title: Larvicidal, Histopathological Efficacy of Penicillium daleae against Larvae of Culex quinquefasciatus and Aedes aegypti Plus Biotoxicity on Artemia nauplii a Non-target Aquatic Organism

abstract: Mosquitoes can transmit the terrible diseases to human beings. Soil-borne fungal products act as potential source for low-cost chemicals, used for developing eco-friendly control agents against mosquito-vector borne diseases. The prime aim of study was to check the larvicidal potential of fungus mycelia (by ethyl acetate solvent) extract from Penicillium daleae (KX387370) against Culex quinquefasciatus and Aedes aegypti and to test the toxicity of brine shrimp Artemia nauplii, by observing the physiological activity. The ethyl acetate extract of P. daleae mycelia (after 15 days) from Potato dextrose broth (PDB) medium revealed better result with least LC50 and LC90 values of I-IV instars larvae of Cx. quinquefasciatus (LC50=127.441, 129.087, 108.683, 93.521; LC90=152.758, 158.169, 139.091,125.918 µg/ml) and Ae. aegypti (LC50=105.077, 83.943, 97.158, 76.513; LC90= 128.035, 106.869, 125.640, 104.606 µg/ml) respectively. At higher concentration (1000 µg/ml) of extracts, mortality begins at 18 h of exposure and attained 100 % mortality after 48 h exposure. Overall, the activity was depends on the dose and time of exposure to the extracts. The stereomicroscopic and histopathological analysis of Ae. aegypti and Cx. quinquefasciatus larvae treated with mycelium ethyl acetate extract showed complete disintegration of abdominal region, particularly the midgut and caeca, loss of cuticular parts and caudal hairs. Morphological characterization of the fungi was performed and taxonomically identified through 5.8s rDNA technique. The phylogenetic analysis of rDNA sequence was carried out to find out the taxonomic and the evolutionary sketch of isolate in relation to earlier described genus Penicillium. Behaviour and swimming speed alteration was analyzed together with mortality. The results of the experiment indicates that Swimming behaviour recorder (SBR) is a appropriate tool to detect individual swimming speed of the Artemia nauplii organisms, since the values have been obtained in accordance with control monitored results showed the 2.75 mm s-1 and after 24h treated found to be 0.72 mm s-1, respectively. The extract-exposed to A. nauplii showed changes in body structures i.e, intestine enlargement, eye formation, outer shell malformations and loss of antennae. In the present study, we aimed to investigate the toxicity of the ethyl acetate extract of P. daleae on A. nauplii larvae


Performance Study of IEEE 802.11 DCF and IEEE 802.11e EDCA


title: Hotel Recommendation System

abstract: In these day, as the e-commerce industry is growing and becoming complex, everyone uses online websites for getting reviews and giving the reviews on the website in the form of comments. This comments varies from worst level to best level. So in order to categorize these comments or to predict the best outcome among the posted comments recommendation is needed. Recommender System is an e cient tool that consider an individual's opinion to identify their content more appropriately and selectively. This system has been applied to various domain, but in the eld of research area, service based recommendation system plays a major role. In recent years, the growth rate of online hotel searching has been increased much faster and makes this online hotel searching a very di cult task due to the abundant amount of online information. Reviews written by the travelers replaces the word-of-mouth but then to searching becomes the time consuming task based on user preference. Reviews crawled from the travelers visiting sites are a common and valuable source of information for recommendation of a hotel, yet little attention has been paid as, how to present the reviews of a reviewers in an understandable format. The purpose of this paper is to recommend the travelers the name of the hotels based on their preferences, by analyzing the other travelers reviews together with the rating value to improve the prediction accuracy. Users do not rate adequate hotels to enable collaborating ltering based recommendation, which can lead to an issue called as cold start problem. To solve this issue the following contributions will be made in this paper like: 1. Paper will combine Collaborative Filtering with Aspect Based Sentiment Analysis to overcome cold start user problem, while ensuring high accuracy. 2. Context like traveler type, user's location and user's preference will be included as an additional information for personalized recommendation. 3. Several experiments will be conducted on tripadvisor datasets and the result is expected that proposed hybrid framework is competitive against classical approaches. This paper will include various elements like semi-supervised clustering algorithm is used to group the features of the same vocabulary into nine, prede ned categories known as hotel aspects. Lexicon based approach will be used to find the sentiment orientation towards each hotel aspects based on the defined context. Item-based ltering technique will be used to predict the unrated items/aspects. Thus, context-based approach will increase the recommendation results.


title: Modeling enjoyment preference from physiological responses in a car racing game

abstract: We propose a framework to estimate player enjoyment preference from physiological signals. This can produce objective measures that could be used to adapt dynamically a game to maintain the player in an optimal status of enjoyment. We present a case study on The Open Racing Car Simulator (TORCS) video game. In particular, we focus both on the experimental protocol, which we designed with special attention to produce physiological responses related to the game experience only, and on signal analysis, which produces a simple and general model good enough to estimate player enjoyment preference in real applications.


title: Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis

abstract: Technology has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. Much of the content being posted and consumed online is multimodal. With billions of phones, tablets and PCs shipping today with built-in cameras and a host of new video-equipped wearables like Google Glass on the horizon, the amount of video on the Internet will only continue to increase. It has become increasingly difficult for researchers to keep up with this deluge of multimodal content, let alone organize or make sense of it. Mining useful knowledge from video is a critical need that will grow exponentially, in pace with the global growth of content. This is particularly important in sentiment analysis, as both service and product reviews are gradually shifting from unimodal to multimodal. We present a novel method to extract features from visual and textual modalities using deep convolutional neural networks. By feeding such features to a multiple kernel learning classifier, we significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets.


title: RFID and Android based smart ticketing and destination announcement system

abstract: In India, the most widely used public transport system is the ready-to-go-bus facility. However, this ‘ready-to-go’ facility is not as smooth as the need of the hour, particularly in today's congested metropolitan cities. Standing in long queues at bus stands, quarrelling with conductors for trifle matters make the journey uncomfortable for the passengers. That is why; we have proposed an idea for implementing smart card technology for ticketing the passengers travelling in bus. The smart card is mainly based on latest Radio Frequency Identification (RFID) technology. For this purpose, an interface is built between RFID setup and driver's mobile phone using a specifically developed Android app “SwipeNgo”. The interface helps to send passenger ID from RFID reader to the driver's mobile phone via Bluetooth. The developed “SwipeNgo” app is installed in driver's mobile phone and receives passenger ID from the RFID card reader via interface when passenger get into the bus. Along with the passenger ID, “SwipeNgo” also keep records of the stoppage name/no. into database in mapping with the Global positioning system (GPS) coordinates. The exact fare between source and destination is calculated and deducted from the balance when the passenger gets down from the bus. This information regarding balance is also sent to the RFID setup where the fare is displayed. There is a separate announcement system which alerts the passengers prior to the next halt.


title: Dual Time-Interleaved Successive Approximation Register ADCs for an Ultra-Wideband Receiver

abstract: Ultra-wideband radio requires Nyquist sampling rates of at least 500 MS/s with low resolutions. While flash is the traditional choice for these specifications, a comparative energy model is used to show the potential energy savings of the time-interleaved successive approximation register architecture, which requires only a linear number of comparisons versus exponential for flash. A dual 500-MS/s, 5-bit ADC chip is implemented in a 0.18-mum CMOS process, with both ADCs synchronized for use in an I/Q UWB receiver. Each ADC uses a 6-way time-interleaved SAR topology with full custom logic, self-timed bit-cycling, and duty cycling of the comparator preamplifiers to enable 500-MS/s operation with 7.8 mW power consumption. The output resolution is adjustable down to the 1-bit level for additional power savings


A framework to build bespoke auto-tuners with structured Bayesian optimisation


title: Collision Avoidance for Quadrotors with a Monocular Camera

abstract: Automatic obstacle detection and avoidance is a key component for the success of micro-aerial vehicles (MAVs) in the future. As the payload of MAVs is highly constrained, cameras are attractive sensors because they are both lightweight and provide rich information about the environment. In this paper, we present an approach that allows a quadrotor with a single monocular camera to locally generate collision-free waypoints. We acquire a small set of images while the quadrotor is hovering from which we compute a dense depth map. Based on this depth map, we render a 2D scan and generate a suitable waypoint for navigation. In our experiments, we found that the pose variation during hovering is already sufficient to obtain suitable depth maps. The computation takes less than one second which renders our approach applicable for obstacle avoidance in real-time. We demonstrate the validity of our approach in challenging environments where we navigate a Parrot Ardrone quadrotor successfully through narrow passages including doors, boxes, and people.


