title: Object-Level Context Modeling For Scene Classification with Context-CNN

abstract: Convolutional Neural Networks (CNNs) have been used extensively for computer vision tasks and produce rich feature representation for objects or parts of an image. But reasoning about scenes requires integration between the low-level feature representations and the high-level semantic information. We propose a deep network architecture which models the semantic context of scenes by capturing object-level information. We use Long Short Term Memory(LSTM) units in conjunction with object proposals to incorporate object-object relationship and object-scene relationship in an end-to-end trainable manner. We evaluate our model on the LSUN dataset and achieve results comparable to the state-of-art. We further show visualization of the learned features and analyze the model with experiments to verify our model's ability to model context.


title: Automatic generation of LEGO building instructions from multiple photographic images of real objects

abstract: We introduce a system to reconstruct large scale LEGO models from multiple two dimensional images of objects taken from different views. We employ a unit voxel with an edge length ratio of 5:5:6 for the shape from silhouette method that reconstructs an octree voxel-based three dimensional model with color information from images. We then convert the resulting voxel model with color information into a LEGO sculpture. In order to minimize the number of LEGO bricks, we use a stochastic global optimization method, simulated annealing, to hollow the model as much as possible but keep its strength for portability. Several real complex LEGO models are provided to demonstrate the effectiveness of the proposed method. Display Omitted Even beginners are able to build realistic complex LEGO models.Our system allows reconstruction of portable large scale models with color information.Easy adjustment of the trade-off between model strength and the total number of bricks is possible.


title: An Optimized RAD51 Inhibitor That Disrupts Homologous Recombination without Requiring Michael Acceptor Reactivity

abstract: Homologous recombination (HR) is an essential process in cells that provides repair of DNA double-strand breaks and lesions that block DNA replication. RAD51 is an evolutionarily conserved protein that is central to HR. Overexpression of RAD51 protein is common in cancer cells and represents a potential therapeutic target in oncology. We previously described a chemical inhibitor of RAD51, called RI-1 (referred to as compound 1 in this report). The chloromaleimide group of this compound is thought to act as a Michael acceptor and react with the thiol group on C319 of RAD51, using a conjugate addition–elimination mechanism. In order to reduce the likelihood of off-target effects and to improve compound stability in biological systems, we developed an analogue of compound 1 that lacks maleimide-based reactivity but retains RAD51 inhibitory activity. This compound, 1-(3,4-dichlorophenyl)-3-(4-methoxyphenyl)-4-morpholino-1H-pyrrole-2,5-dione, named RI-2 (referred to as compound 7a in this report), appears to b...


title: Should bisphosphonates be standard therapy for bone pain?

abstract: We have been studying bisphosphonates since the early 1980s, initially investigating etidronate in the management of hypercalcaemia and, since the mid-1980s, clodronate in the management of hypercalcaemia, bone pain, and skeletal complications in patients with bone metastases. We have also recently reported that bone metastases can be prevented or delayed in patients without evidence of bone disease but with recurrent disease at other sites. Bisphosphonates are now the standard therapy for hypercalcaemia after rehydration. For patients with bone metastases and bone pain, a trial of clodronate 600–1500 mg i.v. in 500 ml normal saline over 3 h given every 1–2 weeks is worthwhile in association with other modalities such as radiotherapy and analgesic medications. Oral clodronate or intravenous pamidronate should be given as a preventive measure in patients with established bone metastases from breast cancer and myeloma. In patients with no evidence of bone metastases, it may be that bisphosphonates can delay the emergence of bone metastases; at present this remains under clinical investigation and our pioneer trials require confirmation. Clinical trials of bisphosphonates in the treatment of hypercalcaemia, bone pain, management of patients with bone metastases and management of patients with recurrent cancer but no evidence of bone metastases will be discussed.


title: Immature Frontal Lobe Contributions to Cognitive Control in Children Evidence from fMRI

abstract: Abstract Event-related fMRI was employed to characterize differences in brain activation between children ages 8–12 and adults related to two forms of cognitive control: interference suppression and response inhibition. Children were more susceptible to interference and less able to inhibit inappropriate responses than were adults. Effective interference suppression in children was associated with prefrontal activation in the opposite hemisphere relative to adults. In contrast, effective response inhibition in children was associated with activation of posterior, but not prefrontal, regions activated by adults. Children failed to activate a region in right ventrolateral prefrontal cortex that was recruited for both types of cognitive control by adults. Thus, children exhibited immature prefrontal activation that varied according to the type of cognitive control required.


title: 3D Mesh Compression: Survey, Comparisons, and Emerging Trends

abstract: 3D meshes are commonly used to represent virtual surface and volumes. However, their raw data representations take a large amount of space. Hence, 3D mesh compression has been an active research topic since the mid 1990s. In 2005, two very good review articles describing the pioneering works were published. Yet, new technologies have emerged since then. In this article, we summarize the early works and put the focus on these novel approaches. We classify and describe the algorithms, evaluate their performance, and provide synthetic comparisons. We also outline the emerging trends for future research.


title: Deep-Syntactic Parsing

abstract: “Deep-syntactic” dependency structures that capture the argumentative, attributive and coordinative relations between full words of a sentence have a great potential for a number of NLPapplications. The abstraction degree of these structures is in-between the output of a syntactic dependency parser (connected trees defined over all words of a sentence and language-specific grammatical functions) and the output of a semantic parser (forests of trees defined over individual lexemes or phrasal chunks and abstract semantic role labels which capture the argument structure of predicative elements, dropping all attributive and coordinative dependencies). We propose a parser that delivers deep syntactic structures as output.


title: Handover Performance in 3GPP Long Term Evolution (LTE) Systems

abstract: The specification of the long term evolution (LTE) of 3G systems is currently ongoing in 3GPP with a target date of ready specification at the end of 2007. The evolved radio access network (RAN) involves a new radio interface based on OFDM technology and a radically different RAN architecture, where radio functionality is distributed into the base stations. The distributed nature of the RAN architecture calls for new radio control algorithms and procedures that operate in a distributed manner, including a distributed handover scheme as well. The most important aspects of the handover procedure in LTE has been already settled in 3GPP except a few details. In this paper we give an overview of the LTE intra-access handover procedure and evaluate its performance focusing on the user perceived performance aspects of it. We investigate the necessity of packet forwarding from a TCP throughput point of view, we analyse the problem of out of order packet delivery during handover and propose a simple solution for it. Finally, we investigate the impact of HARQ/ARQ state discard at handover on the radio efficiency. The results show that neither the user perceived performance nor the radio efficiency are compromised by the relocation based handover procedure of LTE.


title: Geometric continuous-curvature path planning for automatic parallel parking

abstract: This paper deals with path planning for car-like vehicle in parallel parking problems. Our path planning method uses simple geometry of the vehicle kinematic model. The presented strategy consists in two parts: create a simple geometric path for the parallel parking in one or more maneuvers, formed by circle arcs and then transform it to a continuous-curvature path with the use of clothoids. Accordingly, a car-like vehicle can follow such path without reorienting its front wheels at stop. The proposed planning method is independent of the initial position and of the orientation of the vehicle. Control inputs for steering angle and longitudinal velocity are generated so that the vehicle can park by following them.


title: Enhanced Experience Replay Generation for Efficient Reinforcement Learning

abstract: Applying deep reinforcement learning (RL) on real systems suffers from slow data sampling. We propose an enhanced generative adversarial network (EGAN) to initialize an RL agent in order to achieve faster learning. The EGAN utilizes the relation between states and actions to enhance the quality of data samples generated by a GAN. Pre-training the agent with the EGAN shows a steeper learning curve with a 20% improvement of training time in the beginning of learning, compared to no pre-training, and an improvement compared to training with GAN by about 5% with smaller variations. For real time systems with sparse and slow data sampling the EGAN could be used to speed up the early phases of the training process.


title: Co-evolutionary multi-task learning for dynamic time series prediction

abstract: Multi-task learning employs shared representation of knowledge for learning multiple instances from the same or related problems. Time series prediction consists of several instances that are defined by the way they are broken down into fixed windows known as embedding dimension. Finding the optimal values for embedding dimension is a computationally intensive task. Therefore, we introduce a new category of problem called dynamic time series prediction that requires a trained model to give prediction when presented with different values of the embedding dimension. This can be seen a new class of time series prediction where dynamic prediction is needed. In this paper, we propose a co-evolutionary multi-task learning method that provides a synergy between multi-task learning and coevolution. This enables neural networks to retain modularity during training for building blocks of knowledge for different instances of the problem. The effectiveness of the proposed method is demonstrated using one-step-ahead chaotic time series problems. The results show that the proposed method can effectively be used for different instances of the related time series problems while providing improved generalisation performance.


