title: Ant Colony Extended : Experiments on the Travelling Salesman Problem

abstract: This paper studies of the performance of Ant Colony Extended to the Travelling Salesman Problem.The algorithm includes two kinds of ants: patrollers (exploration search) and foragers (exploitation search).The algorithm includes a population dynamics allows the algorithm to self-organise.The algorithm is able to solve the Travelling Salesman Problem successfully compared with other classical ant algorithms. Ant Colony Extended (ACE) is a novel algorithm belonging to the general Ant Colony Optimisation (ACO) framework. Two specific features of ACE are: the division of tasks between two kinds of ants, namely patrollers and foragers, and the implementation of a regulation policy to control the number of each kind of ant during the searching process. In addition, ACE does not employ the construction graph usually employed by classical ACO algorithms. Instead, the search is performed using a state space exploration approach. This paper studies the performance of ACE in the context of the Travelling Salesman Problem (TSP), a classical combinatorial optimisation problem. The results are compared with the results of two well known ACO algorithms: ACS and MMAS. ACE shows better performance than ACS and MMAS in almost every TSP tested instance.


title: Workspace Performance Optimization of Fully Restrained Cable-Driven Parallel Manipulators

abstract: Workspace analysis and optimization is a critical issue in robot manipulator design. For a Cable-Driven Parallel Manipulator (CDPM), due to the unilateral driving properties of the cables, maintaining positive cable tension is essential to maneuver the moving platform. As a result, its workspaces are always determined and characterized by the tension status of its driving cables. It has been realized that the Tension Factor (TF) reflecting the relative tension distribution among the driving cables is an appropriate measure to evaluate the quality of force closure for CDPMs. However, in a fully restrained CDPM, since redundant cables are employed to drive the moving platform, the TF values are not unique even for a particular moving platform pose. Therefore, how to obtain the optimal TF value so as to generate an optimized workspace becomes the major subject of this paper. It is proved that the optimal TF value can be efficiently determined through a linear optimization approach, although it is essentially a nonlinear optimization problem. Subsequently, a global tension index is proposed to evaluate the quality of the entire workspace, which is achieved by integrating the local TF values (i.e., the optimal TF values at every moving platform poses) over the workspace. Computation examples are provided to demonstrate the effectiveness of the proposed algorithms.


title: Security analysis of an image encryption algorithm based on a DNA addition combining with chaotic maps

abstract: In this paper, we propose to cryptanalyse an encryption algorithm which combines a DNA addition and a chaotic map to encrypt a gray scale image. Our contribution consists on, at first, demonstrating that the algorithm, as it is described, is non-invertible, which means that the receiver cannot decrypt the ciphered image even if he posses the secret key. Then, a chosen plaintext attack on the invertible encryption block is described, where, the attacker can illegally decrypt the ciphered image by a temporary access to the encryption machinery.


title: Outlier detection via sampling ensemble

abstract: Outlier detection is a key technique in data ming and machine learning fields. The deviating characters of outliers make huge detrimental effects on the learning tasks. A lot of algorithms are therefore proposed to handle outliers from different perspectives, such as distance, density, angle and so on. Among these approaches, the density-based methods achieve better performance, but also suffer from huge time complexity. Recently, in order to accelerate the speed and improve the performance, the subsampling ensemble method attracts much attention, which has a reasonable theoretical interpretation and high performance. However, existing work only gives the partial picture of outlier detection via row-sampling, the effective portfolio of bi-sampling is still void. In light of this, we propose the general outlier detection framework via bi-sampling, Bi-Sampling Outlier Detection (BSOD) and provide the effective portfolios of the row and column-sampling ratios in a theoretical way. In addition, the benefits of BSOD are fully illustrated in terms of ensemble diversity and divide-and-conquer. Further we employ LOF within BSOD as BI-LOF to conduct extensive experiments. In general, on 30 synthetic and 17 real-world data sets we thoroughly explore the characteristics of BI-LOF with different numbers of instances, features, nearest neighbors, validate the theoretical analysis of BSOD condition on synthetic data sets, and show obvious advantages over other state-of-the-art algorithms in terms of low and high dimensional real-world data sets. And finally we use BI-LOF to conduct image outlier detection and show high quality and stableness of BI-LOF.


title: Stochastic Programming for Multiple-Leg Network Revenue Management

abstract: Airline seat inventory control is a very profitable tool in the airline industry. Mathematical programming models provide booking limits or bid-prices for all itineraries and fare classes based on demand forecasts. But the actual revenue generated in the booking process fails to meet expectations. Simple deterministic models based on expected demand generate better revenue than more advanced probabilistic models. Recently suggested models put even more effort into demand forecasting. We will show that the dynamic booking process rather than the demand forecasts needs to be addressed. In particular the nesting strategies applied in booking control will counter-effect the profitability of advanced estimation of booking limits and bid-prices.


title: Fall Detection System Using Artificial Neural Network

abstract: Falls are a major concern in health care nowadays. The senior citizens and patients suffering from genetic disorders like muscular dystrophy are more likely to be prone to falls as their muscles weaken over the period of time. Apart from the fall itself, most significant consequences are generally the long lie periods associated with falls. This can cause severe side effects and can be dangerous especially if the person is alone or becomes unconscious after falling. So there is a need for a robust fall detector which is able to accurately detect fall and alert the concerned people for assistance so that the adverse effects of long lie period can be mitigated. This project aims in building an efficient fall detection system using neural network. The key to any fall detection system is the algorithm used. This paper proposes the use of machine learning technique specifically, neural network for the classification of an activity as a fall or non fall event.


title: Exploring Engaging Gamification Mechanics in Massive Online Open Courses

abstract: Introduction Massive online open courses (MOOCs) are a current trend for creating online courses for equipping learning institutions to obtain a free and high quality teaching initiative with relevant visibility on the Internet (Johnson, Becker, Cummins, Freeman, Ifenthaler, & Vardaxis, 2013; Pellas, 2014). MOOCs refer to web platforms that allow millions of learners to access various instructional materials and resources without the constraints of time and place, and additional learning opportunities to supplement traditional classroom instruction, such as Coursera, Udacity, and edX (Lin, 2010; Stoel & Lee, 2003). MOOCs are interactive, online learning tools that support the learning of specific concepts by enhancing, amplifying, and guiding the cognitive processes of learners (Altbach, 2014). MOOCs use the increasing popularity of social networking services (SNSs) such as instant messengers (IMs), Facebook, and Twitter, to facilitate increased social interaction and engage millions of teachers, learners, and parents (Lin, 2010). The learner-learner and learner-instructor interaction created by MOOCs is central to knowledge acquisition and the development of learner cognitive skills, and that interaction is intrinsic to effective instructional practice (Lee & Hammer, 2011; Tobarra, Robles-Gomez, Ros, Hernandez, & Caminero, 2014). MOOCs are an alternative to traditional models of face-to-face education, and have even been viewed as a threat to traditional educational institutions and professionals (Millard, Borthwick, Howard, McSweeney, & Hargood, 2013). Thus, the development of MOOCs has received considerable attention from both educators and learning-technology developers. MOOCs have been an increased focus related to learner participation of MOOCs, given rising tuition costs and concerns regarding learner success and retention rates (Pappano, 2012). Although MOOCs are rapidly developing and gaining enormous popularity, most of them fail to help learners to remain focused on learning content and lead to relatively poor learning efficiency and effectiveness. This phenomenon occurs because most MOOC designs do not provide learners with an engaging experience. Certain researchers have mentioned that MOOCs must enhance learner digital engagement, which refers to the learning and everyday engagement of learners with available technologies in their learning ecologies, including both daily life and school contexts (Gurung & Rutledge, 2014). Therefore, improving learner digital engagement is critical to the development of MOOCs. Certain studies have proposed gamification as a potential solution to alleviate this problem (Grunewald, Meinel, Totschnig, & Willems, 2013; Skiba, 2013; Dicheva, Dichev, Agre, & Angelova, 2015). Gamification incorporates game mechanics into nongame settings to increase user engagement and enjoyment of a product or service, and to encourage users to perform certain behaviours (Hsu, Chang, & Lee, 2013). Gamification essentially functions as entertainment that causes learners to enjoy actively participating and engaging with others, such as through reputation points, rewards, and goal setting. Kapp (2012) indicated that gamification is crucial to the development of learning technology because numerous elements of gamification are based on educational psychology and techniques that instructors have been using for years. Simoes, Redondo, and Vilas (2013) developed a learning platform for K-6 learners, and suggested that education is an area with high potential for applying gamification because it substantially promotes learner motivation and engagement with the learning platform. Sung and Hwang (2013) proposed a gamification mechanism for course websites to improve the learning performance of learners in their learning attitudes, learning motivation, self-efficacy, and learning achievements. Because of the importance of gamification to learner engagement, certain popular MOOCs such as Coursera, Udacity, and edX effectively attract and maintain learners through various gamification designs such as rewards and badges. …


title: Are features equally representative? A feature-centric recommendation

abstract: Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, producer). This observation motivates us to consider a feature-centric recommendation approach to item recommendation: instead of directly predicting the rating on items, we predict the rating on the features of items, and use such ratings to derive the rating on an item. This approach offers several advantages over the traditional item-centric approach: it incorporates more information about why a user chooses an item, it generalizes better due to the denser feature rating data, it explains the prediction of item ratings through the predicted feature ratings. Another contribution is turning a principled item-centric solution into a feature-centric solution, instead of inventing a new algorithm that is feature-centric. This approach maximally leverages previous research. We demonstrate this approach by turning the traditional item-centric latent factor model into a feature-centric solution and demonstrate its superiority over item-centric approaches.


