title: Mindfulness meditation-related pain relief: Evidence for unique brain mechanisms in the regulation of pain

abstract: The cognitive modulation of pain is influenced by a number of factors ranging from attention, beliefs, conditioning, expectations, mood, and the regulation of emotional responses to noxious sensory events. Recently, mindfulness meditation has been found attenuate pain through some of these mechanisms including enhanced cognitive and emotional control, as well as altering the contextual evaluation of sensory events. This review discusses the brain mechanisms involved in mindfulness meditation-related pain relief across different meditative techniques, expertise and training levels, experimental procedures, and neuroimaging methodologies. Converging lines of neuroimaging evidence reveal that mindfulness meditation-related pain relief is associated with unique appraisal cognitive processes depending on expertise level and meditation tradition. Moreover, it is postulated that mindfulness meditation-related pain relief may share a common final pathway with other cognitive techniques in the modulation of pain.


title: Software security checklist for the software life cycle

abstract: A formal approach to security in the software life cycle is essential to protect corporate resources. However, little thought has been given to this aspect of software development. Traditionally, software security has been treated as an afterthought leading to a cycle of 'penetrate and patch.' Due to its criticality, security should be integrated as a formal approach in the software life cycle. Both a software security checklist and assessment tools should be incorporated into this life cycle process. The current research at JPL addresses both of these areas through the development of a Software Security Assessment Instrument (SSAI). This paper focuses on the development of a Software Security Checklist (SSC) for the life cycle. It includes the critical areas of requirements gathering and specification, design and code issues, and maintenance and decommissioning of software and systems.


title: One Day Ahead Prediction of Wind Speed and Direction

abstract: This paper presents a new technique for predicting wind speed and direction. This technique is based on using a linear time-series-based model relating the predicted interval to its corresponding one- and two-year old data. The accuracy of the model for predicting wind speeds and directions up to 24 h ahead have been investigated using two sets of data recorded during winter and summer season at Madison weather station. Generated results are compared with their corresponding values when using the persistent model. The presented results validate the effectiveness and accuracy of the proposed prediction model for wind speed and direction.


title: A flexible 3D object localization system for industrial part handling

abstract: We present a flexible system that can scan and localize workpieces in 3D for assembly and pick and place operations. The system contains a vision robot that acquires 3D point clouds by performing sweeps with a laser triangulation sensor. Because the sensor is mounted on a robot, we can choose a viewpoint and a scanner trajectory that is optimal for a given task. For example, we can sweep along a semicircular trajectory in order to recognize and grasp parts from a pallet. With the same vision robot, we can perform a sweep of the packaging container in order to recognize and manipulate elements of cardboard. The system also incorporates software that recognizes and localizes objects based on an acquired 3D point cloud and a CAD model of the object to search for. The core matching algorithm is based on oriented point pairs and a Hough-like voting scheme. The method has been improved with a robust clustering algorithm as well as methods for pose veri?cation and pose re?nement that signi?cantly increase the accuracy and robustness of the system. As an application of the system, an industrial prototype workcell is presented.


title: Computational Anatomy in Theano

abstract: To model deformation of anatomical shapes, non-linear statistics are required to take into account the non-linear structure of the data space. Computer implementations of non-linear statistics and differential geometry algorithms often lead to long and complex code sequences. The aim of the paper is to show how the Theano framework can be used for simple and concise implementation of complex differential geometry algorithms while being able to handle complex and high-dimensional data structures. We show how the Theano framework meets both of these requirements. The framework provides a symbolic language that allows mathematical equations to be directly translated into Theano code, and it is able to perform both fast CPU and GPU computations on high-dimensional data. We show how different concepts from non-linear statistics and differential geometry can be implemented in Theano, and give examples of the implemented theory visualized on landmark representations of Corpus Callosum shapes.


A Novel Synchronization between SQL Server and Mobile Client Databases


title: Circle-based recommendation in online social networks

abstract: Online social network information promises to increase recommendation accuracy beyond the capabilities of purely rating/feedback-driven recommender systems (RS). As to better serve users' activities across different domains, many online social networks now support a new feature of "Friends Circles", which refines the domain-oblivious "Friends" concept. RS should also benefit from domain-specific "Trust Circles". Intuitively, a user may trust different subsets of friends regarding different domains. Unfortunately, in most existing multi-category rating datasets, a user's social connections from all categories are mixed together. This paper presents an effort to develop circle-based RS. We focus on inferring category-specific social trust circles from available rating data combined with social network data. We outline several variants of weighting friends within circles based on their inferred expertise levels. Through experiments on publicly available data, we demonstrate that the proposed circle-based recommendation models can better utilize user's social trust information, resulting in increased recommendation accuracy.


title: Indicators of Good Student Performance in Moodle Activity Data

abstract: In this paper we conduct an analysis of Moodle activity data focused on identifying early predictors of good student performance. The analysis shows that three relevant hypotheses are largely supported by the data. These hypotheses are: early submission is a good sign, a high level of activity is predictive of good results and evening activity is even better than daytime activity. We highlight some pathological examples where high levels of activity correlates with bad results.


title: Semantic node embeddings of distributed graphs using apache spark

abstract: Graphs are widely used to model relationships between entities. Mining for information on large scale social network graphs poses a major challenge in terms of computation and performance due to the sheer size, scale and growth of the graphs involved. Therefore it is important to be able to work with graphs in a distributed setting. Recently, Natural language processing (NLP)-based graph representation algorithms have been developed. These algorithms obtain vector representation for the nodes of the graph in Rn while encoding information about each node's context. Such representational algorithms can be used to preprocess graph input for a variety of graph-based machine learning algorithms such as classification, link prediction etc. In our work, we introduce a distributed version of one such algorithm which can learn node representations from large scale distributed graphs using Apache Spark. Our algorithm is fast, scalable and supports online training for learning representations. We have also modeled the protein-protein interaction problem using graphs and obtained its semantic representation in Rn. The results we have obtained are quite interesting owing to the fact that even when we use just 1% of the data for training, we get an accuracy of close to 95% on prediction.


title: Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays

abstract: Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFESD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimisation solver using the tensor representation in an efficient way.


title: AAWAAZ: A communication system for deaf and dumb

abstract: The paper proposes a framework for recognizing hand gesture which would serve not only as a way of communication between deaf and dumb and mute people, but also, as an instructor. Deaf and dumb individuals lack in proper communication with normal people and find it difficult to properly express themselves. Thus, they are subjected to face many issues in this regard. The sign language is very popular among them and they use it to express themselves. Thus, there is a need of a proper translator. The deaf and dumb are not idle as past, they are working outside and doing great at it. So an efficient system must be set up, to interact with them, to know their views and ideas. The framework here, act as a communication system for deaf and dumb individuals. It would take the sign language as an input which would display the result not only in the form of text but also in the form of audio. Similarly, if there is any input in the form of text, it would display the corresponding image.


title: Car monitoring, alerting and tracking model: Enhancement with mobility and database facilities

abstract: Statistics show that the number of cars is increasing rapidly and so is the number of car theft attempts, locally and internationally. Although there are a lot of car security systems that had been produced lately, but the result is still disappointing as the number of car theft cases still increases. The thieves are inventing cleverer and stronger stealing techniques that need more powerful security systems. This project “Car Monitoring and Tracking System” is being proposed to solve the issue. It introduces the integration between monitoring and tracking system. Both elements are very crucial in order to have a powerful security system. The system can send SMS and MMS to the owner to have fast response especially if the car is nearby. This paper focuses on using MMS and database technology, the picture of the intruder will be sent via local GSM/GPRS service provider to user (and/or) police. The Database offers the required information about car and owner, which will help the police or security authorities in their job. Moreover, in the event of theft local police and user can easily track the car using GPS system that can be link to Google Earth and other mapping software. The implementation and testing results show the success of prototype in sending MMS to owner within 40 seconds and receiving acknowledgment to the database (police or security unit) within 4 minutes. The timing and results are suitable to owner and police to take suitable action against intruder.


title: A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward

abstract: We develop an off-policy actor-critic algorithm for learning an optimal policy from a training set composed of data from multiple individuals. This algorithm is developed with a view towards its use in mobile health.