title: Parsing occluded people by flexible compositions

abstract: This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior that, even in presence of occlusion, the visible nodes form a connected subtree of the graphical model. We call each connected subtree a flexible composition of object parts. This involves a novel method for learning occlusion cues. During inference we need to search over a mixture of different flexible models. By exploiting part sharing, we show that this inference can be done extremely efficiently requiring only twice as many computations as searching for the entire object (i.e., not modeling occlusion). We evaluate our model on the standard benchmarked “We Are Family” Stickmen dataset and obtain significant performance improvements over the best alternative algorithms.


title: 8 amazing secrets for getting more clicks: detecting clickbaits in news streams using article informality

abstract: Clickbaits are articles with misleading titles, exaggerating the content on the landing page. Their goal is to entice users to click on the title in order to monetize the landing page. The content on the landing page is usually of low quality. Their presence in user homepage stream of news aggregator sites (e.g., Yahoo news, Google news) may adversely impact user experience. Hence, it is important to identify and demote or block them on homepages. In this paper, we present a machine-learning model to detect clickbaits. We use a variety of features and show that the degree of informality of a web-page (as measured by different metrics) is a strong indicator of it being a clickbait. We conduct extensive experiments to evaluate our approach and analyze properties of clickbait and non-clickbait articles. Our model achieves high performance (74.9% F-1 score) in predicting clickbaits.


title: 3D to 2D fingerprints: Unrolling and distortion correction

abstract: Touchless 3D fingerprint sensors can capture both 3D depth information and albedo images of the finger surface. Compared with 2D fingerprint images acquired by traditional contact-based fingerprint sensors, the 3D fingerprints are generally free from the distortion caused by non-uniform pressure and undesirable motion of the finger. Several unrolling algorithms have been proposed for virtual rolling of 3D fingerprints to obtain 2D equivalent fingerprints, so that they can be matched with the legacy 2D fingerprint databases. However, available unrolling algorithms do not consider the impact of distortion that is typically present in the legacy 2D fingerprint images. In this paper, we conduct a comparative study of representative unrolling algorithms and propose an effective approach to incorporate distortion into the unrolling process. The 3D fingerprint database was acquired by using a 3D fingerprint sensor being developed by the General Electric Global Research. By matching the 2D equivalent fingerprints with the corresponding 2D fingerprints collected with a commercial contact-based fingerprint sensor, we show that the compatibility between the 2D unrolled fingerprints and the traditional contact-based 2D fingerprints is improved after incorporating the distortion into the unrolling process.


title: HyperLex: Lexical Cartography for Information Retrieval

abstract: Abstract This article describes an algorithm called HyperLex that is capable of automatically determining word uses in a textbase without recourse to a dictionary. The algorithm makes use of the specific properties of word cooccurrence graphs, which are shown as having “small world” properties. Unlike earlier dictionary-free methods based on word vectors, it can isolate highly infrequent uses (as rare as 1% of all occurrences) by detecting “hubs” and high-density components in the cooccurrence graphs. The algorithm is applied here to information retrieval on the Web, using a set of highly ambiguous test words. An evaluation of the algorithm showed that it only omitted a very small number of relevant uses. In addition, HyperLex offers automatic tagging of word uses in context with excellent precision (97%, compared to 73% for baseline tagging, with an 82% recall rate). Remarkably good precision (96%) was also achieved on a selection of the 25 most relevant pages for each use (including highly infrequent ones). Finally, HyperLex is combined with a graphic display technique that allows the user to navigate visually through the lexicon and explore the various domains detected for each word use.


title: Charge Pump Circuits: An Overview on Design Strategies and Topologies

abstract: Due to the continuous power supply reduction, charge pumps circuits are widely used in integrated circuits (ICs) devoted to several kind of applications such as smart power, nonvolatile memories, switched capacitor circuits, operational amplifiers, voltage regulators, SRAMs, LCD drivers, piezoelectric actuators, RF antenna switch controllers, etc. The main focus of this tutorial manuscript is to provide a deep understanding of the charge pumps behavior, to present useful models and key parameters and to organically and in details discuss the optimized design strategies. Finally, an overview of the main different topologies is also included.


title: Intruders and intrusion detection systems — An overview

abstract: With rapid advancement in telecommunication sub-domains like Internet of Things (IoT), Internet of Energy (IoE), Internet of Vehicles (IoV), Next Generation Networks (5G) and Big Data, the strength, intensity and effectiveness of intruders is also evolving. Intruders are ready to challenge modern advancement in the domain of electronic warfare. The traditional TCP/IP protocol stack does not have the capability to defend against modern threats posed by intruders, therefore, major revision, from security point of view, is mandatory. In this paper a brief overview of intruders is provided with respect to each layer of TCP/IP protocol stack. Advancements in Intrusion Detection Systems (IDS) for detection of intruders are provided in chronological order. In an unending war between parallel advancements in technologies and evolutions of intruders, an intrusion detection system is always needed that works at each layer of TCP/IP protocol stack and covers entire spectrum of threats.


title: Cross-lingual Document Retrieval using Regularized Wasserstein Distance

abstract: Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature. Recently, a new promising metric called Word Mover's Distance was proposed to measure the divergence between text passages. In this paper, we demonstrate that this metric can be extended to incorporate term-weighting schemes and provide more accurate and computationally efficient matching between documents using entropic regularization. We evaluate the benefits of both extensions in the task of cross-lingual document retrieval (CLDR). Our experimental results on eight CLDR problems suggest that the proposed methods achieve remarkable improvements in terms of Mean Reciprocal Rank compared to several baselines.


Towards a Neural Conversation Model with Diversity Net Using Determinantal Point Processes


title: Japanese has syllables: a reply to Labrune *

abstract: Labrune (2012b) proposes a syllable-less theory of Japanese, suggesting that Japanese has no syllables, with only moras below the foot. She argues that there is no phonetic or psycholinguistic evidence for the existence of syllables in Japanese. This reply summarises and re-examines previous experimental findings that demonstrate that Japanese does show evidence for syllables both phonetically and psycholinguistically. After an extensive review of previous studies, this reply also takes up a number of phonological and theoretical issues that require an explicit response from the perspective of a syllable proponent. On the basis of these considerations, this paper concludes that Japanese does have syllables.


title: Roombots-mechanical design of self-reconfiguring modular robots for adaptive furniture

abstract: We aim at merging technologies from information technology, roomware, and robotics in order to design adaptive and intelligent furniture. This paper presents design principles for our modular robots, called Roombots, as future building blocks for furniture that moves and self-reconfigures. The reconfiguration is done using dynamic connection and disconnection of modules and rotations of the degrees of freedom. We are furthermore interested in applying Roombots towards adaptive behaviour, such as online learning of locomotion patterns. To create coordinated and efficient gait patterns, we use a Central Pattern Generator (CPG) approach, which can easily be optimized by any gradient-free optimization algorithm. To provide a hardware framework we present the mechanical design of the Roombots modules and an active connection mechanism based on physical latches. Further we discuss the application of our Roombots modules as pieces of a homogenic or heterogenic mix of building blocks for static structures.


title: Sobreperez./Technological Frame Incongruence, Diffusion and non-compliance 1 TECHNOLOGICAL FRAME INCONGRUENCE, DIFFUSION

abstract: The technological frames of reference strand of social shaping of technology theory is used to overlay the issues arising from a case study looking at noncompliance with information systems. A recent review of the theory suggests that although frame content is often addressed, frame structure, the process of framing, and the characteristics and outcomes of frames are largely overlooked. This paper attempts to address this shortfall by applying the indicators identified by case study research to the frames of different groups and using them to highlight differing perceptions and attitudes. In this way, the author suggests that issues surrounding noncompliance should not be dismissed as resistance but instead should be further studied by managers and developers, leading to accommodation of differing views. Further examination of frame incongruence reveals dependence on inefficient or ineffective organizational situations and thus these indicators can be useful in future studies to identify and address procedural, acceptance and cultural issues leading to acts of noncompliance.


title: Digital Double-loop PID Controller for Inverted Pendulum

abstract: Inverted pendulum system is a complicated, unstable and multivariable nonlinear system. In order to control the angle and displacement of inverted pendulum system effectively, a novel double-loop digital PID control strategy is presented in this paper. Based on impulse transfer function, the model of the single linear inverted pendulum system is divided into two parts according to the controlled parameters. The inner control loop that is formed by the digital PID feedback control can control the angle of the pendulum, while in order to control the cart displacement, the digital PID series control is adopted to form the outer control loop. The simulation results show the digital control strategy is very effective to single inverted pendulum and when the sampling period is selected as 50 ms, the performance of the digital control system is similar to that of the analog control system. Copyright © 2013 IFSA.


