Query: Deep Reinforcement Learning for Dialogue Generation

Relevant passages:
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems <=SEP=> Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems covering multiple domains and languages. This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure. The LSTM generator can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates. With fewer heuristics, an objective evaluation in two differing test domains showed the proposed method improved performance compared to previous methods. Human judges scored the LSTM system higher on informativeness and naturalness and overall preferred it to the other systems.

Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System <=SEP=> Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.

Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling <=SEP=> Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability. Conversation modeling will notably benefit from domain knowledge since the relationships between sentences can be clarified due to semantic hints introduced by knowledge. In this paper, a deep neural network is proposed to incorporate background knowledge for conversation modeling. Through a specially designed Recall gate, domain knowledge can be transformed into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance LSTM by cooperating with its local memory to capture the implicit semantic relevance between sentences within conversations. In addition, this paper introduces the loose structured domain knowledge base, which can be built with slight amount of manual work and easily adopted by the Recall gate. Our model is evaluated on the context-oriented response selecting task, and experimental results on both two datasets have shown that our approach is promising for modeling human conversations and building key components of automatic chatting systems.

A Diversity-Promoting Objective Function for Neural Conversation Models <=SEP=> Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g.,I don’t know) regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (responses) given input (messages) is unsuited to response generation tasks. Instead we propose using Maximum Mutual Information (MMI) as objective function in neural models. Experimental results demonstrate that the proposed objective function produces more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets.

Are We There Yet? Research in Commercial Spoken Dialog Systems <=SEP=> In this paper we discuss the recent evolution of spoken dialog systems in commercial deployments. Yet based on a simple finite state machine design paradigm, dialog systems reached today a higher level of complexity. The availability of massive amounts of data during deployment led to the development of continuous optimization strategy pushing the design and development of spoken dialog applications from an art to science. At the same time new methods for evaluating the subjective caller experience are available. Finally we describe the inevitable evolution for spoken dialog applications from speech only to multimodal interaction.

Tonsillectomy and risk of Parkinson's disease: A danish nationwide population-based cohort study. <=SEP=> BACKGROUND
We hypothesized that tonsillectomy modifies the risk of PD.


OBJECTIVES
To test the hypothesis in a nationwide population-based cohort study.


METHODS
We used Danish medical registries to construct a cohort of all patients in Denmark with an operation code of tonsillectomy 1980-2010 (n = 195,169) and a matched age and sex general population comparison cohort (n = 975,845). Patients were followed until PD diagnosis, death, censoring, or end of follow-up 30 November 2013. Using Cox regression, we computed hazard ratios for PD and corresponding 95% confidence intervals, adjusting for age and sex by study design, and potential confounders.


RESULTS
We identified 100 and 568 patients diagnosed with PD among the tonsillectomy and general population comparison cohort, respectively, finding similar risks of PD (adjusted hazard ratio = 0.95 [95% confidence interval: 0.76-1.19]; for > 20 years' follow-up (adjusted hazard ratio = 0.96 [95% confidence interval: 0.64-1.41]).


CONCLUSION
Tonsillectomy is not associated with risk of PD, especially early-onset PD. © 2017 International Parkinson and Movement Disorder Society.

Event Extraction with Complex Event Classification Using Rich Features <=SEP=> Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge.

Acceptance of homegrown enterprise resource planning (ERP) systems in Ethiopia <=SEP=> In the current competitive global market, organizations are implementing information and communication technology (ICT) that could add value to their products, processes, and satisfaction of their users. The adoption, implementation and use of homegrown enterprise resource planning (ERP) systems is one of these mechanisms being globally used for recording, processing, storing, and exchanging organizational information anytime anywhere. Although organizations have been utilizing ERP systems, the acceptance of homegrown ERP systems is given less attention as compared to commercial off-the shelf (COTS) software. Hence, this research studied factors that determine acceptance of homegrown ERP through the extension of unified theory of acceptance and use of technology (UTAUT) model. The finding revealed that performance expectancy, effort expectancy, social influence, competitive advantage, cost effectiveness, and facilitations functions are determinants of homegrown ERP system acceptance in Ethiopia. Moreover, experience and voluntariness are found to be significant moderators of the study.

