title: Wireless Sensor Networks for Early Detection of Forest Fires

abstract: We present the design and evaluation of a wireless sensor network for early detection of forest fires. We first present the key aspects in modeling forest fires. We do this by analyzing the Fire Weather Index (FWI) System, and show how its different components can be used in designing efficient fire detection systems. The FWI System is one of the most comprehensive forest fire danger rating systems in North America, and it is backed by several decades of forestry research. The analysis of the FWI System could be of interest in its own right to researchers working in the sensor network area and to sensor manufacturers who can optimize the communication and sensing modules of their products to better fit forest fire detection systems. Then, we model the forest fire detection problem as a k-coverage problem in wireless sensor networks. In addition, we present a simple data aggregation scheme based on the FWI System. This data aggregation scheme significantly prolongs the network lifetime, because it only delivers the data that is of interest to the application. We validate several aspects of our design using simulation.


title: A novel CMOS-memristor based inverter circuit design

abstract: Computers transform to the smaller, faster and more reliable devices year by year. Accordingly designing more efficient logic gates, as the basic blocks of the VLSI chips, are essential for circuit designers. Since the integration reached to its limits through the conventional technologies mainly the CMOS based VLSI designs the quest for the novel promising technologies was commenced. Memristor is a passive element that has been fabricated recently and taken increasing attentions. Less area and non-volatility are the most important properties of this element. Also Memristors compatibility for fabrication over CMOS structures makes it as a breakthrough for the VLSI design enhancement. Memristor based logic computation are developed primarily. This paper presents a novel CMOS-Memristor based inverter with acceptable performance and also has nonvolatile output. This feature makes this circuit to be utilized not only as an alternative for conventional inverter circuit but also as a SRAM memory cell with the capability of input negation for specific applications. This includes a 0.18um PMOS and two Memristors. The output voltage level, non-volatility and read/write time are analyzed and simulated with Hspice©.


title: Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

abstract: Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by \emph{adversarial examples} that are generated by adding small but purposeful distortions to natural examples. Previous studies to defend against adversarial examples mostly focused on refining the DNN models, but have either shown limited success or required expensive computation. We propose a new strategy, \emph{feature squeezing}, that can be used to harden DNN models by detecting adversarial examples. Feature squeezing reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample. By comparing a DNN model's prediction on the original input with that on squeezed inputs, feature squeezing detects adversarial examples with high accuracy and few false positives. This paper explores two feature squeezing methods: reducing the color bit depth of each pixel and spatial smoothing. These simple strategies are inexpensive and complementary to other defenses, and can be combined in a joint detection framework to achieve high detection rates against state-of-the-art attacks.


title: End-to-End Deep Learning for Person Search.

abstract: Existing person re-identification (re-id) benchmarks and algorithms mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be found from whole images. To close the gap, we investigate how to localize and match query persons from the scene images without relying on the annotations of candidate boxes. Instead of breaking it down into two separate tasks—pedestrian detection and person re-id, we propose an end-to-end deep learning framework to jointly handle both tasks. A random sampling softmax loss is proposed to effectively train the model under the supervision of sparse and unbalanced labels. On the other hand, existing benchmarks are small in scale and the samples are collected from a few fixed camera views with low scene diversities. To address this issue, we collect a largescale and scene-diversified person search dataset, which contains 18,184 images, 8,432 persons, and 99,809 annotated bounding boxes1. We evaluate our approach and other baselines on the proposed dataset, and study the influence of various factors. Experiments show that our method achieves the best result.


title: 6: Can differences in obstetric outcomes be explained by differences in the care provided?

abstract: 6 Can differences in obstetric outcomes be explained by differences in the care provided? William Grobman For the Eunice Kennedy Shriver National Institute of Health and Human Development, Maternal-Fetal Units Network, Bethesda, MD OBJECTIVE: Although many obstetric outcomes have been suggested as quality indicators, there is little direct evidence that institutional differences in these indicators are related to differences in the care provided. The objective of this study was to determine whether variation in the frequency of potential quality indicators could be related to differences in care. STUDY DESIGN: Data were obtained by trained abstractors, with ongoing data edits and audits, from all deliveries on 365 randomly selected days at 25 hospitals over a three-year period. Four outcome measures, selected a priori and rigorously defined, were chosen as potential quality indicators: severe postpartum hemorrhage (PPH), maternal peripartum infection (INF), perineal trauma (3rd or 4th degree laceration) at SVD (LAC), and a composite adverse neonatal outcome (NEO). Because the frequency of outcomes may be related to other patient, physician (e.g. years of experience), and institutional (e.g. in-house obstetrician 24 hours daily) factors, these characteristics were assessed for their associations with the above outcomes of interest through the use of hierarchical logistic regression, which was used to account for potential confounding and clustering of observations. Selected care processes were then placed into the model to assess whether these were independently associated with the outcomes. RESULTS: Data were collected on 115,502 women. After adjustment for patient, physician, and institutional characteristics, differential use of labor induction, cesarean delivery, and episiotomy, were associated with the outcomes of interest (Table). CONCLUSION: After controlling for differences in patients, physicians, and institutional factors, several care processes were found to be associated with variation in predefined adverse outcomes. These associations support the use of these outcomes as quality indicators, and also may suggest process measures that also are reasonable to use as quality indicators. 7 ROLO study: a randomized control trial of low glycemic index diet to prevent macrosomia in euglycemic women Jennifer Walsh, Rhona Mahony, Michael Foley, Fionnuala McAuliffe UCD Obstetrics and Gynaecology, School of Medicine and Medical Science, Dublin, Ireland OBJECTIVE: The macrosomic fetus is predisposed to a variety of adverse obstetric and neonatal outcomes, and significantly increases the risk of birth complications for the mother. In the long term, infants that are at the highest end of the distribution for weight are more likely to be obese in childhood. Eating primarily a high glycaemic carbohydrate diet can result in feto-placental overgrowth, excessive maternal weight gain and fetal macrosomia. Our hypothesis was that altering the source of maternal dietary carbohydrate could prevent macrosomia in euglycemic women. STUDY DESIGN: This is a randomised control intervention trial of 720 secundigravid women whose first baby was macrosomic (birth weight 4.0 Kg). Women were randomized to receive either no dietary intervention or a low glycemic index (GI) diet from early pregnancy. Those with pre-existing or previous gestational diabetes were excluded. The low GI group attended a small group dietetic session at 14-18 weeks’ gestation and continued the diet until term with written information and regular re-enforcement. The primary endpoint of the study was the difference in birth weight. Secondary outcomes included gestational weight gain and the development of glucose intolerance. RESULTS: There was no difference between the control and intervention groups in birthweight, customized birthweight centile or birthweight z-score. Fetal macrosomia recurred in 51% (n 184) of the intervention group and in 52 % (n 187) of controls. Women in the low GI group had significantly less gestational weight gain than those without any dietary intervention. (12.2kg vs 13.6kg p 0.01). The incidence of glucose intolerance was also significantly less in the low GI arm with 16.7% having a glucose challenge test result of 7.8 mmol/L compared with 23.4% of controls (p 0.04). CONCLUSION: Infant birthweight is a complex interplay of environmental and genetic factors. Despite a significant reduction in maternal gestational weight gain and glucose intolerance, a low GI diet in pregnancy has no effect on infant birthweight.


title: Coordinating with the Future: The Anticipatory Nature of Representation

abstract: Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent's predictive capabilities. Therefore, we argue that the ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goal-directed: the mind serves to coordinate with the future.