title: Concentric Tube Robots: The State of the Art and Future Directions

abstract: Seven years ago, concentric tube robots were essentially unknown in robotics, yet today one would be hard pressed to find a major medical robotics forum that does not include several presentations on them. Indeed, we now stand at a noteworthy moment in the history of these robots. The recent maturation of foundational models has created new opportunities for research in control, sensing, planning, design, and applications, which are attracting an increasing number of robotics researchers with diverse interests. The purpose of this review is to facilitate the continued growth of the subfield by describing the state of the art in concentric tube robot research. We begin with current and proposed applications for these robots and then trace their origins (some aspects of which date back to 1985), before proceeding to describe the state of the art in terms of modeling, control, sensing, and design. The paper concludes with forward-looking perspectives, noting that concentric tube robots provide rich opportunities for further research, yet simultaneously appear poised to become viable commercial devices in the near future.


title: Positive Effects of Gossiping at Work

abstract: Imagining the situation: twenty office-workers stay the whole day together but they cannot gossip at work. They work a day, a week, a month, and suddenly, their productivity and work spirit lowers, they begin to fall ill and refuse to work. The reason is obvious—they miss gossiping. People like to discuss other people’s lives; moreover, they cannot live without it. Moreover, if a person reveals his or her thoughts and ideas about something personal, he/she feels better, and it stimulates such a person to work better. On the contrary, when people keep too many secrets in memory and do not share them with anyone, they can fall into depression. It is doubtful whether any business owner wants to have depressive workers. In order to analyze the problem, one should consider that gossiping is a necessary type of communication at work, can benefit the working place in many effective ways, form the good atmosphere at work. Encouraging of positive gossiping can also bring efficient results into the working process.


title: ν-Nonparallel support vector machine for pattern classification

abstract: In this paper, we propose a novel nonparallel hyperplane classifier, named ν-nonparallel support vector machine (ν-NPSVM), for binary classification. Based on our recently proposed method, i.e., nonparallel support vector machine (NPSVM), which has been proved superior to the twin support vector machines, ν-NPSVM is parameterized by the quantity ν to let ones effectively control the number of support vectors. By combining the ν-support vector classification and the ν-support vector regression together to construct the primal problems, ν-NPSVM inherits the advantages of ν-support vector machine so that enables us to eliminate one of the other free parameters of the NPSVM: the accuracy parameter e and the regularization constant C. We describe the algorithm, give some theoretical results concerning the meaning and the choice of ν, and also report the experimental results on lots of data sets to show the effectiveness of our method.


title: Towards robust instruction-level trace alignment of binary code

abstract: Program trace alignment is the process of establishing a correspondence between dynamic instruction instances in executions of two semantically similar but syntactically different programs. In this paper we present what is, to the best of our knowledge, the first method capable of aligning realistically long execution traces of real programs. To maximize generality, our method works entirely on the machine code level, i.e. it does not require access to source code. Moreover, the method is based entirely on dynamic analysis, which avoids the many challenges associated with static analysis of binary code, and which additionally makes our approach inherently resilient to e.g. static code obfuscation. Therefore, we believe that our trace alignment method could prove to be a useful aid in many program analysis tasks, such as debugging, reverse-engineering, investigating plagiarism, and malware analysis. We empirically evaluate our method on 11 popular Linux programs, and show that it is capable of producing meaningful alignments in the presence of various code transformations such as optimization or obfuscation, and that it easily scales to traces with tens of millions of instructions.


title: Finding the weakest link in person detectors

abstract: Detecting people remains a popular and challenging problem in computer vision. In this paper, we analyze parts-based models for person detection to determine which components of their pipeline could benefit the most if improved. We accomplish this task by studying numerous detectors formed from combinations of components performed by human subjects and machines. The parts-based model we study can be roughly broken into four components: feature detection, part detection, spatial part scoring and contextual reasoning including non-maximal suppression. Our experiments conclude that part detection is the weakest link for challenging person detection datasets. Non-maximal suppression and context can also significantly boost performance. However, the use of human or machine spatial models does not significantly or consistently affect detection accuracy.


title: A stochastic approximation method for inference in probabilistic graphical models

abstract: We describe a new algorithmic framework for inference in probabilistic models, and apply it to inference for latent Dirichlet allocation (LDA). Our framework adopts the methodology of variational inference, but unlike existing variational methods such as mean field and expectation propagation it is not restricted to tractable classes of approximating distributions. Our approach can also be viewed as a "population-based" sequential Monte Carlo (SMC) method, but unlike existing SMC methods there is no need to design the artificial sequence of distributions. Significantly, our framework offers a principled means to exchange the variance of an importance sampling estimate for the bias incurred through variational approximation. We conduct experiments on a difficult inference problem in population genetics, a problem that is related to inference for LDA. The results of these experiments suggest that our method can offer improvements in stability and accuracy over existing methods, and at a comparable cost.


title: Supply chain coordination with revenue-sharing contracts: Strengths and limitations. Working paper

abstract: Under a revenue-sharing contract, a retailer pays a supplier a wholesale price for each unit purchased, plus a percentage of the revenue the retailer generates. Such contracts have become more prevalent in the videocassette rental industry relative to the more conventional wholesale price contract. This paper studies revenue-sharing contracts in a general supply chain model with revenues determined by each retailer's purchase quantity and price. Demand can be deterministic or stochastic and revenue is generated either from rentals or outright sales. Our model includes the case of a supplier selling to a classical fixed-price newsvendor or a price-setting newsvendor. We demonstrate that revenue sharing coordinates a supply chain with a single retailer (i.e., the retailer chooses optimal price and quantity) and arbitrarily allocates the supply chain's profit. We compare revenue sharing to a number of other supply chain contracts (e.g., buy-back contracts, price-discount contracts, quantity-flexibility contracts, sales-rebate contracts, franchise contracts, and quantity discounts). We find that revenue sharing is equivalent to buybacks in the newsvendor case and equivalent to price discounts in the price-setting newsvendor case. Revenue sharing also coordinates a supply chain with retailers competing in quantities, e.g., Cournot competitors or competing newsvendors with fixed prices. Despite its numerous merits, we identify several limitations of revenue sharing to (at least partially) explain why it is not prevalent in all industries. In particular, we characterize cases in which revenue sharing provides only a small improvement over the administratively cheaper wholesale price contract. Additionally, revenue sharing does not coordinate a supply chain with demand that depends on costly retail effort. We develop a variation on revenue sharing for this setting.


title: CacheBleed: a timing attack on OpenSSL constant-time RSA

abstract: The scatter–gather technique is a commonly implemented approach to prevent cache-based timing attacks. In this paper, we show that scatter–gather is not constant time. We implement a cache timing attack against the scatter–gather implementation used in the modular exponentiation routine in OpenSSL version 1.0.2f. Our attack exploits cache-bank conflicts on the Sandy Bridge microarchitecture. We have tested the attack on an Intel Xeon E5-2430 processor. For 4096-bit RSA, our attack can fully recover the private key after observing 16,000 decryptions.


title: Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains

abstract: Model-based strategies for control are critical to obtain sample efficient learning. Dyna is a planning paradigm that naturally interleaves learning and planning, by simulating one-step experience to update the action-value function. This elegant planning strategy has been mostly explored in the tabular setting. The aim of this paper is to revisit sample-based planning, in stochastic and continuous domains with learned models. We first highlight the flexibility afforded by a model over Experience Replay (ER). Replay-based methods can be seen as stochastic planning methods that repeatedly sample from a buffer of recent agent-environment interactions and perform updates to improve data efficiency. We show that a model, as opposed to a replay buffer, is particularly useful for specifying which states to sample from during planning, such as predecessor states that propagate information in reverse from a state more quickly. We introduce a semi-parametric model learning approach, called Reweighted Experience Models (REMs), that makes it simple to sample next states or predecessors. We demonstrate that REM-Dyna exhibits similar advantages over replay-based methods in learning in continuous state problems, and that the performance gap grows when moving to stochastic domains, of increasing size.


title: Gaussian mixture models with Wasserstein distance

abstract: Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets. They present, however, subtleties in training often manifesting in the discrete latent being under leveraged. In this paper, we show that such models are more amenable to training when using the Optimal Transport framework of Wasserstein Autoencoders. We find our discrete latent variable to be fully leveraged by the model when trained, without any modifications to the objective function or significant fine tuning. Our model generates comparable samples to other approaches while using relatively simple neural networks, since the discrete latent variable carries much of the descriptive burden. Furthermore, the discrete latent provides significant control over generation.


title: 3D Mapping of Surface Temperature Using Thermal Stereo

abstract: In this paper we present a simple but novel technique to recover 3D temperature map from a pair of calibrated thermal cameras. The method is also useful in estimating depth of an object in the dark environment.