title: Design and implementation of a Mamdani fuzzy inference system on an FPGA

abstract: The growth in number of fuzzy logic applications led to the need of finding efficient ways to implement them. The advantage of using Field Programmable Gate Arrays (FPGA) is the ease with which controllers can be re-designed based on requirements without incurring major non recurring engineering (NRE) costs. In this paper, the implementation of a Mamdani Fuzzy Inference System has been demonstrated with the application of a Home Water Sprinkler System. The design of the Home Water Sprinkler System uses two inputs, Temperature and Soil Humidity, to give a single output, Watering Duration. VHDL programming language is used for creating the design in which simulations are carried out to check the functional verification of individual blocks. The design created on VHDL platform was synthesized, verified and implemented using Xilinx Integrated Software Environment (ISE) Version 6.1. The bit stream created is downloaded onto the Xilinx Virtex 2 FPGA which is mounted on a V2MB1000 board. For the verification of FIS, DIP switches and push buttons were used to simulate inputs. The outputs and inputs were displayed on the two seven segment LED displays. The accuracy of the model on FPGA was compared with simulation results obtained using MATLAB & Fuzzy Logic Tool Box. The results obtained from the two models differed with a Root Mean Square (R.M.S.) error of 0.8%.


title: YoMo: the Arduino-based smart metering board

abstract: Smart meters are an enabling technology for many smart grid applications. This paper introduces a design for a low-cost smart meter system as well as explaining fundamentals of smart metering. The smart meter platform, provided as open hardware, is designed with a connector interface compatible to the Arduino platform, thus opening the possibilities for smart meters with flexible hardware and computation features, starting from low-cost 8 bit micro controllers up to powerful single board computers that can run Linux. The metering platform features a current transformer which allows a non-intrusive installation of the current measurement unit. The suggested design can switch loads, offers a variable sampling frequency, and provides measurement data such as active power, reactive and apparent power. Results indicate that measurement accuracy and resolution of the proposed metering platform are sufficient for a range of different applications and loads from a few watts up to 5 kW.


title: Methodological approach for the design of a complex inclusive human-machine system

abstract: Modern industrial automatic machines and robotic cells are equipped with highly complex human-machine interfaces (HMIs) that often prevent human operators from an effective use of the automatic systems. In particular, this applies to vulnerable users, such as those with low experience or education level, the elderly and the disabled. To tackle this issue, it becomes necessary to design user-oriented HMIs, which adapt to the capabilities and skills of users, thus compensating their limitations and taking full advantage of their knowledge. In this paper, we propose a methodological approach to the design of complex adaptive human-machine systems that might be inclusive of all users, in particular the vulnerable ones. The proposed approach takes into account both the technical requirements and the requirements for ethical, legal and social implications (ELSI) for the design of automatic systems. The technical requirements derive from a thorough analysis of three use cases taken from the European project INCLUSIVE. To achieve the ELSI requirements, the MEESTAR approach is combined with the specific legal issues for occupational systems and requirements of the target users.


title: Exposure limits: the underestimation of absorbed cell phone radiation, especially in children.

abstract: The existing cell phone certification process uses a plastic model of the head called the Specific Anthropomorphic Mannequin (SAM), representing the top 10% of U.S. military recruits in 1989 and greatly underestimating the Specific Absorption Rate (SAR) for typical mobile phone users, especially children. A superior computer simulation certification process has been approved by the Federal Communications Commission (FCC) but is not employed to certify cell phones. In the United States, the FCC determines maximum allowed exposures. Many countries, especially European Union members, use the "guidelines" of International Commission on Non-Ionizing Radiation Protection (ICNIRP), a non governmental agency. Radiofrequency (RF) exposure to a head smaller than SAM will absorb a relatively higher SAR. Also, SAM uses a fluid having the average electrical properties of the head that cannot indicate differential absorption of specific brain tissue, nor absorption in children or smaller adults. The SAR for a 10-year old is up to 153% higher than the SAR for the SAM model. When electrical properties are considered, a child's head's absorption can be over two times greater, and absorption of the skull's bone marrow can be ten times greater than adults. Therefore, a new certification process is needed that incorporates different modes of use, head sizes, and tissue properties. Anatomically based models should be employed in revising safety standards for these ubiquitous modern devices and standards should be set by accountable, independent groups.


title: Kangaroo Care Is Effective in Diminishing Pain Response in Preterm Neonates

abstract: Objective To test the efficacy of maternal skin-to-skin contact, or kangaroo care (KC), on diminishing the pain response of preterm neonates to heel lancing. Design A crossover design was used, in which the neonates served as their own controls. Subjects Preterm neonates (n = 74), between 32 and 36 weeks' postmenstrual age and within 10 days of birth, who were breathing without assistance and who were not receiving sedatives or analgesics in 3 level II to III neonatal intensive care units in Canada. Interventions In the experimental condition, the neonate was held in KC for 30 minutes before the heel-lancing procedure and remained in KC for the duration of the procedure. In the control condition, the neonate was in the prone position in the isolette. The ordering of conditions was random. Main Outcome Measures The primary outcome was the Premature Infant Pain Profile, which is composed of 3 facial actions, maximum heart rate, and minimum oxygen saturation changes from baseline in 30-second blocks. Videotapes, taken with the camera positioned on the neonate's face so that an observer could not tell whether the neonate was being held or was in the isolette, were coded by research assistants who were naive to the purpose of the study. Heart rate and oxygen levels were continuously monitored into a computer for later analysis. A repeated-measures analysis of covariance was used, with order of condition and site as factors and severity of illness as a covariate. Results Premature Infant Pain Profile scores across the first 90 seconds from the heel-lancing procedure were significantly (.002 P Conclusions For preterm neonates who are 32 weeks' postmenstrual age or older, KC seems to effectively decrease pain from heel lancing. Further study is needed to determine if younger neonates or those requiring assistance in breathing, or older infants or toddlers, would benefit from KC, or if it would remain effective over several procedures. Given its effectiveness, and that parents of neonates in critical care units want to participate more in comforting their children, KC is a potentially beneficial strategy for promoting family health.


title: Unsupervised regenerative learning of hierarchical features in Spiking Deep Networks for object recognition

abstract: We present a spike-based unsupervised regenerative learning scheme to train Spiking Deep Networks (SpikeCNN) for object recognition problems using biologically realistic leaky integrate-and-fire neurons. The training methodology is based on the Auto-Encoder learning model wherein the hierarchical network is trained layer wise using the encoder-decoder principle. Regenerative learning uses spike-timing information and inherent latencies to update the weights and learn representative levels for each convolutional layer in an unsupervised manner. The features learnt from the final layer in the hierarchy are then fed to an output layer. The output layer is trained with supervision by showing a fraction of the labeled training dataset and performs the overall classification of the input. Our proposed methodology yields 0.95%/24.58% classification error on MNIST/CIFAR10 datasets which is comparable with state-of-the-art results. The proposed methodology also introduces sparsity in the hierarchical feature representations on account of event-based coding resulting in computationally efficient learning.


title: Artificial neural networks for non-stationary time series

abstract: The use of artificial neural networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary time series. Our result discusses the range of complexities caused by non-stationary behavior and finds that overfitting by ANN could be useful in the analysis of such non-stationary complex financial time series.


title: PrivWatcher: Non-bypassable Monitoring and Protection of Process Credentials from Memory Corruption Attacks

abstract: Commodity operating systems kernels are typically implemented using low-level unsafe languages, which leads to the inevitability of memory corruption vulnerabilities. Multiple defense techniques are widely adopted to mitigate the impact of memory corruption on executable code and control data. Nevertheless, there has not been much attention to defend against corruption of non-control data despite the fact that previous incidents of kernel exploitation showed that corrupting non-control data is a real threat. We present PrivWatcher, a framework for monitoring and protecting the integrity of process credentials and their usage contexts from memory corruption attacks. PrivWatcher solves multiple challenges to achieve this objective. It introduces techniques to isolate and protect the data that define process credentials and guarantee the locality of this data within the protected memory. Then, by adopting a dual reference monitor model, it guarantees the Time of Check To Time of Use (TOCTTOU) consistency between verification and usage contexts for process credentials. Moreover, it provides a secure mechanism that allows the presumably protected kernel code to verify the protected data without relying on unprotected data fields. PrivWatcher provides non-bypassable integrity assurances for process credentials and can be adapted to enforce a variety of integrity policies. In this paper, we demonstrate an application of PrivWatcher that enforces the original semantics of the OS kernel's access control policy: a change in process privileges is legitimate only if an uncompromised kernel would have allowed it. We implemented a PrivWatcher prototype to protect Ubuntu Linux running on x86-64. Evaluation of our prototype showed that PrivWatcher is effective and efficient.