title: A Convergent Gambling Estimate of the Entropy of English

abstract: In his original paper on the subject, Shannon found upper and lower bounds for the entropy of printed English based on the number of trials required for a subject to guess subsequent symbols in a given text. The guessing approach precludes asymptotic consistency of either the upper or lower bounds except for degenerate ergodic processes. Shannon's technique of guessing the next symbol is altered by having the subject place sequential bets on the next symbol of text. If S_{n} denotes the subject's capital after n bets at 27 for 1 odds, and if it is assumed that the subject knows the underlying probability distribution for the process X , then the entropy estimate is \hat{H}_{n}(X)=(1-(1/n) \log_{27}S_{n}) \log_{2} 27 bits/symbol. If the subject does not know the true probability distribution for the stochastic process, then \hat{H}_{n}(X) is an asymptotic upper bound for the true entropy. If X is stationary, E\hat{H}_{n}(X)\rightarrowH(X), H(X) being the true entropy of the process. Moreover, if X is ergodic, then by the Shannon-McMillan-Breiman theorem \hat{H}_{n}(X)\rightarrowH(X) with probability one. Preliminary indications are that English text has an entropy of approximately 1.3 bits/symbol, which agrees well with Shannon's estimate. In his original paper on the subject, Shannon found upper and lower bounds for the entropy of printed English based on the number of trials required for a subject to guess subsequent symbols in a given text. The guessing approach precludes asymptotic consistency of either the upper or lower bounds except for degenerate ergodic processes. Shannon's technique of guessing the next symbol is altered by having the subject place sequential bets on the next symbol of text. If S_{n} denotes the subject's capital after n bets at 27 for 1 odds, and if it is assumed that the subject knows the underlying probability distribution for the process X , then the entropy estimate is \hat{H}_{n}(X)=(1-(1/n) \log_{27}S_{n}) \log_{2} 27 bits/symbol. If the subject does not know the true probability distribution for the stochastic process, then \hat{H}_{n}(X) is an asymptotic upper bound for the true entropy. If X is stationary, E\hat{H}_{n}(X)\rightarrowH(X), H(X) being the true entropy of the process. Moreover, if X is ergodic, then by the Shannon-McMillan-Breiman theorem \hat{H}_{n}(X)\rightarrowH(X) with probability one. Preliminary indications are that English text has an entropy of approximately 1.3 bits/symbol, which agrees well with Shannon's estimate. In his original paper on the subject, Shannon found upper and lower bounds for the entropy of printed English based on the number of trials required for a subject to guess subsequent symbols in a given text. The guessing approach precludes asymptotic consistency of either the upper or lower bounds except for degenerate ergodic processes. Shannon's technique of guessing the next symbol is altered by having the subject place sequential bets on the next symbol of text. If S_{n} denotes the subject's capital after n bets at 27 for 1 odds, and if it is assumed that the subject knows the underlying probability distribution for the process X , then the entropy estimate is \hat{H}_{n}(X)=(1-(1/n) \log_{27}S_{n}) \log_{2} 27 bits/symbol. If the subject does not know the true probability distribution for the stochastic process, then \hat{H}_{n}(X) is an asymptotic upper bound for the true entropy. If X is stationary, E\hat{H}_{n}(X)\rightarrowH(X), H(X) being the true entropy of the process.Moreover, if X is ergodic, then by the Shannon-McMillan-Breiman theorem \hat{H}_{n}(X)\rightarrowH(X) with probability one. Preliminary indications are that English text has an entropy of approximately 1.3 bits/symbol, which agrees well with Shannon's estimate. In his original paper on the subject, Shannon found upper and lower bounds for the entropy of printed English based on the number of trials required for a subject to guess subsequent symbols in a given text. The guessing approach precludes asymptotic consistency of either the upper or lower bounds except for degenerate ergodic processes. Shannon's technique of guessing the next symbol is altered by having the subject place sequential bets on the next symbol of text. If S_{n} denotes the subject's capital after n bets at 27 for 1 odds, and if it is assumed that the subject knows the underlying probability distribution for the process X , then the entropy estimate is \hat{H}_{n}(X)=(1-(1/n) \log_{27}S_{n}) \log_{2} 27 bits/symbol. If the subject does not know the true probability distribution for the stochastic process, then \hat{H}_{n}(X) is an asymptotic upper bound for the true entropy. If X is stationary, E\hat{H}_{n}(X)\rightarrowH(X), H(X) being the true entropy of the process. Moreover, if X is ergodic, then by the Shannon-McMillan-Breiman theorem \hat{H}_{n}(X)\rightarrowH(X) with probability one. Preliminary indications are that English text has an entropy of approximately 1.3 bits/symbol, which agrees well with Shannon's estimate.


title: iDriver - Human Machine Interface for Autonomous Cars

abstract: Modern cars are equipped with a variety of sensors, advanced driver assistance systems and user interfaces nowadays. To benefit from these systems and to optimally support the driver in his monitoring and decision making process, efficient human-machine interfaces play an important part. This paper describes the second release of iDriver, an iPad software solution which was developed to navigate and remote control autonomous cars, to give access to live sensor data and useful data about the car state, as there are, e.g., current speed, engine and gear state. The software was used and evaluated in our two fully autonomous research cars "Spirit of Berlin" and "Made in Germany".


title: An Ontology Design Pattern for Chess Games

abstract: We present an ontology pattern describing records of chess games. Besides being an interesting modeling problem by itself, the fact that chess is one of the most popular game in the world with hundreds of millions of active players, including several millions online players led to a huge amount of chess game data available from various online chess databases. Furthermore, these data are becoming available as linked data with links to prominent datasets, such as DBPedia or GeoNames. However, there is still a lack of a well-designed schema that can ease linked data publishing of chess games. Our pattern is hoped to fill that gap.


title: Spectrum requirement for IoT health sector in Indonesia

abstract: Internet of Things (IoT) has grown rapidlly in various sectors including health sector. There are many technologies used for IoT such as Wi-Fi, Bluetooth, and LTE-Advanced. IoT for health sector in Indonesia is used to improve health services based on telemedicine services roadmap by Indonesian Ministry of Health. This services include teleECG, teleRadiology and teleUSG which utilize mobile technology. In Indonesia, there is a tendency that IoT will be implemented on a large scale. Meanwhile, Indonesia is expected to experience deficiency of 500 MHz mobile spectrum in 2020. Therefore, the calculation of spectrum for telemedicine services is urgently needed. This paper aims to calculate the spectrum that is required for health sector in Indonesia by using mobile technology especially LTE. We use the method of general LTE planning equation. The result is the requirements of spectrum for telemedicine services will be growing 14.44% until 2025. On 2025, telemedicine services are predicted to use 165,47 kHz. Regardless of the spectrum needed by telemedicine services is only in the order of kHz, it should be considered since it will enlarge spectrum deficiency in Indonesia. The government of Indonesia should prepare about strategic spectrum management to anticipate the massive growing of IoT application, such as a roadmap of IoT development and a regulation of specific frequency in the nearest future.


A Methodology for the Integration of Service Repositories


title: Design and development of air temperature and relative humidity monitoring system with AVR processor based web server

abstract: In this paper improved design of previously developed system for temperature and relative humidity measurement and data acquisition is described. Hardware of the system is extended with Arduino Uno and Ethernet Shield boards. SHT11 sensor is used for obtaining information about air temperature and relative humidity. Storing the results is on the micro SD card in CSV file. Main software improvement is adding of embedded Web server which enables immediate access to sensor readings through Internet. The designed system is tested under different conditions and in all tests the system worked stable and accurately.


title: The Parting of the Sulawesi Sea: U.S. Strategy and Transforming the Terrorist Transit Triangle

abstract: [ILLUSTRATION OMITTED] WHILE MOST U.S. efforts in overseas contingency operations focus on the Middle East, Afghanistan, and the Horn of Africa, other efforts center in Southeast Asia on the tri-border region of the Philippines, Indonesia, and Malaysia around the Sulawesi Sea. This area, more commonly known as the "T3"--the Terrorist Transit Triangle--remains the U.S. Pacific Command's primary area of interest for counterterrorism in the Pacific and its primary focus of bilateral military engagement within Southeast Asia. This article discusses the various threats in the T3 region and the reactions of the three nations that surround it. Looking for a Needle in a Stack of Needles The expanse known as T3 centers on the Sulawesi Sea, which separates the Philippines, Indonesia, and Malaysia. The area is much larger and more remote than most Americans appreciate. Just the water area of the Sulawesi Sea is larger than the combined areas of the states of Texas and Louisiana. Even defining the water area is complicated because the Sulawesi Sea separates two archipelagic nations--Indonesia and the Philippines--and adjoins eastern Malaysia. To the southwest, the Sulawesi intersects one of the most important waterways for energy security in the Pacific, the Makassar Straights, which contain the world's second largest operational liquid natural gas field. (1) Several island chains bisect the T3, providing natural corridors for transit. They provided trading routes during the precolonial era in Southeast Asia. Today, along with legitimate trade, they provide relatively safe transit routes for criminal and terrorist elements and for the movement of weapons and personnel to the region's two infamous terrorist groups, the Jemaah Islamiyah (JI) in Southeast Asia, and the Abu Sayyaf Group in the southern Philippines. Four main island chains transit the T3 area. These chains have many unofficial names--"rat lines," "infiltration routes," "terrorist corridors"--and are referred to differently by Department of Defense, Department of State, various intelligence agencies, and Department of Justice officials. From west to east, the first route (Route 1) originates and terminates in northern Mindanao or the central Philippines, and it extends west to the Philippine island of Palawan. Palawan is, in turn, a waypoint for transit into the eastern Malaysian city of Sandakan, another port in the state of Sabah. [ILLUSTRATION OMITTED] The second route (Route 2), a direct line along the Sulu archipelago island chain, is the most obvious and the most infamous of these routes. This line originates and terminates in the southwestern Mindanao city of Zamboanga, and it extends southwest down a chain of islands from Basilan to Jolo, the tiny island of Tapul, Tawi-Tawi, and Sibutu. The route then splits toward either the island of Timbunmata and the Malaysian port town of Tawao or toward the island of Ligitan and the Malaysian port of Lahaddatu. Alternatively, it splits toward the Indonesian port of Nunukan or turns back northwesterly towards Sandakan at the terminus of Route 1. The third route (Route 3) originates and terminates in southern Mindanao near General Santos City, and it crosses the T3 via a group of small islands that lead to Tahuna Island, which is off the extreme northeast tip ofthe island of Sulawesi. From Tahuna, the route follows a southerly path directly to the Indonesian port cities of Manado and Bitung. The fourth and final route (Route 3A) is a branch of the General Santos City-Manado route. It only recently came to light in the wake of sectarian violence in Poso on central Sulawesi in February and March 2007. Weapons and trained cadre intended for Indonesian fundamentalist organizations moved out of the Philippines via this route to support the violence in Poso. (2) The route originates and terminates near General Santos City,and it veers southeast to Karkarekelong Island just inside Indonesian territory, then proceeds on a southerly course to the Indonesian port city of Ternate, on the island of Halmahera. …