title: A fingerprint retrieval system based on level-1 and level-2 features

abstract: This paper proposes a novel fingerprint retrieval system that combines level-1 (local orientation and frequencies) and level-2 (minutiae) features. Various score- and rank-level fusion strategies and a novel hybrid fusion approach are evaluated. Extensive experiments are carried out on six public databases and a systematic comparison is made with eighteen retrieval methods and seventeen exclusive classification techniques published in the literature. The novel approach achieves impressive results: its retrieval accuracy is definitely higher than competing state-of-the-art methods, with error rates that in some cases are even one or two orders of magnitude smaller.


title: A survey on energy efficient techniques in wireless sensor networks

abstract: The myriad of potential applications supported by wireless sensor networks (WSNs) has generated much interest from the research community. Various applications range from small size low industrial monitoring to large scale energy constrained environmental monitoring. In all cases, an operational network is required to fulfill the application missions. In addition, energy consumption of nodes is a great challenge in order to maximize network lifetime. Unlike other networks, it can be hazardous, very expensive or even impossible to charge or replace exhausted batteries due to the hostile nature of environment. Researchers are invited to design energy efficient protocols while achieving the desired network operations. This paper focuses on different techniques to reduce the consumption of the limited energy budget of sensor nodes. After having identified the reasons of energy waste in WSNs, we classify energy efficient techniques into five classes, namely data reduction, control reduction, energy efficient routing, duty cycling and topology control. We then detail each of them, presenting subdivisions and giving many examples. We conclude by a recapitulative table.


title: Semantic Normalization and Matching of Business Dependency Models

abstract: Assessing potential threats and impacts relevant for a company, requires a detailed analysis of a company's business processes and functions down to a level of infrastructure resources, in the form of one business dependency model. Required information is frequently encapsulated in BPMN models per process, but pose an eminent problem of fusing and merging multiple sources into one model. Experts defining BPMN models possibly use different nomenclature, descriptions, and references towards common entities, leading to semantically overlapping partial dependency models. Merging multiple partial dependency models is a novel problem related to the business process matching problem, but origins from an orthogonal perspective. In this paper we propose a business dependency model normalization and matching approach by exploiting structures and dependencies of business resources, which neither requires linguistic processing nor "fuzzy" matching processes.


title: Black-Box Side-Channel Attacks Highlight the Importance of Countermeasures – An Analysis of the Xilinx Virtex-4 and Virtex-5 Bitstream Encryption Mechanism –

abstract: This paper presents a side-channel analysis of the bitstream encryption mechanism provided by Xilinx Virtex FPGAs. This work covers our results analyzing the Virtex-4 and Virtex-5 family showing that the encryption mechanism can be completely broken with moderate effort. The presented results provide an overview of a practical real-world analysis and should help practitioners to judge the necessity to implement side-channel countermeasures. We demonstrate sophisticated attacks on off-the-shelf FPGAs that go far beyond schoolbook attacks on 8-bit AES S-boxes. We were able to perform the key extraction by using only the measurements of a single power-up. Access to the key enables cloning and manipulating a design, which has been encrypted to protect the intellectual property and to prevent fraud. As a consequence, the target product faces serious threats like IP theft and more advanced attacks such as reverse engineering or the introduction of hardware Trojans. To the best of our knowledge, this is the first successful attack against the bitstream encryption of Xilinx Virtex-4 and Virtex-5 reported in open literature.


title: IMPROVING COURSE SUCCESS PREDICTION USING ABET COURSE OUTCOMES AND GRADES

abstract: Modeling and prediction of student success is a critical task in education. In this paper, we employ machine learning methods to predict course grade performance of Computer Engineering students. As features, in addition to the conventional course grades we use fine grained student performance measurements corresponding to different goals (ABET outcomes) of a course. We observe that, compared to using only previous course grades, addition of outcome grades can significantly improve the prediction results. Using the trained model enables interpretation of how different courses affect performance on a specific course in the future. We think that even more detailed and systematically produced course outcome measurements can be beneficial in modeling students university performance.


title: A formal analysis of the role of multi-point crossover in genetic algorithms

abstract: On the basis of early theoretical and empirical studies, genetic algorithms have typically used 1 and 2-point crossover operators as the standard mechanisms for implementing recombination. However, there have been a number of recent studies, primarily empirical in nature, which have shown the benefits of crossover operators involving a higher number of crossover points. From a traditional theoretical point of view, the most surprising of these new results relate to uniform crossover, which involves on the averageL/2 crossover points for strings of lengthL. In this paper we extend the existing theoretical results in an attempt to provide a broader explanatory and predictive theory of the role of multi-point crossover in genetic algorithms. In particular, we extend the traditional disruption analysis to include two general forms of multi-point crossover:n-point crossover and uniform crossover. We also analyze two other aspects of multi-point crossover operators, namely, their recombination potential and exploratory power. The results of this analysis provide a much clearer view of the role of multi-point crossover in genetic algorithms. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested.


title: Adaptive Cleaning for RFID Data Streams

abstract: To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.


title: Towards Biologically Plausible Deep Learning

abstract: Neuroscientists have long criticised deep learning algorithms as incompatible with current knowledge of neurobiology. We explore more biologically plausible versions of deep representation learning, focusing here mostly on unsupervised learning but developing a learning mechanism that could account for supervised, unsupervised and reinforcement learning. The starting point is that the basic learning rule believed to govern synaptic weight updates (Spike-TimingDependent Plasticity) can be interpreted as gradient descent on some objective function so long as the neuronal dynamics push firing rates towards better values of the objective function (be it supervised, unsupervised, or reward-driven). The second main idea is that this corresponds to a form of the variational EM algorithm, i.e., with approximate rather than exact posteriors, implemented by neural dynamics. Another contribution of this paper is that the gradients required for updating the hidden states in the above variational interpretation can be estimated using an approximation that only requires propagating activations forward and backward, with pairs of layers learning to form a denoising auto-encoder. Finally, we extend the theory about the probabilistic interpretation of auto-encoders to justify improved sampling schemes based on the generative interpretation of denoising auto-encoders, and we validate all these ideas on generative learning tasks.


title: $OntoMath^{PRO}$ Ontology: A Linked Data Hub for Mathematics

abstract: In this paper, we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE.


title: Consumer understanding and use of nutrition labelling: a systematic review

abstract: Objective: To explore published and unpublished research into consumer understanding and use of nutrition labelling which is culturally applicable in Europe. Design: A systematic review undertaken between July 2002 and February 2003. Results: One hundred and three papers were identified that reported on consumer understanding or use of nutrition labelling, most originating from North America or northern Europe. Only a few studies (9%) were judged to be of high or medium–high quality. We found that reported use of nutrition labels is high but more objective measures suggest that actual use of nutrition labelling during food purchase may be much lower. Whether or not consumers can understand and use nutrition labelling depends on the purpose of the task. Available evidence suggests that consumers who do look at nutrition labels can understand some of the terms used but are confused by other types of information. Most appear able to retrieve simple information and make simple calculations and comparisons between products using numerical information, but their ability to interpret the nutrition label accurately reduces as the complexity of the task increases. The addition of interpretational aids like verbal descriptors and recommended reference values helps in product comparison and in putting products into a total diet context. Conclusions: Improvements in nutrition labelling could make a small but important contribution towards making the existing point-of-purchase environment more conducive to the selection of healthy choices. In particular, interpretational aids can help consumers assess the nutrient contribution of specific foods to the overall diet.


title: Precise selection techniques for multi-touch screens

abstract: The size of human fingers and the lack of sensing precision can make precise touch screen interactions difficult. We present a set of five techniques, called Dual Finger Selections, which leverage the recent development of multi-touch sensitive displays to help users select very small targets. These techniques facilitate pixel-accurate targeting by adjusting the control-display ratio with a secondary finger while the primary finger controls the movement of the cursor. We also contribute a "clicking" technique, called SimPress, which reduces motion errors during clicking and allows us to simulate a hover state on devices unable to sense proximity. We implemented our techniques on a multi-touch tabletop prototype that offers computer vision-based tracking. In our formal user study, we tested the performance of our three most promising techniques (Stretch, X-Menu, and Slider) against our baseline (Offset), on four target sizes and three input noise levels. All three chosen techniques outperformed the control technique in terms of error rate reduction and were preferred by our participants, with Stretch being the overall performance and preference winner.


