title: Collaborative Filtering Recommendation Based on All-Weighted Matrix Factorization and Fast Optimization

abstract: Collaborative filtering recommendation with implicit feedbacks (e.g., clicks, views, and plays) is regarded as one of the most challenging issues in both academia and industry. From implicit feedbacks, we can only get a small fraction of observed data (positive examples), and the massive unobserved data are the mixture of negative examples and unlabeled positive examples. However, most of the existing efforts either treat unobserved data equally by assigning a uniform weight or uniformly weight observed data while ignoring the hidden information (i.e., visit frequency) in implicit feedbacks. This assumption may not hold in real-life scenarios since they cannot distinguish the contributions of the whole data and it easily leads to prediction bias. Besides, those approaches still suffer from low-efficiency issue. To this end, we propose an all-weighted matrix factorization and fast optimization strategy for effective and efficient recommendation. We first design a frequency-aware weighting scheme for observed data and a user-oriented weighting scheme for unobserved data nonuniformly. Then, the weighting schemes of both observed and unobserved data are combined in a unified way to form an all-weighted matrix factorization model. Afterwards, we present a surrogate objective function and develop a fast optimization strategy to enhance the efficiency. Extensive experimental results on real-world datasets demonstrate that our method outperforms the competitive baselines on several evaluation metrics.


title: Why Video Game Genres Fail: A Classificatory Analysis

abstract: This article explores the current affordances and limitations of video game genre from a library and information science perspective with an emphasis on classification theory. We identify and discuss various purposes of genre relating to video games, including identity, collocation and retrieval, commercial marketing, and educational instruction. Through the use of examples, we discuss the ways in which these purposes are supported by genre classification and conceptualization and the implications for video games. Suggestions for improved conceptualizations such as family resemblances, prototype theory, faceted classification, and appeal factors for video game genres are considered, with discussions of strengths and weaknesses. This analysis helps inform potential future practical applications for describing video games at cultural heritage institutions such as libraries, museums, and archives, as well as furthering the understanding of video game genre and genre classification for game studies at large.


title: Discriminative Regularization for Generative Models

abstract: We explore the question of whether the representations learned by classifiers can be used to enhance the quality of generative models. Our conjecture is that labels correspond to characteristics of natural data which are most salient to humans: identity in faces, objects in images, and utterances in speech. We propose to take advantage of this by using the representations from discriminative classifiers to augment the objective function corresponding to a generative model. In particular we enhance the objective function of the variational autoencoder, a popular generative model, with a discriminative regularization term. We show that enhancing the objective function in this way leads to samples that are clearer and have higher visual quality than the samples from the standard variational autoencoders.


title: Application of Computational Intelligence to Improve Education in Smart Cities

abstract: According to UNESCO, education is a fundamental human right and every nation’s citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student’s knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives’ achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders.


title: An Introduction to Fuzzy Control

abstract: Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...


title: Deep Learning for Hand Gesture Recognition on Skeletal Data

abstract: In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model. We propose a new Convolutional Neural Network (CNN) where sequences of hand-skeletal joints' positions are processed by parallel convolutions; we then investigate the performance of this model on hand gesture sequence classification tasks. Our model only uses hand-skeletal data and no depth image. Experimental results show that our approach achieves a state-of-the-art performance on a challenging dataset (DHG dataset from the SHREC 2017 3D Shape Retrieval Contest), when compared to other published approaches. Our model achieves a 91.28% classification accuracy for the 14 gesture classes case and an 84.35% classification accuracy for the 28 gesture classes case.


title: Comparing the Topological and Electrical Structure of the North American Electric Power Infrastructure

abstract: The topological (graph) structure of complex networks often provides valuable information about the performance and vulnerability of the network. However, there are multiple ways to represent a given network as a graph. Electric power transmission and distribution networks have a topological structure that is straightforward to represent and analyze as a graph. However, simple graph models neglect the comprehensive connections between components that result from Ohm's and Kirchhoff's laws. This paper describes the structure of the three North American electric power interconnections, from the perspective of both topological and electrical connectivity. We compare the simple topology of these networks with that of random, preferential-attachment, and small-world networks of equivalent sizes and find that power grids differ substantially from these abstract models in degree distribution, clustering, diameter and assortativity, and thus conclude that these topological forms may be misleading as models of power systems. To study the electrical connectivity of power systems, we propose a new method for representing electrical structure using electrical distances rather than geographic connections. Comparisons of these two representations of the North American power networks reveal notable differences between the electrical and topological structures of electric power networks.


title: Embedding Words as Distributions with a Bayesian Skip-gram Model

abstract: We introduce a method for embedding words as probability densities in a low-dimensional space. Rather than assuming that a word embedding is fixed across the entire text collection, as in standard word embedding methods, in our Bayesian model we generate it from a word-specific prior density for each occurrence of a given word. Intuitively, for each word, the prior density encodes the distribution of its potential 'meanings'. These prior densities are conceptually similar to Gaussian embeddings. Interestingly, unlike the Gaussian embeddings, we can also obtain context-specific densities: they encode uncertainty about the sense of a word given its context and correspond to posterior distributions within our model. The context-dependent densities have many potential applications: for example, we show that they can be directly used in the lexical substitution task. We describe an effective estimation method based on the variational autoencoding framework. We also demonstrate that our embeddings achieve competitive results on standard benchmarks.


title: An Exploratory Study of “Selfitis” and the Development of the Selfitis Behavior Scale

abstract: In 2014, stories appeared in national and international media claiming that the condition of “selfitis” (the obsessive taking of selfies) was to be classed as a mental disorder by the American Psychiatric Association and that the condition could be borderline, acute, or chronic. However, the stories were a hoax but this did not stop empirical research being carried out into the concept. The present study empirically explored the concept and collected data on the existence of selfitis with respect to the three alleged levels (borderline, acute, and chronic) and developed the Selfitis Behavior Scale (SBS). Initially, focus group interviews with 225 Indian university students were carried out to generate potential items for the SBS. The SBS was then validated using 400 Indian university students via exploratory factor analysis (EFA). Six factors were identified in the EFA comprising environmental enhancement, social competition, attention seeking, mood modification, self-confidence, and social conformity. The findings demonstrate that the SBS appears to be a reliable and valid instrument for assessing selfitis but that confirmatory studies are needed to validate the concept more rigorously.


title: 8T1R: A Novel Low-power High-speed RRAM-based Non-volatile SRAM Design

abstract: With continuous and aggressive technology scaling, suppressing the stand-by power is among the top priorities for SRAM design. Switching off the less-frequently accessed blocks is an efficient way to reduce the stand-by power, provided that the information stored in these blocks can be restored. Non-volatile memories (NVMs) are integrated into SRAM cells to perform the required store and restore functions. Among various types of NVMs, memristors (a.k.a. RRAM) have several advantages including their small device size, low voltage operation, high speed, and CMOS-compatible fabrication process. In this article, we propose a new 8T1R RRAM-based non-volatile SRAM (NV-SRAM) which adds non-volatility to the SRAM with minimum impact on the Write and Read operations. Simulation at cell-level and array-level have confirmed that the new design performs Read and Write operations at a compatible delay, energy and noise margin as the conventional 6T SRAM, and it is among the best of all reported RRAM-based NV-SRAM designs to our knowledge. In addition, since our 8T1R design uses only one RRAM device per cell, the energy required for storing/restoring the SRAM data to/from the RRAM is significantly reduced by 60%/70% compared to the lowest storing/restoring energy of the previously proposed RRAM-based NV-SRAM designs.


title: ASIC implementation of a MIMO-OFDM transceiver for 192 Mbps WLANs

abstract: Next generation wireless local area networks (WLANs) such as the IEEE 802.11a standard are expected to rely on multiple antennas at both transmitter and receiver to increase throughput and link reliability. However, these improvements come at a significant increase in signal processing and hence hardware complexity compared to existing single-antenna systems. This paper presents, to the best of the authors' knowledge, the first 4 /spl times/ 4 MIMO-OFDM WLAN physical layer ASIC based on the OFDM specifications of the IEEE 802.1 la standard. The ASIC achieves an uncoded throughput of 192 Mbps in a 20 MHz channel resulting in a spectral efficiency of 9.6 bits/s/Hz. We describe the hardware architectural differences to single-antenna OFDM systems as well as the extensions made necessary by the use of multiple antennas. Our implementation provides reference for the silicon complexity of MIMO-OFDM systems.


