title: Automated Assessment of ESOL Free Text Examinations

abstract: In this report, we consider the task of automated assessment of English as a Second Language (ESOL) examination scripts written in response to prompts eliciting free text answers. We review and critically evaluate previous work on automated assessment for essays, especially when applied to ESOL text. We formally define the task as discriminative preference ranking and develop a new system trained and tested on a corpus of manually-graded scripts. We show experimentally that our best performing system is very close to the upper bound for the task, as defined by the agreement between human examiners on the same corpus. Finally we argue that our approach, unlike extant solutions, is relatively prompt-insensitive and resistant to subversion, even when its operating principles are in the public domain. These properties make our approach significantly more viable for high-stakes assessment.


DOI 10.1007/s10796-012-9404-7 Semantic similarity measurement using historical google search patterns


title: Broadband Bent Triangular Omnidirectional Antenna for RF Energy Harvesting

abstract: In this letter, a broadband bent triangular omnidirectional antenna is presented for RF energy harvesting. The antenna has a bandwidth for ${\hbox {VSWR}}\leq2$ from 850 MHz to 1.94 GHz. The antenna is designed to receive both horizontal and vertical polarized waves and has a stable radiation pattern over the entire bandwidth. Antenna has also been optimized for energy harvesting application and it is designed for $100~\Omega $ input impedance to provide a passive voltage amplification and impedance matching to the rectifier. A peak efficiency of 60% and 17% is obtained for a load of $500~\Omega $ at 980 and 1800 MHz, respectively. At a cell site while harvesting all bands simultaneously a voltage of 3.76 V for open circuit and 1.38 V across a load of $4.3~\hbox{k} \Omega $ is obtained at a distance of 25 m using an array of two elements of the rectenna.


GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data


title: Least-squares estimation of transformation parameters between two point patterns

abstract: In many applications of computer vision, the following problem is encountered. Two point patterns (sets of points) (x/sub i/) and (x/sub i/); i=1, 2, . . ., n are given in m-dimensional space, and the similarity transformation parameters (rotation, translation, and scaling) that give the least mean squared error between these point patterns are needed. Recently, K.S. Arun et al. (1987) and B.K.P. Horn et al. (1987) presented a solution of this problem. Their solution, however, sometimes fails to give a correct rotation matrix and gives a reflection instead when the data is severely corrupted. The proposed theorem is a strict solution of the problem, and it always gives the correct transformation parameters even when the data is corrupted. >


Addressing Imbalance in Multi-Label Classification Using Structured Hellinger Forests.


title: Incept-N: A Convolutional Neural Network based Classification Approach for Predicting Nationality from Facial Features

abstract: Nationality of a human being is a well-known identifying characteristic used for every major authentication purpose in every country. Albeit advances in application of Artificial Intelligence and Computer Vision in different aspects, its’ contribution to this specific security procedure is yet to be cultivated. With a goal to successfully applying computer vision techniques to predict a human’s nationality based on his facial features, we have proposed this novel method and have achieved an average of 93.6% accuracy with very low mis-classification rate.


title: Perceiving Mass in Mixed Reality through Pseudo-Haptic Rendering of Newton's Third Law

abstract: In mixed reality, real objects can be used to interact with virtual objects. However, unlike in the real world, real objects do not encounter any opposite reaction force when pushing against virtual objects. The lack of reaction force during manipulation prevents users from perceiving the mass of virtual objects. Although this could be addressed by equipping real objects with force-feedback devices, such a solution remains complex and impractical. In this work, we present a technique to produce an illusion of mass without any active force-feedback mechanism. This is achieved by simulating the effects of this reaction force in a purely visual way. A first study demonstrates that our technique indeed allows users to differentiate light virtual objects from heavy virtual objects. In addition, it shows that the illusion is immediately effective, with no prior training. In a second study, we measure the lowest mass difference (JND) that can be perceived with this technique. The effectiveness and ease of implementation of our solution provides an opportunity to enhance mixed reality interaction at no additional cost.


title: Collaborative analytics for data silos

abstract: As a great deal of data has been accumulated in various disciplines, the need for the integrative analysis of separate but relevant data sources is becoming more important. Combining data sources can provide global insight that is otherwise difficult to obtain from individual sources. Because of privacy, regulations, and other issues, many large-scale data repositories remain closed off from the outside, raising what has been termed the data silo issue. The huge volume of today's big data often leads to computational challenges, adding another layer of complexity to the solution. In this paper, we propose a novel method called collaborative analytics by ensemble learning (CABEL), which attempts to resolve the main hurdles regarding the silo issue: accuracy, privacy, and computational efficiency. CABEL represents the data stored in each silo as a compact aggregate of samples called the silo signature. The compact representation provides computational efficiency and privacy preservation but makes it challenging to produce accurate analytics. To resolve this challenge, we formulate the problem of attribute domain sampling and reconstruction, and propose a solution called the Chebyshev subset. To model collaborative efforts to analyze semantically linked but structurally disconnected databases, CABEL utilizes a new ensemble learning technique termed the weighted bagging of base classifiers. We demonstrate the effectiveness of CABEL by testing with a nationwide health-insurance data set containing approximately 4,182,000,000 records collected from the entire population of an Organisation for Economic Co-operation and Development (OECD) country in 2012. In our binary classification tests, CABEL achieved median recall, precision, and F-measure values of 89%, 64%, and 76%, respectively, although only 0.001–0.00001% of the original data was used for model construction, while maintaining data privacy and computational efficiency.


EXTERNAL ENVIRONMENT, TRUST AND EAST–WEST TRADE


title: A multimodal biometrie system for personal identification based on deep learning approaches

abstract: Multimodal biometrie systems seek to alleviate some of the limitations of unimodal biometrie systems by combining multiple pieces of evidence of the same person in the deeision-making process. In this paper, a novel multimodal biometric identification system is proposed based on fusing the results obtained from both the face and the left and right irises using deep learning approaches. Firstly, the facial features are extracted using a Deep Belief Network (DBN) architecture consisting of 3-layers. The first two RBMs are used as features detectors, while the last one is used as a discriminative model associated with softmax units for the multi-class classification purpose. Secondly, an efficient deep learning system is employed for iris recognition, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Extensive experiments on large-scale challenging databases, including FERET, CASIA V1.0 and MMU1, and SDUMLA-HMT have demonstrated the superiority of the proposed approaches by achieving new state-of-the-art Rank-1 identification rates on all the employed databases.


title: Towards Baselines for Shoulder Surfing on Mobile Authentication

abstract: Given the nature of mobile devices and unlock procedures, unlock authentication is a prime target for credential leaking via shoulder surfing, a form of an observation attack. While the research community has investigated solutions to minimize or prevent the threat of shoulder surfing, our understanding of how the attack performs on current systems is less well studied. In this paper, we describe a large online experiment (n = 1173) that works towards establishing a baseline of shoulder surfing vulnerability for current unlock authentication systems. Using controlled video recordings of a victim entering in a set of 4- and 6-length PINs and Android unlock patterns on different phones from different angles, we asked participants to act as attackers, trying to determine the authentication input based on the observation. We find that 6-digit PINs are the most elusive attacking surface where a single observation leads to just 10.8% successful attacks (26.5% with multiple observations). As a comparison, 6-length Android patterns, with one observation, were found to have an attack rate of 64.2% (79.9% with multiple observations). Removing feedback lines for patterns improves security to 35.3% (52.1% with multiple observations). This evidence, as well as other results related to hand position, phone size, and observation angle, suggests the best and worst case scenarios related to shoulder surfing vulnerability which can both help inform users to improve their security choices, as well as establish baselines for researchers.


title: Modeling Two-Dimensional Guillotine Cutting Problems via Integer Programming

abstract: We propose a framework to model general guillotine restrictions in two-dimensional cutting problems formulated as mixed-integer linear programs (MIPs). The modeling framework requires a pseudopolynomial number of variables and constraints, which can be effectively enumerated for medium-size instances. Our modeling of general guillotine cuts is the first one that, once it is implemented within a state-of-the-art MIP solver, can tackle instances of challenging size. We mainly concentrate our analysis on the guillotine two-dimensional knapsack problem (G2KP), for which a model, and an exact procedure able to significantly improve the computational performance, are given. We also show how the modeling of general guillotine cuts can be extended to other relevant problems such as the guillotine two-dimensional cutting stock problem and the guillotine strip packing problem (GSPP). Finally, we conclude the paper discussing an extensive set of computational experiments on G2KP and GSPP benchmark instances from the literature.


title: Concreteness ratings for 40 thousand generally known English word lemmas

abstract: Concreteness ratings are presented for 37,058 English words and 2,896 two-word expressions (such as zebra crossing and zoom in), obtained from over 4,000 participants by means of a norming study using Internet crowdsourcing for data collection. Although the instructions stressed that the assessment of word concreteness would be based on experiences involving all senses and motor responses, a comparison with the existing concreteness norms indicates that participants, as before, largely focused on visual and haptic experiences. The reported data set is a subset of a comprehensive list of English lemmas and contains all lemmas known by at least 85 % of the raters. It can be used in future research as a reference list of generally known English lemmas.


