or steric bulk of the alkyl group increases. No coupling products are observed for aryl or benzyl halides. The current observations provide strong support for the nonradical nickel-based mechanism previously proposed and suggest that the dehydrohalogenation processess in this system involve β -hydride elimination.


title: Counting blessings versus burdens: an experimental investigation of gratitude and subjective well-being in daily life.

abstract: The effect of a grateful outlook on psychological and physical well-being was examined. In Studies 1 and 2, participants were randomly assigned to 1 of 3 experimental conditions (hassles, gratitude listing, and either neutral life events or social comparison); they then kept weekly (Study 1) or dail


IOT BASED AIR AND SOUND POLLUTION MONITORING SYSTEM


title: A Review of the Tripartite Structure of Subjective Well-Being: Implications for Conceptualization, Operationalization, Analysis, and Synthesis

abstract: Subjective well-being (SWB) comprises a global evaluation of life satisfaction and positive and negative affective reactions to one’s life. Despite the apparent simplicity of this tripartite model, the structure of SWB remains in question. In the present review, the authors identify five prominent structural conceptualizations in which SWB is cast variously as three separate components, a hierarchical construct, a causal system, a composite, and as configurations of components. Supporting evidence for each of these models is reviewed, strengths and weaknesses are evaluated, and commonalities and discrepancies among approaches are described. The authors demonstrate how current ambiguities concerning the tripartite structure of SWB have fundamental implications for conceptualization, measurement, analysis, and synthesis. Given these ambiguities, it is premature to propose a definitive structure of SWB. Rather, the authors outline a research agenda comprising both short-term and longer-term steps toward reso...


title: Big data platforms: What's next?

abstract: Three computer scientists from UC Irvine address the question "What's next for big data?" by summarizing the current state of the big data platform space and then describing ASTERIX, their next-generation big data management system.


title: Generalization and Differentiation in Semantic Memory

abstract: According to many theories, semantic representations reflect the parallel activation of information coded across a distributed set of modality-specific association brain cortices. This view is challenged by the neurodegenerative condition known as semantic dementia (SD), in which relatively circumscribed, bilateral atrophy of the anterior temporal lobes results in selective degradation of core semantic knowledge, affecting all types of concept, irrespective of the modality of testing. Research on SD suggests a major revision in our understanding of the neural basis of semantic memory. Specifically, it is proposed that the anterior temporal lobes form amodal semantic representations through the distillation of the multimodal information that is projected to this region from the modality-specific association cortices. Although cross-indexing of modality-specific information could be achieved by a web of direct connections between pairs of these regions, amodal semantic representations enable semantic generalization and inference on the basis of conceptual structure rather than modality-specific features. As expected from this hypothesis, SD is characterized by impaired semantic generalization, both clinically and in formal assessment. The article describes a comprehensive array of under- and overgeneralization errors by patients with SD when engaged in receptive and expressive verbal and nonverbal tasks and everyday behaviors.


Are you living in a computer simulation


title: The social strategy cone: Towards a framework for evaluating social media strategies

abstract: Social media is growing rapidly. Providing both risks and opportunities for organizations as it does. The social strategy cone is developed for evaluating social media strategies. This framework comprises of seven key elements of social media strategies as based on a systematic literature review and case studies. The results of 21 interviews have contributed to the construction of the social media strategy cone for analyzing social media strategies. Three levels of maturity of social media strategy are proposed: initiation, diffusion and maturity. Initiation includes the key elements: ‘target audience’ and ‘channel choice’ while all case organizations studied payed attention to these elements. Diffusion includes the elements: ‘goals’, ‘resources’ and ‘policies’. Maturity adds the elements of ‘monitoring’ and ‘content activities’. Only 3 of the 9 organizations studied are in this phase of maturity. Although, theory suggests the importance of the element of ‘monitoring’ our research shows the need for more attention in practice.


title: Enabling scientific workflows in virtual reality

abstract: To advance research and improve the scientific return on data collection and interpretation efforts in the geosciences, we have developed methods of interactive visualization, with a special focus on immersive virtual reality (VR) environments. Earth sciences employ a strongly visual approach to the measurement and analysis of geologic data due to the spatial and temporal scales over which such data ranges. As observations and simulations increase in size and complexity, the Earth sciences are challenged to manage and interpret increasing amounts of data. Reaping the full intellectual benefits of immersive VR requires us to tailor exploratory approaches to scientific problems. These applications build on the visualization method's strengths, using both 3D perception and interaction with data and models, to take advantage of the skills and training of the geological scientists exploring their data in the VR environment. This interactive approach has enabled us to develop a suite of tools that are adaptable to a range of problems in the geosciences and beyond.


title: A Low-Profile Unidirectional Printed Antenna for Millimeter-Wave Applications

abstract: By combining a printed reflector-backed one-wave-length bowtie antenna and a printed double loop antenna, a new wideband high-gain antenna element is demonstrated. Due to its low-profile structure of ~ 0.05λ0, it is attractive to be used in an array environment. Experimentally, a series of antenna arrays operated at 60 GHz are designed and fabricated. They achieve wide impedance bandwidths covering the 57-64 GHz unlicensed frequency band. Over this band, the measured antenna gains are ranged from 14.5-15.5 dBi, 18.3-20.1 dBi and 22.5-25.2 dBi for 4-element, 14-element and 50-element antenna arrays, respectively. The measured radiation pattern has low cross-polarization level and low back radiation. The proposed low-profile antenna and arrays are low-cost in fabrication as they are simply made on a single-layer printed circuit board.