title: Metallothionein content increased in the liver of mice exposed to magnetic fields

abstract: Although recent studies have shown that various stress can induce metallothionein (MT) synthesis in animal tissues, the induction of MT synthesis by exposure to static magnetic fields (SMF) has not been reported. We measured MT levels in the liver, kidney and brain of mice exposed to SMF and also evaluated the effect of SMF exposure on the induction of hepatic MT by treatment with carbon tetrachloride (CCl4). The MT content in the liver was significantly increased by exposure to 4.7 T of SMF for 6, 24, or 48 h, whereas that in the kidney or brain was not changed compared to the control. The combination of CCl4 injection and SMF exposure induced elevation of the hepatic MT content exceeding that induced by either treatment alone. These results indicate that exposure to the strong SMF induces MT synthesis in the liver in mice and enhances the hepatic MT synthesis induced by CCl4 administration.


title: Architecting the next generation of service-based SCADA/DCS system of systems

abstract: SCADA and DCS systems are in the heart of the modern industrial infrastructure. The rapid changes in the networked embedded systems and the way industrial applications are designed and implemented, call for a shift in the architectural paradigm. Next generation SCADA and DCS systems will be able to foster cross-layer collaboration with the shop-floor devices as well as in-network and enterprise applications. Ecosystems driven by (web) service based interactions will enable stronger coupling of real-world and the business side, leading to a new generation of monitoring and control applications and services witnessed as the integration of large-scale systems of systems that are constantly evolving to address new user needs.


title: Active Inference, homeostatic regulation and adaptive behavioural control

abstract: We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference.


title: Classification of histopathological breast cancer images using iterative VMD aided Zernike moments&textural signatures

abstract: In this paper we present a novel method for an automated diagnosis of breast carcinoma through multilevel iterative variational mode decomposition (VMD) and textural features encompassing Zernaike moments, fractal dimension and entropy features namely, Kapoor entropy, Renyi entropy, Yager entropy features are extracted from VMD components. The proposed method considers the histopathological image as a set of multidimensional spatially-evolving signals. ReliefF algorithm is used to select the discriminatory features and statistically most significant features are fed to squares support vector machine (SVM) for classification. We evaluate the efficiency of the proposed methodology on publicly available Breakhis dataset containing 7,909 breast cancer histological images, collected from 82 patients, of both benign and malignant cases. Experimental results shows the efficacy of the proposed method in outperforming the state of the art while achieving an average classification rates of 89.61% and 88:23% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the pathologist in accurate and reliable diagnosis of biopsy samples. BreaKHis, a publicly dataset available at this http URL


title: Natural language processing for Urdu TTS system

abstract: Natural Language Processing (NLP) provides the foundation for every text-to-speech system. NLP caters all the issues related to the language. NLP for Urdu Textto-Speech System is a part of research project being done at CRULP, NUCES, Lahore. Its objective is to generate phonetic stream from plain Urdu text for the production of natural and intelligible speech. To carry out this transformation from text to speech, the NLP performs a series of steps. These steps are tokenization of input text, generation of phonemic stream, syllabification of words present in text, primary stress assignment on words, application of phonological rules and resyllabification of words. This paper focuses on all these processes from the perspective of Urdu.


title: Robot team learning enhancement using Human Advice

abstract: The paper discusses the augmentation of the Concurrent Individual and Social Learning (CISL) mechanism with a new Human Advice Layer (HAL). The new layer is characterized by a Gaussian Mixture Model (GMM), which is trained on human experience data. The CISL mechanism consists of the Individual Performance and Task Allocation Markov Decision Processes (MDP), and the HAL can provide preferred action selection policies to the individual agents. The data utilized for training the GMM is collected using a heterogeneous team foraging simulation. When leveraging human experience in the multi-agent learning process, the team performance is enhanced significantly.


title: Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning

abstract: Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor λ. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD(λ) for arbitrary λ, for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(λ), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using λ > 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning.


Recommendation System for Netflix


title: Quantum Memristors in Quantum Photonics

abstract: We propose a method to build quantum memristors in quantum photonic platforms. We first design an effective beam splitter, which is tunable in real time, by means of a Mach-Zehnder-type array with two equal 50:50 beam splitters and a tunable retarder, which allows us to control its reflectivity. Then, we show that this tunable beam splitter, when equipped with weak measurements and classical feedback, behaves as a quantum memristor. Indeed, in order to prove its quantumness, we show how to codify quantum information in the coherent beams. Moreover, we estimate the memory capability of the quantum memristor. Finally, we show the feasibility of the proposed setup in integrated quantum photonics.We propose a method to build quantum memristors in quantum photonic platforms. We first design an effective beam splitter, which is tunable in real time, by means of a Mach-Zehnder-type array with two equal 50:50 beam splitters and a tunable retarder, which allows us to control its reflectivity. Then, we show that this tunable beam splitter, when equipped with weak measurements and classical feedback, behaves as a quantum memristor. Indeed, in order to prove its quantumness, we show how to codify quantum information in the coherent beams. Moreover, we estimate the memory capability of the quantum memristor. Finally, we show the feasibility of the proposed setup in integrated quantum photonics.


title: A Survey of Traffic Issues in Machine-to-Machine Communications Over LTE

abstract: Machine-to-machine (M2M) communication, also referred to as Internet of Things (IoT), is a global network of devices such as sensors, actuators, and smart appliances which collect information, and can be controlled and managed in real time over the Internet. Due to their universal coverage, cellular networks and the Internet together offer the most promising foundation for the implementation of M2M communication. With the worldwide deployment of the fourth generation (4G) of cellular networks, the long-term evolution (LTE) and LTE-advanced standards have defined several quality-of-service classes to accommodate the M2M traffic. However, cellular networks are mainly optimized for human-to-human (H2H) communication. The characteristics of M2M traffic are different from the human-generated traffic and consequently create sever problems in both radio access and the core networks (CNs). This survey on M2M communication in LTE/LTE-A explores the issues, solutions, and the remaining challenges to enable and improve M2M communication over cellular networks. We first present an overview of the LTE networks and discuss the issues related to M2M applications on LTE. We investigate the traffic issues of M2M communications and the challenges they impose on both access channel and traffic channel of a radio access network and the congestion problems they create in the CN. We present a comprehensive review of the solutions for these problems which have been proposed in the literature in recent years and discuss the advantages and disadvantages of each method. The remaining challenges are also discussed in detail.


title: Big spatial data mining

abstract: In this paper, spatial data mining is discussed in the context of big data. Firstly, we elaborate the fact that spatial data plays a primary role in big data, attracting academic community, business industry and governments. Secondly, the adverse of spatial data mining is discussed, such as much garbage, heavy pollution and its difficulties in utilization. Finally, we dissect the value in spatial big data, expound the techniques to discover knowledge from spatial big data, and investigate the transformation from knowledge into data intelligences.


title: Mining Social Media for Newsgathering

abstract: Social media is becoming an increasingly important data source for learning about and tracking breaking news. This is possible thanks to mobile devices connected to the Internet, which allow anyone to post updates from anywhere, leading in turn to a growing presence of citizen journalism. Consequently, social media has become a go-to resource for journalists during newsgathering. Use of social media for newsgathering is however challenging, and suitable tools are needed in order to facilitate access to useful information for reporting. In this paper, we provide an overview of research in data mining and natural language processing for mining social media for newsgathering. We discuss seven different tasks that researchers have worked on to mitigate the challenges inherent to social media newsgathering: event detection, summarisation, news recommenders, content verification, finding information sources, development of newsgathering dashboards and other tasks. We outline the progress made so far in the field, summarise the current challenges as well as discuss future directions in the use of computational journalism to assist with social media newsgathering. This survey paper is relevant to computer scientists researching news in social media as well as for interdisciplinary researchers interested in the intersection of computer science and journalism.


title: A controlled-temperature hot-wire anemometer with voltage feedback linearization

abstract: This paper proposes a new architecture of a controlled-temperature hot-wire anemometer using voltage feedback linearization. The voltage feedback linearizes the sensor input-output relationship and the controller is designed to achieve null steady-state error and reduce the system response time. Analysis of the behavior of the architecture modeled using Simulink is presented for a NTC sensor. Simulation results are presented and discussed, and the architecture is compared with the classical constant-temperature anemometer (CTA) one.


