title: Optimal tuning of PID controller using GWO algorithm for speed control in DC motor

abstract: This paper introduces a bio-inspired meta-heuristic soft computing method to optimize the performance of a PID controller used in DC motor speed control. APID controller is designed for an adopted second order DC motor system and the evolutionary algorithm based on Grey Wolf Optimizer is used to optimize the controller. In MATLAB/Simulink environment, the best set of PID parameters thus obtained from the optimization are used with a step input to the DC motor to get the transient response specifications such as rise time, settling time, maximum over shoot and steady state error. These results are then compared with results from other conventional techniques and soft computing methods to look for the most efficient method to get best transient response in a DC Motor.


title: A new database for online handwritten Mongolian word recognition

abstract: A new online handwritten Mongolian word database, MRG-OHMW, is introduced in this paper. This database contains 946 Mongolian words produced by 300 persons from Mongolian ethnic minority. These Mongolian words are composed of one to fourteen Mongolian characters, and selected from large-scale Mongolian text corpus according to the frequencies of usage. The current version of this database is collected using Anoto pen on paper. The database is further annotated using Mongolian word-level string alignment strategy. We partition the samples into training and test sets, and evaluate the database using the CNN-based recognizer as a baseline. Experimental results reveal a big challenge to higher recognition performance. To our knowledge, MRG-OHMW is the first publicly available database for online handwritten Mongolian research. It provides a basic database to compare empirically different algorithms for online handwritten Mongolian word recognition.


title: Proofs of Space-Time and Rational Proofs of Storage.

abstract: We introduce a new cryptographic primitive: Proofs of SpaceTime (PoSTs) and construct a practical protocol for implementing these proofs. A PoST allows a prover to convince a verifier that she spent a “spacetime” resource (storing data—space—over a period of time). Formally, we define the PoST resource as a linear tradeoff between CPU work and space-time (under reasonable cost assumptions, a rational user will prefer to use the lower-cost space-time resource over CPU work). Compared to a proof-of-work, a PoST requires less energy use, as the “difficulty” can be increased by extending the time period over which data is stored without increasing computation costs. Our definition is very similar to “Proofs of Space” [ePrint 2013/796, 2013/805] but, unlike the previous definitions, takes into account amortization attacks and storage duration. Moreover, our protocol uses a very different (and simpler) technique, making use of the fact that we explicitly allow a space-time tradeoff.


title: Multi-task Sparse Structure Learning

abstract: Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. While sometimes the underlying task relationship structure is known, often the structure needs to be estimated from data at hand. In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of task relationships. In particular, we consider a joint estimation problem of the task relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship structure learning component builds on recent advances in structure learning of Gaussian graphical models based on sparse estimators of the precision (inverse covariance) matrix. We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark datasets for regression and classification. We also consider the problem of combining climate model outputs for better projections of future climate, with focus on temperature in South America, and show that the proposed model outperforms several existing methods for the problem.


title: Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera

abstract: Self-reporting (e.g. electronic or paper-and-pencil food diary, 24h dietary recall, FFQ) is the most common method of dietary assessment(1–5). Although this approach is used widely in large cohort studies, its accuracy is limited by its dependence on the willingness of the participant to report and his/her ability to estimate accurately the amount of food consumed(6–8). To improve assessment accuracy, various portion size measurement aids are employed, including pictures (two dimensions) or realistic models (three dimensions) of objects of known sizes (e.g. a life-size picture of a tennis ball or a real tennis ball)(9–12). With the help of portion size measurement aids, an individual’s ability to estimate portion size can be improved significantly, especially after training(13–16). However, the ability of portion size measurement aids to improve accuracy varies with food models, training methods, food type and study population(13–25). For example, Lanerolle and co-workers developed models specifically for Asian foods (e.g. rice, noodles)(21,22). Yuhas et al. compared estimation accuracies among solid foods, liquids and amorphous foods using portion size measurement aids. They concluded that errors were smallest in solid foods and largest in amorphous foods(23). Foster et al. showed the importance of using age-appropriate food photographs for studies in children(24,25). Regardless of these findings, the accuracy of dietary assessment methods still highly depends on the individual’s ability to estimate portion size accurately. ::: ::: Recently a picture-based method for dietary analysis has been reported that uses camera-enabled mobile phones or tablet computers to record pictures of consumed foods and beverages. Pictures are acquired by the participant before and after meal and snack consumption. Food volume is then estimated from the pictures, and converted to energy and nutrient values using a nutritional database(5,26–31). Compared with the method of employing portion size measurement aids, the picture-based method provides more objective estimation of portion size. However, it requires the willingness of the participant to take pictures at each eating event. Hence, the food intake record may be incomplete if the participant forgets or ignores picture taking, especially when a meal involves multiple courses of foods and when picture taking disrupts his/her normal social interaction during eating. To resolve this issue, we developed a wearable device (‘eButton’) that automatically takes pictures at a pre-set rate without interrupting the participant’s eating behaviour. eButton is convenient to use, since the wearer only needs to turn it on and off. However, an important question is whether eButton pictures (which are two-dimensional) can provide accurate food volume (i.e. three-dimensional (3D)) estimates. In the present study we therefore compared food volumes estimated from eButton pictures with actual volumes measured using a seed displacement method(32,33). A few picture-based studies have attempted to analyse volume measurement error, but the food samples used in these studies were limited to those with standard volumes or volumes that could easily be measured by water displacement (e.g. solid fruits)(31,34). In this experiment, we studied real foods prepared or purchased by study participants and consumed at lunch break in the lab (see Fig. 1). The volume of each food item was first measured using the seed displacement method (see ‘Experimental methods’ section and online supplementary material) and then calculated using a software program from eButton images acquired during lunch. Different from water displacement, seed displacement involves no liquids and thus permits volume measurements of a wide variety of foods. For example, an airtight waterproof enclosure is required for measuring hamburgers with water displacement; yet controlling the amount of sealed air appropriately is more difficult. To validate further the accuracy of our software for volume estimation, we recruited three human raters to estimate the volume of each food sample by observing the same eButton-acquired pictures. ::: ::: ::: ::: Fig. 1 ::: ::: (colour online) (a) eButton Prototype; (b) a person wearing an eButton during eating


title: BiUCB: A Contextual Bandit Algorithm for Cold-Start and Diversified Recommendation

abstract: In web-based scenarios, new users and new items frequently join the recommendation system over time without prior events. In addition, users always hold dynamic and diversified preferences. Therefore, cold-start and diversity are two serious challenges of the recommendation system. Recent works show that these problems can be effectively solved by contextual multi-armed bandit (CMAB) algorithms which consider the coldstart and diversified recommendation process as a bandit game. But existing methods only treat either items or users as arms, causing a lower accuracy on the other side. In this paper, we propose a novel bandit algorithm called binary upper confidence bound (BiUCB), which employs a binary UCB to consider both users and items to be arms of each other. BiUCB can deal with the item-user-cold-start problem where there is no information about users and items. Furthermore, BiUCB and k-e-greedy can be combined as a switching algorithm which lead to significant improvement of the temporal diversity of entire recommendation. Extensive experiments on real world datasets demonstrate the precision of BiUCB and the diversity of switching algorithm.


title: Optical quantum random number generator

abstract: A physical random number generator based on the intrinsic randomness of quantum mechanics is described. The random events are realized by the choice of single photons between the two outputs of a beamsplitter. We present a simple device, which minimizes the impact of the photon counters' noise, dead-time and after pulses.


title: An abstraction-refinement approach to verification of artificial neural networks

abstract: A key problem in the adoption of artificial neural networks in safety-related applications is that misbehaviors can be hardly ruled out with traditional analytical or probabilistic techniques In this paper we focus on specific networks known as Multi-Layer Perceptrons (MLPs), and we propose a solution to verify their safety using abstractions to Boolean combinations of linear arithmetic constraints We show that our abstractions are consistent, i.e., whenever the abstract MLP is declared to be safe, the same holds for the concrete one Spurious counterexamples, on the other hand, trigger refinements and can be leveraged to automate the correction of misbehaviors We describe an implementation of our approach based on the HySAT solver, detailing the abstraction-refinement process and the automated correction strategy Finally, we present experimental results confirming the feasibility of our approach on a realistic case study.