title: Dense versus Sparse Approaches for Estimating the Fundamental Matrix

abstract: There are two main strategies for solving correspondence problems in computer vision: sparse local feature based approaches and dense global energy based methods. While sparse feature based methods are often used for estimating the fundamental matrix by matching a small set of sophistically optimised interest points, dense energy based methods mark the state of the art in optical flow computation. The goal of our paper is to show that this separation into different application domains is unnecessary and can be bridged in a natural way. As a first contribution we present a new application of dense optical flow for estimating the fundamental matrix. Comparing our results with those obtained by feature based techniques we identify cases in which dense methods have advantages over sparse approaches. Motivated by these promising results we propose, as a second contribution, a new variational model that recovers the fundamental matrix and the optical flow simultaneously as the minimisers of a single energy functional. In experiments we show that our coupled approach is able to further improve the estimates of both the fundamental matrix and the optical flow. Our results prove that dense variational methods can be a serious alternative even in classical application domains of sparse feature based approaches.


title: Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget

abstract: The millimeter wave (mmWave) frequency band spanning from 30 to 300 GHz constitutes a substantial portion of the unused frequency spectrum, which is an important resource for future wireless communication systems in order to fulfill the escalating capacity demand. Given the improvements in integrated components and enhanced power efficiency at high frequencies, wireless systems can operate in the mmWave frequency band. In this paper, we present a survey of the mmWave propagation characteristics, channel modeling, and design guidelines, such as system and antenna design considerations for mmWave, including the link budget of the network, which are essential for mmWave communication systems. We commence by introducing the main channel propagation characteristics of mmWaves followed by channel modeling and design guidelines. Then, we report on the main measurement and modeling campaigns conducted in order to understand the mmWave band’s properties and present the associated channel models. We survey the different channel models focusing on the channel models available for the 28, 38, 60, and 73 GHz frequency bands. Finally, we present the mmWave channel model and its challenges in the context of mmWave communication systems design.


title: Model-based Pricing for Machine Learning in a Data Marketplace

abstract: Various domains such as business intelligence and journalism have made many achievements with help of data analytics based on machine learning (ML). While a lot of work has studied how to reduce the cost of training, storing, and deploying ML models, there is little work on eliminating the data collection and purchase cost. Existing data markets provide only simplistic mechanism allowing the sale of fixed datasets with fixed price, which potentially hurts not only ML model availability to buyers with limited budget, but market expansion and thus sellers' revenue as well. In this work, we demonstrate Nimbus, a data market framework for ML model exchange. Instead of pricing data, Nimbus prices ML models directly, which we call model-based pricing (MBP). Through interactive interfaces, the audience can play the role of sellers to vend their own ML models with different price requirements, as well as the role of buyers to purchase ML model instances with different accuracy/budget constraints. We will further demonstrate how much gain of sellers' revenue and buyers' affordability Nimbus can achieve with low runtime cost via both real time and offline results.


title: Recognizing emotion in speech

abstract: The paper explores several statistical pattern recognition techniques to classify utterances according to their emotional content. The authors have recorded a corpus containing emotional speech with over a 1000 utterances from different speakers. They present a new method of extracting prosodic features from speech, based on a smoothing spline approximation of the pitch contour. To make maximal use of the limited amount of training data available, they introduce a novel pattern recognition technique: majority voting of subspace specialists. Using this technique, they obtain classification performance that is close to human performance on the task.


title: Contemporary collaborative trends and their effect in education

abstract: Collaborative learning has become an increasingly popular method of engaging students in the learning process. The activities included in this type of learning focus on the interaction that occurs between users when they are working in groups and they often incorporate solutions to real life problems. Collaborative software development to support this process has grown in popularity over the last years. In this paper, we evaluate software tools that are used in Collaborative Learning and investigate how these software tools can affect the learning process and to what extent they have influenced the educational process. In this study we focus on contemporary collaborative trends, their unique characteristics, and their assimilation and implementation to education and we propose learning activities that exploit their advantages. The results, among others, depict the significant extent of new technological achievements' effect on the educational process.


title: Staying Cool when Things Get Hot: Emotion Regulation Modulates Neural Mechanisms of Memory Encoding

abstract: During times of emotional stress, individuals often engage in emotion regulation to reduce the experiential and physiological impact of negative emotions. Interestingly, emotion regulation strategies also influence memory encoding of the event. Cognitive reappraisal is associated with enhanced memory while expressive suppression is associated with impaired explicit memory of the emotional event. However, the mechanism by which these emotion regulation strategies affect memory is unclear. We used event-related fMRI to investigate the neural mechanisms that give rise to memory formation during emotion regulation. Twenty-five participants viewed negative pictures while alternately engaging in cognitive reappraisal, expressive suppression, or passive viewing. As part of the subsequent memory design, participants returned to the laboratory two weeks later for a surprise memory test. Behavioral results showed a reduction in negative affect and a retention advantage for reappraised stimuli relative to the other conditions. Imaging results showed that successful encoding during reappraisal was uniquely associated with greater co-activation of the left inferior frontal gyrus, amygdala, and hippocampus, suggesting a possible role for elaborative encoding of negative memories. This study provides neurobehavioral evidence that engaging in cognitive reappraisal is advantageous to both affective and mnemonic processes.


title: HYDROLASING OF CONTAMINATED UNDERWATER BASIN SURFACES AT THE HANFORD K-AREA

abstract: This paper discusses selecting and Implementing hydrolasing technology to reduce radioactive contamination in preparing to dispose of the K Basins; two highly contaminated concrete basins at the Hanford Site. A large collection of spent nuclear fuel stored for many years underwater at the K Basins has been removed to stable, dry, safe storage. Remediation activities have begun for the remaining highly contaminated water, sludge, and concrete basin structures. Hydrolasing will be used to decontaminate and prepare the basin structures for disposal. The U. S. Department of Energy's (DOE) Hanford Site is considered the world's largest environmental cleanup project. The site covers 1,517 Km{sup 2} (586 square miles) along the Columbia River in an arid region of the northwest United States (U.S.). Hanford is the largest of the US former nuclear defense production sites. From the World War II era of the mid-1940s until the late-1980s when production stopped, Hanford produced 60 percent of the plutonium for nuclear defense and, as a consequence, produced a significant amount of environmental pollution now being addressed. Spent nuclear fuel was among the major challenges for DOE's environmental cleanup mission at Hanford. The end of production left Hanford with about 105,000 irradiated, solid uranium metalmore » fuel assemblies--representing approximately 2,100 metric tons (80 percent of DOE's spent nuclear fuel). The fuel was ultimately stored in the K Basins water-filled, concrete basins attached to Hanford's K East (KE) and K West (KW) reactors. K Basin's fuel accounted for 95 percent of the total radioactivity in Hanford's former reactor production areas. Located about 457 meters (500 yards) from the Columbia River, the K Basins are two indoor, rectangular structures of reinforced concrete; each filled with more than 3.8 million liters (one million gallons) of water that has become highly contaminated with long-lived radionuclides. At the KW Basin, fuel was packaged and sealed in canisters. At the KE Basin, fuel was stored in open canisters that were exposed to water in the basin. The irradiated spent nuclear fuel corroded during long-term, wet storage; resulting in thousands of fuel assemblies becoming severely corroded and/or damaged. Corrosion, especially in the KE Basin, contributed to the formation of a layer of radioactive sludge in the basins. Sludge removal is now progressing and will be followed by dewatering and dispositioning the concrete structures. The DOE Richland Operations Office (RL) has given Fluor Hanford Inc./Fluor Government Group (Fluor) the task of preparing Hanford's K Basins for decontamination and disposal. Prior to dewatering, hydrolasing will be used to decontaminate the basin surfaces to prepare them for disposal. By removing highly contaminated surface layers of concrete, hydrolasing will be used to meet the dose objectives for protecting workers and complying with regulations for transporting demolition debris. Fluor has innovated, tested, and planned the application of the hydrolasing technology to meet the challenge of decontaminating highly radioactive concrete surfaces underwater. Newly existing technology is being adapted to this unique challenge.« less


title: Seeing Through Misinformation: A Framework for Identifying Fake Online News

abstract: The fake news epidemic makes it imperative to develop a diagnostic framework that is both parsimonious and valid to guide present and future efforts in fake news detection. This paper represents one of the very first attempts to fill a void in the research on this topic. The LeSiE (Lexical Structure, Simplicity, Emotion) framework we created and validated allows lay people to identify potential fake news without the use of calculators or complex statistics by looking out for three simple cues.


title: A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

abstract: Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.


title: A Novel Voltage Driving Method Using 3-TFT Pixel Circuit for AMOLED

abstract: A novel voltage driving method using three thin-film transistors (TFTs) for active-matrix organic light-emitting diodes (OLEDs) is presented and verified by automatic integrated circuit modeling SPICE simulation. The proposed novel 3-TFT pixel circuit, which successfully compensates for the threshold voltage variations, uses few TFTs with simplified control signals, and the current nonuniformity of the proposed circuit is 0.19% to 1.99% throughout the entire data range. To compensate for variations in OLED current, the proposed circuit utilizes a novel driving scheme that uses a diode connection current source with a biased voltage.