title: Speech enhancement using Long Short-Term Memory based recurrent Neural Networks for noise robust Speaker Verification

abstract: In this paper we propose to use a state-of-the-art Deep Recurrent Neural Network (DRNN) based Speech Enhancement (SE) algorithm for noise robust Speaker Verification (SV). Specifically, we study the performance of an i-vector based SV system, when tested in noisy conditions using a DRNN based SE front-end utilizing a Long Short-Term Memory (LSTM) architecture. We make comparisons to systems using a Non-negative Matrix Factorization (NMF) based front-end, and a Short-Time Spectral Amplitude Minimum Mean Square Error (STSA-MMSE) based front-end, respectively. We show in simulation experiments that a male-speaker and text-independent DRNN based SE front-end, without specific a priori knowledge about the noise type outperforms a text, noise type and speaker dependent NMF based front-end as well as a STSA-MMSE based front-end in terms of Equal Error Rates for a large range of noise types and signal to noise ratios on the RSR2015 speech corpus.


title: Static and dynamic novelty detection methods for jet engine health monitoring

abstract: Novelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution of energy across the harmonics of a rotating shaft can be learnt by a support vector machine model of normality. The second model is a dynamic model partially learnt from data using an expectation–maximization-based method. This model uses a Kalman filter to fuse performance data in order to characterize normal engine behaviour. Deviations from normal operation are detected using the normalized innovations squared from the Kalman filter.


title: Bro: A System for Detecting Network Intruders in Real-Time

abstract: Abstract We describe Bro, a stand-alone system for detecting network intruders in real-time by passively monitoring a network link over which the intruder's traffic transits. We give an overview of the system's design, which emphasizes high-speed (FDDI-rate) monitoring, real-time notification, clear separation between mechanism and policy, and extensibility. To achieve these ends, Bro is divided into an `event engine' that reduces a kernel-filtered network traffic stream into a series of higher-level events, and a `policy script interpreter' that interprets event handlers written in a specialized language used to express a site's security policy. Event handlers can update state information, synthesize new events, record information to disk, and generate real-time notifications via syslog. We also discuss a number of attacks that attempt to subvert passive monitoring systems and defenses against these, and give particulars of how Bro analyzes the six applications integrated into it so far: Finger, FTP, Portmapper, Ident, Telnet and Rlogin. The system is publicly available in source code form.


title: Isolation Enhancement Between Two Closely Packed Antennas

abstract: This paper introduces a coupling element to enhance the isolation between two closely packed antennas operating at the same frequency band. The proposed structure consists of two antenna elements and a coupling element which is located in between the two antenna elements. The idea is to use field cancellation to enhance isolation by putting a coupling element which artificially creates an additional coupling path between the antenna elements. To validate the idea, a design for a USB dongle MIMO antenna for the 2.4 GHz WLAN band is presented. In this design, the antenna elements are etched on a compact low-cost FR4 PCB board with dimensions of 20times40times1.6 mm3. According to our measurement results, we can achieve more than 30 dB isolation between the antenna elements even though the two parallel individual planar inverted F antenna (PIFA) in the design share a solid ground plane with inter-antenna spacing (Center to Center) of less than 0.095 lambdao or edge to edge separations of just 3.6 mm (0.0294 lambdao). Both simulation and measurement results are used to confirm the antenna isolation and performance. The method can also be applied to different types of antennas such as non-planar antennas. Parametric studies and current distribution for the design are also included to show how to tune the structure and control the isolation.


title: A Transitivity Aware Matrix Factorization Model for Recommendation in Social Networks

abstract: Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users who have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix factorization techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model in a principled way. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.


title: Accuracy-configurable adder for approximate arithmetic designs

abstract: Approximation can increase performance or reduce power consumption with a simplified or inaccurate circuit in application contexts where strict requirements are relaxed. For applications related to human senses, approximate arithmetic can be used to generate sufficient results rather than absolutely accurate results. Approximate design exploits a tradeoff of accuracy in computation versus performance and power. However, required accuracy varies according to applications, and 100% accurate results are still required in some situations. In this paper, we propose an accuracy-configurable approximate (ACA) adder for which the accuracy of results is configurable during runtime. Because of its configurability, the ACA adder can adaptively operate in both approximate (inaccurate) mode and accurate mode. The proposed adder can achieve significant throughput improvement and total power reduction over conventional adder designs. It can be used in accuracy-configurable applications, and improves the achievable tradeoff between performance/power and quality. The ACA adder achieves approximately 30% power reduction versus the conventional pipelined adder at the relaxed accuracy requirement.


title: Transfer of Route Learning from Virtual to Real Environments

abstract: The authors investigated the extent to which route learning in a virtual environment (VE) transfers to the real world. In Experiment 1, active VE exploration, on its own or with a map, produced better transfer of training than either no VE training at all or passive VE training; however, transfer was achieved after shorter training times with the map. Experiment 2 demonstrated that VE + map training was not superior to training with a map alone, and Experiment 3 demonstrated that the poorer performances observed after passive VE training were not simply due to a lack of attention but to the lack of active navigational decisions. The authors concluded that the present VE technology does not provide better route learning than studying a map.


title: Opportunities and Limits of Remote Timing Attacks

abstract: Many algorithms can take a variable amount of time to complete depending on the data being processed. These timing differences can sometimes disclose confidential information. Indeed, researchers have been able to reconstruct an RSA private key purely by querying an SSL Web server and timing the results. Our work analyzes the limits of attacks based on accurately measuring network response times and jitter over a local network and across the Internet. We present the design of filters to significantly reduce the effects of jitter, allowing an attacker to measure events with 15-100μs accuracy across the Internet, and as good as 100ns over a local network. Notably, security-related algorithms on Web servers and other network servers need to be carefully engineered to avoid timing channel leaks at the accuracy demonstrated in this article.


title: Compressive Sensing encryption modes and their security

abstract: Compressed Sensing may offer confidentiality with a good level of security that comes at limited extra costs. By this means, encryption can be added right into the sampling process. Previous proposals of Compressive Sensing encryption schemes are based on the randomness introduced by the sensing matrix. Once the matrix is fixed, the encryption becomes deterministic and therefore it can hardly be secure when encrypting more than a single signal. The work presented in this paper is built on the theoretical results on the secrecy of one-time Compressive Sensing encryption, and it proposes practical realizations, which extend this kind of encryption so that multiple signals may be encrypted under a single key. The confidentiality of the signal is thereby solely given by the inherent secrecy of the compressed measurements. In contrast to recent suggestions, the modes of operation defined in this paper are able to encrypt signals with different energy without exposing the signals energy to an eavesdropper. A general design for Compressive Sensing encryption modes is presented along with two realizations: one designed for parallel processing and another one that is self-synchronizing. The secrecy of the proposed modes is reduced to the secrecy of known and trusted cryptographic primitives. Compressive Sensing encryption modes are useful in a wide range of practical applications by providing a joint end-to-end encryption and compression that starts at the sensor level.