Document Clustering and Text Summarization


title: Learning to Detect Misleading Content on Twitter

abstract: The publication and spread of misleading content is a problem of increasing magnitude, complexity and consequences in a world that is increasingly relying on user-generated content for news sourcing. To this end, multimedia analysis techniques could serve as an assisting tool to spot and debunk misleading content on the Web. In this paper, we tackle the problem of misleading multimedia content detection on Twitter in real time. We propose a number of new features and a new semi-supervised learning event adaptation approach that helps generalize the detection capabilities of trained models to unseen content, even when the event of interest is of different nature compared to that used for training. Combined with bagging, the proposed approach manages to outperform previous systems by a significant margin in terms of accuracy. Moreover, in order to communicate the verification process to end users, we develop a web-based application for visualizing the results.


title: On Generating the Irredundant Conjunctive and Disjunctive Normal Forms of Monotone Boolean Functions

abstract: Let f : {0,1} n → {0,1} be a monotone Boolean function whose value at any point x ∈ {0,1} n can be determined in time t. Denote by c=Λ I ∈ C ∨ i ∈ I x i the irredundant CNF off, where C is the set of the prime implicates of f. Similarly, let d=∨ J ∈ D Λ j ∈ J x j be the irredundant DNF of the same function, where D is the set of the prime implicants of f. We show that given subsets C' ⊆ C and D' ⊆ D such that (C',D') ¬= (C,D), a new term in (C\C')∪(D\D') can be found in time O(n(t+n))+m o(log m) , where m=|C'|+|D'|. In particular, if f(x) can be evaluated for every x ∈ {0,1} n in polynomial time, then the forms c and d can be jointly generated in incremental quasi-polynomial time. On the other hand, even for the class of Λ, ∨-formulae f of depth 2, i.e., for CNFs or DNFs, it is unlikely that uniform sampling from within the set of the prime implicates and implicants of f can be carried out in time bounded by a quasi-polynomial 2 polylog(.) in the input size of f. We also show that for some classes of polynomial-time computable monotone Boolean functions it is NP-hard to test either of the conditions D' =D or C' =C. This provides evidence that for each of these classes neither conjunctive nor disjunctive irredundant normal forms can be generated in total (or incremental) quasi-polynomial time. Such classes of monotone Boolean functions naturally arise in game theory, networks and relay contact circuits, convex programming, and include a subset of Λ, ∨-formulae of depth 3.


title: Orthogonality regularizer for question answering

abstract: Learning embeddings of words and knowledge base elements is a promising approach for open domain question answering. Based on the remark that relations and entities are distinct object types lying in the same embedding space, we analyze the benefit of adding a regularizer favoring the embeddings of entities to be orthogonal to those of relations. The main motivation comes from the observation that modifying the embeddings using prior knowledge often helps performance. The experiments show that incorporating the regularizer yields better results on a challenging question answering benchmark.


title: Exploiting Congestion Games to Achieve Distributed Service Chaining in NFV Networks

abstract: The network function virtualization (NFV) paradigm has gained increasing interest in both academia and industry as it promises scalable and flexible network management and orchestration. In NFV networks, network services are provided as chains of different virtual network functions (VNFs), which are instantiated and executed on dedicated VNF-compliant servers. The problem of composing those chains is referred to as the service chain composition problem. In contrast to centralized solutions that suffer from scalability and privacy issues, in this paper, we leverage non-cooperative game theory to achieve a low-complexity distributed solution to the above-mentioned problem. Specifically, to account for selfish and competitive behavior of users, we formulate the service chain composition problem as an atomic weighted congestion game with unsplittable flows and player-specific cost functions. We show that the game possesses a weighted potential function and admits a Nash equilibrium (NE). We prove that the price of anarchy is upper-bounded, and also propose a distributed and privacy-preserving algorithm which provably converges toward an NE of the game in polynomial time. Finally, through extensive numerical results, we assess the performance of the proposed distributed solution to the service chain composition problem.


title: Self-indexing inverted files for fast text retrieval

abstract: Query-processing costs on large text databases are dominated by the need to retrieve and scan the inverted list of each query term. Retrieval time for inverted lists can be greatly reduced by the use of compression, but this adds to the CPU time required. Here we show that the CPU component of query response time for conjunctive Boolean queries and for informal ranked queries can be similarly reduced, at little cost in terms of storage, by the inclusion of an internal index in each compressed inverted list. This method has been applied in a retrieval system for a collection of nearly two million short documents. Our experimental results show that the self-indexing strategy adds less than 20% to the size of the compressed inverted file, which itself occupies less than 10% of the indexed text, yet can reduce processing time for Boolean queries of 5-10 terms to under one fifth of the previous cost. Similarly, ranked queries of 40-50 terms can be evaluated in as little as 25% of the previous time, with little or no loss of retrieval effectiveness.


title: Binary stirring: self-randomizing instruction addresses of legacy x86 binary code

abstract: Unlike library code, whose instruction addresses can be randomized by address space layout randomization (ASLR), application binary code often has static instruction addresses. Attackers can exploit this limitation to craft robust shell codes for such applications, as demonstrated by a recent attack that reuses instruction gadgets from the static binary code of victim applications. This paper introduces binary stirring, a new technique that imbues x86 native code with the ability to self-randomize its instruction addresses each time it is launched. The input to STIR is only the application binary code without any source code, debug symbols, or relocation information. The output is a new binary whose basic block addresses are dynamically determined at load-time. Therefore, even if an attacker can find code gadgets in one instance of the binary, the instruction addresses in other instances are unpredictable. An array of binary transformation techniques enable STIR to transparently protect large, realistic applications that cannot be perfectly disassembled due to computed jumps, code-data interleaving, OS callbacks, dynamic linking and a variety of other difficult binary features. Evaluation of STIR for both Windows and Linux platforms shows that stirring introduces about 1.6% overhead on average to application runtimes.


title: Internet Of Things (IOT) enabled smart autonomous hospital management system - A real world health care use case with the technology drivers

abstract: A worldwide network of Internet Of Things (IOT) is going to be the future network, which connects objects of different application fields, functionality and technology. These objects are uniquely addressable and use standard communication protocol and communicate in a heterogeneous networking environment. Anytime, anyplace connecting anything idea brought out significant advancements in the healthcare domain. This paper discusses with the implemented real world scenario of smart autonomous hospital management with the IOT. This paper aims at explaining in detail the technology drivers behind the IOT and health care with the information on data modeling of medical devices, data validation of critical incident data, data mapping of existing IOT data into different other associated system data, workflow or the process flow behind the technical operations of the remote device coordination, the architecture of network, middleware, databases, application services. The challenges and the associated solution in this field is discussed with the use case.


title: Modulation Scheme for Improved Operation of an RB-IGBT-Based Resonant Inverter Applied to Domestic Induction Heating

abstract: Domestic induction appliances require power converters that feature high efficiency and accurate power control in a wide range of operating conditions. To achieve this modulation techniques play a key role to optimize the power converter operation. In this paper, a series resonant inverter featuring reverse-blocking insulated gate bipolar transistors and an optimized modulation technique are proposed. An analytical study of the converter operation is performed, and the main simulation results are shown. The proposed topology reduces both conduction and switching losses, increasing significantly the power converter efficiency. Moreover, the proposed modulation technique achieves linear output power control, improving the final appliance performance. The results derived from this analysis are tested by means of an experimental prototype, verifying the feasibility of the proposed converter and modulation technique.


title: XGBoost: A Scalable Tree Boosting System

abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.


On Dimensionality Reduction Techniques for Cross-Language Information Retrieval


title: Simulation, Analysis, and Verification of Substrate Currents for Layout Optimization of Smart Power ICs

abstract: Today circuit failures in Smart Power ICs due to substrate couplings are partially addressed during the circuit design phase. The state-of-the-art guidelines for the optimization of parasitic couplings provide mainly qualitative rules, which are difficult to implement and to verify during the design of a complex Smart Power circuit. These rules are often based on the physical device simulations or on the empirical results extracted from predefined benchmark structures. In this paper, a novel approach is proposed for designing robust circuits integrating accurate and specific analysis of substrate couplings already into the design flow. First, substrate currents injected by power transistors are discussed to show the spatial distribution of voltage and currents into the substrate. A set of guidelines to optimize substrate currents is presented as a summary of the studied test cases. Then, an H-Bridge output driver was implemented in a $0.35$ - $\mu$ m HVCMOS technology to investigate substrate currents by both measurements and simulations. Reverse currents were deliberately injected into the chip to activate substrate lateral and vertical parasitic bipolar junction transistors and measured data closely match circuit simulation results in both cases.


title: Estimation of Tire Forces for Application to Vehicle Stability Control

abstract: Estimated tire forces can be used to implement unified-chassis-control (UCC) systems. This paper presents a scheme for longitudinal/lateral tire-force estimation. The longitudinal and lateral tire-force-estimation scheme has been designed, and this consists of the following five steps: vertical tire-force estimation, shaft torque estimation, longitudinal tire-force estimation based on a simplified wheel-dynamics model, lateral tire-force estimation based on a planar model, and the combined tire-force estimation. The combined tire-force-estimation scheme has been designed to compensate for the longitudinal/lateral tire-force estimator, which uses a random-walk Kalman filter. The proposed estimation scheme has been integrated into a UCC system. The performance of the UCC system, including the estimator, has been evaluated via computer simulations conducted using the vehicle dynamic software CARSIM, the ASM vehicle model, and the UCC system coded with Matlab/Simulink.