title: Peer Relationships and Social and Recreational Activities Among Adolescents and Adults with Autism

abstract: In this study, we investigate peer relationships and participation in social and recreational activities among 235 adolescents and adults with autism who live at home. The prevalence of having friendships, peer relationships, and participating in social and recreational activities were all low and comparable to previous research. Both individual and environmental factors were investigated as predictors of having peer relationships and participation in social and recreational activities. Having peer relationships was predicted by individual characteristics (younger age, and less impairment in social interaction skills), but not by characteristics of the environment. Greater participation in social and recreational activities was predicted by characteristics of the individual with autism (greater functional independence, less impairment in social interaction skills, higher levels of internalizing behaviors) and characteristics of the environment (greater maternal participation in social and recreational activities, greater number of services received, and inclusion in integrated settings while in school).


title: Impedance control of VToL UAVs with a momentum-based external generalized forces estimator

abstract: An estimator of external generalized forces (force plus moments) acting on aerial platforms, and based on the momentum of the mechanical system, is proposed for the control of VToL UAVs together with a hierarchical architecture separating the translational and rotational dynamics of the vehicle. The closed-loop system equations are shaped as mechanical impedances, programmable through the controller gains, and forced by the residuals given by the estimation error. This arrangement allows the VToL UAVs to perform hovering and tracking tasks without a precise knowledge of the vehicle dynamics and in presence of external disturbances and unmodeled aerodynamic effects. Experiments are presented to evaluate the performance of the proposed control design.


title: A CMOS Fish Spoilage Detector for IoT Applications of Fish Markets

abstract: In this paper, a novel complementary metal–oxide–semiconductor fish spoilage detector is proposed as an application of Internet of Things (IoT) in fish markets. The proposed analog processing circuits were successfully used for detecting the freshness of swordfish. The proposed detector can be a low cost and easy-to-use alternative for housewives or for IoT applications in fish markets. Moreover, the detector can be directly delivered through transmission media of IoT devices because of its digitized output. The functions and performance of the proposed detector were evaluated and validated through measurements. The measured resistance of the proposed chip ranged from 20 $\text{k}\Omega $ to 1 $\text{M}\Omega $ , and the corresponding output period range was 6.21– $313~\mu \text{s}$ . The maximum linear error was 0.53%, and the sensitivity was 0.313 $\mu \text{s}/\text{k}\Omega $ . The chip area was measured to be $1.26\times 1.24$ mm2. The freshness detection time for the swordfish was 0–50 min, and the corresponding output period range was 107.51– $14.66~\mu \text{s}$ . The freshness of the fish could thus be easily determined based on the output period of the proposed chip. The proposed chip is suitable for IoT applications for detecting fish spoilage, such as remotely detecting fish spoilage in fish markets.


title: Applying Supervised Opinion Mining Techniques on Online User Reviews

abstract: In recent years, the spectacular development of web technologies, lead to an enormous quantity of user generated information in online systems. This large amount of information on web platforms make them viable for use as data sources, in applications based on opinion mining and sentiment analysis. The paper proposes an algorithm for detecting sentiments on movie user reviews, based on naive Bayes classifier. We make an analysis of the opinion mining domain, techniques used in sentiment analysis and its applicability. We implemented the proposed algorithm and we tested its performance, and suggested directions of development.


title: Generative Adversarial Residual Pairwise Networks for One Shot Learning

abstract: Deep neural networks achieve unprecedented performance levels over many tasks and scale well with large quantities of data, but performance in the low-data regime and tasks like one shot learning still lags behind. While recent work suggests many hypotheses from better optimization to more complicated network structures, in this work we hypothesize that having a learnable and more expressive similarity objective is an essential missing component. Towards overcoming that, we propose a network design inspired by deep residual networks that allows the efficient computation of this more expressive pairwise similarity objective. Further, we argue that regularization is key in learning with small amounts of data, and propose an additional generator network based on the Generative Adversarial Networks where the discriminator is our residual pairwise network. This provides a strong regularizer by leveraging the generated data samples. The proposed model can generate plausible variations of exemplars over unseen classes and outperforms strong discriminative baselines for few shot classification tasks. Notably, our residual pairwise network design outperforms previous state-of-theart on the challenging mini-Imagenet dataset for one shot learning by getting over 55% accuracy for the 5-way classification task over unseen classes.


title: Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?

abstract: Identifying whether an utterance is a statement, question, greeting, and so forth is integral to effective automatic understanding of natural dialog. Little is known, however, about how such dialog acts (DAs) can be automatically classified in truly natural conversation. This study asks whether current approaches, which use mainly word information, could be improved by adding prosodic information.The study is based on more than 1000 conversations from the Switchboard corpus. DAs were hand-annotated, and prosodic features (duration, pause, F0, energy, and speaking rate) were automatically extracted for each DA. In training, decision trees based on these features were inferred; trees were then applied to unseen test data to evaluate performance. Performance was evaluated for prosody models alone, and after combining the prosody models with word information—either from true words or from the output of an automatic speech recognizer.For an overall classification task, as well as three subtasks, prosody made s...


title: Modeling Bitcoin Contracts by Timed Automata

abstract: Bitcoin is a peer-to-peer cryptographic currency system. Since its introduction in 2008, Bitcoin has gained noticeable popularity, mostly due to its following properties: (1) the transaction fees are very low, and (2) it is not controlled by any central authority, which in particular means that nobody can “print” the money to generate inflation. Moreover, the transaction syntax allows to create the so-called contracts, where a number of mutually-distrusting parties engage in a protocol to jointly perform some financial task, and the fairness of this process is guaranteed by the properties of Bitcoin. Although the Bitcoin contracts have several potential applications in the digital economy, so far they have not been widely used in real life. This is partly due to the fact that they are cumbersome to create and analyze, and hence risky to use.


title: Computer vision signal processing on graphics processing units

abstract: This paper shows speedups attained by using computer graphics hardware for implementation of computer vision algorithms by efficiently mapping mathematical operations of computer vision onto modem computer graphics architecture. As an example computer vision algorithm, we implement a real-time projective camera motion tracking routine on modern, GeForce FX class GPUs (graphics processing units). Algorithms are implemented using OpenGL and the nVIDIA Cg fragment shaders. Trade-offs between computer vision requirements and GPU resources are discussed. Algorithm implementation is examined closely, and hardware bottlenecks are addressed to examine the performance of GPU architecture for computer vision. It is shown that significant speedups can be achieved, while leaving the CPU free for other signal processing tasks. Applications of our work include wearable, computer mediated reality systems that use both computer vision and computer graphics, and require realtime processing with low-latency and high throughput provided by modem GPUs.


FFSSE: Flexible Forward Secure Searchable Encryption with Efficient Performance.


title: SnapApp: Reducing Authentication Overhead with a Time-Constrained Fast Unlock Option

abstract: We present SnapApp, a novel unlock concept for mobile devices that reduces authentication overhead with a time-constrained quick-access option. SnapApp provides two unlock methods at once: While PIN entry enables full access to the device, users can also bypass authentication with a short sliding gesture ("Snap"). This grants access for a limited amount of time (e.g. 30 seconds). The device then automatically locks itself upon expiration. Our concept further explores limiting the possible number of Snaps in a row, and configuring blacklists for app use during short access (e.g. to exclude banking apps). We discuss opportunities and challenges of this concept based on a 30-day field study with 18 participants, including data logging and experience sampling methods. Snaps significantly reduced unlock times, and our app was perceived to offer a good tradeoff. Conceptual challenges include, for example, supporting users in configuring their blacklists.


title: Modeling and Control of Brushless Doubly-Fed Induction Generators in Wind Energy Applications

abstract: The development of the brushless doubly-fed induction generator (BDFIG) system and flexible power flow controller for the wind energy conversion is proposed in this paper. The system employs two cascaded induction machines to eliminate the brushes and copper rings in the traditional DFIG. The dynamic model of BDFIG with two machines' rotor electromechanically interconnected is presented and the control strategy for flexible power flow control is developed. The independent control of the active and reactive power flows is achieved by means of a four quadrant power converter under the closed-loop stator flux oriented control scheme. The experimental results obtained verify the proposed controller scheme, which allows wide operational range and reactive power control.