title: Computational Approaches to Sentence Completion

abstract: This paper studies the problem of sentence-level semantic coherence by answering SAT-style sentence completion questions. These questions test the ability of algorithms to distinguish sense from nonsense based on a variety of sentence-level phenomena. We tackle the problem with two approaches: methods that use local lexical information, such as the n-grams of a classical language model; and methods that evaluate global coherence, such as latent semantic analysis. We evaluate these methods on a suite of practice SAT questions, and on a recently released sentence completion task based on data taken from five Conan Doyle novels. We find that by fusing local and global information, we can exceed 50% on this task (chance baseline is 20%), and we suggest some avenues for further research.


title: Compact Physics-Based Circuit Models for Graphene Nanoribbon Interconnects

abstract: Physics-based equivalent circuit models are presented for armchair and zigzag graphene nanoribbons (GNRs), and their conductances have been benchmarked against those of carbon nanotubes and copper wires. Atomically thick GNRs with smooth edges can potentially have smaller resistances compared with copper wires with unity aspect ratios for widths below 8 nm and stacks of noninteracting GNRs can have substantially smaller resistivities compared to Cu wires. It is shown that rough edges can increase the resistance of narrow GNRs by an order of magnitude. This fact highlights the need for patterning methods that can produce relatively smooth edges to fabricate low resistance GNR interconnects.


title: Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion

abstract: In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today, the latency between consecutive arriving poses would damage the user experience in mobile AR/VR. Thus, a visual-inertial based real-time motion tracking for mobile AR/VR is proposed in this paper. By means of high frequency and passive outputs from the inertial sensor, the real-time performance of arriving poses for mobile AR/VR is achieved. In addition, to alleviate the jitter phenomenon during the visual-inertial fusion, an adaptive filter framework is established to cope with different motion situations automatically, enabling the real-time 6-DoF motion tracking by balancing the jitter and latency. Besides, the robustness of the traditional visual-only based motion tracking is enhanced, giving rise to a better mobile AR/VR performance when motion blur is encountered. Finally, experiments are carried out to demonstrate the proposed method, and the results show that this work is capable of providing a smooth and robust 6-DoF motion tracking for mobile AR/VR in real-time.


title: Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions

abstract: Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely \emph{unsupervised} fashion. It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge. We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.


title: Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation

abstract: An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the former teaches the summarization model how to look for salient questioning-worthy details, and the latter teaches the model how to rewrite a summary which is a directed-logical subset of the input document. We also propose novel multi-task architectures with high-level (semantic) layer-specific sharing across multiple encoder and decoder layers of the three tasks, as well as soft-sharing mechanisms (and show performance ablations and analysis examples of each contribution). Overall, we achieve statistically significant improvements over the state-of-the-art on both the CNN/DailyMail and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also present several quantitative and qualitative analysis studies of our model's learned saliency and entailment skills.


title: Pruning Filters and Classes: Towards On-Device Customization of Convolutional Neural Networks

abstract: In recent years, we have witnessed more and more mobile applications based on deep learning. Widely used as they may be, those applications provide little flexibility to cater to the diversified needs of different groups of users. For users facing a classification problem, it is natural that some classes are more important to them, while the rest are not. We thus propose a lightweight method that allows users to prune the unneeded classes together with associated filters from convolutional neural networks (CNNs). Such customization can result in substantial reduction in computational costs at test time. Early results have shown that after pruning the Network-in-Network (NIN) model on CIFAR-10 dataset\cite{lim2013network} down to a 5-class classifier, we can trade a 3\% loss in accuracy for a 1.63$\times$ gain in energy consumption and a 1.24$\times$ improvement in latency when experimenting on an off-the-shelf smartphone, while the procedure incurs with very little overhead. After pruning, the custom-tailored model can still achieve a higher classification accuracy than the unmodified classifier because of a smaller problem space that more accurately reflects users' needs.


Creating Capsule Wardrobes from Fashion Images ( Supplemental Material )


title: Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity

abstract: We examine two recent artificial intelligence (AI) based deep learning algorithms for visual blending in convolutional neural networks (Mordvintsev et al. 2015, Gatys et al. 2015). To investigate the potential value of these algorithms as tools for computational creativity research, we explain and schematize the essential aspects of the algorithms' operation and give visual examples of their output. We discuss the relationship of the two algorithms to human cognitive science theories of creativity such as conceptual blending theory and honing theory, and characterize the algorithms with respect to generation of novelty and aesthetic quality.


title: 22q11.2 deletion syndrome in diverse populations

abstract: 22q11.2 deletion syndrome (22q11.2 DS) is the most common microdeletion syndrome and is underdiagnosed in diverse populations. This syndrome has a variable phenotype and affects multiple systems, making early recognition imperative. In this study, individuals from diverse populations with 22q11.2 DS were evaluated clinically and by facial analysis technology. Clinical information from 106 individuals and images from 101 were collected from individuals with 22q11.2 DS from 11 countries; average age was 11.7 and 47% were male. Individuals were grouped into categories of African descent (African), Asian, and Latin American. We found that the phenotype of 22q11.2 DS varied across population groups. Only two findings, congenital heart disease and learning problems, were found in greater than 50% of participants. When comparing the clinical features of 22q11.2 DS in each population, the proportion of individuals within each clinical category was statistically different except for learning problems and ear anomalies (P < 0.05). However, when Africans were removed from analysis, six additional clinical features were found to be independent of ethnicity (P ≥ 0.05). Using facial analysis technology, we compared 156 Caucasians, Africans, Asians, and Latin American individuals with 22q11.2 DS with 156 age and gender matched controls and found that sensitivity and specificity were greater than 96% for all populations. In summary, we present the varied findings from global populations with 22q11.2 DS and demonstrate how facial analysis technology can assist clinicians in making accurate 22q11.2 DS diagnoses. This work will assist in earlier detection and in increasing recognition of 22q11.2 DS throughout the world.


title: Study on adaptive ability of Gaussian mixture background model

abstract: Gaussian mixture background model is an online parameterized statistical model,and the presenting way of pixel sample pattern observed from time window has great influence on the model's learning result.According to the characteristics of dynamic background changes,issues such as the stability and plasticity of modal learning,the modal residual and activation,which affected the model's adaptive ability,were studied.The simulation results show that Gaussian mixture background model has a robust selective adaptability to gradual change,but a limited adaptability to transient variation of background configuration provided by modal residual and activation mechanism.


title: Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

abstract: Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.


title: A compact rotary series elastic actuator for knee joint assistive system

abstract: Precise and large torque generation, back-drivability, low output impedance, and compactness of hardware are important requirements for human assistive robots. In this paper, a compact rotary series elastic actuator (cRSEA) is designed considering these requirements. To magnify the torque generated by an electric motor in the limited space of the compact device, a worm gear is utilized. However, the actual torque amplification ratio provided by the worm gear is different from the nominal speed reduction ratio due to friction, which makes the controller design challenging. In this paper, the friction effect is considered in the model of cRSEA, and a robust control algorithm is designed to precisely control the torque output in the presence of nonlinearities such as the friction. The mechanical design and dynamic model of the proposed device and the design of a robust control algorithm are discussed, and actuation performance is verified by experiments.


title: Proper complex random processes with applications to information theory

abstract: The covariance of complex random variables and processes, when defined consistently with the corresponding notion for real random variables, is shown to be determined by the usual complex covariance together with a quantity called the pseudo-covariance. A characterization of uncorrelatedness and wide-sense stationarity in terms of covariance and pseudo-covariance is given. Complex random variables and processes with a vanishing pseudo-covariance are called proper. It is shown that properness is preserved under affine transformations and that the complex-multivariate Gaussian density assumes a natural form only for proper random variables. The maximum-entropy theorem is generalized to the complex-multivariate case. The differential entropy of a complex random vector with a fixed correlation matrix is shown to be maximum if and only if the random vector is proper, Gaussian, and zero-mean. The notion of circular stationarity is introduced. For the class of proper complex random processes, a discrete Fourier transform correspondence is derived relating circular stationarity in the time domain to uncorrelatedness in the frequency domain. An application of the theory is presented. >