title: Towards simultaneous recognition, localization and mapping for hand-held and wearable cameras

abstract: This paper presents a system which combines single-camera SLAM (simultaneous localization and mapping) with established methods for feature recognition. Besides using standard salient image features to build an on-line map of the camera's environment, this system is capable of identifying and localizing known planar objects in the scene, and incorporating their geometry into the world map. Continued measurement of these mapped objects improves both the accuracy of estimated maps and the robustness of the tracking system. In the context of hand-held or wearable vision, the system's ability to enhance generated maps with known objects increases the map's value to human operators, and also enables meaningful automatic annotation of the user's surroundings. The presented solution lies between the high order enriching of maps such as scene classification, and the efforts to introduce higher geometric primitives such as lines into probabilistic maps.


title: A Deeper Look at Dataset Bias

abstract: The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid development of deep learning architectures, the activation values of Convolutional Neural Networks (CNN) are emerging as reliable and robust image descriptors. In this paper we propose to verify the potential of the DeCAF features when facing the dataset bias problem. We conduct a series of analyses looking at how existing datasets differ among each other and verifying the performance of existing debiasing methods under different representations. We learn important lessons on which part of the dataset bias problem can be considered solved and which open questions still need to be tackled.


title: Use of eye movements for video game control

abstract: We present a study that explores the use of a commercially available eye tracker as a control device for video games. We examine its use across multiple gaming genres and present games that utilize the eye tracker in a variety of ways. First, we describe a first-person shooter that uses the eyes to control orientation. Second, we study the use of eye movements for more natural interaction with characters in a role playing game. And lastly, we examine the use of eye tracking as a means to control a modified version of the classic action/arcade game Missile Command. Our results indicate that the use of an eye tracker can increase the immersion of a video game and can significantly alter the gameplay experience.


title: Physically based hydraulic erosion simulation on graphics processing unit

abstract: Visual simulation of natural erosion on terrains has always been a fascinating research topic in the field of computer graphics. While there are many algorithms already developed to improve the visual quality of terrain, the recent simulation methods revolve around physically-based hydraulic erosion because it can generate realistic natural-looking terrains. However, many of such algorithms were tested only on low resolution terrains. When simulated on a higher resolution terrain, most of the current algorithms become computationally expensive. This is why in many applications today, terrains are generated off-line and loaded during the application runtime. This method restricts the number of terrains which can be stored if there is a limitation on storage capacity. Recently, graphics hardware has evolved into an indispensable tool in improving the speed of computation. This has motivated us to develop an erosion algorithm to map to graphics hardware for faster terrain generation. In this paper, we propose a fast and efficient hydraulic erosion procedural technique that utilizes the GPUs powerful computation capability in order to generate high resolution erosion on terrains. Our method is based on the Newtonian physics approach that is implemented on a two-dimensional data structure which stores height fields, water amount, and dissolved sediment and water velocities. We also present a comprehensive comparison between the CPU and GPU implementations together with the visual results and the statistics on simulation time taken.


title: Casual games discussion

abstract: Digital games have become a remarkable cultural phenomenon in the last ten years. The casual games sector especially has been growing rapidly in the last few years. However, there is no clear view on what is "casual" in games cultures and the area has not previously been rigorously studied. In the discussions on casual games, "casual" is often taken to refer to the player, the game or the playing style, but other factors such as business models and accessibility are also considered as characteristic of "casual" in games. Views on casual vary and confusion over different meanings can lead to paradoxical readings, which is especially the case when "casual gamer" is taken to mean both "someone who plays casual games" and someone who "plays casually". In this article we will analyse the ongoing discussion by providing clarification of the different meanings of casual and a framework for an overall understanding of casual in the level of expanded game experience.


title: NECTAR: non-interactive smart contract protocol using blockchain technology

abstract: Blockchain-driven technologies are considered disruptive because of the availability of dis-intermediated, censorship-resistant and tamper-proof digital platforms of distributed trust. Among these technologies, smart contract platforms have the potential to take over functions usually done by intermediaries like banks, escrow or legal services. In this paper, we introduce a novel protocol aiming to execute smart contracts as part of a blockchain transaction validation. We enable extensions in the execution of smart contracts while guaranteeing their privacy, correctness and verifiability. Man-in-the-middle attacks are prevented, since no communication between participants is requested, and contract validations do not imply the re-execution of the code by all the nodes in the network. However, proofs of correct execution are stored on the blockchain and can be verified by multiple parties. Our solution is based on programming tools which optimize the time execution and the required memory while preserving the embedded functionality.


title: Audio Classification of Bird Species: A Statistical Manifold Approach

abstract: Our goal is to automatically identify which species of bird is present in an audio recording using supervised learning. Devising effective algorithms for bird species classification is a preliminary step toward extracting useful ecological data from recordings collected in the field. We propose a probabilistic model for audio features within a short interval of time, then derive its Bayes risk-minimizing classifier, and show that it is closely approximated by a nearest-neighbor classifier using Kullback-Leibler divergence to compare histograms of features. We note that feature histograms can be viewed as points on a statistical manifold, and KL divergence approximates geodesic distances defined by the Fisher information metric on such manifolds. Motivated by this fact, we propose the use of another approximation to the Fisher information metric, namely the Hellinger metric. The proposed classifiers achieve over 90% accuracy on a data set containing six species of bird, and outperform support vector machines.


title: Using Embeddings to Correct for Unobserved Confounding

abstract: We consider causal inference in the presence of unobserved confounding. In particular, we study the case where a proxy is available for the confounder but the proxy has non-iid structure. As one example, the link structure of a social network carries information about its members. As another, the text of a document collection carries information about their meanings. In both these settings, we show how to effectively use the proxy to do causal inference. The main idea is to reduce the causal estimation problem to a semi-supervised prediction of both the treatments and outcomes. Networks and text both admit high-quality embedding models that can be used for this semi-supervised prediction. Our method yields valid inferences under suitable (weak) conditions on the quality of the predictive model. We validate the method with experiments on a semi-synthetic social network dataset. We demonstrate the method by estimating the causal effect of properties of computer science submissions on whether they are accepted at a conference.


title: Learning of grasp selection based on shape-templates

abstract: The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algorithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situations. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configurations for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.


title: Evolution patterns of open-source software systems and communities

abstract: Open-Source Software (OSS) development is regarded as a successful model of encouraging "natural product evolution". To understand how this "natural product evolution" happens, we have conducted a case study of four typical OSS projects. Unlike most previous studies on software evolution that focus on the evolution of the system per se, our study takes a broader perspective: It examines not only the evolution of OSS systems, but also the evolution of the associated OSS communities, as well as the relationship between the two types of evolution.Through the case study, we have found that while collaborative development within a community is the essential characteristic of OSS, different collaboration models exist, and that the difference in collaboration model results in different evolution patterns of OSS systems and communities. To treat such differences systematically, we propose to classify OSS into three types: Exploration-Oriented, Utility-Oriented, and Service-Oriented. Such a classification can provide guidance on the creation and maintenance of sustainable OSS development and communities.


title: Image Watermarking Using LSB (Least Significant Bit)

abstract: With the rapid development and wide use of Internet, information transmission faces a big challenge of security. People need a safe and secured way to transmit information. Digital watermarking is a technique of data hiding, which provide security of data. This paper presents a watermarking technique which least significant bits (LSB), its steps and its process with matlab images.


Endogenous Market Formation : Theory and Evidence from Chilean College Admissions


title: Introduction to Artificial Intelligence and Expert Systems

abstract: Introduction to artificial intelligence knowledge representation knowledge organization and manipulation perception and communication knowledge acquisition.


title: A Fitts Law comparison of eye tracking and manual input in the selection of visual targets

abstract: We present a Fitts' Law evaluation of a number of eye tracking and manual input devices in the selection of large visual targets. We compared performance of two eye tracking techniques, manual click and dwell time click, with that of mouse and stylus. Results show eye tracking with manual click outperformed the mouse by 16%, with dwell time click 46% faster. However, eye tracking conditions suffered a high error rate of 11.7% for manual click and 43% for dwell time click conditions. After Welford correction eye tracking still appears to outperform manual input, with IPs of 13.8 bits/s for dwell time click, and 10.9 bits/s for manual click. Eye tracking with manual click provides the best tradeoff between speed and accuracy, and was preferred by 50% of participants. Mouse and stylus had IPs of 4.7 and 4.2 respectively. However, their low error rate of 5% makes these techniques more suitable for refined target selection.


title: Cross-border logistics with fleet management: A goal programming approach

abstract: In this paper, we introduce the cross-border logistics problem with fleet management. A major phenomenon of implementation of open-door policy in China is the move of Hong Kong-based manufacturers' production lines to China, crossing the border to take advantages of lower production costs, lower wages and lower rental costs. The finished products are then transshipped to Hong Kong, an efficient logistics hub well-equipped with reliable transportation facility, for exporting. We present a preemptive goal programming model for multi-objective cross-border logistics problem, in which three objectives are optimized hierarchically. We also describe a framework for incorporating decision-makers' opinions for determination of goal priorities and target values. A set of Hong Kong data have been used to test the effectiveness and efficiency of the proposed model. Results demonstrate the decision-makers can find the flexibility and robustness of the proposed model by adjusting the goal priorities with respect to the importance of each objective.