title: The Prevalence of code Over Models: Turning it Around with Transparency

abstract: Over the last ten years, there has been an increasing shift in software production from plandriven processes towards lean software processes. These lean processes are based either on agile strategies or in open source environments. One instance of this trend is the growing use of GitHub as a software construction platform. More and more, software developers are relying on text as representation and collaboration as a way of producing quality software. As such, the adoption of more abstract representations (model oriented), as the research community have been forecasting, is yet to happen. Taking a bias position, anchored on the concept of transparency as a quality, we look at the factors for the prevalence of code over models. We try to forecast possible ways to foster the use of models in software production. Our vision is based on bringing transparency to collaborative modeling.


title: Geometry-Contrastive GAN for Facial Expression Transfer

abstract: In this paper, we propose a Geometry-Contrastive Generative Adversarial Network (GC-GAN) for transferring continuous emotions across different subjects. Given an input face with certain emotion and a target facial expression from another subject, GC-GAN can generate an identity-preserving face with the target expression. Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions. In order to handle the misalignment across different subjects or emotions, contrastive learning is used to transform geometry manifold into an embedded semantic manifold of facial expressions. Therefore, the embedded geometry is injected into the latent space of GANs and control the emotion generation effectively. Experimental results demonstrate that our proposed method can be applied in facial expression transfer even there exist big differences in facial shapes and expressions between different subjects.


title: A Simple Baseline Algorithm for Graph Classification

abstract: Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approaches offer promising results with different respective strengths and weaknesses. However, most of them rely on complex mathematics and require heavy computational power to achieve their best performance. We propose a simple and fast algorithm based on the spectral decomposition of graph Laplacian to perform graph classification and get a first reference score for a dataset. We show that this method obtains competitive results compared to state-of-the-art algorithms.


title: Computational pan-genomics: status, promises and challenges

abstract: Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.


title: Radiosensitization and nanoparticles.

abstract: Nanomaterials have been shown to have physical and chemical properties that have opened new avenues for cancer diagnosis and therapy. Nanoconstructs that enhance existing treatments for cancer, such as radiation therapy, are being explored in several different ways. Two general paths toward nanomaterial-enabled radiosensitization have been explored: (1) improving the effectiveness of ionizing radiation and (2) modulating cellular pathways leading to a disturbance of cellular homeostasis, thus rendering the cells more susceptible to radiation-induced damage. A variety of different agents that work via one of these two approaches have been explored, many of which modulate direct and indirect DNA damage (gold), radiosensitivity through hyperthermia (Fe), and different cellular pathways. There have been many in vitro successes with the use of nanomaterials for radiosensitization, but in vivo testing has been less efficacious, predominantly because of difficulty in targeting the nanoparticles. As improved methods for tumor targeting become available, it is anticipated that nanomaterials can become clinically useful radiosensitizers for radiation therapy.


title: SiNA: A Scalable Iterative Network Aligner

abstract: Given two graphs, network alignment asks for a potentially partial mapping between the vertices of the two graphs. This arises in many applications where data from different sources need to be integrated. Recent graph aligners use the global structure of input graphs and additional information given for the edges and vertices. We present SINA, an efficient, shared memory parallel implementation of such an aligner. Our experimental evaluations on a 32-core shared memory machine showed that SINA scales well for aligning large real-world graphs: SINA can achieve up to 28.5x speedup, and can reduce the total execution time of a graph alignment problem with 2M vertices and 100M edges from 4.5 hours to under 10 minutes. To the best of our knowledge, SINA is the first parallel aligner that uses global structure and vertex and edge attributes to handle large graphs.


title: Data-Specific Adaptive Threshold for Face Recognition and Authentication

abstract: Many face recognition systems boost the performance using deep learning models, but only a few researches go into the mechanisms for dealing with online registration. Although we can obtain discriminative facial features through the state-of-the-art deep model training, how to decide the best threshold for practical use remains a challenge. We develop a technique of adaptive threshold mechanism to improve the recognition accuracy. We also design a face recognition system along with the registering procedure to handle online registration. Furthermore, we introduce a new evaluation protocol to better evaluate the performance of an algorithm for real-world scenarios. Under our proposed protocol, our method can achieve a 22% accuracy improvement on the LFW dataset.


title: A 112GB/S PAM4 Wireline Receiver Using a 64-Way Time-Interleaved SAR ADC in 16NM FinFET

abstract: A 112Gb/s PAM4 wireline receiver testchip is implemented in 16nm FinFET. The receiver consists of continuous-time linear equalizers, a peaking capacitance buffer, and a 56GSa/s 64-way time-interleaved SAR ADC. The receiver achieves 2e-5 BER over a 20dB loss channel at 28GHz Nyquist while consuming 590mW power, excluding DSP.


title: Correlation filter based visual trackers for person pursuit using a low-cost Quadrotor

abstract: We propose two model-free visual object trackers for following targets using the low-cost quadrotor Parrot AR.Drone 2.0 at low altitudes. Our trackers employ correlation filters for short-term tracking and a redetection strategy based on tracking-learning-detection (TLD). We performed an extensive quantitative evaluation of our trackers and a wide variety of existing trackers on person pursuit sequences. The results show that our trackers outperform the existing trackers. In addition, we demonstrate the applicability of our proposed trackers in a series of flight experiments in unconstrained environments using human targets and an existing visual servoing controller.


title: Time-frequency MSE analysis of linear channel estimation methods for the LTE downlink

abstract: Orthogonal frequency-division multiplexing OFDM is widely used in modern communication systems thanks to the ease of its implementation as well as to a simple design of channel estimation methods. Pilot-based schemes are commonly employed to recover channel information and implement efficient channel equalisation. From the symbols transmitted over the pilot carriers, the channel coefficients for a whole OFDM are estimated. In this paper, we propose a method to compute in a closed form the mean square estimation error of different linear approximation and interpolation algorithms in the time-frequency domain. Specifically, by exploiting the linear relationship between the estimated channel coefficients for the whole frame and those relative to the pilots, the proposed approach permits to obtain the mean square error relative to all OFDM symbols in a frame. The method can be used for any linear frequency/time interpolation/approximation method and for any choice of pilot positions and channel statistics. Thanks to our derivations, several pilot-aided channel estimation and interpolation algorithms can be compared without resorting to burdensome computer simulations. In order to demonstrate the effectiveness of the method, the downlink of the long term evolution system is taken as a case study: some popular channel estimators have been selected and the best strategy in a realistic wireless scenario is investigated. The performance of the method is also verified by computing, by means of simulations, bit error rate BER curves for the long term evolution LTE multiple input multiple output MIMO downlink, using 2×2 and 4×4 Alamouti space-frequency coding configurations, in both uncoded and coded communications. Copyright © 2014 John Wiley & Sons, Ltd.


title: Rethinking erasure codes for cloud file systems: Minimizing I/O for recovery and degraded reads

abstract: To reduce storage overhead, cloud file systems are transitioning from replication to erasure codes. This process has revealed new dimensions on which to evaluate the performance of different coding schemes: the amount of data used in recovery and when performing degraded reads. We present an algorithm that finds the optimal number of codeword symbols needed for recovery for any XOR-based erasure code and produces recovery schedules that use a minimum amount of data. We differentiate popular erasure codes based on this criterion and demonstrate that the differences improve I/O performance in practice for the large block sizes used in cloud file systems. Several cloud systems [15, 10] have adopted Reed-Solomon (RS) codes, because of their generality and their ability to tolerate larger numbers of failures. We define a new class of rotated Reed-Solomon codes that perform degraded reads more efficiently than all known codes, but otherwise inherit the reliability and performance properties of Reed-Solomon codes.


title: Machine Learning and Lexicon Based Methods for Sentiment Classification: A Survey

abstract: Sentiment classification is an important subject in text mining research, which concerns the application of automatic methods for predicting the orientation of sentiment present on text documents, with many applications on a number of areas including recommender and advertising systems, customer intelligence and information retrieval. In this paper, we provide a survey and comparative study of existing techniques for opinion mining including machine learning and lexicon-based approaches, together with evaluation metrics. Also cross-domain and cross-lingual approaches are explored. Experimental results show that supervised machine learning methods, such as SVM and naive Bayes, have higher precision, while lexicon-based methods are also very competitive because they require few effort in human-labeled document and isn't sensitive to the quantity and quality of the training dataset.