Personal Factors Associated with Leadership: A Survey of the Literature


title: Digital manipulatives: new toys to think with

abstract: In many educational settings, manipulative materials (such as Cuisenaire Rods and Pattern Blocks) play an important role in children’s learning, enabling children to explore mathematical and scientific concepts (such as number and shape) through direct manipulation of physical objects. Our group at de MJT Media Lab has developed a new generation of “digital manipulatives”-computationallyenhanced versions of traditional children’s toys. These new manipulatives enable children to explore a new set of concepts (im particular, “systems concepts” such as feedback and emergence) that have previously been considered “too advanced” for children to learn. In this paper, we discuss four of our digital manipulatives-computationallyaugmented versions of blocks, beads, balls, and badges.


title: The Author-Topic-Community model for author interest profiling and community discovery

abstract: In this paper, we propose a generative model named the author-topic-community (ATC) model for representing a corpus of linked documents. The ATC model allows each author to be associated with a topic distribution and a community distribution as its model parameters. A learning algorithm based on variational inference is derived for the model parameter estimation where the two distributions are essentially reinforcing each other during the estimation. We compare the performance of the ATC model with two related generative models using first synthetic data sets and then real data sets, which include a research community data set, a blog data set, a news-sharing data set, and a microblogging data set. The empirical results obtained confirm that the proposed ATC model outperforms the existing models for tasks such as author interest profiling and author community discovery. We also demonstrate how the inferred ATC model can be used to characterize the roles of users/authors in online communities.


title: Undressing the fungal cell wall/cell membrane--the antifungal drug targets.

abstract: Being external, the fungal cell wall plays a crucial role in the fungal life. By covering the underneath cell, it offers mechanical strength and acts as a barrier, thus protecting the fungus from the hostile environment. Chemically, this cell wall is composed of different polysaccharides. Because of their specific composition, the fungal cell wall and its underlying plasma membrane are unique targets for the development of drugs against pathogenic fungal species. The objective of this review is to consolidate the current knowledge on the antifungal drugs targeting the cell wall and plasma membrane, mainly of Aspergillus and Candida species – the most prevalent fungal pathogens, and also to present challenges and questions conditioning the development of new antifungal drugs targeting the cell wall.


title: JUMPER: Learning When to Make Classification Decisions in Reading

abstract: In early years, text classification is typically accomplished by feature-based machine learning models; recently, deep neural networks, as a powerful learning machine, make it possible to work with raw input as the text stands. However, exiting end-to-end neural networks lack explicit interpretation of the prediction. In this paper, we propose a novel framework, JUMPER, inspired by the cognitive process of text reading, that models text classification as a sequential decision process. Basically, JUMPER is a neural system that scans a piece of text sequentially and makes classification decisions at the time it wishes. Both the classification result and when to make the classification are part of the decision process, which is controlled by a policy network and trained with reinforcement learning. Experimental results show that a properly trained JUMPER has the following properties: (1) It can make decisions whenever the evidence is enough, therefore reducing total text reading by 30-40% and often finding the key rationale of prediction. (2) It achieves classification accuracy better than or comparable to state-of-the-art models in several benchmark and industrial datasets.


title: A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials

abstract: Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word"big-data", we refer to this challenge as"micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.


title: Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure

abstract: Model-based compression is an effective, facilitating, and expanded model of neural network models with limited computing and low power. However, conventional models of compression techniques utilize crafted features [2,3,12] and explore specialized areas for exploration and design of large spaces in terms of size, speed, and accuracy, which usually have returns Less and time is up. This paper will effectively analyze deep auto compression (ADC) and reinforcement learning strength in an effective sample and space design, and improve the compression quality of the model. The results of compression of the advanced model are obtained without any human effort and in a completely automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher than the manual compression model for VGG-16 in ImageNet.


title: Real-Time and Spatio-Temporal Crowd-Sourced Social Network Data Publishing with Differential Privacy

abstract: Nowadays gigantic crowd-sourced data from mobile devices have become widely available in social networks, enabling the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of crowd-sourced social network data to the public will pose considerable threats to mobile users’ privacy. In this paper, we investigate the problem of real-time spatio-temporal data publishing in social networks with privacy preservation. Specifically, we consider continuous publication of population statistics and design RescueDP—an online aggregate monitoring framework over infinite streams with $w$ -event privacy guarantee. Its key components including adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. Moreover, we further propose an enhanced RescueDP with neural networks to accurately predict the values of statistics and improve the utility of released data. Both RescueDP and the enhanced RescueDP are proved satisfying $w$ -event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two $w$ -event privacy-assured representative methods. Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.


title: Mathematically defined tissue engineering scaffold architectures prepared by stereolithography

abstract: The technologies employed for the preparation of conventional tissue engineering scaffolds restrict the materials choice and the extent to which the architecture can be designed. Here we show the versatility of stereolithography with respect to materials and freedom of design. Porous scaffolds are designed with computer software and built with either a poly(d,l-lactide)-based resin or a poly(d,l-lactide-co-e-caprolactone)-based resin. Characterisation of the scaffolds by micro-computed tomography shows excellent reproduction of the designs. The mechanical properties are evaluated in compression, and show good agreement with finite element predictions. The mechanical properties of scaffolds can be controlled by the combination of material and scaffold pore architecture. The presented technology and materials enable an accurate preparation of tissue engineering scaffolds with a large freedom of design, and properties ranging from rigid and strong to highly flexible and elastic


title: Subset feature learning for fine-grained category classification

abstract: Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy.


title: A low-phase-noise 0.004-ppm/step DCXO with guaranteed monotonicity in the 90-nm CMOS process

abstract: This paper presents an integrated 26-MHz digitally controlled crystal oscillator (DCXO) for GSM standard that achieves a phase noise of -140 dBc/Hz at 1 kHz and -152 dBc/Hz at 10 kHz offset. The measured supply pushing at 26 MHz is 0.5 ppm/V. Frequency tuning is done by a predistorted 14-bit MOS capacitor digital-to-analog converter (DAC) where the top 10 bits are thermometer coded and the bottom 4 bits are /spl Sigma//spl Delta/ modulated. Monotonicity of /spl sim/0.004 ppm/step across the entire /spl sim/70 ppm tuning range is guaranteed by applying a sliding /spl Sigma//spl Delta/ modulation to the last exercised thermometer element. This DCXO consumes 3 mW at 1.4 V supply and occupies 0.18 mm/sup 2/ in the 90-nm CMOS process.


title: Model-Integrating Microservices: A Vision Paper

abstract: Model-integrating development is a novel approach that aims to provide a comprehensive conceptual framework for the engineering of flexible software systems. The atomic building blocks for architecting model-integrating software are modelintegrating components which support the modular cooperation of flexible models and efficient code at runtime. Model-integrating components achieve flexibility by using models at runtime and operations on them like querying, transforming and interpreting. Microservices achieve flexibility by upgrading whole components at runtime. In this short paper, we sketch the vision of Model-Integrating Microservices (MIMs) that combine the strengths of model-integrating components with microservices to support continuous software engineering. With this early work, we intent to initiate a fruitful discussion about architectural design considerations in the community. 1 Background and Motivation Model-Integrating Development (MID) [Der15] is a novel approach that aims to provide a comprehensive conceptual framework for the design and development of flexible software that can be adapted easily and – in parts – even autonomously [ST09]. Moreover, being able to make changes quickly and to evolve software easily also supports longevity [GRG15]. We observe that these capabilities are similarly useful in the emerging trend of Continuous Software Engineering (CSE). The building blocks for MID are Model-Integrating Components (MoCos) which we have presented and implemented in previous research [DEIE14]. MoCos result from the symbiosis of Component-Based Development (CBD) [SGM02] and Model-Driven Development (MDD) [BCW12] concepts. A MoCo is a non-redundant, reusable and executable combination of logically related models and code in an integrated form where both parts are stored together in one component. In traditional MDD, models (e.g., UML activity models, feature models, component models) are used at design time. In contrast, in MID models are used directly at runtime, too. As a result, the internals of MoCos can be easily monitored and analyzed using model queries, as well as systematically modified using repeatable model transformations. The same techniques can be used for evolving components using an editor during development and maintenance, and adapting them at runtime using an administration panel or an autonomic adaptation manager component. By combining the strengths of modeling languages (e.g., abstraction, separation of conCopyright © 2016 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors. M. Derakhshanmanesh, M. Grieger: Model-Integrating Microservices


title: ASAP: Prioritizing Attention via Time Series Smoothing

abstract: Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users' attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning, pixel-aware preaggregation, and on-demand refresh. We demonstrate that ASAP can improve users' accuracy in identifying long-term deviations in time series by up to 38.4% while reducing response times by up to 44.3%. Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.