Explaining contradictory relations between risk perception and risk taking. <=SEP=> Different studies have documented opposite relations between perceived risk and behavior. The present study tested a theoretical explanation that reconciles these conflicting results. Adolescents (N= 596) completed alternative measures of risk perception that differed in cue specificity and response format. As predicted by fuzzy-trace theory, measures that emphasized verbatim retrieval and quantitative processing produced positive correlations between perceived risk and risky behavior; risk perceptions reflected the extent to which adolescents engaged in risky behavior. In contrast, measures that assessed global, gist-based judgments of risk produced negative correlations; higher risk perceptions were associated with less risk taking, a protective rather than reflective relation. Endorsement of simple values and principles provided the greatest protection against risk taking. Results support a dual-processes interpretation of the relation between risk perception and risk taking according to which observed relations depend on whether the cues in questions trigger verbatim or gist processing.

Local optima smoothing for global optimization <=SEP=> It is widely believed that in order to solve large scale global optimization problems an appropriate mixture of local approximation and global exploration is necessary. Local approximation, if first order information on the objective function is available, is efficiently performed by means of local optimization methods. Unfortunately, global exploration, in absence of some kind of global information on the problem, is a “blind” procedure, aimed at placing observations as evenly as possible in the search domain. Often this procedure reduces to uniform random sampling (like in Multistart algorithms, or in techniques based on clustering). In this paper we propose a new framework for global exploration which tries to guide random exploration towards the region of attraction of low-level local optima. The main idea originated by the use of smoothing techniques (based on gaussian convolutions): the possibility of applying a smoothing transformation not to the objective function but to the result of local searches seems to have never been explored yet. Although an exact smoothing of the results of local searches is impossible to implement, in this paper we propose a computational approximation scheme which has proven to be very efficient and (maybe more important) extremely robust in solving large scale global optimization problems with huge numbers of local optima. ∗Email: b.addis@ing.unifi.it Dip. Sistemi e Informatica Università di Firenze †Email: locatell@di.unito.it Dip. Informatica Università di Torino ‡Email: schoen@ing.unifi.it Dip. Sistemi e Informatica Università di Firenze

Efficient Subgraph Matching on Billion Node Graphs <=SEP=> The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear indexing space. Unfortunately, for very large graphs, super-linear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billion-node graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on super-linear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on web-scale graph data.

A Temporal Logic of Nested Calls and Returns <=SEP=> Model checking of linear temporal logic (LTL) specifications with respect to pushdown systems has been shown to be a useful tool for analysis of programs with potentially recursive procedures. LTL, however, can specify only regular properties, and properties such as correctness of procedures with respect to pre and post conditions, that require matching of calls and returns, are not regular. We introduce a temporal logic of calls and returns (CARET) for specification and algorithmic verification of correctness requirements of structured programs. The formulas of CARET are interpreted over sequences of propositional valuations tagged with special symbols call and ret. Besides the standard global temporal modalities, CARET admits the abstract-next operator that allows a path to jump from a call to the matching return. This operator can be used to specify a variety of non-regular properties such as partial and total correctness of program blocks with respect to pre and post conditions. The abstract versions of the other temporal modalities can be used to specify regular properties of local paths within a procedure that skip over calls to other procedures. CARET also admits the caller modality that jumps to the most recent pending call, and such caller modalities allow specification of a variety of security properties that involve inspection of the call-stack. Even though verifying context-free properties of pushdown systems is undecidable, we show that model checking CARET formulas against a pushdown model is decidable. We present a tableau construction that reduces our model checking problem to the emptiness problem for a Büchi pushdown system. The complexity of model checking CARET formulas is the same as that of checking LTL formulas, namely, polynomial in the model and singly exponential in the size of the specification. Comments From the 10th International Conference, TACAS 2004, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2004, Barcelona, Spain, March 29 April 2, 2004. This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/203 A Temporal Logic of Nested Calls and Returns ? Rajeev Alur, Kousha Etessami, and P. Madhusudan 1 University of Pennsylvania 2 University of Edinburgh Abstract. Model checking of linear temporal logic (LTL) speci cations Model checking of linear temporal logic (LTL) speci cations with respect to pushdown systems has been shown to be a useful tool for analysis of programs with potentially recursive procedures. LTL, however, can specify only regular properties, and properties such as correctness of procedures with respect to pre and post conditions, that require matching of calls and returns, are not regular. We introduce a temporal logic of calls and returns (CaRet) for speci cation and algorithmic veri cation of correctness requirements of structured programs. The formulas of CaRet are interpreted over sequences of propositional valuations tagged with special symbols call and ret. Besides the standard global temporal modalities, CaRet admits the abstract-next operator that allows a path to jump from a call to the matching return. This operator can be used to specify a variety of non-regular properties such as partial and total correctness of program blocks with respect to pre and post conditions. The abstract versions of the other temporal modalities can be used to specify regular properties of local paths within a procedure that skip over calls to other procedures. CaRet also admits the caller modality that jumps to the most recent pending call, and such caller modalities allow speci cation of a variety of security properties that involve inspection of the call-stack. Even though verifying contextfree properties of pushdown systems is undecidable, we show that model checking CaRet formulas against a pushdown model is decidable. We present a tableau construction that reduces our model checking problem to the emptiness problem for a B uchi pushdown system. The complexity of model checking CaRet formulas is the same as that of checking LTL formulas, namely, polynomial in the model and singly exponential in the size of the speci cation.

