title: Diabetes mellitus and health-related quality of life among older adults findings from the behavioral risk factor surveillance system (BRFSS)

abstract: The aim of the present study was to examine associations between the presence of diabetes mellitus and health-related quality of life (HRQOL) among older adults. Using data from 37,054 adults aged 50 years or older who participated in the 2001 BRFSS, we examined the independent association between diabetes and four measures of HRQOL developed by the U.S. Centers for Disease Control and Prevention. Multivariate logistic regression was used to obtain adjusted odds ratios (ORs) and 95% confidence intervals (CIs). On average, older adults with diabetes reported nearly twice as many unhealthy days (physical or mental) as those without the condition (mean: 10.1 [S.E.: 0.32] versus 5.7 [0.43]) after age adjustment. The proportion of older adults reporting 14 or more unhealthy days (physical or mental) was significantly higher among those with diabetes (n = 4032; 11%) compared to those without the condition (OR: 1.64; 95% CI: 1.20, 2.23) after multivariate adjustment. Among older diabetic adults, the adjusted relative odds of having 14 or more unhealthy days (physical or mental) was 1.71 (95% CI: 1.31, 2.22) times greater for those treated with insulin compared to those not treated with insulin. Diabetes is independently associated with lower levels of HRQOL among older adults. These results reinforce the importance of preventing diabetes and its complications through health education messages stressing a balanced diet and increased physical activity. © 2003 Elsevier Ireland Ltd. All rights reserved.


title: Codecatch: extracting source code snippets from online sources

abstract: Nowadays, developers rely on online sources to find example snippets that address the programming problems they are trying to solve. However, contemporary API usage mining methods are not suitable for locating easily reusable snippets, as they provide usage examples for specific APIs, thus requiring the developer to know which library to use beforehand. On the other hand, the approaches that retrieve snippets from online sources usually output a list of examples, without aiding the developer to distinguish among different implementations and without offering any insight on the quality and the reusability of the proposed snippets. In this work, we present CodeCatch, a system that receives queries in natural language and extracts snippets from multiple online sources. The snippets are assessed both for their quality and for their usefulness/preference by the developers, while they are also clustered according to their API calls to allow the developer to select among the different implementations. Preliminary evaluation of CodeCatch in a set of indicative programming problems indicates that it can be a useful tool for the developer.


title: Plug-ins for GNU Radio Companion

abstract: This paper gives an insight on how to develop plug-ins (signal processing blocks) for GNU Radio Companion. GRC is on the monitoring computer and does bulk of the signal processing before transmission and after reception. The coding done in order to develop any block is discussed. A block that performs Huffman coding has been built. Huffman coding is a coding technique that gives a prefix code. A block that performs convolution coding at any desired rate using any generator polynomial has also been built. Both Huffman and Convolution coding are done on data stored in file sources by these blocks. This paper thus describes the ease of signal processing that can be attained by developing blocks in demand by changing the C++ and PYTHON codes of the HOWTO package. Being an open source it is available to all, is highly cost effective and is a field with great potential.


title: VNS: An integrated framework for vehicular networks simulation

abstract: Vehicular networks are becoming one of the most important emerging technologies to enable Intelligent Transportation Systems (ITS). A strong research effort has been made in the last few years in the design and development of new systems using vehicle-to-vehicle or vehicle-to-infrastructure communications. Therefore, a large-scale evaluation of these networks is of critical importance. However, due to the prohibitive costs of real test-beds, computer simulation is the only viable solution. Building upon previous work, we extend the state-of-the-art in the field of vehicular simulation by proposing an integrated framework for Vehicular Networks Simulation (VNS). The key point of this framework is the complete integration between the mobility and network components, given the fact not only the networking dynamics are greatly dependent on the mobility aspect, but also the network-enabled applications can influence the mobility patterns of ITS systems. Finally, to demonstrate the capabilities of this framework, we provide a large-scale analysis of 802.11p-based communications when used for the exchange of status advertisements between vehicles. We ran simulations for different scenarios including the urban scenario of the city of Porto with a realistic mobility pattern.


title: Energy hubs for the future

abstract: This paper presents a project named "Vision of Future Energy Networks", which aims at a greenfield approach for future energy systems. The definition of energy hubs and the conception of combined interconnector devices represent key approaches towards a multicarrier greenfield layout. Models and tools for technical, economical and environmental investigations in multicarrier energy systems have been developed and used in various case studies


title: Vision-Based Nighttime Vehicle Detection Using CenSurE and SVM

abstract: In this paper, we propose a method for detecting vehicles from a nighttime driving scene taken by an in-vehicle monocular camera. Since it is difficult to recognize the shape of the vehicles during nighttime, vehicle detection is based on the headlights and the taillights, which are bright areas of pixels called blobs. Many research studies using automatic multilevel thresholding are being conducted, but these methods are prone to get affected by the ambient light because it uses the luminance of the whole image to derive the thresholds. Owing to such reasons, we focused on the Laplacian of Gaussian operator, which derives the response of luminance difference between the blob and its surroundings. Compared with automatic multilevel thresholding, Laplacian of Gaussian operator is more robust to the ambient light. However, the computational cost to derive the response of this operator is large. Therefore, we used a method called Center Surround Extremas to detect the blobs in high speed. Since the detected blobs include nuisance lights, we had to determine whether the blob is a light of a vehicle or not. Thus, we classified them according to the features of the blob using support vector machines. Then, we detected vehicle traffic lane and specified the region where the vehicle may exist. Finally, we classified the blobs based on the movements across the frames. We applied the proposed method to nighttime driving sequences and confirmed the effectiveness of the classification process used in this method and that it could process within frame rate.


title: A Wideband Omnidirectional Antenna Array With Low Gain Variation

abstract: An omnidirectional antenna array with wide band and low gain variation in the horizontal plane is proposed in this letter. The antenna unit of the array is composed of two vertically arrayed omnidirectional elements and the associated feeding network. 3-D dipoles with vertical arms and parasitic dipoles are introduced in the antenna unit to significantly enhance the bandwidth and decrease the gain variation. An array with three vertically installed units is fabricated. Each unit is 120 ° ::: rotated compared to the neighboring one for a lower gain variation. The array exhibits a broad bandwidth of 56.4% (1.54-2.75 GHz) for VSWR ≤ 1.5, a gain around 7 dBi, and a low gain variation around 1.5 dB, which may find applications in base station for wireless communication or spectrum monitoring/jamming systems .


title: Rethinking Vehicular Communications: Merging VANET with cloud computing

abstract: Despite the surge in Vehicular Ad Hoc NETwork (VANET) research, future high-end vehicles are expected to under-utilize the on-board computation, communication, and storage resources. Olariu et al. envisioned the next paradigm shift from conventional VANET to Vehicular Cloud Computing (VCC) by merging VANET with cloud computing. But to date, in the literature, there is no solid architecture for cloud computing from VANET standpoint. In this paper, we put forth the taxonomy of VANET based cloud computing. It is, to the best of our knowledge, the first effort to define VANET Cloud architecture. Additionally we divide VANET clouds into three architectural frameworks named Vehicular Clouds (VC), Vehicles using Clouds (VuC), and Hybrid Vehicular Clouds (HVC). We also outline the unique security and privacy issues and research challenges in VANET clouds.


title: Monitoring body positions and movements during sleep using wisps

abstract: Sleep monitoring is very important for elderly people as inadequate and irregular sleep are often related to serious diseases such as depression and diabetes. In many cases, it is necessary to monitor the body positions and movements made while sleeping because of their relationships to particular diseases (i.e., sleep apnea and restless legs syndrome). Analyzing movements during sleep also helps in determining sleep quality and irregular sleeping patterns. This paper presents a sleep monitoring system based on the WISP platform - active RFID-based sensors equipped with accelerometers. We show how our system accurately infers fine-grained body positions from accelerometer data collected from the WISPs attached to the bed mattress. Movements and their duration are also detected by the system. We present the results of our empirical study from 10 subjects on three different mattresses in controlled experiments to show the accuracy of our inference algorithms. Finally, we evaluate the accuracy of the movement detection and body position inference for six nights on one subject, and compare these results with two baseline systems: one that uses bed pressure sensors and the other is an iPhone application.


title: Is investment-cashflow sensitivity a good measure of financial constraints?

abstract: Abstract Since Fazzari et al. (1988) , investment–cashflow sensitivity has been one of the most important indicators for testing and measuring the external financial constraints of corporations. This study analyzes the effect of changes in the relative cost of internal and external financing on investment decisions in response to tax changes. China’s 2004 VAT reform decreased companies’ effective tax rates, leading to an increase in operating cashflow. This, in turn, reduced the internal cost of financing and the value of the tax shield and increased the cost of debt financing. This study shows that in the case of the VAT reform, investment–cashflow sensitivity increased significantly, whereas cash holdings–cashflow sensitivity and borrowing-slack sensitivity did not significantly change. We conclude that investment–cashflow sensitivity is not an effective measure of financial constraints under information asymmetry, but cash–cashflow sensitivity and borrowing-slack sensitivity may be useful alternatives.


title: Causal relation extraction using cue phrase and lexical pair probabilities

abstract: This work aims to extract causal relations that exist between two events expressed by noun phrases or sentences. The previous works for the causality made use of causal patterns such as causal verbs. We concentrate on the information obtained from other causal event pairs. If two event pairs share some lexical pairs and one of them is revealed to be causally related, the causal probability of another event pair tends to increase. We introduce the lexical pair probability and the cue phrase probability. These probabilities are learned from raw corpus in unsupervised manner. With these probabilities and the Naive Bayes classifier, we try to resolve the causal relation extraction problem. Our inter-NP causal relation extraction shows the precision of 81.29%, that is 7.05% improvement over the baseline model. The proposed models are also applied to inter-sentence causal relation extraction.


title: A spectral algorithm for computing social balance

abstract: We consider social networks in which links are associated with a sign; a positive (negative) sign indicates friendship (animosity) between the connected nodes. Recent work studies such large online signed networks by applying theories that stem from the notion of social balance. Computing the social balance of a signed network requires counting the distinct configurations of the signed edges within all possible triangles that appear in the network. A naive algorithm for such counting would require time that is cubic to the total number of nodes; such an algorithm is infeasible for large signed networks that are generated from online applications. ::: ::: In this paper, we present an efficient spectral algorithm that computes approximate counts of the signed-triangle configurations. The essence of the algorithm lies in associating the eigenvalues of the adjacency matrix of a signed network with its signed-triangle configurations. Our experiments demonstrate that our algorithm introduces only a small error in the computed quantities while, at the same time, it achieves significant computational speedups.


