Query: Head pose estimation based on face symmetry analysis

Relevant passages:
The CMU Pose, Illumination, and Expression Database <=SEP=> In the Fall of 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this the CMU Pose, Illumination, and Expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.

Combining Models and Exemplars for Face Recognition : An Illuminating Example <=SEP=> We propose a modeland exemplar-based approach for face recognition. This problem has been previously tackled using either models or exemplars, with limited success. Our idea uses models to synthesize many more exemplars, which are then used in the learning stage of a face recognition system. To demonstrate this, we develop a statistical shape-fromshading model to recover face shape from a single image, and to synthesize the same face under new illumination. We then use this to build a simple and fast classifier that was not possible before because of a lack of training data.

From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination <=SEP=> Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearance-based methods which, unlike previous appearance-based approaches, require only a small set of training images to generate a rich representation that models this variability. Speci cally, from as few as three images of an object in xed pose seen under slightly varying but unknown lighting, a surface and an albedo map are reconstructed. These are then used to generate synthetic images with large variations in pose and illumination and thus build a representation useful for object recognition. Our methods have been tested within the domain of face recognition on a subset of the Yale Face Database B containing 4050 images of 10 faces seen under variable pose and illumination. This database was speci cally gathered for testing these generative methods. Their performance is shown to exceed that of popular existing methods.

Face recognition: A literature survey <=SEP=> As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Optical Flow Constraints on Deformable Models with Applications to Face Tracking <=SEP=> Optical flow provides a constraint on the motion of a deformable model. We derive and solve a dynamic system incorporating flow as a hard constraint, producing a model-based least-squares optical flow solution. Our solution also ensures the constraint remains satisfied when combined with edge information, which helps combat tracking error accumulation. Constraint enforcement can be relaxed using a Kalman filter, which permits controlled constraint violations based on the noise present in the optical flow information, and enables optical flow and edge information to be combined more robustly and efficiently. We apply this framework to the estimation of face shape and motion using a 3D deformable face model. This model uses a small number of parameters to describe a rich variety of face shapes and facial expressions. We present experiments in extracting the shape and motion of a face from image sequences which validate the accuracy of the method. They also demonstrate that our treatment of optical flow as a hard constraint, as well as our use of a Kalman filter to reconcile these constraints with the uncertainty in the optical flow, are vital for improving the performance of our system.

Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking <=SEP=> Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.

A New Word Language Model Evaluation Metric for Character Based Languages <=SEP=> Perplexity is a widely used measure to evaluate word prediction power of a word-based language model. It can be computed independently and has shown good correlation with word error rate (WER) in speech recognition. However, for character based languages, character error rate (CER) is commonly used instead of WER as the measure for speech recognition, although language model is still word based. Due to the fact that different word segmentation strategies may result in different word vocabulary for the same text corpus, in many cases, wordbased perplexity is incompetent to evaluate the combined effect of word segmentation and language model training to predict final CER. In this paper, a new word-based language model evaluation measure is proposed to account for the effect of word segmentation and the goal of predicting CER. Experiments were conducted on Chinese speech recognition. Compared to the traditional word-based perplexity, the new measure is more robust to word segmentation and shows much more consistent correlation with CER in a large vocabulary continuous Chinese speech recognition task.

Joint wavelet-based spectrum sensing and FBMC modulation for cognitive mmWave small cell networks <=SEP=> Millimetre-wave (mmWave) 5G communications is an emerging technology to enhance the capacity of existing systems by thousand-fold improvement. Heterogeneous networks employing densely distributed small cells can optimise the available coverage and throughput of 5G systems. Efficiently utilising the spectrum bands by small cells is one of the approaches that will considerably increase the available data rate and capacity of the heterogeneous networks. This challenging task can be achieved by spectrum sensing capability of cognitive radios and new modulation techniques for data transmission. In this study, a wavelet-based filter bank is proposed for spectrum sensing and modulation in 5G heterogeneous networks. The proposed technique can mitigate the spectral leakage and interference by adapting the subcarriers according to cognitive information provided by wavelet packet based spectrum sensing (WPSS) and lowering sidelobes using wavelet-based filter bank multicarrier modulation. The performance improvement of WPSS compared with Fourier-based spectrum sensing is verified in terms of power spectral density comparison and probabilities of detection and false alarm. Meanwhile, the bit error rate performance demonstrates the superiority of the proposed wavelet-based system compared with its Fourier-based counterpart over the 60 GHz mmWave channel.