title: 3D binary signatures

abstract: In this paper, we propose a novel binary descriptor for 3D point clouds. The proposed descriptor termed as 3D Binary Signature (3DBS) is motivated from the matching efficiency of the binary descriptors for 2D images. 3DBS describes keypoints from point clouds with a binary vector resulting in extremely fast matching. The method uses keypoints from standard keypoint detectors. The descriptor is built by constructing a Local Reference Frame and aligning a local surface patch accordingly. The local surface patch constitutes of identifying nearest neighbours based upon an angular constraint among them. The points are ordered with respect to the distance from the keypoints. The normals of the ordered pairs of these keypoints are projected on the axes and the relative magnitude is used to assign a binary digit. The vector thus constituted is used as a signature for representing the keypoints. The matching is done by using hamming distance. We show that 3DBS outperforms state of the art descriptors on various evaluation metrics.


title: Sparse representation for blind image quality assessment

abstract: Blind image quality assessment (BIQA) is an important yet difficult task in image processing related applications. Existing algorithms for universal BIQA learn a mapping from features of an image to the corresponding subjective quality or divide the image into different distortions before mapping. Although these algorithms are promising, they face the following problems: 1) they require a large number of samples (pairs of distorted image and its subjective quality) to train a robust mapping; 2) they are sensitive to different datasets; and 3) they have to be retrained when new training samples are available. In this paper, we introduce a simple yet effective algorithm based upon the sparse representation of natural scene statistics (NSS) feature. It consists of three key steps: extracting NSS features in the wavelet domain, representing features via sparse coding, and weighting differential mean opinion scores by the sparse coding coefficients to obtain the final visual quality values. Thorough experiments on standard databases show that the proposed algorithm outperforms representative BIQA algorithms and some full-reference metrics.


title: Network forensics: Review, taxonomy, and open challenges

abstract: In recent years, a number of network forensics techniques have been proposed to investigate the increasing number of cybercrimes. Network forensics techniques assist in tracking internal and external network attacks by focusing on inherent network vulnerabilities and communication mechanisms. However, investigation of cybercrime becomes more challenging when cyber criminals erase the traces in order to avoid detection. Therefore, network forensics techniques employ mechanisms to facilitate investigation by recording every single packet and event that is disseminated into the network. As a result, it allows identification of the origin of the attack through reconstruction of the recorded data. In the current literature, network forensics techniques are studied on the basis of forensic tools, process models and framework implementations. However, a comprehensive study of cybercrime investigation using network forensics frameworks along with a critical review of present network forensics techniques is lacking. In other words, our study is motivated by the diversity of digital evidence and the difficulty of addressing numerous attacks in the network using network forensics techniques. Therefore, this paper reviews the fundamental mechanism of network forensics techniques to determine how network attacks are identified in the network. Through an extensive review of related literature, a thematic taxonomy is proposed for the classification of current network forensics techniques based on its implementation as well as target data sets involved in the conducting of forensic investigations. The critical aspects and significant features of the current network forensics techniques are investigated using qualitative analysis technique. We derive significant parameters from the literature for discussing the similarities and differences in existing network forensics techniques. The parameters include framework nature, mechanism, target dataset, target instance, forensic processing, time of investigation, execution definition, and objective function. Finally, open research challenges are discussed in network forensics to assist researchers in selecting the appropriate domains for further research and obtain ideas for exploring optimal techniques for investigating cyber-crimes.


title: From TagME to WAT: a new entity annotator

abstract: In this paper we propose a novel entity annotator for texts which hinges on TagME's algorithmic technology, currently the best one available. The novelty is twofold: from the one hand, we have engineered the software in order to be modular and more efficient; from the other hand, we have improved the annotation pipeline by re-designing all of its three main modules: spotting, disambiguation and pruning. In particular, the re-design has involved the detailed inspection of the performance of these modules by developing new algorithms which have been in turn tested over all publicly available datasets (i.e. AIDA, IITB, MSN, AQUAINT, and the one of the ERD Challenge). This extensive experimentation allowed us to derive the best combination which achieved on the ERD development dataset an F1 score of 74.8%, which turned to be 67.2% F1 for the test dataset. This final result was due to an impressive precision equal to 87.6%, but very low recall 54.5%. With respect to classic TagME on the development dataset the improvement ranged from 1% to 9% on the D2W benchmark, depending on the disambiguation algorithm being used. As a side result, the final software can be interpreted as a flexible library of several parsing/disambiguation and pruning modules that can be used to build up new and more sophisticated entity annotators. We plan to release our library to the public as an open-source project.


A Systematic Literature Mining of Sponge City: What Has Been Done and The Challenges Standing Ahead


title: Development of a quad rotor tail-sitter VTOL UAV without control surfaces and experimental verification

abstract: This paper presents development of a quad rotor tail-sitter VTOL UAV (Vertical Takeoff and Landing Unmanned Aerial Vehicle) which is composed of four rotors and a fixed wing. The conventional VTOL UAVs have a drawback in the accuracy of the attitude control in stationary hovering because they were developed based on a fixed-wing aircraft and they used the control surfaces, such as aileron, elevator, and rudder for the attitude control. To overcome such a drawback, we developed a quad rotor tail-sitter VTOL UAV. The quad rotor tail-sitter VTOL UAV realizes high accuracy in the attitude control with four rotors like a quad rotor helicopter and achieves level flight like a fixed-wing airplane. The remarkable characteristic of the developed quad rotor tail-sitter VTOL UAV is that it does not use any control surfaces even in the level flight. This paper shows the design concept of the developed UAV and experimental verification of all flight modes including hovering, transition flight and level flight.


title: An Alternative View: When Does SGD Escape Local Minima?

abstract: Stochastic gradient descent (SGD) is widely used in machine learning. Although being commonly viewed as a fast but not accurate version of gradient descent (GD), it always finds better solutions than GD for modern neural networks. ::: In order to understand this phenomenon, we take an alternative view that SGD is working on the convolved (thus smoothed) version of the loss function. We show that, even if the function $f$ has many bad local minima or saddle points, as long as for every point $x$, the weighted average of the gradients of its neighborhoods is one point convex with respect to the desired solution $x^*$, SGD will get close to, and then stay around $x^*$ with constant probability. More specifically, SGD will not get stuck at "sharp" local minima with small diameters, as long as the neighborhoods of these regions contain enough gradient information. The neighborhood size is controlled by step size and gradient noise. ::: Our result identifies a set of functions that SGD provably works, which is much larger than the set of convex functions. Empirically, we observe that the loss surface of neural networks enjoys nice one point convexity properties locally, therefore our theorem helps explain why SGD works so well for neural networks.


title: Forthcoming, Journal of Political Economy WHY DO MORE OPEN ECONOMIES HAVE BIGGER GOVERNMENTS?

abstract: This paper demonstrates that there is a robust empirical association between the extent to which an economy is exposed to trade and the size of its government sector. This association holds for a large cross-section of countries, in low- as well as high-income samples, and is robust to the inclusion of a wide range of controls. The explanation appears to be that government consumption plays a risk-reducing role in economies exposed to a significant amount of external risk. When openness is interacted with explicit measures of external risk, such as terms-of-trade uncertainty and product concentration of exports, it is the interaction terms that enter significantly, and the openness term loses its significance (or turns negative). The paper also demonstrates that government consumption is the majority of countries.


title: Negation-Aware Framework for Sentiment Analysis in Arabic Reviews

abstract: Sentiment analysis deals with identifying polarity orientation embedded in users' comments and reviews. It aims at discriminating positive reviews from negative ones. Sentiment is related to culture and language morphology. In this paper, we investigate the effects of language morphology on sentiment analysis in reviews written in the Arabic language. In particular, we investigate, in details, how negation affects sentiments. We also define a set of rules that capture the morphology of negations in Arabic. These rules are then used to detect sentiment taking care of negated words. Experimentations prove that our suggested approach is superior to several existing methods that deal with sentiment detection in Arabic reviews.