How the relationship between the crisis life cycle and mass media content can better inform crisis communication


[A telemetric transmission system for in vivo measuring of hip joint force with instrument-implanted prostheses].


title: A Correction to the Algorithm in Reiter's Theory of Diagnosis

abstract: Reiter [3] has developed a general theory of diagnosis based on first principles. His algorithm computes all diagnoses which explain the differences between the predicted and observed behavior of a given system. Unfortunately, Reiter's description of the algorithm is incorrect in that some diagnoses can be missed under certain conditions. This note presents a revised algorithm and a proof of its correctness.


title: A facial expression model with generative albedo texture

abstract: A facial expression model (FEM) is developed which can synthesize various face shapes and albedo textures. The face shape varies with individuals and expressions. FEM synthesizes these shape variations by using a bilinear face model built from the Face Warehouse Database. On the other hand, the generative albedo texture is directly extracted from a neutral face model — the Basel Face Model. In this paper, we elaborate the model construction process and demonstrate its application in face reconstruction and expression tracking.


title: Array Tomography: A New Tool for Imaging the Molecular Architecture and Ultrastructure of Neural Circuits

abstract: SUMMARY Many biological functions depend critically upon fine details of tissue molecular architecture that have resisted exploration by existing imaging techniques. This is particularly true for nervous system tissues, where information processingfunctiondependsonintricatecircuit and synaptic architectures. Here, we describe a new imaging method, called array tomography, which combines and extends superlative features of modern optical fluorescence and electron microscopy methods. Based on methods for constructing and repeatedly staining and imaging ordered arrays of ultrathin (50‐200 nm), resin-embedded serial sections on glass microscope slides, array tomography allows for quantitative, high-resolution, largefield volumetric imaging of large numbers of antigens, fluorescent proteins, and ultrastructure in individual tissue specimens. Compared to confocal microscopy, array tomography offers the advantage of better spatial resolution, in particular along the z axis, as well as depthindependent immunofluorescent staining. The application of array tomography can reveal important but previously unseen features of brain molecular architecture.


title: Comparing two software design process theories

abstract: This paper explores an ongoing conflict concerning the nature of software design This conflict manifests itself as antagonism between managers and developers, debates about agile vs plan-driven methodologies and aspiring developers' dissatisfaction with their courses One side views design as a plan-driven information processing task involving rational decision-making (the Reason-Centric Perspective), while the other views design as an improvised, creative task involving naturalized decision-making (Action-Centric Perspective) Each perspective includes an epistemology, theory of human action and a software design process theory (an explanation of how software is created in practice) This paper reports the results of an exploratory questionnaire study that comparatively and empirically evaluated the two process theories Results clearly favor the Action-Centric process theory: the Sensemaking-Coevolution-Implementation Framework.


title: Information Hiding using Least Significant Bit Steganography and Blowfish Algorithm

abstract: Security is the most challenging aspects in the World Wide Web. In present time information sharing and transfer has increased exponentially. So to find out best solution this is providing necessary protection of our data against malicious attacks from intruders. Cryptography and Steganography are the two major techniques for secret communication. Cryptography converts information from its original form (plaintext) into unreadable form (cipher text); where as in steganography the secret message is hidden into the cover medium. There are many different techniques are available for cryptography and stagnography. In this paper two techniques BLOWFISH algorithm for cryptography and LSB approach for stagnography are used. First encryption of data is done by using BLOWFISH algorithm which is one of the most powerful techniques and then hide encrypted message using LSB approach. Our proposed model gives two layers of security for secret data.


title: Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network

abstract: We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network. Our proposed method, named TD-CEDN, solves two important issues in this low-level vision problem: (1) learning multi-scale and multi-level features; and (2) applying an effective top-down refined approach in the networks. TD-CEDN performs the pixel-wise prediction by means of leveraging features at all layers of the net. Unlike skip connections and previous encoder-decoder methods, we first learn a coarse feature map after the encoder stage in a feedforward pass, and then refine this feature map in a top-down strategy during the decoder stage utilizing features at successively lower layers. Therefore, the deconvolutional process is conducted stepwise, which is guided by Deeply-Supervision Net providing the integrated direct supervision. The above proposed technologies lead to a more precise and clearer prediction. Our proposed algorithm achieved the state-of-the-art on the BSDS500 dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of 0.588), and and the NYU Depth dataset (ODS F-score of 0.735).


title: Assessing the Travel Impacts of Subnetworks for Automated Driving: An Exploratory Study

abstract: This study explores a network configuration concept for vehicle automation levels 3–4 (according to SAE classifications) in an urban road network having mixed traffic and demonstrates its potential impacts. We assume automated driving will be allowed on a selection of roads. For the remaining roads, manual driving (although supported by assisting driving automation systems) will be compulsory. Accordingly, we introduce an approach for road selection and present relevant operational concepts. To evaluate the impacts of this configuration and model different vehicles’ route choice behavior in mixed traffic, a static multi-class stochastic user equilibrium traffic assignment with a path-size logit route choice model and a Monte Carlo labeling route-set generation is adapted. Two user-classes are distinguished: vehicles with automation levels 0–2 and vehicles with automation levels 3–4 having a different passenger car unit to account for lower driving headways, lower value of time, and higher fuel efficiency. The results indicate a decrease in total travel cost with the increase in market penetration rate of higher automation levels, a decrease in total travel time, and a minor increase in total travel distance. Although in most cases vehicles with higher automation levels benefit more from the improvements, no deterioration in travel conditions is observed for the rest of the vehicles in any scenario. Furthermore, a noticeable shift of traffic from roads with access function to roads with flow function and distributors is observed. Sensitivity analysis shows that the extent of changes in the impacts is not strongly dependent on the input parameters.


title: Study of reconfigurable suspended cable-driven parallel robots for airplane maintenance

abstract: This paper discusses the use of large-dimension reconfigurable suspended cable-driven parallel robots (CDPR) to substitute for conventional gantry nacelles that carry workers in an airplane maintenance workshop. The reconfiguration of the CDPR is considered as a multi-objective optimization problem with two performance indices. One criterion is the sum of the cable tensions which is directly related to the power consumption of the CDPR. The other criterion is the normalized upper bound on the infinitesimal displacement of the mobile platform, used here as a means to quantify the stiffness of the CDPR. A systematic procedure is proposed to find optimal reconfigurations of the CDPR while dealing efficiently with the tradeoff between these two criteria.


Credit Card Fraud Detection with Unsupervised Algorithms


title: Efficient handling of concept drift and concept evolution over Stream Data

abstract: To decide if an update to a data stream classifier is necessary, existing sliding window based techniques monitor classifier performance on recent instances. If there is a significant change in classifier performance, these approaches determine a chunk boundary, and update the classifier. However, monitoring classifier performance is costly due to scarcity of labeled data. In our previous work, we presented a semi-supervised framework SAND, which uses change detection on classifier confidence to detect a concept drift. Unlike most approaches, it requires only a limited amount of labeled data to detect chunk boundaries and to update the classifier. However, SAND is expensive in terms of execution time due to exhaustive invocation of the change detection module. In this paper, we present an efficient framework, which is based on the same principle as SAND, but exploits dynamic programming and executes the change detection module selectively. Moreover, we provide theoretical justification of the confidence calculation, and show effect of a concept drift on subsequent confidence scores. Experiment results show efficiency of the proposed framework in terms of both accuracy and execution time.


title: Research Priorities for Robust and Beneficial Artificial Intelligence

abstract: Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.


title: A 0.8-V 230-$\mu$ W 98-dB DR Inverter-Based $\Sigma \Delta$ Modulator for Audio Applications

abstract: This paper presents a ΣΔ modulator based on a gain-boost class-C inverter for audio applications. The gain-boost class-C inverter behaves as a low-voltage subthreshold amplifier and boosts its dc gain for the high-precision requirement. Meanwhile, an on-chip body bias is used to compensate the performance degradation of the inverter at a slow process corner or low supply voltage. The proposed inverter-based modulator is fabricated in a 65-nm mixed-signal CMOS process with a die area of 0.3 mm 2. The experimental chip achieves 91-dB peak signal-to-noise-plus-distortion ratio (SNDR), 94-dB signal-to-noise ratio (SNR) and 98-dB dynamic range (DR) over a 20-KHz audio band with a 5-MHz sampling frequency and a 0.8-V supply voltage consuming only 230-μW power, which demonstrates that the gain-boost class-C inverter is particularly suitable for low-voltage micro-power high-resolution applications.


title: "Play PRBLMS": Identifying and Correcting Less Accessible Content in Voice Interfaces

abstract: Voice interfaces often struggle with specific types of named content. Domain-specific terminology and naming may push the bounds of standard language, especially in domains like music where artistic creativity extends beyond the music itself. Artists may name themselves with symbols (e.g. M S C RA) that most standard automatic speech recognition (ASR) systems cannot transcribe. Voice interfaces also experience difficulty surfacing content whose titles include non-standard spellings, symbols or other ASCII characters in place of English letters, or are written using a non-standard dialect. We present a generalizable method to detect content that current voice interfaces underserve by leveraging differences in engagement across input modalities. Using this detection method, we develop a typology of content types and linguistic practices that can make content hard to surface. Finally, we present a process using crowdsourced annotations to make underserved content more accessible.