Linguistically Rich Vector Representations of Supertags for TAG Parsing.


title: emPHasis-10: development of a health-related quality of life measure in pulmonary hypertension

abstract: 1. School of Nursing, Midwifery and Social Work, University of Manchester, Manchester UK 2. Department of Respiratory Medicine, Freeman Hospital, Newcastle UK 3. Pulmonary Hypertension Unit, Mater Misericordiae University Hospital, Dublin, Ireland 4. National Heart and Lung Institute, Imperial College London UK 5. Pulmonary Hypertension Service, Hammersmith Hospital, London UK 6. Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK 7. Pulmonary Hypertension Unit, Royal Brompton and Harefield Foundation NHS Trust, London UK 8. Scottish Vascular Unit, Golden Jubilee National Hospital, Glasgow UK 9. Pulmonary Hypertension Association UK, Sheffield UK Corresponding author: Dr Janelle Yorke, Senior Lecturer, School of Nursing, Midwifery & Social Work University of Manchester, Room 5.320 Jean McFarlane Building, Oxford Road, Manchester, M13 9PL Telephone: (+44)161 3067780 Email: Janelle.Yorke@manchester.ac.uk . Published on November 14, 2013 as doi: 10.1183/09031936.00127113 ERJ Express


title: Panako - A Scalable Acoustic Fingerprinting System Handling Time-Scale and Pitch Modification

abstract: In this paper a scalable granular acoustic fingerprinting system robust against time and pitch scale modification is presented. The aim of acoustic fingerprinting is to identify identical, or recognize similar, audio fragments in a large set using condensed representations of audio signals, i.e. fingerprints. A robust fingerprinting system generates similar fingerprints for perceptually similar audio signals. The new system, presented here, handles a variety of distortions well. It is designed to be robust against pitch shifting, time stretching and tempo changes, while remaining scalable. After a query, the system returns the start time in the reference audio, and the amount of pitch shift and tempo change that has been applied. The design of the system that offers this unique combination of features is the main contribution of this research. The fingerprint itself consists of a combination of key points in a Constant-Q spectrogram. The system is evaluated on commodity hardware using a freely available reference database with fingerprints of over 30.000 songs. The results show that the system responds quickly and reliably on queries, while handling time and pitch scale modifications of up to ten percent.


title: Hilbert Space Embeddings of Hidden Markov Models

abstract: Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations. And, learning algorithms for HMMs have predominantly relied on local search heuristics, with the exception of spectral methods such as those described below. We propose a nonparametric HMM that extends traditional HMMs to structured and non-Gaussian continuous distributions. Furthermore, we derive a local-minimum-free kernel spectral algorithm for learning these HMMs. We apply our method to robot vision data, slot car inertial sensor data and audio event classification data, and show that in these applications, embedded HMMs exceed the previous state-of-the-art performance.


title: Complex Adaptive Systems Modeling: A multidisciplinary Roadmap

abstract: Abstract PAC Codes 07.05.Tp, 89.75.-k, 89.75.Fb Mathematics Subject Classification (2010) 05C82, 68T42, 00A72, 92C42


title: Dynamic Non-Rigid Objects Reconstruction with a Single RGB-D Sensor

abstract: This paper deals with the 3D reconstruction problem for dynamic non-rigid objects with a single RGB-D sensor. It is a challenging task as we consider the almost inevitable accumulation error issue in some previous sequential fusion methods and also the possible failure of surface tracking in a long sequence. Therefore, we propose a global non-rigid registration framework and tackle the drifting problem via an explicit loop closure. Our novel scheme starts with a fusion step to get multiple partial scans from the input sequence, followed by a pairwise non-rigid registration and loop detection step to obtain correspondences between neighboring partial pieces and those pieces that form a loop. Then, we perform a global registration procedure to align all those pieces together into a consistent canonical space as guided by those matches that we have established. Finally, our proposed model-update step helps fixing potential misalignments that still exist after the global registration. Both geometric and appearance constraints are enforced during our alignment; therefore, we are able to get the recovered model with accurate geometry as well as high fidelity color maps for the mesh. Experiments on both synthetic and various real datasets have demonstrated the capability of our approach to reconstruct complete and watertight deformable objects.


title: Survey on Software Vulnerability Analysis Method Based on Machine Learning

abstract: With the increasingly rich of vulnerability related data and the extensive application of machine learning methods, software vulnerability analysis methods based on machine learning is becoming an important research area of information security. In this paper, the up-to-date and well-known works in this research area were analyzed deeply. A framework for software vulnerability analysis based on machine learning was proposed. And the existing works were described and compared, the limitations of these works were discussed. The future research directions on software vulnerability analysis based on machine learning were put forward in the end.


title: The extracellular space of rat brain following perfusion fixation with glutaraldehyde and hydroxyadipaldehyde

abstract: SummaryFollowing perfusion fixation of rat brain with hydroxyadipaldehyde a greater extracellular space can be observed in electron micrographs than in similar studies of tissue fixed with glutaraldehyde. This increased space cannot be reduced to normal size by a variation of the osmolality of the perfusion medium, by an alteration in the height of the perfusion column, or by the use of a physiological buffer in the perfusion medium. Therefore the size of the extracellular compartment is believed to be due to a chemical effect of the fixative compound upon cell membranes and/or intracellular protein. The possible relationship of this larger space to the extracellular space of living animals is discussed.


title: On conditional parity as a notion of non-discrimination in machine learning

abstract: We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We show that conditional parity is amenable to statistical analysis by studying randomization as a general mechanism for achieving conditional parity and a kernel-based test of conditional parity.


title: A Comparison of Three Distributed File System Architectures: Vnode, Sprite, and Plan 9

abstract: This paper compares three distributed file system architectures: the vnode architecture found in SunOS, the architecture used in the Sprite distributed file system, and the architecture used in the Plan 9 distributed file system' The emphasis of the comparison is on generalized support for remote access to file system resources, which include periph- eral devices and IPC communication channels as well as regular files. The vnode architecture is an evolu- tion of sffuctures and interfaces used in the original, stand-alone UNIX file system' The Sprite architecture provides a network-wide shared name space and em- phasizes a strong separation of the internal naming and VO interfaces to easily provide remote access to a variety of resources. The Plan 9 architecture relies on per-process name spaces and is organized around a singte frle system protocol, 9P, through which a variety of devices and system services are accessed'


title: Cytokine storm and sepsis disease pathogenesis

abstract: Infectious diseases are a leading cause of death worldwide. Sepsis is a severe clinical syndrome related to the host response to infection. The severity of infections is due to an activation cascade that will lead to an autoamplifying cytokine production: the cytokine storm. Cytokines are a broad category of relatively small proteins (<40 kDa) that are produced and released with the aim of cell signaling. Our understanding of the processes that trigger this tremendous amount of cytokine production has made dramatic progress over the last decades, but unfortunately, these findings could not translate yet into effective treatments; so far, all clinical trials targeting cytokine production or effects failed. This review aims to summarize the pathophysiology of the cytokine storm; to describe the type, effects, and kinetics of cytokine production; and to discuss the therapeutic challenges of targeting cytokines. New promising therapeutic strategies focusing on the endothelium, as a source and a target of cytokines, are described.


title: Hanamogera speech robot which makes a person feel a talking is fun

abstract: We propose that a robot speaks a Hanamogera (a semantic-free speech) when the robot speaks with a person. Hanamogera is semantic-free speech and the sound of the speech is a sound of the words which consists of phonogram characters. The consisted characters can be changed freely because the Hanamogera speech does not have to have any meaning. Each sound of characters of a Hanamogera is thought to have an impression according to the contained consonant/vowel in the characters. The Hanamogera is expected to make a listener feel that the talking with a robot which speaks a Hanamogera is fun because of a sound of the Hanamogera. We conducted an experiment of talking with a NAO and an experiment of evaluating to Hanamogera speeches. The results of the experiment showed that a talking with a Hanamogera speech robot was better fun than a talking with a nodding robot.


title: REVIEW-ARTICLE Bioinformatics: an overview and its applications

abstract: Technological advancements in recent years have promoted a marked progress in understanding the genetic basis of phenotypes. In line with these advances, genomics has changed the paradigm of biological questions in full genome-wide scale (genome-wide), revealing an explosion of data and opening up many possibilities. On the other hand, the vast amount of information that has been generated points the challenges that must be overcome for storage (Moore's law) and processing of biological information. In this context, bioinformatics and computational biology have sought to overcome such challenges. This review presents an overview of bioinformatics and its use in the analysis of biological data, exploring approaches, emerging methodologies, and tools that can give biological meaning to the data generated.