Model-Driven Game Development Addressing Architectural Diversity and Game Engine-Integration <=SEP=> 

The NumPy Array: A Structure for Efficient Numerical Computation <=SEP=> In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.

Survey of Event Correlation Techniques for Attack Detection in Early Warning Systems <=SEP=> In the context of early warning systems for detecting Internet worms and other attacks, event correlation techniques are needed for two reasons. First, network attack detection is usually based on distributed sensors, e.g. intrusion detection systems. During attacks but even in normal operation, the generated amount of events is hard to handle in order to evaluate the current attack situation for a larger network. Thus, the concept of event or alert correlation has been introduced. This survey was motivated by recent work on early warning systems. We summarize and clarify the typical terminology used in this context and present a requirement analysis from an early warning system’s point of view. In the main part of this survey, we summarize and classify event correlation techniques as described in the literature.

Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped <=SEP=> Robotics controllers often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. Simulation can aid in optimizing these controllers if parameters learned in simulation transfer to hardware. Unfortunately, this is often not the case in legged locomotion, necessitating learning directly on hardware. This motivates using data-efficient learning techniques like Bayesian Optimization (BO) to minimize collecting expensive data samples. BO is a black-box data-efficient optimization scheme, though its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge, with a focus on bipedal locomotion. In our previous work, we proposed a feature transformation that projected a 16-dimensional locomotion controller to a 1-dimensional space using knowledge of human walking. When optimizing a human-inspired neuromuscular controller in simulation, this feature transformation enhanced sample efficiency of BO over traditional BO with a Squared Exponential kernel. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot, in both simulation and hardware. We present three different walking controllers and two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.