title: IDENTIFICATION AND CLASSIFICATION OF VALUE DRIVERS FOR A NEW PRODUCTION HOMEBUILDING SUPPLY CHAIN

abstract: The United States homebuilding industry experienced significant consolidation over the past ten years. The largest builders in the industry grew from building around 7,500 homes per year to over 30,000 homes per year. The supply chains for homebuilding were not designed to facilitate this level of mass production of homes. To achieve economies of scale in the Denver production homebuilding market, recent process improvements have been made by USBuild Corporation, a new player in the supply chain. These improvements have focused on using the Internet, a proprietary information system, and a new distribution channel to integrate information and material flows. The resulting opportunities for value creation in USBuild’s “e-chain” supply chain during the first six months of operation in 2001 were significant. This paper identifies the value drivers in the case of USBuild’s supply chain intermediation in the Denver market. Further, the value drivers are classified using an established framework for how Internet-enabled information technologies are impacting the extended supply chain.


title: Downlink and Uplink Decoupling: a Disruptive Architectural Design for 5G Networks

abstract: Cell association in cellular networks has traditionally been based on the downlink received signal power only, despite the fact that uplink and downlink transmission powers and interference levels differed significantly. This approach was adequate in homogeneous networks with macro base stations all having similar transmission power levels. However, with the growth of heterogeneous networks where there is a big disparity in the transmit power of the different base station types, this approach is highly inefficient. In this paper, we study the notion of Downlink and Uplink Decoupling (DUDe) where the downlink cell association is based on the downlink received power while the uplink is based on the pathloss. We present the motivation and assess the gains of this 5G design approach with simulations that are based on Vodafone's LTE field trial network in a dense urban area, employing a high resolution ray-tracing pathloss prediction and realistic traffic maps based on live network measurements.


title: An early introduction to STEM education: Teaching computer programming principles to 5th graders through an e-learning platform: A game-based approach

abstract: E-learning systems and environments have progressively infiltrated all grades of Education, either in a complementary role to face-to-face instruction in class under blended learning scenarios or in purely remote education. The relative merits of face-to-face instruction versus e-learning, especially in Primary Education, are still an open research issue, though. Another challenge that Primary Education faces is the development of STEM thinking and STEM-friendly attitudes in pupils of early ages. An educational intervention that attempts to address both issues is designed, implemented and evaluated in this paper. Motivation and engagement of young pupils are sought through the gamification of the learning process. The present study investigates the feasibility and the efficiency of an asynchronous e-learning scenario involving an on-line game, for pupils of 10–11 years, in the 5'h grade of Primary School. A basic STEM subject, Computer Programming Principles, is selected as the learning material to be taught purely remotely, through the e-learning environment designed and developed in moodle, for the purposes of this research. The educational intervention provides data on the learning outcomes of the pupils and on the efficiency of the approach. Evaluation results verify both the feasibility and the efficiency of this approach. Pupils and parents emphasize the role of the teacher who provides support and guidance through the platform communication tools; their attitudes are seen to change in favor of e-learning.


title: Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces

abstract: Users of software applications generate vast amounts of unstructured log-trace data. These traces contain clues to the intentions and interests of those users, but service providers may find it difficult to uncover and exploit those clues. In this paper, we propose a framework for personalizing software and web services by leveraging such unstructured traces. We use 6 months of Photoshop usage history and 7 years of interaction records from 67K Behance users to design, develop, and validate a user-modeling technique that discovers highly discriminative representations of Photoshop users; we refer to the model as cutilization-to-vector, util2vec. We demonstrate the promise of this approach for three sample applications: (1) a practical user-tagging system that automatically predicts areas of focus for millions of Photoshop users; (2) a two-phase recommendation model that enables cold-start personalized recommendations for many new Behance users who have Photoshop usage data, improving recommendation quality ([email protected]) by 21.2% over a popularity-based recommender; and (3) a novel inspiration engine that provides real-time personalized inspirations to artists. We believe that this work demonstrates the potential impact of unstructured usage-log data for personalization.


title: Framework for Zombie Detection Using Neural Networks

abstract: One of the most important threats to personal and corporate Internet security is the proliferation of Zombie PCs operating as an organized network. Zombie detection is currently performed at the host level and/or network level, but these options have some important drawbacks: antivirus, anti-spyware and personal firewalls are ineffective in the detection of hosts that are compromised via new or target-specific malicious software, while network firewalls and Intrusion Detection Systems were developed to protect the network from external attacks but they were not designed to detect and protect against vulnerabilities that are already present inside the local area network. This paper presents a new approach, based on neural networks, that is able to detect Zombie PCs based on the historical traffic profiles presented by "licit" and "illicit" network applications. The evaluation of the proposed methodology relies on traffic traces obtained in a controlled environment and composed by licit traffic measured from normal activity of network applications and malicious traffic synthetically generated using the SubSeven backdoor. The results obtained show that the proposed methodology is able to achieve good identification results, being at the same time computationally efficient and easy to deploy in real network scenarios.


title: Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric

abstract: In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.


title: Machine Learning Algorithms in Bipedal Robot Control

abstract: Over the past decades, machine learning techniques, such as supervised learning, reinforcement learning, and unsupervised learning, have been increasingly used in the control engineering community. Various learning algorithms have been developed to achieve autonomous operation and intelligent decision making for many complex and challenging control problems. One of such problems is bipedal walking robot control. Although still in their early stages, learning techniques have demonstrated promising potential to build adaptive control systems for bipedal robots. This paper gives a review of recent advances on the state-of-the-art learning algorithms and their applications to bipedal robot control. The effects and limitations of different learning techniques are discussed through a representative selection of examples from the literature. Guidelines for future research on learning control of bipedal robots are provided in the end.


On the learnability of fully-connected neural networks


title: Approximation and Inapproximation for the Influence Maximization Problem in Social Networks under Deterministic Linear Threshold Model

abstract: Influence Maximization is the problem of finding a certain amount of people in a social network such that their aggregation influence through the network is maximized. In the past this problem has been widely studied under a number of different models. In 2003, Kempe \emph{et al.} gave a $(1-{1 \over e})$-approximation algorithm for the \emph{linear threshold model} and the \emph{independent cascade model}, which are the two main models in the social network analysis. In addition, Chen \emph{et al.} proved that the problem of exactly computing the influence given a seed set in the two models is $\#$P-hard. Both the \emph{linear threshold model} and the \emph{independent cascade model} are based on randomized propagation. However such information might be obtained by surveys or data mining techniques, which makes great difference on the properties of the problem. In this paper, we study the Influence Maximization problem in the \emph{deterministic linear threshold model}. As a contrast, we show that in the \emph{deterministic linear threshold model}, there is no polynomial time $n^{1-\epsilon}$-approximation unless P=NP even at the simple case that one person needs at most two active neighbors to become active. This inapproximability result is derived with self-contained proofs without using PCP theorem. In the case that a person can be activated when one of its neighbors become active, there is a polynomial time ${e\over e-1}$-approximation, and we prove it is the best possible approximation under a reasonable assumption in the complexity theory, $NP \not\subset DTIME(n^{\log\log n})$. We also show that the exact computation of the final influence given a seed set can be solved in linear time in the \emph{deterministic linear threshold model}. The Least Seed Set problem, which aims to find a seed set with least number of people to activate all the required people in a given social network, is discussed. Using an analysis framework based on Set Cover, we show a $O($log$n)$-approximation in the case that a people become active when one of its neighbors is activated.


title: Automated segmentation of gallstones in ultrasound images

abstract: Gallstones are solid particles formed from bile in the gall bladder. In this paper, we propose a technique to automatically detect Gallstones in ultrasound images, christened as, Automated Gallstone Segmentation (AGS) Technique. Speckle Noise in the ultrasound image is first suppressed using Anisotropic Diffusion Technique. The edges are then enhanced using Unsharp Filtering. NCUT Segmentation Technique is then put to use to segment the image. Afterwards, edges are detected using Sobel Edge Detection. Further, Edge Thickening Process is used to smoothen the edges and probability maps are generated using Floodfill Technique. Then, the image is scribbled using Automatic Scribbling Technique. Finally, we get the segmented gallstone within the gallbladder using the Closed Form Matting Technique.