title: Feed Forward and Backward Run in Deep Convolution Neural Network

abstract: Convolution Neural Networks (CNN), known as ConvNets are widely used in many visual imagery application, object classification, speech recognition. After the implementation and demonstration of the deep convolution neural network in Imagenet classification in 2012 by krizhevsky, the architecture of deep Convolution Neural Network is attracted many researchers. This has led to the major development in Deep learning frameworks such as Tensorflow, caffe, keras, theno. Though the implementation of deep learning is quite possible by employing deep learning frameworks, mathematical theory and concepts are harder to understand for new learners and practitioners. This article is intended to provide an overview of ConvNets architecture and to explain the mathematical theory behind it including activation function, loss function, feedforward and backward propagation. In this article, grey scale image is taken as input information image, ReLU and Sigmoid activation function are considered for developing the architecture and cross-entropy loss function are used for computing the difference between predicted value and actual value. The architecture is developed in such a way that it can contain one convolution layer, one pooling layer, and multiple dense layers


title: A Weight-Scaling Algorithm for Min-Cost Imperfect Matchings in Bipartite Graphs

abstract: Call a bipartite graph G = (X, Y, E) "balanced" when |X| = |Y|. Given a balanced bipartite graph G with edge costs, the "assignment problem" asks for a perfect matching in G of minimum total cost. The Hungarian Method can solve assignment problems in time O(mn + n^2 log n), where n := |X| = |Y| and m := |E|. If the edge weights are integers bounded in magnitude by C > 1, then algorithms using "weight scaling", such as that of Gabow and Tarjan, can lower the time to O(m sqrt(n) log(nC)). There are important applications in which G is "unbalanced", with |X| not equal |Y|, and we require a min-cost matching of size r := min(|X|, |Y|) or, more generally, of some specified size s than or equal to r. The Hungarian Method extends easily to find such a matching in time O(ms + s2 log r), but weightscaling algorithms do not extend so easily. We introduce new machinery to find such a matching in time O(mps log(sC)) via weight scaling. Our results provide some insight into the design space of efficient weight-scaling matching algorithms.


title: Big data privacy: a technological perspective and review

abstract: Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.


title: A Markov random field model for term dependencies

abstract: This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases. We explore full independence, sequential dependence, and full dependence variants of the model. A novel approach is developed to train the model that directly maximizes the mean average precision rather than maximizing the likelihood of the training data. Ad hoc retrieval experiments are presented on several newswire and web collections, including the GOV2 collection used at the TREC 2004 Terabyte Track. The results show significant improvements are possible by modeling dependencies, especially on the larger web collections.


title: Geotagging with local lexicons to build indexes for textually-specified spatial data

abstract: The successful execution of location-based and feature-based queries on spatial databases requires the construction of spatial indexes on the spatial attributes. This is not simple when the data is unstructured as is the case when the data is a collection of documents such as news articles, which is the domain of discourse, where the spatial attribute consists of text that can be (but is not required to be) interpreted as the names of locations. In other words, spatial data is specified using text (known as a toponym) instead of geometry, which means that there is some ambiguity involved. The process of identifying and disambiguating references to geographic locations is known as geotagging and involves using a combination of internal document structure and external knowledge, including a document-independent model of the audience's vocabulary of geographic locations, termed its spatial lexicon. In contrast to previous work, a new spatial lexicon model is presented that distinguishes between a global lexicon of locations known to all audiences, and an audience-specific local lexicon. Generic methods for inferring audiences' local lexicons are described. Evaluations of this inference method and the overall geotagging procedure indicate that establishing local lexicons cannot be overlooked, especially given the increasing prevalence of highly local data sources on the Internet, and will enable the construction of more accurate spatial indexes.


title: Jumping to conclusions: the psychology of delusional reasoning

abstract: Delusions are a key symptom of psychosis and they are frequently distressing and disabling. Existing treatments, both pharmacological and psychological, are only partially effective. It is important to develop new treatment approaches based on theoretically derived and empirically tested processes. Delusions are associated with a reasoning bias: the jumping to conclusions (JTC) bias involves gathering limited information to reach decisions. It is proposed that this bias influences appraisals of psychotic experiences leading to the formation and persistence of delusions. Existing treatments do not influence JTC. A new intensive treatment approach – ‘reasoning training’ – is described. It aims to encourage participants to gather information, consider alternative explanations for events and review the evidence before reaching a decision. Preliminary data suggest that it is possible to change the JTC bias and that this improves belief flexibility and may reduce delusional conviction. The concepts and methods of this new approach have implications for clinical practice.


title: INTERNATIONAL RESEARCH AND JUST SHARING OF BENEFITS IN MEXICO

abstract: International research enrolling human subjects has raised an ethical concern regarding the just distribution of benefits between the countries that design the research and the host communities. Although several universal declarations have expressed this concern, a gap between theory and practice continues to exist, as well as a significant divergence between the design of the research protocol and the social context where it will be implemented. Although institutional review boards have made a valuable effort to evaluate international research, their sensitivity to the just sharing of research benefits as well as their attention to the social context must be evaluated. This article analyzes the distribution of benefits in a review of international research in Mexico and produces an ethical reflection based on the results.


title: MPPT of Photovoltaic Systems Using Sensorless Current-Based Model Predictive Control

abstract: Variability in the solar irradiance level and ambient temperature of photovoltaic (PV) systems necessitates the use of maximum power point tracking (MPPT) of PV systems to ensure continuous harvesting of maximum power. This paper presents a sensorless current (SC) MPPT algorithm using model predictive control (MPC). The main contribution of this paper is the use of model-based predictive control principle to eliminate the current sensor that is usually required for well-known MPPT techniques such as perturb and observe (P&O). By predicting the PV system states in horizon of time, the proposed method becomes an elegant, embedded controller that allows faster response and lower power ripple in steady state than the conventional P&O technique under rapidly changing atmospheric conditions. This becomes possible without requiring expensive sensing and communications equipment and networks for direct measurement of solar irradiation changes. The performance of the proposed SC-MPC-MPPT with reduced load sensitivity is evaluated on the basis of industrial European Efficiency Test, EN 50530, that assesses the performance of PV systems under dynamic environmental conditions. The proposed control technique is implemented experimentally using dSPACE DS1007 platform to verify the simulation results.


title: A benchmark for the evaluation of RGB-D SLAM systems

abstract: In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. The sequences contain both the color and depth images in full sensor resolution (640 × 480) at video frame rate (30 Hz). The ground-truth trajectory was obtained from a motion-capture system with eight high-speed tracking cameras (100 Hz). The dataset consists of 39 sequences that were recorded in an office environment and an industrial hall. The dataset covers a large variety of scenes and camera motions. We provide sequences for debugging with slow motions as well as longer trajectories with and without loop closures. Most sequences were recorded from a handheld Kinect with unconstrained 6-DOF motions but we also provide sequences from a Kinect mounted on a Pioneer 3 robot that was manually navigated through a cluttered indoor environment. To stimulate the comparison of different approaches, we provide automatic evaluation tools both for the evaluation of drift of visual odometry systems and the global pose error of SLAM systems. The benchmark website [1] contains all data, detailed descriptions of the scenes, specifications of the data formats, sample code, and evaluation tools.


title: Discrete Cosserat approach for soft robot dynamics: A new piece-wise constant strain model with torsion and shears

abstract: Modeling and control of soft robots is an up-to-date and exciting area of research which has been tackled with complementary approaches so far. In this paper, we modify the existing continuum Cosserat approach optimizing it for soft robot arms which can be discretized in a finite number of sections and degrees of freedom. The resulting new piece-wise constant strain model extends the existing piece-wise constant curvature model by allowing torsion and shears strains which are fundamental to cope with out-of-the-plane external forces as appearing for example during ground locomotion. A first experimental comparison has been also conducted using one fluidic actuated leg of the soft crawler FASTT.


title: Interactive Data Integration through Smart Copy&Paste

abstract: In many scenarios, such as emergency response or ad hoc collaboration, it is critical to reduce the overhead in integrating data. Here, the goal is often to rapidly integrate “enough” data to answer a specific question. Ideally, one could perform the entire process interactively under one unified interface: defining extractors and wrappers for sources, creating a mediated schema, and adding schema mappings — while seeing how these impact the integrated view of the data, and refining the design accordingly. We propose a novel smart copy and paste (SCP) model and architecture for seamlessly combining the design-time and run-time aspects of data integration, and we describe an initial prototype, the CopyCat system. In CopyCat, the user does not need special tools for the different stages of integration: instead, the system watches as the user copies data from applications (including the Web browser) and pastes them into CopyCat’s spreadsheet-like workspace. CopyCat generalizes these actions and presents proposed auto-completions, each with an explanation in the form of provenance. The user provides feedback on these suggestions — through either direct interactions or further copy-and-paste operations — and the system learns from this feedback. This paper provides an overview of our prototype system, and identifies key research challenges in achieving SCP in its full generality.


title: Visibility Based Preconditioning for bundle adjustment

abstract: We present Visibility Based Preconditioning (VBP) a new technique for efficiently solving the linear least squares problems that arise in bundle adjustment [24]. Using the camera-point visibility structure of the scene, we describe the construction of two preconditioners. These preconditioners when combined with an inexact step Levenberg-Marquardt algorithm[25] offer state of the art performance on the BAL data set [1], with 3–5× reduction in execution time over currently available methods while delivering comparable or better solution quality.


title: The complexity of relational query languages (Extended Abstract)

abstract: Two complexity measures for query languages are proposed. Data complexity is the complexity of evaluating a query in the language as a function of the size of the database, and expression complexity is the complexity of evaluating a query in the language as a function of the size of the expression defining the query. We study the data and expression complexity of logical languages - relational calculus and its extensions by transitive closure, fixpoint and second order existential quantification - and algebraic languages - relational algebra and its extensions by bounded and unbounded looping. The pattern which will be shown is that the expression complexity of the investigated languages is one exponential higher then their data complexity, and for both types of complexity we show completeness in some complexity class.