GENDER AND LEADERSHIP BEHAVIOR AMONG SENIOR PASTORS <=SEP=> Research findings have been equivocal as to the existence of gender difference in leadership across settings. However, some studies based on transformational leadership theory and employing the Multifactor Leadership Questionnaire to measure leadership behaviors have indicated a difference in the leadership styles of female and male leaders. This study sought to test whether there were gender differences in the use of transformational, transactional, and laissez-faire leadership behaviors by Senior Pastors in an Australian Christian Church. The study did not detect any significant gender differences in leadership behaviors. Gender and Leadership Behavior among Senior Pastors Gender and Leadership Leadership has been typically a male prerogative in most sectors of society, including the corporate, political, military, and church sectors (Eagly & Karau, 2002). However, over the last 30 years women have made steady progress in moving into leadership roles. In 1972, women held 17% of all management and professional positions in Fortune 500 Companies. By 2006, this number had grown to 50.3% (Hoyt, Simon, and Reid, 2009). Women typically tend to occupy lower and middle management ranks while men cluster around the most powerful positions at the top. Women managers still receive significantly less remuneration for their work, with female managers receiving 24 percent less pay than men performing the equivalent function (Haslam and Ryan, 2008). Nonetheless, despite continuing 138 Asian Journal of Pentecostal Studies 12:2 (2009) inequity, it is clear that women are gradually occupying an increasing number of management and leadership positions. Although women have increasingly gained access to supervisory and middle management positions, they remain quite rare as elite leaders. For example, in 2006 women represented 5.2% of top earners, 14.7% of board members, 7.9% of the highest earners, and less than 2% of CEOs in Fortune 500 Companies (Hoyt, Simon, and Reid, 2009). This phenomenon has been explained by use of the idea of a “glass ceiling” – an invisible barrier preventing the rise of women within leadership ranks (Haslam & Ryan, 2008). Eagly and Karau (2002) describe it as “a barrier of prejudice and discrimination that excludes women from higher leadership positions” (p. 573). It is evident in the lower number of women in leadership positions, and particularly in high-level leadership positions. Yukl (2006) observes that “In the complete absence of sex-based discrimination, the number of women in chief executive positions in business and government should be close to 50 percent” (p. 427). A variety of explanations have been offered for the existence of gender-based discrimination in the appointment of organizational leaders. These include: (1) gender stereotypes suggesting that men are more suited to leadership positions and that women are more suited to support roles (Yukl, 2006); (2) overt sexism in the workplace (Schwartz, 1971); (3) perceived incompatibilities between women’s abilities and the requirements of leadership (Arvey, 1979); (4) women’s competing responsibilities in the home (Schwartz, 1994); and (5) women’s fear of success (Horner, 1972). The explanations are not mutually exclusive and they may combine to create significant barriers to the advancement of women. Martell and DeSmet (2001) found that a contributing reason for the glass ceiling and the continued absence of women in the upper ranks of management is “the existence of gender-based stereotypes in the leadership domain” (p. 1227). Gender stereotypes are “categorical beliefs composed of the traits and behavioral characteristics assigned to women and men only on the basis of the group label” (Martell & DeSmet, 2001, p. 1223). Such stereotypes serve as a type of expectation regarding the likely abilities of group members and, if left unchallenged, can translate into discriminatory behavior. Eagly and Karau (2002) point out that a “potential for prejudice exists when social perceivers hold a stereotype about a social group that is incongruent with the attributes that are thought to be required for success in certain classes of social roles” (p. 574). Prejudice against women as leaders Fogarty, Gender and Leadership 139 “follows from the incongruity that many people perceive between the characteristics of women and the requirements of leader roles” (Eagly & Karau, 2002, p. 574). Gender and Leadership within Christian Churches Within Christian churches gender based discrimination has been reinforced by theological perspectives (Barron, 1990; Bridges, 1998; Franklin, 2008; Scholz, 2005). The case that women are forbidden by scripture and church tradition to assume leadership within the church has been made frequently (Barron, 1990). This exclusion has been predominantly based on two Pauline texts (1 Timothy 2:11-15 and 1 Corinthians 14:33-34) and a broader theological position which sees men and women as being ontologically equal but functionally different. Its basic logic is that “God designed women to be subordinate to men in role and function” and therefore “women should not operate in positions of authority over men” (Franklin, 2008, 14). For example, Piper and Grudem (1991) state that “we are persuaded that the Bible teaches that only men should be pastors and elders. That is, men should bear primary responsibility for Christlike leadership and teaching in the church. So it is unbiblical, we believe, and therefore detrimental, for women to assume this role” (p. 60-61). Complementary to this theological position is the suggestion that women do not have the capacity for effective church leadership (Bridges, 1998). Piper (1991) exemplifies this position when he writes: “At the heart of mature masculinity is a sense of benevolent responsibility to lead, provide for and protect women in ways appropriate to a man’s differing relationships” and “At the heart of mature femininity is a freeing disposition to affirm, receive, and nurture strength and leadership from worthy men in ways appropriate to a woman’s differing relationships’ (p. 35-36). The understanding portrayed is that men have the capacity to lead and that women do not. The assumption implicit within this understanding is that leadership does not involve affirming and nurturing behaviors.

A double-tail sense amplifier for low-voltage SRAM in 28nm technology <=SEP=> A double-tail sense amplifier (DTSA) is designed as a drop-in replacement for a conventional SRAM sense amplifier (SA), to enable a robust read operation at low voltages. A pre-amplification stage helps reduce the offset voltage of the sense amplifier by magnifying the input of the regeneration stage. The self-timed regenerative latch simplifies the timing logic so the DTSA can replace the SA with no area overhead. A test chip in 28nm technology achieves 56% error rate reduction at 0.44V. The proposed scheme achieves 50mV of VDDmin reduction compared to commercial SRAM with a faster timing option that demonstrates a smaller bitline swing.

Sampling cube: a framework for statistical olap over sampling data <=SEP=> Sampling is a popular method of data collection when it is impossible or too costly to reach the entire population. For example, television show ratings in the United States are gathered from a sample of roughly 5,000 households. To use the results effectively, the samples are further partitioned in a multidimensional space based on multiple attribute values. This naturally leads to the desirability of OLAP (Online Analytical Processing) over sampling data. However, unlike traditional data, sampling data is inherently uncertain, i.e., not representing the full data in the population. Thus, it is desirable to return not only query results but also the confidence intervals indicating the reliability of the results. Moreover, a certain segment in a multidimensional space may contain none or too few samples. This requires some additional analysis to return trustable results.
 In this paper we propose a Sampling Cube framework, which efficiently calculates confidence intervals for any multidimensional query and uses the OLAP structure to group similar segments to increase sampling size when needed. Further, to handle high dimensional data, a Sampling Cube Shell method is proposed to effectively reduce the storage requirement while still preserving query result quality.