title: Giant Magnetoresistive Biosensor Array for Detecting Magnetorelaxation

abstract: In this paper, a time-domain magnetorelaxometry biosensing scheme is presented using giant magnetoresistive (GMR) sensors to measure the fast relaxation response of superparamagnetic magnetic nanoparticles (MNPs) in a pulsed magnetic field. The system consists of an 8 × 10 GMR sensor array, a Helmholtz coil, an electromagnet driver, and an integrator-based analog front-end needed to capture the fast relaxation dynamics of MNPs. A custom designed electromagnet driver and Helmholtz coil improve the switch-off speed to >5 Oe/μs, limiting the dead zone time to <10 μs, and thus enables the system to monitor fast relaxation processes of 30 nm MNPs. A magnetic correlated double sampling technique is proposed to reduce sensor-to-sensor variation by 99.98% while also reducing temperature drift, circuit offset, and nonlinearity below the noise level. An optimum integration time is calculated and experimentally verified to maximize the SNR. Experiments with dried MNPs have shown successful relaxation detection, and immunoassay experiments have demonstrated their binding kinetics.


title: IMDS: intelligent malware detection system

abstract: The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic and new, previously unseen malicious executables. In this paper, resting on the analysis of Windows API execution sequences called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of King-Soft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system out perform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques.


title: An Emotion Cause Corpus for Chinese Microblogs with Multiple-User Structures

abstract: A notably challenging problem in emotion analysis is recognizing the cause of an emotion. Although there have been a few studies on emotion cause detection, most of them work on news reports or a few of them focus on microblogs using a single-user structure (i.e., all texts in a microblog are written by the same user). In this article, we focus on emotion cause detection for Chinese microblogs using a multiple-user structure (i.e., texts in a microblog are successively written by several users). First, based on the fact that the causes of an emotion of a focused user may be provided by other users in a microblog with the multiple-user structure, we design an emotion cause annotation scheme which can deal with such a complicated case, and then provide an emotion cause corpus using the annotation scheme. Second, based on the analysis of the emotion cause corpus, we formalize two emotion cause detection tasks for microblogs (current-subtweet-based emotion cause detection and original-subtweet-based emotion cause detection). Furthermore, in order to examine the difficulty of the two emotion cause detection tasks and the contributions of texts written by different users in a microblog with the multiple-user structure, we choose two popular classification methods (SVM and LSTM) to do emotion cause detection. Our experiments show that the current-subtweet-based emotion cause detection is much more difficult than the original-subtweet-based emotion cause detection, and texts written by different users are very helpful for both emotion cause detection tasks. This study presents a pilot study of emotion cause detection which deals with Chinese microblogs using a complicated structure.


title: Grief after second-trimester termination for fetal anomaly: a qualitative study.

abstract: OBJECTIVES ::: We aimed to qualitatively evaluate factors that contribute to and alleviate grief associated with termination of a pregnancy for a fetal anomaly and how that grief changes over time. ::: ::: ::: STUDY DESIGN ::: We conducted a longitudinal qualitative study of decision satisfaction, grief and coping among women undergoing termination (dilation and evacuation or induction termination) for fetal anomalies and other complications. We conducted three post-procedure interviews at 1-3 weeks, 3 months and 1 year. We used a generative thematic approach to analyze themes related to grief using NVivo software program. ::: ::: ::: RESULTS ::: Of the 19 women in the overall study, 13 women's interviews were eligible for analysis of the grief experience. Eleven women completed all three interviews, and two completed only the first interview. Themes that contributed to grief include self-blame for the diagnosis, guilt around the termination decision, social isolation related to discomfort with abortion and grief triggered by reminders of pregnancy. Social support and time are mechanisms that serve to alleviate grief. ::: ::: ::: CONCLUSIONS ::: Pregnancy termination in this context is experienced as a significant loss similar to other types of pregnancy loss and is also associated with real and perceived stigma. Women choosing termination for fetal anomalies may benefit from tailored counseling that includes dispelling misconceptions about cause of the anomaly. In addition, efforts to decrease abortion stigma and increase social support may improve women's experiences and lessen their grief response. ::: ::: ::: IMPLICATIONS ::: The nature and course of grief after second-trimester termination for fetal anomaly are, as of yet, poorly understood. With improved understanding of how women grieve over time, clinicians can better recognize the significance of their patients' suffering and offer tools to direct their grief toward positive coping.


title: Predicting Complete 3D Models of Indoor Scenes

abstract: One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of walls, which must conform to a Manhattan structure but is otherwise flexible, and the layout and extent of objects, modeled with CAD-like 3D shapes. We represent both the visible and occluded portions of the scene, producing a $complete$ 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scene. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and overall consistency. We demonstrate encouraging results on the NYU v2 dataset and highlight a variety of interesting directions for future work.


title: Duct fan based wall climbing robot for concrete surface crack inspection

abstract: Degradation inspection of inaccessible parts of concrete structures is always a difficult task. An efficient and low cost solution is the use of wall climbing robots in such areas. In this paper, we propose a duct fan based wall climbing robot for the crack inspection on concrete wall surfaces. The thrust force provided by the duct fan holds the robot on to the wall. This wireless robot moves like a four wheeled vehicle on the wall surface and captures images of the wall under inspection using a camera mounted on it. Cracks are detected from these images using percolation method of image processing.


title: Vascular dementia Diagnostic criteria for research studies: Report of the NINDS‐AIREN International Workshop*

abstract: Criteria for the diagnosis of vascular dementia (VaD) that are reliable, valid, and readily applicable in a variety of settings are urgently needed for both clinical and research purposes. To address this need, the Neuroepidemiology Branch of the National Institute of Neurological Disorders and Stroke (NINDS) convened an International Workshop with support from the Association Internationale pour la Recherche et l9Enseignement en Neurosciences (AIREN), resulting in research criteria for the diagnosis of VaD. Compared with other current criteria, these guidelines emphasize (1) the heterogeneity of vascular dementia syndromes and pathologic subtypes including ischemic and hemorrhagic strokes, cerebral hypoxic-ischemic events, and senile leukoencephalopathic lesions; (2) the variability in clinical course, which may be static, remitting, or progressive; (3) specific clinical findings early in the course (eg, gait disorder, incontinence, or mood and personality changes) that support a vascular rather than a degenerative cause; (4) the need to establish a temporal relationship between stroke and dementia onset for a secure diagnosis; (5) the importance of brain imaging to support clinical findings; (6) the value of neuropsychological testing to document impairments in multiple cognitive domains; and (7) a protocol for neuropathologic evaluations and correlative studies of clinical, radiologic, and neuropsychological features. These criteria are intended as a guide for case definition in neuroepidemiologic studies, stratified by levels of certainty (definite, probable, and possible). They await testing and validation and will be revised as more information becomes available.


title: Loss minimization of electric drive systems using a DC/DC converter and an optimized battery voltage in automotive applications

abstract: Voltage source inverters generally feed the traction motor from a battery in automotive applications. By implementing a DC/DC converter between battery and inverter, the DC-link voltage is adjustable. This paper discusses the benefits of the variable DC-link voltage as an additional degree of freedom for a loss minimized control of electric drives in battery supplied vehicles. Inverter systems with and without DC/DC converter are compared and operating areas of improved efficiency are identified. Energy saving potentials resulting from an optimized battery voltage are discussed for different driving cycles.


title: The ShangRing device for simplified adult circumcision

abstract: Adult male circumcision reduces HIV transmission through vaginal intercourse and is being promoted in areas where HIV is widespread. Conventional surgical circumcision involves suturing and thus requires practitioners with surgical skills. It is also associated with complications, including bleeding and infection, especially in resource-poor settings. The ShangRing (Wuhu Snnda Medical Treatment Appliance Technology Co. Ltd, Wuhu City, China) has been used to perform thousands of circumcisions in adult men, principally in China. It consists of two concentric plastic rings that sandwich the foreskin of the penis, allowing circumcision without stitches or notable bleeding. As well as substantially reduced operative times, ShangRing adult male circumcision is associated with a low complication rate, and the technique can easily be taught to both physician and nonphysician personnel. The simple technique and successful outcomes associated with the ShangRing procedure mean that the device could enable standardization of adult male circumcision, helping HIV prevention efforts throughout the developing world.


title: Pseudoangiomatous Stromal Hyperplasia of the Breast: Mammosonography and Elastography Findings with a Histopathological Correlation

abstract: Pseudoangiomatous stromal hyperplasia (PASH) is a rare benign mesenchymal proliferative lesion of the breast. In this study, we aimed to show a case of PASH with mammographic and sonographic features, which fulfill the criteria for benign lesions and to define its recently discovered elastography findings. A 49-year-old premenopausal female presented with breast pain in our outpatient surgery clinic. In ultrasound images, a hypoechoic solid mass located at the 3 o'clock position in the periareolar region of the right breast was observed. Due to it was not detected on earlier mammographies, the patient underwent a tru-cut biopsy, although the mass fulfilled the criteria for benign lesions on mammography, ultrasound, and elastography. Elastography is a new technique differentiating between benign and malignant lesions. It is also useful to determine whether a biopsy is necessary or follow-up is sufficient.