An Anti-collision Algorithm Based on Unequal Timeslots in Radio Frequency Identification System: An Anti-collision Algorithm Based on Unequal Timeslots in Radio Frequency Identification System


title: Influence Maximization in Continuous Time Diffusion Networks

abstract: The problem of finding the optimal set of source nodes in a diffusion network that maximizes the spread of information, influence, and diseases in a limited amount of time depends dramatically on the underlying temporal dynamics of the network. However, this still remains largely unexplored to date. To this end, given a network and its temporal dynamics, we first describe how continuous time Markov chains allow us to analytically compute the average total number of nodes reached by a diffusion process starting in a set of source nodes. We then show that selecting the set of most influential source nodes in the continuous time influence maximization problem is NP-hard and develop an efficient approximation algorithm with provable near-optimal performance. Experiments on synthetic and real diffusion networks show that our algorithm outperforms other state of the art algorithms by at least ~20% and is robust across different network topologies.


title: Ambient intelligence and wearable computing : Sensors on the body , in the home , and beyond

abstract: Ambient intelligence has a history of focusing on technologies that are integrated into a person's environment. However, ambient intelligence can be found on a person's body as well. In this thematic issue we examine the role of wearable computing in the field of ambient intelligence. In this article we provide an overview of the field of wearable computing and discuss its relationship to the fields of smart environments and ambient intelligence. In addition, we introduce the papers presented in the thematic issue highlighting a number of research projects which are defining the state of the art in wearable computing and ambient intelligence.


title: Turning scientists into data explorers

abstract: Nowadays scientists receive increasingly large volumes of data daily. These volumes and accompanying metadata that describes them are collected in scientific file repositories. Today's scientists need a data management tool that makes these file repositories accessible and performs a number of exploration steps near-instantly. Current database technology, however, has a long data-to-insight time, and does not provide enough interactivity to shorten the exploration time. We envision that exploiting metadata helps solving these problems. To this end, we propose a novel query execution paradigm, in which we decompose the query execution into two stages. During the first stage, we process only metadata, whereas the rest of the data is processed during the second stage. So that, we can exploit metadata to boost interactivity and to ingest only required data per query transparently. Preliminary experiments show that up-front ingestion time is reduced by orders of magnitude, while query performance remains similar. Motivated by these results, we identify the challenges on the way from the new paradigm to efficient interactive data exploration.


title: Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing

abstract: Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.


Blood glucose monitoring techniques: recent advances, challenges and future perspectives


title: Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

abstract: In this paper we present a new methodology of class discovery and clustering validation tailored to the task of analyzing gene expression data. The method can best be thought of as an analysis approach, to guide and assist in the use of any of a wide range of available clustering algorithms. We call the new methodology consensus clustering, and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters. The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions. Finally, it provides for a visualization tool to inspect cluster number, membership, and boundaries. We present the results of our experiments on both simulated data and real gene expression data aimed at evaluating the effectiveness of the methodology in discovering biologically meaningful clusters.


title: Deep Feature Fusion for VHR Remote Sensing Scene Classification

abstract: The rapid development of remote sensing technology allows us to get images with high and very high resolution (VHR). VHR imagery scene classification has become an important and challenging problem. In this paper, we introduce a framework for VHR scene understanding. First, the pretrained visual geometry group network (VGG-Net) model is proposed as deep feature extractors to extract informative features from the original VHR images. Second, we select the fully connected layers constructed by VGG-Net in which each layer is regarded as separated feature descriptors. And then we combine between them to construct final representation of the VHR image scenes. Third, discriminant correlation analysis (DCA) is adopted as feature fusion strategy to further refine the original features extracting from VGG-Net, which allows a more efficient fusion approach with small cost than the traditional feature fusion strategies. We apply our approach to three challenging data sets: 1) UC MERCED data set that contains 21 different areal scene categories with submeter resolution; 2) WHU-RS data set that contains 19 challenging scene categories with various resolutions; and 3) the Aerial Image data set that has a number of 10 000 images within 30 challenging scene categories with various resolutions. The experimental results demonstrate that our proposed method outperforms the state-of-the-art approaches. Using feature fusion technique achieves a higher accuracy than solely using the raw deep features. Moreover, the proposed method based on DCA fusion produces good informative features to describe the images scene with much lower dimension.


title: Body fat, fitness and level of everyday physical activity in adolescents and young adults with meningomyelocele.

abstract: OBJECTIVE ::: Data on body fat and fitness in adolescents and young adults with meningomyelocele are scarce. The aim of this study was to assess body fat and fitness in this patient group. In addition, we explored whether the level of everyday physical activity is related to body fat and fitness. ::: ::: ::: SUBJECTS AND METHODS ::: Body fat (skinfold thickness), fitness (VO2peak), and everyday physical activity (Activity Monitor, based on accelerometry) were measured in 14 patients with meningomyelocele (8 men, 6 women; age range 14-26 years). ::: ::: ::: RESULTS ::: Peak VO2 was 20-30% lower than reference values and 4 patients were obese. Level of everyday activity was related to fitness (rs = 0.65, p = 0.01) but not to body fat. ::: ::: ::: CONCLUSION ::: Young patients with meningomyelocele are at risk for developing obesity and have a distinctly subnormal fitness. Level of everyday physical activity is related to fitness in this patient group.


title: DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

abstract: In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small.


title: Sleep and memory in healthy children and adolescents – A critical review

abstract: There is mounting evidence that sleep is important for learning, memory and the underlying neural plasticity. This article aims to review published studies that evaluate the association between sleep, its distinct stages and memory systems in healthy children and adolescents. Furthermore it intends to suggest directions for future research. A computerised search of the literature for relevant articles published between 1966 and March 2008 was performed using the keywords “sleep”, “memory”, “learn”, “child”, “adolescents”, “adolescence” and “teenager”. Fifteen studies met the inclusion criteria. Published studies focused on the impact of sleep on working memory and memory consolidation. In summary, most studies support the hypothesis that sleep facilitates working memory as well as memory consolidation in children and adolescents. There is evidence that performance in abstract and complex tasks involving higher brain functions declines more strongly after sleep deprivation than the performance in simple memory tasks. Future studies are needed to better understand the impact of a variety of variables potentially modulating the interplay between sleep and memory, such as developmental stage, socioeconomic burden, circadian factors, or the level of post-learning sensory and motor activity (interference). This line of research can provide valuable input relevant to teaching, learning and public health policy.


title: Adaptive traffic light control system

abstract: Conventional traffic light control systems are based on fixed time intervals of the traffic lights. These conventional fixed traffic light controllers have limitations and are less efficient because they use a hardware, which functions according to the program that lacks the flexibility of modification and adaptation on a real time basis. Thus due to the fixed time intervals of green and red signals there is excess and unnecessary waiting time on roads and vehicles consume more fuel. This eventually adds up to the environmental pollution and creates several health issues among the people on road and residing nearby. Also these conventional traffic light control systems do not have any provisions to provide any information on traffic densities on various roads, which leads to traffic congestions. Thus, to make traffic light controlling and traffic regulation more efficient, we exploit the emergence of new technique called as Adaptive Traffic Light Control System (ATLCS). The proposed system makes the use of network of array of sensors for sensing the traffic. On categorising this sensed traffic the timing intervals of red and green lights at each crossing of roads are intelligently decided and varied so as to keep the waiting time minimum. Thus, optimization of the traffic light switching increases the road capacity, saves time for travelling and prevents traffic congestions. The system also aims at incorporating special provisions for making immediate way for the emergency vehicles. GSM cell phone interface provides traffic information to drivers on demand and also helps in efficiently regulating the traffic and alternate route taking decisions. Efficiently regulated traffic also reduces pollution. The performance of this proposed system is compared with the conventional fixed time traffic light control system. The various performance evaluation parameters are efficient operation of sensor assembly, time saved per cycle, signal switching frequency, efficient emergency mode operation and the satisfactory operation of SMS using GSM mobile. The effectiveness of this system is shown and discussed in this paper by means of simulation results and on-board display.


title: Anomaly detection in wireless sensor networks: A survey

abstract: Since security threats to WSNs are increasingly being diversified and deliberate, prevention-based techniques alone can no longer provide WSNs with adequate security. However, detection-based techniques might be effective in collaboration with prevention-based techniques for securing WSNs. As a significant branch of detection-based techniques, the research of anomaly detection in wired networks and wireless ad hoc networks is already quite mature, but such solutions can be rarely applied to WSNs without any change, because WSNs are characterized by constrained resources, such as limited energy, weak computation capability, poor memory, short communication range, etc. The development of anomaly detection techniques suitable for WSNs is therefore regarded as an essential research area, which will enable WSNs to be much more secure and reliable. In this survey paper, a few of the key design principles relating to the development of anomaly detection techniques in WSNs are discussed in particular. Then, the state-of-the-art techniques of anomaly detection in WSNs are systematically introduced, according to WSNs' architectures (Hierarchical/Flat) and detection technique categories (statistical techniques, rule based, data mining, computational intelligence, game theory, graph based, and hybrid, etc.). The analyses and comparisons of the approaches that belong to a similar technique category are represented technically, followed by a brief discussion towards the potential research areas in the near future and conclusion.