title: A Century of Gestalt Psychology in Visual Perception: I. Perceptual Grouping and Figure–Ground Organization

abstract: In 1912, Max Wertheimer published his paper on phi motion, widely recognized as the start of Gestalt psychology. Because of its continued relevance in modern psychology, this centennial anniversary is an excellent opportunity to take stock of what Gestalt psychology has offered and how it has changed since its inception. We first introduce the key findings and ideas in the Berlin school of Gestalt psychology, and then briefly sketch its development, rise, and fall. Next, we discuss its empirical and conceptual problems, and indicate how they are addressed in contemporary research on perceptual grouping and figure–ground organization. In particular, we review the principles of grouping, both classical (e.g., proximity, similarity, common fate, good continuation, closure, symmetry, parallelism) and new (e.g., synchrony, common region, element and uniform connectedness), and their role in contour integration and completion. We then review classic and new image-based principles of figure–ground organization, how it is influenced by past experience and attention, and how it relates to shape and depth perception. After an integrated review of the neural mechanisms involved in contour grouping, border ownership, and figure–ground perception, we conclude by evaluating what modern vision science has offered compared to traditional Gestalt psychology, whether we can speak of a Gestalt revival, and where the remaining limitations and challenges lie. A better integration of this research tradition with the rest of vision science requires further progress regarding the conceptual and theoretical foundations of the Gestalt approach, which is the focus of a second review article.


title: Negotiating privacy concerns and social capital needs in a social media environment

abstract: Social network sites (SNSs) are becoming an increasingly popular resource for both students and adults, who use them to connect with and maintain relationships with a variety of ties. For many, the primary function of these sites is to consume and distribute personal content about the self. Privacy concerns around sharing information in a public or semi-public space are amplified by SNSs’ structural characteristics, which may obfuscate the true audience of these disclosures due to their technical properties (e.g., persistence, searchability) and dynamics of use (e.g., invisible audiences, context collapse) (boyd 2008b). Early work on the topic focused on the privacy pitfalls of Facebook and other SNSs (e.g., Acquisti and Gross 2006; Barnes 2006; Gross and Acquisti 2005) and argued that individuals were (perhaps inadvertently) disclosing information that might be inappropriate for some audiences, such as future employers, or that might enable identity theft or other negative outcomes.


title: Which Are the Best Features for Automatic Verb Classification

abstract: In this work, we develop and evaluate a wide range of feature spaces for deriving Levinstyle verb classifications (Levin, 1993). We perform the classification experiments using Bayesian Multinomial Regression (an efficient log-linear modeling framework which we found to outperform SVMs for this task) with the proposed feature spaces. Our experiments suggest that subcategorization frames are not the most effective features for automatic verb classification. A mixture of syntactic information and lexical information works best for this task.


title: Secure control against replay attacks

abstract: This paper analyzes the effect of replay attacks on a control system. We assume an attacker wishes to disrupt the operation of a control system in steady state. In order to inject an exogenous control input without being detected the attacker will hijack the sensors, observe and record their readings for a certain amount of time and repeat them afterwards while carrying out his attack. This is a very common and natural attack (we have seen numerous times intruders recording and replaying security videos while performing their attack undisturbed) for an attacker who does not know the dynamics of the system but is aware of the fact that the system itself is expected to be in steady state for the duration of the attack. We assume the control system to be a discrete time linear time invariant gaussian system applying an infinite horizon Linear Quadratic Gaussian (LQG) controller. We also assume that the system is equipped with a χ2 failure detector. The main contributions of the paper, beyond the novelty of the problem formulation, consist in 1) providing conditions on the feasibility of the replay attack on the aforementioned system and 2) proposing a countermeasure that guarantees a desired probability of detection (with a fixed false alarm rate) by trading off either detection delay or LQG performance, either by decreasing control accuracy or increasing control effort.


Corpora and collocations


title: Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events

abstract: The standard life events methodology for the prediction of psychological symptoms was compared with one focusing on relatively minor events, namely, the hassles and uplifts of everyday life. Hassles and Uplifts Scales were constructed and administered once a month for 10 consecutive months to a community sample of middle-aged adults. It was found that the Hassles Scale was a better predictor of concurrent and subsequent psychological symptoms than were the life events scores, and that the scale shared most of the variance in symptoms accounted for by life events. When the effects of life events scores were removed, hassles and symptoms remained significantly correlated. Uplifts were positively related to symptoms for women but not for men. Hassles and uplifts were also shown to be related, although only modestly so, to positive and negative affect, thus providing discriminate validation for hassles and uplifts in comparison to measures of emotion. It was concluded that the assessment of daily hassles and uplifts may be a better approach to the prediction of adaptational outcomes than the usual life events approach.


title: Zequanox: A Potential Solution to Zebra Mussels

abstract: Zebra mussels are an invasive species that are very harmful to the environment and therefore cost society a considerable amount of money. Most of the previous attempts to eliminate zebra mussels were either ineffective or caused damage to other organisms. Zequanox is a biopesticide made by Marrone Bio Innovations, and its ultimate purpose is to eliminate zebra mussels. However, Zequanox can change the water quality for very short periods of time and can harm other aquatic organisms in large doses. After extensive research on this controversial subject, Zequanox has been found to be a viable option if used in regulated doses.


title: An efficient and cost effective FPGA based implementation of the Viola-Jones face detection algorithm

abstract: Abstract We present an field programmable gate arrays (FPGA) based implementation of the popular Viola-Jones face detection algorithm, which is an essential building block in many applications such as video surveillance and tracking. Our implementation is a complete system level hardware design described in a hardware description language and validated on the affordable DE2-115 evaluation board. Our primary objective is to study the achievable performance with a low-end FPGA chip based implementation. In addition, we release to the public domain the entire project. We hope that this will enable other researchers to easily replicate and compare their results to ours and that it will encourage and facilitate further research and educational ideas in the areas of image processing, computer vision, and advanced digital design and FPGA prototyping.


title: Acquiring evolvability through adaptive representations

abstract: Adaptive representations allow evolution to explore the space of phenotypes by choosing the most suitable set of genotypic parameters. Although such an approach is believed to be efficient on complex problems, few empirical studieshave been conducted in such domains. In this paper, three neural network representations, a direct encoding, a complexifying encoding, and an implicit encoding capable of adapting the genotype-phenotype mapping are compared on Nothello, a complex game playing domain from the AAAI General Game Playing Competition. Implicit encoding makes the search more efficient and uses several times fewer parameters. Random mutation leads to highly structured phenotypic variation that is acquired during the course of evolution rather than built into the representation itself. Thus, adaptive representations learn to become evolvable, and furthermore do so in a way that makes search efficient on difficult coevolutionary problems.


Understanding Undergraduate Students' Adoption of Mobile Learning Model: A Perspective of the Extended UTAUT2


title: A syntactic language model based on incremental CCG parsing

abstract: Syntactically-enriched language models (parsers) constitute a promising component in applications such as machine translation and speech-recognition. To maintain a useful level of accuracy, existing parsers are non-incremental and must span a combinatorially growing space of possible structures as every input word is processed. This prohibits their incorporation into standard linear-time decoders. In this paper, we present an incremental, linear-time dependency parser based on Combinatory Categorial Grammar (CCG) and classification techniques. We devise a deterministic transform of CCG-bank canonical derivations into incremental ones, and train our parser on this data. We discover that a cascaded, incremental version provides an appealing balance between efficiency and accuracy.


title: Analysis and Validation of Perception Sensor Models in an Integrated Vehicle and Environment Simulation

abstract: The number of Advanced Driver Assistance Systems (ADAS) in future vehicle generations will increase steadily in order to support drivers by means of comfort-, safety- and ecology-functions. Along with the ascent of ADAS functions, the challenge for developers to prove the safety and reliability of the overall system increases. The risk for people and test equipment involved in potentially dangerous real world test scenarios and the great efforts required to achieve reproducible results in real driving tests make an alternative test method necessary. Therefore, Audi is working together with partners on the development of "Virtual Test Drive" (VTD) [VIR01], a modular, computer-based system for the integrated simulation of a virtual vehicle in a virtual environment. VTD supports engineers throughout the development, testing and validation process of ADAS. It contains reusable components, interfaces, models and tools which can be shared by different simulation variants (Software-, Hardware-, Model-, Driver- and Vehicle-in-the-loop) and applied at different stages of the development and testing process. The VTD simulation environment enables realistic closed-loop simulations to analyze the interaction between simulation components, such as sensor systems, actuators and a model of the vehicle environment as well as the assistance or safety functions under test. This paper presents in particular a method for the analysis and validation of perceptive sensor models generating synthetic sensor data (e.g. Video Camera, RADAR, LIDAR, etc.) in VTD. The simulated perception sensor data is compared to real sensor data in a number of selected scenarios. The process of generating synthetic sensor data with VTD using perception sensor models starts with the recording of a real vehicle test drive in a real world test scenario. GPS trajectory coordinates as well as vehicle state data and perception sensor data are recorded during defined approach and collision scenarios between the ego-vehicle and target objects. In a second step, these data is imported into VTD and synthetic sensor data is generated by feeding the recorded trajectory and vehicle state data through VTD sensor models. In a final step the synthetic sensor data is converted to the same format as the recorded real sensor data. The aim of this conversion step is to evaluate and validate the synthetic data by using the same toolchain as it is done for the real sensor data. The novelty of the method presented in this paper is its reusability for different sensor models, functions and test scenarios and moreover the high level of automation reachable.