title: Tester for photovoltaic charger using NI cRIO

abstract: This paper presents a developed system for testing the photovoltaic chargers for Lithium-ion battery. The system is based on the NI cRIO platform using LabVIEW graphical programming language. The tested chargers are based on dedicated chips that are using maximum power point tracking algorithms for extracting the maximum energy from solar panels.


title: PACT: Parameterized Clipping Activation for Quantized Neural Networks

abstract: Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost. To address this cost, a number of quantization schemeshave been proposed - but most of these techniques focused on quantizing weights, which are relatively smaller in size compared to activations. This paper proposes a novel quantization scheme for activations during training - that enables neural networks to work well with ultra low precision weights and activations without any significant accuracy degradation. This technique, PArameterized Clipping acTi-vation (PACT), uses an activation clipping parameter α that is optimized duringtraining to find the right quantization scale. PACT allows quantizing activations toarbitrary bit precisions, while achieving much better accuracy relative to publishedstate-of-the-art quantization schemes. We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets. We also show that exploiting these reduced-precision computational units in hardware can enable a super-linear improvement in inferencing performance dueto a significant reduction in the area of accelerator compute engines coupled with the ability to retain the quantized model and activation data in on-chip memories.


title: A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings

abstract: Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios. This work proposes an alternative approach based on a fully unsupervised initialization that explicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively improves this solution. Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems. Our implementation is released as an open source project at this https URL


title: Electrical Stimulation of Broca's Area Enhances Implicit Learning of an Artificial Grammar

abstract: Artificial grammar learning constitutes a well-established model for the acquisition of grammatical knowledge in a natural setting. Previous neuroimaging studies demonstrated that Broca's area (left BA 44/45) is similarly activated by natural syntactic processing and artificial grammar learning. The current study was conducted to investigate the causal relationship between Broca's area and learning of an artificial grammar by means of transcranial direct current stimulation (tDCS). Thirty-eight healthy subjects participated in a between-subject design, with either anodal tDCS (20 min, 1 mA) or sham stimulation, over Broca's area during the acquisition of an artificial grammar. Performance during the acquisition phase, presented as a working memory task, was comparable between groups. In the subsequent classification task, detecting syntactic violations, and specifically, those where no cues to superficial similarity were available, improved significantly after anodal tDCS, resulting in an overall better performance. A control experiment where 10 subjects received anodal tDCS over an area unrelated to artificial grammar learning further supported the specificity of these effects to Broca's area. We conclude that Broca's area is specifically involved in rule-based knowledge, and here, in an improved ability to detect syntactic violations. The results cannot be explained by better tDCS-induced working memory performance during the acquisition phase. This is the first study that demonstrates that tDCS may facilitate acquisition of grammatical knowledge, a finding of potential interest for rehabilitation of aphasia.


title: Mining DNS for malicious domain registrations

abstract: Millions of new domains are registered every day and the many of them are malicious. It is challenging to keep track of malicious domains by only Web content analysis due to the large number of domains. One interesting pattern in legitimate domain names is that many of them consist of English words or look like meaningful English while many malicious domain names are randomly generated and do not include meaningful words. We show that it is possible to transform this intuitive observation into statistically informative features using second order Markov models. Four transition matrices are built from known legitimate domain names, known malicious domain names, English words in a dictionary, and based on a uniform distribution. The probabilities from these Markov models, as well as other features extracted from DNS data, are used to build a Random Forest classifier. The experimental results demonstrate that our system can quickly catch malicious domains with a low false positive rate.


title: Design and implementation of smart HealthCare system using IoT

abstract: Diagnosis, and monitoring of health is a very important task in healthcare industry. Due to time constraint, people are not visiting hospitals, which might and possibly lead to a lot of health issues in one instant of time. Predominantly most of the healthcare systems have been developed to predict and diagnose the health of the patients by which people who are busy in their schedule can also monitor their health at regular intervals. Many studies show that early prediction is the best way to cure health because early diagnosis will help and alert the patients to know the health status. Healthcare being a global issue more particularly India being a most populated nation where majority of which live in villages deprived of healthcare facilities on real time basis continuously and regularly. With the increasing use of technology, there is an urgent need to have such a smart health monitoring system that can communicate between network devices and application which will help the patients and doctors to monitor, track and record the patient's sensitive data containing medical information. This paper depicts the idea of solving health issues using the latest technology, Internet of Things (IoT). It presents the architectural review of smart healthcare system using Internet of Things(IoT) which is aimed to provide a Better HealthCare to everyone. Using this system architecture, patient's body parameters can be measured in real time. Sensors collect patients body parameters and transfers that data to Microcontroller ATMEL 89s52 which further transfer that data to the MySQL database server. This MySQL database server manages the data and provides accessibility. The patient can view this data with the help of Android App. Which one can install in Smartphone, or Tablet. If data is abnormal then patient gets notification also care takers will get emergency message. With the help of different decision making algorithms decisions can be made easily and fast and according to it people can have access to the database. The patient can check their medical record Hence, the system provides a Better HealthCare to everyone and error free and smooth communication to patients.


title: An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange

abstract: Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm is capable of accurately predicting stock market return. We attempt to model and predict the return on stock price index of the Istanbul Stock Exchange (ISE) with ANFIS. We use six macroeconomic variables and three indices as input variables. The experimental results reveal that the model successfully forecasts the monthly return of ISE National 100 Index with an accuracy rate of 98.3%. ANFIS provides a promising alternative for stock market prediction. ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock price index return.


title: An Analytical Method to Evaluate and Design Hybrid Switched-Capacitor and Multilevel Converters

abstract: This paper investigates the use of multilevel conversion in dc–dc applications that require a large voltage conversion ratio. A quantitative method that can serve as a guide to compare and design multilevel topologies for large conversion ratio applications is presented. The proposed method keeps the conduction loss and switching loss constant across the different converters and employs the passive component volume as the single performance metric. As examples, flying capacitor multilevel converters and hybrid switched-capacitor (SC) converters are compared to conventional two-level buck converters, and are shown analytically to have significantly reduced passive component size. Three converter prototypes are implemented, based on the presented methodology to experimentally validate the method as well as demonstrate the advantages of multilevel and hybrid SC converters.


title: A Novel Quad-Band Diversity Antenna for LTE and Wi-Fi Applications With High Isolation

abstract: A novel quad-band diversity antenna is presented with two radiating elements suitable for long-term evolution (LTE) and wireless fidelity (Wi-Fi) applications in handheld devices. The radiating elements are two planar inverted F-shaped antennas (PIFA) constructed by a meandered line and folded patch with interdigitated capacitive strips connected to the folded patch. To achieve high isolation between the two radiating elements, a defected ground plane (a combination of rectangular slot ring and inverted T-shaped slot stub) is employed. The antenna is designed to operate in the 746-787 MHz, 1850-1990 MHz, 1920-2170 MHz, and 3600-3700 MHz frequency bands. The characterization of the antenna in free-space as well as in the proximity of the user head and hand is presented. The results confirm the excellent performance of the proposed diversity antenna.


title: Nonlinear analysis and control of a reaction wheel-based 3D inverted pendulum

abstract: This paper presents the nonlinear analysis and control design of the Cubli, a reaction wheel-based 3D inverted pendulum. Using the concept of generalized momenta, the key properties of a reaction wheel-based 3D inverted pendulum are compared to the properties of a 1D case in order to come up with a relatively simple and intuitive nonlinear controller. Finally, the proposed controller is implemented on the Cubli, and the experimental results are presented.


title: LCL filter design and performance analysis for small wind turbine systems

abstract: The use of power converter is becoming very popular nowadays to maximize the power extraction from wind as well as interfacing the wind generator with the power grid. LCL filter plays an important role for interconnecting the inverter to the utility grid. Although several researchers designed LCL filtering based inverters, it has been still lacking in the literature a consisted and systematic design methodology that considers statespace mathematical models for delta and wye connected capacitors, considering grounding options and related harmonics mitigation. This paper discusses a comprehensive design of the output LCL filter for the grid-interconnected inverters used in small scale wind energy conversion system, with a practical industrial point of view. The proposed design methodology can also be used in medium and large scale grid connected wind and photovoltaic systems. This paper has practical applicability by describing all the procedural methodology and analysis in how to design LCL filters supported by an in-depth simulation study.