title: A Hole Filling Approach Based on Background Reconstruction for View Synthesis in 3D Video

abstract: The depth image based rendering (DIBR) plays a key role in 3D video synthesis, by which other virtual views can be generated from a 2D video and its depth map. However, in the synthesis process, the background occluded by the foreground objects might be exposed in the new view, resulting in some holes in the synthetized video. In this paper, a hole filling approach based on background reconstruction is proposed, in which the temporal correlation information in both the 2D video and its corresponding depth map are exploited to construct a background video. To construct a clean background video, the foreground objects are detected and removed. Also motion compensation is applied to make the background reconstruction model suitable for moving camera scenario. Each frame is projected to the current plane where a modified Gaussian mixture model is performed. The constructed background video is used to eliminate the holes in the synthetized video. Our experimental results have indicated that the proposed approach has better quality of the synthetized 3D video compared with the other methods.


title: Internal Nasal Valve Incompetence Is Effectively Treated Using Batten Graft Functional Rhinoplasty

abstract: Introduction. Internal nasal valve incompetence (INVI) has been treated with various surgical methods. Large, single surgeon case series are lacking, meaning that the evidence supporting a particular technique has been deficient. We present a case series using alar batten grafts to reconstruct the internal nasal valve, all performed by the senior author. Methods. Over a 7-year period, 107 patients with nasal obstruction caused by INVI underwent alar batten grafting. Preoperative assessment included the use of nasal strips to evaluate symptom improvement. Visual analogue scale (VAS) assessment of nasal blockage (NB) and quality of life (QOL) both pre- and postoperatively were performed and analysed with the Wilcoxon signed rank test. Results. Sixty-seven patients responded to both pre- and postoperative questionnaires. Ninety-one percent reported an improvement in NB and 88% an improvement in QOL. The greatest improvement was seen at 6 months (median VAS 15 mm and 88 mm resp., with a P value of <0.05 for both). Nasal strips were used preoperatively and are a useful tool in predicting patient operative success in both NB and QOL (odds ratio 2.15 and 2.58, resp.). Conclusions. Alar batten graft insertion as a single technique is a valid technique in treating INVI and produces good outcomes.


title: The role of vection, eye movements and postural instability in the etiology of motion sickness

abstract: Motion sickness is a term that is commonly used to describe the ill effects of many provocative motion (e.g. seagoing or air travel) and apparent motion (e.g. IMAX movies and virtual reality) environments on human well-being and performance. It can be extremely debilitating and yet we do not have a precise understanding of its cause. This study evaluates the importance of three factors that are purported to be involved in the etiology of motion sickness (MS). Most provocative motion environments cause three distinct, but possibly related, responses: reflexive eye movements (EM), sensory conflict (SC), and postural instability (PS). Three current theories, concerning the etiology of motion sickness, emphasize one of these responses, but deny the importance of the others. Such theoretical approaches preclude the possibility of a synergistic interaction of these factors. This experiment employed a three-factor experimental design wherein each factor was manipulated alone or in combination with the others. The independent variables involved two levels of: PS (induced by having the subject stand on a stationary platform or on a posturally challenging platform mounted atop a partially inflated rubber inner tube); SC (with or without illusory self movement elicited visually by whole field stimulation); and EM (unrestricted or controlled by a stable fixation point). Analysis of measures of PS, SC and EM confirmed the effectiveness of these manipulations. Analysis of MS measures (questionnaires, magnitude ratings, tolerance times) revealed a main effect of SC ( p< 0.01), increased MS found with illusory self motion conditions. In addition, measures of MS symptomatology revealed a significant three-way interaction between SC, PS and EM ( p< 0.05), greater amounts of MS found with conditions of illusory self motion, postural challenge, and unrestricted EM. This suggests support for a multi-factorial approach to the study of MS etiology. These findings suggest a major role of SC in the elicitation of MS, but also suggest important contributions from the EM and PS mechanisms.


title: Azhary: An Arabic Lexical Ontology

abstract: Arabic language is the most spoken languages in the Semitic languages group, and one of the most common languages in the world spoken by more than 422 million. It is also of paramount importance to Muslims, it is a sacred language of the Islamic Holly Book (Quran) and prayer (and other acts of worship) in Islam is performed only by mastering some of Arabic words. Arabic is also a major ritual language of a number of Christian churches in the Arab world and it is also used in writing several intellectual and religious Jewish books in the Middle Ages. Despite this, there is no semantic Arabic lexicon which researchers can depend on. In this paper we introduce Azhary as a lexical ontology for the Arabic language. It groups Arabic words into sets of synonyms called synsets, and records a number of relationships between words such as synonym, antonym, hypernym, hyponym, meronym, holonym and association relations. The ontology contains 26,195 words organized in 13,328 synsets. It has been developed and contrasted against AWN which is the most common available Arabic lexical ontology.


title: A fast algorithm and system for the inverse kinematics of general serial manipulators

abstract: We present fast and robust algorithms for the inverse kinematics of serial manipulators consisting of six or fewer joints. When stated mathematically, the problem of inverse kinematics reduces to simultaneously solving a system of algebraic equations. In this paper, we use a series of algebraic and numeric transformations to reduce the problem to computing the eigenstructure of a matrix pencil. To efficiently compute the eigenstructure, we make use of the symbolic formulation of the matrix and use a number of techniques from linear algebra and matrix computations. The resulting algorithm computes all the solution of a serial manipulator with six or fewer joints in the order of tens of milliseconds on the current workstations. It has been implemented as part of a generic package, KINEM, for the inverse kinematics of serial manipulators. >


title: Impact of Social Influence in E-Commerce Decision Making

abstract: Purchasing decisions are often strongly influenced by people who the consumer knows and trusts. Moreover, many online shoppers tend to wait for the opinions of early adopters before making a purchase decision to reduce the risk of buying a new product. Web-based social communities, actively fostered by E-commerce companies, allow consumers to share their personal experiences by writing reviews, rating reviews, and chatting among trusting members. They drive the volume of traffic to retail sites and become a starting point for Web shoppers. E-commerce companies have recently started to capture data on the social interaction between consumers in their websites, with the potential objective of understanding and leveraging social influence in customers' purchase decision making to improve customer relationship management and increase sales. In this paper, we present an overview of the impact of social influence in E-commerce decision making to provide guidance to researchers and companies who have an interest in related issues. We identify how data about social influence can be captured from online customer behaviors and how social influence can be used by E-commerce websites to aid the user decision making process. We also provide a summary of technology for social network analysis and identify the research challenges of measuring and leveraging the impact of social influence on E-commerce decision making.


title: Linking service quality, customer satisfaction, and behavioral intention.

abstract: Based on the service quality and script theory literature, a framework of relationships among service quality, customer satisfaction, and behavioral intention for service purchases is proposed. Specific models are developed from the general framework and the models are applied and tested for the highly complex and divergent consumer service of overnight hospital care. Service quality, customer satisfaction, and behavioral intention data were collected from recent patients of two hospitals. The findings support the specific models and general framework. Implications for theory, service marketing, and future research are discussed.