Q # : A Quantum Programming Language by Microsoft <=SEP=> The multi-paradigm quantum programming language Q# was analysed and used to study and create novel programs that were able to go beyond the capabilities of any classical program. This language was built by Microsoft to succeed LIQUi|> and is a Domain-Specific Language (DSL) that can be used within Microsoft’s Quantum Development Kit (QDK). The quantum programs are run on a quantum simulator which possesses properties that a real quantum computer would from a behavioural aspect. It uses the .NET Code SDK, allowing for easy creation, building and running of quantum projects via the command line. Initially, the main features and libraries available were studied and experimented with by analysing implementations of the Quantum Teleportation and Deutsch-Jozsa algorithms. Thereafter, an algorithm to solve an arbitrary 2 × 2 matrix system of linear equations was implemented, demonstrating a theoretical quantum speed-up with time complexities of order O(logN) compared to the classical O(N) for sparse matrices. Running the algorithm for a particular matrix achieved results that were within the range of theoretical predictions. Subsequently, as an extension to the project, concepts within Quantum Game Theory were explored. This led to the Mermin-Peres Magic Square game successfully being simulated in Q#; a game where no classical winning strategy exists, yet a quantum strategy is able to win in all possible cases of the game.

Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm <=SEP=> A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are used as dataset. The segmented cell nuclei results on microscopic breast cancer images using Gram-Schmidt method shows that the most of MSE values are below 0.1 and the average MSE of segmented cell nuclei results is 0.08. The average accuracy of separated cell nuclei counting using cluster validation algorithm is 73% compares with the manual counting.

TCS-TRA-1260 TCS Technical Report ZDD-Based Computation of the Number of Paths in a Graph by <=SEP=> (Abstract) Counting the number of paths in a graph, for example the number of nonintersecting (or self-avoiding) rook paths joining opposite corners of an n × n grid, is not an easy problem since no mathematical formula is found and the number becomes too large to enumerate one by one except for small graphs. We have implemented software based on the ZDD technique in Knuth's " The Art of Computer Programming " carefully so as to achieve better memory efficiency, and have succeeded in computing the exact numbers for some graphs that were not known until now.

Approximate model for interactive-tendon driven mechanism of a multiple-DoFs myoelectric prosthetic hand <=SEP=> For practical use, a myoelectric prosthetic hand needs to (1) have a human-like structure, (2) be lightweight, (3) have multiple degrees of freedom (DoFs), and (4) have a high grip force. We have developed a myoelectric prosthetic hand with an interactive-tendon driven mechanism. This paper describes the control method by which the interactive-tendon driven mechanism produces fine and precise actions, as well as an approximate model for the control method. The approximate model was developed based on a geometry model and an equilibrium model for the joint torque. Experimental results show that the joint motions of the actual robotic hand are controlled with errors of between 9 and 15% using the approximate model.

Modeling punctuation prediction as machine translation <=SEP=> Punctuation prediction is an important task in Spoken Language Translation. The output of speech recognition systems does not typically contain punctuation marks. In this paper we analyze different methods for punctuation prediction and show improvements in the quality of the final translation output. In our experiments we compare the different approaches and show improvements of up to 0.8 BLEU points on the IWSLT 2011 English French Speech Translation of Talks task using a translation system to translate from unpunctuated to punctuated text instead of a language model based punctuation prediction method. Furthermore, we do a system combination of the hypotheses of all our different approaches and get an additional improvement of 0.4 points in BLEU.

Sneaking around concatMap: efficient combinators for dynamic programming <=SEP=> We present a framework of dynamic programming combinators that provides a high-level environment to describe the recursions typical of dynamic programming over sequence data in a style very similar to algebraic dynamic programming (ADP). Using a combination of type-level programming and stream fusion leads to a substantial increase in performance, without sacrificing much of the convenience and theoretical underpinnings of ADP.
 We draw examples from the field of computational biology, more specifically RNA secondary structure prediction, to demonstrate how to use these combinators and what differences exist between this library, ADP, and other approaches.
 The final version of the combinator library allows writing algorithms with performance close to hand-optimized C code.