title: A Rank-Switching, Open-Row DRAM Controller for Time-Predictable Systems

abstract: We introduce ROC, a Rank-switching, Open-row Controller for Double Data Rate Dynamic RAM (DDR DRAM). ROC is optimized for mixed-criticality multicore systems using modern DDR devices: compared to existing real-time memory controllers, it provides significantly lower worst case latency bounds for hard real-time tasks and supports throughput-oriented optimizations for soft real-time applications. The key to improved performance is an innovative rank-switching mechanism which hides the latency of write-read transitions in DRAM devices without requiring unpredictable request reordering. We further employ open row policy to take advantage of the data caching mechanism (row buffering) in each device. ROC provides complete timing isolation between hard and soft tasks and allows for compositional timing analysis over the number of cores and memory ranks in the system. We implemented and synthesized the ROC back end in Verilog RTL, and evaluated its performance on both synthetic tasks and a set of representative benchmarks.


title: Semantic-Based Location Recommendation With Multimodal Venue Semantics

abstract: In recent years, we have witnessed a flourishing of location -based social networks. A well-formed representation of location knowledge is desired to cater to the need of location sensing, browsing, navigation and querying. In this paper, we aim to study the semantics of point-of-interest (POI) by exploiting the abundant heterogeneous user generated content (UGC) from different social networks. Our idea is to explore the text descriptions, photos, user check-in patterns, and venue context for location semantic similarity measurement. We argue that the venue semantics play an important role in user check-in behavior. Based on this argument, a unified POI recommendation algorithm is proposed by incorporating venue semantics as a regularizer. In addition to deriving user preference based on user-venue check-in information, we place special emphasis on location semantic similarity. Finally, we conduct a comprehensive performance evaluation of location semantic similarity and location recommendation over a real world dataset collected from Foursquare and Instagram. Experimental results show that the UGC information can well characterize the venue semantics, which help to improve the recommendation performance.


title: How Deep Are Effects of Language on Thought? Time Estimation

abstract: How deep are effects of language on thought? Time estimation in speakers of English, Indonesian, Greek, and Spanish Daniel Casasanto † Lera Boroditsky Webb Phillips Jesse Greene Shima Goswami Simon Bocanegra-Thiel Ilia Santiago-Diaz MIT Department of Brain & Cognitive Sciences, 77 Massachusetts Avenue NE20-457 Cambridge, MA 02139 USA Olga Fotokopoulu Ria Pita Aristotle University of Thessaloniki, Greece David Gil Max Planck Center for Evolutionary Anthropology Jakarta Field Station, Indonesia Abstract from many previous inquiries into relations between language and thought: objectively evaluable linguistic data, and language-independent psychological data. A notorious fallacy, attributable in part to Whorf, illustrates the need for methodological rigor. Whorf (1939) argued that Eskimos must conceive of snow differently than English speakers because the Eskimo lexicon contains multiple words that distinguish different types of snow, whereas English has only one word to describe all types. The exact number of snow words the Eskimos were purported to have is not clear. (This number has now been inflated by the popular press to as many as two-hundred.) According to a Western Greenlandic Eskimo dictionary published in Whorf’s time, however, Eskimos may have had as few as two distinct words for snow (Pullum, 1991). Setting aside Whorf’s imprecision and the media’s exaggeration, there remain two problems with Whorf’s argument, which are evident in much subsequent ‘Language and Thought’ research, as well. First, although Whorf asserted an objective difference between Eskimo and English snow vocabularies, his comparative linguistic data were subjective and unfalsifiable: it is a matter of opinion whether any cross-linguistic difference in the number of snow words existed. As Geoffrey Pullum (1991) points out, English could also be argued to have multiple terms for snow in its various manifestations: slush, sleet, powder, granular, blizzard, drift, etc. The problem of unfalsifiability would be addressed if cross-linguistic differences could be demonstrated empirically, and ideally, if the magnitude of the differences could be quantified. A second problem with Whorf’s argument (and others like it in the contemporary Cognitive Linguistics literature) is that it uses purely linguistic data to motivate inferences about non-linguistic thinking. Steven Pinker illustrates the resulting circularity of Whorf’s claim in this parody of his reasoning: “[Eskimos] speak differently so they must think differently. How do we know that they think differently? Just listen to the way they speak!” (Pinker, 1994, pg. 61). This circularity would be escaped if non-linguistic evidence could be produced to show that two groups of speakers who talk differently also think differently in corresponding ways. Do the languages that we speak affect how we experience the world? This question was taken up in a linguistic survey and two non-linguistic psychophysical experiments conducted in native speakers of English, Indonesian, Greek, and Spanish. All four of these languages use spatial metaphors to talk about time, but the particular metaphoric mappings between time and space vary across languages. A linguistic corpus study revealed that English and Indonesian tend to map duration onto linear distance (e.g., a long time), whereas Greek and Spanish preferentially map duration onto quantity (e.g., much time). Two psychophysical time estimation experiments were conducted to determine whether this cross-linguistic difference has implications for speakers’ temporal thinking. Performance on the psychophysical tasks reflected the relative frequencies of the ‘time as distance’ and ‘time as quantity’ metaphors in English, Indonesian, Greek, and Spanish. This was true despite the fact that the tasks used entirely non- linguistic stimuli and responses. Results suggest that: (1.) The spatial metaphors in our native language may profoundly influence the way we mentally represent time. (2.) Language can shape even primitive, low-level mental processes such as estimating brief durations – an ability we share with babies and non-human animals. Introduction “Are our own concepts of ‘time,’ ‘space,’ and ‘matter’ given in substantially the same form by experience to all men, or are they in part conditioned by the structure of particular languages?” (Whorf, 1939/2000, pg. 138.) This question, posed by Benjamin Whorf over half a century ago, is currently the subject of renewed interest and debate. Does language shape thought? The answer yes would call for a reexamination of some foundational theories that have guided Cognitive Science for decades, which assume both the universality and the primacy of non-linguistic concepts (Chomsky, 1975; Fodor, 1975). Yet despite unreserved belief among the general public that people who talk differently also think differently (ask anyone about the Eskimos’ words for snow), it has remained widely agreed among linguists and psychologists that they do not. Skepticism about some Whorfian claims has been well founded. Two crucial kinds of evidence have been missing Corresponding author: Daniel Casasanto (djc@mit.edu)


title: Lonrho and Oil Sanctions against Rhodesia in the 1960s

abstract: This article assesses how the British multinational company Lonrho, led by its buccaneering managing-director, Roland ‘Tiny’ Rowland, attempted to navigate oil sanctions against Rhodesia after the British colony had made its Unilateral Declaration of Independence (UDI) in November 1965. Rowland had built, and possessed, a majority shareholding in the company which operated the oil pipeline that provided crude oil to Rhodesia's Feruka refinery near Umtali from the port of Beira on the Mozambique coast. After voluntarily ceasing to supply Ian Smith's rebel regime with oil soon after UDI, Lonrho became involved in a series of discussions with the British government over the future of the pipeline and oil sanctions more generally. In examining these discussions, I will argue that the pipeline was of too much symbolic importance for the British government to significantly allow Lonrho to influence its Rhodesia policy. Despite clear evidence that the Beira naval blockade was to all intents and purposes ineffect...


title: Ensemble Classifiers for Steganalysis of Digital Media

abstract: Today, the most accurate steganalysis methods for digital media are built as supervised classifiers on feature vectors extracted from the media. The tool of choice for the machine learning seems to be the support vector machine (SVM). In this paper, we propose an alternative and well-known machine learning tool-ensemble classifiers implemented as random forests-and argue that they are ideally suited for steganalysis. Ensemble classifiers scale much more favorably w.r.t. the number of training examples and the feature dimensionality with performance comparable to the much more complex SVMs. The significantly lower training complexity opens up the possibility for the steganalyst to work with rich (high-dimensional) cover models and train on larger training sets-two key elements that appear necessary to reliably detect modern steganographic algorithms. Ensemble classification is portrayed here as a powerful developer tool that allows fast construction of steganography detectors with markedly improved detection accuracy across a wide range of embedding methods. The power of the proposed framework is demonstrated on three steganographic methods that hide messages in JPEG images.


title: A Dataset for Web-Scale Knowledge Base Population

abstract: For many domains, structured knowledge is in short supply, while unstructured text is plentiful. Knowledge Base Population (KBP) is the task of building or extending a knowledge base from text, and systems for KBP have grown in capability and scope. However, existing datasets for KBP are all limited by multiple issues: small in size, not open or accessible, only capable of benchmarking a fraction of the KBP process, or only suitable for extracting knowledge from title-oriented documents (documents that describe a particular entity, such as Wikipedia pages). We introduce and release CC-DBP, a web-scale dataset for training and benchmarking KBP systems. The dataset is based on Common Crawl as the corpus and DBpedia as the target knowledge base. Critically, by releasing the tools to build the dataset, we enable the dataset to remain current as new crawls and DBpedia dumps are released. Also, the modularity of the released tool set resolves a crucial tension between the ease that a dataset can be used for a particular subtask in KBP and the number of different subtasks it can be used to train or benchmark.