SMART: a SysteM of Augmented Reality for Teaching 2nd grade students


title: Properties of inductive reasoning

abstract: This paper reviews the main psychological phenomena of inductive reasoning, covering 25 years of experimental and model-based research, in particular addressing four questions. First, what makes a case or event generalizable to other cases? Second, what makes a set of cases generalizable? Third, what makes a property or predicate projectable? Fourth, how do psychological models of induction address these results? The key results in inductive reasoning are outlined, and several recent models, including a new Bayesian account, are evaluated with respect to these results. In addition, future directions for experimental and model-based work are proposed.


title: Application of Perceived Maternal Parenting Self-Efficacy (PMP S-E) Questionnaire in a Mid-West Community Medical Center NICU in the United States

abstract: Preterm birth and infants’ admission to neonatal intensive care units (NICU) are associated with significant emotional and psychological stresses on mothers that interfere with normal mother-infant relationship. Maternal selfefficacy in parenting ability may predict long-term outcome of mother-infant relationship as well as neurodevelopmental and behavioral development of preterm infants. The Perceived Maternal Parenting Self-Efficacy (PMP S-E) tool was developed to measure self-efficacy in mothers of premature infants in the United Kingdom. The present study determined if maternal and neonatal characteristics could predict PMP S-E scores of mothers who were administered to in a mid-west community medical center NICU. Mothers whose infants were born less than 37 weeks gestational age and admitted to a level III neonatal intensive care unit participated. Participants completed the PMP S-E and demographic survey prior to discharge. A logistic regression analysis was conducted from PMP SE scores involving 103 dyads using maternal education, race, breast feeding, maternal age, infant’s gestational age, Apgar 5-minute score, birth weight, mode of delivery and time from birth to completion of PMP S-E questionnaire. Time to completion of survey and gestational age were the significant predictors of PMP S-E scores. The finding of this study concerning the utilization of the PMP S-E in a United States mid-west tertiary neonatal center suggest that interpretation of the score requires careful consideration of these two variables.


title: Engineering self-adaptive systems through feedback loops

abstract: To deal with the increasing complexity of software systems and uncertainty of their environments, software engineers have turned to self-adaptivity. Self-adaptive systems are capable of dealing with a continuously changing environment and emerging requirements that may be unknown at design-time. However, building such systems cost-effectively and in a predictable manner is a major engineering challenge. In this paper, we explore the state-of-the-art in engineering self-adaptive systems and identify potential improvements in the design process. ::: ::: Our most important finding is that in designing self-adaptive systems, the feedback loops that control self-adaptation must become first-class entities. We explore feedback loops from the perspective of control engineering and within existing self-adaptive systems in nature and biology. Finally, we identify the critical challenges our community must address to enable systematic and well-organized engineering of self-adaptive and self-managing software systems.


title: Detection and prevention system against cyber attacks and botnet malware for information systems and Internet of Things

abstract: The explosion of interconnected devices and the Internet of Things has triggered new important challenges in the area of internet security, due to the various device vulnerabilities and increased potential for cyber-attacks. This paper touches on the areas of Cybersecurity, intrusion detection, prevention systems and artificial intelligence. Our aim is to create a system capable of understanding, detecting and preventing malicious connections using applied concepts of machine learning. We emphasize the importance of selecting and extracting features that can lead to an accurate decision of classification for malware and intrusion attacks. We propose a solution that combines features that extract correlations from the packet history for the same and different services and hosts, based on the rate of REJ, SYN and ACK flags and connection states, with HTTP features extracted from URI and RESTful methods. Our proposed solution is able to detect network intrusions and botnet communications with a precision of 98.4% on the binary classification problem.


title: State-trajectory control of LLC converter implemented by microcontroller

abstract: This paper investigates how to integrate state-trajectory control functions of LLC resonant converter within a commercial low-cost microcontroller, including Simplified Optimal Trajectory Control (SOTC) for load transient, burst mode and soft start up. Simplified calculation process is proposed for SOTC to minimize digital delay. Guideline to select a proper microcontroller is presented to limit calculation delay and ADC delay within 1 switching cycle to ensure good transient performance. Optimized transient processes are proposed to eliminate large oscillation between burst mode and normal operation. Soft start-up is implemented with sensing the output voltage only. The whole control system structure is proposed to integrate all these control functions and for the first time all these control functions are integrated in one low-cost MCU. The results are demonstrated on a 130kHz 300W 380V/12V halfbridge LLC resonant converter with MCU TMS320F2808.


title: “Their Sleep Means More Harmony”: Maternal Perspectives and Experiences of Preschoolers’ Sleep in Ethnically and Socioeconomically Diverse Families in Aotearoa/New Zealand

abstract: Viewing sleep through a socioecological lens, maternal perceptions, and experiences of preschoolers’ sleep were explored using semistructured interviews with 15 Māori (indigenous) and 16 non-Māori ...


title: Gratitude in Intermediate Affective Terrain: Links of Grateful Moods to Individual Differences and Daily Emotional Experience

abstract: Two studies were conducted to explore gratitude in daily mood and the relationships among various affective manifestations of gratitude. In Study 1, spiritual transcendence and a variety of positive affective traits were related to higher mean levels of gratitude across 21 days. Study 2 replicated these findings and revealed that on days when people had more grateful moods than was typical for them, they also reported more frequent daily episodes of grateful emotions, more intense gratitude per episode, and more people to whom they were grateful than was typical for them. In addition, gratitude as an affective trait appeared to render participants’ grateful moods somewhat resistant to the effects of discrete emotional episodes of gratitude.


title: Controller for TORCS created by imitation

abstract: This paper is an initial approach to create a controller for the game TORCS by learning how another controller or humans play the game. We used data obtained from two controllers and from one human player. The first controller is the winner of the WCCI 2008 Simulated Car Racing Competition, and the second one is a hand coded controller that performs a complete lap in all tracks. First, each kind of controller is imitated separately, then a mix of the data is used to create new controllers. The imitation is performed by means of training a feed forward neural network with the data, using the backpropagation algorithm for learning.


title: Multilevel Image Thresholding Selection Based on the Firefly Algorithm

abstract: The multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the firefly algorithm is proposed. This proposed method is called the maximum entropy based firefly thresholding method. Four different methods are implemented for comparing to this proposed method: the exhaustive search, the particle swarm optimization, the hybrid cooperative-comprehensive learning based PSO algorithm and the honey bee mating optimization. The experimental results demonstrated that the proposed MEFFT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the PSO and HCOCLPSO, the segmentation results of using the MEFFT algorithm is significantly improved and the computation time of the proposed MEFFT algorithm is shortest.


title: Successive Cancellation List Polar Decoder using Log-likelihood Ratios

abstract: Successive cancellation list (SCL) decoding algorithm is a powerful method that can help polar codes achieve excellent error-correcting performance. However, the current SCL algorithm and decoders are based on likelihood or log-likelihood forms, which render high hardware complexity. In this paper, we propose a log-likelihood-ratio (LLR)-based SCL (LLR-SCL) decoding algorithm, which only needs half the computation and storage complexity than the conventional one. Then, based on the proposed algorithm, we develop low-complexity VLSI architectures for LLR-SCL decoders. Analysis results show that the proposed LLR-SCL decoder achieves 50% reduction in hardware and 98% improvement in hardware efficiency.


title: Weakly Supervised Object Localization Using Things and Stuff Transfer

abstract: We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations. The source and target classes might share similar appearance (e.g. bear fur is similar to cat fur) or appear against similar background (e.g. horse and sheep appear against grass). To exploit this, we acquire three types of knowledge from the source set: a segmentation model trained on both thing and stuff classes; similarity relations between target and source classes; and co-occurrence relations between thing and stuff classes in the source. The segmentation model is used to generate thing and stuff segmentation maps on a target image, while the class similarity and co-occurrence knowledge help refining them. We then incorporate these maps as new cues into a multiple instance learning framework (MIL), propagating the transferred knowledge from the pixel level to the object proposal level. In extensive experiments, we conduct our transfer from the PASCAL Context dataset (source) to the ILSVRC, COCO and PASCAL VOC 2007 datasets (targets). We evaluate our transfer across widely different thing classes, including some that are not similar in appearance, but appear against similar background. The results demonstrate significant improvement over standard MIL, and we outperform the state-of-the-art in the transfer setting.