title: Life-stage Prediction for Product Recommendation in E-commerce

abstract: Although marketing researchers and sociologists have recognized the large impact of life stage on consumer's purchasing behaviors, existing recommender systems have not taken this impact into consideration. In this paper, we found obvious correlation between life stage and purchasing behavior in many E-commerce categories. For example, a mum may look for different suitable products when her baby is at different ages. Motivated by this, we introduce the conception of life stage into recommender systems and propose to predict a user's current life-stage and recommend products correspondingly. We propose a new Maximum Entropy Semi Markov Model to segment and label consumer life stage based on the observed purchasing data over time. In the mom-baby product category where the life stage transition is deterministic, we develop an efficient approximate solution using large scale logistic regression and a Viterbi-like algorithm. We also propose a Gaussian mixture model to efficiently handle multi-kids life stage prediction problem. We integrate the life stage information predicted into the recommender system behind the largest online shopping website taobao.com. Both offline and online experiments demonstrate the effectiveness of the proposed life-stage based recommendation approach.


title: Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs

abstract: Collaborative filtering is an important topic in data mining and has been widely used in recommendation system. In this paper, we proposed a unified model for collaborative filtering based on graph regularized weighted nonnegative matrix factorization. In our model, two graphs are constructed on users and items, which exploit the internal information (e.g. neighborhood information in the user-item rating matrix) and external information (e.g. content information such as user’s occupation and item’s genre, or other kind of knowledge such as social trust network). The proposed method not only inherits the advantages of model-based method, but also owns the merits of memory-based method which considers the neighborhood information. Moreover, it has the ability to make use of content information and any additional information regarding user-user such as social trust network. Due to the use of these internal and external information, the proposed method is able to find more interpretable lowdimensional representations for users and items, which is helpful for improving the recommendation accuracy. Experimental results on benchmark collaborative filtering data sets demonstrate that the proposed methods outperform the state of the art collaborative filtering methods a lot.


title: ELM variants comparison on applications of time series data forecasting

abstract: Extreme learning machine (ELM) which belongs to randomized algorithm categories, is versatile and an emerging learning algorithm. ELM has been developed for different application starting from pattern recognition, function estimation, regression analysis, time series analysis, and big data analysis etc. Unlike feed forward neural networks where slow convergence rate, imprecise learning parameters, presence of local minima are major bottles neck, This paper addresses these problems using different variants of ELM on some bench mark time series data. ELM and its variants where hidden nodes parameters like weights and biases are randomly generated and fixed during the time of learning process, also give results of weights as an output of single hidden layer feed forward neural networks (SLFNs) analytically. The paper performs experiments on two time series data and demonstrates that variants of ELM delivers good performance in generalized manner in several cases without compromising on accuracy.


title: Towards a design space explorer for media facades

abstract: Collaborative design projects are often complex affairs in which a number of resources, concerns, and sources of inspiration are brought into play in the shaping of future design concepts. This paper presents the Design Space Explorer, a framework for managing these multiple sources of information and domain concerns in collaborative design projects. The Design Space Explorer captures and gives an overview of design materials and forms, domain locations and situations, interaction styles, and content types. Furthermore, it provides a platform for designers to combine these aspects into scenarios for design concepts. We present and discuss the use of the Design Space Explorer in two specific design cases in the domain of interactive media facades, part of the emerging field of digital urban living.


title: Evolving multimodal controllers with HyperNEAT

abstract: Natural brains effectively integrate multiple sensory modalities and act upon the world through multiple effector types. As researchers strive to evolve more sophisticated neural controllers, confronting the challenge of multimodality is becoming increasingly important. As a solution, this paper presents a principled new approach to exploiting indirect encoding to incorporate multimodality based on the HyperNEAT generative neuroevolution algorithm called the multi-spatial substrate (MSS). The main idea is to place each input and output modality on its own independent plane. That way, the spatial separation of such groupings provides HyperNEAT an a priori hint on which neurons are associated with which that can be exploited from the start of evolution. To validate this approach, the MSS is compared with more conventional approaches to HyperNEAT substrate design in a multiagent domain featuring three input and two output modalities. The new approach both significantly outperforms conventional approaches and reduces the creative burden on the user to design the layout of the substrate, thereby opening formerly prohibitive multimodal problems to neuroevolution.


title: A math-aware search engine for math question answering system

abstract: We propose a math-aware search engine that is capable of handling both textual keywords as well as mathematical expressions. Our math feature extraction and representation framework captures the semantics of math expressions via a Finite State Machine model. We adapt the passive aggressive online learning binary classifier as the ranking model. We benchmarked our approach against three classical information retrieval (IR) strategies on math documents crawled from Math Overflow, a well-known online math question answering system. Experimental results show that our proposed approach can perform better than other methods by more than 9%.


title: Privacy-Preserving ECG Classification With Branching Programs and Neural Networks

abstract: Privacy protection is a crucial problem in many biomedical signal processing applications. For this reason, particular attention has been given to the use of secure multiparty computation techniques for processing biomedical signals, whereby nontrusted parties are able to manipulate the signals although they are encrypted. This paper focuses on the development of a privacy preserving automatic diagnosis system whereby a remote server classifies a biomedical signal provided by the client without getting any information about the signal itself and the final result of the classification. Specifically, we present and compare two methods for the secure classification of electrocardiogram (ECG) signals: the former based on linear branching programs (a particular kind of decision tree) and the latter relying on neural networks. The paper deals with all the requirements and difficulties related to working with data that must stay encrypted during all the computation steps, including the necessity of working with fixed point arithmetic with no truncation while guaranteeing the same performance of a floating point implementation in the plain domain. A highly efficient version of the underlying cryptographic primitives is used, ensuring a good efficiency of the two proposed methods, from both a communication and computational complexity perspectives. The proposed systems prove that carrying out complex tasks like ECG classification in the encrypted domain efficiently is indeed possible in the semihonest model, paving the way to interesting future applications wherein privacy of signal owners is protected by applying high security standards.


title: Low-Rank Matrix Factorization under General Mixture Noise Distributions

abstract: Many computer vision problems can be posed as learning a low-dimensional subspace from high dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problem using L_1 norm and L_2 norm, which mainly deal with Laplacian and Gaussian noise, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as Mixture of Exponential Power (MoEP) distributions and proposes a penalized MoEP model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture is adapted from a series of preliminary super-or sub-Gaussian candidates. An Expectation Maximization (EM) algorithm is also designed to infer the parameters involved in the proposed PMoEP model. The advantage of our method is demonstrated by extensive experiments on synthetic data, face modeling and hyperspectral image restoration.


title: Identifying Appropriate Support for Propositions in Online User Comments

abstract: The ability to analyze the adequacy of supporting information is necessary for determining the strength of an argument.1 This is especially the case for online user comments, which often consist of arguments lacking proper substantiation and reasoning. Thus, we develop a framework for automatically classifying each proposition as UNVERIFIABLE, VERIFIABLE NONEXPERIENTIAL, or VERIFIABLE EXPERIENTIAL2, where the appropriate type of support is reason, evidence, and optional evidence, respectively3. Once the existing support for propositions are identified, this classification can provide an estimate of how adequately the arguments have been supported. We build a goldstandard dataset of 9,476 sentences and clauses from 1,047 comments submitted to an eRulemaking platform and find that Support Vector Machine (SVM) classifiers trained with n-grams and additional features capturing the verifiability and experientiality exhibit statistically significant improvement over the unigram baseline, achieving a macro-averaged F1 of 68.99%.


title: Multilevel inverter based class D audio amplifier for capacitive transducers

abstract: The reduced semiconductor voltage stress makes the multilevel inverters especially interesting, when driving capacitive transducers for audio applications. A ± 300 V flying capacitor class D audio amplifier driving a 100 nF load in the midrange region of 0.1-3.5 kHz with Total Harmonic Distortion plus Noise (THD+N) below 1 % is presented.


title: An Idiom-independent Representation of Chords for Computational Music Analysis and Generation.

abstract: In this paper we focus on issues of harmonic representation and computational analysis. An attempt is made to propose a new idiom-independent representation of chord types that is appropriate for encoding tone simultaneities in any harmonic context (such as tonal, modal, jazz, octatonic, atonal). The General Chord Type (GCT) representation, allows the re-arrangement of the notes of a harmonic simultaneity such that abstract idiom-specific types of chords may be derived; this encoding is inspired by the standard roman numeral chord type labeling, but is more general and flexible. Given a consonancedissonance classification of intervals (that reflects culturally-dependent notions of consonance/dissonance), and a scale, the GCT algorithm finds the maximal subset of notes of a given note simultaneity that contains only consonant intervals; this maximal subset forms the base upon which the chord type is built. The proposed representation is ideal for hierarchic harmonic systems such as the tonal system and its many variations, but adjusts to any other harmonic system such as post-tonal, atonal music, or traditional polyphonic systems. In this paper, the GCT representation is applied to a small set of examples from diverse musical idioms, and its output is illustrated and analysed showing its potential, especially, for computational music analysis & music information retrieval.