Diagnostic assessment of the borg MOEA for many-objective product family design problems <=SEP=> The recently introduced Borg multiobjective evolutionary algorithm (MOEA) framework features auto-adaptive search that tailors itself to effectively explore different problem spaces. A key auto-adaptive feature of the Borg MOEA is the dynamic allocation of search across a suite of recombination and mutation operators. This study explores the application of the Borg MOEA on a real-world product family design problem: the severely constrained, ten objective General Aviation Aircraft (GAA) problem. The GAA problem represents a promising benchmark problem that strongly highlights the importance of using auto-adaptive search to discover how to exploit multiple recombination strategies cooperatively. The auto-adaptive behavior of the Borg MOEA is rigorously compared against its ancestor algorithm, the ε-MOEA, by employing global sensitivity analysis across each algorithm's feasible parameter ranges. This study provides the first Sobol' sensitivity analysis to determine the individual and interactive parameter sensitivities of MOEAs on a real-world many-objective problem.

Foundations of RDF⋆ and SPARQL⋆ (An Alternative Approach to Statement-Level Metadata in RDF) <=SEP=> The standard approach to annotate statements in RDF with metadata has a number of shortcomings including data size blow-up and unnecessarily complicated queries. We propose an alternative approach that is based on nesting of RDF triples and of query patterns. The approach allows for a more compact representation of data and queries, and it is backwards compatible with the standard. In this paper we present the formal foundations of our proposal and of different approaches to implement it. More specifically, we formally capture the necessary extensions of the RDF data model and its query language SPARQL, and we define mappings based on which our extended notions can be converted back to ordinary RDF and SPARQL. Additionally, for such type of mappings we define two desirable properties, information preservation and query result equivalence, and we show that the introduced mappings possess these properties.

Learning to Decode for Future Success <=SEP=> We introduce a general strategy for improving neural sequence generation by incorporating knowledge about the future. Our decoder combines a standard sequence decoder with a ‘soothsayer’ prediction function Q that estimates the outcome in the future of generating a word in the present. Our model draws on the same intuitions as reinforcement learning, but is both simpler and higher performing, avoiding known problems with the use of reinforcement learning in tasks with enormous search spaces like sequence generation. We demonstrate our model by incorporating Q functions that incrementally predict what the future BLEU or ROUGE score of the completed sequence will be, its future length, and the backwards probability of the source given the future target sequence. Experimental results show that future prediction yields improved performance in abstractive summarization and conversational response generation and the state-of-the-art in machine translation, while also enabling the decoder to generate outputs that have specific properties.

Aspect extraction for opinion mining with a deep convolutional neural network <=SEP=> In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about. We used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.

Design, fabrication and preliminary results of a novel anthropomorphic hand for humanoid robotics: RCH-1 <=SEP=> Among social infrastructure technologies, robot technology (RT) is expected to play an important role in solving the problems of both decrease of birth rate and increase of elderly people in the 21st century, specially but not only in Japan. In order to achieve this objective, the new generation of personal robots should be capable of a natural communication with humans by expressing human-like emotion. In this sense, human hands play a fundamental role in communication, because they have grasping, sensing and emotional expression ability. This paper presents the recent results of the collaboration between the Takanishi Lab of Waseda University, Tokyo, Japan, and the Arts Lab of Scuola Superiore Sant'Anna, Pisa, Italy, and ROBOCASA. In this paper, the development of a novel anthropomorphic hand for humanoid robotics RCH-1 (ROBOCASA Hand No.1) and its integration into a humanoid robotic platform, named WE-4RII (Waseda Eye No.4 Refined II) is presented.

Educational System for the Holy Quran and Its Sciences for Blind and Handicapped People Based on Google Speech API <=SEP=> There is a great need to provide educational environments for blind and handicapped people. There are many Islamic websites and applications dedicated to the educational services for the Holy Quran and Its Sciences (Quran Recitations, the interpretations, etc.) on the Internet. Unfortunately, blind and handicapped people could not use these services. These people cannot use the keyboard and the mouse. In addition, the ability to read and write is essential to benefit from these services. In this paper, we present an educational environment that allows these people to take full advantage of the scientific materials. This is done through the interaction with the system using voice commands by speaking directly without the need to write or to use the mouse. Google Speech API is used for the universal speech recognition after a preprocessing and post processing phases to improve the accuracy. For blind people, responses of these commands will be played back through the audio device instead of displaying the text to the screen. The text will be displayed on the screen to help other people make use of the system.