title: Will Cybersecurity Dictate the Outcome of Future Wars?

abstract: A recent workshop envisions the impacts that information and communications technology advances will have on warfare by 2050 and the challenges these pose for command and control.


title: An Efficient and Trustworthy P2P and Social Network Integrated File Sharing System

abstract: Efficient and trustworthy file querying is important to the overall performance of peer-to-peer (P2P) file sharing systems. Emerging methods are beginning to address this challenge by exploiting online social networks (OSNs). However, current OSN-based methods simply cluster common-interest nodes for high efficiency or limit the interaction between social friends for high trustworthiness, which provides limited enhancement or contradicts the open and free service goal of P2P systems. Little research has been undertaken to fully and cooperatively leverage OSNs with integrated consideration of proximity and interest. In this work, we analyze a BitTorrent file sharing trace, which proves the necessity of proximity- and interest-aware clustering. Based on the trace study and OSN properties, we propose a social network integrated P2P file sharing system with enhanced efficiency and trustworthiness (SOCNET) to fully and cooperatively leverage the common-interest, proximity-close, and trust properties of OSN friends. SOCNET uses a hierarchical distributed hash table (DHT) to cluster common-interest nodes, then further cluster proximity-close nodes into a subcluster, and connects the nodes in a subcluster with social links. Thus, when queries travel along trustable social links, they also gain higher probability of being successfully resolved by proximity-close nodes, and it simultaneously enhances efficiency and trustworthiness. We further propose different strategies to guide nodes to forward a file query to friends that are more trustworthy and more likely to resolve the queries or forward the query to file holders. We also propose follower- and cluster-based file replication algorithms to enhance file search efficiency. The results of trace-driven experiments on the real-world PlanetLab testbed demonstrate the higher efficiency, trustworthiness, and dynamism-resilience of SOCNET compared with other systems. Experimental results also confirm the effectiveness of the proposed strategies to improve SOCNET’s performance.


title: Investigating remote sensor placement for practical haptic sensing with EndoWrist surgical tools

abstract: It has been frequently argued that the addition of haptic sensing to tele-operated surgical robots would benefit surgeon performance. Conventional haptic sensing technologies are impractical for application to minimally invasive surgery, due to size, sterilization robustness and cost vs. tool disposability. In this work we validate the concept of remote force measurement, where force interactions at the tip of a surgical tool are observed via simple torque sensors near the tool's actuators. This method provides reusable sensors located outside of the human body and so sidesteps many key issues that have limited practical haptic sensing in this scenario. Though such methods have been proposed and criticized by several groups in the past, we have been unable to locate quantitative results in the literature. Here, we provide initial remote force interaction measurements on a da Vinci EndoWrist needle driver tool. The measurements were obtained via simple custom torque sensors which easily attach to any EndoWrist tool. The complex cable-pulley transmission of the tool introduces expected nonlinearities over distal measurements. These effects are more pronounced in flexion than abduction DOFs. Despite these effects, the unprocessed torque data identifies contact with synthetic soft tissue at various actuator velocities and during external shaft loading.


title: Circularly polarized slot array antenna based on substrate integrated waveguide

abstract: This paper proposed a new approach to implement circularly polarized slot array antenna based on substrate integrated waveguide (SIW), which could be a potential technique in many fields, such as satellite communication etc. The design mainly includes a compact directional coupler, alternating phase power divider and a pair of symmetrical +45deg/-45deg linearly polarized slot array antennas. The slot array is simulated and optimized with full-wave electromagnetic (EM) software and then fabricated with a normal low cost printed circuit board (PCB) process. The measured results show good agreement with simulations. The antenna shows a good performance with the 3 dB axial ratio bandwidth of 9% and the gain of 17.13 dB at 11 GHz.


title: Multiband millimeter wave antenna array for 5G communication

abstract: This paper presents a simulated design of millimeter wave square patch antenna 1×6 array on silicon and Roger RO4003 substrate for prominent multiple bands i.e. 58GHz–60GHz, 65GHz–68GHz, 72GHz–77GHz. Designed antenna can serve 5G cellular network as well as advance device-to-device (D2D) network which is special feature of 5G communication system to reduce end-to-end latency and to implement Mission Critical Push-To-Talk Communication (MCPTT) and Vehicle-to-Anything (V2X) Communication. Designed antenna has peakgain of 9 dB and very high efficiency. Return loss for given bands at their resonant frequencies are as low as −35dB and total bandwidth of 9.57 GHz. Silicon is used under feeding network to enhance the bandwidth and reduce the size of feeding network and low dielectric material under patch to reduce dielectric loss thus maintaining the efficiency. Symmetrical parallel feeding network is used to enhance gain. Inset fed with quarter wave transformers are used for feeding and matching, along with maintaining the conformity. A novel design is used to kill the spurious radiation due to feed network, thus shaping the radiation pattern for cellular application. Overall size of antenna is 6.7mm×30mm×1.2mm compatible with miniaturized devices and is printable.


title: Real time traffic accident detection system using wireless sensor network

abstract: Automatic vehicle accident detection is a life-saving application that is vital in today's high speed motorways. In case of motorway accidents, notification to the proper authorities must be done efficiently and expediently. The main objective of this paper is to create a Real Time Traffic Accident Detection System (RTTADS) using Wireless Sensor Network (WSN) and Radio-Frequency Identification (RFID) Technologies. This paper explains the hardware prototype setup for RTTADS, the algorithms used, the advantages and the limitations of the entire system. Also the configuration of the setup and application software is elaborated. Sensors installed in a vehicle detect the accident's location, the vehicle's speed just before the accident and the number of passengers in the vehicle. The sensors then send an alert signal to a monitoring station. The monitoring station, in turn, tracks the location where the accident has occurred and directs casualty alert to the authorities concerned.


title: Influence analysis of Github repositories

abstract: With the support of cloud computing techniques, social coding platforms have changed the style of software development. Github is now the most popular social coding platform and project hosting service. Software developers of various levels keep entering Github, and use Github to save their public and private software projects. The large amounts of software developers and software repositories on Github are posing new challenges to the world of software engineering. This paper tries to tackle one of the important problems: analyzing the importance and influence of Github repositories. We proposed a HITS based influence analysis on graphs that represent the star relationship between Github users and repositories. A weighted version of HITS is applied to the overall star graph, and generates a different set of top influential repositories other than the results from standard version of HITS algorithm. We also conduct the influential analysis on per-month star graph, and study the monthly influence ranking of top repositories.


title: ‘The dots just don’t join up’: Understanding the support needs of families of children on the autism spectrum:

abstract: Much research has documented the elevated levels of stress experienced by families of autistic children. Yet remarkably little research has examined the types of support that these families perceive to be beneficial to their lives. This study, co-produced by researchers and school-based professionals, sought to establish these families’ support needs from their own perspectives. In total, 139 parents of autistic children with additional intellectual disabilities and limited spoken communication, all attending an inner-city London school, participated in an initial survey examining parental wellbeing, self-efficacy and the extent to which they felt supported. Semi-structured interviews were conducted with a subgroup of parents (n = 17), some of whom reported in the survey that they felt unsupported, in order to gain their in-depth perspectives. The results from both the survey and the interviews suggested that existing support (particularly from formal support services) was not meeting parents’ needs, whic...


title: Painful paediatric hip: frog-leg lateral view only!

abstract: Objective ::: For hip complaints in the paediatric population, it is common practice to acquire both anteroposterior (AP) and frog-leg lateral (FL) radiographs. This combination of views provides a high diagnostic yield, but also doubles radiation exposure. We investigated the diagnostic accuracy of obtaining a solitary FL view as compared to a combination of the AP and FL view.


title: Modeling and design of a tendon actuated soft robotic exoskeleton for hemiparetic upper limb rehabilitation

abstract: Robotic technology has recently been explored as a means to rehabilitate and assist individuals suffering from hemiparesis of their upper limbs. Robotic approaches allow for targeted rehabilitation routines which are more personalized and adaptable while providing quantitative measurements of patient outcomes. Development of these technologies into inherently safe and portable devices has the potential to extend the therapy outside of the clinical setting and into the patient's home with benefits to the cost and accessibility of care. To this end, a soft, cable actuated robotic glove and sleeve was designed, modeled, and constructed to provide assistance of finger and elbow movements in a way that mimics the biological function of the tendons. The resulting design increases safety through greater compliance as well as greater tolerance for misalignment with the user's skeletal frame over traditional rigid exoskeletons. Overall this design provides a platform to expand and study the concepts around soft robotic rehabilitation.