Game Design as Narrative Architecture


title: Novel approach to network function development

abstract: This article considers a novel approach to network function development. Transmitting speed and amount of data in the networks are exponentially increasing, which makes middle-boxes to be less efficient due to cost, deployment, inflexibility, scalability and other issues. Network function virtualization technology, on the other hand, was proposed to solve this problem by moving hardware functionality to be developed as a software and deployed to commodity hardware. However, this approach brought several new problems: slow speed of network functions' development, lower performance compared to the middle-boxes, virtual machines scaling and deployment issues. Our approach presents a framework for rapid development of performant, scalable virtualized network functions.


title: Implementation of MOSFET based capacitors for digital applications

abstract: To compensate for the growing effect of process, temperature and voltage variations in digital ICs, several dynamic approaches have been proposed. These approaches require the use of capacitors to offset the effects of variations. Although MOSFET based capacitors are a natural choice, such capacitances vary significantly depending on the applied voltage. In this paper, we propose techniques to make the capacitance of MOSFET based capacitors relatively constant over the applied voltage. We study approaches that are based on gate as well as diffusion capacitors. We study the trade-off (in terms of capacitance variation, requirement of a bias voltage and area efficiency) of all the proposed schemes. Simulation results are presented for two process technologies. We show that by using our techniques the capacitance variation across applied voltage can be reduced from 75% down to 3% for a 70nm process technology.


title: Easy Calibration of a Blind-Spot-Free Fisheye Camera System Using a Scene of a Parking Space

abstract: Mounting three fisheye cameras, on the sides and rear of a vehicle, can help a driver maneuver his/her vehicle in restricted environments by providing a top view that is generated from these three fisheye cameras. To generate the top view, the pose of each fisheye camera must first be calibrated. In this paper, we propose an easy method of calibrating such a fisheye camera system by observing the scene of a parking space. First, each camera pose relative to the ground is estimated from the typical line pattern of a parking space. Then, the relative pose among the three cameras is refined using the overlapping region of the ground between the neighboring cameras. Finally, if necessary, any small deviation of the pose of the camera system can manually be adjusted by an interactive interface. Since the calibration of the fisheye camera system can be performed without preparing a specific calibration pattern for this particular purpose beforehand, the proposed method can reduce the workload on the user. Experimental results reveal the effectiveness of the proposed method.


title: OpenSurfaces: a richly annotated catalog of surface appearance

abstract: The appearance of surfaces in real-world scenes is determined by the materials, textures, and context in which the surfaces appear. However, the datasets we have for visualizing and modeling rich surface appearance in context, in applications such as home remodeling, are quite limited. To help address this need, we present OpenSurfaces, a rich, labeled database consisting of thousands of examples of surfaces segmented from consumer photographs of interiors, and annotated with material parameters (reflectance, material names), texture information (surface normals, rectified textures), and contextual information (scene category, and object names). Retrieving usable surface information from uncalibrated Internet photo collections is challenging. We use human annotations and present a new methodology for segmenting and annotating materials in Internet photo collections suitable for crowdsourcing (e.g., through Amazon's Mechanical Turk). Because of the noise and variability inherent in Internet photos and novice annotators, designing this annotation engine was a key challenge; we present a multi-stage set of annotation tasks with quality checks and validation. We demonstrate the use of this database in proof-of-concept applications including surface retexturing and material and image browsing, and discuss future uses. OpenSurfaces is a public resource available at http://opensurfaces.cs.cornell.edu/.


title: One switching cycle current control strategy for triple active bridge phase-shifted DC-DC converter

abstract: The paper presents two types of one cycle current control method for Triple Active Bridge(TAB) phase-shifted DC-DC converter integrating Renewable Energy Source(RES), Energy Storage System(ESS) and a output dc bus. The main objective of the current control methods is to control the transformer current in each cycle so that dc transients are eliminated during phase angle change from one cycle to the next cycle. In the proposed current control methods, the transformer currents are sampled within a switching cycle and the phase shift angles for the next switching cycle are generated based on sampled current values and current references. The discussed one cycle control methods also provide an inherent power decoupling feature for the three port phase shifted triple active bridge converter. Two different methods, (a) sampling and updating twice in a switching cycle and (b) sampling and updating once in a switching cycle, are explained in this paper. The current control methods are experimentally verified using digital implementation technique on a laboratory made hardware prototype.


title: Speech recognition by machines and humans

abstract: Abstract This paper reviews past work comparing modern speech recognition systems and humans to determine how far recent dramatic advances in technology have progressed towards the goal of human-like performance. Comparisons use six modern speech corpora with vocabularies ranging from 10 to more than 65,000 words and content ranging from read isolated words to spontaneous conversations. Error rates of machines are often more than an order of magnitude greater than those of humans for quiet, wideband, read speech. Machine performance degrades further below that of humans in noise, with channel variability, and for spontaneous speech. Humans can also recognize quiet, clearly spoken nonsense syllables and nonsense sentences with little high-level grammatical information. These comparisons suggest that the human-machine performance gap can be reduced by basic research on improving low-level acoustic-phonetic modeling, on improving robustness with noise and channel variability, and on more accurately modeling spontaneous speech.


title: Design of passive-quenching active-reset circuit with adjustable hold-off time for single-photon avalanche diodes

abstract: Single-photon avalanche diodes (SPADs) are gaining popularity in applications where low intensity light needs to be detected. Since they are used in Geiger mode, where the self-sustaining avalanche needs to be quenched, an important part of the detection circuitry is the quenching circuit. First, we examine the operation of a basic passive quenching circuit consisting of the SPAD and two series resistors and measure the SPAD's dark count rate. Then we implement a passive quenching circuit with active reset (PQAR). Without a sufficiently long hold-off time between quenching and reset the circuit does not operate properly. Because of that, a hold-off time is introduced by means of an adjustable time delay circuit. The behavior of the PQAR circuit for different hold-off times is then examined, and the minimum hold-off time of 1 μs, which still allows for correct operation with the given circuitry is determined. Finally, a comparison is made between the passive and the PQAR circuit, focusing on the advantages of active over passive reset.


title: Biclustering via Sparse Singular Value Decomposition

abstract: Summary. Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row–column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets.


title: The Outer-connected Domination Number of Sierpiński-like Graphs

abstract: An outer-connected dominating set in a graph G = (V, E) is a set of vertices D ⊆ V satisfying the condition that, for each vertex v ? D, vertex v is adjacent to some vertex in D and the subgraph induced by V?D is connected. The outer-connected dominating set problem is to find an outer-connected dominating set with the minimum number of vertices which is denoted by ?~c(G)$\tilde {\gamma }_{c}(G)$. In this paper, we determine ?~c(S(n,k))$\tilde {\gamma }_{c}(S(n,k))$, ?~c(S+(n,k))$\tilde {\gamma }_{c}(S^{+}(n,k))$, ?~c(S++(n,k))$\tilde {\gamma }_{c}(S^{++}(n,k))$, and ?~c(Sn)$\tilde {\gamma }_{c}(S_{n})$, where S(n, k), S+(n, k), S++(n, k), and Sn are Sierpin?$\acute {\mathrm {n}}$ski-like graphs.


title: A review of issues and challenges in designing Iris recognition Systems for noisy imaging environment

abstract: Iris recognition is a challenging task in a noisy imaging environment. Nowadays researcher's primary focus is to develop reliable Iris recognition System that can work in noisy imaging environment and to increase the iris recognition rate on different iris database. But there are major issues involved in designing such systems like occlusion by eyelashes, eyelids, glass frames, off-angle imaging, presence of contact lenses, poor illumination, motion blur, close-up of iris image acquired at a large standoff distance and specular reflections etc. Because of these issues the quality of acquired iris image gets affected. The performance of the iris based recognition system will deteriorate abruptly, when the iris mask is not accurate. This results in lower recognition rate. In this review paper different challenges in designing iris recognition systems for noisy imaging environment are reviewed and methodologies involved in overcoming these issues are discussed. At the end, some measures to improve the accuracy of such systems are suggested.