title: Geoparsing and Geosemantics for Social Media: Spatiotemporal Grounding of Content Propagating Rumors to Support Trust and Veracity Analysis during Breaking News

abstract: In recent years, there has been a growing trend to use publicly available social media sources within the field of journalism. Breaking news has tight reporting deadlines, measured in minutes not days, but content must still be checked and rumors verified. As such, journalists are looking at automated content analysis to prefilter large volumes of social media content prior to manual verification. This article describes a real-time social media analytics framework for journalists. We extend our previously published geoparsing approach to improve its scalability and efficiency. We develop and evaluate a novel approach to geosemantic feature extraction, classifying evidence in terms of situatedness, timeliness, confirmation, and validity. Our approach works for new unseen news topics. We report results from four experiments using five Twitter datasets crawled during different English-language news events. One of our datasets is the standard TREC 2012 microblog corpus. Our classification results are promising, with F1 scores varying by class from 0.64 to 0.92 for unseen event types. We lastly report results from two case studies during real-world news stories, showcasing different ways our system can assist journalists filter and cross-check content as they examine the trust and veracity of content and sources.


title: Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality

abstract: Deep neural network representations play an important role in computer vision, speech, computational linguistics, robotics, reinforcement learning and many other data-rich domains. In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning. I will demonstrate this with several examples from my research team: learning to learn by gradient descent by gradient descent, neural programmers and interpreters, and learning communication.


title: Probiotics and the Gut Immune System: Indirect Regulation

abstract: The gastrointestinal tract (GIT) represents the largest interface between the human organism and the external environment. In the lumen and upper part of the mucus layer, this organ hosts an enormous number of microorganisms whose composition affects the functions of the epithelial barrier and the gut immune system. Consequentially, the microorganisms in the GIT influence the health status of the organism. Probiotics are living microorganisms which, in specific conditions, confer a health benefit to the host. Among others, probiotics have immunomodulatory properties that usually act directly by (a) increasing the activity of macrophages or natural killer cells, (b) modulating the secretion of immunoglobulins or cytokines, or indirectly by (c) enhancing the gut epithelial barrier, (d) altering the mucus secretion, and (e) competitive exclusion of other (pathogenic) bacteria. This review focuses on specific bacteria strains with indirect immunomodulatory properties. Particularly, we describe here the mechanisms through which specific probiotics enhance the gut epithelial barrier and modulate mucus production. Moreover, we describe the antimicrobial properties of specific bacteria strains. Recent data suggest that multiple pathologies are associated with an unbalanced gut microflora (dysbiosis). Although the cause-effect relationship between pathology and gut microflora is not yet well established, consumption of specific probiotics may represent a powerful tool to re-establish gut homeostasis and promote gut health.


title: Exploring consumers' impulse buying behavior on social commerce platform: The role of parasocial interaction

abstract: Task-related features affect perceived usefulness.Mood-related features affect perceived enjoyment.Social-relevant features determine users' parasocial interaction (PSI).PSI affects impulse buying tendency.Perceived enjoyment and impulse buying tendency affect urge to buy impulsively. Social commerce, a recent branch of e-commerce, has made the experience of consumers on social commerce platform (SCP) different from other contexts, as the consumers have social interactions with each other. Growing evidence also shows that consumers on these platforms are prone to impulse buying behavior owing to the social interactions. However, existing research on online impulse buying was limited when illustrating the behavior of consumers on SCPs as social relationship constructs were not included. New theoretical developments are needed in regard to fill the research gap. In this research, parasocial interaction (PSI) theory is introduced in this research to examine the influence of social relationship factors on the formation of impulse buying behavior. An empirical research has been conducted on Mogujie (www.mogujie.com), one of the most popular image-sharing SCPs in China. Results indicate that PSI exerts an impact on impulse buying tendency, the social-relevant features of the SCP determine PSI, and perceived usefulness and PSI both significantly affect perceived enjoyment. In addition, consumers' perceived enjoyment and impulse buying tendencies significantly affect their urge to buy impulsively. The implications, limitations, and discussions are provided.


title: EpicFlow: Edge-preserving interpolation of correspondences for optical flow

abstract: We propose a novel approach for optical flow estima-tion, targeted at large displacements with significant oc-clusions. It consists of two steps: i) dense matching by edge-preserving interpolation from a sparse set of matches; ii) variational energy minimization initialized with the dense matches. The sparse-to-dense interpolation relies on an appropriate choice of the distance, namely an edge-aware geodesic distance. This distance is tailored to han-dle occlusions and motion boundaries, two common and difficult issues for optical flow computation. We also pro-pose an approximation scheme for the geodesic distance to allow fast computation without loss of performance. Sub-sequent to the dense interpolation step, standard one-level variational energy minimization is carried out on the dense matches to obtain the final flow estimation. The proposed approach, called Edge-Preserving Interpolation of Corre-spondences (EpicFlow) is fast and robust to large displace-ments. It significantly outperforms the state of the art on MPI-Sintel and performs on par on KITTI and Middlebury.


Deep Neural Network based system for solving Arithmetic Word problems


title: BLEU deconstructed: Designing a Better MT Evaluation Metric

abstract: BLEU is the de facto standard automatic evaluation metric in machine translation. While BLEU is undeniably useful, it has a number of limitations. Although it works well for large documents and multiple references, it is unreliable at the sentence or sub-sentence levels, and with a single reference. In this paper, we propose new variants of BLEU which address these limitations, resulting in a more flexible metric which is not only more reliable, but also allows for more accurate discriminative training. Our best metric has better correlation with human judgements than standard BLEU, despite using a simpler formulation. Moreover, these improvements carry over to a system tuned for our new metric.


title: Distributionally Robust Games with Risk-averse Players

abstract: We present a new model of incomplete information games without private information in which the players use a distributionally robust optimization approach to cope with the payoff uncertainty. With some specific restrictions, we show that our "Distributionally Robust Game" constitutes a true generalization of three popular finite games. These are the Complete Information Games, Bayesian Games and Robust Games. Subsequently, we prove that the set of equilibria of an arbitrary distributionally robust game with specified ambiguity set can be computed as the component-wise projection of the solution set of a multi-linear system of equations and inequalities. For special cases of such games we show equivalence to complete information finite games (Nash Games) with the same number of players and same action spaces. Thus, when our game falls within these special cases one can simply solve the corresponding Nash Game. Finally, we demonstrate the applicability of our new model of games and highlight its importance.


title: Research and Design of Smart Home System Based on Zigbee Technology

abstract: A system of wireless Smart home sensor network based on ZigBee technology was proposed in this paper. A common control unit and binding idea are used in design of this system, which improves the development efficiency and makes it easy to expand and upgrade various functions. The proposed system has high availability and reliability. It could meet the requirements of daily life such as the control of commonly used household appliances and the real-time monitoring of family environment.


title: Classification of ECoG motor imagery tasks based on CSP and SVM

abstract: This paper mainly studies about the data processing of brain computer interface(BCI) and presents a kind of method for classifying the ECoG motor imagery tasks. Both the training and testing ECoG datasets were filtered with the frequency band of 8–30Hz according to the event-related desynchronization and synchronization(ERD/ERS) phenomenon. The features were extracted by using Common Spatial Pattern(CSP) and then the best starting and ending points were decided through a ten-fold cross validation(CV) of the training dataset. All the features were fed into a linear support vector machine(SVM), and the final classification accuracy of testing dataset is 90%.


title: Size and Topology Optimization for Supercapacitor-Based Sub-Watt Energy Harvesters

abstract: This paper explores sizing and topology reconfiguration strategies for charging and discharging multiple supercapacitors as energy storage in sub-Watt-scale energy harvesters. To maximize energy efficiency for storage ( $\eta_{E_{\rm storage}}$ ), total leakage power is kept low by selecting the supercapacitors to charge sequentially, alternately, or in series based on their voltages. To maximize energy efficiency for driving load ( $\eta_{E_{\rm driving}}$ ), residual energy is minimized by switching to series composition of the supercapacitors in order to raise the voltage above the minimum input voltage of the load-side dc–dc converter. Due to the nonlinear, stateful, load-dependent behavior of such harvesters, parameter sweeping is used for system-level optimization. A topology-reconfigurable structure consisting of two larger, symmetric “reservoir” supercapacitors and one voltage-raising “bootcap” proves to be efficient and practical when considering combinations of symmetric and asymmetric capacitance values. Experimental results on an actual implementation show that for charging, series topology is best when input power is high due to lower leakage, and individual topology is best when input power is low due to lower voltage; for discharging, series topology is effective in reducing residual energy while individual topology with alternating discharging effectively minimizes leakage.


title: Automated test generation for detection of leaks in Android applications

abstract: Android devices have limited hardware resources (e.g., memory). Excessive consumption of such resources may lead to crashes, poor responsiveness, battery drain, and negative user experience. We propose an approach for systematic automated test generation to expose resource leak defects in Android applications. We first define the notion of a neutral sequence of GUI events. Intuitively, such a sequence can be expected to have "neutral" effects on resource consumption, and repeated executions of this sequence should not exhibit resource growth. Using a state-of-the-art static control-flow model of an Android application, we demonstrate how to achieve automated generation of such sequences. We then define test generation algorithms for two important categories of neutral sequences, based on common leak patterns specific to Android. Our experimental evaluation compares this approach with a non-automated approach from prior work. The results from this evaluation strongly indicate that it is possible to achieve effective, general, and automated test generation for resource leaks in Android applications using the proposed techniques.