title: Radiographic diagnosis of proximal caries—influence of experience and gender of the dental staff

abstract: OBJECTIVES ::: The aim of this study was to examine the influence of experience and gender on the radiographic detection of proximal caries lesions and on therapeutic decisions. ::: ::: ::: MATERIALS AND METHODS ::: A survey of 220 examiners (students and employees at three universities and dentists in the field) was taken using a standardized written questionnaire concerning radiographic diagnosis and therapy planning. An expert opinion of four dentists was determined as radiographic reference. A mixed effect logistic regression model was used for statistical evaluation and the odds ratio and p values were calculated (α = 0.05). ::: ::: ::: RESULTS ::: Examiners with experience had an almost four times greater chance of a correct assessment, if proximal caries lesion was present or not, than examiners with low experience (OR 3.7 (95% CI 2.4-5.8)). No gender-specific differences were discovered (OR women vs. men 0.9 (95% CI 0.6-1.4)). There was a relationship between the severity of the burnout effect on the x-ray and false positive caries diagnosis. Overall, 43% of respondents would plan invasive treatment in the enamel on a patient at low risk of caries and 78% on a high-risk patient. The results showed that the more experienced practitioners would be more likely to postpone restorative therapy on proximal caries until the lesion reached a later stage. ::: ::: ::: CONCLUSIONS ::: The results of this study suggest that examiner's experience influences the radiographic diagnosis of proximal lesions. No gender-specific differences could be found. ::: ::: ::: CLINICAL RELEVANCE ::: Examiner's professional experience is an important factor when radiography is included for detection and treatment planning of proximal lesions.


title: A review of industrial wireless networks in the context of Industry 4.0

abstract: There have been many recent advances in wireless communication technologies, particularly in the area of wireless sensor networks, which have undergone rapid development and been successfully applied in the consumer electronics market. Therefore, wireless networks (WNs) have been attracting more attention from academic communities and other domains. From an industrial perspective, WNs present many advantages including flexibility, low cost, easy deployment and so on. Therefore, WNs can play a vital role in the Industry 4.0 framework, and can be used for smart factories and intelligent manufacturing systems. In this paper, we present an overview of industrial WNs (IWNs), discuss IWN features and related techniques, and then provide a new architecture based on quality of service and quality of data for IWNs. We also propose some applications for IWNs and IWN standards. Then, we will use a case from our previous achievements to explain how to design an IWN under Industry 4.0. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make IWNs truly ubiquitous for a wide range of applications.


title: Controllable Video Generation with Sparse Trajectories

abstract: Video generation and manipulation is an important yet challenging task in computer vision. Existing methods usually lack ways to explicitly control the synthesized motion. In this work, we present a conditional video generation model that allows detailed control over the motion of the generated video. Given the first frame and sparse motion trajectories specified by users, our model can synthesize a video with corresponding appearance and motion. We propose to combine the advantage of copying pixels from the given frame and hallucinating the lightness difference from scratch which help generate sharp video while keeping the model robust to occlusion and lightness change. We also propose a training paradigm that calculate trajectories from video clips, which eliminated the need of annotated training data. Experiments on several standard benchmarks demonstrate that our approach can generate realistic videos comparable to state-of-the-art video generation and video prediction methods while the motion of the generated videos can correspond well with user input.


title: Dynamic Video Segmentation Network

abstract: In this paper, we present a detailed design of dynamic video segmentation network (DVSNet) for fast and efficient semantic video segmentation. DVSNet consists of two convolutional neural networks: a segmentation network and a flow network. The former generates highly accurate semantic segmentations, but is deeper and slower. The latter is much faster than the former, but its output requires further processing to generate less accurate semantic segmentations. We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score. Frame regions with a higher expected confidence score traverse the flow network. Frame regions with a lower expected confidence score have to pass through the segmentation network. We have extensively performed experiments on various configurations of DVSNet, and investigated a number of variants for the proposed decision network. The experimental results show that our DVSNet is able to achieve up to 70.4% mIoU at 19.8 fps on the Cityscape dataset. A high speed version of DVSNet is able to deliver an fps of 30.4 with 63.2% mIoU on the same dataset. DVSNet is also able to reduce up to 95% of the computational workloads.


title: What does it take to generate natural textures

abstract: Natural image generation is currently one of the most actively explored fields in Deep Learning. Many approaches, e.g. for state-of-the-art artistic style transfer or natural texture synthesis, rely on the statistics of hierarchical representations in supervisedly trained deep neural networks. It is, however, unclear what aspects of this feature representation are crucial for natural image generation: is it the depth, the pooling or the training of the features on natural images? We here address this question for the task of natural texture synthesis and show that none of the above aspects are indispensable. Instead, we demonstrate that natural textures of high perceptual quality can be generated from networks with only a single layer, no pooling and random filters.


title: NASA's Genesis and Rapid Intensification Processes (GRIP) Field Experiment

abstract: In August–September 2010, NASA, NOAA, and the National Science Foundation (NSF) conducted separate but closely coordinated hurricane field campaigns, bringing to bear a combined seven aircraft with both new and mature observing technologies. NASA's Genesis and Rapid Intensification Processes (GRIP) experiment, the subject of this article, along with NOAA's Intensity Forecasting Experiment (IFEX) and NSF's Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) experiment, obtained unprecedented observations of the formation and intensification of tropical cyclones. The major goal of GRIP was to better understand the physical processes that control hurricane formation and intensity change, specifically the relative roles of environmental and inner-core processes. A key focus of GRIP was the application of new technologies to address this important scientific goal, including the first ever use of the unmanned Global Hawk aircraft for hurricane science operations. NASA and NOAA conducted coord...


title: Convergent Learning: Do different neural networks learn the same representations?

abstract: Recent success in training deep neural networks have prompted active investigation into the features learned on their intermediate layers. Such research is difficult because it requires making sense of non-linear computations performed by millions of parameters, but valuable because it increases our ability to understand current models and create improved versions of them. In this paper we investigate the extent to which neural networks exhibit what we call convergent learning, which is when the representations learned by multiple nets converge to a set of features which are either individually similar between networks or where subsets of features span similar low-dimensional spaces. We propose a specific method of probing representations: training multiple networks and then comparing and contrasting their individual, learned representations at the level of neurons or groups of neurons. We begin research into this question using three techniques to approximately align different neural networks on a feature level: a bipartite matching approach that makes one-to-one assignments between neurons, a sparse prediction approach that finds one-to-many mappings, and a spectral clustering approach that finds many-to-many mappings. This initial investigation reveals a few previously unknown properties of neural networks, and we argue that future research into the question of convergent learning will yield many more. The insights described here include (1) that some features are learned reliably in multiple networks, yet other features are not consistently learned; (2) that units learn to span low-dimensional subspaces and, while these subspaces are common to multiple networks, the specific basis vectors learned are not; (3) that the representation codes show evidence of being a mix between a local code and slightly, but not fully, distributed codes across multiple units.


title: Random Projection Ensemble Classifiers

abstract: We introduce a novel ensemble model based on random projections. The contribution of using random projections is two-fold. First, the randomness provides the diversity which is required for the construction of an ensemble model. Second, random projections embed the original set into a space of lower dimension while preserving the dataset’s geometrical structure to a given distortion. This reduces the computational complexity of the model construction as well as the complexity of the classification. Furthermore, dimensionality reduction removes noisy features from the data and also represents the information which is inherent in the raw data by using a small number of features. The noise removal increases the accuracy of the classifier.


title: Look me in the Eyes: A Survey of Eye and Gaze Animation for Virtual Agents and Artificial Systems

abstract: A person's emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: "The face is the portrait of the mind; the eyes, its informers.". This presents a huge challenge for computer graphics researchers in the generation of artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human-human interactions. This State of the Art Report provides an overview of the efforts made on tackling this challenging task. As with many topics in Computer Graphics, a cross-disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user. We discuss the movement of the eyeballs, eyelids, and the head from a physiological perspective and how these movements can be modelled, rendered and animated in computer graphics applications. Further, we present recent research from psychology and sociology that seeks to understand higher level behaviours, such as attention and eye-gaze, during the expression of emotion or during conversation, and how they are synthesised in Computer Graphics and Robotics.


title: The Economics of Click Fraud

abstract: Click fraud is a substantial threat in the cyberworld. Here, the author examines the contexts, mechanisms, and processes associated with the click-fraud industry from an economics viewpoint. The nature of electronic channels, characterized by asymmetric hypermediation, provides a fertile ground for such fraud.


title: FPGA-based remote pulse rate detection using photoplethysmographic imaging

abstract: This paper presents first several steps towards an FPGA-based electronic system for remote pulse rate (PR) measurement. The system uses a low-cost digital camera as an image sensor, which operates at up to 30 frames per second (fps) in WXGA (1200×800 pixels) resolution. A novel algorithm for PR measurement was implemented using an FPGA development board. A commercially-available photoplethysmography module (TP-TSD200A from BIOPAC) was used as a golden standard to verify the performance of the suggested system. Ten male subjects were simultaneously examined using both the suggested system and the golden standard, and the results were compared. The proposed system leads to a potential means for providing mobile healthcare using smart phones and other mobile consumer products.