Resting state networks and consciousness Alterations of multiple resting state network connectivity in physiological , pharmacological , and pathological consciousness states <=SEP=> *Correspondence: Athena Demertzi , Coma Science Group, Cyclotron Research Center, Allée du 6 août no 8, Sart Tilman B30, University of Liège, 4000 Liège, Belgium. e-mail: a.demertzi@ulg.ac.be In order to better understand the functional contribution of resting state activity to conscious cognition, we aimed to review increases and decreases in functional magnetic resonance imaging (fMRI) functional connectivity under physiological (sleep), pharmacological (anesthesia), and pathological altered states of consciousness, such as brain death, coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state.The reviewed resting state networks were the DMN, left and right executive control, salience, sensorimotor, auditory, and visual networks. We highlight some methodological issues concerning resting state analyses in severely injured brains mainly in terms of hypothesis-driven seed-based correlation analysis and data-driven independent components analysis approaches. Finally, we attempt to contextualize our discussion within theoretical frameworks of conscious processes.We think that this “lesion” approach allows us to better determine the necessary conditions under which normal conscious cognition takes place. At the clinical level, we acknowledge the technical merits of the resting state paradigm. Indeed, fast and easy acquisitions are preferable to activation paradigms in clinical populations. Finally, we emphasize the need to validate the diagnostic and prognostic value of fMRI resting state measurements in non-communicating brain damaged patients.

tress and anxiety detection using facial cues from videos <=SEP=> This study develops a framework for the detection and analysis of stress/anxiety emotional states through video-recorded facial cues. A thorough experimental protocol was established to induce systematic variability in affective states (neutral, relaxed and stressed/anxious) through a variety of external and internal stressors. The analysis was focused mainly on non-voluntary and semi-voluntary facial cues in order to estimate the emotion representation more objectively. Features under investigation included eye-related events, mouth activity, head motion parameters and heart rate estimated through camerabased photoplethysmography. A feature selection procedure was employed to select the most robust features followed by classification schemes discriminating between stress/anxiety and neutral states nxiety tress link rate ead motion with reference to a relaxed state in each experimental phase. In addition, a ranking transformation was proposed utilizing self reports in order to investigate the correlation of facial parameters with a participant perceived amount of stress/anxiety. The results indicated that, specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.

Unsupervised Concept Categorization and Extraction from Scientific Document Titles <=SEP=> This paper studies the automated categorization and extraction of scientific concepts from titles of scientific articles, in order to gain a deeper understanding of their key contributions and facilitate the construction of a generic academic knowledgebase. Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the first phase of our algorithm we proposePhraseType, a probabilistic generative model which exploits textual features and limited POS tags to broadly segment text snippets into aspect-typed phrases. We extend this model to simultaneously learn aspect-specific features and identify academic domains in multi-domain corpora, since the two tasks mutually enhance each other. In the second phase, we propose an approach based on adaptor grammars to extract fine grained concept mentions from the aspect-typed phrases without the need for any external resources or human effort, in a purely data-driven manner. We apply our technique to study literature from diverse scientific domains and show significant gains over state-of-the-art concept extraction techniques. We also present a qualitative analysis of the results obtained.

Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution <=SEP=> Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.

A Secure and Efficient Uniqueness-and-Anonymity-Preserving Remote User Authentication Scheme for Connected Health Care <=SEP=> Connected health care has several applications including telecare medicine information system, personally controlled health records system, and patient monitoring. In such applications, user authentication can ensure the legality of patients. In user authentication for such applications, only the legal user/patient himself/herself is allowed to access the remote server, and no one can trace him/her according to transmitted data. Chang et al. proposed a uniqueness-and-anonymity-preserving remote user authentication scheme for connected health care (Chang et al., J Med Syst 37:9902, 2013). Their scheme uses the user’s personal biometrics along with his/her password with the help of the smart card. The user’s biometrics is verified using BioHashing. Their scheme is efficient due to usage of one-way hash function and exclusive-or (XOR) operations. In this paper, we show that though their scheme is very efficient, their scheme has several security weaknesses such as (1) it has design flaws in login and authentication phases, (2) it has design flaws in password change phase, (3) it fails to protect privileged insider attack, (4) it fails to protect the man-in-the middle attack, and (5) it fails to provide proper authentication. In order to remedy these security weaknesses in Chang et al.’s scheme, we propose an improvement of their scheme while retaining the original merit of their scheme. We show that our scheme is efficient as compared to Chang et al.’s scheme. Through the security analysis, we show that our scheme is secure against possible attacks. Further, we simulate our scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. In addition, after successful authentication between the user and the server, they establish a secret session key shared between them for future secure communication.