title: Order-Embeddings of Images and Language

abstract: Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval.


title: Scene text detection in video by learning locally and globally

abstract: There are a variety of grand challenges for text extraction in scene videos by robots and users, e.g., heterogeneous background, varied text, nonuniform illumination, arbitrary motion and poor contrast. Most previous video text detection methods are investigated with local information, i.e., within individual frames, with limited performance. In this paper, we propose a unified tracking based text detection system by learning locally and globally, which uniformly integrates detection, tracking, recognition and their interactions. In this system, scene text is first detected locally in individual frames. Second, an optimal tracking trajectory is learned and linked globally with all detection, recognition and prediction information by dynamic programming. With the tracking trajectory, final detection and tracking results are simultaneously and immediately obtained. Moreover, our proposed techniques are extensively evaluated on several public scene video text databases, and are much better than the state-of-the-art methods.


THE INFLUENCE OF MANAGERIAL LEADERSHIP STYLE ON EMPLOYEE JOB SATISFACTION IN JORDANIAN RESORT


title: Optical Flow Measurement using Lucas kanade Method

abstract: Motion estimation is demanding field among researchers to compute independent estimation of motion at each pixel in most of general. Motion estimation generally known as optical or optic flow. In this paper, overview of some basic concepts of motion estimation, optical flow and Lucas kanade method has been provided by us. Lucas kanade method is one of the methods for optical flow measurement. It is a differential method for optical flow estimation.


title: Type I collagen is thermally unstable at body temperature

abstract: Measured by ultra-slow scanning calorimetry and isothermal circular dichroism, human lung collagen monomers denature at 37°C within a couple of days. Their unfolding rate decreases exponentially at lower temperature, but complete unfolding is observed even below 36°C. Refolding of full-length, native collagen triple helices does occur, but only below 30°C. Thus, contrary to the widely held belief, the energetically preferred conformation of the main protein of bone and skin in physiological solution is a random coil rather than a triple helix. These observations suggest that once secreted from cells collagen helices would begin to unfold. We argue that initial microunfolding of their least stable domains would trigger self-assembly of fibers where the helices are protected from complete unfolding. Our data support an earlier hypothesis that in fibers collagen helices may melt and refold locally when needed, giving fibers their strength and elasticity. Apparently, Nature adjusts collagen hydroxyproline content to ensure that the melting temperature of triple helical monomers is several degrees below rather than above body temperature.


title: An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis

abstract: We present a newly collected data set of 8,868 gold-standard annotated Arabic twitter feeds. The corpus is manually labelled for subjectivity and sentiment analysis (SSA) (κ = 0.816). In addition, the corpus is annotated with a variety of linguistically motivated feature-sets that have previously shown positive impact on classification performance. The paper highlights issues posed by twitter as a genre, such as a mixture of language varieties and topic-shifts. Our next step is to extend the current corpus, using online semi-supervised learning. A first sub-corpus will be released via the ELRA repository as part of this submission.


title: A Model of Events based on a Foundational Ontology

abstract: The lack of a formal event model hinders interoperability in distributed event-based systems. Consequently, we present in this paper a formal model of events, called F. The model bases on an upper-level ontology and pro-vides comprehensive support for all aspects of events such as time and space, objects and persons involved, as well as the structural aspects, namely mereological, causal, and correlational relationships. The event model provides a flexible means for event composition, modeling of event causality and correlation, and allows for representing different interpretations of the same event. The foundational event model F is developed in a pattern-oriented approach, modularized in different ontologies, and can be easily extended by domain specifific ontologies.


title: A queueing analysis of max-min fairness, proportional fairness and balanced fairness. Queueing Systems: Theory and Applications

abstract: We compare the performance of three usual allocations, namely max-min fairness, proportional fairness and balanced fairness, in a communication network whose resources are shared by a random number of data flows. The model consists of a network of processor-sharing queues. The vector of service rates, which is constrained by some compact, convex capacity set representing the network resources, is a function of the number of customers in each queue. This function determines the way network resources are allocated. We show that this model is representative of a rich class of wired and wireless networks. We give in this general framework the stability condition of max-min fairness, proportional fairness and balanced fairness and compare their performance on a number of toy networks.


title: Dynamic integrated scheduling of hard real-time, soft real-time, and non-real-time processes

abstract: Real-time systems are growing in complexity and real-time and soft real-time applications are becoming common in general-purpose computing environments. Thus, there is a growing need for scheduling solutions that simultaneously support processes with a variety of different timeliness constraints. Toward this goal we have developed the resource allocation/dispatching (RAD) integrated scheduling model and the rate-based earliest deadline (RBED) integrated multi-class real-time scheduler based on this model. We present RAD and the RBED scheduler and formally prove the correctness of the operations that RBED employs. We then describe our implementation of RBED and present results demonstrating how RBED simultaneously and seamlessly supports hard real-time, soft real-time, and best-effort processes.