Comparison of eSports and Traditional Sports Consumption Motives <=SEP=> With recognition of the need for studying eSports in this interactive digital communication era, this study explored 14 motivational factors affecting the time spent on eSports gaming. Using a sample of 515 college students and athletic event attendees, we further compared eSports game patterns to their non-eSport or traditional sport involvements (game participation, game attendance, sports viewership, sports readership, sports listenership, Internet usage specific to sports, and purchase of team merchandise). Multiple regression results indicated that competition and skill had a statistically significant impact on the time spent on eSports games while peer pressure had marginal significance. Related to the overall findings, developing tailored messages that drives consumption behaviors of target audiences to specific eSports games will provide a better chance for marketers to fulfill their strategic goals of increased purchasing and larger market shares. Understanding that the interest in competition and skill are critical to eSports gamers may influence marketers to focus on creating games and opportunities for gamers to compete against each other and give tangible rewards to the winner. The use of peer pressure may be another motivational factor for playing. Consequently, those marketing dollars could be spent more on the interactive nature of game design. The subsequent analysis on cross-validation check suggests that the results of the regression analysis could be generalized.

Web 3.0 Emerging <=SEP=> While Web 3.0 technologies are difficult to define precisely, the outline of emerging applications has become clear over the past year. We can thus essentially view Web 3.0 as semantic Web technologies integrated into, or powering, large-scale Web applications. The base of Web 3.0 applications resides in the resource description framework (RDF) for providing a means to link data from multiple Web sites or databases. With the SPARQL query language, a SQL-like standard for querying RDF data, applications can use native graph-based RDF stores and extract RDF data from traditional databases.

Multiple Handshakes Security of TLS 1.3 Candidates <=SEP=> The Transport Layer Security (TLS) protocol is by far the most widely deployed protocol for securing communications and the Internet Engineering Task Force (IETF) is currently developing TLS 1.3 as the next-generation TLS protocol. The TLS standard features multiple modes of handshake protocols and supports many combinational running of successive TLS handshakes over multiple connections. Although each handshake mode is now well-understood in isolation, their composition in TLS 1.2 remains problematic, and yet it is critical to obtain practical security guarantees for TLS. In this paper, we present the first formal treatment of multiple handshakes protocols of TLS 1.3 candidates. First, we introduce a multi-level&stage security model, an adaptation of the Bellare-Rogaway authenticated key exchange model, covering all kinds of compositional interactions between different TLS handshake modes and providing reasonably strong security guarantees. Next, we prove that candidate handshakes of TLS 1.3 draft meet our strong notion of multiple handshakes security. Our results confirm the soundness of TLS 1.3 security protection design. Such a multi-level&stage approach is convenient for analyzing the compositional design of the candidates with different session modes, as they establish dependencies of multiple sessions. We also identify the triple handshake attack of Bhargavan et al. on TLS 1.2 within our multiple handshakes security model. We show generically that the proposed fixes (RFC 7627) for TLS 1.2 offer good protection against multiple handshakes attacks.

Improving Design of Systems Supporting Creativity-intensive Processes - A Cross-industry Focus Group Evaluation <=SEP=> 

Exploring Users' Attitudes and Intentions toward the Adoption of Cloud Computing in Saudi Arabia: an Empirical Investigation <=SEP=> Over the past few years, cloud computing has evolve d as one of the major advances in the field of Information Technology (IT) utilizing third-party s ervices. Therefore, trust in cloud vendors as well as the determination of potential risks, such as privacy a nd security issues, are crucial for ensuring the su ccessful adoption of an appropriate cloud. Prior research ha ve ddressed the technical aspects of cloud-based environments, such as cloud virtualization, scalabi lity and security. Nevertheless, it is argued that the biggest obstacle of cloud computing is not technolo gical, rather it is perceptual or attitudinal. The adoption of cloud computing has been central to several scho larly research areas, particularly user acceptance. This study presents an extended Technology Acceptance Mo del (TAM), which integrates Trust (TR), Anxiety (ANX) and Perceived Risk (PR), to investigate users ’ attitudes and intentions toward the adoption of c l ud computing. The proposed model was empirically exami ned using the Structural Equation Model (SEM) to analyze data gathered by a survey of both IT profes sionals and end users. The results herein suggested that trust, anxiety and PR can be successfully integrate d within the TAMs. Trust has demonstrated to have a strong positive influence on Perceived Ease of Use (PEOU), but it had no significant effects on Perceived Use fuln ss (PU). Both anxiety and PR were found to have signif icant negative effects on PEOU and PU. In addition, Behavioral Intention (BI) to use the cloud can be p r dicted by trust, attitudes and PU, as PR was show n t have no significant effect on BI. The proposed model, including PR, trust and anxiety , has been demonstrated to be a true predictor of user intentions, toward t he use of cloud computing, within the context of Sa udi Arabia.