title: Predicting human activities using spatio-temporal structure of interest points

abstract: Early recognition and prediction of human activities are of great importance in video surveillance, e.g., by recognizing a criminal activity at its beginning stage, it is possible to avoid unfortunate outcomes. We address early activity recognition by developing a Spatial-Temporal Implicit Shape Model (STISM), which characterizes the space-time structure of the sparse local features extracted from a video. The early recognition of human activities is accomplished by pattern matching through STISM. To enable efficient and robust matching, we propose a new random forest structure, called multi-class balanced random forest, which makes a good trade-off between the balance of the trees and the discriminative abilities. The prediction is done simultaneously for multiple classes, which saves both the memory and computational cost. The experiments show that our algorithm significantly outperforms the state of the arts for the human activity prediction problem.


title: A wait-free queue as fast as fetch-and-add

abstract: Concurrent data structures that have fast and predictable performance are of critical importance for harnessing the power of multicore processors, which are now ubiquitous. Although wait-free objects, whose operations complete in a bounded number of steps, were devised more than two decades ago, wait-free objects that can deliver scalable high performance are still rare. In this paper, we present the first wait-free FIFO queue based on fetch-and-add (FAA). While compare-and-swap (CAS) based non-blocking algorithms may perform poorly due to work wasted by CAS failures, algorithms that coordinate using FAA, which is guaranteed to succeed, can in principle perform better under high contention. Along with FAA, our queue uses a custom epoch-based scheme to reclaim memory; on x86 architectures, it requires no extra memory fences on our algorithm's typical execution path. An empirical study of our new FAA-based wait-free FIFO queue under high contention on four different architectures with many hardware threads shows that it outperforms prior queue designs that lack a wait-free progress guarantee. Surprisingly, at the highest level of contention, the throughput of our queue is often as high as that of a microbenchmark that only performs FAA. As a result, our fast wait-free queue implementation is useful in practice on most multi-core systems today. We believe that our design can serve as an example of how to construct other fast wait-free objects.


title: The Relationship Between Facebook and the Well-Being of Undergraduate College Students

abstract: Abstract We investigated how Facebook use and attitudes relate to self-esteem and college adjustment, and expected to find a positive relationship between Facebook and social adjustment, and a negative relationship between Facebook, self-esteem, and emotional adjustment. We examined these relationships in first-year and upper-class students and expected to find differences between the groups. Seventy undergraduate students completed Facebook measures (time, number of friends, emotional and social connection to Facebook), the Rosenberg Self-Esteem Scale, and the Student Adaptation to College Scale. First-year students had a stronger emotional connection to and spent more time on Facebook while they reported fewer friends than upper-class students did. The groups did not differ in the adjustment scores. The number of Facebook friends potentially hinders academic adjustment, and spending a lot of time on Facebook is related to low self-esteem. The number of Facebook friends was negatively associated with emo...


title: Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape

abstract: The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be defined as unplanned, unauthorized, and/or unstructured housing. Techniques for efficiently mapping these settlement boundaries can benefit various decision making bodies. From a remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other types of structures (e.g., industrial, commercial, and formal residential). These spatial characteristics are often captured in high spatial resolution satellite imagery. We analyzed the role of spatial, structural, and contextual features (e.g., GLCM, Histogram of Oriented Gradients, Line Support Regions, Lacunarity) for urban neighborhood mapping, and computed several low-level image features at multiple scales to characterize local neighborhoods. The decision parameters to classify formal-, informal-, and non-settlement classes were learned under Decision Trees and a supervised classification framework. Experiments were conducted on high-resolution satellite imagery from the CitySphere collection, and four different cities (i.e., Caracas, Kabul, Kandahar, and La Paz) with varying spatial characteristics were represented. Overall accuracy ranged from 85% in La Paz, Bolivia, to 92% in Kandahar, Afghanistan. While the disparities between formal and informal neighborhoods varied greatly, many of the image statistics tested proved robust.


title: Automatic Extraction of Data from Bar Charts

abstract: Scientific charts are an effective tool to visualize numerical data trends. They appear in a wide range of contexts, from experimental results in scientific papers to statistical analyses in business reports. The abundance of scientific charts in the web has made it inevitable for search engines to include them as indexed content. However, the queries based on only the textual data used to tag the images can limit query results. Many studies exist to address the extraction of data from scientific diagrams in order to improve search results. In our approach to achieving this goal, we attempt to enhance the semantic labeling of the charts by using the original data values that these charts were designed to represent. In this paper, we describe a method to extract data values from a specific class of charts, bar charts. The extraction process is fully automated using image processing and text recognition techniques combined with various heuristics derived from the graphical properties of bar charts. The extracted information can be used to enrich the indexing content for bar charts and improve search results. We evaluate the effectiveness of our method on bar charts drawn from the web as well as charts embedded in digital documents.


title: Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps

abstract: While user-modeling and recommender systems successfully utilize items like emails, news, and movies, they widely neglect mind-maps as a source for user modeling. We consider this a serious shortcoming since we assume user modeling based on mind maps to be equally effective as user modeling based on other items. Hence, millions of mind-mapping users could benefit from user-modeling applications such as recommender systems. The objective of this doctoral thesis is to develop an effective user-modeling approach based on mind maps. To achieve this objective, we integrate a recommender system in our mind-mapping and reference-management software Docear. The recommender system builds user models based on the mind maps, and recommends research papers based on the user models. As part of our research, we identify several variables relating to mind-map-based user modeling, and evaluate the variables' impact on user-modeling effectiveness with an offline evaluation, a user study, and an online evaluation based on 430,893 recommendations displayed to 4,700 users. We find, among others, that the number of analyzed nodes, modification time, visibility of nodes, relations between nodes, and number of children and siblings of a node affect the effectiveness of user modeling. When all variables are combined in a favorable way, this novel approach achieves click-through rates of 7.20%, which is nearly twice as effective as the best baseline. In addition, we show that user modeling based on mind maps performs about as well as user modeling based on other items, namely the research articles users downloaded or cited. Our findings let us to conclude that user modeling based on mind maps is a promising research field, and that developers of mind-mapping applications should integrate recommender systems into their applications. Such systems could create additional value for millions of mind-mapping users.


title: An automatic approach for ontology-based feature extraction from heterogeneous textualresources

abstract: Data mining algorithms such as data classification or clustering methods exploit features of entities to characterise, group or classify them according to their resemblance. In the past, many feature extraction methods focused on the analysis of numerical or categorical properties. In recent years, motivated by the success of the Information Society and the WWW, which has made available enormous amounts of textual electronic resources, researchers have proposed semantic data classification and clustering methods that exploit textual data at a conceptual level. To do so, these methods rely on pre-annotated inputs in which text has been mapped to their formal semantics according to one or several knowledge structures (e.g. ontologies, taxonomies). Hence, they are hampered by the bottleneck introduced by the manual semantic mapping process. To tackle this problem, this paper presents a domain-independent, automatic and unsupervised method to detect relevant features from heterogeneous textual resources, associating them to concepts modelled in a background ontology. The method has been applied to raw text resources and also to semi-structured ones (Wikipedia articles). It has been tested in the Tourism domain, showing promising results.


title: A Survey of Music Recommendation Systems and Future Perspectives

abstract: Along with the rapid expansion of digital music formats, managing and searching for songs has become significant. Though music information retrieval (MIR) techniques have been made successfully in last ten years, the development of music recommender systems is still at a very early stage. Therefore, this paper surveys a general framework and state-of-art approaches in recommending music. Two popular algorithms: collaborative filtering (CF) and content-based model (CBM), have been found to perform well. Due to the relatively poor experience in finding songs in long tail and the powerful emotional meanings in music, two user-centric approaches: context-based model and emotion-based model, have been paid increasing attention. In this paper, three key compo- nents in music recommender - user modelling, item profiling, and match algorithms are discussed. Six recommendation models and four potential issues towards user experience, are explained. However, subjective music recommendation system has not been fully investigated. To this end, we propose a motivation-based model using the empirical studies of human behaviour, sports education, music psychology.