title: Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization

abstract: Sparse signal representation, analysis, and sensing have received a lot of attention in recent years from the signal processing, optimization, and learning communities. On one hand, learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. On the other hand, the framework of compressed sensing (CS) has shown that sparse signals can be recovered from far less samples than those required by the classical Shannon-Nyquist Theorem. The samples used in CS correspond to linear projections obtained by a sensing projection matrix. It has been shown that, for example, a nonadaptive random sampling matrix satisfies the fundamental theoretical requirements of CS, enjoying the additional benefit of universality. On the other hand, a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy or further reduce the necessary number of samples. In this paper, we introduce a framework for the joint design and optimization, from a set of training images, of the nonparametric dictionary and the sensing matrix. We show that this joint optimization outperforms both the use of random sensing matrices and those matrices that are optimized independently of the learning of the dictionary. Particular cases of the proposed framework include the optimization of the sensing matrix for a given dictionary as well as the optimization of the dictionary for a predefined sensing environment. The presentation of the framework and its efficient numerical optimization is complemented with numerous examples on classical image datasets.


title: Pharmacological potential of Bidens pilosa L. and determination of bioactive compounds using UHPLC-QqQLIT-MS/MS and GC/MS

abstract: BACKGROUND ::: Research of natural products from traditionally used medicinal plants to fight against the human ailments is fetching attention of researchers worldwide. Bidens pilosa Linn. var. Radiata (Asteraceae) is well known for its folkloric medicinal use against various diseases from many decades. Mizoram, North East India, has high plant diversity and the use of this plant as herbal medicine is deep rooted in the local tribes. The present study was executed to understand the pharmacological potential of B. pilosa leaves extract. ::: ::: ::: METHODS ::: The antimicrobial potential was determined using agar well diffusion and broth microdilution method against bacterial and yeast pathogens. Cytotoxicity was evaluated using MTT and apoptotic DNA fragmentation assays. Further, the antioxidant ability of the extract was analysed using DPPH and ABTS free radical scavenging assay. Mosquitocidal activity was evaluated against third in-star larvae of C. quinquefasciatus using dose response and time response larvicidal bioassay. Additionally, the major phenolic and volatile compounds were determined using UHPLC-QqQLIT-MS/MS and GC/MS respectively. ::: ::: ::: RESULTS ::: We found that the extract showed highest antimicrobial activity against E. coli (MIC 80 μg/mL and IC50 110.04 μg/mL) and showed significant cytotoxicity against human epidermoid carcinoma (KB-3-1) cells with IC50 values of 99.56 μg/mL among the tested cancer cell lines. The IC50 values for scavenging DPPH and ABTS was 80.45 μg/mL and 171.6 μg/mL respectively. The extract also showed the high phenolics (72 μg GAE/mg extract) and flavonoids (123.3 μg Quercetin /mg extract). Lastly, five bioactive and six volatile compounds were detected using UHPLC-QqQLIT-MS/MS and GC-MS respectively which may be responsible for the plant's bioactivities. An anticancerous compound, Paclitaxel was detected and quantified for the first time from B. pilosa leaves extract, which further showed the anticancerous potential of the tested extract. ::: ::: ::: CONCLUSION ::: On the basis of the present investigation, we propose that the leaf extract of B. pilosa might be a good candidate for the search of efficient environment friendly natural bioactive agent and pharmaceutically important compounds.


title: A Survey of Digital Evidences Forensic and Cybercrime Investigation Procedure

abstract: Due to the development of networks, cybercrime has many crime types, including network attack, mail fraud, intimidation, copyright infringement, and so on. For network attacks, many approaches have been proposed and used to detect and defense. However, after the network attack is confirmed or other crime exists, it still need to execute the investigation procedure by the investigators, collect the evidences related to the crime, find the perpetrators, and prosecute them. Therefore, in this paper, we collect the researches of investigation procedure of cybercrime in the recent years. By introducing the research investigation procedure of these papers, we will discover the features of every procedure. Then we compare these investigation procedures via the traditional investigative procedures compatibility, cybercrime behavior analysis, evidence forensic procedures, case analysis and verification, the methods of evidence collection and analysis, and the area of judicial jurisdiction. Finally, we will propose the viewpoints of cybercrime investigation and forensic procedures, and we wish this paper will help the research of investigation and forensic procedures.


title: SUN attribute database: Discovering, annotating, and recognizing scene attributes

abstract: In this paper we present the first large-scale scene attribute database. First, we perform crowd-sourced human studies to find a taxonomy of 102 discriminative attributes. Next, we build the “SUN attribute database” on top of the diverse SUN categorical database. Our attribute database spans more than 700 categories and 14,000 images and has potential for use in high-level scene understanding and fine-grained scene recognition. We use our dataset to train attribute classifiers and evaluate how well these relatively simple classifiers can recognize a variety of attributes related to materials, surface properties, lighting, functions and affordances, and spatial envelope properties.


title: Some Effective Techniques for Naive Bayes Text Classification

abstract: While naive Bayes is quite effective in various data mining tasks, it shows a disappointing result in the automatic text classification problem. Based on the observation of naive Bayes for the natural language text, we found a serious problem in the parameter estimation process, which causes poor results in text classification domain. In this paper, we propose two empirical heuristics: per-document text normalization and feature weighting method. While these are somewhat ad hoc methods, our proposed naive Bayes text classifier performs very well in the standard benchmark collections, competing with state-of-the-art text classifiers based on a highly complex learning method such as SVM


title: Measurement system with leaky coaxial cables operating as distributed antennas for UHF-RFID readers

abstract: This paper presents a measurement system for UHF-RFID tag detection employing leaky coaxial cable as distributed reader antenna. The proposed system is characterized through an experimental analysis in terms of S-parameters. Then the leaky feeder is employed in a measurement setup with commercial UHF-RFID reader. Measurement results show the leaky feeder capability to detect tags independently on its orientation.