title: Leadership Effectiveness in Global Virtual Teams

abstract: The trend toward physically dispersed work groups has necessitated a fresh inquiry into the role and nature of team leadership in virtual settings. To accomplish this, we assembled thirteen culturally diverse global teams from locations in Europe, Mexico, and the United States, assigning each team a project leader and task to complete. The findings suggest that effective team leaders demonstrate the capability to deal with paradox and contradiction by performing multiple leadership roles simultaneously (behavioral complexity). Specifically, we discovered that highly effective virtual team leaders act in a mentoring role and exhibit a high degree of understanding (empathy) toward other team members. At the same time, effective leaders are also able to assert their authority without being perceived as overbearing or inflexible. Finally, effective leaders are found to be extremely effective at providing regular, detailed, and prompt communication with their peers and in articulating role relationships (responsibilities) among the virtual team members. This study provides useful insights for managers interested in developing global virtual teams, as well as for academics interested in pursuing virtual team research.


title: Environmental factors that influence the cutaneous production of vitamin D

abstract: All vertebrates, including humans, obtain most of their daily vitamin D requirement from casual exposure to sunlight. During exposure to sunlight, the solar ultraviolet B photons (290-315 nm) penetrate into the skin where they cause the photolysis of 7-dehydrocholesterol to precholecalciferol. Once formed, precholecalciferol undergoes a thermally induced rearrangement of its double bonds to form cholecalciferol. An increase in skin pigmentation, aging, and the topical application of a sunscreen diminishes the cutaneous production of cholecalciferol. Latitude, season, and time of day as well as ozone pollution in the atmosphere influence the number of solar ultraviolet B photons that reach the earth's surface, and thereby, alter the cutaneous production of cholecalciferol. In Boston, exposure to sunlight during the months of November through February will not produce any significant amounts of cholecalciferol in the skin. Because windowpane glass absorbs ultraviolet B radiation, exposure of sunlight through glass windows will not result in any production of cholecalciferol. It is now recognized that vitamin D insufficiency and vitamin D deficiency are common in elderly people, especially in those who are infirm and not exposed to sunlight or who live at latitudes that do not provide them with sunlight-mediated cholecalciferol during the winter months. Vitamin D insufficiency and deficiency exacerbate osteoporosis, cause osteomalacia, and increase the risk of skeletal fractures. Vitamin D insufficiency and deficiency can be prevented by encouraging responsible exposure to sunlight and/or consumption of a multivitamin tablet that contains 10 micrograms (400 IU) vitamin D.


title: Grounding spatial relations for outdoor robot navigation

abstract: We propose a language-driven navigation approach for commanding mobile robots in outdoor environments. We consider unknown environments that contain previously unseen objects. The proposed approach aims at making interactions in human-robot teams natural. Robots receive from human teammates commands in natural language, such as “Navigate around the building to the car left of the fire hydrant and near the tree”. A robot needs first to classify its surrounding objects into categories, using images obtained from its sensors. The result of this classification is a map of the environment, where each object is given a list of semantic labels, such as “tree” and “car”, with varying degrees of confidence. Then, the robot needs to ground the nouns in the command. Grounding, the main focus of this paper, is mapping each noun in the command into a physical object in the environment. We use a probabilistic model for interpreting the spatial relations, such as “left of” and “near”. The model is learned from examples provided by humans. For each noun in the command, a distribution on the objects in the environment is computed by combining spatial constraints with a prior given as the semantic classifier's confidence values. The robot needs also to ground the navigation mode specified in the command, such as “navigate quickly” and “navigate covertly”, as a cost map. The cost map is also learned from examples, using Inverse Optimal Control (IOC). The cost map and the grounded goal are used to generate a path for the robot. This approach is evaluated on a robot in a real-world environment. Our experiments clearly show that the proposed approach is efficient for commanding outdoor robots.


title: Generation of PLC Ladder Diagram Using Modular Structure

abstract: In this research work, auto-generation of PLC ladder diagram program is explained. This work is carried out in real time operation of PLCs in the plant and simulation of the models in the PlcStudioreg. Involved modelling and simulation tasks are performed in PlcStudioreg which is research product of digital manufacturing system (DMS) team in Ajou University, consists of input/output model and graphics tool for PLC based industries. Object-oriented approach DEVS (discrete event system) is employed in formalism of process structures, later, formalized model is simulated using plc-DEVS simulator, which is extended from conventional DEVS. Generated sequence of operation (SOP) from the simulation and mapping it into input/output table to generate executable ladder diagram is explained consecutively in the paper. The algorithm developed to connect plc-DEVS and graphics module of PlcStudioreg is elaborated. In the mean time, to show the applicability of methodology, pallet conveyor system is described.


title: DOLPHIN: an autonomous indoor positioning system in ubiquitous computing environment

abstract: Determining physical location of indoor objects is one of the key issues in development of context-aware applications in ubiquitous computing environments. This is mainly because context information obtained from sensor networks is meaningful only when the physical location of the context information source is determined. Recently, several indoor location information systems, such as Active Bat and Cricket, have been developed for precise indoor object localization. However, to provide accurate physical location tracking in large-scale space, those systems requires a lot of manual configuration for all the ultrasonic sensor nodes. To reduce configuration costs, we developed a new indoor positioning system called DOLPHIN. The DOLPHIN system consists of distributed wireless sensor nodes which are capable of sending and receiving RF and ultrasonic signals. These nodes are attached to various indoor objects. And using a novel distributed positioning algorithm in the nodes, DOLPHIN enables autonomous positioning of the objects with minimal manual configuration. This paper describes the design and implementation of the DOLPHIN system, and evaluates basic performance through several experiments in an indoor environment.


Weighting Finite-State Transductions With Neural Context


title: Design and Implementation of Broadband MEMS RHCP/LHCP Reconfigurable Arrays Using Rotated E-Shaped Patch Elements

abstract: A novel broadband RHCP/LHCP reconfigurable patch antenna array using an E-shaped patch antenna element is investigated. By applying particle swarm optimization (PSO), a challenging, combined $\mathbf{S}_{11}$ -AR bandwidth of 17% was achieved and verified through measurement for the isolated element using MEMS switches at an overall substrate thickness of $0.092 \boldsymbol{\lambda}_0$ . The achieved bandwidth is significantly higher than the current state-of-the-art in single-layer, single-feed circularly polarized (CP) patch element designs with similar substrate thickness. A small percentage of the upper frequency band experiences a pronounced beam squint similar to other thick substrate CP patch antennas. To overcome the beam squint, a novel rotated-element configuration is implemented to force pattern symmetry. Derivations of pattern symmetry and network effects are also shown. The final design prototype using rotated elements provides a measured 20% $\mathbf{S}_{11}$ -AR bandwidth with good radiation pattern stability.


title: Swarm intelligence based approach for real time UAV team coordination in search operations

abstract: This paper proposes swarm intelligence based approach for the real time coordination of groups of UAVs (Unmanned Aerial Vehicles) in tasks where values that are sensed from the aerial platform can be used to qualify the individuals. In particular, as an example application, here we consider environmental monitoring UAV teams. Their function is to monitor an area and when some undesired environmental condition arises, coordinate themselves to find the source as fast as possible. The swarm based algorithm has been extensively tested using a 3D simulation platform and validated with real UAVs flying over an industrial area.


title: Similar response of agonist and antagonist muscles after eccentric exercise revealed by electromyography and mechanomyography.

abstract: The purpose of this study was to investigate the influence of eccentric contractions (ECC) on the biceps (BB) and triceps brachii (TB) muscles during maximal voluntary contraction (MVC) of elbow flexors using electrical (EMG) and mechanomyographical activities (MMG). Each of 18 male students performed 25 submaximal contractions (50% MVC) of the elbow flexors. Root mean square amplitude (RMS) and median frequency (MDF) were calculated for the EMG and MMG signals recorded during MVC. All measurements were taken before, immediately after, 24, 48, 72, and 120 h post-ECC from the BB and TB muscles. MVC was reduced by 34% immediately after exercise and did not return to the resting value within 120 h (P0.05). The EMG MDF decreased significantly (P< or =0.05) in both muscles after ECC. The MMG RMS at 24h, 48, 72 and 120 h post-ECC was significantly lower compared to that recorded immediately after ECC in both muscles (P< or =0.05). The present research showed that (i) there were similar changes in electrical and mechanical activities during MVC after submaximal ECC in agonist and antagonist muscles suggesting a common drive controlling the agonist and antagonist motoneuron pool, (ii) the ECC induced different changes in EMG than in MMG immediately after ECC and during 120 h of recovery that suggested an increased tremor and contractile impairments, i.e., reduced rate of calcium release from the sarcoplasmic reticulum (acute effect), and changes in motor control mechanisms of agonist and antagonist muscles, and increased muscle stiffness (chronic effect).


title: Trajectory dancing modelling of humanoid robot dancing 33 degree of freedom

abstract: This paper described about humanoid robot dancing which designed like human and can dance like the real dancer with flexibility of human dancer. Part of link and frame of this humanoid robot dancing was built from 3D printing technique. Dance movement will be resemble the motion system possessed by humans which design of robot seize on mechanical approach, and will be controlled by a motion program to be included in the motion system. Humanoid currently developed to resemble human beings, including the shape of human skeleton and their functions. During this period, humanoid robot dancing rare develop a complex system of abdominal motion system. Therefore, withdrawn hypothesis presence of abdominal motion system can improve their existing motion systems. Some possible methods used in these systems, such as inverse kinematic, and forward kinematic. Trajectory dancing style rise from two kinds of kinematic. Inverse kinematic will controlled any motion of leg motion system. This system will hand over degree value in joint space as output. Position in Cartesian coordinate space as input. Timing control for every step from primitive style is needed. Therefore, actuator of ankle will not burden in execution of working because it will hand over some smoothness in every single last step in execution. Forward kinematic controlled abdominal motion system and give Cartesian coordinate space as output. From that method we were successfully built trajectory dancing modelling of humanoid robot dancing.