title: Speed and power scaling of SRAM's

abstract: Simple models for the delay, power, and area of a static random access memory (SRAM) are used to determine the optimal organizations for an SRAM and study the scaling of their speed and power with size and technology. The delay is found to increase by about one gate delay for every doubling of the RAM size up to 1 Mb, beyond which the interconnect delay becomes an increasingly significant fraction of the total delay. With technology scaling, the nonscaling of threshold mismatches in the sense amplifiers is found to significantly impact the total delay in generations of 0.1 /spl mu/m and below.


title: Cost-Effective Active Learning for Melanoma Segmentation

abstract: We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at this https URL .


title: Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex

abstract: In this paper, we propose an improved image dehazing algorithm using dark channel prior and Multi-Scale Retinex. Main improvement lies in automatic and fast acquisition of transmission map of the scene. We implement the Multi-scale Retinex algorithm on the luminance component in YCbCr space, obtain a pseudo transmission map whose function is similar to the transmission map in original approach. Combining with the haze image model and the dark channel prior, we can recover a high quality haze-free image. Compared with the original method, our algorithm has two main advantages: (i) no user interaction is needed, and (ii) restoring the image much faster while maintaining comparable dehazing performance.


title: Existence and uniqueness of fixed point for contractive mapping of integral type

abstract: In this paper, we prove a fixed point theorem for contractive mapping of integral type in complete metric spaces by using iterative approximations. This paper extends and improves results of Rakotch 1962, Branciari 2002, and several other results announced by many authors. An example and few remarks are given to show that our result is a proper extension of existing ones.


title: The Ontogenesis of Social Dominance: A Strategy-Based Evolutionary Perspective

abstract: Abstract Social dominance results when members of a social group vary in their ability to acquire resources in the presence of others (i.e., compete). Traditional approaches to social dominance often emphasize coercive behavior, but nonetheless suggest that dominant individuals are socially central (e.g., watched, attractive social partners). These patterns, however, apply to humans only up to a certain age. This apparent discontinuity may give the false impression that social dominance is less relevant to human social organization than it is to animal social organization. This paper reintroduces the ethological concept of social dominance, but reinterprets it from a strategy-based perspective. That is, if social dominance is defined as differential ability to control resources—without reference to how this is done—then children evidently employ different strategies to compete with peers (e.g., coercive and prosocial). Furthermore, the type of strategy children employ and peers' responses to it depend largely on the ages of the children. By adopting a strategy-based approach to social dominance and explicitly incorporating developmental processes and uniquely human capacities, human social dominance patterns appear to be more similar to primate patterns than commonly believed. Implications for social competence, peer relationships, and the development of the self are discussed.


title: Design of rotor position detecting circuit with band-pass filters in sensorless BLDC motor drives

abstract: The structure and characteristic of the band-pass filter are analyzed, and parameters of components are calculated, and a Back-EMF zero-cross detection circuit for BLDC motor sensorless control is designed in this paper. A simplified scheme of the circuit is proposed. The simulation results and the experimental results of the filter and the circuit are presented.


title: Morphology of Cell-Substratum Adhesion Influence of Receptor Heterogeneity and Nonspecific Forces

abstract: Many cell types modulate growth, differentiation, and motility through changes in cell substrate adhesion, including regulation of focal contact formation. Clustering of cell surface adhesion receptors is an essential early step in the development of focal contacts, and thus may influence cell physiology. In this paper, we present a theoretical framework to examine how cell surface chemistry affects receptor clustering. Our one-dimensional tape-peeling model couples the equations of mechanical equilibrium for a cell membrane with kinetic receptor-ligand binding relations. We considered two distinct model scenarios: Adhesion mediated by multiple receptor-ligand interactions of different length and specific binding of a single receptor type occurs in the presence of van der Waals attraction and nonspecific repulsion. In each case, nonuniform (wave-like) membrane morphologies are observed in certain parameter ranges that support the clustering of adhesion receptors. The formation of these morphologies is described in terms of a balance of membrane stresses; when cell-surface potential as a function of separation distance is symmetric between two potential energy minima, nonuniform morphologies are obtained. Increases in the chemical binding energy between receptor and ligand (e.g., increases in ligand density) or decreases in the membrane rigidity result in smaller wavelengths for nonuniform interfaces. Additionally, we show wave-like geometries appear only when the mechanical compliance of receptor-ligand bonds is within an intermediate range, and examine how the mobility of "repellers"--glycocalyx molecules that exert a nonspecific repulsive force--influences membrane morphology. We find fully mobile repellers always redistribute to prevent nonuniform morphologies.


title: An all-digital PWM generator with 62.5ps resolution in 28nm CMOS technology

abstract: This paper presents an all-digital pulse width modulator (PWM) for application in integrated DC-DC converters. Based on a multi-phase clock signal a PWM resolution of 62.5ps is achieved, resulting in up to 16-Bit PWM resolution at 4096ns period. The PWM signal duty cycle and period can be arbitrarily changed within a single cycle which allow efficient alldigital implementation of spread spectrum clocking schemes. The circuit has been implemented in 28nm SLP CMOS technology. It consumes 0.3mW when operating from a 1.0V supply.


title: Efficient Human Pose Estimation from Single Depth Images

abstract: We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features and parallelizable decision forests, both approaches can run super-real time on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities.


title: A high-efficiency linear power amplifier for 28GHz mobile communications in 40nm CMOS

abstract: This paper presents a high-efficiency, linear power amplifier (PA) for 28GHz mobile communications in 40nm CMOS technology. The design and layout are optimized for high linearity while maintaining high gain and output power. A capacitance neutralized differential pair with source degeneration inductor for linearity enhancement is discussed. The inductive degeneration technique greatly increases the optimal load impedance, which enables a low loss parallel power combining. The complete PA achieves a measured saturated output power of 18.1dBm with 41.5% power-added efficiency (PAE). With 6 Gb/s QAM-64 signals, the proposed PA achieves an average output power of 8.4dBm and 8.8% PAE, with −25 dBc EVM. All measurements are performed with a fixed bias condition.


An empirical analysis of machine learning models for automated essay grading


title: Automatic Accident Detection: Assistance Through Communication Technologies and Vehicles

abstract: In this article, e-NOTIFY system is presented, which allows fast detection of traffic accidents, improving the assistance to injured passengers by reducing the response time of emergency services through the efficient communication of relevant information about the accident using a combination of V2V and V2I communications. The proposed system requires installing OBUs in the vehicles, in charge of detecting accidents and notifying them to an external CU, which will estimate the severity of the accident and inform the appropriate emergency services about the incident. This architecture replaces the current mechanisms for notification of accidents based on witnesses, who may provide incomplete or incorrect information after a long time. The development of a low-cost prototype shows that it is feasible to massively incorporate this system in existing vehicles.


title: Therapeutic strategies for patients with micropenis or penile dysmorphic disorder

abstract: Micropenis in adults is defined as a stretched length of <7.5 cm. Many aetiologies exist, including congenital and endocrinological causes as well as pathological conditions, such as penile lichen sclerosus, trauma and genital cancer. The resulting reduction in functional penile length can lead to considerable psychosexual morbidity. Furthermore, the subset of patients with micropenis who also suffer from penile dysmorphic disorder require careful and intensive psychological counselling. Corrective surgery for micropenis can be performed in patients with realistic expectations. Total phalloplasty using radial-artery-based forearm skin flaps can offer restoration of normal penile length in selected patients. More-conservative surgical techniques to improve length or girth are limited by minimal enhancement but associated with a significantly lower rate of complications and comorbidity compared to total phalloplasty. Emerging tissue engineering techniques might represent a suitable alternative to penile replacement surgery in the future.