S3: A Symbolic String Solver for Vulnerability Detection in Web Applications <=SEP=> Motivated by the vulnerability analysis of web programs which work on string inputs, we present S3, a new symbolic string solver. Our solver employs a new algorithm for a constraint language that is expressive enough for widespread applicability. Specifically, our language covers all the main string operations, such as those in JavaScript. The algorithm first makes use of a symbolic representation so that membership in a set defined by a regular expression can be encoded as string equations. Secondly, there is a constraint-based generation of instances from these symbolic expressions so that the total number of instances can be limited. We evaluate S3 on a well-known set of practical benchmarks, demonstrating both its robustness (more definitive answers) and its efficiency (about 20 times faster) against the state-of-the-art.

NANOFIBRILLATED CELLULOSE AS PAPER ADDITIVE IN EUCALYPTUS PULPS <=SEP=> In this work, the physical and mechanical properties of bleached Eucalyptus pulp reinforced with nanofibrillated cellulose (NFC) are compared with those of traditional beaten pulp used in the making of writing/printing and offset printing papers. For this purpose, three different types of hardwood slurries were prepared: beaten pulps, unbeaten pulps reinforced with NFC, and slightly beaten pulps also reinforced with NFC. Physical and mechanical tests were performed on handsheets from these different slurries. The results showed that adding NFC to unbeaten pulps results in physical and mechanical properties similar to those in pulps used for printing/writing papers. Nevertheless, the best results were obtained in slurries previously beaten at slight conditions and subsequently reinforced with NFC. These results demonstrate that the addition of NFC allows a reduction in beating intensity without decreasing the desired mechanical properties for this specific purpose.

Make Evasion Harder: An Intelligent Android Malware Detection System <=SEP=> To combat the evolving Android malware attacks, in this paper, instead of only using Application Programming Interface (API) calls, we further analyze the different relationships between them and create higher-level semantics which require more efforts for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning to make predictions. Promising experimental results based on real sample collections from Comodo Cloud Security Center demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.

Learning Latent Vector Spaces for Product Search <=SEP=> We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.

Improving Login Authorization by Providing Graphical Password (Security) <=SEP=> Usable security has unique usability challenges because the need for security often means that standard humancomputer-interaction approaches cannot be directly applied. An important usability goal for authentication systems is to support users in selecting better passwords. Users often create memorable passwords that are easy for attackers to guess, but strong system-assigned passwords are difficult for users to remember. So researchers of modern days have gone for alternative methods where in graphical pictures are used as passwords. Graphical passwords essentially use images or representation of images as passwords. Human brain is good in remembering picture than textual character. There are various graphical password schemes or graphical password software in the market. However, very little research has been done to analyze graphical passwords that are still immature. There for, this project work merges persuasive selective click points and password guessing resistant protocol. The major goal of this work is to reduce the guessing attacks as well as encouraging users to select more random, and difficult passwords to guess. Well known security threats like brute force attacks and dictionary attacks can be successfully abolished using this method.

A novel corner-fed patch to reduce cross-polarization for a microstrip antenna array <=SEP=> This paper presents a novel corner-fed microstrip square patch. On the patch there is a slot along the diagonal. With this slot, the current distribution on the patch is changed. The currents related to co-polarization become more dominant and those related to cross-polarization become more symmetrical. These two factors result in a reduction in the cross-polarization level. An eight element shunt connected series-fed linear array has been designed, manufactured and tested. The measured results show that the cross-polarization level is reduced by 10.5 dB, while the other performances of the array are nearly the same as those of a standard one.

Advertising Keyword Recommendation based on Supervised Link Prediction in Multi-Relational Network <=SEP=> In the sponsored search system, advertisers bid on keywords that are related to their products or services to participate in repeated auctions and display their ads on the search result pages. Since there is a huge inventory of possible terms, instructive keyword recommendation is an important component to help advertisers optimize their campaigns and improve ad monetization. In this paper, by constructing a heterogeneous network which contains four types of links between advertisers and keywords based on different data resources and mining complex representations of network structure and task-guided attributes of nodes, we propose an approach to keyword recommendation based on supervised link prediction in multi-relational network. This method can retrieve ample candidates and provide informative ranking scores for recommended list of keywords, experimental results with real sponsored search data validate the effectiveness of the proposed algorithm in producing valuable keywords for advertisers.