title: A survey on Adversarial Attacks and Defenses in Text.

abstract: Deep neural networks (DNNs) have shown an inherent vulnerability to adversarial examples which are maliciously crafted on real examples by attackers, aiming at making target DNNs misbehave. The threats of adversarial examples are widely existed in image, voice, speech, and text recognition and classification. Inspired by the previous work, researches on adversarial attacks and defenses in text domain develop rapidly. To the best of our knowledge, this article presents a comprehensive review on adversarial examples in text. We analyze the advantages and shortcomings of recent adversarial examples generation methods and elaborate the efficiency and limitations on countermeasures. Finally, we discuss the challenges in adversarial texts and provide a research direction of this aspect.


title: Hand joints-based gesture recognition for noisy dataset using nested interval unscented Kalman filter with LSTM network

abstract: Hand joints-based gesture recognition using a neural network provides excellent performance in hand gesture recognition. However, during the collection of sequential skeletal datasets, joints identification through hand pose estimations usually includes noise and even errors, which often diminish the accuracy of gesture recognition. To promote the availability of hand gesture recognition for such noisy datasets, this paper presents a nested interval unscented Kalman filter (UKF) with long short-term memory (NIUKF-LSTM) network to improve the accuracy of hand gesture recognition from noisy datasets. This nested interval method with the UKF changes the distribution of the sigma points based on two sampling intervals. By considering the information of previous frames, the nested interval method helps the NIUKF-LSTM network revise the noise in the sequential hand skeletal data and improve the recognition accuracy. The experimental results showing the removal of noisy skeletal data from the dynamic hand gesture dataset demonstrate the effectiveness of our NIUKF-LSTM network, which achieves better performance than do other state-of-the-art methods.


title: Forest fire forecasting using ensemble learning approaches

abstract: Frequent and intense forest fires have posed severe challenges to forest management in many countries worldwide. Since human experts may overlook important signals, the development of reliable prediction models with various types of data generated by automatic tools is crucial for establishing rigorous and effective forest firefighting plans. This study applied recently emerged ensemble learning methods to predict the burned area of forest fires and the occurrence of large-scale forest fires using the forest fire dataset from the University of California, Irvine machine learning repository collected from the northeastern region of Portugal. The results showed that the tuned random forest approach performed better than other regression models did with regard to the prediction accuracy of the burned area. In addition, extreme gradient boosting outperformed other classification models in terms of its predictive accuracy of large-scale fire occurrences. The findings showed that ensemble learning methods not only have great potential for broader application in forest fire automatic precaution and prevention systems but also provide important techniques for forest firefighting decision making in terms of fire resource allocation and strategies, which can ultimately improve the efficiency of forest fire management worldwide.


title: Personality and Patterns of Facebook Usage

abstract: The gamut of the present study was to explore the relationship of Big Five with facebook usage among 200 students of Himachal Pradesh University (H.P.U) within the age range of 21-30 years with equal number of males and females. Data were analyzed in terms of Correlation analysis and Regression analysis. In males, extraversion explained the maximum variance (15%), followed by conscientiousness (5%), in all these variables have explained a variance of 20%. In females, agreeableness has explained the maximum variance (15%), followed by extraversion and conscientiousness; both explaining a variance of 5% each. On the whole, these variables have explained a total variance of 25% in females. The results have shown extraversion and conscientiousness as common variables in explaining the maximum


title: Harmonic radar transceiver design: miniature tags for insect tracking

abstract: The design and operation along with verifying measurements of a harmonic radar transceiver, or tag, developed for insect tracking are presented. A short length of wire formed the antenna while a beam lead Schottky diode across a resonant loop formed the frequency doubler circuit yielding a total tag mass of less than 3 mg. Simulators using the method-of-moments for the antenna, finite-integral time-domain for the loop, and harmonic balance for the nonlinear diode element were used to predict and optimize the transceiver performance. This performance is compared to the ideal case and to measurements performed using a pulsed magnetron source within an anechoic chamber. A method for analysis of the tag is presented and used to optimize the design by creating the largest possible return signal at the second harmonic frequency for a particular incident power density. These methods were verified through measurement of tags both in isolation and mounted on insects. For excitation at 9.41 GHz the optimum tag in isolation had an antenna length of 12 mm with a loop diameter of 1 mm which yielded a harmonic cross-section of 40 mm/sup 2/. For tags mounted on Colorado potato beetles, optimum performance was achieved with an 8 mm dipole fed 2 mm from the beetle attached end. A theory is developed that describes harmonic radar in a fashion similar to the conventional radar range equation but with harmonic cross-section replacing the conventional radar cross-section. This method provides a straightforward description of harmonic radar system performance as well as provides a means to describe harmonic radar tag performance.


title: Limiting the Spread of Fake News on Social Media Platforms by Evaluating Users' Trustworthiness

abstract: Today's social media platforms enable to spread both authentic and fake news very quickly. Some approaches have been proposed to automatically detect such"fake"news based on their content, but it is difficult to agree on universal criteria of authenticity (which can be bypassed by adversaries once known). Besides, it is obviously impossible to have each news item checked by a human. In this paper, we a mechanism to limit the spread of fake news which is not based on content. It can be implemented as a plugin on a social media platform. The principle is as follows: a team of fact-checkers reviews a small number of news items (the most popular ones), which enables to have an estimation of each user's inclination to share fake news items. Then, using a Bayesian approach, we estimate the trustworthiness of future news items, and treat accordingly those of them that pass a certain"untrustworthiness"threshold. We then evaluate the effectiveness and overhead of this technique on a large Twitter graph. We show that having a few thousands users exposed to one given news item enables to reach a very precise estimation of its reliability. We thus identify more than 99% of fake news items with no false positives. The performance impact is very small: the induced overhead on the 90th percentile latency is less than 3%, and less than 8% on the throughput of user operations.


title: Topic Model Based Opinion Mining and Sentiment Analysis

abstract: This paper discusses a new topic model based approach for opinion mining and sentiment analysis of text reviews posted in web forums or social media site which are mostly in unstructured in nature. In recent years, opinions are exchanged in clouds about any product, person, event or any interested topic. These opinions help in decision making for choosing a product or getting feedback about any topic. Opinion mining and sentiment analysis are related in a sense that opining mining deals with analyzing and summarizing expressed opinions whereas sentiment analysis classifies opinionated text into positive and negative. Aspect extraction is a crucial problem in sentiment analysis. Model proposed in the paper utilizes topic model for aspect extraction and support vector machine learning technique for sentiment classification of textual reviews. The goal is to automate the process of mining attitudes, opinions and hidden emotions from text.


title: Why is My Baby Crying? An In-Depth Analysis of Paralinguistic Features and Classical Machine Learning Algorithms for Baby Cry Classification

abstract: As human beings, we begin interacting with the world by expressing our basic needs through crying. Parents strive to identify and timely address these needs before hysterical crying sets in. However, first-time parents usually fail, and this leads to frustration and feelings of helplessness. In this context, our work focuses on creating an automatic system able to distinguish between different infant needs based on crying. We extract various sets of paralinguistic features from the baby-cry audio signals and we train various rule-based or statistical classifiers. We evaluate and in-depth compare the results and obtain up to 70% accuracy on the evaluation dataset.


title: Comparing the maintainability of selenium WebDriver test suites employing different locators: a case study

abstract: Test suite maintenance tends to have the biggest impact on the overall cost of test automation. Frequently modifications applied on a web application lead to have one or more test cases broken and repairing the test suite is a time-consuming and expensive task. This paper reports on an industrial case study conducted in a small Italian company investigating on the analysis of the effort to repair web test suites implemented using different UI locators (e.g., Identifiers and XPath). The results of our case study indicate that ID locators used in conjunction with LinkText is the best solution among the considered ones in terms of time required (and LOCs to modify) to repair the test suite to the new release of the application.


title: Magnetic MIMO: how to charge your phone in your pocket

abstract: This paper bridges wireless communication with wireless power transfer. It shows that mobile phones can be charged remotely, while in the user's pocket by applying the concept of MIMO beamforming. However, unlike MIMO beamforming in communication systems which targets the radiated field, we transfer power by beamforming the non-radiated magnetic field and steering it toward the phone. We design MagMIMO, a new system for wireless charging of cell phones and portable devices. MagMIMO consumes as much power as existing solutions, yet it can charge a phone remotely without being removed from the user's pocket. Furthermore, the phone need not face the charging pad, and can charge independently of its orientation. We have built MagMIMO and demonstrated its ability to charge the iPhone and other smart phones, while in the user's pocket.