title: The Role of Psychopathy and Sexuality in a Female Serial Killer

abstract: The case of Aileen Wuornos, executed in Florida for the serial killing of seven men, is studied to determine her degree of psychopathy and the presence or absence of sexuality or sexual sadism as a motivation or gratification for her crimes. The authors, one of whom evaluated the subject shortly before her death, determined that she evidenced a psychopathic personality (PCL-R score 32). She also met DSM-IV-TR criteria for Antisocial Personality Disorder and Borderline Personality Disorder. While her killings ostensibly were carried out during routine acts of prostitution, there was ambiguous evidence that her crimes were sexually motivated or gratifying. Her articulated motivation was robbery and elimination of the witness/victim. After carefully considering all available data, the authors concluded there was no convincing evidence of sexual sadism in either her personal history or her method of committing serial murder, and it remains unclear whether sexual gratification was to some degree a motivating factor in her commission of these offenses. The confluence of early childhood attachment disruptions, severe psychopathy, other personality disorder pathology, and a traumagenic abuse history likely contributed to her having serially murdered seven victims.


title: Digital Forensics and Cyber Crime Datamining

abstract: Digital forensics is the science of identifying, extracting, analyzing and presenting the digital evidence that has been stored in the digital devices. Various digital tools and techniques are being used to achieve this. Our paper explains forensic analysis steps in the storage media, hidden data analysis in the file system, network forensic methods and cyber crime data mining. This paper proposes a new tool which is the combination of digital forensic investigation and crime data mining. The proposed system is designed for finding motive, pattern of cyber attacks and counts of attacks types happened during a period. Hence the proposed tool enables the system administrators to minimize the system vulnerability.


title: Analyzing lock contention in multithreaded applications

abstract: Many programs exploit shared-memory parallelism using multithreading. Threaded codes typically use locks to coordinate access to shared data. In many cases, contention for locks reduces parallel efficiency and hurts scalability. Being able to quantify and attribute lock contention is important for understanding where a multithreaded program needs improvement. This paper proposes and evaluates three strategies for gaining insight into performance losses due to lock contention. First, we consider using a straightforward strategy based on call stack profiling to attribute idle time and show that it fails to yield insight into lock contention. Second, we consider an approach that builds on a strategy previously used for analyzing idleness in work-stealing computations; we show that this strategy does not yield insight into lock contention. Finally, we propose a new technique for measurement and analysis of lock contention that uses data associated with locks to blame lock holders for the idleness of spinning threads. Our approach incurs ≤ 5% overhead on a quantum chemistry application that makes extensive use of locking (65M distinct locks, a maximum of 340K live locks, and an average of 30K lock acquisitions per second per thread) and attributes lock contention to its full static and dynamic calling contexts. Our strategy, implemented in HPCToolkit, is fully distributed and should scale well to systems with large core counts.


title: Plasmon-modulated photoluminescence from gold nanostructures and its dependence on plasmon resonance, excitation energy, and band structure

abstract: Two distinct single-photon plasmon-modulated photoluminescence processes are generated from nanostructured gold surfaces by tuning the spectral overlap of the incident laser source, localized surface plasmon resonance band, and the interband transitions between the d and sp bands, near the X-and L-symmetry points of the electronic band structure of gold. In the main section of the article, the characteristics of these photoluminescence processes are described and discussed. In the last section, the background continuum accompanying surface-enhanced Raman scattering (SERS) spectra from benzenethiol and 4-mercaptopyridine self-assembled monolayers chemisorbed on nanostructured gold surfaces is shown to originate from plasmon-modulated photoluminescence.


Wireless sensor network security: A survey,” in book chapter of Security


title: Retroperitoneal lipomatosis — Report of two cases with an unusual distribution

abstract: Two cases of retroperitoneal lipomatosis with an unusual distribution are reported. In the first case the fat was confined to the perirectal soft tissues and the sigmoid mesentery, whereas in the second case it was peripancreatic and extended into the small bowel mesentery. The lipomatous nature of these retroperitoneal masses was suggested by the lucency of the fat on the plain abdominal roentgenograms. These cases were studied prior to the availability of computed tomography, so that the diagnoses were confirmed by surgical exploration, which may have been avoided by this currently available noninvasive technique.


title: Using stochastic computing to implement digital image processing algorithms

abstract: As device scaling continues to nanoscale dimensions, circuit reliability will continue to become an ever greater problem. Stochastic computing, which performs computing with random bits (stochastic bits streams), can be used to enable reliable computation using those unreliable devices. However, one of the major issues of stochastic computing is that applications implemented with this technique are limited by the available computational elements. In this paper, first we will introduce and prove a stochastic absolute value function. Second, we will demonstrate a mathematical analysis of a stochastic tanh function, which is a key component used in a stochastic comparator. Third, we will present a quantitative analysis of a one-parameter linear gain function, and propose a new two-parameter version. The validity of the present stochastic computational elements is demonstrated through four basic digital image processing algorithms: edge detection, frame difference based image segmentation, median filter based noise reduction, and image contrast stretching. Our experimental results show that stochastic implementations tolerate more noise and consume less hardware than their conventional counterparts.


title: Vehicle Detection by Independent Parts for Urban Driver Assistance

abstract: In this paper, we introduce vehicle detection by independent parts (VDIP) for urban driver assistance. In urban environments, vehicles appear in a variety of orientations, i.e., oncoming, preceding, and sideview. Additionally, partial vehicle occlusions are common at intersections, during entry and exit from the camera's field of view, or due to scene clutter. VDIP provides a lightweight robust framework for detecting oncoming, preceding, sideview, and partially occluded vehicles in urban driving. In this paper, we use active learning to train independent-part detectors. A semisupervised approach is used for training part-matching classification, which forms sideview vehicles from independently detected parts. The hierarchical learning process yields VDIP, featuring efficient evaluation and robust performance. Parts and vehicles are tracked using Kalman filtering. The fully implemented system is lightweight and runs in real time. Extensive quantitative analysis on real-world on-road data sets is provided.


title: Pupil size as a measure of within‐task learning

abstract: Pupillometry is commonly used in research to determine how much mental effort an individual is exerting while completing tasks. Traditionally, larger pupils are associated with increased mental effort when completing more difficult tasks. However, little research has investigated how pupils change as individuals learn a new task. In theory, as one repeatedly completes a task, the task demands should reduce, reliance on working memory should decrease, and the task should become more automatic. This should translate to faster completion times and smaller peak pupil dilations. We tested this hypothesis by having participants complete multiple trials of a cognitive task that requires individuals to orient themselves in space relative to a target. We found that trial completion times and maximum pupil size significantly reduced across trials. These data suggest that measuring changes in pupil dilation may help researchers determine whether individuals have shifted from a learned procedure to an automatic processing of information when learning a new task.


title: A Characterization of the Utility of Using Artificial Intelligence to Test Two Artificial Intelligence Systems

abstract: An artificial intelligence system, designed for operations in a real-world environment faces a nearly infinite set of possible performance scenarios. Designers and developers, thus, face the challenge of validating proper performance across both foreseen and unforeseen conditions, particularly when the artificial intelligence is controlling a robot that will be operating in close proximity, or may represent a danger, to humans. While the manual creation of test cases allows limited testing (perhaps ensuring that a set of foreseeable conditions trigger an appropriate response), this may be insufficient to fully characterize and validate safe system performance. An approach to validating the performance of an artificial intelligence system using a simple artificial intelligence test case producer (AITCP) is presented. The AITCP allows the creation and simulation of prospective operating scenarios at a rate far exceeding that possible by human testers. Four scenarios for testing an autonomous navigation control system are presented: single actor in two-dimensional space, multiple actors in two-dimensional space, single actor in three-dimensional space, and multiple actors in three-dimensional space. The utility of using the AITCP is compared to that of human testers in each of these scenarios.


title: Detection Learning Style Vark for Out of School Children (OSC)

abstract: Learning style is different for every learner especially for out of school children or OSC. They are not like formal students, they are learners but they don't have a teacher as a guide for learning. E-learning is one of the solutions to help OSC to get education. E-learning should have preferred learning styles of learners. Data for identifying the learning style in this study were collected with a VARK questionnaire from 25 OSC in junior high school level from 5 municipalities in Palembang. The validity of the questionnaire was considered on basis of experts' views and its reliability was calculated by using Cronbach's alpha coefficients (α=0.68). Overall, 55% preferred to use a single learning style (Uni-modal). Of these, 27,76% preferred Aural, 20,57% preferred Reading Writing, 33,33% preferred Kinaesthetic and 23,13% preferred Visual. 45% of OSC preferred more than one style, 30% chose two-modes (bimodal), and 15% chose three-modes (tri-modal). The Most preferred Learning style of OSC is kinaesthetic learning. Kinaesthetic learning requires body movements, interactivities, and direct contacts with learning materials, these things can be difficult to implement in eLearning, but E-learning should be able to adopt any learning styles which are flexible in terms of time, period, curriculum, pedagogy, location, and language.


title: Exploiting GNU radio and USRP: An economical test bed for real time communication systems

abstract: Communication experiments using normal lab setup, which includes more hardware and less software raises the cost of the total system. The method proposed here provides a new approach through which all the analog and digital experiments can be performed using a single hardware-USRP (Universal Software Radio Peripheral) and software-GNU Radio Companion (GRC). Initially, networking setup is formulated using SDR technology. Later on, one of the analog communication experiments is demonstrated in real time using the GNU Radio Companion, RTL-SDR and USRP. The entire communication system is less expensive as the system uses a single reprogrammable hardware and most of the focus of the system deals with the software part.