title: Optimizing Locations for a Vehicle Sharing System

abstract: Car-sharing business provides members access to a fleet of shared-use vehicles in a network of locations on a short-term, as-needed basis. It allows individuals to gain the benefits of private vehicle use without the costs and responsibilities of ownership. The primary advantage of car sharing as opposed to the car rental business is that it provides flexibility of using the vehicles for shorter periods of time. Secondly if you are a registered customer of the company, the process of accessing and using the vehicle is quite simple, self-serving and hassle-free. One of the main problems faced by the car-sharing businesses is that of finding the best stations to place the facility. These locations should be chosen by attractiveness of various parameters such as the socio-demographic-economic profile of the population that resides or works at the location and accessibility of other forms of public transport in that location. However the presence of one or more stations in the vicinity and the type of vehicles placed in a station are also critical for decision-making. It must be noted that the attractiveness factor of a parameter would be shared between different stations if they are present in the sphere of influence of that parameter. This study analyzes the performance of an electric car sharing service across different stations in and around the city of Nice. This service is operated by a major public transport operator in France and commenced its operations in April 2011. The main measure of performance of the stations is the average number of rides (usages) per day. The objective of this study is two-fold one, to analyze the performance of the car-sharing service across all stations and estimate the key drivers of demand, and secondly, to use these drivers to identify future station locations, such that the overall system performance is maximized. The methodology used by us to optimize the station locations follows two steps. In the first step, we perform an extensive data analysis and determine all those factors that we expect to be driving the demand for the service and build a linear regression model with station performance as the dependent variable that is explained by a host of other independent variables, such as public transport ridership, share of car-users in the locality, share of high income / education groups in the locality population, population density and presence of other mobility generators such as hotels, colleges and commercial centers. In the second and final step, we use the attractiveness of the different localities to optimally locate the new stations for the service. The main trade-off decision made by the model involves locating more stations in highly attractive localities versus locating new stations in less attractive but untapped localities. We validated and reported our results on a set of new stations that were opened quite recently, after the commencement of our study.


source computing framework unifies streaming , batch , and interactive big data workloads to unlock new applications


title: Characterization of the Use of Social Media in Natural Disasters: A Systematic Review

abstract: Social media sites are playing a significant role in rapid propagation of information when disasters occur. This effective communication platform is a great useful tool for emergency (disaster) management agencies during all phases of disaster management life cycle: prevention (mitigation), preparedness, response, and recovery. This study has conducted a systematic review of social media use in disaster management literature to identify how social media sites have been used during these four critical phases of disaster management life cycle in order to recommend strategies for government officials. A systematic method has been used to search four major academic databases for this review. The search resulted in 40 articles and categorized the findings in six main themes: situational awareness, data collection methods, distributed sensor systems, news and rumors, sentiment analysis, and digital volunteerism.


title: Deep stacking networks for information retrieval

abstract: Deep stacking networks (DSN) are a special type of deep model equipped with parallel and scalable learning. We report successful applications of DSN to an information retrieval (IR) task pertaining to relevance prediction for sponsor search after careful regularization methods are incorporated to the previous DSN methods developed for speech and image classification tasks. The DSN-based system significantly outperforms the LambdaRank-based system which represents a recent state-of-the-art for IR in normalized discounted cumulative gain (NDCG) measures, despite the use of mean square error as DSN's training objective. We demonstrate desirable monotonic correlation between NDCG and classification rate in a wide range of IR quality. The weaker correlation and more flat relationship in the high IR-quality region suggest the need for developing new learning objectives and optimization methods.


title: Learning to Compose Neural Networks for Question Answering

abstract: We describe a question answering model that applies to both images and structured knowledge bases. The model uses natural language strings to automatically assemble neural networks from a collection of composable modules. Parameters for these modules are learned jointly with network-assembly parameters via reinforcement learning, with only (world, question, answer) triples as supervision. Our approach, which we term a dynamic neural model network, achieves state-of-the-art results on benchmark datasets in both visual and structured domains.


title: Co-Training for Cross-Lingual Sentiment Classification

abstract: The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data. Machine translation services are used for eliminating the language gap between the training set and test set, and English features and Chinese features are considered as two independent views of the classification problem. We propose a cotraining approach to making use of unlabeled Chinese data. Experimental results show the effectiveness of the proposed approach, which can outperform the standard inductive classifiers and the transductive classifiers.


title: Recent Advances in the Inverted Planar Structure of Perovskite Solar Cells

abstract: Inorganic-organic hybrid perovskite solar cells research could be traced back to 2009, and initially showed 3.8% efficiency. After 6 years of efforts, the efficiency has been pushed to 20.1%. The pace of development was much faster than that of any type of solar cell technology. In addition to high efficiency, the device fabrication is a low-cost solution process. Due to these advantages, a large number of scientists have been immersed into this promising area. In the past 6 years, much of the research on perovskite solar cells has been focused on planar and mesoporous device structures employing an n-type TiO2 layer as the bottom electron transport layer. These architectures have achieved champion device efficiencies. However, they still possess unwanted features. Mesoporous structures require a high temperature (>450 °C) sintering process for the TiO2 scaffold, which will increase the cost and also not be compatible with flexible substrates. While the planar structures based on TiO2 (regular structure) usually suffer from a large degree of J-V hysteresis. Recently, another emerging structure, referred to as an "inverted" planar device structure (i.e., p-i-n), uses p-type and n-type materials as bottom and top charge transport layers, respectively. This structure derived from organic solar cells, and the charge transport layers used in organic photovoltaics were successfully transferred into perovskite solar cells. The p-i-n structure of perovskite solar cells has shown efficiencies as high as 18%, lower temperature processing, flexibility, and, furthermore, negligible J-V hysteresis effects. In this Account, we will provide a comprehensive comparison of the mesoporous and planar structures, and also the regular and inverted of planar structures. Later, we will focus the discussion on the development of the inverted planar structure of perovskite solar cells, including film growth, band alignment, stability, and hysteresis. In the film growth part, several methods for obtaining high quality perovskite films are reviewed. In the interface engineering parts, the effect of hole transport layer on subsequent perovskite film growth and their interface band alignment, and also the effect of electron transport layers on charge transport and interface contact will be discussed. As concerns stability, the role of charge transport layers especially the top electron transport layer in the devices stability will be concluded. In the hysteresis part, possible reasons for hysteresis free in inverted planar structure are provided. At the end of this Account, future development and possible solutions to the remaining challenges facing the commercialization of perovskite solar cells are discussed.


title: Performance evaluation of synchronous rectification in front-end full-bridge rectifiers

abstract: In this paper, performance evaluation of synchronous rectification in front-end full-bridge rectifiers is presented. Specifically, the implementation of the full-bridge rectifier with the two diode rectifiers connected to the negative output terminal replaced by two synchronous rectifiers (SRs) and the implementation with all four diode rectifiers replaced by four SRs are considered. In both implementations, the SRs are N-channel MOSFETs controlled by sensing the line voltage. Two methods of line-voltage sensing are presented. First, direct line-voltage sensing, and second, indirect line-voltage sensing, i.e., sensing the line voltage between an input terminal and the negative output terminal. The proposed implementations also include a protection method of preventing accidental short circuit between the input or output terminals. In addition, SR power-management methods such as turning off the SRs at light loads and at no load are provided. The protection of SRs at large inrush currents is also discussed. Experimental results obtained on a 90-W (19.5-V/4.6-A) laptop adapter for the universal line voltage range (90–264 Vrms) are given. In the experimental circuit with four SRs, the efficiency improvement at 90-Vrms line voltage and full load is 1.31%.


title: Application of Non-negative sparse matrix factorization in occluded face recognition

abstract: In order to reduce the impact of block for the rate of face recognition ,in this paper, through the control of sparseness in the non-negative matrix factorization , the face image do non-negative sparse coding to obtain the eigenspace for the image. The experiment uses the ORL face database. The experimental results show that using NMFs obtains Eigenfaces with the local features of face and has a strong ability to express the occluded human face. The algorithm has good adaptability to partial occlusion, and has better robustness than PCA algorithm


title: The value of celebrity endorsements: A stock market perspective

abstract: Are celebrity endorsements worthwhile investments in advertising? To answer this question, we analyze a unique sample of 101 announcements made between 1996 and 2008 by firms listed in the USA. Internet is the main medium of communication for these announcements. We employ event study methodology and document statistically insignificant abnormal returns around the announcement dates. This finding is consistent with the notion that the incremental benefits from celebrity endorsements closely match the incremental costs due to such contracts. Further, we investigate if the announcement date return depends on a number of characteristics that are often used in the endorsement literature. As a result, we find that endorsements of technology industry products coincide with significant positive abnormal returns around the announcement dates. Finally, we find weak support for the match-up hypothesis between celebrities and endorsed products.


title: Embodied simulation: from neurons to phenomenal experience

abstract: The same neural structures involved in the unconscious modeling of our acting body in space also contribute to our awareness of the lived body and of the objects that the world contains. Neuroscientific research also shows that there are neural mechanisms mediating between the multi-level personal experience we entertain of our lived body, and the implicit certainties we simultaneously hold about others. Such personal and body-related experiential knowledge enables us to understand the actions performed by others, and to directly decode the emotions and sensations they experience. A common functional mechanism is at the basis of both body awareness and basic forms of social understanding: embodied simulation. It will be shown that the present proposal is consistent with some of the perspectives offered by phenomenology.