Analysis the Gene Expression of Apelin ( APLN ) in Colorectal Cancer Based on Oncomine


title: ReviewChain: untampered product reviews on the blockchain

abstract: Online portals include an increasing amount of user feedback in form of ratings and reviews. Recent research highlighted the importance of this feedback and confirmed that positive feedback improves product sales figures and thus its success. Online portals' operators act as central authorities throughout the overall review process. In the worst case, operators can exclude users from submitting reviews, modify existing reviews, and introduce fake reviews by fictional users. This paper presents R eview C hain , a decentralized review approach. Our approach avoids central authorities by using blockchain technologies, decentralized apps and storage. It enables users to submit and retrieve untampered reviews. We highlight the implementation challenges encountered when realizing our approach on the public Ethereum blockchain. Then, we discuss possible design alternatives and their trade-offs regarding costs, security, and trustworthiness. Finally, we analyze which design decision should be chosen to support specific trade-offs and present resulting combinations of decentralized blockchain technologies, also with conventional centralized technologies.


title: High-Resolution Multibeam and Subbottom Surveys of Submarine Canyons, Deep-Sea Fan Channels, and Gas Seeps Using the MBARI Mapping AUV

abstract: The Monterey Bay Aquarium Research Institute (MBARI) has developed an autonomous seafloor mapping capability for high resolution mapping of the deep ocean seafloor. The MBARI Mapping AUV is a 21" diameter, Dorado class autonomous underwater vehicle (AUV) equipped with a 200 kHz multibeam sonar, 110 kHz and 410 kHz sidescan sonars, and a 2-16 kHz subbottom profiler. All components of the vehicle are rated to 6000 m depth. Using precise navigation and attitude data from an laser-ring-gyro-based inertial navigation system (INS) integrated with a Doppler velocity log sonar (DVL), the Mapping AUV can image the deep-ocean seafloor and shallow subsurface structure with much greater resolution than is possible with hull-mounted sonars. The system can also be operated in an ROV-mounted configuration, allowing near-bottom surveys in very restricted terrain. The Mapping AUV has conducted several autonomous surveys of Monterey Canyon and Smooth Ridge in Monterey Bay, and ROV-mounted surveys of five sites along the Monterey Canyon axis during 2005 and 2006. During June 2006, the Mapping AUV surveyed mounds and channel deposits in the Santa Monica Basin. The bathymetric surveys have achieved a vertical precision of 0.3 m; surveys from a 20 m altitude achieve sub-meter lateral resolution and surveys from 50-100 m altitudes achieve lateral resolutions less than 2 m. The subbottom profile data provides resolution of ~0.1 m with penetrations up to 50 m in soft sediments. The combination of high-resolution bathymetry with closely spaced subbottom profiles allow us to Additional Mapping AUV expeditions are planned for 2006, including surveys of Barclay submarine canyon and the summits of Davidson and Axial seamounts


title: VIROME: a standard operating procedure for analysis of viral metagenome sequences

abstract: One consistent finding among studies using shotgun metagenomics to analyze whole viral communities is that most viral sequences show no significant homology to known sequences. Thus, bioinformatic analyses based on sequence collections such as GenBank nr, which are largely comprised of sequences from known organisms, tend to ignore a majority of sequences within most shotgun viral metagenome libraries. Here we describe a bioinformatic pipeline, the Viral Informatics Resource for Metagenome Exploration (VIROME), that emphasizes the classification of viral metagenome sequences (predicted open-reading frames) based on homology search results against both known and environmental sequences. Functional and taxonomic information is derived from five annotated sequence databases which are linked to the UniRef 100 database. Environmental classifications are obtained from hits against a custom database, MetaGenomes On-Line, which contains 49 million predicted environmental peptides. Each predicted viral metagenomic ORF run through the VIROME pipeline is placed into one of seven ORF classes, thus, every sequence receives a meaningful annotation. Additionally, the pipeline includes quality control measures to remove contaminating and poor quality sequence and assesses the potential amount of cellular DNA contamination in a viral metagenome library by screening for rRNA genes. Access to the VIROME pipeline and analysis results are provided through a web-application interface that is dynamically linked to a relational back-end database. The VIROME web-application interface is designed to allow users flexibility in retrieving sequences (reads, ORFs, predicted peptides) and search results for focused secondary analyses.


title: On the psychological impact of food colour

abstract: Colour is the single most important product-intrinsic sensory cue when it comes to setting people’s expectations regarding the likely taste and flavour of food and drink. To date, a large body of laboratory research has demonstrated that changing the hue or intensity/saturation of the colour of food and beverage items can exert a sometimes dramatic impact on the expectations, and hence on the subsequent experiences, of consumers (or participants in the lab). However, should the colour not match the taste, then the result may well be a negatively valenced disconfirmation of expectation. Food colours can have rather different meanings and hence give rise to differing expectations, in different age groups, not to mention in different cultures. Genetic differences, such as in a person’s taster status, can also modulate the psychological impact of food colour on flavour perception. By gaining a better understanding of the sensory and hedonic expectations elicited by food colour in different groups of individuals, researchers are coming to understand more about why it is that what we see modulates the multisensory perception of flavour, as well as our appetitive and avoidance-related food behaviours.


title: Model-checking markov chains in the presence of uncertainties

abstract: We investigate the problem of model checking Interval-valued Discrete-time Markov Chains (IDTMC). IDTMCs are discrete-time finite Markov Chains for which the exact transition probabilities are not known. Instead in IDTMCs, each transition is associated with an interval in which the actual transition probability must lie. We consider two semantic interpretations for the uncertainty in the transition probabilities of an IDTMC. In the first interpretation, we think of an IDTMC as representing a (possibly uncountable) family of (classical) discrete-time Markov Chains, where each member of the family is a Markov Chain whose transition probabilities lie within the interval range given in the IDTMC. This semantic interpretation we call Uncertain Markov Chains (UMC). In the second semantics for an IDTMC, which we call Interval Markov Decision Process (IMDP), we view the uncertainty as being resolved through non-determinism. In other words, each time a state is visited, we adversarially pick a transition distribution that respects the interval constraints, and take a probabilistic step according to the chosen distribution. We show that the PCTL model checking problem for both Uncertain Markov Chain semantics and Interval Markov Decision Process semantics is decidable in PSPACE. We also prove lower bounds for these model checking problems.


Urban Solid Waste Management in Low-Income Countries of Asia How to Cope with the Garbage Crisis


title: Improved Adam Optimizer for Deep Neural Networks

abstract: Adaptive optimization algorithms, such as Adam and RMSprop, have witnessed better optimization performance than stochastic gradient descent (SGD) in some scenarios. However, recent studies show that they often lead to worse generalization performance than SGD, especially for training deep neural networks (DNNs). In this work, we identify the reasons that Adam generalizes worse than SGD, and develop a variant of Adam to eliminate the generalization gap. The proposed method, normalized direction-preserving Adam (ND-Adam), enables more precise control of the direction and step size for updating weight vectors, leading to significantly improved generalization performance. Following a similar rationale, we further improve the generalization performance in classification tasks by regularizing the softmax logits. By bridging the gap between SGD and Adam, we also hope to shed light on why certain optimization algorithms generalize better than others.