title: Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms

abstract: PURPOSE: We develop a practical, iterative algorithm for image-reconstruction in under-sampled tomographic systems, such as digital breast tomosynthesis (DBT). METHOD: The algorithm controls image regularity by minimizing the image total $p$-variation (TpV), a function that reduces to the total variation when $p=1.0$ or the image roughness when $p=2.0$. Constraints on the image, such as image positivity and estimated projection-data tolerance, are enforced by projection onto convex sets (POCS). The fact that the tomographic system is under-sampled translates to the mathematical property that many widely varied resultant volumes may correspond to a given data tolerance. Thus the application of image regularity serves two purposes: (1) reduction of the number of resultant volumes out of those allowed by fixing the data tolerance, finding the minimum image TpV for fixed data tolerance, and (2) traditional regularization, sacrificing data fidelity for higher image regularity. The present algorithm allows for this dual role of image regularity in under-sampled tomography. RESULTS: The proposed image-reconstruction algorithm is applied to three clinical DBT data sets. The DBT cases include one with microcalcifications and two with masses. CONCLUSION: Results indicate that there may be a substantial advantage in using the present image-reconstruction algorithm for microcalcification imaging.


title: Inverse Perspective Mapping based road curvature estimation

abstract: This paper proposes a solution for road curvature estimation. This information is relevant for the task of autonomous navigation. The proposed method is based on the Inverse Perspective Mapping (IPM) which gets rid of perspective distortion. From the IPM with multi scale lane markers appear as the parallel lines. Then, the lane markers are detected by using color segmentation, Hough transform and RANSAC. The detected lines are used for calculating the road curvature which is used to estimate a vehicle trajectory for autonomous vehicle or driver assistance system. In the current experiments, the correct detection rate reached 88.45%.


title: A Data Mining view on Class Room Teaching Language

abstract: From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.


title: A polynomial-time approximation scheme for maximizing the minimum machine completion time

abstract: We consider the problem of assigning a set of n jobs to a system of m identical parallel machines so as to maximize the earliest machine completion time (without using idle times). This problem has applications in the sequencing of maintenance actions for modular gas turbine aircraft engines. Up to now, only approximation algorithms were known whose worst case ratio was bounded strictly away from one. In this note, we derive the first polynomial-time approximation scheme for this problem. The time complexity of our algorithms is O(c"@?nlogm), where c"@? is a constant that depends on the desired precision @?.


title: Learning Graphical State Transitions

abstract: Graph-structured data is important in modeling relationships between multiple entities, and can be used to represent states of the world as well as many data structures. Li et al. (2016) describe a model known as a Gated Graph Sequence Neural Network (GGS-NN) that produces sequences from graph-structured input. In this work I introduce the Gated Graph Transformer Neural Network (GGT-NN), an extension of GGS-NNs that uses graph-structured data as an intermediate representation. The model can learn to construct and modify graphs in sophisticated ways based on textual input, and also to use the graphs to produce a variety of outputs. For example, the model successfully learns to solve almost all of the bAbI tasks (Weston et al., 2016), and also discovers the rules governing graphical formulations of a simple cellular automaton and a family of Turing machines.


title: What Makes Online Content Viral

abstract: Why are certain pieces of online content (e.g., advertisements, videos, news articles) more viral than others? This article takes a psychological approach to understanding diffusion. Using a unique...


title: Internet of Things: challenges and research opportunities

abstract: Faster development of sensor and network technologies is facilitating immense deployment of Internet of Things (IoT) towards creating a smart world. In IoT, a massive number of heterogeneous resource–constraint devices communicate with each other without any human intervention and generate a huge amount of data. Unique research challenges posed by IoT are fascinating the research community. This paper presents some of the critical issues along with state of the art solutions towards them. In-depth discussion is provided on various key issues like heterogeneity and interoperability, scalability, QoS, and security. Directions for further researches in those areas are also pointed out.


title: Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

abstract: We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.


title: Synthetic fingerprint-database generation

abstract: This work complements our previous efforts in generating realistic fingerprint images for test purposes. The main variability which characterizes the acquisition of a fingerprint through an on-line sensor is modeled and a sequence of steps is defined to derive a series of impressions from the same master-fingerprint. This allows large fingerprint databases to be randomly generated according to some given parameters. The experimental results validate our technique and prove that it can be very useful for performance evaluation, learning and testing in fingerprint-based systems.


title: Smart Home automation system using IR, bluetooth, GSM and android

abstract: In this busy life while away from home we may tend to forget to switch off the AC appliances which cause wastage of energy. Misuse of power energy can be curtailed by automating the devices and appliances. In Home automation household appliances and residential house features like gates, curtains etc are controlled automatically or semi-automatically. Some of the currently available systems provide a view of the house from a web application; but this can cause trouble to the user. Because user must access the web each time he/she wishes to view the status of the home appliances. In this paper a home automation system that uses IR remote, Bluetooth and GSM to control AC appliances using android app is introduced that is easy to use over the traditional method of the switch. Therefore, the motivation behind the development of this system is to let people know about these technologies, and make the system as simple as possible for an ordinary person to understand. The result of this research is the implementation of home automation system which involves control and automation of home appliances through mobile application from remote locations.


Applications of Lucid Dreaming in Sports


title: Probabilistic wavelet transform for partial discharge measurement of transformer

abstract: Partial discharge (PD) measurement provides a means for online monitoring and diagnosis of transformers. However, extensive interferences and noise can significantly jeopardize the measured PD signals and cause ambiguity in PD measurement interpretation. Necessary PD signal de-noising techniques need to be adopted and wavelet transform is one of such techniques. Mother wavelet selection, decomposition level determination and thresholding are important processes for effective PD extraction using wavelet transform. Various methods have been proposed in the literature to improve the above processes of wavelet transform. In these methods a single threshold is normally adopted at each decomposition level and a binary decision is made to indicate whether an extracted signal is PD signal or noise. However, in online PD measurements it is difficult to find a threshold, which can be used for extracting only PD signals without including any noise. As such, the signals determined by a single threshold cannot be assured as PD signals with certainty. To address the limitations caused by the single thresholding method in wavelet transform for PD signals extraction, this paper proposes quantile based multi-scale thresholding method at each decomposition level, which can thus provide probability indexes for the extracted signals evaluating the likelihood of these signals to be PD signals. To evaluate the proposed method, PD measurements have been conducted on both experimental PD models and inservice transformers at substation. The results are provided in the paper.


title: Correspondence and Depth-Image Based Rendering a Hybrid Approach for Free-Viewpoint Video

abstract: We present a novel approach to free-viewpoint video. Our main contribution is the formulation of a hybrid approach between image morphing and depth-image based rendering. When rendering the scene from novel viewpoints, we use both dense pixel correspondences between image pairs as well as an underlying, view-dependent geometrical model. Our novel reconstruction scheme iteratively refines geometric and correspondence information. By combining the strengths of both depth and correspondence estimation, our approach enables free-viewpoint video also for challenging scenes as well as for recordings that may violate typical constraints in multiview reconstruction. For example, our method is robust against inaccurate camera calibration, asynchronous capture, and imprecise depth reconstruction. Rendering results for different scenes and applications demonstrate the versatility and robustness of our approach.


title: A SDN-IoT Architecture with NFV Implementation

abstract: It is anticipated that SDN coupled with NFV and cloud computing, will become a critical enabling technology to radically revolutionize the way network operators will architect and monetize their infrastructure. On the other hand, the Internet of Things (IoT) is transforming the interaction between cyberspace and the physical space with a tremendous impact on everyday life. The effectiveness of these technologies will require new methodological and engineering approaches due to the impressive scale of the problem and the new challenging requests in terms of performance, security and reliability. This paper presents a simple and general SDN-IoT architecture with NFV implementation with specific choices on where and how to adopt SDN and NFV approaches to address the new challenges of the Internet of Things. The architecture will accelerate innovations in the IoT sector, thanks to its flexibility opening new perspectives for fast deployment of software-enabled worldwide services. The paper also look at the business perspective by considering SDN and NFV as enablers of new added value services on top to the existing infrastructure providing more opportunities for revenues leveraging fast deployed services in the value chain.