title: An Optical Interconnect Network Design for Dynamically Composable Data Centers

abstract: Virtual Data Center (VDC) is becoming the real world tendency of IT model that offers flexible and on-demand service, most of which adopt Software-Defined Networking (SDN) as their network architecture to simplify network management and ensure network isolation. In our paper, by following our Just-in-Time Architecture (JITA) design, we further studied the composable architecture in optical domain. For this architecture to work efficiently, Terabit per second (Tbps) bandwidth and negligible latency (i.e., less than microsecond latency) are needed to support disaggregated infrastructures. Therefore, in this paper, we present an all-optical packets switch solution with proposed SDN controlling scheme. The modeling of the all-optical switch is also proposed, implemented and studied. Based on the modeling of the switch, the output Optical Signal-to-Noise Ratio (OSNR) and latency performance are studied. With the result of the simulation, we find the limits and dominated factors for designing the large scale all-optical switch in the composable data centers. An example of analyzing the maximum throughput and the latency of the all-optical switch is given at the end.


title: Examining Crowd Work and Gig Work Through The Historical Lens of Piecework

abstract: The internet is empowering the rise of crowd work, gig work, and other forms of on-demand labor. A large and growing body of scholarship has attempted to predict the socio-technical outcomes of this shift, especially addressing three questions: 1) What are the complexity limits of on-demand work?, 2) How far can work be decomposed into smaller microtasks?, and 3) What will work and the place of work look like for workers? In this paper, we look to the historical scholarship on piecework — a similar trend of work decomposition, distribution, and payment that was popular at the turn of the 20th century — to understand how these questions might play out with modern on-demand work. We identify the mechanisms that enabled and limited piecework historically, and identify whether on-demand work faces the same pitfalls or might differentiate itself. This approach introduces theoretical grounding that can help address some of the most persistent questions in crowd work, and suggests design interventions that learn from history rather than repeat it.


title: Non-dissipative battery cell balancing using half-bridge switching circuit

abstract: Cells in an electric vehicle battery pack tend to become out of balance after several charging and discharging cycles, resulting in capacity under-utilization and over-sizing of battery packs to achieve extended driving range. This makes it necessary to have a cell balancing mechanism onboard of the battery management system. This paper introduces a half-bridge DC/DC switching circuit for the balancing of lithium-ion cells, and highlights its advantages over existing cell balancing techniques. Furthermore, details on planar transformer design, control feedback, simulation results in MATLAB/Simulink, and experimental observations are included, showing greater controllability and improvement in efficiency in comparison with existing systems.


title: Bayesian Cognitive Modeling: A Practical Course

abstract: Part I. Getting Started: 1. The basics of Bayesian analysis 2. Getting started with WinBUGS Part II. Parameter Estimation: 3. Inferences with binomials 4. Inferences with Gaussians 5. Some examples of data analysis 6. Latent mixture models Part III. Model Selection: 7. Bayesian model comparison 8. Comparing Gaussian means 9. Comparing binomial rates Part IV. Case Studies: 10. Memory retention 11. Signal detection theory 12. Psychophysical functions 13. Extrasensory perception 14. Multinomial processing trees 15. The SIMPLE model of memory 16. The BART model of risk taking 17. The GCM model of categorization 18. Heuristic decision-making 19. Number concept development.


title: A comparison of modeling and statistical evaluation of hydrologic change

abstract: The U.S. Department of Agriculture Hydrograph Laboratory model of watershed hydrology is adapted for use on a research watershed where land use changed drastically as a result of reforestation and subsequent thinning. Eight years of measured runoff from a mature forest are used to estimate two of the model parameters; the remaining 39 are based on physical measurements. Model simulations properly evaluated both the magnitude and statistical significance of the hydrologic changes with much less data than were needed for standard statistical analyses.


title: ArgRewrite: A Web-based Revision Assistant for Argumentative Writings

abstract: While intelligent writing assistants have become more common, they typically have little support for revision behavior. We present ArgRewrite, a novel web-based revision assistant that focus on rewriting analysis. The system supports two major functionalities: 1) to assist students as they revise, the system automatically extracts and analyzes revisions; 2) to assist teachers, the system provides an overview of students’ revisions and allows teachers to correct the automatically analyzed results, ensuring that students get the correct feedback.


title: Corpus-based stemming using cooccurrence of word variants

abstract: Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots. It is one of the simplest applications of natural-language processing to IR and is one of the most effective in terms of user acceptance and consistency, though small retrieval improvements. Current stemming techniques do not, however, reflect the language use in specific corpora, and this can lead to occasional serious retrieval failures. We propose a technique for using corpus-based word variant cooccurrence statistics to modify or create a stemmer. The experimental results generated using English newspaper and legal text and Spanish text demonstrate the viability of this technique and its advantages relative to conventional approaches that only employ morphological rules.


2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)


title: The Relationship Between Accessibility, Communication and Motivation Towards Online Learning

abstract: There are higher education institutions in Malaysia which are at the stage of transforming from teacher-centred teaching in the classroom to a more learner-centred teaching environment. A number of innovations in teaching styles including the use of technology such as online websites, games, elearning applications and so on will be introduced to improve the understanding of students. With the assistance of technology, various learning tools or techniques were introduced and incorporated into teaching and learning process. This includes the online learning environment which supports synchronous and asynchronous learning. Regardless of the advantages that online learning offers, a variety of factors should be identified as crucial to the success of online courses such as motivation. It is a key factor in learning and achievement in online learning environments. This paper examines the relationship between accessibility, communication and motivation towards online leaning. A case study was conducted on 331 students of Universiti Teknologi Mara, Negeri Sembilan Malaysia. The results show that accessibility and communication are positively significant influence on students’ motivation to learn online. Hence, online platforms for learning should be convenient for students to access with feedback support from instructors and peers.


title: Building an E-contract management system using Google Docs

abstract: This paper introduces a way for approaching electronic contracts management involving two parties. Contract monitoring automation is achieved by giving legal clauses a formal representation. Inspired from the file system properties, a mechanism for dynamic reorganization of contracts on Google Docs is proposed.


title: USED: a large-scale social event detection dataset

abstract: Event discovery from single pictures is a challenging problem that has raised significant interest in the last decade. During this time, a number of interesting solutions have been proposed to tackle event discovery in still images. However, a large scale benchmarking image dataset for the evaluation and comparison of event discovery algorithms from single images is still lagging behind. To this aim, in this paper we provide a large-scale properly annotated and balanced dataset of 490,000 images, covering every aspect of 14 different types of social events, selected among the most shared ones in the social network. Such a large scale collection of event-related images is intended to become a powerful support tool for the research community in multimedia analysis by providing a common benchmark for training, testing, validation and comparison of existing and novel algorithms. In this paper, we provide a detailed description of how the dataset is collected, organized and how it can be beneficial for the researchers in the multimedia analysis domain. Moreover, a deep learning based approach is introduced into event discovery from single images as one of the possible applications of this dataset with a belief that deep learning can prove to be a breakthrough also in this research area. By providing this dataset, we hope to gather research community in the multimedia and signal processing domains to advance this application.


title: A plant growth-promoting rhizobacteria (PGPR) mixture does not display synergistic effects, likely by biofilm but not growth inhibition

abstract: Combination of different PGPR strains with complementary characteristics as a mixture to reduce possible instability under fluctuating environment has been considered practical. However, PGPR mixtures do not always play synergistic roles in growth promotion or biological control as reflected in our previous findings and other publications. In this work, we accidentally discovered that a mixture containing two well compatible PGPR strains, Bacillus pumilus WP8 and Erwinia persicinus RA2, did not synergize in growth promotion or biological control of tomato bacterial wilt under field conditions. Considering the importance of PGPR biofilm formation in growth promotion and biocontrol activities, we hypothesized that this phenomenon may be related to inhibition of biofilm formation. In vitro experiments showed that biofilm-formation ability of WP8 was inhibited by both RA2 cells and filtered supernatants collected from RA2 cultures at 12 h (RA2-12) rather than 48 h (RA2-48), even at high-temperatures (within 100°C). An in vivo experiment derived from crystal violet staining yielded similar results. Using liquid chromatography-mass spectrometry (LC-MS), we compared primary and secondary metabolites of RA2 (namely RA2-12 and RA2-48) and found D-glutamine, abundant in RA2-12, as the putative inhibitory factor. Trace amounts of jasmonic acid together with viscous extracellular polysaccharides in RA2-48 likely promoted the rescue of robust biofilm formation. This work suggests that inhibition of biofilm formation should be considered in PGPR mixture development.


title: Indian mate preferences: Continuity, sex differences, and cultural change across a quarter of a century

abstract: Abstract Expressed mate preferences provide unique windows into evolved mating psychology. The current study used two research instruments—one ranking and one rating procedure—to examine mate preferences in India. We compared modern Indians (n = 536) with a more modest Indian sample studied a quarter of a century earlier (n = 105) to test the hypothesis that sex-specific mate preferences—as hypothesized by parental investment theory—would persist during this time period. Mate preferences for mutual attraction and love remained important and invariant over time, despite India’s history of arranged marriages. Sex differences in mate preferences for cues to fertility (youth, physical attractiveness) and resources (good financial prospects, social status) remained relatively invariant over time. Several changes in mate preferences emerged, including a greater preference for mates who are “creative and artistic,” “ambitious and industrious,” and “a good cook and housekeeper” for both sexes. Despite cultural changes in India over the past 25 years, evolved mate preferences have persisted during this time period. Discussion highlights limitations of this research.