title: Design of an Assistive Communication Glove Using Combined Sensory Channels

abstract: This paper presents a new design of a wireless sensor glove developed for American Sign Language finger spelling gesture recognition. Five contact sensors are installed on the glove, in addition to five flex sensors on the fingers and a 3D accelerometer on the back of the hand. Each pair of flex and contact sensors are combined into the same input channel on the BSN node in order to save the number of channels and the installation area. After which, the signal is analyzed and separated back into flex and contact features by software. With electrical contacts and wirings made of conductive fabric and threads, the glove design has become thinner and more flexible. For validation, ASL finger spelling gesture recognition experiments have been performed on signals collected from six speech-impaired subjects and a normal subject. With the new sensor glove design, the experimental results have shown a significant increase in classification accuracy.


title: Secure Double-Layered Defense against HTTP-DDoS Attacks

abstract: A major cyber-security concern to date for webservers are Distributed Denial of Service (DDoS) attacks. Previously we proposed a novel overlay-based method consisting of distributed network of public servers (PS) for preparation, and access nodes (AN) for actual communication. The AN's performance is evaluated under difficult to detect HTTP(S)-DDoS attacks. Yet, attackers may attempt service denial by attacking the PS instead. The focus in this paper is on mitigating complex slow-requesting HTTP-DDoS attacks that target the PS. A proof-of-concept prototype is implemented with simplified countermeasures and tested. We report on the results of two experiments. Results suggest that the simple PS role can enable high mitigation factors of both high-rate and low-rate attack traffic per source, even with 10,000 unique attack sources per target PS, acting as a second layer of defense with the AN. Yet, with a cost of longer time to load the requested resource file in comparison to direct access.


title: Impact of 3-D Advertising on Product Knowledge, Brand Attitude, and Purchase Intention: The Mediating Role of Presence

abstract: Abstract The conceptualization of a virtual experience has emerged because advancements in computer technology have led to a movement toward more multisensory online experiences. Two studies designed to explore the concepts of virtual experience and presence are presented, with the results largely supporting the proposition that 3-D advertising is capable of enhancing presence and, to varying degrees, ultimately influencing the product knowledge, brand attitude, and purchase intention of consumers. The marketing implications are immediate because the ability to create a compelling virtual product experience is not beyond the current capability of interactive advertising. By creating compelling on-line virtual experiences, advertisers can potentially enhance the value of product information presented and engage consumers in an active user-controlled product experience.s


title: Testing non-wearable fall detection methods in the homes of older adults

abstract: In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a two-webcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.


title: Analysis and QoE evaluation of cloud gaming service adaptation under different network conditions: The case of NVIDIA GeForce NOW

abstract: Cloud gaming represents a highly interactive service whereby game logic is rendered in the cloud and streamed as a video to end devices. While benefits include the ability to stream high-quality graphics games to practically any end user device, drawbacks include high bandwidth requirements and very low latency. Consequently, a challenge faced by cloud gaming service providers is the design of algorithms for adapting video streaming parameters to meet the end user system and network resource constraints. In this paper, we conduct an analysis of the commercial NVIDIA GeForce NOW game streaming platform adaptation mechanisms in light of variable network conditions. We further conduct an empirical user study involving the GeForce NOW platform to assess player Quality of Experience when such adaptation mechanisms are employed. The results provide insight into limitations of the currently deployed mechanisms, as well as aim to provide input for the proposal of designing future video encoding adaptation strategies.


title: Ultra Reliable UAV Communication Using Altitude and Cooperation Diversity

abstract: The use of unmanned aerial vehicles (UAVs) serving as aerial base stations is expected to become predominant in the next decade. However, in order, for this technology, to unfold its full potential, it is necessary to develop a fundamental understanding of the distinctive features of air-to-ground (A2G) links. As a contribution in this direction, this paper proposes a generic framework for the analysis and optimization of the A2G systems. In contrast to the existing literature, this framework incorporates both height-dependent path loss exponent and small-scale fading, and unifies a widely used ground-to-ground channel model with that of A2G for the analysis of large-scale wireless networks. We derive analytical expressions for the optimal UAV height that minimizes the outage probability of an arbitrary A2G link. Moreover, our framework allows us to derive a height-dependent closed-form expression for the outage probability of an A2G cooperative communication network. Our results suggest that the optimal location of the UAVs with respect to the ground nodes does not change by the inclusion of ground relays. This enables interesting insights about the deployment of future A2G networks, as the system reliability could be adjusted dynamically by adding relaying nodes without requiring changes in the position of the corresponding UAVs. Finally, to optimize the network for multiple destinations, we derive an optimum altitude of the UAV for maximum coverage region by guaranteeing a minimum outage performance over the region.


title: Solid state circuit breakers for DC micrgrids: Current status and future trends

abstract: Short circuit protection remains one of the major technical barriers in DC microgrids. This paper reviews state of the art of DC solid state circuit breakers (SSCBs). A new concept of a self-powered SSCB using normally-on wideband gap (WBG) semiconductor devices as the main static switch is described in this paper. The new SSCB detects short circuit faults by sensing its terminal voltage rise, and draws power from the fault condition itself to turn and hold off the static switch. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or extra wiring. Challenges and future trends in protecting low voltage distribution microgrids against short circuit and other faults are discussed.


title: Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems

abstract: Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms. This paper presents a new data-driven framework for root-cause analysis, based on a spatiotemporal graphical modeling approach built on the concept of symbolic dynamics for discovering and representing causal interactions among sub-systems of complex CPSs. We formulate the root-cause analysis problem as a minimization problem via the proposed inference based metric and present two approximate approaches for root-cause analysis, namely the sequential state switching ($S^3$, based on free energy concept of a restricted Boltzmann machine, RBM) and artificial anomaly association ($A^3$, a classification framework using deep neural networks, DNN). Synthetic data from cases with failed pattern(s) and anomalous node(s) are simulated to validate the proposed approaches. Real dataset based on Tennessee Eastman process (TEP) is also used for comparison with other approaches. The results show that: (1) $S^3$ and $A^3$ approaches can obtain high accuracy in root-cause analysis under both pattern-based and node-based fault scenarios, in addition to successfully handling multiple nominal operating modes, (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy, and (3) the proposed framework is robust and adaptive in different fault conditions and performs better in comparison with the state-of-the-art methods.


Question Generation via Overgenerating Transformations and Ranking


title: Altering the Intestinal Microbiota during a Critical Developmental Window Has Lasting Metabolic Consequences

abstract: Summary Acquisition of the intestinal microbiota begins at birth, and a stable microbial community develops from a succession of key organisms. Disruption of the microbiota during maturation by low-dose antibiotic exposure can alter host metabolism and adiposity. We now show that low-dose penicillin (LDP), delivered from birth, induces metabolic alterations and affects ileal expression of genes involved in immunity. LDP that is limited to early life transiently perturbs the microbiota, which is sufficient to induce sustained effects on body composition, indicating that microbiota interactions in infancy may be critical determinants of long-term host metabolic effects. In addition, LDP enhances the effect of high-fat diet induced obesity. The growth promotion phenotype is transferrable to germ-free hosts by LDP-selected microbiota, showing that the altered microbiota, not antibiotics per se, play a causal role. These studies characterize important variables in early-life microbe-host metabolic interaction and identify several taxa consistently linked with metabolic alterations. PaperClip


title: JAIST: Combining multiple features for Answer Selection in Community Question Answering

abstract: In this paper, we describe our system for SemEval-2015 Task 3: Answer Selection in Community Question Answering. In this task, the systems are required to identify the good or potentially good answers from the answer thread in Community Question Answering collections. Our system combines 16 features belong to 5 groups to predict answer quality. Our final model achieves the best result in subtask A for English, both in accuracy and F1score.


title: Survey on Automatic Number Plate Recognition (ANR)

abstract: Day by day we have been heard about the news of vehicle getting stolen from parking or from any other place in the city. So, to keep track of that stolen vehicle we should have to install the CCTV camera on every signal in the city. Also we have to install the number plate detection system which can detect the number plate of every vehicle on the traffic signal. For detecting the number plate from the moving vehicle there are many algorithm has been developed but still this area always remain evolving each every year, In number plate detection system image processing plays an important role, the system consist of basic operations preprocessing, image conversion from RGB to Gray, apply edge detection, apply morphological operators on same image then extract plate region from image and last process the plate region using OCR (optical character recognition).Every algorithm in this category always follows this basic steps, each algorithm has some pros and cons, because same algorithm cannot be useful for different environmental condition. The Algorithm’s efficiency is totally depends upon the quality of input image. E.g. resolution of camera, intensity of the image, illumination of image, shadow effect etc. In this paper we are focusing on which are different algorithm has been developed so far to improve the efficiency of the number plate detection system.


