Query: A Geometric Method for Detecting Semantic Coercion

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Semi-productive Polysemy and Sense Extension <=SEP=> In this paper we discuss various aspects of systematic or conventional polysemy and their formal treatment within an implemented constraint based approach to linguistic representation. We distinguish between two classes of systematic polysemy: constructional polysemy, where a single sense assigned to a lexical entry is contextually specialised, and sense extension, which predictably relates two or more senses. Formally the first case is treated as instantiation of an underspecified lexical entry and the second by use of lexical rules. The problems of distinguishing between these two classes are discussed in detail. We illustrate how lexical rules can be used both to relate fully conventionalised senses and also applied productively to recognise novel usages and how this process can be controlled to account for semi-productivity by utilising probabilities.

Type shifting in construction grammar : An integrated approach to aspectual coercion * <=SEP=> Implicit type shifting, or coercion, appears to indicate a modular grammatical architecture, in which the process of semantic composition may add meanings absent from the syntax in order to ensure that certain operators, e.g., the progressive, receive suitable arguments (Jackendo¤ 1997; De Swart 1998). I will argue that coercion phenomena actually provide strong support for a sign-based model of grammar, in which rules of morphosyntactic combination can shift the designations of content words with which they combine. On this account, enriched composition is a by-product of the ordinary referring behavior of constructions. Thus, for example, the constraint which requires semantic concord between the syntactic sisters in the string a bottle is also what underlies the coerced interpretation found in a beer. If this concord constraint is stated for a rule of morphosyntactic combination, we capture an important generalization: a single combinatory mechanism, the construction, is responsible for both coerced and compositional meanings. Since both type-selecting constructions (e.g., the French Imparfait) and type-shifting constructions (e.g., English progressive aspect) require semantic concord between syntactic sisters, we account for the fact that constructions of both types perform coercion. Coercion data suggest that aspectual sensitivity is not merely a property of formally di¤erentiated past tenses, as in French and Latin, but a general property of tense constructions, including the English present and past tenses.

Glove: Global Vectors for Word Representation <=SEP=> Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

Efficient Estimation of Word Representations in Vector Space <=SEP=> We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.

Grammatical Constructions and Linguistic Generalizations : the What ' s X doing Y ? Construction 1 <=SEP=> To adopt a constructional approach is to undertake a commitment in principle to account for the entirety of each language. This means that the relatively general patterns of the language, such as the one licensing the ordering of a finite auxiliary verb before its subject in English as illustrated in (1), and the more idiomatic patterns, such as those exemplified in (2), stand on an equal footing as data for which the grammar must provide an account.

On the relationship between math anxiety and math achievement in early elementary school: The role of problem solving strategies. <=SEP=> Even at young ages, children self-report experiencing math anxiety, which negatively relates to their math achievement. Leveraging a large dataset of first and second grade students' math achievement scores, math problem solving strategies, and math attitudes, we explored the possibility that children's math anxiety (i.e., a fear or apprehension about math) negatively relates to their use of more advanced problem solving strategies, which in turn relates to their math achievement. Our results confirm our hypothesis and, moreover, demonstrate that the relation between math anxiety and math problem solving strategies is strongest in children with the highest working memory capacity. Ironically, children who have the highest cognitive capacity avoid using advanced problem solving strategies when they are high in math anxiety and, as a result, underperform in math compared with their lower working memory peers.

Selective harmonic elimination modulation technique applied on four-leg NPC <=SEP=> In this paper the selective harmonic elimination pulse width modulation (SHE-PWM) technique is proposed to control the three-level four-leg neutral point clamped (NPC) inverter in order to have both advantages of low switching frequency of SHE and neutral point of fourth leg in four wire systems. In this proposed method, the obtained switching angles of inverter phase legs are used to eliminate the non-triplen, 5th to 23th harmonics orders from the output voltage. As well, switching angles calculated for the fourth leg are considered to eliminate the triplen harmonics containing 3th, 9th, 15th, 21th and 27th orders from phase voltage. The efficiency of the proposed modulation technique is verified by simulations of a four leg NPC inverter as an UPS feeding different types of dynamic and unbalanced loads.

Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures <=SEP=> Conventional sentiment analysis usually neglects semantic information between (sub-)clauses, as it merely implements so-called bag-of-words approaches, where the sentiment of individual words is aggregated independently of the document structure. Instead, we advance sentiment analysis by the use of rhetoric structure theory (RST), which provides a hierarchical representation of texts at document level. For this purpose, texts are split into elementary discourse units (EDU). These EDUs span a hierarchical structure in the form of a binary tree, where the branches are labeled according to their semantic discourse. Accordingly, this paper proposes a novel combination of weighting and grid search to aggregate sentiment scores from the RST tree, as well as feature engineering for machine learning. We apply our algorithms to the especially hard task of predicting stock returns subsequent to financial disclosures. As a result, machine learning improves the balanced accuracy by 8.6 percent compared to the baseline.

Evaluation of direct attacks to fingerprint verification systems <=SEP=> The vulnerabilities of fingerprint-based recognition systems to direct attacks with and without the cooperation of the user are studied. Two different systems, one minutiae-based and one ridge feature-based, are evaluated on a database of real and fake fingerprints. Based on the fingerprint images quality and on the results achieved on different operational scenarios, we obtain a number of statistically significant observations regarding the robustness of the systems.

Conjunctive representation of position, direction, and velocity in entorhinal cortex. <=SEP=> Grid cells in the medial entorhinal cortex (MEC) are part of an environment-independent spatial coordinate system. To determine how information about location, direction, and distance is integrated in the grid-cell network, we recorded from each principal cell layer of MEC in rats that explored two-dimensional environments. Whereas layer II was predominated by grid cells, grid cells colocalized with head-direction cells and conjunctive grid x head-direction cells in the deeper layers. All cell types were modulated by running speed. The conjunction of positional, directional, and translational information in a single MEC cell type may enable grid coordinates to be updated during self-motion-based navigation.

Compliance through Informed Consent: Semantic Based Consent Permission and Data Management Model <=SEP=> The General Data Protection Regulations (GDPR) imposes greater restrictions on obtaining valid user consents involving the use of personal data. A semantic model of consent can make the concepts of consent explicit, establish a common understanding and enable re-use of consent. Therefore, forming a semantic model of consent will satisfy the GDPR requirements of specificity and unambiguity and is an important step towards ensuring compliance. In this paper, we discuss obtaining an open vocabulary of expressing consent leveraging existing semantic models of provenance, processes, permission and obligations. We also present a reference architecture for the management of data processing according to consent permission. This data management model utilizes the open vocabulary of consent and incorporates the change of context into the data processing activity. By identifying and incorporating changes to the relational context between data controllers and data subjects into the data processing model, it aims to improve the integration of data management across different information systems specifically adhering to the GDPR and helping controllers to demonstrate compliance.