title: Digging Deeper Into the Outcomes of Social Media Use among College Students: The Mediating Effect of Academic Self-Efficacy

abstract: The use of social media is prevalent among college students. Additionally, youth and high school students, many of whom will eventually enroll in college, also use social media at high levels. With the near ubiquitous use of social media and the culture that comes with it, it is important for researchers to understand the effects and relationships that social media use has on students and the different aspects of students’ lives. This study specifically considers the outcomes of academic performance and satisfaction with life and digs deeper into these relationships by looking at the mediating role of academic self e fficacy and controlling for the effects of self regulation. Results from a sample of 234 college students indicate that social media use is negatively associated with academic self efficacy and academic performance an d that academic self efficacy mediates the relationsh ips with academic performance and satisfaction with life.


title: A Spinning Current Circuit for Hall Measurements Down to the Nanotesla Range

abstract: The spinning current modulation technique is shown not only to be able to suppress all the offset and low-frequency noise contributions from the Hall sensing element and its preamplifier, but it may also suppress the parasitic signals, such as pickup noise and thermal electromotive force contributed by the interconnects in a hybrid Hall plate/driving electronics system with remote Hall sensor head. The selection process of an adequate modulation sequence achieving this goal is detailed. The optimization of the elements involved in the analog circuitry, including the analog switches, differential amplifiers, filters, interconnects, and so on, for given measurement conditions is discussed. A printed circuit board aimed at optimizing the operating conditions for a broad spectrum of Hall sensor types and applications, using pluggable modules for adapting the gain and transfer function, is presented. The modulation sequence, modulation frequency, and fine-tuning of the delays involved in the spinning sequence are adjustable by software. With this circuit board and the optimized spinning sequence, we show that the parasitics originating from the interconnection in a hybrid Hall microsystem with remote sensor 1.5 m away can indeed be suppressed. We demonstrate an offset reduction by about four decades, from 630 $\mu \text{T}$ down to less than 100 nT for a system with +/−35-mT full scale. The residual noise has a white spectrum down to at least 60 mHz, close to the Johnson–Nyquist thermal noise. A detectivity of 30 nT down to the sub-Hz range is demonstrated.


title: Global analysis of Expectation Maximization for mixtures of two Gaussians

abstract: Expectation Maximization (EM) is among the most popular algorithms for estimating parameters of statistical models. However, EM, which is an iterative algorithm based on the maximum likelihood principle, is generally only guaranteed to find stationary points of the likelihood objective, and these points may be far from any maximizer. This article addresses this disconnect between the statistical principles behind EM and its algorithmic properties. Specifically, it provides a global analysis of EM for specific models in which the observations comprise an i.i.d. sample from a mixture of two Gaussians. This is achieved by (i) studying the sequence of parameters from idealized execution of EM in the infinite sample limit, and fully characterizing the limit points of the sequence in terms of the initial parameters; and then (ii) based on this convergence analysis, establishing statistical consistency (or lack thereof) for the actual sequence of parameters produced by EM.


title: Occluded object search by relational affordances

abstract: Searching for objects in occluded spaces is one of the problems robots need to solve when tackling mobile manipulation tasks. Most approaches focus only on searching for a specific object. In this paper, we use the concept of relational affordances to improve occluded object search performance. Affordances define action possibilities on an object in the environment and play a role in basic cognitive capabilities. Relational affordances extend this concept by modelling relations between multiple objects. By learning and using a relational affordance model we can search for any of the multiple objects that afford a given action, each object type having a probability distribution over possible sizes and shapes, and where spatial relations between objects such as co-occurrence and stacking are modelled. The experimental results show the viability of the relational affordance models for occluded object search.


title: Strategic sensemaking and organizational performance: linkages among scanning, interpretation, action, and outcomes.

abstract: This study investigated the strategic “sensemaking” processes of scanning, interpretation, and action and how those activities are linked to organizational performance. Using path analyses on data ...


title: On combining side information and unlabeled data for heterogeneous multi-task metric learning

abstract: Distance metric learning (DML) is critical for a wide variety of machine learning algorithms and pattern recognition applications. Transfer metric learning (TML) leverages the side information (e.g., similar/dissimilar constraints over pairs of samples) from related domains to help the target metric learning (with limited information). Current TML tools usually assume that different domains exploit the same feature representation, and thus are not applicable to tasks where data are drawn from heterogeneous domains. Heterogeneous transfer learning approaches handle heterogeneous domains by usually learning feature transformations across different domains. The learned transformation can be used to derive a metric, but these approaches are mostly limited by their capability of only handling two domains. This motivates the proposed heterogeneous multi-task metric learning (HMTML) framework for handling multiple domains by combining side information and unlabeled data. Specifically, HMTML learns the metrics for all different domains simultaneously by maximizing their high-order correlation (parameterized by feature covariance of unlabeled data) in a common subspace, which is induced by the transformations derived from the metrics. Extensive experiments on both multi-language text categorization and multi-view social image annotation demonstrate the effectiveness of the proposed method.


title: A fuzzing test for dynamic vulnerability detection on Android Binder mechanism

abstract: Binder, which helps to package the functional codes of system processes into inter-process invocable interfaces for application-level processes, is the core mechanism to implement the Inter-Process Communication(IPC) in Android. This paper, for the first time, attempts to study the system-level security properties of this mechanism. The universal injection interface and the model of IPC data are proposed to implement a fuzzing test. A test case generation technique based on mutation algorithm of pre-captured IPC data is introduced in order to improve the fuzzing test efficiency. Two high-risk vulnerabilities are detected in Android 5.1.0. Analysis of these vulnerabilities highlights a critical design issue in the system services of Binder mechanism.


title: Hunting for invisibility: Characterizing and detecting malicious web infrastructures through server visibility analysis

abstract: Nowadays, cyber criminals often build web infrastructures rather than a single server to conduct their malicious activities. In order to continue their malevolent activities without being detected, cyber criminals make efforts to conceal the core servers (e.g., C&C servers, exploit servers, and drop-zone servers) in the malicious web infrastructure. Such deliberate invisibility of those concealed malicious servers, however, makes them particularly distinguishable from benign web servers that are usually promoted to be public. In this paper, we conduct the first large-scale measurement study to investigate the visibility of both malicious and benign servers. From our intensive analysis of over 100,000 benign servers, 45,000 malicious servers and 40,000 redirections, we identify a set of distinct features of malicious web infrastructures from their locations, structures, roles, and relationships perspectives, and propose a lightweight yet effective detection system called VisHunter. VisHunter identifies malicious redirections from visible servers to invisible servers at the entryway of malicious web infrastructures. We evaluate VisHunter on both online public data and large-scale enterprise network traffic, and demonstrate that VisHunter can achieve an average 96.2% detection rate with only 0.9% false positive rate on the real enterprise network traffic.


title: Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction

abstract: Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants.


title: AIDA compiler: a code synthesizer from programs in pictures

abstract: AIDA is a language for programming (modeling and documenting) in pictures within F-modeling environment where pictures and moving pictures are used as super-characters for representing features of computational algorithms, corresponding application models and possible related documentation. Generic pictures of the algorithmic super-characters are used to compose compound pictures defining algorithmic steps. The generic and compound pictures, as well as their series are acquired in special galleries of an open type where supportive pictures of various annotations are also included. These acquisitions allow very compact algorithmic specifications from which a set of program slices with different levels of details are synthesized. The lowest level slice is a C++ program. The F-modeling environment provides a set of editors for different views and annotations. In this paper, a structure of the synthesizer is presented and a distribution of its functions among the environment subsystems is provided. A special attention is payed to internal objects, template programs and algorithms supporting the C++ program generation.


title: A Remote Attestation Security Model Based on Privacy-Preserving Blockchain for V2X

abstract: The vehicle to everything (V2X) requires the real-time integration of all kinds of information on roads, pedestrians, the environment, and vehicles themselves. This information also needs to be shared with other vehicles. The effective integration of information and the strong privacy protection are the key restrictions on the development of the V2X. The previous privacy protection model has mainly focused on the centralized network, and there were problems with the centralized gateway and single-point decision, which were not suitable for the decentralized scenario. Therefore, this paper proposes a remote attestation security model based on a privacy-preserving blockchain. The overall model involves two core steps. First, the vehicle provides the network with an evidence of a credible identity and integrity. Secured, the vehicles in the network calculate the nodes to make their respective decisions, and the accounting nodes summarize the sub-conclusions, form the final results, and write them into data blocks. The analysis shows that it possesses the security features of decentralization, traceability, anonymity, irreplaceability, and high efficiency. The model framework, core block chain structure, and protocol process are described in detail. The experimental results based on a realistic infrastructure are presented. These experimental results demonstrate that our scheme can effectively enhance the security of the communications of intelligent vehicles in the V2X.