Measuring the Effectiveness of Embedded Phishing Exercises


title: Collaborative personalized tweet recommendation

abstract: Twitter has rapidly grown to a popular social network in recent years and provides a large number of real-time messages for users. Tweets are presented in chronological order and users scan the followees' timelines to find what they are interested in. However, an information overload problem has troubled many users, especially those with many followees and thousands of tweets arriving every day. In this paper, we focus on recommending useful tweets that users are really interested in personally to reduce the users' effort to find useful information. Many kinds of information on Twitter are available for helping recommendation, including the user's own tweet history, retweet history and social relations between users. We propose a method of making tweet recommendations based on collaborative ranking to capture personal interests. It can also conveniently integrate the other useful contextual information. Our final method considers three major elements on Twitter: tweet topic level factors, user social relation factors and explicit features such as authority of the publisher and quality of the tweet. The experiments show that all the proposed elements are important and our method greatly outperforms several baseline methods.


title: Trajectory pattern mining

abstract: The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.


Artificial Intelligence Algorithms


A Frames Approach to Semantic Analysis


title: Collective Sensemaking in Online Health Forums

abstract: Online health communities collect vast amounts of information and opinions in regards to health and wellness management. However, these opinions are usually stored within lengthy and loosely structured discussion threads; synthesizing information in these threads can be challenging. In this mixed-methods study, grounded in the theoretical perspective of collective sensemaking, we examined patterns of communication within an online diabetes community TuDiabetes. The results of the study suggest that members of TuDiabetes often construct shared meaning through deep discussions, back and forth negotiation of perspectives, and resolution of conflicts in opinions. However, unlike participants of other sensemaking communities, members of TuDiabetes often value multiplicity of opinions rather than consensus. We use study results to draw implications for the design of computing platforms for facilitating collective sensemaking that promote construction of shared knowledge yet embrace diversity of opinions.


title: ProCom: Designing and Evaluating a Mobile and Wearable System to Support Proximity Awareness for People with Autism

abstract: People with autism are at risk for social isolation due to differences in their perception and engagement with the social world. In this work, we aim to address one specific concern related to socialization the understanding, awareness, and use of interpersonal space. Over the course of a year, we iteratively designed and tested a series of concepts for supporting children with autism in perceiving, understanding, and responding to physical proximity with other people. During this process, we developed ProCom, a prototype system for measuring proximity without requiring instrumentation of the environment or another person. We used a variety of low and high fidelity prototypes, culminating in ProCom, to assess the feasibility, utility, and challenges of this approach. The results of these iterative design engagements indicate that wearable assistive technologies can support people in developing awareness of physical proximity in social settings. However, challenges related to both personal and collective use remain


title: ThrowBot: design considerations for a man-portable throwable robot

abstract: The pocket-sized ThrowBot is a sub-kilogram-class robot that provides short-range remote eyes and ears for urban combat. This paper provides an overview of lessons learned from experience, testing, and evaluation of the iRobot ThrowBot developed under the Defense Advanced Research Projects Agency (DARPA) Tactical Mobile Robots (TMR) program. Emphasis has been placed on investigating requirements for the next generation of ThrowBots to be developed by iRobot Corporation and SPAWAR Systems Center, San Diego (SSC San Diego) Unmanned Systems Branch. Details on recent evaluation activities performed at the Military Operations in Urban Terrain (MOUT) test site at Fort Benning, GA, are included, along with insights obtained throughout the development of the ThrowBot since its inception in 1999 as part of the TMR program.


title: Maple: a coverage-driven testing tool for multithreaded programs

abstract: Testing multithreaded programs is a hard problem, because it is challenging to expose those rare interleavings that can trigger a concurrency bug. We propose a new thread interleaving coverage-driven testing tool called Maple that seeks to expose untested thread interleavings as much as possible. It memoizes tested interleavings and actively seeks to expose untested interleavings for a given test input to increase interleaving coverage. We discuss several solutions to realize the above goal. First, we discuss a coverage metric based on a set of interleaving idioms. Second, we discuss an online technique to predict untested interleavings that can potentially be exposed for a given test input. Finally, the predicted untested interleavings are exposed by actively controlling the thread schedule while executing for the test input. We discuss our experiences in using the tool to expose several known and unknown bugs in real-world applications such as Apache and MySQL.


title: Self‐medication among Finnish young men in the beginning of common military service

abstract: Purpose ::: ::: Little is known about self-medication in adolescent and young adult males, particularly in ones exposed to increased stress. The objective of this article is to analyze self-medication and its predictors in young Finnish men at entry into common military service. ::: ::: ::: ::: Methods ::: ::: The responding men (n = 857) from nine brigades anonymously completed a self-administered questionnaire during the first conscription days before active military training in July 1999. The effect of pre-disposing, need and health behavior variables on self-medication was assessed using Andersen et al. theoretical health care utilization model. ::: ::: ::: ::: Results ::: ::: In the 2 weeks preceding the survey, 65% of the men reported self-medication. Self-medication for pain or common cold symptoms was reported by 54% while 8% had taken caffeine tablets or other stimulants. Multivariate analysis on overall self-medication showed an association with brigade and using health services before the conscription. Along with pre-disposing variables, need variables were associated with self-medication for pain or common cold symptoms and self-medication with stimulants. Stimulant use was also associated with health behaviors such as trial with illegal drugs during lifetime. ::: ::: ::: ::: Conclusions ::: ::: Young men frequently self-medicate at the initial stage of conscription that may pose them at an increased risk for adverse effects of medicines or other medical problems. The military and civilian primary health care providers should discuss the proper use of self-medication with young men. Copyright © 2004 John Wiley & Sons, Ltd.


title: Radial basis function neural network-based face recognition using firefly algorithm

abstract: This paper presents an adaptive technique for obtaining centers of the hidden layer neurons of radial basis function neural network (RBFNN) for face recognition. The proposed technique uses firefly algorithm to obtain natural sub-clusters of training face images formed due to variations in pose, illumination, expression and occlusion, etc. Movement of fireflies in a hyper-dimensional input space is controlled by tuning the parameter gamma (γ) of firefly algorithm which plays an important role in maintaining the trade-off between effective search space exploration, firefly convergence, overall computational time and the recognition accuracy. The proposed technique is novel as it combines the advantages of evolutionary firefly algorithm and RBFNN in adaptive evolution of number and centers of hidden neurons. The strength of the proposed technique lies in its fast convergence, improved face recognition performance, reduced feature selection overhead and algorithm stability. The proposed technique is validated using benchmark face databases, namely ORL, Yale, AR and LFW. The average face recognition accuracies achieved using proposed algorithm for the above face databases outperform some of the existing techniques in face recognition.


title: Automated Crash Filtering Using Interprocedural Static Analysis for Binary Codes

abstract: This paper introduces a static binary analysis tool called CrashFilter, which classifies the crashes arisen during the test, according to the risk levels. It has advantages in accuracy and provides wider coverage of analysis, due to newly introduced analyses–Memory Location Analysis and Inter-Procedure Analysis.


title: Combining Word and Entity Embeddings for Entity Linking

abstract: The correct identification of the link between an entity mention in a text and a known entity in a large knowledge base is important in information retrieval or information extraction. The general approach for this task is to generate, for a given mention, a set of candidate entities from the base and, in a second step, determine which is the best one. This paper proposes a novel method for the second step which is based on the joint learning of embeddings for the words in the text and the entities in the knowledge base. By learning these embeddings in the same space we arrive at a more conceptually grounded model that can be used for candidate selection based on the surrounding context. The relative improvement of this approach is experimentally validated on a recent benchmark corpus from the TAC-EDL 2015 evaluation campaign.


title: Root Distribution of a Plantation‐Grown Red Pine in an Outwash Soil

abstract: Root distribution and mass of a single dominant sample tree (17 m high, 20 cm diameter) in a 39—year—old Pinus resinosa Ait. plantation supported by a deep, stratified, outwash sandy soil were related to soil horizonation characteristics and aboveground tree components (foilage, branches, bolebark, and bolewood). All roots were excavated by hand in the A_p and B horizons and by 0.3—m depths in the C strata, were separated into five size classes, cleaned, dried, and weighed. Root—depth penetration terminated in a great number of fine roots in a thick, very fine sandy—silty stratum below the coarser textured solum at 2.7 m. Lateral root distribution was extremely variable; the maximum radial extent was approximately 9 m from the stump. Total root weight was approximately twice the total needle weight. See full-text article at JSTOR


title: Multi-Scale Dense Networks for Resource Efficient Image Classification

abstract: In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network's prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and 2. budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across "easier" and "harder" inputs. In contrast to most prior work, such as the popular Viola and Jones algorithm, our approach is based on convolutional neural networks. We train multiple classifiers with varying resource demands, which we adaptively apply during test time. To maximally re-use computation between the classifiers, we incorporate them as early-exits into a single deep convolutional neural network and inter-connect them with dense connectivity. To facilitate high quality classification early on, we use a two-dimensional multi-scale network architecture that maintains coarse and fine level features all-throughout the network. Experiments on three image-classification tasks demonstrate that our framework substantially improves the existing state-of-the-art in both settings.