Training RNNs with global-local losses


title: Designing motivational agents: The role of praise, social comparison, and embodiment in computer feedback

abstract: The present study draws on theories of attribution, social comparison, and social facilitation to investigate how computers might use principles of motivation and persuasion to provide user feedback. In an online experiment, 192 participants performed a speed-reading task. The independent variables included whether or not the verbal feedback from the computer involved praise, whether the objective feedback showed that the participants were performing better or worse from their peers, and whether or not the feedback was presented by an on-screen agent. The main dependent variables included a subjective measure of participants' intrinsic motivation and an objective measure of their task persistence. Results showed that providing participants with praise or comparative information on others' performance improved intrinsic motivation. When praised, participants whose performances were comparatively low persisted in the task longer than those whose performances were comparatively high did. Additionally, the mere presence of an embodied agent on the screen increased participants' motivation. Together, these results indicate that praise and social comparison can serve as effective forms of motivational feedback and that humanlike embodiment further improves user motivation.


title: Truthful Color Reproduction in Spatial Augmented Reality Applications

abstract: Spatial augmented reality is especially interesting for the design process of a car, because a lot of virtual content and corresponding real objects are used. One important issue in such a process is that the designer can trust the visualized colors on the real object, because design decisions are made on basis of the projection. In this paper, we present an interactive visualization technique which is able to exactly compute the RGB values for the projected image, so that the resulting colors on the real object are equally perceived as the real desired colors. Our approach computes the influences of the ambient light, the material, the pose and the color model of the projector to the resulting colors of the projected RGB values by using a physically based computation. This information allows us to compute the adjustment for the RGB values for varying projector positions at interactive rates. Since the amount of projectable colors does not only depend on the material and the ambient light, but also on the pose of the projector, our method can be used to interactively adjust the range of projectable colors by moving the projector to arbitrary positions around the real object. We further extend the mentioned method so that it is applicable to multiple projectors. All methods are evaluated in a number of experiments.


title: Tweeting is believing?: understanding microblog credibility perceptions

abstract: Twitter is now used to distribute substantive content such as breaking news, increasing the importance of assessing the credibility of tweets. As users increasingly access tweets through search, they have less information on which to base credibility judgments as compared to consuming content from direct social network connections. We present survey results regarding users' perceptions of tweet credibility. We find a disparity between features users consider relevant to credibility assessment and those currently revealed by search engines. We then conducted two experiments in which we systematically manipulated several features of tweets to assess their impact on credibility ratings. We show that users are poor judges of truthfulness based on content alone, and instead are influenced by heuristics such as user name when making credibility assessments. Based on these findings, we discuss strategies tweet authors can use to enhance their credibility with readers (and strategies astute readers should be aware of!). We propose design improvements for displaying social search results so as to better convey credibility.


title: Component-Based Software Engineering

abstract: Components are "hot," and changing at a fast pace. Here are industry forecasts from two leaders in the field, as well as their choices for inclusion in this focus: a state-of-the-practice overview and some of the latest work in CBSE.


title: Reducing off-chip memory traffic in deep CNNs using stick buffer cache

abstract: Recent studies show that traffic between the Convolutional Neural Network (CNN) accelerators and off-chip memory becomes critical with respect to the energy consumption, as the networks become deeper in order to improve performance. This is especially important for low power embedded applications. Since on-chip data transfer is much less expensive in terms of power consumption, significant improvement can be obtained by caching and reusing previously transferred off-chip data. However, due to unique caching pattern, which is adequate for calculations of convolutions within CNNs, standard cache memories would not be efficient for this purpose. In this paper, we propose an intelligent on-chip memory architecture which allows caching and significant reduction of feature map transfer from off-chip memory, during computations of convolutional layers in CNNs. Experiment results show that the proposed scheme can reduce off-chip feature map traffic up to 98.5% per convolutional layer for AlexNet and 89% for each convolutional layer of VGG-16.


title: Android phone forensic: Tools and techniques

abstract: Today when there are more than 1 billion Android users all over the world, it shows that its popularity has no equal. These days mobile phones have become so intrusive in our daily lives that when they needed can give huge amount of information to forensic examiners. Till the date of writing this paper there are many papers citing the need of mobile device forensic and ways of getting the vital artifacts through mobile devices for different purposes. With vast options of popular and less popular forensic tools and techniques available today, this papers aims to bring them together under a comparative study so that this paper could serve as a starting point for several android users, future forensic examiners and investigators. During our survey we found scarcity for papers on tools for android forensic. In this paper we have analyzed different tools and techniques used in android forensic and at the end tabulated the results and findings.


title: A Fast, Minimal Memory, Consistent Hash Algorithm

abstract: We present jump consistent hash, a fast, minimal memory, consistent hash algorithm that can be expressed in about 5 lines of code. In comparison to the algorithm of Karger et al., jump consistent hash requires no storage, is faster, and does a better job of evenly dividing the key space among the buckets and of evenly dividing the workload when the number of buckets changes. Its main limitation is that the buckets must be numbered sequentially, which makes it more suitable for data storage applications than for distributed web caching.


title: The Trace Amines: Recent Overview and Future Pointers

abstract: At the 1st International Conference on the Trace Amines held in Edmonton, Canada in 1983, [Proceedings of which have been published Boulton et al. (1984)], not only was the new work up to the end of 1982 described but in addition, the general properties of the trace amines and their identification and quantitative analysis was reviewed. This 2nd meeting held in Tubingen, West Germany covers work completed since then. Clearly everything could not be included and so the organizers have chosen to concentrate on the neuro-and psycho-pharmacology of the trace amines and some bioclinical findings. Not all the trace amines could be included, for example, histamine, the polyamines, aliphatic amines and the classical biogenic amines when present in ‘trace’ concentrations, were ommitted as was the burgeoning role of the trace amines in invertebrate systems; rather, as in 1983, it was the arylalkylamines on which we focussed.


title: A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content

abstract: Many studies have demonstrated that the sensory and motor systems are activated during conceptual processing. Such results have been interpreted as indicating that concepts, and important aspects of cognition more broadly, are embodied. That conclusion does not follow from the empirical evidence. The reason why is that the empirical evidence can equally be accommodated by a ‘disembodied’ view of conceptual representation that makes explicit assumptions about spreading activation between the conceptual and sensory and motor systems. At the same time, the strong form of the embodied cognition hypothesis is at variance with currently available neuropsychological evidence. We suggest a middle ground between the embodied and disembodied cognition hypotheses – grounding by interaction. This hypothesis combines the view that concepts are, at some level, ‘abstract’ and ‘symbolic’, with the idea that sensory and motor information may ‘instantiate’ online conceptual processing.


title: Decision-free true positive estimation with grid maps for multi-object tracking

abstract: A challenge for future advanced driver assistant systems is to establish a reliable environment perception. Recently, advanced multi-object tracking algorithms were presented. These algorithms consider spatial uncertainties and clutter detections from several different sensors and a fusion process combines all the information in a probabilistic framework. Especially the true positive probability of object measurements is taken into account by these algorithms. This contribution presents an approach to estimate the true positive probability of object measurements in order to improve the tracking results. One source of information is a grid map based motion estimation, which applies the Dempster-Shafer theory of evidence. The other source is the result of an object classification algorithm based on the outer appearance of vehicles. First results of an evaluation with real world data are presented.


title: Vaccine Hesitancy: Causes, Consequences, and a Call to Action.

abstract: Vaccine hesitancy reflects concerns about the decision to vaccinate oneself or one's children. There is a broad range of factors contributing to vaccine hesitancy, including the compulsory nature of vaccines, their coincidental temporal relationships to adverse health outcomes, unfamiliarity with vaccine-preventable diseases, and lack of trust in corporations and public health agencies. Although vaccination is a norm in the U.S. and the majority of parents vaccinate their children, many do so amid concerns. The proportion of parents claiming non-medical exemptions to school immunization requirements has been increasing over the past decade. Vaccine refusal has been associated with outbreaks of invasive Haemophilus influenzae type b disease, varicella, pneumococcal disease, measles, and pertussis, resulting in the unnecessary suffering of young children and waste of limited public health resources. Vaccine hesitancy is an extremely important issue that needs to be addressed because effective control of vaccine-preventable diseases generally requires indefinite maintenance of extremely high rates of timely vaccination. The multifactorial and complex causes of vaccine hesitancy require a broad range of approaches on the individual, provider, health system, and national levels. These include standardized measurement tools to quantify and locate clustering of vaccine hesitancy and better understand issues of trust; rapid, independent, and transparent review of an enhanced and appropriately funded vaccine safety system; adequate reimbursement for vaccine risk communication in doctors' offices; and individually tailored messages for parents who have vaccine concerns, especially first-time pregnant women. The potential of vaccines to prevent illness and save lives has never been greater. Yet, that potential is directly dependent on parental acceptance of vaccines, which requires confidence in vaccines, healthcare providers who recommend and administer vaccines, and the systems to make sure vaccines are safe.