title: Interactive Data Exploration with Smart Drill-Down

abstract: We present smart drill-down , an operator for interactively exploring a relational table to discover and summarize “interesting” groups of tuples. Each group of tuples is described by a rule . For instance, the rule $(a, b, \star, 1000)$ tells us that there are 1,000 tuples with value $a$ in the first column and $b$ in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are NP-Hard , and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms.


title: Optimal Coding and Allocation for Perfect Secrecy in Multiple Clouds

abstract: For a user to store data in the cloud, using services provided by multiple cloud storage providers (CSPs) is a promising approach to increase the level of data availability and confidentiality, as it is unlikely that different CSPs are out of service at the same time or collude with each other to extract information of a user. This paper investigates the problem of storing data reliably and securely in multiple CSPs constrained by given budgets with minimum cost. Previous works, with variations in problem formulations, typically tackle the problem by decoupling it into sub-problems and solve them separately. While such a decoupling approach is simple, the resultant solution is suboptimal. This paper is the first one which considers the problem as a whole and derives a jointly optimal coding and storage allocation scheme, which achieves perfect secrecy with minimum cost. The analytical result reveals that the optimal coding scheme is the nested maximum-distance-separable code and the optimal amount of data to be stored in the CSPs exhibits a certain structure. The exact parameters of the code and the exact storage amount to each CSP can be determined numerically by simple 2-D search.


title: Online algorithms in high-frequency trading

abstract: The challenges faced by competing HFT algorithms.


title: Tactile Teaching: Exploring Protein Structure/Function Using Physical Models

abstract: The technology now exists to construct physical models of proteins based on atomic coordinates of solved structures. We review here our recent experiences in using physical models to teach concepts of protein structure and function at both the high school and the undergraduate levels. At the high school level, physical models are used in a professional development program targeted to biology and chemistry teachers. This program has recently been expanded to include two student enrichment programs in which high school students participate in physical protein modeling activities. At the undergraduate level, we are currently exploring the usefulness of physical models in communicating concepts of protein structure and function that have been traditionally difficult to teach. We discuss our recent experience with two such examples: the close-packed nature of an enzyme active site and the pH-induced conformational change of the influenza hemagglutinin protein during virus infection.


title: A hybrid heuristic approach to the problem of the location of vehicle charging stations

abstract: In order to reduce the negative impact of fuel-powered vehicles on the environment, the use of alternative-fuel vehicles (AFVs), which produce far less pollution than traditional fuel-powered vehicles, is being introduced in many countries around the world. However, compared to the fuel-powered vehicles, AFVs such as electric vehicles require frequent recharging of their electrical energy storages (batteries), which results in a short vehicle driving range. Thus, AFV users who want to travel from home to a terminal location and back again must consider whether their AFVs can be recharged on the way. One of the approaches to solve this problem is to install alternative fuel charging stations on suitable locations to provide recharging services. However, when the budget is limited, the selection of locations and the types of alternative fuel charging stations becomes a decision problem, since it will directly affect the number of potential AFV users that can be served. This paper develops a mixed-integer programming model to address this problem and to maximize the number of people who can complete round-trip itineraries. A hybrid heuristic approach is proposed to solve this model. Numerical results show that the proposed heuristic approach only requires a small amount of CPU time to attain confident solutions.


title: Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

abstract: Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.


title: Web Services Metrics: A Survey and A Classification

abstract: Web Services have emerged as a new web-based technology paradigm for exchanging information on the Internet using platform-neutral standards, such as XML and adopting Internet-based protocols. They have become a promising technology to design and build complex inter-enterprise business applications. However, Web Services are problematic to measure, control, and manage. Software metrics is vital for the management, control, and measurement of software development and despite the vast amount of techniques/mechanisms and metrics for traditional and object-oriented software, there has been a few research and techniques that deals with metrics for Web Services. As companies increasingly invest and relies on Web Services, the importance of metrics for those services continues to grow. In this paper the author present and classify the existing metrics for Web Services, and discussed their usage and benefits. In addition, the author highlight the problems found in using some of the metrics and discussed what is still lacking in this domain.


title: Humans can learn new information during sleep

abstract: Although it is well-known that sleep can strengthen existing memories, this study demonstrates that people can acquire completely new associations (between distinct tones and pleasant/unpleasant smells) during sleep, which are preserved during the awake state.


title: Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition

abstract: Spatio-temporal feature encoding is essential for encoding the dynamics in video sequences. Recurrent neural networks, particularly long short-term memory (LSTM) units, have been popular as an efficient tool for encoding spatio-temporal features in sequences. In this work, we investigate the effect of mode variations on the encoded spatio-temporal features using LSTMs. We show that the LSTM retains information related to the mode variation in the sequence, which is irrelevant to the task at hand (e.g. classification facial expressions). Actually, the LSTM forget mechanism is not robust enough to mode variations and preserves information that could negatively affect the encoded spatio-temporal features. We propose the mode variational LSTM to encode spatio-temporal features robust to unseen modes of variation. The mode variational LSTM modifies the original LSTM structure by adding an additional cell state that focuses on encoding the mode variation in the input sequence. To efficiently regulate what features should be stored in the additional cell state, additional gating functionality is also introduced. The effectiveness of the proposed mode variational LSTM is verified using the facial expression recognition task. Comparative experiments on publicly available datasets verified that the proposed mode variational LSTM outperforms existing methods. Moreover, a new dynamic facial expression dataset with different modes of variation, including various modes like pose and illumination variations, was collected to comprehensively evaluate the proposed mode variational LSTM. Experimental results verified that the proposed mode variational LSTM encodes spatio-temporal features robust to unseen modes of variation.


title: ScreenGlint: Practical, In-situ Gaze Estimation on Smartphones

abstract: Gaze estimation has widespread applications. However, little work has explored gaze estimation on smartphones, even though they are fast becoming ubiquitous. This paper presents ScreenGlint, a novel approach which exploits the glint (reflection) of the screen on the user's cornea for gaze estimation, using only the image captured by the front-facing camera. We first conduct a user study on common postures during smartphone use. We then design an experiment to evaluate the accuracy of ScreenGlint under varying face-to-screen distances. An in-depth evaluation involving multiple users is conducted and the impact of head pose variations is investigated. ScreenGlint achieves an overall angular error of 2.44o without head pose variations, and 2.94o with head pose variations. Our technique compares favorably to state-of-the-art research works, indicating that the glint of the screen is an effective and practical cue to gaze estimation on the smartphone platform. We believe that this work can open up new possibilities for practical and ubiquitous gaze-aware applications.


title: TFDASH: A Fairness, Stability, and Efficiency Aware Rate Control Approach for Multiple Clients Over DASH

abstract: Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet and adopted in the industry. It, however, does not impose any adaptation logic for selecting the quality of video fragments requested by clients and suffers from lackluster performance with respect to a number of desirable properties: efficiency, stability, and fairness when multiple players compete for a bottleneck link. In this paper, we propose a throughput-friendly DASH (TFDASH) rate control scheme for video streaming with multiple clients over DASH to well balance the trade-offs among efficiency, stability, and fairness. The core idea behind guaranteeing fairness and high efficiency (bandwidth utilization) is to avoid OFF periods during the downloading process for all clients, i.e., the bandwidth is in perfect-subscription or over-subscription with bandwidth utilization approach to 100\%. We also propose a dual-threshold buffer model to solve the instability problem caused by the above idea. As a result, by integrating these novel components, we also propose a probability-driven rate adaption logic taking into account several key factors that most influence visual quality, including buffer occupancy, video playback quality, video bit-rate switching frequency and amplitude, to guarantee high-quality video streaming. Our experiments evidently demonstrate the superior performance of the proposed method.


title: Calm Before the Storm: The Challenges of Cloud Computing in Digital Forensics

abstract: Cloud computing is a rapidly evolving information technology (IT) phenomenon. Rather than procure, deploy and manage a physical IT infrastructure to host their software applications, organizations are increasingly deploying their infrastructure into remote, virtualized environments, often hosted and managed by third parties. This development has significant implications for digital forensic investigators, equipment vendors, law enforcement, as well as corporate compliance and audit departments (among others). Much of digital forensic practice assumes careful control and management of IT assets (particularly data storage) during the conduct of an investigation. This paper summarises the key aspects of cloud computing and analyses how established digital forensic procedures will be invalidated in this new environment. Several new research challenges addressing this changing context are also identified and discussed.