title: Learning to Select and Order Vacation Photographs

abstract: We propose the problem of automated photo album creation from an unordered image collection. The problem is difficult as it involves a number of complex perceptual tasks that facilitate selection and ordering of photos to create a compelling visual narrative. To help solve this problem, we collect (and will make available) a new benchmark dataset based on Flickr images. Flickr Album Dataset and provides a variety of annotations useful for the task, including manually created albums of various lengths. We analyze the problem and provide experimental evidence, through user studies, that both selection and ordering of photos within an album is important for human observers. To capture and learn rules of album composition, we propose a discriminative structured model capable of encoding simple preferences for contextual layout of the scene (e.g., spatial layout of faces, global scene context, and presence/absence of attributes) and ordering between photos (e.g., exclusion principles or correlations). The parameters of the model are learned using a structured SVM framework. Once learned, the model allows automatic composition of photo albums from unordered and untagged collections of images. We quantitatively evaluate the results obtained using our model against manually created albums and baselines on a dataset of 63 personal photo collections from 5 different topics.


title: CoseDroid: Effective Computation- and Sensing-Offloading for Android Apps

abstract: Smartphone applications are becoming increasingly popular. However, these applications can suffer limited power budgets or malfunctioned sensing issues from their host devices. Computation offloading addresses this issue by delegating local computation workloads to remote servers. In this paper, we present Cose Droid, a framework that supports dynamic computation- and sensing-offloading across different Android mobile devices. This enables Android applications to virtually "borrow" computation or sensing resources from other devices. We experimentally evaluated Cose Droid with real-world Android applications. The experimental results confirmed Cose Droid's effectiveness in on-demand offloading, as well as supporting sensor variety and spontaneous sensing recovery.


title: The Role of Internal and External Social Capital in Crowdfunding: Evidence from China

abstract: How does a proponent’s internal and external social capital impact crowdfunding performance? Based on the theory of social capital, we developed a research model and conducted an econometric analysis using the objective data collected from a Chinese comprehensive crowdfunding platform, ZhongchouNet. Results of an econometric analysis of a sample of 862 projects show that two types of the proponent’s external social capital have significant effect on crowdfunding performance alone. Moreover, the effect of internal and external social capital on the success of a campaign is fully moderated by the type of the project. These results suggest that, to improve crowdfunding performance, the proponents, especially proponents of for-profit projects, should make good use of the mechanisms of social capital construction existed inside the crowdfunding platform.


title: Hystor: making the best use of solid state drives in high performance storage systems

abstract: With the fast technical improvement, flash memory based Solid State Drives (SSDs) are becoming an important part of the computer storage hierarchy to significantly improve performance and energy efficiency. However, due to its relatively high price and low capacity, a major system research issue to address is on how to make SSDs play their most effective roles in a high-performance storage system in cost- and performance-effective ways. In this paper, we will answer several related questions with insights based on the design and implementation of a high performance hybrid storage system, called Hystor. We make the best use of SSDs in storage systems by achieving a set of optimization objectives from both system deployment and algorithm design perspectives. Hystor manages both SSDs and hard disk drives (HDDs) as one single block device with minimal changes to existing OS kernels. By monitoring I/O access patterns at runtime, Hystor can effectively identify blocks that (1) can result in long latencies or (2) are semantically critical (e.g. file system metadata), and stores them in SSDs for future accesses to achieve a significant performance improvement. In order to further leverage the exceptionally high performance of writes in the state-of-the-art SSDs, Hystor also serves as a write-back buffer to speed up write requests. Our measurements on Hystor implemented in the Linux kernel 2.6.25.8 show that it can take advantage of the performance merits of SSDs with only a few lines of changes to the stock Linux kernel. Our system study shows that in a highly effective hybrid storage system, SSDs should play a major role as an independent storage where the best suitable data are adaptively and timely migrated in and retained, and it can also be effective to serve as a write-back buffer.


title: Intelligent technical analysis based equivolume charting for stock trading using neural networks

abstract: It has been long recognized that trading volume provides valuable information for understanding stock price movement. As such, equivolume charting was developed to consider how stocks appear to move in a volume frame of reference as opposed to a time frame of reference. Two technical indicators, namely the volume adjusted moving average (VAMA) and the ease of movement (EMV) indicator, are developed from equivolume charting. This paper explores the profitability of stock trading by using a neural network model developed to assist the trading decisions of the VAMA and EMV. The generalized regression neural network (GRNN) is chosen and utilized on past S&P 500 index data. For the VAMA, the GRNN is used to predict the future stock prices, as well as the future width size of the equivolume boxes typically utilized on an equivolume chart, for calculating the future value of the VAMA. For the EMV, the GRNN is also used to predict the future value of the EMV. The idea is to further exploit the equivolume potential by using a forecasting system to predict the future equivolume measurements, allowing investors to enter or exit trades earlier. The results show that the stock trading using the neural network with the VAMA and EMV outperforms the results of stock trading generated from the VAMA and EMV without neural network assistance, the simple moving averages (MA) in isolation, and the buy-and-hold trading strategy.


title: Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks

abstract: Recent results suggest that attacks against supervised machine learning systems are quite effective, while defenses are easily bypassed by new attacks. However, the specifications for machine learning systems currently lack precise adversary definitions, and the existing attacks make diverse, potentially unrealistic assumptions about the strength of the adversary who launches them. We propose the FAIL attacker model, which describes the adversary's knowledge and control along four dimensions. Our model allows us to consider a wide range of weaker adversaries who have limited control and incomplete knowledge of the features, learning algorithms and training instances utilized. To evaluate the utility of the FAIL model, we consider the problem of conducting targeted poisoning attacks in a realistic setting: the crafted poison samples must have clean labels, must be individually and collectively inconspicuous, and must exhibit a generalized form of transferability, defined by the FAIL model. By taking these constraints into account, we design StingRay, a targeted poisoning attack that is practical against 4 machine learning applications, which use 3 different learning algorithms, and can bypass 2 existing defenses. Conversely, we show that a prior evasion attack is less effective under generalized transferability. Such attack evaluations, under the FAIL adversary model, may also suggest promising directions for future defenses.


title: Real-Time Visibility-Based Fusion of Depth Maps

abstract: We present a viewpoint-based approach for the quick fusion of multiple stereo depth maps. Our method selects depth estimates for each pixel that minimize violations of visibility constraints and thus remove errors and inconsistencies from the depth maps to produce a consistent surface. We advocate a two-stage process in which the first stage generates potentially noisy, overlapping depth maps from a set of calibrated images and the second stage fuses these depth maps to obtain an integrated surface with higher accuracy, suppressed noise, and reduced redundancy. We show that by dividing the processing into two stages we are able to achieve a very high throughput because we are able to use a computationally cheap stereo algorithm and because this architecture is amenable to hardware-accelerated (GPU) implementations. A rigorous formulation based on the notion of stability of a depth estimate is presented first. It aims to determine the validity of a depth estimate by rendering multiple depth maps into the reference view as well as rendering the reference depth map into the other views in order to detect occlusions and free- space violations. We also present an approximate alternative formulation that selects and validates only one hypothesis based on confidence. Both formulations enable us to perform video-based reconstruction at up to 25 frames per second. We show results on the multi-view stereo evaluation benchmark datasets and several outdoors video sequences. Extensive quantitative analysis is performed using an accurately surveyed model of a real building as ground truth.


title: Neural Simpletrons - Minimalistic Directed Generative Networks for Learning with Few Labels

abstract: Deep learning is intensively studied using supervised and unsupervised learning, and by applying probabilistic, deterministic, and bio-inspired approaches. Comparisons of different approaches such as generative and discriminative neural networks is made difficult, however, because of differences in the semantics of their graphical descriptions, different learning methods, different benchmarking objectives and different scalability. To allow for a direct functional comparison, we here study a generative multi-layer neural network in a form and setting as similar to standard discriminative networks as possible. Based on normalized Poisson mixtures, we derive a minimalistic deep neural network with local activation and learning rules. The network learns in a semi-supervised setting and can be scaled using standard deep learning tools for parallelized implementations. Empirical evaluations on standard benchmarks show that for weakly labeled data the derived minimalistic network improves on all standard deep learning approaches and is competitive with their recent variants. In comparison to recent bio-inspired approaches it suggests further improvements through top-down connections. Furthermore, we find that the studied network is the best performing monolithic (`non-hybrid') system for few labels, and that it can be applied in the limit of very few labels, where no other system has been reported to operate so far.


title: Design and Modelling of Omnibola©, A Spherical Mobile Robot

abstract: A spherical mobile robot called Omnibola© is introduced and analyzed in this article. Some advantages of this kind of robot compared with typical wheeled robots are described. Its geometry and its features are presented, emphasizing on those which make it different from other ball-shaped robots. A mathematical model has been developed to have a tool to study our robot dynamics. We conducted some experiments to confirm that model results are similar to experimental results observed in the real robot. Either in experiments or in simulations, the robot's behavior is quite oscillatory. Because of this, a simple control law is proposed to stabilize that oscillatory motion.