Arabic Corpora for Credibility Analysis.


title: Magnetostrictive-ring type torque sensor using two Hall ICs with differential magnetic field detection

abstract: A novel magnetostrictive-ring type torque sensor has been developed which used a new detection scheme for detecting the differential magnetic field and signal conditioning circuitry with the temperature drift compensation. Torque of a shaft was measured by detecting the leakage magnetic field emanating from the magnetostrictive-ring made of permendur Fe49Co49V2 which was attached to the stainless shaft. The magnetic sensor was constructed using a pair of commercial Hall ICs and a permalloy magnetic yoke embedded onto the circuit board. The temperature offset adjustment was accomplished by adding only four diodes and resistors to differential amplifiers. The measured sensitivity was 45 mV/Nm for the applied torque range from -4 Nm to +4 Nm.


title: Paying a price: Culture, trust, and negotiation consequences

abstract: Three studies contrasting Indian and American negotiators tested hypotheses derived from theory proposing why there are cultural differences in trust and how cultural differences in trust influence negotiation strategy. Study 1 (a survey) documented that Indian negotiators trust their counterparts less than American negotiators. Study 2 (a negotiation simulation) linked American and Indian negotiators’ self-reported trust and strategy to their insight and joint gains. Study 3 replicated and extended Study 2 using independently coded negotiation strategy data, allowing for stronger causal inference. Overall, the strategy associated with Indian negotiators’ reluctance to extend interpersonal (as opposed to institutional) trust produced relatively poor outcomes. Our data support an expanded theoretical model of negotiation, linking culture to trust, strategies, and outcomes.


title: WIDEBAND CIRCULARLY POLARIZED UHF RFID READER ANTENNA WITH HIGH GAIN AND WIDE AXIAL RATIO BEAMWIDTHS

abstract: A broadband circularly polarized patch antenna with high gain and wide axial ratio beamwidths is proposed for ultra-high- frequency (UHF) RF identiflcation (RFID) applications in this paper. The antenna is composed of a square patch, a feed network printed on the bottom side of the substrates and an antenna radome. The CP radiation of the proposed antenna is excited by four cylinder probes which transmit four signals that have equal amplitude with quadrature phase difierence (0 - , 90 - , 180 - , and 270 - ) generated from the feed network. To obtain an optimum peak gain and a broad CP bandwidth, 100› isolation resistor is omitted in the feed network for obtaining high radiation e-ciency, and the efiects of varying the feed positions and dimensions of the various parameters on the antenna performances are respectively investigated. Simulation results are compared with the measurements, and a good agreement is obtained. The measured results show that the proposed antenna can provide broad impendence bandwidth of 19.7% (815{993MHz) (re∞ection coe-cient less than i15dB), a maximum gain of 9.65dBi, and a 3-dB axial ratio (AR) bandwidth of about 11% (860{960MHz). The results indicate that the proposed antenna is an excellent candidate for UHF RFID reader system. At last, read performance of the proposed antenna array in RFID systems is presented, which verify the superior features of the proposed antenna in practical RFID system applications.


title: Single motor-variable stiffness actuator using bistable switching mechanisms for independent motion and stiffness control

abstract: This paper presents a proof of concept of a variable stiffness actuator (VSA) that uses only one (high power) input motor. In general, VSAs use two (high power) motors to be able to control both the output position and the output stiffness, which possibly results in a heavy, and bulky system. In this work, two small and light-weight clutches are used to lock either one of the degrees of freedom, allowing the other to be controlled by the input motor. These clutches are realized by friction belts that can engage to a surrounding cylinder. The clutches are operated by solenoids, and small bistable mechanisms ensure that no electrical energy is lost in keeping a degree of freedom locked or unlocked. An experiment with a prototype of the system is performed which validates the proof of concept of this Single Motor-VSA.


title: The Adoption of E-commerce in SMEs in Vietnam: A Study of Users and Prospectors

abstract: The adoption of e-commerce by SMEs in Vietnam will enable these companies to do business in a much more efficient way. E-commerce will make these enterprises, as well as their products and services, better known, while at the same time making it possible for them to conduct transactions not only in that country, but throughout the world. The sample for this study consisted of 300 small and medium-sized enterprises in Vietnam. The results indicate that the size of the enterprise, its resources and its strategic orientation, the employees’ knowledge of e-commerce, the attitudes of managers towards innovation and their knowledge of the new technologies and of e-commerce, the intensity of the competition, the degree of support of the government, the national infrastructure, the perceived relative advantages, and the complexity and compatibility of e-commerce all have an influence on the adoption of e-commerce in SMEs in Vietnam, but that this influence differs depending on whether it involves users or prospectors.


title: System design of two wheeler self-balanced vehicle

abstract: Any two wheeler vehicles usually cannot balance by itself when it is in rest or running at a lower speed. The purpose of this research is to make a vehicle that would be stable by itself in such situations and can be stabilized against any physical impact. Two heavy rotating disks with hub motors have been used here to develop the mechanism of compensating the tilt of the vehicle and get it stabilized. To measure the tilt angle, an android device has been used and then the angle of tilt has sent to a Bluetooth receiver which is connected with the vehicle. An android application has been developed which takes the tilt angle as input from its gyroscope and sends data to that receiver accordingly. This arduino controls the motor which has been used to control the tilt direction of both of the rotating disks according to the signal. This vehicle is designed to provide the safety that two wheeler vehicle does not have during an impact or at zero velocity. Hence, this research is aimed to design a safe, cost effective and fuel efficient self-balanced vehicle for third world countries like Bangladesh.


title: Domain-Adversarial Training of Neural Networks

abstract: We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. ::: ::: The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. ::: ::: We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.


Mental Models in Human-Computer Interaction


title: Domain Specific Modeling Language for Cyber Physical Systems

abstract: The benefits of Domain Specific Modeling Languages (DSML), for modeling and design of cyber physical systems, have been acknowledged in previous years. In contrast to general purpose modeling languages, such as Unified Modeling Language, DSML facilitates the modeling of domain specific concepts. The objective of this work is to develop a simple graphical DSML for cyber physical systems, which allow the unified modeling of the structural and behavioral aspects of a system in a single model, and provide model transformation and design verification support in future. The proposed DSML was defined in terms of its abstract and concrete syntax. The applicability of the proposed DSML was demonstrated by its application in two case studies: Traffic Signal and Arbiter case studies. The results showed that the proposed DSML produce simple and unified models with possible model transformation and verification support.


title: Reaching Unanimous Agreements Within Agent-Based Negotiation Teams With Linear and Monotonic Utility Functions

abstract: In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model.


title: Profiling the Potential of Web Tables for Augmenting Cross-domain Knowledge Bases

abstract: Cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph have gained increasing attention over the last years and are starting to be deployed within various use cases. However, the content of such knowledge bases is far from being complete, far from always being correct, and suffers from deprecation (i.e. population numbers become outdated after some time). Hence, there are efforts to leverage various types of Web data to complement, update and extend such knowledge bases. A source of Web data that potentially provides a very wide coverage are millions of relational HTML tables that are found on the Web. The existing work on using data from Web tables to augment cross-domain knowledge bases reports only aggregated performance numbers. The actual content of the Web tables and the topical areas of the knowledge bases that can be complemented using the tables remain unclear. In this paper, we match a large, publicly available Web table corpus to the DBpedia knowledge base. Based on the matching results, we profile the potential of Web tables for augmenting different parts of cross-domain knowledge bases and report detailed statistics about classes, properties, and instances for which missing values can be filled using Web table data as evidence. In order to estimate the potential quality of the new values, we empirically examine the Local Closed World Assumption and use it to determine the maximal number of correct facts that an ideal data fusion strategy could generate. Using this as ground truth, we compare three data fusion strategies and conclude that knowledge-based trust outperforms PageRank- and voting-based fusion.


title: Tracking Performance of the RLS Algorithm Applied to an Antenna Array in a Realistic Fading Environment

abstract: In this paper, frequency domain techniques are used to derive the tracking properties of the recursive least squares (RLS) algorithm applied to an adaptive antenna array in a mobile fading environment, expanding the use of such frequency domain approaches for nonstationary RLS tracking to the interference canceling problem that characterizes the use of antenna arrays in mobile wireless communications. The analysis focuses on the effect of the exponential weighting of the correlation estimation filter and its effect on the estimations of the time variant autocorrelation matrix and cross-correlation vector. Specifically, the case of a flat Rayleigh fading desired signal applied to an array in the presence of static interferers is considered with an AR2 fading process approximating the Jakes' fading model. The result is a mean square error (MSE) performance metric parameterized by the fading bandwidth and the RLS exponential weighting factor, allowing optimal parameter selection. The analytic results are verified and demonstrated with a simulation example.


title: An optimistic approach to lock-free FIFO queues

abstract: First-in-first-out (FIFO) queues are among the most fundamental and highly studied concurrent data structures. The most effective and practical dynamic-memory concurrent queue implementation in the literature is the lock-free FIFO queue algorithm of Michael and Scott, included in the standard Java TM Concurrency Package. This work presents a new dynamic-memory concurrent lock-free FIFO queue algorithm that in a variety of circumstances performs better than the Michael and Scott queue. The key idea behind our new algorithm is a novel way of replacing the singly-linked list of Michael and Scott, whose pointers are inserted using a costly compare-and-swap (CAS) operation, by an “optimistic” doubly - linked list whose pointers are updated using a simple store, yet can be “fixed” if a bad ordering of events causes them to be inconsistent. We believe it is the first example of such an “optimistic” approach being applied to a real world data structure.