title: Artificial Swarm Intelligence vs human experts

abstract: Artificial Swarm Intelligence (ASI) strives to facilitate the emergence of a super-human intellect by connecting groups of human users in closed-loop systems modeled after biological swarms. Prior studies have shown that “human swarms” can make more accurate predictions than traditional methods for tapping the wisdom of groups, such as votes and polls. To further test the predictive ability of swarms, 75 random sports fans were assembled in the UNU platform for human swarming and tasked with predicting College Bowl football games against the spread. Expert predictions from ESPN were compared. The results are as follows: (i) Individuals - when working alone, test subjects achieved on average, 5 correct predictions out of 10 games (50% accuracy); (ii) Group Poll - aggregating data across all 75 subjects, the group achieved 6 correct predictions out of 10 games (60% accuracy); (iii) Experts - as published by ESPN, the college football experts averaged 5 correct predictions out of 10 games (50% accuracy); and (iv) Swarm - when the 75 subjects worked together as a real-time swarm, they achieved 7 correct predictions out of 10 games (70% accuracy). Thus by forming a real-time swarm intelligence, the group of random sports fans boosted their collective performance and out-performed experts.


title: From the neuron doctrine to neural networks

abstract: Cajal's description of the neuron as the structural and functional unit of the nervous system formed the basis of much subsequent neuroscientific research. In this Timeline article, Yuste considers the contribution of the 'neuron doctrine' to neuroscience and discusses the recent transition in paradigm from the single neuron to neural networks.


title: Real-time procedural generation of `pseudo infinite' cities

abstract: We present an approach to procedural generation of `pseudo infinite' virtual cities in real-time. The cities contain geometrically varied buildings that are generated as needed. The building generation parameters are created by a pseudo random number generator, seeded with an integer derived from the building's position. The varied building geometries are extruded from a set of floor plans. The floor plans for each building are created by combining randomly generated polygons in an iterative process. A display list caching and frustum filling approach manages the generation of buildings and the use of system resources. This approach has been implemented on commodity PC hardware, resulting in interactive frame rates.


title: Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition

abstract: Rich semantic relations are important in a variety of visual recognition problems. As a concrete example, group activity recognition involves the interactions and relative spatial relations of a set of people in a scene. State of the art recognition methods center on deep learning approaches for training highly effective, complex classifiers for interpreting images. However, bridging the relatively low-level concepts output by these methods to interpret higher-level compositional scenes remains a challenge. Graphical models are a standard tool for this task. In this paper, we propose a method to integrate graphical models and deep neural networks into a joint framework. Instead of using a traditional inference method, we use a sequential inference modeled by a recurrent neural network. Beyond this, the appropriate structure for inference can be learned by imposing gates on edges between nodes. Empirical results on group activity recognition demonstrate the potential of this model to handle highly structured learning tasks.


title: Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T)

abstract: User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords into more cohesive subsets, or suggest keywords similar to the selected ones.


title: An exhaustive review for infix to postfix conversion with applications and benefits

abstract: In Computer Science, Reverse Polish notation has simplified calculations and has benefited a new face to technology. Since 1960, RPN is used in calculators because its implementation is very easy and simple as well as it gives good performance. In this paper, we have introduced a new approach for infix to postfix expressions conversion by following some rules and highlighted some of the applications and advantages of existing methods. The proposed algorithm differs from other existing algorithms through its human readability. We have discussed a comparative analysis of RPN, Shunting Yard algorithm and a new approach to Post fix conversion named as PKR algorithm.


Learning physics from dynamical scenes


title: Design and analysis of a social botnet

abstract: Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of today's web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for 8weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80%, (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of today's underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security.


title: Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

abstract: The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs). Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM) motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG). Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.


title: Defending Connected Vehicles Against Malware: Challenges and a Solution Framework

abstract: Vehicles face growing security threats as they become increasingly connected with the external world. Hackers, researchers, and car hobbyists have compromised security keys used by the electronic control units (ECUs) on vehicles, modified ECU software, and hacked wireless transmissions from vehicle key fobs and tire monitoring sensors, using low-cost commercially available tools. However, the most damaging security threats to vehicles are only emerging. One such threat is malware, which can infect vehicles in a variety of ways and cause severe consequences. Defending vehicles against malware attacks must address many unique challenges that have not been well addressed in other types of networks. This paper identifies those vehicle-specific challenges, discusses existing solutions and their limitations, and presents a cloud-assisted vehicle malware defense framework that can address these challenges.


title: Architecture exploration for ambient energy harvesting nonvolatile processors

abstract: Energy harvesting has been widely investigated as a promising method of providing power for ultra-low-power applications. Such energy sources include solar energy, radio-frequency (RF) radiation, piezoelectricity, thermal gradients, etc. However, the power supplied by these sources is highly unreliable and dependent upon ambient environment factors. Hence, it is necessary to develop specialized systems that are tolerant to this power variation, and also capable of making forward progress on the computation tasks. The simulation platform in this paper is calibrated using measured results from a fabricated nonvolatile processor and used to explore the design space for a nonvolatile processor with different architectures, different input power sources, and policies for maximizing forward progress.


title: Acoustic Modeling Using Deep Belief Networks

abstract: Gaussian mixture models are currently the dominant technique for modeling the emission distribution of hidden Markov models for speech recognition. We show that better phone recognition on the TIMIT dataset can be achieved by replacing Gaussian mixture models by deep neural networks that contain many layers of features and a very large number of parameters. These networks are first pre-trained as a multi-layer generative model of a window of spectral feature vectors without making use of any discriminative information. Once the generative pre-training has designed the features, we perform discriminative fine-tuning using backpropagation to adjust the features slightly to make them better at predicting a probability distribution over the states of monophone hidden Markov models.


title: Tourist Satisfaction Enhancement Using Mobile QR Code Payment: An Empirical Investigation

abstract: Innovative technologies have greatly changed people’s lives, including their travel experiences. This study investigates the antecedents and outcomes of the quick response (QR) code payment technology used in tourism to provide empirical evidence that mobile technologies can be used to enhance tourist satisfaction. An empirical analysis using 247 field survey responses reveals that relative advantage, compatibility, and observability innovation attributes significantly affect tourists’ attitudes positively toward QR code payment services, which results in their use of the technology while traveling. However, image—the subjective norm in innovation diffusion—has no effect on such use. Furthermore, the study confirms that the use of the QR code payment technology in tourism influences an individual’s transaction satisfaction and travel satisfaction, suggesting that this technology can be used to advance the tourism industry. Theoretical and practical implications of the findings and future research directions are also discussed.


title: Actuator fault diagnosis in mechanical systems — Fault power estimation approach

abstract: A systematic method is introduced to deal with actuator faults in a class of nonlinear mechanical control systems. The presence of the fault is detected by monitoring the fault induced modifications in the control system’s energy balance. From the energy deviation the residual generator estimates the power of the fault. For fault isolation and identification the set of fault power pattern signals is defined. Using signal processing methods the elements of this set are compared to the residual signal to determine the characteristics and the magnitude of the fault. The method does not require assumptions on the system’s model and it can handle parameter uncertainties. The applicability of the proposed method is shown through mechatronic examples.


title: Static-noise margin analysis of MOS SRAM cells

abstract: The stability of both resistor-load (R-load) and full-CMOS SRAM cells is investigated analytically as well as by simulation. Explicit analytic expressions for the static-noise margin (SNM) as a function of device parameters and supply voltage are derived. The expressions are useful in predicting the effect of parameter changes on the stability as well as in optimizing the design of SRAM cells. An easy-to-use SNM simulation method is presented, the results of which are in good agreement with the results predicted by the analytic SNM expressions. It is further concluded that full-CMOS cells are much more stable than R-local cells at a low supply voltage.


title: Parametric higher-order shelving filters

abstract: The main characteristic of shelving filters, as commonly used in audio equalization, is to amplify or attenuate a certain frequency band by a given gain. For parametric equalizers, a filter structure is desirable that allows independent adjustment of the width and center frequency of the band, and the gain. In this paper, we present a design for arbitrary-order shelving filters and a suitable parametric digital filter structure. A low-shelving filter design based on Butterworth filters is decomposed such that the gain can be easily adjusted. Transformation to the digital domain is performed, keeping gain and denormalized cut-off frequency independently controllable. Finally, we obtain band- and high-shelving filters using a simple manipulation, providing the desired parametric filter structure.