title: Virtual channel space-time processing with dual-polarization discrete lens antenna arrays

abstract: This paper discusses channel capacity improvements with optimal multiantenna wireless communications using a discrete lens array (DLA) at the analog front end. It is first shown that optimal signaling over multiantenna channels via virtual spatial models (VSMs) has a direct hardware correspondence in a multibeam spatially fed antenna architecture. A multibeam dual-polarized DLA is designed and characterized, and the effects of measured antenna radiation patterns on calculated capacity are quantified. In particular, the paper examines: 1) effects of cross-polarization for a dual-polarized multibeam array; 2) effects of beam overlap in a multibeam array; and 3) effects of gain variations for different scan angles. It is demonstrated that adding an ideal dual-polarized array achieves a capacity improvement of up to 88% compared to the single-polarization case. In practice, the two polarizations are weakly coupled, and the measured cross-polarization level of -14 dB decreases the capacity of the system by less than 2%. Furthermore, it is shown that a dual-polarized multibeam DLA is nearly insensitive to polarization rotation in a rich scattering environment, whereas single-polarized antennas incur a capacity degradation of up to 24%. Finally, capacity decrease due to measured nonuniform amplitudes on the antenna aperture is shown to be less than 10%.


Current And Vibration Monitoring For Fault Diagnosis And Root Cause Analysis Of Induction Motor Drives.


title: A Probabilistic Approach to Fast Pattern Matching in Time Series Databases

abstract: The problem of efficiently and accurately locating patterns of interest in massive time series data sets is an important and non-trivial problem in a wide variety of applications, including diagnosis and monitoring of complex systems, biomedical data analysis, and exploratory data analysis in scientific and business time series. In this paper a probabilistic approach is taken to this problem. Using piecewise linear segmentations as the underlying representation, local features (such as peaks, troughs, and plateaus) are defined using a prior distribution on expected deformations from a basic template. Global shape information is represented using another prior on the relative locations of the individual features. An appropriately defined probabilistic model integrates the local and global information and directly leads to an overall distance measure between sequence patterns based on prior knowledge. A search algorithm using this distance measure is shown to efficiently and accurately find matches for a variety of patterns on a number of data sets, including engineering sensor data from space Shuttle mission archives. The proposed approach provides a natural framework to support user-customizable "query by content" on time series data, taking prior domain information into account in a principled manner.


title: Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom

abstract: In this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.


title: Low-RCS and Polarization-Reconfigurable Antenna Using Cross-Slot-Based Metasurface

abstract: This letter presents a polarization-reconfigurable compact slot antenna with reduced radar cross section (RCS) using an asymmetric cross-shaped metasurface (MS). The proposed MS can reconfigure the polarization of the slot antenna between right-hand circular polarization (RHCP), left-hand circular polarization (LHCP), and linear polarization (LP) by rotating it with respect to the center of the slot antenna. In addition, the MS reduces the RCS of the slot antenna significantly in all polarization states. The cross-slot MS is placed just over the planar slot antenna without any air gap. The simulated monostatic RCS of $- $ 19.5 dBsm is observed at 4.4 GHz for LHCP and RHCP cases and $- $ 17.0 dBsm for LP mode of operation. Antenna performance in terms of its input matching, far-field parameters, monostatic RCS, and axial ratio are measured at its three polarization states, which are in agreement with simulated results.


title: Reading in A Digital Age: e-Books Are Students Ready For This Learning Object?

abstract: E-Books are a type of e-content based learning object whose benefits may include: hyper linking, nonlinearity, data density, customizability, greater distribution, low costs, search ability, and other multimedia features (Shiratuddin, Hassan, & Landoni, 2003). Originally introduced in the late 1990’s, the growth of e-books has been sluggish. Midgley reported (as cited in Wilson, 2003) that while proponents believe that e-books will come to change the way we understand reading and represent the future of reading in this digital age, critics explain that reading on a screen is an unpleasant experience that has, and will continue to, stymie the growth of e-books (Weeks, 2002). Concurrently, Prensky (2001) reports that the new generation of students entering higher education, the “Millennials”, are fascinated by new technologies and considers it as a natural part of their environment. This paper represents the findings of students’ reported experiences and perceptions of e-books at a historically Black university.


title: Using ADO to Manage SQL Server Database in VC

abstract: This paper introduced the advantage of ADO and the way of using ADO to connect and manage SQL Server database in environment of Visual C++6.0.


title: CMMI guided process improvement for DevOps projects: an exploratory case study

abstract: Very recently, an increasing number of software companies adopted DevOps to adapt themselves to the ever-changing business environment. While it is important to mature adoption of the DevOps for these companies, no dedicated maturity models for DevOps exist. Meanwhile, maturity models such as CMMI models have demonstrated their effects in the traditional paradigm of software industry, however, it is not clear whether the CMMI models could guide the improvements with the context of DevOps. This paper reports a case study aiming at evaluating the feasibility to apply the CMMI models to guide process improvement for DevOps projects and identifying possible gaps. Using a structured method(i.e., SCAMPI C), we conducted a case study by interviewing four employees from one DevOps project. Based on evidence we collected in the case study, we managed to characterize the maturity/capability of the DevOps project, which implies the possibility to use the CMMI models to appraise the current processes in this DevOps project and guide future improvements. Meanwhile, several gaps also are identified between the CMMI models and the DevOps mode. In this sense, the CMMI models could be taken as a good foundation to design suitable maturity models so as to guide process improvement for projects adopting the DevOps.


title: Self-Improving Algorithms

abstract: We investigate ways in which an algorithm can improve its expected performance by fine-tuning itself automatically with respect to an unknown input distribution $\mathcal{D}$. We assume here that $\mathcal{D}$ is of product type. More precisely, suppose that we need to process a sequence $I_1,I_2,\ldots$ of inputs $I=(x_1,x_2,\ldots,x_n)$ of some fixed length $n$, where each $x_i$ is drawn independently from some arbitrary, unknown distribution $\mathcal{D}_i$. The goal is to design an algorithm for these inputs so that eventually the expected running time will be optimal for the input distribution $\mathcal{D}=\prod_i\mathcal{D}_i$. We give such self-improving algorithms for two problems: (i) sorting a sequence of numbers and (ii) computing the Delaunay triangulation of a planar point set. Both algorithms achieve optimal expected limiting complexity. The algorithms begin with a training phase during which they collect information about the input distribution, followed by a stationary regime in which the algorithms settle to their optimized incarnations.


title: Propagation Modeling for Accurate Indoor WLAN RSS-Based Localization

abstract: WLAN RSS-based localization has been a hot research topic for the last years. To obtain high accuracy in the noisy wireless channel, WLAN location determination systems usually use a calibration phase, where a radio map, capturing the signal strength signatures at different locations in the area of interest, is built. The radio map construction process takes a lot of time and effort, reducing the value of WLAN localization systems. In this paper, we propose 3D ray tracing as a way for automatically generating a highly accurate radiomap. We compare this method to previously used propagation modeling-based methods like the Wall Attenuation Factor and 2D ray tracing models. We evaluate the performance of each method and its computational cost in a typical residential environment. We also examine the sensitivity of the localization accuracy to inaccurate material parameters. Our results quantify the accuracy- complexity trade-off of the different proposed techniques with 3D ray tracing giving the best localization accuracy compared to measurements with acceptable computational requirements on a typical PC.


title: Bounded Least General Generalization

abstract: We study a generalization of Plotkin’s least general generalization. We introduce a novel concept called bounded least general generalization w.r.t. a set of clauses and show an instance of it for which polynomial-time reduction procedures exist. We demonstrate the practical utility of our approach in experiments on several relational learning datasets.


title: Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers

abstract: Clinical research has demonstrated the efficacy of closed-loop control of anesthesia using the bispectral index of the electroencephalogram as the controlled variable. These controllers have evolved to yield patient-specific anesthesia, which is associated with improved patient outcomes. Despite progress, the problem of patient-specific anesthesia remains unsolved. A variety of factors confound good control, including variations in human physiology, imperfect measures of drug effect, and delayed, hysteretic response to drug delivery. Reinforcement learning (RL) appears to be uniquely equipped to overcome these challenges; however, the literature offers no precedent for RL in anesthesia. To begin exploring the role RL might play in improving anesthetic care, we investigated the method's application in the delivery of patient-specific, propofol-induced hypnosis in human volunteers. When compared to performance metrics reported in the anesthesia literature, RL demonstrated patient-specific control marked by improved accuracy and stability. Furthermore, these results suggest that RL may be considered a viable alternative for solving other difficult closed-loop control problems in medicine. More rigorous clinical study, beyond the confines of controlled human volunteer studies, is needed to substantiate these findings.