title: Architectural design recovery using data mining techniques

abstract: The paper presents a technique for recovering the high level design of legacy software systems according to user defined architectural plans. Architectural plans are represented using a description language and specify system components and their interfaces. Such descriptions are viewed as queries that are applied on a large database which stores information extracted from the source code of the subject legacy system. Data mining techniques and a modified branch and bound search algorithm are used to control the matching process, by which the query is satisfied and query variables are instantiated. The matching process allows the alternative results to be ranked according to data mining associations and clustering techniques and, finally, be presented to the user.


title: Motion control of a novel robotic wrist exoskeleton via pneumatic muscle actuators

abstract: In this article, the motion control problem of a robotic EXOskeletal WRIST (EXOWRIST) prototype is considered. This novel robotic appliance's motion is achieved via pneumatic muscle actuators, a pneumatic form of actuation possessing crucial attributes for the development of an exoskeleton that is safe, reliable, portable and low-cost. The EXOWRIST's properties are presented in detail and compared to the recent wrist exoskeleton technology, while its two degrees-of-freedom movement capabilities (extension-flexion, ulnar-radial deviation) are experimentally evaluated on a healthy human volunteer via an advanced nonlinear PID-based control algorithm.


title: A novel algorithm for enumerating maximal bicliques from a symmetric matrix

abstract: We address the problem of enumerating maximal bicliques from a symmetric matrix. We propose a novel algorithm named TWINBLADE which exploits the properties of symmetric matrix, and reduces the search space to a larger extent and it generates not even a single duplicate. Our algorithm is compared with LCM-MBC algorithm, in the experiment conducted, ours take almost only half the running time of LCM-MBC for sparse and comparatively performs better for dense datasets.


title: Why do you say they are similar ? Interpretable Semantic Textual Similarity

abstract: We address interpretability, the ability of machines to explain their reasoning.We formalize it for textual similarity as graded typed alignment between 2 sentences.We release an annotated dataset and build and evaluate a high performance system.We show that the output of the system can be used to produce explanations.2 user studies show preliminary evidence that explanations help humans perform better. User acceptance of artificial intelligence agents might depend on their ability to explain their reasoning to the users. We focus on a specific text processing task, the Semantic Textual Similarity task (STS), where systems need to measure the degree of semantic equivalence between two sentences. We propose to add an interpretability layer (iSTS for short) formalized as the alignment between pairs of segments across the two sentences, where the relation between the segments is labeled with a relation type and a similarity score. This way, a system performing STS could use the interpretability layer to explain to users why it returned that specific score for the given sentence pair. We present a publicly available dataset of sentence pairs annotated following the formalization. We then develop an iSTS system trained on this dataset, which given a sentence pair finds what is similar and what is different, in the form of graded and typed segment alignments. When evaluated on the dataset, the system performs better than an informed baseline, showing that the dataset and task are well-defined and feasible. Most importantly, two user studies show how the iSTS system output can be used to automatically produce explanations in natural language. Users performed the two tasks better when having access to the explanations, providing preliminary evidence that our dataset and method to automatically produce explanations do help users understand the output of STS systems better.


title: RGB-D object modelling for object recognition and tracking

abstract: This work presents a flexible system to reconstruct 3D models of objects captured with an RGB-D sensor. A major advantage of the method is that unlike other modelling tools, our reconstruction pipeline allows the user to acquire a full 3D model of the object. This is achieved by acquiring several partial 3D models in different sessions—each individual session presenting the object of interest in different configurations that reveal occluded parts of the object — that are automatically merged together to reconstruct a full 3D model. In addition, the 3D models acquired by our system can be directly used by state-of-the-art object instance recognition and object tracking modules, providing object-perception capabilities to complex applications requiring these functionalities (e.g. human-object interaction analysis, robot grasping, etc.). The system does not impose constraints in the appearance of objects (textured, untextured) nor in the modelling setup (moving camera with static object or turn-table setups with static camera). The proposed reconstruction system has been used to model a large number of objects resulting in metrically accurate and visually appealing 3D models.


title: Factors influencing job satisfaction amongst plastic surgical trainees: experience from a regional unit in the United Kingdom

abstract: Relationships have been reported between job satisfaction and productivity, consumer satisfaction, turnover, absenteeism, stress and burnout. We investigated job satisfaction amongst plastic surgical trainees at a regional plastic surgical unit. We aim to identify facets of the job influencing overall satisfaction. All plastic surgical trainees were recruited. A validated Job Satisfaction Survey was used to identify facets of their jobs resulting in satisfaction. Trainees in this unit expressed high satisfaction from the nature of their work and level of supervision. The trainees reported least satisfaction with the working conditions of their job. This study highlights the important factors influencing job satisfaction in a group of plastic surgical trainees. Identification of these factors allows trainers to ensure satisfaction of their trainees and subsequent higher performance, productivity and lower turnover and absenteeism.