Risk and parameter convergence of logistic regression <=SEP=> The logistic loss is strictly convex and does not attain its infimum; consequently the solutions of logistic regression are in general off at infinity. This work provides a convergence analysis of stochastic and batch gradient descent for logistic regression. Firstly, under the assumption of separability, stochastic gradient descent minimizes the population risk at rate O(ln(t)/t) with high probability. Secondly, with or without separability, batch gradient descent minimizes the empirical risk at rate O(ln(t)/t). Furthermore, parameter convergence can be characterized along a unique pair of complementary subspaces defined by the problem instance: one subspace along which strong convexity induces parameters to converge at rate O(ln(t)/ √ t), and its orthogonal complement along which separability induces parameters to converge in direction at rate O(ln ln(t)/ ln(t)). 1 Overview Logistic regression is the task of finding a vector w ∈ R which approximately minimizes the population or empirical logistic risk, namely Rlog(w) := E [ `log(〈w,−yx〉) ] or R̂log(w) := 1 n n ∑ i=1 `log(〈w,−yixi〉), where `log(r) := ln ( 1 + exp(r) ) is the logistic loss. A traditional way to minimize Rlog or R̂log is to pick an arbitrary w0, and from there recursively construct stochastic gradient descent (SGD) or gradient descent (GD) iterates (wj)j≥0 via wj+1 := wj − ηjgj , where (ηj)j≥0 are step sizes, and gj := ` ′ log (〈 wj ,−yjxj 〉) (−yjxj) for SGD or gj := ∇R̂`log(wj) for GD. A common assumption is that the distribution on (x, y) is separable (Novikoff, 1962; Soudry et al., 2017). Under this assumption, the first result here is that SGD with constant step sizes minimizes the population risk Rlog at rate Õ(1/t) with high probability. Theorem 1.1 (Simplification of Theorem 2.1). Suppose there exists a unit vector ū with 〈ū, yx〉 ≥ γ, and |yx| ≤ 1 almost surely. Consider SGD iterates (wj)j≥0 as above, with w0 = 0 and ηj = 1. Then for any t ≥ 1, |wt| ≤ O(ln(t)), and with probability at least 1− δ, the average iterate ŵt := t−1 ∑ j<t wj satisfies Rlog(ŵt) ≤ O ( ln(t) + ln(t) ln(1/δ) t ) . This result improves upon prior work in the following ways. 1. Since `log exhibits an exponential tail and Lipschitz continuity, standard analyses give a Õ(1/ √ t) rate of SGD with step size ηj = 1/ √ j + 1, but only in expectation (Nesterov, 2004; Bubeck, 2015). With the O(ln(t)) bound on |wt| given by Theorem 1.1, Azuma-Hoeffding’s inequality gives a high-probability Õ(1/ √ t) rate. However, since standard analyses do not ensure a vanishing risk with constant step sizes, they do not give a Õ(1/t) rate. 1 ar X iv :1 80 3. 07 30 0v 2 [ cs .L G ] 1 J un 2 01 8 2. The only other prior high-probability Õ(1/t) rate is under strong convexity (e.g., (Rakhlin et al., 2012)): |wt − w̄| ≤ Õ(1/t) with high probability, where w̄ is the global optimum. However, `log is not strongly convex on R, and even in trivial cases the optimum of logistic regression may be off at infinity: suppose examples (xi) n i=1 lie on the positive real line, and labels (yi) n i=1 are all +1; then infw∈R R̂log(w) = 0, but R̂log(w) > 0 for every w ∈ R. Furthermore, not only do the guarantees here avoid strong convexity; the proofs are different. The proof of Theorem 1.1 is based on the perceptron proof, and might be of independent interest. Now consider batch gradient descent on empirical risk. In this case, a stronger result is possible: no assumption is made (separability is dropped), and the parameters may be precisely characterized. Theorem 1.2 (Simplification of Theorems 3.1, 3.2 and 3.6). Let examples ((xi, yi)) n i=1 be given with |xiyi| ≤ 1, along with a loss ` ∈ {`log, exp}, with corresponding empirical risk R̂ as above. Consider GD iterates (wj)j≥0 as above, with w0 = 0. 1. (Convergence in risk.) For any step sizes ηj ≤ 1 and any t ≥ 1, R̂(wt)− inf w∈Rd R̂(w) = O ( 1 t + ln(t) ∑ j<t ηj ) = { O(ln(t)/t) ηj = Ω(1), O(ln(t)/ √ t) ηj = Ω(1/ √ j + 1). 2. (Convergence in parameters.) There exists a unique subspace S, unique unit vector ū ∈ S⊥, and unique vector v̄ ∈ S which characterize convergence as follows. Fixing step size ηj := 1/ √ j + 1, letting ΠS denote orthogonal projection onto S, and defining optimal iterates w̄t := inf { R̂(w) : |w| ≤ |wt| } , if t = Ω(n ln(t)), then |ΠSwt| = Θ(1) and max { |ΠSwt − v̄|, |ΠSw̄t − v̄| } = O ( ln(t) √ t ) , |ΠS⊥wt| = Θ(ln(t)) and max ∣∣∣∣ wt |wt| − ū ∣∣∣∣2 , ∣∣∣∣ w̄t |w̄t| − ū ∣∣∣∣2 } = O ( ln ln t ln t ) . In particular, wt/|wt|→ ū and ΠSwt → v̄. To make sense of this, recall firstly the earlier discussion that the solutions are off at infinity in general. As will be made precise in Section 3.1, if examples are separable, then S = {0}, v̄ = 0, and ū is the unique maximum margin separator. The GD iterates not only keep driving R̂ to its infimum 0, but also closely track ū in direction, meaning wt/|wt|→ ū. Note that this is a strong mode of convergence: R̂(wt) = Õ(1/t) and |wt| = Θ(ln(t)) do not exclude the possibility that there is always a large angle between wt/|wt| and ū. The full part 2 of Theorem 1.2 gives a characterization of parameter convergence in the general setting: the parameter space can be split into a subspace S along which optimization is strongly convex, and its complement S⊥ along which the problem behaves as though separable. While there does exist recent prior work on parameter convergence of gradient descent for logistic regression (Soudry et al., 2017; Gunasekar et al., 2018; Nacson et al., 2018), it assumes separability. The analysis essentially for free gives convergence not only to (v̄, ū), but also to the optimal iterates w̄t. Consequently, logistic regression is implicitly regularized in a strong sense: it produces a sequence of iterates which closely track the outputs of a sequence of constraint optimization problems. This paper is organized as follows. SGD population risk convergence (Section 2). An online guarantee is given by a perceptron-style analysis, and transformed into a high-probability bound via a martingale Bernstein bound. Empirical samples structure (Section 3.1). The first step of the analysis is to derive the unique complementary subspaces (S, S⊥), along with unique optimal direction ū ∈ S⊥ and unique vector v̄ ∈ S. (S, S⊥) and ū are unique given the data ((xi, yi)) n i=1 and are not affected by the choice of ` or optimization method; v̄ depends on ` but not on the optimization method.