title: Updated Guidelines for the Medical Assessment and Care of Children Who May Have Been Sexually Abused

abstract: The medical evaluation is an important part of the clinical and legal process when child sexual abuse is suspected. Practitioners who examine children need to be up to date on current recommendations regarding when, how, and by whom these evaluations should be conducted, as well as how the medical findings should be interpreted. A previously published article on guidelines for medical care for sexually abused children has been widely used by physicians, nurses, and nurse practitioners to inform practice guidelines in this field. Since 2007, when the article was published, new research has suggested changes in some of the guidelines and in the table that lists medical and laboratory findings in children evaluated for suspected sexual abuse and suggests how these findings should be interpreted with respect to sexual abuse. A group of specialists in child abuse pediatrics met in person and via online communication from 2011 through 2014 to review published research as well as recommendations from the Centers for Disease Control and Prevention and the American Academy of Pediatrics and to reach consensus on if and how the guidelines and approach to interpretation table should be updated. The revisions are based, when possible, on data from well-designed, unbiased studies published in high-ranking, peer-reviewed, scientific journals that were reviewed and vetted by the authors. When such studies were not available, recommendations were based on expert consensus.


title: Topology-Controlled Volume Rendering

abstract: Topology provides a foundation for the development of mathematically sound tools for processing and exploration of scalar fields. Existing topology-based methods can be used to identify interesting features in volumetric data sets, to find seed sets for accelerated isosurface extraction, or to treat individual connected components as distinct entities for isosurfacing or interval volume rendering. We describe a framework for direct volume rendering based on segmenting a volume into regions of equivalent contour topology and applying separate transfer functions to each region. Each region corresponds to a branch of a hierarchical contour tree decomposition, and a separate transfer function can be defined for it. The novel contributions of our work are: 1) a volume rendering framework and interface where a unique transfer function can be assigned to each subvolume corresponding to a branch of the contour tree, 2) a runtime method for adjusting data values to reflect contour tree simplifications, 3) an efficient way of mapping a spatial location into the contour tree to determine the applicable transfer function, and 4) an algorithm for hardware-accelerated direct volume rendering that visualizes the contour tree-based segmentation at interactive frame rates using graphics processing units (GPUs) that support loops and conditional branches in fragment programs


title: Latent factor transition for dynamic collaborative filtering

abstract: User preferences change over time and capturing such changes is essential for developing accurate recommender systems. Despite its importance, only a few works in collaborative filtering have addressed this issue. In this paper, we consider evolving preferences and we model user dynamics by introducing and learning a transition matrix for each user’s latent vectors between consecutive time windows. Intuitively, the transition matrix for a user summarizes the time-invariant pattern of the evolution for the user. We first extend the conventional probabilistic matrix factorization and then improve upon this solution through its fully Bayesian model. These solutions take advantage of the model complexity and scalability of conventional Bayesian matrix factorization, yet adapt dynamically to user’s evolving preferences. We evaluate the effectiveness of these solutions through empirical studies on six large-scale real life data sets.


title: Two-Stream Flow-Guided Convolutional Attention Networks for Action Recognition

abstract: This paper proposes a two-stream flow-guided convolutional attention networks for action recognition in videos. The central idea is that optical flows, when properly compensated for the camera motion, can be used to guide attention to the human foreground. We thus develop cross-link layers from the temporal network (trained on flows) to the spatial network (trained on RGB frames). These cross-link layers guide the spatial-stream to pay more attention to the human foreground areas and be less affected by background clutter. We obtain promising performances with our approach on the UCF101, HMDB51 and Hollywood2 datasets.


title: Interpretable Basis Decomposition for Visual Explanation

abstract: Explanations of the decisions made by a deep neural network are important for human end-users to be able to understand and diagnose the trustworthiness of the system. Current neural networks used for visual recognition are generally used as black boxes that do not provide any human interpretable justification for a prediction. In this work we propose a new framework called Interpretable Basis Decomposition for providing visual explanations for classification networks. By decomposing the neural activations of the input image into semantically interpretable components pre-trained from a large concept corpus, the proposed framework is able to disentangle the evidence encoded in the activation feature vector, and quantify the contribution of each piece of evidence to the final prediction. We apply our framework for providing explanations to several popular networks for visual recognition, and show it is able to explain the predictions given by the networks in a human-interpretable way. The human interpretability of the visual explanations provided by our framework and other recent explanation methods is evaluated through Amazon Mechanical Turk, showing that our framework generates more faithful and interpretable explanations (The code and data are available at https://github.com/CSAILVision/IBD).


Exploring N-gram Character Presentation in Bidirectional RNN-CRF for Chinese Clinical Named Entity Recognition


title: Onset Headache Predicts Good Outcome in Patients With First-Ever Ischemic Stroke

abstract: BACKGROUND AND PURPOSE ::: The study aimed to assess whether onset headache is an ominous sign in patients with first-ever ischemic stroke. ::: ::: ::: METHODS ::: A large population of ischemic stroke patients was obtained from the Taiwan Stroke Registry. Stroke subtypes were classified by the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria. On the basis of the International Classification of Headache Disorders, second version, onset headache was defined as a new headache that developed at the onset of ischemic stroke. Clinical features and impact on stroke outcomes, including in-hospital stroke in evolution, changes in National Institutes of Health Stroke Scale on discharge, and Barthel index and modified Rankin scale ≤6 months after stroke were compared between those with and without onset headache. ::: ::: ::: RESULTS ::: Among 11 523 patients with first-ever ischemic stroke, 848 had onset headache (7.4%). Patients with specific cause, large-artery atherosclerosis, or cardioembolism were more likely to have onset headache. Patients with onset headache were younger, predominantly female, and more likely to have posterior circulation ischemic lesions. Compared with patients without onset headache, those with onset headache had a lower frequency of stroke in evolution (4.5% versus 6.7%; adjusted relative risk, 0.64; 95% confidence interval, 0.52-0.79), greater improvement in National Institutes of Health Stroke Scale score on discharge (0.08 versus -0.20; P=0.02), higher mean Barthel index scores (86.5±20.0 versus 83.9±23.3; adjusted difference, 1.43; 95% confidence interval, 0.28-2.89), and a lower frequency of modified Rankin scale higher than 2 (27.6% versus 31.5%; adjusted relative risk, 0.85; 95% confidence interval, 0.72-0.95) at 1-month follow-up. There was also a trend for better functional outcome in 3- and 6-month follow-ups. ::: ::: ::: CONCLUSIONS ::: By adopting standard classification criteria, this large-scale study demonstrated that onset headache was associated with modest but significantly better outcomes after ischemic stroke.


title: Two basic double-quantitative rough set models of precision and grade and their investigation using granular computing

abstract: The precision and grade of the approximate space are two fundamental quantitative indexes that measure the relative and absolute quantitative information, respectively. The double quantification of the precision and grade is a relatively new subject, and its effective implementation remains an open problem. This paper approaches the double quantification problem using basic rough set models. The Cartesian product is a natural operator for combining the two indexes given their completeness and complementary natures, and we construct two new models using this strategy. The fundamental items (i.e., the complete system, quantitative semantics and optimal computing) of the model regions are studied using granular computing. First, the model regions (MR granules) and basic model regions (BMR granules) are defined in the traditional fashion using logical double-quantitative semantics; basic semantics (BS) is provided for the double-semantic description, and the semantic extraction of the MR and BMR granules is realized within the BS framework. Computing granules (BMRC granules) are then proposed for the basic model regions to optimize the computation, and a two-dimensional plane and granular hierarchical structure are provided. Two basic algorithms for computing the MR and BMR granules are proposed and analyzed, and the BMRC-granules algorithm generally exhibits superior performance in terms of the temporal and spatial complexity. We also explore the properties of the approximation operators and the notions of attribute approximate dependence and reduction. Finally, we provide an example application from the medical field. The two models provide a basic double quantification of the precision and grade and have concrete double-quantitative semantics; they also represent a quantitatively complete expansion of the Pawlak model.