title: Lane detection and tracking using B-Snake

abstract: In this paper, we proposed a B-Snake based lane detection and tracking algorithm without any cameras' parameters. Compared with other lane models, the B-Snake based lane model is able to describe a wider range of lane structures since B-Spline can form any arbitrary shape by a set of control points. The problems of detecting both sides of lane markings (or boundaries) have been merged here as the problem of detecting the mid-line of the lane, by using the knowledge of the perspective parallel lines. Furthermore, a robust algorithm, called CHEVP, is presented for providing a good initial position for the B-Snake. Also, a minimum error method by Minimum Mean Square Error (MMSE) is proposed to determine the control points of the B-Snake model by the overall image forces on two sides of lane. Experimental results show that the proposed method is robust against noise, shadows, and illumination variations in the captured road images. It is also applicable to the marked and the unmarked roads, as well as the dash and the solid paint line roads.


title: A principal component analysis of 39 scientific impact measures

abstract: BACKGROUND ::: The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. ::: ::: ::: METHODOLOGY ::: We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. ::: ::: ::: CONCLUSIONS ::: Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.


title: Sensitivity analysis of a 1 to 18 GHz broadband DRGH antenna

abstract: In this paper some properties of a 1-18 GHz double ridged guide horn antenna (DRGH) with a feeding section including coaxial input and a back shorting plate are rigorously investigated. Most desired electromagnetic characteristics of this antenna is achieved by empirically finding sizes for different parameters, however there is no explanation for the effect of most of them in open literature. In order to have a clear idea of the effects of different parameters, a 1-18 GHz DRGH has been simulated with HFSS. It is understood from the results that the parameters near feeding point such as the initial distance between ridges, the distance between the center of the probe and the cavity, and the radius of the inserted probe play a significant role in controlling VSWR and gain and in shaping the radiation pattern for high frequencies.


title: A survey of middleware for sensor network and challenges

abstract: In recent years, wireless sensor network (WSN) has emerged as a highly important research area. Middleware for WSN facilitates development and deployment of a large number of applications such as smart environments, weather forecasting, bridge monitoring, health applications, etc. for sensor networks. But due to resource constraints, unreliability of wireless networks, and diversity in available sensor hardware, middleware for WSN presents a number of new challenges. In this paper, we try to find out and elaborate various challenges associated with the development of middleware for WSN. We present a comparative study of several existing middleware and how they address those challenges. In doing so, we point out the limitations of present generation middleware for sensor networks. We also illustrate how much more work needs to be done to make middleware for WSN suitable for general purpose usage in real world.


title: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

abstract: In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Levy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.


An OLAP Endpoint for RDF Data Analysis Using Analysis Graphs


title: Night time road curvature estimation based on Convolutional Neural Networks

abstract: Detecting the road geometry at night time is an essential precondition to provide optimal illumination for the driver and the other traffic participants. In this paper we propose a novel approach to estimate the current road curvature based on three sensors: A far infrared camera, a near infrared camera and an imaging radar sensor. Various Convolutional Neural Networks with different configuration are trained for each input. By fusing the classifier responses of all three sensors, a further performance gain is achieved. To annotate the training and evaluation dataset without costly human interaction a fully automatic curvature annotation algorithm based on inertial navigation system is presented as well.


title: What Aspects of Their Memories Do College Students Most Want to Improve

abstract: Previous research investigated what aspects of their memories general audiences of people in memory-improvement seminars most wanted to improve. This research examined the same question among college students in memory-skills classes. Students rated the importance of each of 12 aspects of memory. The most important aspects were schoolwork, remembering what they read, remembering facts and details, and remembering people's names. The ratings showed few relationships with gender, grade point average, or year in school, suggesting that there might be more similarities than differences among different kinds of students in their memory concerns. The ratings also showed several similarities with the general audiences. ********** Many studies have investigated the aspects of their memories for which people report failures and problems; this is usually done by measuring the reported frequency of occurrence of specified memory problems (cf., Gilewski & Zelinski, 1986). Higbee (2003) took a different approach to studying people's memory concerns, by investigating what aspects of memory were most important to people. A general audience of people attending a memory-improvement seminar responded to an open-ended question on what aspects of memory they most wanted to improve, and another general audience answered a questionnaire developed from those responses. The most important aspect was remembering people's names, and there were some gender and age differences in other aspects of memory. Other research has investigated what kinds of remembering are important to more-limited populations, such as the elderly (e.g., Leirer, Morrow, Sheikh, & Pariante, 1990; Reese, Cherry, & Norris, 1999). The purpose of the present research was to examine the same question among college students, and to see how the responses of students compare with those of the general audiences. In a pilot study, 41 students in a memory-skills course answered the following question at the beginning of the course--"What are the main benefits you hope to get from taking a memory-improvement class? (That is, what are the memory questions or problems that you most want to have answered or solved?)." The answers to this question were categorized and tallied. The four categories with the most frequent answers were schoolwork (e.g., study skills, specific course material) = 51%, people's names and faces # = 24%, everyday tasks (e.g., things to do, where I put something) = 15%, and what I read = 10%. Chi-square comparisons between sub-groups found no significant differences between males and females, between low-GPA and high-GPA students, or among years in school. The findings of no statistically significant differences among subgroups in the pilot study might have some practical significance, suggesting the possibility that there might be more similarities than differences in memory concerns among different kinds of students. However, another possible explanation might be the lack of statistical power in the comparisons, which led to the present study. This study was conducted to further investigate what aspects of their memories college students most want to improve, using interval-level data that would allow more-powerful statistical analyses than the chi-square tests used on the nominal data in the pilot study. Method Like the pilot study, the present study was a quasi-experiment. The participants were college students who were enrolled in another section of the memory-skills course at the same university. The course was taught by the author and is described elsewhere (Higbee, 1999). The 36 participants included 23 males and 13 females, ages 17-26 (M = 21.4, SD = 2.3), with 17 Psychology majors and the other 19 students representing 13 other majors. Years in school included 3 freshmen, 6 sophomores, 10 juniors, and 17 seniors. At the beginning of the course, all participants completed the Memory Improvement Questionnaire (MIQ), which was developed from the categories of responses that had been given to the open-ended question that students had answered in the pilot study (as well as responses by the general audience of Higbee, 2003, Experiment 1). …