Customised Visualisations of Linked Open Data


title: Androgenetic Alopecia: An Evidence-Based Treatment Update

abstract: Background ::: Androgenetic alopecia (AGA) is one of the most common chronic problems seen by dermatologists worldwide. It is characterized by progressive hair loss, especially of scalp hair, and has distinctive patterns of loss in women versus men, but in both genders the central scalp is most severely affected. It often begins around puberty and is known to effect self-esteem and the individual’s quality of life. In contrast to the high prevalence of AGA, approved therapeutic options are limited. In addition to the scarce pharmacologic treatments, there are numerous nonprescription products claimed to be effective in restoring hair in androgenetic alopecia.


title: Bridging the tenant-provider gap in cloud services

abstract: The disconnect between the resource-centric interface exposed by today's cloud providers and tenant goals hurts both entities. Tenants are encumbered by having to translate their performance and cost goals into the corresponding resource requirements, while providers suffer revenue loss due to un-informed resource selection by tenants. Instead, we argue for a "job-centric" cloud whereby tenants only specify high-level goals regarding their jobs and applications. To illustrate our ideas, we present Bazaar, a cloud framework offering a job-centric interface for data analytics applications. Bazaar allows tenants to express high-level goals and predicts the resources needed to achieve them. Since multiple resource combinations may achieve the same goal, Bazaar chooses the combination most suitable for the provider. Using large-scale simulations and deployment on a Hadoop cluster, we demonstrate that Bazaar enables a symbiotic tenant-provider relationship. Tenants achieve their performance goals. At the same time, holistic resource selection benefits providers in the form of increased goodput.


title: Distributional Sentence Entailment Using Density Matrices

abstract: Categorical compositional distributional model of Coecke et al. (2010) suggests a way to combine grammatical composition of the formal, type logical models with the corpus based, empirical word representations of distributional semantics. This paper contributes to the project by expanding the model to also capture entailment relations. This is achieved by extending the representations of words from points in meaning space to density operators, which are probability distributions on the subspaces of the space. A symmetric measure of similarity and an asymmetric measure of entailment is defined, where lexical entailment is measured using von Neumann entropy, the quantum variant of Kullback-Leibler divergence. Lexical entailment, combined with the composition map on word representations, provides a method to obtain entailment relations on the level of sentences. Truth theoretic and corpus-based examples are provided.


Affordance Prediction via Learned Object Attributes


title: Notification, Disruption, and Memory: Effects of Messaging Interruptions on Memory and Performance

abstract: We describe a study on the influence of instant messaging (IM) on ongoing computing tasks. The study both replicates and extends earlier work on the cost of sending notifications at different times and the sensitivity of different tasks to interruption. We investigate alternative hypotheses about the nature of disruption for a list evaluation task, an activity identified as being particularly costly to interrupt. Our findings once again show the generally disruptive effects of IM, especially during fast, stimulus-driven search tasks. In addition, we show that interruptions coming early during a search task are more likely to result in the user forgetting the primary task goal than interruptions that arrive later on. These findings have implications for the design of user interfaces and notification policies that minimize the disruptiveness of notifications.


title: A Linearly-Convergent Stochastic L-BFGS Algorithm

abstract: We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions. Our algorithm draws heavily from a recent stochastic variant of L-BFGS proposed in Byrd et al. (2014) as well as a recent approach to variance reduction for stochastic gradient descent from Johnson and Zhang (2013). We demonstrate experimentally that our algorithm performs well on large-scale convex and non-convex optimization problems, exhibiting linear convergence and rapidly solving the optimization problems to high levels of precision. Furthermore, we show that our algorithm performs well for a wide-range of step sizes, often differing by several orders of magnitude.


title: Structured light 3D scanning in the presence of global illumination

abstract: Global illumination effects such as inter-reflections, diffusion and sub-surface scattering severely degrade the performance of structured light-based 3D scanning. In this paper, we analyze the errors caused by global illumination in structured light-based shape recovery. Based on this analysis, we design structured light patterns that are resilient to individual global illumination effects using simple logical operations and tools from combinatorial mathematics. Scenes exhibiting multiple phenomena are handled by combining results from a small ensemble of such patterns. This combination also allows us to detect any residual errors that are corrected by acquiring a few additional images. Our techniques do not require explicit separation of the direct and global components of scene radiance and hence work even in scenarios where the separation fails or the direct component is too low. Our methods can be readily incorporated into existing scanning systems without significant overhead in terms of capture time or hardware. We show results on a variety of scenes with complex shape and material properties and challenging global illumination effects.


title: Agents or Stewards: Using Theory to Understand the GovernmentNonprofit Social Service Contracting Relationship

abstract: Using agency and stewardship theories, this study examines how public administrators manage contracting relationships with nonprofit organizations. Interviews were conducted with public and nonprofit managers involved in social services contract relationships at the state and county level in New York State. The use of trust, reputation, and monitoring as well as other factors influence the manner in which contract relationships are managed. The findings suggest that the manner in which nonprofits are managed evolves over time from a principal-agent to a principal-steward relationship but with less variance than the theories would suggest. This results in part from the contextual conditions that include the type of service, lack of market competitiveness, and management capacity constraints. The intergovernmental environment in which social services are implemented and delivered presents complex challenges for public managers responsible for managing contract relationships. The findings from this study document those challenges and the corresponding management practices used with nonprofit contractors.


title: An algorithm for addressing the real interval eigenvalue problem

abstract: In this paper we present an algorithm for approximating the range of the real eigenvalues of interval matrices. Such matrices could be used to model real-life problems, where data sets suffer from bounded variations such as uncertainties (e.g. tolerances on parameters, measurement errors), or to study problems for given states. The algorithm that we propose is a subdivision algorithm that exploits sophisticated techniques from interval analysis. The quality of the computed approximation and the running time of the algorithm depend on a given input accuracy. We also present an efficient C++ implementation and illustrate its efficiency on various data sets. In most of the cases we manage to compute efficiently the exact boundary points (limited by floating point representation).


title: Authentication Scheme for Flexible Charging and Discharging of Mobile Vehicles in the V2G Networks

abstract: Navigating security and privacy challenges is one of the crucial requirements in the vehicle-to-grid ( V2G ) network. Since electric vehicles ( EVs ) need to provide their private information to aggregators/servers when charging/discharging at different charging stations, privacy of the vehicle owners can be compromised if the information is misused, traced, or revealed. In a wide V2G network, where vehicles can move outside of their home network to visiting networks, security and privacy become even more challenging due to untrusted entities in the visiting networks. Although some privacy-preserving solutions were proposed in the literature to tackle this problem, they do not protect against well-known security attacks and generate a huge overhead. Therefore, we propose a mutual authentication scheme to preserve privacy of the EV’s information from aggregators/servers in the home as well as distributed visiting V2G networks. Our scheme, based on a bilinear pairing technique with an accumulator performing batch verification, yields higher system efficiency, defeats various security attacks, and maintains untraceability, forward privacy, and identity anonymity. A performance analysis shows that our scheme, in comparison with the existing solutions, significantly generates lower communication and computation overheads in the home and centralized V2G networks, and comparable overheads in the distributed visiting V2G networks.


title: Information technology service management: an emerging area for academic research and pedagogical development

abstract: The economic landscape has changed for the industrialized nations of the world. The most mature nations on the planet have transitioned from agriculture and manufacturing economies to service-based economies. Services constitute more than 75% of industrialized nations' economies. These services are dependent on Information & Communication Technology that is supported by local and global information technology functions or the combination of both. In this paper, we review the modern methods and standards of information technology service management, such as ISO/IEC 20000; emerging pedagogical developments; and alternative approaches to ITSM research.


title: HotelOpinion: An opinion mining system on hotel reviews in Thailand

abstract: This paper reports on the extension of our previous work on feature-based opinion mining for Thai language. In this paper, we present an approach for automatically constructing two main lexicons: features and polar words based on syntactic pattern analysis. The evaluation is performed with a case study on hotel reviews. The experimental results show that our approach is effective in performing its task. To illustrate the potential application, we implement a system called HotelOpinion for summarizing the hotel reviews written in the Thai language. Our system can also generate a comparison between hotels based upon the user's preferred features and then present the results in a user-friendly visualization. Results from our system can be used to determine public perceptions regarding selected hotels in order to allow the business to improve their customer intimacy and satisfaction.


Periodically controlled hybrid systems: Verifying a controller for an autonomous vehicle