title: doi: 10.1111/j.1469-185X.2010.00165.x Hippocampal memory consolidation during sleep: a comparison of mammals and birds

abstract: The transition from wakefulness to sleep is marked by pronounced changes in brain activity. The brain rhythms that characterize the two main types of mammalian sleep, slow-wave sleep (SWS) and rapid eye movement (REM) sleep, are thought to be involved in the functions of sleep. In particular, recent theories suggest that the synchronous slow-oscillation of neocortical neuronal membrane potentials, the defining feature of SWS, is involved in processing information acquired during wakefulness. According to the Standard Model of memory consolidation, during wakefulness the hippocampus receives input from neocortical regions involved in the initial encoding of an experience and binds this information into a coherent memory trace that is then transferred to the neocortex during SWS where it is stored and integrated within preexisting memory traces. Evidence suggests that this process selectively involves direct connections from the hippocampus to the prefrontal cortex (PFC), a multimodal, high-order association region implicated in coordinating the storage and recall of remote memories in the neocortex. The slow-oscillation is thought to orchestrate the transfer of information from the hippocampus by temporally coupling hippocampal sharp-wave/ripples (SWRs) and thalamocortical spindles. SWRs are synchronous bursts of hippocampal activity, during which waking neuronal firing patterns are reactivated in the hippocampus and neocortex in a coordinated manner. Thalamocortical spindles are brief 7–14 Hz oscillations that may facilitate the encoding of information reactivated during SWRs. By temporally coupling the readout of information from the hippocampus with conditions conducive to encoding in the neocortex, the slow-oscillation is thought to mediate the transfer of information from the hippocampus to the neocortex. Although several lines of evidence are consistent with this function for mammalian SWS, it is unclear whether SWS serves a similar function in birds, the only taxonomic group other than mammals to exhibit SWS and REM sleep. Based on our review of research on avian sleep, neuroanatomy, and memory, although involved in some forms of memory consolidation, avian sleep does not appear to be involved in transferring hippocampal memories to other brain regions. Despite exhibiting the slow-oscillation, SWRs and spindles have not been found in birds. Moreover, although birds independently evolved a brain region—the caudolateral nidopallium (NCL)—involved in performing high-order cognitive functions similar to those performed by the PFC, direct connections between the NCL and hippocampus have not been found in birds, and evidence for the transfer of information from the hippocampus to the NCL or other extra-hippocampal regions is lacking. Although based on the absence of evidence for various traits, collectively, these findings suggest that unlike mammalian SWS, avian SWS may not be involved in transferring memories from the hippocampus. Furthermore, it suggests that the slow-oscillation, the defining feature of mammalian and avian SWS, may serve a more general function independent of that related to coordinating the transfer of information from the hippocampus to the PFC in mammals. Given that SWS is homeostatically regulated (a process intimately related to the slow-oscillation) in mammals and birds, functional hypotheses linked to this process may apply to both taxonomic groups.


title: ADAMS Re-Trace: a traceability recovery tool

abstract: We present the traceability recovery tool developed in the ADAMS artefact management system. The tool is based on an information retrieval technique, namely latent semantic indexing and aims at supporting the software engineer in the identification of the traceability links between artefacts of different types. We also present a case study involving seven student projects, which represented an ideal workbench for the tool. The results emphasise the benefits provided by the tool in terms of new traceability links discovered, in addition to the links manually traced by the software engineer. Moreover, the tool was also helpful in identifying cases of lack of similarity between artefacts manually traced by the software engineer, thus revealing inconsistencies in the usage of domain terms in these artefacts. This information is valuable to assess the quality of the produced artefacts.


title: Printed square quadirifilar helix antenna (QHA) for XM Satellite Radio receiver

abstract: In this paper, a new printed square quadrifilar helix antenna (QHA) with circular polarization (CP) applied for XM Satellite Radio is proposed. To improve the radiation efficiency of QHA, the optimum matching method considering both the reflection coefficients at each feeding port and the mutual coupling coefficients between each antenna is used. The measured results show that the proposed antenna has a 3-dB beam-width of more than 150° and a front-to-back ratio of more than 15 dB. Also, the proposed antenna shows the peak gain of about 0 dBic and the axial ratio less than 2 dB in a targeted frequency band. The center frequency is designed at 2.335GHz.


title: Tetrabot: Resonance based locomotion for harsh enviroments

abstract: We describe a robotic architecture that combines the benefits of existing enclosed robots with passive dynamics. This combination results in a mobile robot with no moving parts exposed to the environment, making it ideal for tasks where wheeled or legged robots fail. Instead of suppressing resonance, the new robot morphology relies on the dynamics of resonance for locomotion. Actuators mounted on a central sphere excite a natural mode of vibration by pulling on strings through which the sphere is mounted to a tetrahedral frame. A simple open loop controller is sufficient to cause directed motion by hopping and sliding in a prototype. Rolling as a further gait is investigated theoretically. Fully enclosed resonant dynamic robots could lead to a new type of robot locomotion powered from a vibration source only. This is useful in the microscale where traditional actuators are not available, and at the macroscale where the robot's high ruggedness is favorable.


title: Question Answering Using Enhanced Lexical Semantic Models

abstract: In this paper, we study the answer sentence selection problem for question answering. Unlike previous work, which primarily leverages syntactic analysis through dependency tree matching, we focus on improving the performance using models of lexical semantic resources. Experiments show that our systems can be consistently and significantly improved with rich lexical semantic information, regardless of the choice of learning algorithms. When evaluated on a benchmark dataset, the MAP and MRR scores are increased by 8 to 10 points, compared to one of our baseline systems using only surface-form matching. Moreover, our best system also outperforms pervious work that makes use of the dependency tree structure by a wide margin.


title: Effective ways to use Internet of Things in the field of medical and smart health care

abstract: The recent advancements in technology and the availability of the Internet make it possible to connect various devices that can communicate with each other and share data. The Internet of Things (IoT) is a new concept that allows users to connect various sensors and smart devices to collect real-time data from the environment. However, it has been observed that a comprehensive platform is still missing in the e-Health and m-Health architectures to use smartphone sensors to sense and transmit important data related to a patient's health. In this paper, our contribution is twofold. Firstly, we critically evaluate the existing literature, which discusses the effective ways to deploy IoT in the field of medical and smart health care. Secondly, we propose a new semantic model for patients' e-Health. The proposed model named as `k-Healthcare' makes use of 4 layers; the sensor layer, the network layer, the Internet layer and the services layer. All layers cooperate with each other effectively and efficiently to provide a platform for accessing patients' health data using smart phones.


title: AutoDIAL: Automatic Domain Alignment Layers

abstract: Classifiers trained on given databases perform poorly when tested on data acquired in different settings. This is explained in domain adaptation through a shift among distributions of the source and target domains. Attempts to align them have traditionally resulted in works reducing the domain shift by introducing appropriate loss terms, measuring the discrepancies between source and target distributions, in the objective function. Here we take a different route, proposing to align the learned representations by embedding in any given network specific Domain Alignment Layers, designed to match the source and target feature distributions to a reference one. Opposite to previous works which define a priori in which layers adaptation should be performed, our method is able to automatically learn the degree of feature alignment required at different levels of the deep network. Thorough experiments on different public benchmarks, in the unsupervised setting, confirm the power of our approach.


title: Design 13.56MHz 10 kW resonant inverter using GaN HEMT for wireless power transfer systems

abstract: This paper presents the design process of a 13.56 MHz 10 kW and high efficiency inverter for wireless power transfer systems. The new 650V E-mode GaN is used in multiphase resonant topology to obtain high output power. The optimized efficiency design and the driver design are presented in detail. The circuit simulation results show that the inverter achieves 10 kW output power with the efficiency of over 98%. The experiment system is tested at 4 kW output power with the efficiency of 96.5%.


title: Coupled person orientation estimation and appearance modeling using spherical harmonics

abstract: Abstract We present a novel approach for the estimation of a person's overall body orientation, 3D shape and texture, from overlapping cameras. A distinguishing aspect of our approach is the use of spherical harmonics for 3D shape- and texture-representation; it offers a compact, low-dimensional representation, which elegantly copes with rotation estimation. The estimation process alternates between the estimation of texture, orientation and shape. Texture is estimated by sampling image intensities with the predicted 3D shape (i.e. torso and head) and the predicted orientation, from the last time step. Orientation (i.e. rotation around torso major axis) is estimated by minimizing the difference between a learned texture model in a canonical orientation and the current texture estimate. The newly estimated orientation allows to update the 3D shape estimate, taking into account the new 3D shape measurement obtained by volume carving. We investigate various components of our approach in experiments on synthetic and real-world data. We show that our proposed method has lower orientation estimation error than other methods that use fixed 3D shape models, for data involving persons.