title: MQTT based air quality monitoring

abstract: Air quality plays very important role in safety, security, and health of the mankind. While increasing large-scale industrialization and urbanization creates huge cities, these activities have been creating various ill-effects to the environment. Likewise, one problem is deterioration of air quality in various Indian cities. The primary contributor to air pollution is the particulate matter (PM2.5), causing human health issues, like asthma and other respiratory illnesses. A study has found out that the persons inhaling particulate matter in air are at more risk of lung cancer and comparable to non-smoker susceptible to secondhand smoke. In this work, GP2Y1010AU0F, a sharp dust sensor is used for measuring smoke and dust particles in the environment. The measured data is sent using ESP8266 based NodeMCU-12E, a wireless micro-controller to the cloud server by using Message Queue Telemetry Transport protocol. The MQTT uses publish, subscriber, and broker services. Also, carbon monoxide sensor (MQ-7) is used for sensing carbon monoxide level in the air. The measured values of carbon monoxide (MQ-7) and of sharp dust sensor GP2Y1010AU0F are shown using web page and mobile application.


title: Complete Riemannian metrics with holonomy group G 2 on deformations of cones over S 3 × S 3

abstract: Complete Riemannian metrics with holonomy group G2 on manifolds obtained by deformation of cones over S3 × S3 are constructed.


title: Double Thompson Sampling for Dueling Bandits

abstract: In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit problems. As indicated by its name, D-TS selects both the first and the second candidates according to Thompson Sampling. Specifically, D-TS maintains a posterior distribution for the preference matrix, and chooses the pair of arms for comparison by sampling twice from the posterior distribution. This simple algorithm applies to general Copeland dueling bandits, including Condorcet dueling bandits as its special case. For general Copeland dueling bandits, we show that D-TS achieves $O(K^2 \log T)$ regret. For Condorcet dueling bandits, we further simplify the D-TS algorithm and show that the simplified D-TS algorithm achieves $O(K \log T + K^2 \log \log T)$ regret. Simulation results based on both synthetic and real-world data demonstrate the efficiency of the proposed D-TS algorithm.


title: A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification

abstract: High dimensionality of text can be a deterrent in applying complex learners such as Support Vector Machines to the task of text classification. Feature clustering is a powerful alternative to feature selection for reducing the dimensionality of text data. In this paper we propose a new information-theoretic divisive algorithm for feature/word clustering and apply it to text classification. Existing techniques for such "distributional clustering" of words are agglomerative in nature and result in (i) sub-optimal word clusters and (ii) high computational cost. In order to explicitly capture the optimality of word clusters in an information theoretic framework, we first derive a global criterion for feature clustering. We then present a fast, divisive algorithm that monotonically decreases this objective function value. We show that our algorithm minimizes the "within-cluster Jensen-Shannon divergence" while simultaneously maximizing the "between-cluster Jensen-Shannon divergence". In comparison to the previously proposed agglomerative strategies our divisive algorithm is much faster and achieves comparable or higher classification accuracies. We further show that feature clustering is an effective technique for building smaller class models in hierarchical classification. We present detailed experimental results using Naive Bayes and Support Vector Machines on the 20Newsgroups data set and a 3-level hierarchy of HTML documents collected from the Open Directory project (www.dmoz.org).


title: Graphene transistors.

abstract: Graphene has changed from being the exclusive domain of condensed-matter physicists to being explored by those in the electron-device community. In particular, graphene-based transistors have developed rapidly and are now considered an option for post-silicon electronics. However, many details about the potential performance of graphene transistors in real applications remain unclear. Here I review the properties of graphene that are relevant to electron devices, discuss the trade-offs among these properties and examine their effects on the performance of graphene transistors in both logic and radiofrequency applications. I conclude that the excellent mobility of graphene may not, as is often assumed, be its most compelling feature from a device perspective. Rather, it may be the possibility of making devices with channels that are extremely thin that will allow graphene field-effect transistors to be scaled to shorter channel lengths and higher speeds without encountering the adverse short-channel effects that restrict the performance of existing devices. Outstanding challenges for graphene transistors include opening a sizeable and well-defined bandgap in graphene, making large-area graphene transistors that operate in the current-saturation regime and fabricating graphene nanoribbons with well-defined widths and clean edges.


title: Smart energy metering and power theft control using arduino & GSM

abstract: Energy theft is a very common problem in countries like India where consumers of energy are increasing consistently as the population increases. Utilities in electricity system are destroying the amounts of revenue each year due to energy theft. The newly designed AMR used for energy measurements reveal the concept and working of new automated power metering system but this increased the Electricity theft forms administrative losses because of not regular interval checkout at the consumer's residence. It is quite impossible to check and solve out theft by going every customer's door to door. In this paper, a new procedure is followed based on MICROCONTROLLER Atmega328P to detect and control the energy meter from power theft and solve it by remotely disconnect and reconnecting the service (line) of a particular consumer. An SMS will be sent automatically to the utility central server through GSM module whenever unauthorized activities detected and a separate message will send back to the microcontroller in order to disconnect the unauthorized supply. A unique method is implemented by interspersed the GSM feature into smart meters with Solid state relay to deal with the non-technical losses, billing difficulties, and voltage fluctuation complication.


title: Algorithms for Non-negative Matrix Factorization

abstract: Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence. The monotonic convergence of both algorithms can be proven using an auxiliary function analogous to that used for proving convergence of the Expectation-Maximization algorithm. The algorithms can also be interpreted as diagonally rescaled gradient descent, where the rescaling factor is optimally chosen to ensure convergence.


title: Dynamic Filter Networks

abstract: In a traditional convolutional layer, the learned filters stay fixed after training. In contrast, we introduce a new framework, the Dynamic Filter Network, where filters are generated dynamically conditioned on an input. We show that this architecture is a powerful one, with increased flexibility thanks to its adaptive nature, yet without an excessive increase in the number of model parameters. A wide variety of filtering operations can be learned this way, including local spatial transformations, but also others like selective (de)blurring or adaptive feature extraction. Moreover, multiple such layers can be combined, e.g. in a recurrent architecture. We demonstrate the effectiveness of the dynamic filter network on the tasks of video and stereo prediction, and reach state-of-the-art performance on the moving MNIST dataset with a much smaller model. By visualizing the learned filters, we illustrate that the network has picked up flow information by only looking at unlabelled training data. This suggests that the network can be used to pretrain networks for various supervised tasks in an unsupervised way, like optical flow and depth estimation.


title: Concerto: A High Concurrency Key-Value Store with Integrity

abstract: Verifying the integrity of outsourced data is a classic, well-studied problem. However current techniques have fundamental performance and concurrency limitations for update-heavy workloads. In this paper, we investigate the potential advantages of deferred and batched verification rather than the per-operation verification used in prior work. We present Concerto, a comprehensive key-value store designed around this idea. Using Concerto, we argue that deferred verification preserves the utility of online verification and improves concurrency resulting in orders-of-magnitude performance improvement. On standard benchmarks, the performance of Concerto is within a factor of two when compared to state-of-the-art key-value stores without integrity.


title: A Temporal Sequence Learning for Action Recognition and Prediction

abstract: In this work1, we present a method to represent a video with a sequence of words, and learn the temporal sequencing of such words as the key information for predicting and recognizing human actions. We leverage core concepts from the Natural Language Processing (NLP) literature used in sentence classification to solve the problems of action prediction and action recognition. Each frame is converted into a word that is represented as a vector using the Bag of Visual Words (BoW) encoding method. The words are then combined into a sentence to represent the video, as a sentence. The sequence of words in different actions are learned with a simple but effective Temporal Convolutional Neural Network (T-CNN) that captures the temporal sequencing of information in a video sentence. We demonstrate that a key characteristic of the proposed method is its low-latency, i.e. its ability to predict an action accurately with a partial sequence (sentence). Experiments on two datasets, UCF101 and HMDB51 show that the method on average reaches 95% of its accuracy within half the video frames. Results, also demonstrate that our method achieves compatible state-of-the-art performance in action recognition (i.e. at the completion of the sentence) in addition to action prediction.


title: PCA as a practical indicator of OPLS-DA model reliability

abstract: Background ::: Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation.