title: Padova Smart City: An urban Internet of Things experimentation

abstract: “Smart City” is a powerful paradigm that applies the most advanced communication technologies to urban environments, with the final aim of enhancing the quality of life in cities and provide a wide set of value-added services to both citizens and administration. A fundamental step towards the practical realization of the Smart City concept consists in the development of a communication infrastructure capable of collecting data from a large variety of different devices in a mostly uniform and seamless manner, according to the Internet of Things (IoT) paradigm. While the scientific and commercial interest in IoT has been constantly growing in the last years, practical experimentation of IoT systems has just begun. In this paper, we present and discuss the Padova Smart City system, an experimental realization of an urban IoT system designed within the Smart City framework and deployed in the city of Padova, Italy. We describe the system architecture and discuss the fundamental technical choices at the base of the project. Then, we analyze the data collected by the system and show how simple data processing techniques can be used to gain insights on the functioning of the monitored system, public traffic lighting in our specific case, as well as other information concerning the urban environment.


title: Membership Inference Attacks Against Machine Learning Models

abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.


title: Attention Solves Your TSP.

abstract: We propose a framework for solving combinatorial optimization problems of which the output can be represented as a sequence of input elements. As an alternative to the Pointer Network, we parameterize a policy by a model based entirely on (graph) attention layers, and train it efficiently using REINFORCE with a simple and robust baseline based on a deterministic (greedy) rollout of the best policy found during training. We significantly improve over state-of-the-art results for learning algorithms for the 2D Euclidean TSP, reducing the optimality gap for a single tour construction by more than 75% (to 0.33%) and 50% (to 2.28%) for instances with 20 and 50 nodes respectively.


title: Protein kinase C and annexins: Unusual calcium response elements in the cell

abstract: Abstract Protein kinase C and the annexins appear to share some unusual and potentially important membrane -and calcium-binding properties. While these proteins are calcium response elements, they are not calcium-binding proteins in the formal sense; at intracellular calcium concentrations, they only bind significant amounts of calcium when membranes or other suitable surfaces are present. The number of calcium ions bound per protein is large (> 8) and this stoichiometry, at the protein-membrane interface, may provide the large number of contact points needed for the very high-affinity interaction that is observed. The further ability of annexins and PKC to form structures with properties of integral membrane proteins may be important to provide a type of long-term cell signalling that produces a contitutively active kinase or ion channel activity. Selectivity for phospholipids in bilayer form is modest with respect to the acidic phospholipids but there is a surprising preference for phosphatidylethanomaline as the neutral phospholipid matrix. Along with other unusual properties, these proteins offer the potential for unique types of cell regulation events.


title: All Polarization Receiving Rectenna With Harmonic Rejection Property for Wireless Power Transmission

abstract: In this paper, a high conversion efficiency rectenna for wireless power transmission at 2.45 GHz is proposed. The rectenna is fabricated on a low-cost FR-4 substrate. The proposed design contains a dual circularly polarized patch antenna in which a high-order harmonic rejection property is embedded, and a pair of rectifying circuits without harmonic rejection filters. The dimension for the proposed rectenna design is 100×100×3.8 mm ::: 3 ::: . The proposed rectenna collects waves in all polarization senses simultaneously with a suitable air gap width, chosen to maximize antenna gain, and provide high isolation between the rectifying circuits. The rectifying circuits are fed by the wave power of orthogonally polarized waves. The conversion efficiency is improved by blocking the second and the third harmonic noise from reemission without suffering loss from filters: the second harmonic noise is rejected with a T-shape slot, while the third is rejected by a U-shape resonator. The measured conversion efficiency of the proposed rectenna reaches 82.3% when the inputs of the rectifying circuits are fed at 22 dBm, surpassing the majority of existing rectenna designs in the current literature. The functionality of reception at all polarizations is also verified, distinguishing our design from other proposals.


title: Yo-Yo Attack: Vulnerability In Auto-scaling Mechanism

abstract: In the last few years, more and more public and private networks rely on cloud and virtualization to provide the service while meeting their SLA commitments. One attractive property of the cloud is its support for rapid elasticity the ability to scale the number of machines up and down according to the load on the machine, which can be configured to occur automatically, according to customer-set thresholds. This auto-scaling mechanism provides an ability to cope with many of the basic Distributed Denial of Service (DDoS) attacks (as describe in [4]), but opens the door to a new type of attack, the Economic Denial of Sustainability attacks (EDoS) [2]. In DDoS, an attacker overwhelms the victim with bogus traffic, blocking the service from legitimate users. With a cloud-based operation, the auto-scaling mechanism ensures that a victim can cope with an attack by providing the victim with more resources to handle the attack. This solution, however, comes with an economic penalty termed EDoS, since the victim needs to pay for the extra not beneficial resources that process the bogus traffic. In many DoS attacks, the danger of the attack impact is mitigated by the expected cost to the attacker: the more effort required on the side of the attacker, who has to invest in generating large amounts of traffic, the less likely it is to occur. In this work we present the ’Yo-Yo attack’, an efficient attack on the auto-scaling mechanism, which results in an Economic Denial of Sustainability attack (EDoS) that is difficult to detect. The attack cycles between two phases repeatedly: In the on-attack phase, the attacker sends a short burst of traffic that causes the auto-scaling mechanism to perform a scale up. In the off-attack phase, the attacker stops sending the excess traffic. This second phase takes


title: 100 kHz MEMS Vibratory Gyroscope

abstract: The behavior of MEMS rate gyroscopes depends on the sensor's resonance frequency f0. Key features like robustness against external vibrations and ease of integration can be optimized by increasing f0. We designed and fabricated a device with f0=100 kHz for experimental and theoretical investigation of MEMS vibratory gyroscopes with increased f0. The frequency of 100 kHz is significantly above the typical f0 of current gyroscopes which is in the range of 10 kHz to 30 kHz. Measurements prove that key parameters that are theoretically derived from a 15 kHz reference model can be scaled to 100 kHz with good accuracy. It is shown that the increase of f0 to 100 kHz has-on the one hand-design trade offs like a much lower sensitivity and higher quadrature and-on the other hand-major advantages like the significantly improved vibration robustness.


title: An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer

abstract: Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due to random morphological, color and intensity variations of cell nuclei (immunopositive and immunonegative), manual/subjective assessment of Ki-67 scoring is error-prone and time-consuming. Hence, several machine learning approaches have been reported; nevertheless, none of them had worked on deep learning based hotspots detection and proliferation scoring. In this article, we suggest an advanced deep learning model for computerized recognition of candidate hotspots and subsequent proliferation rate scoring by quantifying Ki-67 appearance in breast cancer immunohistochemical images. Unlike existing Ki-67 scoring techniques, our methodology uses Gamma mixture model (GMM) with Expectation-Maximization for seed point detection and patch selection and deep learning, comprises with decision layer, for hotspots detection and proliferation scoring. Experimental results provide 93% precision, 0.88% recall and 0.91% F-score value. The model performance has also been compared with the pathologists’ manual annotations and recently published articles. In future, the proposed deep learning framework will be highly reliable and beneficial to the junior and senior pathologists for fast and efficient Ki-67 scoring.


title: Secure Role-Based Workflow Models

abstract: In this paper we introduce a series of reference models for Secure Role-Based Workflow systems. We build our models over the well-known RBAC96 framework. The RBAC96 model supports the notion of abstract permissions. The nature of permissions is highly dependent upon the implementation details of the system, so we interpret the permissions for a Workflow system in terms of its components such as tasks, instances of the tasks and operations on them like execute, commit, abort etc. With this interpretation, we show that most of the components of RBAC96 still remain intact. The only components that change are the nature of permissions and their assignment to roles. The models are developed using the recently introduced four-layer OM-AM framework (comprising objective, model, architecture and mechanism layers). In this paper, we focus on the top two layers of OM-AM. We systematically describe our security objectives and construct our models to address these objectives. We also formally describe the models in terms of their components and their interactions. The main purpose for proposing these models is to articulate requirements for building Secure Role-Based Workflow Systems.


title: 1D-Leaky Wave Antenna Employing Parallel-Plate Waveguide Loaded With PRS and HIS

abstract: A new type of one-dimensional leaky-wave antenna (LWA) with independent control of the beam-pointing angle and beamwidth is presented. The antenna is based on a simple structure composed of a bulk parallel-plate waveguide (PPW) loaded with two printed circuit boards (PCBs), each one consisting of an array of printed dipoles. One PCB acts as a partially reflective surface (PRS), and the other grounded PCB behaves as a high impedance surface (HIS). It is shown that an independent control of the leaky-mode phase and leakage rate can be achieved by changing the lengths of the PRS and HIS dipoles, thus resulting in a flexible adjustment of the LWA pointing direction and directivity. The leaky-mode dispersion curves are obtained with a simple Transverse Equivalent Network (TEN), and they are validated with three-dimensional full-wave simulations. Experimental results on fabricated prototypes operating at 15 GHz are reported, demonstrating the versatile and independent control of the LWA performance by changing the PRS and HIS parameters.


title: Calculation methods of tubular linear induction motor

abstract: Among different linear motors, tubular linear induction motors (TLIM) have been employed in many applications in industry due to their high efficiency and low construction cost. However mathematical simulation of such motors is different from common rotating motors by reason of end effects Proposed simplified methods are based on detailed equivalent magnetic circuits (DEMC) and axisymmetric 2D finite element method (FEM) not requiring the computing power. To validate the proposed methods, the obtained results are compared with 3D FEM calculation and experimental data.


title: Unsupervised Cipher Cracking Using Discrete GANs

abstract: This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of fidelity and for vocabularies much larger than previously achieved. We present how CycleGAN can be made compatible with discrete data and train in a stable way. We then prove that the technique used in CipherGAN avoids the common problem of uninformative discrimination associated with GANs applied to discrete data.