Word Segmentation in Sanskrit Using Path Constrained Random Walks


title: Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection

abstract: Abstract Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies on how to fuse the two modalities effectively. In this paper, we deeply compare six different convolutional network fusion architectures and analyse their adaptations, enabling a vanilla architecture to obtain detection performances comparable to the state-of-the-art results. Further, we discover that pedestrian detection confidences from color or thermal images are correlated with illumination conditions. With this in mind, we propose an Illumination-aware Faster R-CNN (IAF R-CNN). Specifically, an Illumination-aware Network is introduced to give an illumination measure of the input image. Then we adaptively merge color and thermal sub-networks via a gate function defined over the illumination value. The experimental results on KAIST Multispectral Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.


title: On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes

abstract: Working memory (WM) is the set of mental processes holding limited information in a temporarily accessible state in service of cognition. We provide a theoretical framework to understand the relation between WM and aptitude measures. The WM measures that have yielded high correlations with aptitudes include separate storage-and-processing task components, on the assumption that WM involves both storage and processing. We argue that the critical aspect of successful WM measures is that rehearsal and grouping processes are prevented, allowing a clearer estimate of how many separate chunks of information the focus of attention circumscribes at once. Storage-and-processing tasks correlate with aptitudes, according to this view, largely because the processing task prevents rehearsal and grouping of items to be recalled. In a developmental study, we document that several scope-of-attention measures that do not include a separate processing component, but nevertheless prevent efficient rehearsal or grouping, also correlate well with aptitudes and with storage-and-processing measures. So does digit span in children too young to rehearse.


Design Space Analysis: Bridging from Theory to Practice via Design Rationale


Blockchain and its uses


title: A subcellular map of the human proteome

abstract: Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.


[Biomechanical basis of occlusal masticatory surface design].


title: A floating high-voltage level-shifter with high area efficiency for biomedical implants

abstract: A major problem in State-of-the-Art digital, floating level-shifters is, that they either require a large area when employing positive feedback, or lack accuracy with respect to the floating digital supply, if an open-loop solution is used. In order to reach accurate, floating, digital supply levels, while using the area efficient, static level-shifter topology, a control-loop for the reference current is introduced. Thereby process, temperature and time related variations are compensated and the adaptive reference can be distributed to an arbitrarily large array of level-shifters. The whole architecture has been simulated, layouted and manufactured in a standard 0.18 µm HV CMOS process. Simulation results show, that the design is capable of shifting digital signals from 0/3V to a floating 6/9V and −6/−9V domain with a delay of 250 ns and 150 ns respectively. By reducing the amount of high voltage transistors and utilizing low voltage diodes in the floating voltage domain, an area of only 65 µm × 33 µm per level-shifter can be realized. This is especially advantageous for the intended application in a multichannel neural stimulator, where many level-shifters are needed to switch the dedicated functions in the floating high voltage domains. The required bias current control-loop has a simulated power consumption of only 94 µW and requires an area of 137 µm × 97 µm.


title: Improving GPGPU resource utilization through alternative thread block scheduling

abstract: High performance in GPGPU workloads is obtained by maximizing parallelism and fully utilizing the available resources. The thousands of threads are assigned to each core in units of CTA (Cooperative Thread Arrays) or thread blocks - with each thread block consisting of multiple warps or wavefronts. The scheduling of the threads can have significant impact on overall performance. In this work, explore alternative thread block or CTA scheduling; in particular, we exploit the interaction between the thread block scheduler and the warp scheduler to improve performance. We explore two aspects of thread block scheduling - 1) LCS (lazy CTA scheduling) which restricts the maximum number of thread blocks allocated to each core, and 2) BCS (block CTA scheduling) where consecutive thread blocks are assigned to the same core. For LCS, we leverage a greedy warp scheduler to help determine the optimal number of thread blocks by only measuring the number of instructions issued while for BCS, we propose an alternative warp scheduler that is aware of the “block” of CTAs allocated to a core. With LCS and the observation that maximum number of CTAs does not necessary maximize performance, we also propose mixed concurrent kernel execution that enables multiple kernels to be allocated to the same core to maximize resource utilization and improve overall performance.


title: Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology

abstract: Abstract Efforts to develop more effective depression treatments are limited by assessment methods that rely on patient-reported or clinician judgments of symptom severity. Depression also affects speech. Research suggests several objective voice acoustic measures affected by depression can be obtained reliably over the telephone. Thirty-five physician-referred patients beginning treatment for depression were assessed weekly, using standard depression severity measures, during a 6-week observational study. Speech samples were also obtained over the telephone each week using an interactive voice response system to automate data collection. Several voice acoustic measures correlated significantly with depression severity. Patients responding to treatment had significantly greater pitch variability, paused less while speaking, and spoke faster than at baseline. Patients not responding to treatment did not show similar changes. Telephone standardization for obtaining voice data was identified as a critical factor influencing the reliability and quality of speech data. This study replicates and extends previous research with a larger sample of patients assessing clinical change associated with treatment. The feasibility of obtaining voice acoustic measures reflecting depression severity and response to treatment using computer-automated telephone data collection techniques is also established. Insight and guidance for future research needs are also identified.


title: Flint: batch-interactive data-intensive processing on transient servers

abstract: Cloud providers now offer transient servers, which they may revoke at anytime, for significantly lower prices than on-demand servers, which they cannot revoke. The low price of transient servers is particularly attractive for executing an emerging class of workload, which we call Batch-Interactive Data-Intensive (BIDI), that is becoming increasingly important for data analytics. BIDI workloads require large sets of servers to cache massive datasets in memory to enable low latency operation. In this paper, we illustrate the challenges of executing BIDI workloads on transient servers, where revocations (akin to failures) are the common case. To address these challenges, we design Flint, which is based on Spark and includes automated checkpointing and server selection policies that i) support batch and interactive applications and ii) dynamically adapt to application characteristics. We evaluate a prototype of Flint using EC2 spot instances, and show that it yields cost savings of up to 90% compared to using on-demand servers, while increasing running time by


Technical considerations in performing posterior component separation with transverse abdominis muscle release


title: Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism

abstract: With the rapid development of electronic commerce, the number of transactions by credit cards are increasing rapidly. As online shopping becomes the most popular transaction mode, cases of transaction fraud are also increasing. In this paper, we propose a novel fraud detection method that composes of four stages. To enrich a cardholder’s behavioral patterns, we first utilize the cardholders’ historical transaction data to divide all cardholders into different groups such that the transaction behaviors of the members in the same group are similar. We thus propose a window-sliding strategy to aggregate the transactions in each group. Next, we extract a collection of specific behavioral patterns for each cardholder based on the aggregated transactions and the cardholder’s historical transactions. Then we train a set of classifiers for each group on the base of all behavioral patterns. Finally, we use the classifier set to detect fraud online and if a new transaction is fraudulent, a feedback mechanism is taken in the detection process in order to solve the problem of concept drift. The results of our experiments show that our approach is better than others.


title: Compact Wideband Bandstop Filter With Four Transmission Zeros

abstract: This work presents a new type of compact wideband bandstop filter (BSF) with four transmission zeros in the stopband. The proposed filter topology consists of a coupled line directional coupler connected in parallel to an interference transmission line. The filter characteristics can be easily controlled by the coupling factor of the coupled line coupler and the characteristic admittances of both the coupler and the transmission line. Design equations and curves are provided for the filter design. To validate the proposed topology, a compact microstrip BSF centered at 2.0 GHz with a measured 20 dB rejection bandwidth of 100% and sharp rejection characteristics is designed and built.


title: Attribute Based Encryption for Secure Access to Cloud Based EHR Systems

abstract: Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients' end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR's access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.


title: Emergence of Digital Twins

abstract: Multiple forms of digital transformation are imminent. Digital Twins represent one concept. It is gaining momentum because it may offer real-time transparency. Rapid diffusion of digital duplicates faces hurdles due to lack of semantic interoperability between architectures, standards and ontologies. The technologies necessary for automated discovery are in short supply. Progression of the field depends on convergence of information technology, operational technology and protocol-agnostic telecommunications. Making sense of the data, ability to curate data and perform data analytics at the edge (or mist rather than in the fog or cloud) is key to value. Delivering engines to the edge are crucial for analytics at the edge when latency is critical. The confluence of these and other factors may chart the future path for Digital Twins. The number of unknown unknowns and the known unknowns in this process makes it imperative to create global infrastructures and organize groups to pursue the development of fundamental building blocks and new ideas through research.