Emotional Implications of Metaphor : Consequences of Metaphor Framing for Mindset about Hardship


title: Ester enolates from .alpha.-acetoxy esters. Synthesis of arylmalonic and .alpha.-arylalkanoic esters from aryl nucleophiles and .alpha.-keto esters

abstract: Eater enolates are generated by reductive α-deacetoxylation of α-acetoxy-α-arylmalonic esters or a-acetoxy-α-arylalkanoic esters with lithium in liquid ammonia or sodium α-(dimethylamino)naphthalenide in hexa- methylphosphoramide-benzene. Since the requisite α-acetoxy esters are available from aryl nucleophiles. the reductions provide effective new synthetic routes to arylmalonic esters and α-arylalkanoic esters. For example, 2-(p-isobutylphenyl)propionic acid (ibuprofen. a commercially important nonsteroidal antiinflammatory agent) is obtained in 73% yield overall from isobutylbenzene. Arenes, aryllithiums, or aryhrtagnesium halides react with α-keto esters, e.g., diethyl oromalonate, ethyl pyruvate, methyl phenylglyouxalate, or alkyl glyoxylates, to afford α-hydroxy esters. These are acetylated with acetic anhydride-triethylamine and p-(dimethylamino)pyridine as a catalyst. Reductive α-deoxygenation then allows replacement of the acetoxy group by hydrogen or an alkyl group.


title: Crowd disasters as systemic failures: analysis of the Love Parade disaster

abstract: Each year, crowd disasters happen in different areas of the world. How and why do such disasters happen? Are the fatalities caused by relentless behavior of people or a psychological state of panic that makes the crowd ‘go mad’? Or are they a tragic consequence of a breakdown of coordination? These and other questions are addressed, based on a qualitative analysis of publicly available videos and materials, which document the planning and organization of the Love Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our analysis reveals a number of misunderstandings that have widely spread. We also provide a new perspective on concepts such as ‘intentional pushing’, ‘mass panic’, ‘stampede’, and ‘crowd crushes’. The focus of our analysis is on the contributing causal factors and their mutual interdependencies, not on legal issues or the judgment of personal or institutional responsibilities. Video recordings show that people stumbled and piled up due to a ‘domino effect’, resulting from a phenomenon called ‘crowd turbulence’ or ‘crowd quake’. Crowd quakes are a typical reason for crowd disasters, to be distinguished from crowd disasters resulting from ‘mass panic’ or ‘crowd crushes’. In Duisburg, crowd turbulence was the consequence of amplifying feedback and cascading effects, which are typical for systemic instabilities. Accordingly, things can go terribly wrong in spite of no bad intentions from anyone. Comparing the incident in Duisburg with others, we give recommendations to help prevent future crowd disasters. In particular, we introduce a new scale to assess the criticality of conditions in the crowd. This may allow preventative measures to be taken earlier on. Furthermore, we discuss the merits and limitations of citizen science for public investigation, considering that today, almost every event is recorded and reflected in the World Wide Web.


title: Multi-Domain Transfer Component Analysis for Domain Generalization

abstract: This paper presents the domain generalization methods Multi-Domain Transfer Component Analysis (Multi-TCA) and Multi-Domain Semi-Supervised Transfer Component Analysis (Multi-SSTCA) which are extensions of the domain adaptation method Transfer Component Analysis to multiple domains. Multi-TCA learns a shared subspace by minimizing the dissimilarities across domains, while maximally preserving the data variance. The proposed methods are compared to other state-of-the-art methods on three public datasets and on a real-world case study on climate control in residential buildings. Experimental results demonstrate that Multi-TCA and Multi-SSTCA can improve predictive performance on previously unseen domains. We perform sensitivity analysis on model parameters and evaluate different kernel distances, which facilitate further improvements in predictive performance.


title: A multi-task learning formulation for predicting disease progression

abstract: Alzheimer's Disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Identifying markers that can track the progress of the disease has recently received increasing attentions in AD research. A definitive diagnosis of AD requires autopsy confirmation, thus many clinical/cognitive measures including Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog) have been designed to evaluate the cognitive status of the patients and used as important criteria for clinical diagnosis of probable AD. In this paper, we propose a multi-task learning formulation for predicting the disease progression measured by the cognitive scores and selecting markers predictive of the progression. Specifically, we formulate the prediction problem as a multi-task regression problem by considering the prediction at each time point as a task. We capture the intrinsic relatedness among different tasks by a temporal group Lasso regularizer. The regularizer consists of two components including an L2,1-norm penalty on the regression weight vectors, which ensures that a small subset of features will be selected for the regression models at all time points, and a temporal smoothness term which ensures a small deviation between two regression models at successive time points. We have performed extensive evaluations using various types of data at the baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for predicting the future MMSE and ADAS-Cog scores. Our experimental studies demonstrate the effectiveness of the proposed algorithm for capturing the progression trend and the cross-sectional group differences of AD severity. Results also show that most markers selected by the proposed algorithm are consistent with findings from existing cross-sectional studies.


title: An improved k-means clustering algorithm

abstract: K-means algorithm used in the initial cluster centers are randomly generated, then clustering results are unstable and susceptible to the noise data-objects. In this paper presents a density-based algorithm to determine the initial cluster centers, eliminate the clustering results depend on the initial cluster centers. While, optimized the methods of cluster centers re-calculation and the distance from data-object to the cluster center, reduce noise impact on the clustering results, which meets the clustering of asymmetry density cluster. Experiments on UCI datasets show that the improved algorithm can eliminate the clustering results depend on the initial cluster centers, obtain more compact cluster. Therefore, the improved K-means clustering algorithm is effective.


title: Supernovae detection by using convolutional neural networks

abstract: During the last couple of years Astronomy, as many other fields, has been facing the problem of automatic processing of massive data. This has been primarily driven by large survey instruments, which scan large areas of the sky aiming to catalog everything they find. The High Cadence Transient Survey (HiTS) is one of these surveys that searches for phenomena of limited duration (such as supernovae) called transients. The HiTS pipeline produces transient candidates by subtracting science images from a template. In this paper we present a convolutional neural network (ConvNet) approach to classify sources found in the difference images as real transients or artifacts. We compare our results against a random forest classification model that has been created by the HiTS team during the last two years through feature engineering. Our ConvNet model obtains features automatically and obtains a 99.32% accuracy as compared to the previous 98.89% accuracy from the random forest model. This approach will be very useful when processing data from next generation instruments such as the Large Synoptic Survey Telescope (LSST).


title: On enhancing accessible smart buildings using IoT

abstract: Moving is one of the most important issues as regards independence while conducting daily activities, in both indoor and outdoor environments. This is considered particularly true for people with disabilities and it should be guaranteed in order to enable and facilitate their integration and their independence. In this context, IoT could be a means to define and obtain smart and more accessible buildings. In this paper, we present UniSAS, a system which has been designed to improve the accessibility of buildings thanks to an architecture based on Raspberry Pi and users' mobile devices. The paper describes the system architecture and a prototype we have developed. Some users' scenarios are illustrated, so as to discuss the feasibility of our approach.


title: A Slew Controlled LVDS Output Driver Circuit in 0.18 $\mu$m CMOS Technology

abstract: This article presents a power-efficient low-voltage differential signaling (LVDS) output driver circuit. The proposed approach helps to reduce the total input capacitance of the LVDS driver circuit and hence relaxes the tradeoffs in designing a low-power pre-driver stage. A slew control technique has also been introduced to reduce the impedance mismatch effect between the output driver circuit and the line. The pre-driver stage shows a total input capacitance of 50 fF and also controls the voltage swing and common-mode voltage at the input of the LVDS driver output stage. This makes the operation at low supply voltages using a conventional 0.18 mum CMOS technology feasible. The output driver circuit consumes 4.5 mA while driving an external 100 Omega resistor with an output voltage swing of VOD = 400 mV, achieving a normalized power dissipation of 3.42 mW/Gbps. The area of the LVDS driver circuit is 0.067 mm2 and the measured output jitter is sigmarms = 4.5 ps. Measurements show that the proposed LVDS driver can be used at frequencies as high as 2.5 Gbps where the speed will be limited by the load RC time constant.


title: Multiple operating points model-based control of a two-wheeled inverted pendulum mobile robot

abstract: Literature review shows that, most of the linear controllers proposed for control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot are based on dynamic models that are linearized around a single operating point. Thus, this paper presents the controller design for a TWIP mobile robot based on its multiple operating point linearized model. The nonlinear dynamical equations of motion of the TWIP mobile robot were firstly obtained. The nonlinear equations were then linearized around different operating points resulting in a number of linear models with each represented a local region of the original nonlinear system. Based on the multiple operating points linearized models a state feedback control scheme was proposed using pole placement technique for balancing and tracking control of the TWIP mobile robot. The performance of the proposed controller investigated in terms of reference command tracking, parameter variation and external disturbance rejection via simulations. The simulation result shows the effectiveness of the proposed control scheme.