title: Substrate Integrated Waveguide (SIW) Rotman Lens and Its Ka-Band Multibeam Array Antenna Applications

abstract: A new type of substrate integrated waveguide (SIW) multibeam antenna is proposed for mobile satellite communications using beam switching and diversity techniques. It employs an SIW Rotman lens as the beamforming network. The prototype of a single multibeam antenna is implemented at 28.5 GHz with seven input ports and an antenna array constructed by nine SIW linear slot arrays, which can generate a corresponding number of beams along one dimension. Several such antennas are grouped in two different ways to cover a 2-D solid angle with multiple beams. Experiment results show that the 2-D solid angle around (-40deg, 40deg) X (-35deg, 35deg) or (-25deg, 25deg) x (-35deg, 35deg) are covered with 20 or 25 beams with 5 -dB beamwidth, respectively. It is demonstrated that this type of printed multibeam antenna is a good choice for communication applications where mobility and high gain are simultaneously required.


DIGITAL MATURITY IN TRADITIONAL INDUSTRIES – AN EXPLORATORY ANALYSIS


title: Multi-relational Link Prediction in Heterogeneous Information Networks

abstract: Many important real-world systems, modeled naturally as complex networks, have heterogeneous interactions and complicated dependency structures. Link prediction in such networks must model the influences between heterogenous relationships and distinguish the formation mechanisms of each link type, a task which is beyond the simple topological features commonly used to score potential links. In this paper, we introduce a novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks, which we use to demonstrate the potential benefits of diverse evidence, particularly in cases where homogeneous relationships are very sparse. However, we also expose some fundamental flaws of traditional a priori link prediction. In accordance with previous research on homogeneous networks, we further demonstrate that a supervised approach to link prediction can enhance performance and is easily extended to the heterogeneous case. Finally, we present results on three diverse, real-world heterogeneous information networks and discuss the trends and tradeoffs of supervised and unsupervised link prediction in a multi-relational setting.


title: Quantum Communication in Rindler Spacetime

abstract: A state that an inertial observer in Minkowski space perceives to be the vacuum will appear to an accelerating observer to be a thermal bath of radiation. We study the impact of this Davies-Fulling-Unruh noise on communication, particularly quantum communication from an inertial sender to an accelerating observer and private communication between two inertial observers in the presence of an accelerating eavesdropper. In both cases, we establish compact, tractable formulas for the associated communication capacities assuming encodings that allow a single excitation in one of a fixed number of modes per use of the communications channel. Our contributions include a rigorous presentation of the general theory of the private quantum capacity as well as a detailed analysis of the structure of these channels, including their group-theoretic properties and a proof that they are conjugate degradable. Connections between the Unruh channel and optical amplifiers are also discussed.


title: Unsupervised Footwear Impression Analysis and Retrieval from Crime Scene Data

abstract: Footwear impressions are one of the most frequently secured types of evidence at crime scenes. For the investigation of crime series they are among the major investigative notes. In this paper, we introduce an unsupervised footwear retrieval algorithm that is able to cope with unconstrained noise conditions and is invariant to rigid transformations. A main challenge for the automated impression analysis is the separation of the actual shoe sole information from the structured background noise. We approach this issue by the analysis of periodic patterns. Given unconstrained noise conditions, the redundancy within periodic patterns makes them the most reliable information source in the image. In this work, we present four main contributions: First, we robustly measure local periodicity by fitting a periodic pattern model to the image. Second, based on the model, we normalize the orientation of the image and compute the window size for a local Fourier transformation. In this way, we avoid distortions of the frequency spectrum through other structures or boundary artefacts. Third, we segment the pattern through robust point-wise classification, making use of the property that the amplitudes of the frequency spectrum are constant for each position in a periodic pattern. Finally, the similarity between footwear impressions is measured by comparing the Fourier representations of the periodic patterns. We demonstrate robustness against severe noise distortions as well as rigid transformations on a database with real crime scene impressions. Moreover, we make our database available to the public, thus enabling standardized benchmarking for the first time.