Soft Robotics : A Perspective — Current Trends and Prospects for the Future


title: Customer churn analysis for a software-as-a-service company

abstract: SaaS companies generate revenues by charging recurring subscription fees for using their software services. The fast growth of SaaS companies is usually accompanied with huge upfront costs in marketing expenses targeted at their potential customers. Customer retention is a critical issue for SaaS companies because it takes twelve months on average to break-even with the expenses for a single customer. This study describes a methodology for helping SaaS companies manage their customer relationships. We investigated the time-dependent software feature usage data, for example, login numbers and comment numbers, to predict whether a customer would churn within the next three months. Our study compared model performance across four classification algorithms. The XGBoost model yielded the best results for identifying the most important software usage features and for classifying customers as either churn type or non-risky type. Our model achieved a 10-fold cross-validated mean AUC score of 0.7941. Companies can choose to move along the ROC curve to accommodate to their marketing capability. The feature importance output from the XGBoost model can facilitate SaaS companies in identifying the most significant software features to launch more effective marketing campaigns when facing prospective customers.


title: A rating approach based on sentiment analysis

abstract: With the advent to social media the number of reviews for any particular product is in millions, as there exist thousand of websites where that particular product exists. As the numbers of reviews are very high the user ends up spending a lot of time for searching best product based on the experiences shared by review writers. This paper presents a sentiment based rating approach for food recipes which sorts food recipes present on various website on the basis of sentiments of review writers. The results are shown with the help of a mobile application: Foodoholic. The output of the application is an ordered list of recipes with user input as their core ingredient.


title: Zero-Shot Open Entity Typing as Type-Compatible Grounding

abstract: The problem of entity-typing has been studied predominantly in supervised learning fashion, mostly with task-specific annotations (for coarse types) and sometimes with distant supervision (for fine types). While such approaches have strong performance within datasets, they often lack the flexibility to transfer across text genres and to generalize to new type taxonomies. In this work we propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. Given a type taxonomy defined as Boolean functions of FREEBASE "types", we ground a given mention to a set of type-compatible Wikipedia entries and then infer the target mention's types using an inference algorithm that makes use of the types of these entries. We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain. Our system is shown to be competitive with state-of-the-art supervised NER systems and outperforms them on out-of-domain datasets. We also show that our system significantly outperforms other zero-shot fine typing systems.


title: Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

abstract: The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.


title: Computer network security: Then and now

abstract: In 1986, this author presented a paper at a conference, giving a sampling of computer and network security issues, and the tools of the day to address them. The purpose of this current paper is to revisit the topic of computer and network security, and see what changes, especially in types of attacks, have been brought about in 30 years. This paper starts by presenting a review of the state of computer and network security in 1986, along with how certain facets of it have changed. Next, it talks about today's security environment, and finally discusses some of today's many computer and network attack methods that are new or greatly updated since 1986. Many references for further study are provided. The classes of attacks that are known today are the same as the ones known in 1986, but many new methods of implementing the attacks have been enabled by new technologies and the increased pervasiveness of computers and networks in today's society. The threats and specific types of attacks faced by the computer community 30 years ago have not gone away. New threat methods and attack vectors have opened due to advancing technology, supplementing and enhancing, rather than replacing the long-standing threat methods.


title: THE [14C]DEOXYGLUCOSE METHOD FOR THE MEASUREMENT OF LOCAL CEREBRAL GLUCOSE UTILIZATION: THEORY, PROCEDURE, AND NORMAL VALUES IN THE CONSCIOUS AND ANESTHETIZED ALBINO RAT

abstract: Abstract— A method has been developed for the simultaneous measurement of the rates of glucose consumption in the various structural and functional components of the brain in vivo. The method can be applied to most laboratory animals in the conscious state. It is based on the use of 2-deoxy-D-[14C]glucose ([14C]DG) as a tracer for the exchange of glucose between plasma and brain and its phosphorylation by hexokinase in the tissues. [14C]DG is used because the label in its product, [14C]deoxyglucose-6-phosphate, is essentially trapped in the tissue over the time course of the measurement. A model has been designed based on the assumptions of a steady state for glucose consumption, a first order equilibration of the free [14C]DG pool in the tissue with the plasma level, and relative rates of phosphorylation of [14C]DG and glucose determined by their relative concentrations in the precursor pools and their respective kinetic constants for the hexokinase reaction. An operational equation based on this model has been derived in terms of determinable variables. A pulse of [14C]DG is administered intravenously and the arterial plasma [14C]DG and glucose concentrations monitored for a preset time between 30 and 45min. At the prescribed time, the head is removed and frozen in liquid N2-chilled Freon XII, and the brain sectioned for autoradiography. Local tissue concentrations of [14C]DG are determined by quantitative autoradiography. Local cerebral glucose consumption is calculated by the equation on the basis of these measured values. ::: ::: The method has been applied to normal albino rats in the conscious state and under thiopental anesthesia. The results demonstrate that the local rates of glucose consumption in the brain fall into two distinct distributions, one for gray matter and the other for white matter. In the conscious rat the values in the gray matter vary widely from structure to structure (54-197 μmol/100 g/min) with the highest values in structures related to auditory function, e.g. medial geniculate body, superior olive, inferior colliculus, and auditory cortex. The values in white matter are more uniform (i.e. 33–40 μmo1/100 g/min) at levels approximately one-fourth to one-half those of gray matter. Heterogeneous rates of glucose consumption are frequently seen within specific structures, often revealing a pattern of cytoarchitecture. Thiopental anesthesia markedly depresses the rates of glucose utilization throughout the brain, particularly in gray matter, and metabolic rate throughout gray matter becomes more uniform at a lower level.


A New Perspective on Magnetic Field Sensing


title: Rectangular waveguide with dielectric-filled corrugations supporting backward waves

abstract: A new application for corrugated waveguides as left-handed (LH) meta-material guided-wave structures is investigated. The waveguide is operated below the cutoff of the dominant mode, where the waveguide has an inherent shunt inductance. The dielectric-filled corrugations are used to provide a series capacitance, which, along with the shunt inductance, create the necessary environment to support backward waves. A simple equivalent-circuit model is constructed, and proves quite accurate in determining the dispersion, as well as the scattering characteristics of the structure. Experimental verification of the occurrence of backward waves in the corrugated waveguide is presented. Very good agreement between the results obtained using the equivalent-circuit model and the full-wave finite-difference time-domain solution is achieved. The effect of the various design parameters on the LH propagation bandwidth is investigated. The advantages and possible applications of the structure are discussed.


title: Nanocrystalline porous α-LiFeO2–C composite—an environmentally friendly cathode for the lithium-ion battery

abstract: A novel nanocrystalline porous α-LiFeO2–C composite with a high surface area of around 115 m2 g−1 has been synthesized by a simple molten salt method, followed by a carbon coating process. The structure and morphology were confirmed by X-ray diffraction, field emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM). FE-SEM observations demonstrated that the morphology consists of α-LiFeO2–C nanoclusters composed of very tiny nanoparticles joined together by a porous architecture. TEM investigations revealed that amorphous carbon was incorporated into the pores among the nanoparticles and that some nanoparticles were covered by a thin layer of carbon as well. Electrochemical measurements showed that the α-LiFeO2–C nanocomposite delivered a significantly higher reversible capacity and excellent cycle stability (230 mA h g−1 at 0.5 C after 100 cycles). Even at the high rate of 3 C, the electrode showed more than 50% of the capacity at low rate (0.1 C). The excellent electrochemical performance of the α-LiFeO2–C nanocomposite electrode can be attributed to the porous conductive architecture among the nanoparticles, which not only has benefits in terms of decreasing the absolute volume changes and increasing the mobility of lithium ions, but also offers conductive pathways along the whole interconnected wall in the structure, which is favourable for the transport of electrons, promotes liquid electrolyte diffusion into the bulk materials, and acts as a buffer zone to absorb the volume changes.


title: Observer pattern using aspect-oriented programming

abstract: This paper discusses the representation and implementation of the Observer design pattern using aspect-oriented techniques.


Stance Classification on PTT Comments


title: Cortical coordination dynamics and cognition

abstract: Abstract New imaging techniques in cognitive neuroscience have produced a deluge of information correlating cognitive and neural phenomena. Yet our understanding of the inter-relationship between brain and mind remains hampered by the lack of a theoretical language for expressing cognitive functions in neural terms. We propose an approach to understanding operational laws in cognition based on principles of coordination dynamics that are derived from a simple and experimentally verified theoretical model. When applied to the dynamical properties of cortical areas and their coordination, these principles support a mechanism of adaptive inter-area pattern constraint that we postulate underlies cognitive operations generally.


title: The identity crisis within the is discipline: defining and communicating the discipline's core properties

abstract: We are concerned that the IS research community is making the discipline's central identity ambiguous by, all too frequently, under-investigating phenomena intimately associated with IT-based systems and over-investigating phenomena distantly associated with IT-based systems. In this commentary, we begin by discussing why establishing an identity for the IS field is important. We then describe what such an identity may look like by proposing a core set of properties, i.e., concepts and phenomena, that define the IS field. Next, we discuss research by IS scholars that either fails to address this core set of properties (labeled as error of exclusion) or that addresses concepts/phenomena falling outside this core set (labeled as error of inclusion). We conclude by offering suggestions for redirecting IS scholarship toward the concepts and phenomena that we argue define the core of the IS discipline.