title: Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

abstract: The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.


title: A Survey of Parallel Programming Models and Tools in the Multi and Many-Core Era

abstract: In this work, we present a survey of the different parallel programming models and tools available today with special consideration to their suitability for high-performance computing. Thus, we review the shared and distributed memory approaches, as well as the current heterogeneous parallel programming model. In addition, we analyze how the partitioned global address space (PGAS) and hybrid parallel programming models are used to combine the advantages of shared and distributed memory systems. The work is completed by considering languages with specific parallel support and the distributed programming paradigm. In all cases, we present characteristics, strengths, and weaknesses. The study shows that the availability of multi-core CPUs has given new impulse to the shared memory parallel programming approach. In addition, we find that hybrid parallel programming is the current way of harnessing the capabilities of computer clusters with multi-core nodes. On the other hand, heterogeneous programming is found to be an increasingly popular paradigm, as a consequence of the availability of multi-core CPUs+GPUs systems. The use of open industry standards like OpenMP, MPI, or OpenCL, as opposed to proprietary solutions, seems to be the way to uniformize and extend the use of parallel programming models.


title: Effectiveness of a computerised working memory training in adolescents with mild to borderline intellectual disabilities

abstract: Background: The goal of this study is to evaluate the effectiveness of a computerised working memory (WM) training on memory, response inhibition, fluid intelligence, scholastic abilities and the recall of stories in adolescents with mild to borderline intellectual disabilities attending special education. Method: A total of 95 adolescents with mild to borderline intellectual disabilities were randomly assigned to either a training adaptive to each child's progress in WM, a non-adaptive WM training, or to a control group. Results: Verbal short-term memory (STM) improved significantly from pre- to post-testing in the group who received the adaptive training compared with the control group. The beneficial effect on verbal STM was maintained at follow-up and other effects became clear at that time as well. Both the adaptive and non-adaptive WM training led to higher scores at follow-up than at post-intervention on visual STM, arithmetic and story recall compared with the control condition. In addition, the non-adaptive training group showed a significant increase in visuo-spatial WM capacity. Conclusion: The current study provides the first demonstration that WM can be effectively trained in adolescents with mild to borderline intellectual disabilities.


title: Face Recognition Using the Improved Bag of Words Model

abstract: Bag of words (BoW) model, which was originally used for document processing field, has been introduced to computer vision field recently and used in object recognition successfully. However, in face recognition, the order less collection of local patches in BoW model cannot provide strong distinctive information since the objects (face images) belong to the same category. A new framework for extracting facial features based on BoW model is proposed in this paper, which can maintain holistic spatial information. Experimental results show that the improved method can obtain better face recognition performance on face images of AR database with extreme expressions, variant illuminations, and partial occlusions.


Potassium and periodic paralysis; a metabolic study and physiological considerations.


title: Medical Question Answering for Clinical Decision Support

abstract: The goal of modern Clinical Decision Support (CDS) systems is to provide physicians with information relevant to their management of patient care. When faced with a medical case, a physician asks questions about the diagnosis, the tests, or treatments that should be administered. Recently, the TREC-CDS track has addressed this challenge by evaluating results of retrieving relevant scientific articles where the answers of medical questions in support of CDS can be found. Although retrieving relevant medical articles instead of identifying the answers was believed to be an easier task, state-of-the-art results are not yet sufficiently promising. In this paper, we present a novel framework for answering medical questions in the spirit of TREC-CDS by first discovering the answer and then selecting and ranking scientific articles that contain the answer. Answer discovery is the result of probabilistic inference which operates on a probabilistic knowledge graph, automatically generated by processing the medical language of large collections of electronic medical records (EMRs). The probabilistic inference of answers combines knowledge from medical practice (EMRs) with knowledge from medical research (scientific articles). It also takes into account the medical knowledge automatically discerned from the medical case description. We show that this novel form of medical question answering (Q/A) produces very promising results in (a) identifying accurately the answers and (b) it improves medical article ranking by 40\%.


title: Practical program repair via bytecode mutation

abstract: Automated Program Repair (APR) is one of the most recent advances in automated debugging, and can directly fix buggy programs with minimal human intervention. Although various advanced APR techniques (including search-based or semantic-based ones) have been proposed, they mainly work at the source-code level and it is not clear how bytecode-level APR performs in practice. Also, empirical studies of the existing techniques on bugs beyond what has been reported in the original papers are rather limited. In this paper, we implement the first practical bytecode-level APR technique, PraPR, and present the first extensive study on fixing real-world bugs (e.g., Defects4J bugs) using JVM bytecode mutation. The experimental results show that surprisingly even PraPR with only the basic traditional mutators can produce genuine fixes for 17 bugs; with simple additional commonly used APR mutators, PraPR is able to produce genuine fixes for 43 bugs, significantly outperforming state-of-the-art APR, while being over 10X faster. Furthermore, we performed an extensive study of PraPR and other recent APR tools on a large number of additional real-world bugs, and demonstrated the overfitting problem of recent advanced APR tools for the first time. Lastly, PraPR has also successfully fixed bugs for other JVM languages (e.g., for the popular Kotlin language), indicating PraPR can greatly complement existing source-code-level APR.


title: Issues in packet switching network design

abstract: The goals of this paper are to identify several of the key design choices that must be made in specifying a packetswitching network and to provide some insight in each area. Through our involvement in the design, evolution, and operation of the ARPA Network over the last five years (and our consulting in the design of several other networks), we have learned to appreciate both the opportunities and the hazards of this new technical domain.


title: The Effects of Positive and Negative Mood on Cognition and Motivation in Multimedia Learning Environment

abstract: Introduction In a typical e-learning and computer-based learning environment, learners are confronted with information presented in the format of text, narration, animation, and graphics. Hence, learners engage in the cognitive process known as multimedia learning (Mayer, 2008). The cognitive process of multimedia learning describes an information processing in which learners select relevant visual and verbal materials, organize these visual and verbal mental representations in coherent structures in working memory, and integrate the visual and the verbal mental representations with one another and with prior knowledge (Mayer, 2008). Complementing this process model, the cognitive load theory (CLT) provides a capacity model of multimedia learning (Kalyuga, 2011). The CLT model highlights the inherent limitation of working memory and describes three types of cognitive load: intrinsic, germane, and extraneous. Intrinsic cognitive load is the inherent level of difficulty associated with a specific instructional topic. Germane load defines the amount of mental effort invested by the learner in comprehending the materials, whereas extraneous load describes processing demands of information that are not directly related to the learning task (Kalyuga, 2011). Multimedia learning design has been primarily based on cognitive models such as dual-coding theory and cognitive load theory (Mayer & Moreno, 2003). However, Moreno's (2006) Cognitive-Affective Theory of Learning with Media extended the framework by integrating emotional and motivational factors in multimedia learning design. According to this framework, the process of multimedia learning is mediated by emotional factors that influence learner's willingness to invest mental effort to the learning process (Park et al., 2015). In addition, the framework assumes that multimedia learning process is mediated by learner's metacognition such as cognitive styles and abilities (Moreno & Duran, 2004; Park et al., 2015). Hence, a learner's working memory, motivation (i.e., germane load), and cognitive style is affected by his or her emotional states. Recent studies have attempted to validate the importance of emotional factors in the design of multimedia learning environments. In a seminal work by Um et al. (2012), it was demonstrated that learners who were induced with positive mood outperformed learners who were induced with neutral mood in comprehension and transfer tests. The researchers also showed that positive emotion can be transferred to learners through interface designs, such as anthropomorphism and bright colors. In the attempt to replicate these findings, Plass et al. (2014) tested the effects of color and anthropomorphism on learners' emotion, motivational and cognitive outcomes. It was shown that positive emotional design in multimedia learning environments enhanced positive emotion, motivation, and comprehension. Moreover, positive emotional design in a multimedia interface significantly reduced learners' perception of task difficulty. However, the study failed to replicate the facilitating effect of positive emotional design on transfer scores. Mayer and Estrella (2014) tested the effects of positive emotional design in multimedia learning environments. Mirroring earlier findings (Plass et al., 2014; Um et al., 2012;), results from their experiments showed that positive emotional design via anthropomorphism and appealing colors enhanced retention scores. In an eye tracking experiment, Park et al. (2015) revealed that learners who were induced with positive mood had higher learning outcomes in comprehension and transfer tests and showed longer fixation durations on the relevant information of the multimedia learning environment than learners who were induced with neutral mood. Their findings empirically indicate that emotional state influences cognitive and attentional processing in multimedia learning. Moods in multimedia learning environment There are different constructs when defining "emotion" (Ekkekakis, 2012; Russell, 2003). …