title: Body temperature measurement for remote health monitoring system

abstract: Remote health monitoring system has been an interesting topic recently among medical practitioners, engineers as well as IT professionals. However, the application of remote health monitoring system where doctor's can monitor patients' vital signs via web is practically new in Malaysia and other countries. Remote health monitoring system is beneficial to the patients and society where the implementation of such system will save hospital bill, waiting time and reduce traffics in the hospital. The objective of this project is to design and develop body temperature measurement device that can be observe by the doctor in real time as well as history data via internet with an alarm/indication in case of abnormalities. In the proposed health monitoring system, heart rate and body temperature wireless sensors were developed, however this paper only focus on body temperature wireless monitoring system. The temperature sensors will send the readings to a microcontroller using Xbee wireless communication. To send the real-time data to health monitoring database, wireless local area network (WLAN) has been used. Arduino with Ethernet shield based on IEEE 802.11 standard has been used for this purpose. Test results from a group of voluntary shows the real-time temperature reading successfully monitored locally (at home) and remotely (at doctor's computer) and the readings are comparable to commercial thermometer.


title: ANN based distance protection of long transmission lines by considering the effect of fault resistance

abstract: Distance relays are widely used for protection of transmission lines. Traditionally used electromechanical distance relays for protection of transmission lines are prone to effects of fault resistance. Each fault condition corresponds to a particular pattern. So use of a pattern recognizer can improve the relay performance. This paper presents a new approach, known as artificial neural network (ANN) to overcome the effect of fault resistance on relay mal-operation. In this paper effect of fault resistance for single line to ground type of fault is considered. The scheme utilizes the magnitudes of resistance and reactance as inputs. Once trained with a large number of patterns corresponding to various conditions, it can classify unknown patterns.


title: Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces

abstract: Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity even when the traffic is protected by encryption, a VPN, or some other anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain up to 98% accuracy against Tor. Existing WF defenses are either too expensive in terms of bandwidth and latency overheads (e.g. 2-3 times as large or slow) or ineffective against the latest attacks. In this paper, we explore a novel defense, Adv-DWF, based on the idea of adversarial examples that have been shown to undermine machine learning classifiers in other domains. Our Adv-DWF defense adds padding to a traffic trace in a manner that fools the classifier into classifying it as coming from a different site. The technique drops the accuracy of the state-of-the-art attack augmented with adversarial training from 98% to 35%, while incurring a reasonable 56% bandwidth overhead. For most of the cases, the state-of-the-art attack's accuracies of our defense are at least 45% and 14% lower than state-of-the-art defenses WTF-PAD and Walkie-Talkie (W-T), respectively. The Top-2 accuracy of our defense is at best 56.9%, while it is over 98% for W-T. In addition, for the most cases, the bandwidth overheads of our defense are at least 8% and 6% lower than WTF-PAD and W-T, respectively, showing its promise as a possible defense for Tor.


title: Study on Sequential Feeding Networks for Subarrays of Circularly Polarized Elliptical Dielectric Resonator Antenna

abstract: Circularly polarized (CP) dielectric resonator antenna (DRA) subarrays have been numerically studied and experimentally verified. Elliptical CP DRA is used as the antenna element, which is excited by either a narrow slot or a probe. The elements are arranged in a 2 by 2 subarray configuration and are excited sequentially. In order to optimize the CP bandwidth, wideband feeding networks have been designed. Three different types of feeding network are studied; they are parallel feeding network, series feeding network and hybrid ring feeding network. For the CP DRA subarray with hybrid ring feeding network, the impedance matching bandwidth (S11<-10 dB) and 3-dB AR bandwidth achieved are 44% and 26% respectively


title: Analyst Disagreement and Aggregate Volatility Risk

abstract: The paper explains why firms with high dispersion of analyst forecasts earn low future returns. These firms beat the CAPM in periods of increasing aggregate volatility and thereby provide a hedge against aggregate volatility risk. The aggregate volatility risk factor can explain the abnormal return differential between high and low disagreement firms. This return differential is higher for firms with abundant real options, and this fact can be explained by aggregate volatility risk. Aggregate volatility risk can also explain why the link between analyst disagreement and future returns is stronger for firms with high short-sale constraints.


Abduction, reason, and science: processes of discovery and explanation-a review


title: Clause Restructuring For Statistical Machine Translation

abstract: We describe a method for incorporating syntactic information in statistical machine translation systems. The first step of the method is to parse the source language string that is being translated. The second step is to apply a series of transformations to the parse tree, effectively reordering the surface string on the source language side of the translation system. The goal of this step is to recover an underlying word order that is closer to the target language word-order than the original string. The reordering approach is applied as a pre-processing step in both the training and decoding phases of a phrase-based statistical MT system. We describe experiments on translation from German to English, showing an improvement from 25.2% Bleu score for a baseline system to 26.8% Bleu score for the system with reordering, a statistically significant improvement.


title: The Work Design Questionnaire (WDQ): Developing and validating a comprehensive measure for assessing job design and the nature of work.

abstract: Although there are thousands of studies investigating work and job design, existing measures are incomplete. In an effort to address this gap, the authors reviewed the work design literature, identified and integrated previously described work characteristics, and developed a measure to tap those work characteristics. The resultant Work Design Questionnaire (WDQ) was validated with 540 incumbents holding 243 distinct jobs and demonstrated excellent reliability and convergent and discriminant validity. In addition, the authors found that, although both task and knowledge work characteristics predicted satisfaction, only knowledge characteristics were related to training and compensation requirements. Finally, the results showed that social support incrementally predicted satisfaction beyond motivational work characteristics but was not related to increased training and compensation requirements. These results provide new insight into how to avoid the trade-offs commonly observed in work design research. Taken together, the WDQ appears to hold promise as a general measure of work characteristics that can be used by scholars and practitioners to conduct basic research on the nature of work or to design and redesign jobs in organizations.