Enabling ECN in Multi-Service Multi-Queue Data Centers <=SEP=> Recent proposals have leveraged Explicit Congestion Notification (ECN) to achieve high throughput low latency data center network (DCN) transport. However, most of them implicitly assume each switch port has one queue, making the ECN schemes they designed inapplicable to production DCNs where multiple service queues per port are employed to isolate different traffic classes through weighted fair sharing. In this paper, we reveal this problem by leveraging extensive testbed experiments to explore the intrinsic tradeoffs between throughput, latency, and weighted fair sharing in multi-queue scenarios. Using the guideline learned from the exploration, we design MQ-ECN, a simple yet effective solution to enable ECN for multi-service multiqueue production DCNs. Through a series of testbed experiments and large-scale simulations, we show that MQ-ECN breaks the tradeoffs by delivering both high throughput and low latency simultaneously, while still preserving weighted fair sharing.

Differentiable Parameterization of Catmull-Clark Subdivision Surfaces <=SEP=> Subdivision-based representations are recognized as important tools for the generation of high-quality surfaces for Computer Graphics. In this paper we describe two parameterizations of Catmull-Clark subdivision surfaces that allow a variety of algorithms designed for other types of parametric surfaces (i.e., B-splines) to be directly applied to subdivision surfaces. In contrast with the natural parameterization of subdivision surfaces characterized by diverging first order derivatives around extraordinary vertices of valence higher than four, the derivatives associated with our proposed methods are defined everywhere on the surface. This is especially important for Computer-Aided Design (CAD) applications that seek to address the limitations of NURBS-based representations through the more flexible subdivision framework.

Quantization Mimic: Towards Very Tiny CNN for Object Detection <=SEP=> In this paper, we propose a simple and general framework for training very tiny CNNs (e.g. VGG with the number of channels reduced to 1 32 ) for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks. We utilize two types of acceleration methods: mimic and quantization. Mimic improves the performance of a student network by transfering knowledge from a teacher network. Quantization converts a full-precision network to a quantized one without large degradation of performance. If the teacher network is quantized, the search scope of the student network will be smaller. Using this feature of the quantization, we propose Quantization Mimic. It first quantizes the large network, then mimic a quantized small network. The quantization operation can help student network to better match the feature maps from teacher network. To evaluate our approach, we carry out experiments on various popular CNNs including VGG and Resnet, as well as different detection frameworks including Faster R-CNN and R-FCN. Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic algorithm can be applied on various settings and outperforms state-of-the-art model acceleration methods given limited computing resouces.