title: Semi-Dense Depth Interpolation using Deep Convolutional Neural Networks

abstract: With advances of recent technologies, augmented reality systems and autonomous vehicles gained a lot of interest from academics and industry. Both these areas rely on scene geometry understanding, which usually requires depth map estimation. However, in case of systems with limited computational resources, such as smartphones or autonomous robots, high resolution dense depth map estimation may be challenging. In this paper, we study the problem of semi-dense depth map interpolation along with low resolution depth map upsampling. We present an end-to-end learnable residual convolutional neural network architecture that achieves fast interpolation of semi-dense depth maps with different sparse depth distributions: uniform, sparse grid and along intensity image gradient. We also propose a loss function combining classical mean squared error with perceptual loss widely used in intensity image super-resolution and style transfer tasks. We show that with some modifications, this architecture can be used for depth map super-resolution. Finally, we evaluate our results on both synthetic and real data, and consider applications for autonomous vehicles and creating AR/MR video games.


title: Automatic Readability Classification of Crowd-Sourced Data based on Linguistic and Information-Theoretic Features

abstract: This paper presents a classifier of text readability based on information-theoretic features. The classifier was developed based on a linguistic approach to readability that explores lexical, syntactic and semantic features. For this evaluation we extracted a corpus of 645 articles from Wikipedia together with their quality judgments. We show that information-theoretic features perform as well as their linguistic counterparts even if we explore several linguistic levels at once.


title: Parallelized Rabin-Karp method for exact string matching

abstract: Exact string matching refers to the search of each and any occurrences of a string in another string. Nowadays, this issue presents itself in various segments in a great deal, starting from standard routines for exact search, which routines are implemented into programs for text editing and processing, through databases and all the way to their various applications in other sciences. One of the sciences where, among other, this kind of search has been applied on a substantial level is biology, and especially in the segment concerning DNA chains. There are numerous different more or less efficient algorithms to solution of this problem. One of more efficient algorithms is Rabin-Karp algorithm, whose complexity is linear. This work provides us with one way to parallelize this algorithm for performance on multiprocessor systems.


Influence of Word Normalization on Text Classification


title: 3D shape histograms for similarity search and classification in spatial databases

abstract: Classification is one of the basic tasks of data mining in modern database applications including molecular biology, astronomy, mechanical engineering, medical imaging or meteorology. The underlying models have to consider spatial properties such as shape or extension as well as thematic attributes. We introduce 3D shape histograms as an intuitive and powerful similarity model for 3D objects. Particular flexibility is provided by using quadratic form distance functions in order to account for errors of measurement, sampling, and numerical rounding that all may result in small displacements and rotations of shapes. For query processing, a general filter-refinement architecture is employed that efficiently supports similarity search based on quadratic forms. An experimental evaluation in the context of molecular biology demonstrates both, the high classification accuracy of more than 90% and the good performance of the approach.


title: Predicting fault incidence using software change history

abstract: This paper is an attempt to understand the processes by which software ages. We define code to be aged or decayed if its structure makes it unnecessarily difficult to understand or change and we measure the extent of decay by counting the number of faults in code in a period of time. Using change management data from a very large, long-lived software system, we explore the extent to which measurements from the change history are successful in predicting the distribution over modules of these incidences of faults. In general, process measures based on the change history are more useful in predicting fault rates than product metrics of the code: For instance, the number of times code has been changed is a better indication of how many faults it will contain than is its length. We also compare the fault rates of code of various ages, finding that if a module is, on the average, a year older than an otherwise similar module, the older module will have roughly a third fewer faults. Our most successful model measures the fault potential of a module as the sum of contributions from all of the times the module has been changed, with large, recent changes receiving the most weight.


title: Algorithmic Songwriting with ALYSIA

abstract: This paper introduces ALYSIA: Automated LYrical SongwrIting Application. ALYSIA is based on a machine learning model using Random Forests, and we discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA was used to create original pop songs that were subsequently recorded and produced. Finally, we discuss our vision for the future of Automated Songwriting for both co-creative and autonomous systems.


title: DIoT: A Self-learning System for Detecting Compromised IoT Devices

abstract: IoT devices are being widely deployed. Many of them are vulnerable due to insecure implementations and configuration. As a result, many networks already have vulnerable devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. Existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DIoT, a system for detecting compromised IoT devices effectively. In contrast to prior work, DIoT uses a novel self-learning approach to classify devices into device types and build normal communication profiles for each of these that can subsequently be used to detect anomalous deviations in communication patterns. DIoT is completely autonomous and can be trained in a distributed crowdsourced manner without requiring human intervention or labeled training data. Consequently, DIoT can cope with the emergence of new device types as well as new attacks. By systematic experiments using more than 30 off-the-shelf IoT devices, we show that DIoT is effective (94% detection rate) and fast (2 s.) at detecting devices compromised by the infamous Mirai malware. DIoT reported no false alarms when evaluated in a real-world deployment setting.


title: Sensorless vector control of asynchronous machine based on reduced order Kalman filter

abstract: The article describes sensorless vector control of the asynchronous machine based on Kalman filter. A control system block diagram is presented along with a functional scheme of the original speed observer using dynamic system state vector filter estimator. The synthesis of current regulator using machine parameters determined by inverse r model is shown. The method of a control matrix dimension reduction is proposed by removal the flux linkage calculations out of Kalman filter algorithms. Current and speed regulators, as well as a flux estimator, were implemented in the Mexbios Development Studio simulation environment. The robustness of a speed observer and the efficiency of the asynchronous machine sensorless control system were confirmed by the simulation results.


title: Risk Factors for Severe Neonatal Hyperbilirubinemia in Low and Middle-Income Countries: A Systematic Review and Meta-Analysis

abstract: Background ::: Available evidence suggests that low- and middle-income countries (LMICs) bear the greatest burden of severe neonatal hyperbilirubinemia characterized by disproportionately high rates of morbidity, mortality and neurodevelopmental disorders compared to high-income countries. We set out to identify the risk factors that contribute to the burden of severe hyperbilirubinemia in the most developmentally disadvantaged LMICs to highlight areas for action and further research. ::: ::: ::: Methods ::: We systematically searched PubMed, Scopus, Ovid EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), WHO Library Database (WHOLIS), African Index Medicus (AIM), African Journals Online (AJOL), LILACS, and IndMed for reports published between January 1990 and June 2014. We included only studies that controlled for the effects of confounding variables in determining maternal and infant risk factors for severe hyperbilirubinemia. We conducted meta-analysis of the eligible studies and computed the summary risk estimates with random effects models. ::: ::: ::: Results ::: A total of 13 studies with 1,951 subjects and 32,208 controls from India, Nigeria, Pakistan, Nepal and Egypt were identified and analyzed. The pooled data showed that primiparity (OR, 1.59; 95% CI:1.26-2.00), delivery outside public hospitals (OR, 6.42; 95% CI:1.76-23.36), ABO incompatibility (OR, 4.01; 95% CI:2.44-6.61), Rhesus hemolytic disease (OR, 20.63; 95% CI:3.95-107.65), G6PD deficiency (OR, 8.01; 95% CI:2.09-30.69), UGT1A1 polymorphisms (OR, 4.92; 95% CI:1.30-18.62), low gestational age (OR, 1.71; 95% CI:1.40-2.11), underweight/weight loss (OR, 6.26; 95% CI:1.23-31.86), sepsis (OR, 9.15; 95% CI:2.78-30.10) and high transcutaneous/total serum bilirubin levels (OR, 1.46; 95% CI:1.10-1.92) placed infants at increased risk of severe hyperbilirubinemia or bilirubin induced neurologic dysfunctions. Low social class was not associated with an increased risk of severe hyperbilirubinemia. ::: ::: ::: Conclusions ::: Infants at risk of severe hyperbilirubinemia in LMICs are associated with maternal and neonatal factors that can be effectively addressed by available interventions to curtail the disease burden prevailing in the affected countries.


title: Unsupervised Person Image Synthesis in Arbitrary Poses

abstract: We present a novel approach for synthesizing photo-realistic images of people in arbitrary poses using generative adversarial learning. Given an input image of a person and a desired pose represented by a 2D skeleton, our model renders the image of the same person under the new pose, synthesizing novel views of the parts visible in the input image and hallucinating those that are not seen. This problem has recently been addressed in a supervised manner, i.e., during training the ground truth images under the new poses are given to the network. We go beyond these approaches by proposing a fully unsupervised strategy. We tackle this challenging scenario by splitting the problem into two principal subtasks. First, we consider a pose conditioned bidirectional generator that maps back the initially rendered image to the original pose, hence being directly comparable to the input image without the need to resort to any training image. Second, we devise a novel loss function that incorporates content and style terms, and aims at producing images of high perceptual quality. Extensive experiments conducted on the DeepFashion dataset demonstrate that the images rendered by our model are very close in appearance to those obtained by fully supervised approaches.


title: Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering

abstract: This paper presents a new baseline for visual question answering task. Given an image and a question in natural language, our model produces accurate answers according to the content of the image. Our model, while being architecturally simple and relatively small in terms of trainable parameters, sets a new state of the art on both unbalanced and balanced VQA benchmark. On VQA 1.0 open ended challenge, our model achieves 64.6% accuracy on the test-standard set without using additional data, an improvement of 0.4% over state of the art, and on newly released VQA 2.0, our model scores 59.7% on validation set outperforming best previously reported results by 0.5%. The results presented in this paper are especially interesting because very similar models have been tried before but significantly lower performance were reported. In light of the new results we hope to see more meaningful research on visual question answering in the future.