Forward+: Bringing Deferred Lighting to the Next Level


title: Securing the Internet of Things: A Meta-Study of Challenges, Approaches, and Open Problems

abstract: The Internet of Things (IoT) is becoming a key infrastructure for the development of smart ecosystems. However, the increased deployment of IoT devices with poor security has already rendered them increasingly vulnerable to cyber attacks. In some cases, they can be used as a tool for committing serious crimes. Although some researchers have already explored such issues in the IoT domain and provided solutions for them, there remains the need for a thorough analysis of the challenges, solutions, and open problems in this domain. In this paper, we consider this research gap and provide a systematic analysis of security issues of IoT-based systems. Then, we discuss certain existing research projects to resolve the security issues. Finally, we highlight a set of open problems and provide a detailed description for each. We posit that our systematic approach for understanding the nature and challenges in IoT security will motivate researchers to addressing and solving these problems.


title: The Imprecisions of Precision Measures in Process Mining

abstract: In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all models and logs. This paper fills this gap by postulating a number of axioms for quantifying precision consistently for any log and any model. Further, we show through counter-examples that none of the existing measures consistently quantifies precision.


title: Comparing Mobile Privacy Protection through Cross-Platform Applications

abstract: With the rapid growth of the mobile market, security of mobile platforms is receiving increasing attention from both research community as well as the public. In this paper, we make the first attempt to establish a baseline for security comparison between the two most popular mobile platforms. We investigate applications that run on both Android and iOS and examine the difference in the usage of their security sensitive APIs (SS-APIs). Our analysis over 2,600 applications shows that iOS applications consistently access more SS-APIs than their counterparts on Android. The additional privileges gained on iOS are often associated with accessing private resources such as device ID, camera, and users’ contacts. A possible explanation for this difference in SS-API usage is that privileges obtained by an application on the current iOS platform are invisible to end users. Our analysis shows that: 1) third-party libraries (specifically advertising and analytic libraries) on iOS invoke more SS-APIs than those on Android; 2) Android application developers avoid requesting unnecessary privileges which will be shown in the permission list during application installation. Considering the fact that an Android application may gain additional privileges with privilege-escalation attacks and iOS provides a more restricted privilege set accessible by third-party applications, our results do not necessarily imply that Android provides better privacy protection than iOS. However, our evidence suggests that Apple’s application vetting process may not be as effective as Android’s privilege notification mechanism, particularly in protecting sensitive resources from third-party applications.


Investigating Robustness and Interpretability of Link Prediction via Adversarial Attacks


title: Restaurant Survival Analysis with Heterogeneous Information

abstract: For shopkeepers, one of their biggest common concerns is whether their business will thrive or fail in the future. With the development of new ways to collect business data, it is possible to leverage multiple domains' knowledge to build an intelligent model for business assessment. In this paper, we discuss what the potential indicators are for the long-term survival of a physical store. To this end, we study factors from four pillars: geography, user mobility, user rating, and review text. We start by exploring the impact of geographic features, which describe the location environment of the retailer store. The location and nearby places play an important role in the popularity of the shop, and usually less competitiveness and more heterogeneity is better. Then we study user mobility. It can be viewed as supplementary to the geographical placement, showing how the location can attract users from anywhere. Another important factor is how the shop can serve and satisfy users. We find that restaurant survival prediction is a hard task that can not be solved simply using consumers' ratings or sentiment metrics. Compared with conclusive and well-formatted ratings, the various review words provide more insight of the shop and deserve in-depth mining. We adopt several language models to fully explore the textual message. Comprehensive experiments demonstrate that review text indeed have the strongest predictive power. We further compare different cities' models and find the conclusions are highly consistent. Although we focus on the class of restaurant in this paper, the method can be easily extended to other shop categories.


title: Investigation of machine learning based network traffic classification

abstract: Timely and accurate traffic classification and application characterization are becoming increasingly important with many applications in wired and wireless networks, e.g., traffic engineering, security monitoring, and quality of service (QoS). In particular, Software Defined Networking (SDN) is a new networking paradigm that has great impact on future IP networks and 5G wireless networks. In SDN networks, application awareness is essential for functionalities such as virtual network resource slicing and fast routing. Compared to traditional classification methods such as port-based and payload-based algorithms, machine learning (ML) approaches offer a better choice in Internet traffic characterization by using payload-independent traffic statistics. In this paper, two ML algorithms, namely supervised Support Vector Machine (SVM) and unsupervised K-means clustering, are studied for traffic classification. It has been found that an overall accuracy of over 95% can be achieved. Meanwhile, the system performance can be further improved with model tuning and feature selection.


title: ML-SOR : Message routing using multi-layer social networks in opportunistic communications

abstract: Opportunistic networks are a generalization of DTNs in which disconnections are frequent and encounter patterns between mobile devices are unpredictable. In such scenarios, message routing is a fundamental issue. Social-based routing protocols usually exploit the social information extracted from the history of encounters between mobile devices to find an appropriate message relay. Protocols based on encounter history, however, take time to build up a knowledge database from which to take routing decisions. While contact information changes constantly and it takes time to identify strong social ties, other types of ties remain rather stable and could be exploited to augment available partial contact information. In this paper, we start defining a multi-layer social network model combining the social network detected through encounters with other social networks and investigate the relationship between these social network layers in terms of node centrality, community structure, tie strength and link prediction. The purpose of this analysis is to better understand user behavior in a multi-layered complex network combining online and offline social relationships. Then, we propose a novel opportunistic routing approach ML-SOR (Multi-layer Social Network based Routing) which extracts social network information from such a model to perform routing decisions. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. Trace driven simulations show that a routing metric combining social information extracted from multiple social network layers allows users to achieve good routing performance with low overhead cost.


title: An on-chip wideband and low-loss duplexer for 3G/4G CMOS radios

abstract: A wideband integrated RF duplexer supports 3G/4G bands I, II, III, IV, and IX, and achieves a TX-to-RX isolation of more than 55dB in the transmit-band, and greater than 45dB in the corresponding receive-band across 200MHz of bandwidth. A 65nm CMOS duplexer/LNA achieves a transmit insertion loss of 2.5dB, and a cascaded receiver noise figure of 5dB with more than 27dB of gain, exceeding the commercial external duplexers performance at considerably lower cost and area.


title: Supporting Exploration Awareness in Information Visualization

abstract: When users want to continue an analysis performed in the past, done by themselves or by a collaborator, they need an overview of what has been done and found so far. Such an overview helps them to gain a shared knowledge about each otherspsila analysis strategy and continue the analysis. We aim to support users in this process, and thereby support their exploration awareness. We present an information visualization framework with three linked processes: overview, search and retrieve for this purpose. First, we present a userpsilas information interest model that captures key aspects of the exploration process. Exploration overview, and keyword and similarity based search mechanisms are designed based on these key aspects. A metadata view is used to visualize the search results and help users to retrieve specific visualizations from past analysis. Finally, we present three case studies and discuss the support offered by the framework for developing exploration awareness.


title: Formal language models for finding groups of experts

abstract: We introduce a new information retrieval task: given a topic, try to find knowledgeable groups that have expertise on the topic.Five probabilistic language models are proposed to tackle the challenge of automatically finding groups of experts in heterogeneous document collections.For evaluation purpose, a data set is created based on a publicly downloadable corpus used in the TREC Enterprise 2005 and 2006 tracks and three types of ground truth are defined.We provide a detailed analysis of the performance of the proposed group finding models. The task of finding groups or teams has recently received increased attention, as a natural and challenging extension of search tasks aimed at retrieving individual entities. We introduce a new group finding task: given a query topic, we try to find knowledgeable groups that have expertise on that topic. We present five general strategies for this group finding task, given a heterogenous document repository. The models are formalized using generative language models. Two of the models aggregate expertise scores of the experts in the same group for the task, one locates documents associated with experts in the group and then determines how closely the documents are associated with the topic, whilst the remaining two models directly estimate the degree to which a group is a knowledgeable group for a given topic. For evaluation purposes we construct a test collection based on the TREC 2005 and 2006 Enterprise collections, and define three types of ground truth for our task. Experimental results show that our five knowledgeable group finding models achieve high absolute scores. We also find significant differences between different ways of estimating the association between a topic and a group.


title: A Fuzzy-based approach to programming language independent source-code plagiarism detection

abstract: Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Fuzzy clustering approaches are a suitable solution to detecting source-code plagiarism due to their capability to capture the qualitative and semantic elements of similarity. This paper proposes a novel Fuzzy-based approach to source-code plagiarism detection, based on Fuzzy C-Means and the Adaptive-Neuro Fuzzy Inference System (ANFIS). In addition, performance of the proposed approach is compared to the Self- Organising Map (SOM) and the state-of-the-art plagiarism detection Running Karp-Rabin Greedy-String-Tiling (RKR-GST) algorithms. The advantages of the proposed approach are that it is programming language independent, and hence there is no need to develop any parsers or compilers in order for the fuzzy-based predictor to provide detection in different programming languages. The results demonstrate that the performance of the proposed fuzzy-based approach overcomes all other approaches on well-known source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.