3D People Tracking with Gaussian Process Dynamical Models <=SEP=> We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of human motion data, with a density function that gives higher probability to poses and motions close to the training data. With Bayesian model averaging a GPDM can be learned from relatively small amounts of data, and it generalizes gracefully to motions outside the training set. Here we modify the GPDM to permit learning from motions with significant stylistic variation. The resulting priors are effective for tracking a range of human walking styles, despite weak and noisy image measurements and significant occlusions.

Data Approximation Using Biarcs <=SEP=> . An algorithm for data approximation with biarcs is presented. The method uses a specific formulation of biarcs appropriate for parametric curves in Bézier or NURBS formulation. A base curve is applied to obtain tangents and anchor points for the individual arcs joining in G 1 continuity. Data sampled from circular arcs or straight line segments is represented precisely by one biarc. The method is most useful in numerical control to drive the cutter along straight line or circular paths.

An efficient selection of HOG feature for SVM classification of vehicle <=SEP=> Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of SVM is to reduce the dimension of HOG feature. In this paper, we examine the effect of the number of HOG bins on the vehicle detection and the symmetric characteristics of HOG feature of vehicle. And we successfully demonstrate the speed-up of SVM classifier for vehicle detection by about three times while maintaining the detection performance.

Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs <=SEP=> SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80% which sets a new state-of-the-art on the SimpleQuestions dataset.

Discriminative Optimization: Theory and Applications to Computer Vision <=SEP=> Many computer vision problems are formulated as the optimization of a cost function. This approach faces two main challenges: designing a cost function with a local optimum at an acceptable solution, and developing an efficient numerical method to search for this optimum. While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data. In practice, this can result in undesirable local optima or not having a local optimum in the expected place. On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search. To overcome these limitations, we propose Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function. DO explicitly learns a sequence of updates in the search space that leads to stationary points that correspond to the desired solutions. We provide a formal analysis of DO and illustrate its benefits in the problem of 3D registration, camera pose estimation, and image denoising. We show that DO outperformed or matched state-of-the-art algorithms in terms of accuracy, robustness, and computational efficiency.

Hypervisor Memory Forensics <=SEP=> Memory forensics is the branch of computer forensics that aims at extracting artifacts from memory snapshots taken from a running system. Even though it is a relatively recent field, it is rapidly growing and it is attracting considerable attention from both industrial and academic researchers. In this paper, we present a set of techniques to extend the field of memory forensics toward the analysis of hypervisors and virtual machines. With the increasing adoption of virtualization techniques (both as part of the cloud and in normal desktop environments), we believe that memory forensics will soon play a very important role in many investigations that involve virtual environments. Our approach, implemented in an open source tool as an extension of the Volatility framework, is designed to detect both the existence and the characteristics of any hypervisor that uses the Intel VT-x technology. It also supports the analysis of nested virtualization and it is able to infer the hierarchy of multiple hypervisors and virtual machines. Finally, by exploiting the techniques presented in this paper, our tool can reconstruct the address space of a virtual machine in order to transparently support any existing Volatility plugin allowing analysts to reuse their code for the analysis of virtual environments.

A Scalable SIEM Correlation Engine and Its Application to the Olympic Games IT Infrastructure <=SEP=> The security event correlation scalability has become a major concern for security analysts and IT administrators when considering complex IT infrastructures that need to handle gargantuan amounts of events or wide correlation window spans. The current correlation capabilities of Security Information and Event Management (SIEM), based on a single node in centralized servers, have proved to be insufficient to process large event streams. This paper introduces a step forward in the current state of the art to address the aforementioned problems. The proposed model takes into account the two main aspects of this field: distributed correlation and query parallelization. We present a case study of a multiple-step attack on the Olympic Games IT infrastructure to illustrate the applicability of our approach.

Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments <=SEP=> Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.

Fourier Transform Decoding of Non-Binary LDPC Codes <=SEP=> Binary low-density parity-check (LDPC) codes have recently emerged as a promising alternative to turbo codes and have been shown to achieve excellent coding gains in AWGN channels. Non-binary LDPC codes have demonstrated improved performance over binary LDPC codes but their decoding is computationally intensive. Fourier transform decoding can reduce the decoding complexity but has a number of problems which can be avoided by implementing this algorithm in the log domain. In this paper Fourier transform decoding of non-binary LDPC codes in the log domain is described. The computational complexity and performance of this algorithm is then discussed. Differential evolution is then used to search for optimal binary and non-binary LDPC codes and it is shown by simulation that non-binary LDPC codes are superior.

Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations <=SEP=> Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited contextual information, and they are sparse, noisy and ambiguous, and hence, automatically learning topics from them remains an important challenge. To tackle this problem, in this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of the corpus. The SeaNMF model is solved using a block coordinate descent algorithm. We also develop a sparse variant of the SeaNMF model which can achieve a better model interpretability. Extensive quantitative evaluations on various realworld short text datasets demonstrate the superior performance of the proposed models over several other state-of-the-art methods in terms of topic coherence and classification accuracy. The qualitative semantic analysis demonstrates the interpretability of our models by discovering meaningful and consistent topics. With a simple formulation and the superior performance, SeaNMF can be an effective standard topic model for short texts.

Empirical research on consumers' initial trust and gender differences in B2C e-business <=SEP=> Based on the Technology Acceptance Model and related literatures, this paper proposes an integrated conceptual model of consumers' initial trust in B2C e-business. Empirical research results show that, perceived usefulness, perceived safety, perceived website reputation and propensity to trust significantly positively affect consumers' initial trust in e-vendor, which further positively influence consumers' intention to purchase online, by the complete mediation of attitude; however, the effect of perceived use of use on initial trust doesn't show the statistic significance. Furthermore, there are significant gender differences in the effects of perceived ease of use, perceived safety, perceived website reputation on consumers' initial trust.

Digital array radar: Technology and trends <=SEP=> This paper presents the architecture of digital array radar and analysis the key technologies, digital T/R modules, waveform generation and amplitude-phase control module based on DDS, frequency up/down convert, high efficiency power amplifier, hybrid digital/microwave multilayer circuit and high performance computing are described as the main technologies. The correlation between the microsystems technologies and digital array architectures trends is also discussed.

ACCIDENT AVOIDANCE AND DETECTION ON HIGHWAYS <=SEP=> Technological approaches for detecting and monitoring fatigue levels of driver fatigue continue to emerge and many are now in the development, validation testing, or early implementation stages. Previous studies have reviewed available fatigue detection and prediction technologies and methodologies. As the name indicates this project is about advanced technologies in cars for making it more intelligent and interactive for avoiding accidents on roads. By using ARM7 this system becomes more efficient, reliable & effective. There are very less number of systems implemented on human behaviour detection in or with cars. In this paper, we describe a real-time online safety prototype that controls the vehicle speed under driver fatigue. The purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control the speed of vehicle to avoid accidents. The main components of the system consist of number of real time sensors like gas, eye blink, alcohol, fuel, impact sensors and a software interface with GPS and Google Maps APIs for location.

A cryptosystem based on Vigenère cipher with varying key <=SEP=> — Privacy is one of the key issues information Security addresses. Through encryption one can prevent a third party from understanding raw data during signal transmission. The encryption methods for enhancing the security of digital contents has gained high significance in the current era of breach of security and misuse of the confidential information intercepted and misused by the unauthorized parties. This paper sets out to contribute to the general body of knowledge in the area of cryptography application and by developing a new way of implementing Vigenère cipher encryption algorithm by automatically changing the cipher key after each encryption step. The new method will use successive keys that will be dependent on the initial key value during the encryption process. The algorithm ultimately makes it possible for encryption and decryption of the text. The ciphertext will have different encryption key pattern and the Vigenère cryptosystem will be more difficult to decipher using frequency attack. The implementation will be done using java programming.

Learning to recommend with explicit and implicit social relations <=SEP=> Recommender systems have been well studied and developed, both in academia and in industry recently. However, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real-world observations where we always turn to our trusted friends for recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework which naturally fuses the users' tastes and their trusted friends' favors together. The proposed framework is quite general, and it can also be applied to pure user-item rating matrix even if we do not have explicit social trust information among users. In this framework, we coin the term social trust ensemble to represent the formulation of the social trust restrictions on the recommender systems. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method outperforms state-of-the-art approaches.