title: A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

abstract: Abstract In order to effectively manage the use of existing infrastructures and prevent the emergency caused by the large gathered crowd, the short-term passenger flow forecasting technology becomes more and more significant in the field of intelligent transportation system. However, there are few studies discussing how to predict different kinds of passenger flows in the subway system. In this paper, a novel hybrid model Wavelet-SVM is proposed, and it combines the complementary advantages of Wavelet and SVM models, and meanwhile overcomes their shortcomings respectively. The Wavelet-SVM forecasting approach consists of three important stages. The first stage decomposes the passenger flow data into different high frequency and low frequency series by wavelet. During the prediction stage, the SVM method is applied to learn and predict the corresponding high frequency and low frequency series. In the last stage, the diverse predicted sequences are reconstructed by wavelet. The experimental results show that the approach not only has the best forecasting performance compared with the state-of-the-art techniques but also appears to be the most promising and robust based on the historical passenger flow data in Beijing subway system and several standard evaluation measures.


title: Testing a tool for assessing the risk of bias for nonrandomized studies showed moderate reliability and promising validity.

abstract: OBJECTIVES ::: To develop and validate a new risk-of-bias tool for nonrandomized studies (NRSs). ::: ::: ::: STUDY DESIGN AND SETTING ::: We developed the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS). A validation process with 39 NRSs examined the reliability (interrater agreement), validity (the degree of correlation between the overall assessments of RoBANS and Methodological Index for Nonrandomized Studies [MINORS], obtained by plotting the overall risk of bias relative to effect size and funding source), face validity with eight experts, and completion time for the RoBANS approach. ::: ::: ::: RESULTS ::: RoBANS contains six domains: the selection of participants, confounding variables, the measurement of exposure, the blinding of the outcome assessments, incomplete outcome data, and selective outcome reporting. The interrater agreement of the RoBANS tool except the measurement of exposure and selective outcome reporting domains ranged from fair to substantial. There was a moderate correlation between the overall risks of bias determined using RoBANS and MINORS. The observed differences in effect sizes and funding sources among the assessed studies were not correlated with the overall risk of bias in these studies. The mean time required to complete RoBANS was approximately 10 min. The external experts who were interviewed evaluated RoBANS as a "fair" assessment tool. ::: ::: ::: CONCLUSIONS ::: RoBANS shows moderate reliability, promising feasibility, and validity. The further refinement of this tool and larger validation studies are required.


title: Neural representation of abstract and concrete concepts: A meta-analysis of neuroimaging studies

abstract: A number of studies have investigated differences in neural correlates of abstract and con- crete concepts with disagreement across results. A quantitative, coordinate-based meta-analysis com- bined data from 303 participants across 19 functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies to identify the differences in neural representation of abstract and concrete concepts. Studies that reported peak activations in standard space in contrast of abstract > con- crete or concrete > abstract concepts at a whole brain level in healthy adults were included in this meta- analysis. Multilevel kernel density analysis (MKDA) was performed to identify the proportion of acti- vated contrasts weighted by sample size and analysis type (fixed or random effects). Meta-analysis results indicated consistent and meaningful differences in neural representation for abstract and con- crete concepts. Abstract concepts elicit greater activity in the inferior frontal gyrus and middle tempo- ral gyrus compared to concrete concepts, while concrete concepts elicit greater activity in the posterior cingulate, precuneus, fusiform gyrus, and parahippocampal gyrus compared to abstract concepts. These results suggest greater engagement of the verbal system for processing of abstract concepts and greater engagement of the perceptual system for processing of concrete concepts, likely via mental im- agery. Hum Brain Mapp 31:1459-1468, 2010. V C 2010 Wiley-Liss, Inc.


Building strong brands in a modern marketing communications environment


title: On Unlimited Sampling

abstract: Shannon's sampling theorem provides a link between the continuous and the discrete realms stating that bandlimited signals are uniquely determined by its values on a discrete set. This theorem is realized in practice using so called analog--to--digital converters (ADCs). Unlike Shannon's sampling theorem, the ADCs are limited in dynamic range. Whenever a signal exceeds some preset threshold, the ADC saturates, resulting in aliasing due to clipping. The goal of this paper is to analyze an alternative approach that does not suffer from these problems. Our work is based on recent developments in ADC design, which allow for ADCs that reset rather than to saturate, thus producing modulo samples. An open problem that remains is: Given such modulo samples of a bandlimited function as well as the dynamic range of the ADC, how can the original signal be recovered and what are the sufficient conditions that guarantee perfect recovery? In this paper, we prove such sufficiency conditions and complement them with a stable recovery algorithm. Our results are not limited to certain amplitude ranges, in fact even the same circuit architecture allows for the recovery of arbitrary large amplitudes as long as some estimate of the signal norm is available when recovering. Numerical experiments that corroborate our theory indeed show that it is possible to perfectly recover function that takes values that are orders of magnitude higher than the ADC's threshold.


Multi-Source Multi-Class Fake News Detection


Space Object Classification using Fused Features of Time Series Data


title: The Importance of Concepts of Knowledge Management and Learning Organization in Managing the Knowledge-Flow in Organizations

abstract: Knowledge economy imposes new demands and imperatives. To achieve a sustainable competitive advantage today means to achieve primacy in knowledge. Therefore it is not surprising that the move towards knowledge-based competitive advantage has become an imperative, not only for individuals, but also for organizational and national strategies. The drastic shift from products to services, observing knowledge as the product, constant innovation, the necessity of managing intangible assets, and the free flow of knowledge, present some of the postulates for modern businesses and organizations. Organizations today seek not only the simple generation of knowledge, but also to provide the knowledge that will create added value to the organization. In this sense, special attention is paid to the concept (s) of knowledge management and learning organization, which should provide the right knowledge at the right place at the right time. By applying the above (s) of concepts, organizations achieve their competitive advantage based on what they know, how effectively and quickly they acquire and use their knowledge. The objectives of this paper are: (1) to determine whether and how the organizations are conscious of the importance of knowledge as the key issue in modern businesses, (2) to explore the ways that organizations use to modernize their knowledge in order to achieve sustainable competitive advantage and (3) to analyze how knowledge is exchanged between employees and what are the main barriers to knowledge-flow in organizations.


title: A Deep Learning Based Method For 3D Human Pose Estimation From 2D Fisheye Images

abstract: We propose a deep learning based method to directly estimate the human joint positions in 3D space from 2D fisheye images captured in an egocentric manner. The core of our method is a novel network architecture based on Inception-v3 [4], featuring the asymmtric convolutional filter size, the long short-term memory module, and the anthropomorphic weights on the training loss. We demonstrate our method outperform state-of-the-art method under different tasks. Our method can be helpful to develop useful deep learning network for human-machine interaction and VR/AR applications.


title: Real-time eye blink and wink detection for object selection in HCI systems

abstract: This paper presents an approach for real-time detection of three types of eye blinks: eye blink (blinking both eyes simultaneously), left and right winks. The process of blink detection has been divided into four parts viz. face localization in facial images acquired through a video camera, eye pair localization, pixels’ motion analysis using optical flow technique, and classification of eye blinks. Blink detection has been performed using a video camera and MATLAB software with image processing and computer vision toolbox. The algorithm takes about 60 ms time for processing a frame and 250 ms time for confirmation and classification of the detected blink. An experiment was conducted to evaluate the performance of the proposed approach in which 10 users voluntarily participated. The performance of the proposed method has been tested under two lighting conditions: natural lighting conditions and controlled lighting conditions. Also, the performance has been tested by varying the distance of the user from the camera. Here, it is observed that the system gives best performance when used under controlled lighting conditions and the user sitting at a distance of about 0.5 m. Accuracy of the proposed approach has been found to be 96, 92 and 88% for detection of eye blink, left wink and right wink, respectively. The proposed method has also been tested on ZJU dataset where it has given precision, detection accuracy and false alarm rate of values 94.11, 91.2 and 1.54%, respectively. The proposed system has been used and evaluated for performing various mouse analogous functions using eye blinks and winks. It has given an accuracy of 90, 80 and 90% in performing left click, double click, and right click operations, respectively.


title: Ambient Sensors for Elderly Care and Independent Living: A Survey

abstract: Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.