Software-Defined LANs for Interconnected Smart Environment <=SEP=> In this paper, we propose a solution to delegate the control and the management of the network connecting the many devices of a smart environment to a software entity, while keeping end-users in control of what is happening in their networks. For this, we rely on the logical manipulation of all connected devices through device abstraction and network programmability. Applying Software Defined Networking (SDN) principles, we propose a software-based solution that we call Software-Defined LANs in order to interconnect devices of smart environments according to the services the users are requesting or expecting.We define the adequate virtualization framework based on Virtual Objects and Communities of Virtual Objects. Using these virtual entities, we apply the SDN architectural principles to define a generic architecture that can be applied to any smart environment. Then we describe a prototype implementing these concepts in the home networking context, through a scenario in which users of two different homes can easily interconnect two private but shareable DLNA devices in a dedicated video-delivery SD-LAN. Finally we provide a discussion of the benefits and challenges of our approach regarding the generalization of SDN principles, autonomic features, Internet of Things scalability, security and privacy aspects enabled by SD-LANs intrinsic properties.

Named entity recognition on Indonesian Twitter posts using long short-term memory networks <=SEP=> The task of Named-Entity Recognition (NER) can support the higher-level tasks such as question answering, text summarization, and information retrieval. This work views NER on Indonesian Twitter posts as a sequence labeling problem using supervised machine learning approach. The architecture used is Long Short-Term Memory Networks (LSTMs), with word embedding and POS tag as the model features. As the result, our model can give a performance with an F1 score of 77.08%.

Scrutinizing WPA2 Password Generating Algorithms in Wireless Routers <=SEP=> A wireless router is a networking device that enables a user to set up a wireless connection to the Internet. A router can offer a secure channel by cryptographic means which provides authenticity and confidentiality. Nowadays, almost all routers use a secure channel by default that is based on Wi-Fi Protected Access II (WPA2). This is a security protocol which is believed not to be susceptible to practical key recovery attacks. However, the passwords should have sufficient entropy to avert bruteforce attacks. In this paper, we compose a strategy on how to reverse-engineer embedded routers. Furthermore, we describe a procedure that can instantly gather a complete wireless authentication trace which enables an offline password recovery attack. Finally, we present a number of use cases where we identify extremely weak password generating algorithms in various routers which are massively deployed in The Netherlands. The algorithms are used to generate the default WPA2 password. Such a password is loaded during device initialization and hardware reset. Users that did not explicitly change their wireless password are most likely vulnerable to practical attacks which can recover their password within minutes. A stolen password allows an adversary to abuse someone else’s internet connection, for instance compromising the firewall, making a fraudulent transaction or performing other criminal activities. Together with the Dutch National Cyber Security Centre we have initiated a responsible disclosure procedure. However, since these routers are also used by many other companies in various countries, our findings seem to relate an international industry wide security issue.

A Simplification-Translation-Restoration Framework for Cross-Domain SMT Applications <=SEP=> Integration of domain specific knowledge into a general purpose statistical machine translation (SMT) system poses challenges due to insufficient bilingual corpora. In this paper we propose a simplification-translation-restoration (STR) framework for domain adaptation in SMT by simplifying domain specific segments of a text. For an in-domain text, we identify the critical segments and modify them to alleviate the data sparseness problem in the out-domain SMT system. After we receive the translation result, these critical segments are then restored according to the provided in-domain knowledge. We conduct experiments on an English-toChinese translation task in the medical domain and evaluate each step of the STR framework. The translation results show significant improvement of our approach over the out-domain and the naïve in-domain SMT systems. 用於跨領域統計式機器翻譯系統之簡化-翻譯-還原架構