title: Computer puppetry: An importance-based approach

abstract: Computer puppetry maps the movements of a performer to an animated character in real-time. In this article, we provide a comprehensive solution to the problem of transferring the observations of the motion capture sensors to an animated character whose size and proportion may be different from the performer's. Our goal is to map as many of the important aspects of the motion to the target character as possible, while meeting the online, real-time demands of computer puppetry. We adopt a Kalman filter scheme that addresses motion capture noise issues in this setting. We provide the notion of dynamic importance of an end-effector that allows us to determine what aspects of the performance must be kept in the resulting motion. We introduce a novel inverse kinematics solver that realizes these important aspects within tight real-time constraints. Our approach is demonstrated by its application to broadcast television performances.


title: An integrated process for game balancing

abstract: Game balancing is a recurring problem that currently requires a lot of manual work, usually following a game designer's intuition or rules-of-thumb. To what extent can or should the balancing process be automated? We establish a process model that integrates both manual and automated balancing approaches. Artificial agents are employed to automatically assess the desirability of a game. We demonstrate the feasibility of implementing the model and analyze the resulting solutions from its application to a simple video game.


title: Series-connected self-reconfigurable multicell battery

abstract: This paper proposes a novel series-connected, self-reconfigurable, multicell battery design. A new cell switching circuit topology is proposed and implemented by using high-efficiency semiconductor devices for dynamic configuration of the multicell battery. Each cell in the battery only uses two switches to fully control its operation independently. The proposed design can maximally utilize the battery's capacity and is tolerant to failures of single or multiple cells. These prolong the operating time and lifespan and enhance the reliability of the battery system. By using the proposed design, additional monitoring, control, protection and optimization functions can be readily added to each cell and the battery system to produce a smart battery. The proposed design is validated by simulation and experimental results for a six-cell polymer lithium-ion battery pack. The proposed design is universal and can be applied to any type and size of battery cells.


title: Reconstructing vehicles from a single image: Shape priors for road scene understanding

abstract: We present an approach for reconstructing vehicles from a single (RGB) image, in the context of autonomous driving. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in \emph{shape priors}, which are learnt over a small keypoint-annotated dataset. We then formulate a shape-aware adjustment problem that uses the learnt shape priors to recover the 3D pose and shape of a query object from an image. For shape representation and inference, we leverage recent successes of Convolutional Neural Networks (CNNs) for the task of object and keypoint localization, and train a novel cascaded fully-convolutional architecture to localize vehicle \emph{keypoints} in images. The shape-aware adjustment then robustly recovers shape (3D locations of the detected keypoints) while simultaneously filling in occluded keypoints. To tackle estimation errors incurred due to erroneously detected keypoints, we use an Iteratively Re-weighted Least Squares (IRLS) scheme for robust optimization, and as a by-product characterize noise models for each predicted keypoint. We evaluate our approach on autonomous driving benchmarks, and present superior results to existing monocular, as well as stereo approaches.


title: A survey on anti-forensics techniques

abstract: Digital forensics methodologies and tools have become a crucial part for investigation of cybercrimes and collecting digital evidences in a case. Digital forensics experts usually follow a common workflow and use known methodologies and tools while investigating a case. Attackers and cybercriminals also know which methodologies are used in an investigation and how digital forensics tools work. As a consequence, they started to find and implement a new methodology which is called anti-forensics for deceiving investigator or having a case lasts longer than expected. Anti-forensics has been acknowledged as a legitimate field of study recently, therefore it can be considered as an emerging area of interest and there is a lack of knowledge about anti-forensics techniques. This paper aims to make mention of the anti-forensics techniques such as Data Pooling; Non-Standart RAID'ed Disks; Manipulating File Signatures; Restricted Filenames; Manipulating MACE (file Modified, Accessed, Created and Entry) Times; Loop References; Hash Collisions; Dummy HDDs and proposals for mitigating these techniques.


A constant degree of anoxemia obtained by the administration of a gas of variable oxygen concentration.


title: Incomplete Multisource Transfer Learning

abstract: Transfer learning is generally exploited to adapt well-established source knowledge for learning tasks in weakly labeled or unlabeled target domain. Nowadays, it is common to see multiple sources available for knowledge transfer, each of which, however, may not include complete classes information of the target domain. Naively merging multiple sources together would lead to inferior results due to the large divergence among multiple sources. In this paper, we attempt to utilize incomplete multiple sources for effective knowledge transfer to facilitate the learning task in target domain. To this end, we propose an incomplete multisource transfer learning through two directional knowledge transfer, i.e., cross-domain transfer from each source to target, and cross-source transfer. In particular, in cross-domain direction, we deploy latent low-rank transfer learning guided by iterative structure learning to transfer knowledge from each single source to target domain. This practice reinforces to compensate for any missing data in each source by the complete target data. While in cross-source direction, unsupervised manifold regularizer and effective multisource alignment are explored to jointly compensate for missing data from one portion of source to another. In this way, both marginal and conditional distribution discrepancy in two directions would be mitigated. Experimental results on standard cross-domain benchmarks and synthetic data sets demonstrate the effectiveness of our proposed model in knowledge transfer from incomplete multiple sources.