title: Perceptual correlates of Cantonese tones

abstract: Abstract This study investigated measurements related to average f0 level and f0 change over eight consecutive sections of the whole vocalic segment, for their roles as both acoustic and perceptual correlates of Cantonese lexical tones. Twenty-four CV Cantonese words, making up four sets of words that minimally contrasted in the six Cantonese tones (tones 55, 25, 33, 21, 23, and 22) were read by 10 speakers once in random order. Ten expert listeners and twenty naive listeners were asked to identify the words. Discriminant analysis showed that, in both production and perception, direction and magnitude of f0 change over the later part (6th and 7th sections) of the vocalic segment were important correlates of tone identity. f0 change provided separation both between contour tones (tones 25, 23, 21) and level tones (tones 55, 33, 22) as well as among contour tones. Average f0 provided separation among the level tones. Findings correspond well with description of tones based on Wang's phonological features of ‘rising’, ‘contour’, ‘falling’, and ‘height’.


title: Who Said What: Modeling Individual Labelers Improves Classification

abstract: Data are often labeled by many different experts with each expert only labeling a small fraction of the data and each data point being labeled by several experts. This reduces the workload on individual experts and also gives a better estimate of the unobserved ground truth. When experts disagree, the standard approaches are to treat the majority opinion as the correct label or to model the correct label as a distribution. These approaches, however, do not make any use of potentially valuable information about which expert produced which label. To make use of this extra information, we propose modeling the experts individually and then learning averaging weights for combining them, possibly in sample-specific ways. This allows us to give more weight to more reliable experts and take advantage of the unique strengths of individual experts at classifying certain types of data. Here we show that our approach leads to improvements in computer-aided diagnosis of diabetic retinopathy. We also show that our method performs better than competing algorithms by Welinder and Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative approach for dealing with the myriad real-world settings that use expert opinions to define labels for training.


Personality at Work: Individual Differences in the Workplace


title: Word of mouth and interpersonal communication: A review and directions for future research

abstract: People often share opinions and information with their social ties, and word of mouth has an important impact on consumer behavior. But what drives interpersonal communication and why do people talk about certain things rather than others? This article argues that word of mouth is goal driven and serves five key functions (i.e., impression management, emotion regulation, information acquisition, social bonding, and persuasion). Importantly, I suggest these motivations are predominantly self- (rather than other) serving and drive what people talk about even without their awareness. Further, these drivers make predictions about the types of news and information people are most likely to discuss. This article reviews the five proposed functions and well as how contextual factors (i.e., audience and communication channel) may moderate which functions play a larger role. Taken together, the paper provides insight into the psychological factors that shape word of mouth and outlines additional questions that deserve further study.


title: Analysis of different PM machines with concentrated windings and flux barriers in stator core

abstract: Φ Abstract - The new stator structure with magnetic flux-barriers in the stator yoke or tooth region represents an efficient method for reducing the sub-harmonics of electric machines with fractional slots, tooth-concentrated windings. In this paper the both flux-barriers techniques are considered during the analysis of different PM machines. The 12-teeth single-layer and double-layer concentrated winding in combination with a 10-poles and 14-poles PM rotor are investigated. For the all machine topologies the new stator design is used to improve their performances and characteristics. The flux-barrier effects on the main machine parameters, such as in the air-gap flux density harmonics, dq-machine parameters, characteristic currents, electromagnetic torque, and so on, are studied carefully. Comparisons performed with the analogous conventional machines (with conventional stator) show that, the new stator design offers significant advantages.


title: Entity-driven Type Hierarchy Construction for Freebase

abstract: The hierarchical structure of a knowledge base system can lead to various valuable applications; however, many knowledge base systems do not have such property. In this paper, we propose an entity-driven approach to automatically construct the hierarchical structure of entities for knowledge base systems. By deriving type dependencies from entity information, the initial graph of types will be constructed, and then modified to become a hierarchical structure by several graph algorithms. Experimental results show the effectiveness of our method in terms of constructing reasonable type hierarchy for knowledge base systems.


title: Mutations in EZH2 Cause Weaver Syndrome

abstract: We used trio-based whole-exome sequencing to analyze two families affected by Weaver syndrome, including one of the original families reported in 1974. Filtering of rare variants in the affected probands against the parental variants identified two different de novo mutations in the enhancer of zeste homolog 2 (EZH2). Sanger sequencing of EZH2 in a third classically-affected proband identified a third de novo mutation in this gene. These data show that mutations in EZH2 cause Weaver syndrome.


title: Supporting visual quality assessment with machine learning

abstract: Objective metrics for visual quality assessment often base their reliability on the explicit modeling of the highly non-linear behavior of human perception; as a result, they may be complex and computationally expensive. Conversely, machine learning (ML) paradigms allow to tackle the quality assessment task from a different perspective, as the eventual goal is to mimic quality perception instead of designing an explicit model the human visual system. Several studies already proved the ability of ML-based approaches to address visual quality assessment; nevertheless, these paradigms are highly prone to overfitting, and their overall reliability may be questionable. In fact, a prerequisite for successfully using ML in modeling perceptual mechanisms is a profound understanding of the advantages and limitations that characterize learning machines. This paper illustrates and exemplifies the good practices to be followed.


title: Calibration drift in regression and machine learning models for acute kidney injury

abstract: Objective ::: Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population. ::: ::: ::: Materials and Methods ::: Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years. ::: ::: ::: Results ::: Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods. ::: ::: ::: Conclusions ::: Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.


title: Alleviating the “dark side” of smart phone use

abstract: Technology offers great potential to reshape our relationship to work, but the form of that reshaping should not be allowed to happen haphazardly. As work and technology use become increasingly intertwined, a number of issues deserve re-examination. Some of these relate to work intensification and/or longer hours and possible exchange for flexibility. Recent research on use of employer-supplied smart phones offers some insight into employee perceptions of why the company supplies this technology and whether there is risk to declining the opportunity. Because dangers are more readily apparent, current limitations of technology use have been approached more often through laws related to driving than through general policies or regulation about the work itself. However, there are other concerns that may translate into employer liability beyond the possibility of car accidents. A variety of these concerns are covered in this article, along with related suggestion for actions by employers, their advisory groups, technology companies, government and employees themselves.


title: Telepresence Interaction by Touching Live Video Images

abstract: This paper presents a telepresence interaction framework and system based on touch screen and telepresence robot technologies. The system is composed of a telepresence robot and tele-interactive devices in a remote environment (presence space), the touching live video image user interface (TIUI) used by an operator (user) in an operation space, and wireless network connecting the two spaces. A tele-interactive device refers to a real object with its identification, actuator, and Wireless communication. A telepresence robot is used as the embodiment of an operator to go around in the presence space to actively capture live videos. The TIUI is our new user interface which allows an operator simply uses a pad to access the system anywhere to not only remotely operate the telepresence robot but also interact with a tele-interactive device, just by directly touching its live video image as if him/her to do it in the presence. The preliminary evaluation and demonstration show the efficacy and promising of our framework and system.


title: Partially connected hybrid beamforming for large antenna arrays in multi-user MISO systems

abstract: Hybrid beamforming (HBF) is a promising approach to be employed in millimeter-wave massive MIMO systems. In this paper, four HBF algorithms with partially connected radio frequency architecture are proposed for large antenna arrays in multi-user MISO systems. The first two algorithms aim at minimizing the difference between either the fully digital zero forcing (ZF) or maximum ratio transmission (MRT) beamformer, and the hybrid beamformer of each user. The other two algorithms apply either ZF or MRT HBF solution to each subarray. The average sum rate performance of the proposed algorithms are evaluated using a realistic geometry-based stochastic channel model, and compared with digital ZF and MRT approaches. Numerical results demonstrate that the subarray-based ZF algorithm is superior to other proposed hybrid methods in all simulation cases.


title: DOI: 10.1080/13576500444000317 Self-compassion, Achievement Goals, and Coping with Academic Failure

abstract: Two studies examined the relationship between self-compassion, academic achievement goals, and coping with perceived academic failure among undergraduates. Selfcompassion entails being kind to oneself in instances of failure, perceiving one’s experiences as part of the larger human experience, and holding painful feelings in mindful awareness. Study 1 (N = 222) found that self-compassion was positively associated with mastery goals and negatively associated with performance goals, a relationship that was mediated by the lesser fear of failure and greater perceived competence of self-compassionate individuals. Study 2 confirmed these findings among students who perceived their recent midterm grade as a failure (N = 110), with results also indicating that self-compassion was positively associated with emotion-focused coping strategies and negatively associated with avoidance-oriented strategies.


title: AI’s War on Manipulation: Are We Winning?

abstract: We provide an overview of more than two decades of work, mostly in AI, that studies computational complexity as a barrier against manipulation in elections.


title: Predictive Control of Power Converters: Designs With Guaranteed Performance

abstract: In this work, a cost function design based on Lyapunov stability concepts for finite control set model predic- tive control is proposed. This predictive controller design allows one to characterize the performance of the controlled converter, while providing sufficient conditions for local stability for a class of power converters. Simulation and experimental results on a buck dc-dc converter and a two-level dc-ac inverter are conducted to validate the effectiveness of our proposal.