title: Using a Foundational Ontology for Reengineering a Software Process Ontology

abstract: Normal 0 21 During project planning , knowledge about software processes is useful in several situations: software processes are defined, activities are scheduled, and people are allocated to these activities. In this context, standard software processes are used as basis for defining project processes, and tools are used to support scheduling, people allocation, and so on. Ideally, people and tools should share a common conceptualization regarding this domain for allowing interoperability, and correct use of the tools. A domain ontology can be used to define an explicit representation of this shared conceptualization . Moreover, for a domain ontology to adequately serve as a reference model, it should be built explicitly taking foundational concepts into account . This paper discusses the reengineering of part of a Software Process Ontology based on the Unified Foundational Ontology (UFO). The part reengineered concerns standard processes, project processes, and activities, which are analyzed at the light of UFO concepts.


title: A Survey of Distributed Query Optimization

abstract: The distributed query optimization is one of the hardest problems in the database area. The great commercial success of database systems is partly due to the development of sophisticated query optimization technology where users pose queries in a declarative way using SQL or OQL and the optimizer of the database system finds a good way (i. e. plan) to execute these queries. The optimizer, for example, determines which indices should be used to execute a query and in which order the operations of a query (e. g. joins, selects, and projects) should be executed. To this end, the optimizer enumerates alternative plans, estimates the cost of every plan using a cost model, and chooses the plan with lowest cost. There has been much research into this field. In this paper, we stud y the problem of distributed query optimization; we focus on the basic components of the distributed query optimizer, i. e. search space, search strategy, and cost model. A survey of the available work into this field is given. Finally, some future work is highlighted based on some recent work that uses mobile agent technologies.


title: The Change of Residents' Subjective Well-Being after the Tenth Anniversary of Macao's Handover

abstract: On December 20, 1999, China resumed the exercise of sovereignty over Macao. From then on, Macao entered a completely new era in its history, and that brought changes to residents' subject well-being. In order to analyze and compare this change when before and after Macao's handover, we investigated 450 residents in Macao with General Well-Being Schedule (GWBS) and Self-Compiled Questionnaire, and ana- lyzed from aspects of sex, age, religion, marriage states, school record, and monthly income. The results showed a significantly higher Subject well-being after Macao's return no matter on 6 demographic variables, on GWBS overall or on 6 dimensions of GWBS (Satisfaction and interest toward life, worry about health, energy, mind of depression or delight, the control of emotion and behavior, relax or tension). We do think that the changes of residents' subject well-being should be attributed to political system's creation, economic took-off, better public security, a higher international status, and so on.


title: Wireless power transfer and applications to sensor networks

abstract: Energy constraints are widely regarded as a fundamental limitation of wireless and mobile devices. For sensor networks, a limited lifetime due to battery constraint poses a performance bottleneck and barrier for large scale deployment. Recently, wireless power transfer has emerged as a promising technology to address energy and lifetime bottlenecks in a sensor network. In this article, we give a review of the history of wireless power transfer and describe its recent developments. We show how such technologies can be applied to sensor networks and address their energy constraints.


title: A Survey on Bio-inspired Networking

abstract: The developments in the communication and networking technologies have yielded many existing and envisioned information network architectures such as cognitive radio networks, sensor and actor networks, quantum communication networks, terrestrial next generation Internet, and InterPlaNetary Internet. However, there exist many common significant challenges to be addressed for the practical realization of these current and envisioned networking paradigms such as the increased complexity with large scale networks, their dynamic nature, resource constraints, heterogeneous architectures, absence or impracticality of centralized control and infrastructure, need for survivability, and unattended resolution of potential failures. These challenges have been successfully dealt with by Nature, which, as a result of millions of years of evolution, have yielded many biological systems and processes with intrinsic appealing characteristics such as adaptivity to varying environmental conditions, inherent resiliency to failures and damages, successful and collaborative operation on the basis of a limited set of rules and with global intelligence which is larger than superposition of individuals, self-organization, survivability, and evolvability. Inspired by these characteristics, many researchers are currently engaged in developing innovative design paradigms to address the networking challenges of existing and envisioned information systems. In this paper, the current state-of-the-art in bio-inspired networking is captured. The existing bio-inspired networking and communication protocols and algorithms devised by looking at biology as a source of inspiration, and by mimicking the laws and dynamics governing these systems are presented along with open research issues for the bio-inspired networking. Furthermore, the domain of bio-inspired networking is linked to the emerging research domain of nanonetworks, which bring a set of unique challenges. The objective of this survey is to provide better understanding of the potentials for bio-inspired networking which is currently far from being fully recognized, and to motivate the research community to further explore this timely and exciting topic.


title: A survey on data center networking for cloud computing

abstract: Data Center Networks (DCNs) are an essential infrastructure that impact the success of cloud computing. A scalable and efficient data center is crucial in both the construction and operation of stable cloud services. In recent years, the growing importance of data center networking has drawn much attention to related issues including connective simplification and service stability. However, existing DCNs lack the necessary agility for multi-tenant demands in the cloud, creating poor responsiveness and limited scalability. In this paper, we present an overview of data center networks for cloud computing and evaluate construction prototypes based on these issues. We provide, specifically, detailed descriptions of several important aspects: the physical architecture, virtualized infrastructure, and DCN routing. Each section of this work discusses and evaluates resolution approaches, and presents the use cases for cloud computing service. In our attempt to build insight relevant to future research, we also present some open research issues. Based on experience gained in both research and industrial trials, the future of data center networking must include careful consideration of the interactions between the important aspects mentioned above.


Are Generic UX Evaluation Techniques Enough? A study on the UX Evaluation of the Edmodo Learning Management System


title: Design of circularly polarized waveguide crossed slotted array antenna at Ka band

abstract: Present paper highlights an improvement over the use of conventional cross slot radiator in waveguide broadwall for circular polarization by using a combination of cross slot and inclined slot pair. Accordingly, a circularly polarized travelling wave slotted array antenna at Ka band (38.6GHz) of 20dB gain has been designed and verified in a nonstandard waveguide using the modified cross slot radiator.


title: Formal verification problems in a big data world: towards a mighty synergy

abstract: Formal verification requires high performance data processing software for extracting knowledge from the unprecedented amount of data coming from analyzed systems. Since cloud based computing resources have became easily accessible, there is an opportunity for verification techniques and tools to undergo a deep technological transition to exploit the new available architectures. This has created an increasing interest in parallelizing and distributing verification techniques. In this paper we introduce a distributed approach which exploits techniques typically used by the bigdata community to enable verification of very complex systems using bigdata approaches and cloud computing facilities.


title: Heart rate variability with deep breathing as a clinical test of cardiovagal function

abstract: Research into heart rate variability (HRV) and respiration over the past 150 years has led to the insight that HRV with deep breathing (HRVdb) is a highly sensitive measure of cardiovagal or parasympathetic cardiac function. This sensitivity makes HRVdb an important part of the battery of cardiovascular autonomic function tests used in clinical autonomic laboratories. HRVdb is a reliable and sensitive clinical test for early detection of cardiovagal dysfunction in a wide range of autonomic disorders.


title: Research and simulation of an improved Halbach arrays

abstract: Halbach array is new permanent structure. It was proposed by physicist Dr. K. Halbach of the U.S. Lawrence Berkeley National Laboratory. This paper proposes an improved magnet structure of Halbach array. By simulation of finite element software-Ansoft's Maxwell, the results show that improved magnet structure can effectively improve the uniformity of the magnetic field within the array comparing with one-on-pole Halbach magnet array Composed by fan-based units.


title: Current and Future Challenges in Radiation Effects on CMOS Electronics

abstract: Advances in microelectronics performance and density continue to be fueled by the engine of Moore's law. Although lately this engine appears to be running out of steam, recent developments in advanced technologies have brought about a number of challenges and opportunities for their use in radiation environments. For example, while many advanced CMOS technologies have generally shown improving total dose tolerance, single-event effects continue to be a serious concern for highly scaled technologies. In this paper, we examine the impact of recent developments and the challenges they present to the radiation effects community. Topics covered include the impact of technology scaling on radiation response and technology challenges for both total dose and single-event effects. We include challenges for hardening and mitigation techniques at the nanometer scale. Recent developments leading to hardness assurance challenges are covered. Finally, we discuss future radiation effects challenges as the electronics industry looks beyond Moore's law to alternatives to traditional CMOS technologies.


title: Phase spectrum prediction of audio signals

abstract: Modeling the phases of audio signals has received significantly less attention in comparison to the modeling of magnitudes. This paper proposes to use linear least squares and neural networks to predict phases from the neighboring points only in the phase spectrum. The simulation results show that there is a structure in the phase components which could be used in further analysis algorithms based on the phase spectrum.


title: Field mice: extracting hand geometry from electric field measurements

abstract: Several members of a family of techniques called Electric Field Sensing are described. Each sensing technique can be understood as a measurement of the value of a different component in an effective circuit diagram that summarizes all possible current pathways involving the body to be sensed and the sensing electrodes. An analytical model of the sensor response is presented, and then a probabilistic framework for inferring geometrical information from field measurements is described. The inference framework is demonstrated for the cases of a two-dimensional and a three-dimensional mouse.