Requirements for Safe Robots : Measurements , Analysis and New Insights. Physical human–robot interaction and cooperation has become a topic of increasing importance and of major focus in robotics research. An essential requirement of a robot designed for high mobility and direct interaction with human users or uncertain environments is that it must in no case pose a threat to the human. Until recently, quite a few attempts were made to investigate real-world threats via collision tests and use the outcome to considerably improve safety during physical human–robot interaction. In this paper, we give an overview of our systematic evaluation of safety in human–robot interaction, covering various aspects of the most significant injury mechanisms. In order to quantify the potential injury risk emanating from such a manipulator, impact tests with the DLR-Lightweight Robot III were carried out using standard automobile crash test facilities at the German Automobile Club (ADAC). Based on these tests, several industrial robots of different weight have been evaluated and the influence of the robot mass and velocity have been investigated. The evaluated non-constrained impacts would only partially capture the nature of human–robot safety. A possibly constrained environment and its effect on the resulting human injuries are discussed and evaluated from different perspectives. As well as such impact tests and simulations, we have analyzed the problem of the quasi-static constrained impact, which could pose a serious threat to the human even for low-inertia robots under certain circumstances. Finally, possible injuries relevant in robotics are summarized and systematically classified. KEY WORDS—Physical Human-Robot Interaction, Simulation, Flexible Arms, Mechanics, Design and Control, Force Control The International Journal of Robotics Research Vol. 00, No. 00, Xxxxxxxx 2009, pp. 000–000 DOI: 10.1177/0278364909343970 c The Author(s), 2009. Reprints and permissions: http://www.sa

Question: What is the difference between a robot and a robot?


Accessory Axillary Breast Tissues. Dr. Emsen recently published a case report describing a patient with accessory axillary breast tissue treated with ultrasound-guided liposuction [3]. We recently treated a similar case of bilateral axillary breast tissue, and we offer our contribution to the debate (Figs. 1, 2). Accessory axillary breast tissues are common embriogenic alterations found in 1% to 6% of women that often manifest bilaterally [1,2]. Ectopic breast tissue is at risk for benign and malignant breast disease with reported diagnoses of fibrocystic disease, mastitis, fibroadenoma, atypical hyperplasia, and carcinoma [4 6]. In a review of 82 ectopic breast cancer cases, Marshall et al. [5] found an increased incidence of cancer in aberrant breast tissue. Ectopic cancers located in the axilla seemed to present with more extensive disease at an earlier age, suggesting that aberrant breast tissue may be at increased risk for malignant change. According to this view, as for any ectopic mass, tissue diagnosis is required and mandatory [4]. We recently treated a 43-year-old woman who was asymptomatic until her second pregnancy (2004), when two bilateral masses appeared in axillae and began to increase in size. Six months after parturition, a clear fluid was continuously secreted throughout few small pores on the skin. Ultrasounddiagnosed breast tissue and excision with histologic examination confirmed the radiologic findings. In this case, we preoperatively contemplated, as Dr. Emsen did, less invasive and aesthetic methods such as liposuction. However, a collegial discussion with our pathologist and an analysis of literature data suggested en bloc removal for a more precise analysis not only of cellular, but also of histologic characteristics of suspected mammary ducts. We believe that liposuction, more traumatic on tissues than simple excision, does not allow for this. Furthermore, although liposuction leaves minimal incision scars, the Fig. 1. A 43-year-old woman

Question: What is the difference between liposuction and excision?


How to Prove Yourself: Practical Solutions to Identification and Signature Problems. In this paper we describe simple identification and signature schemes which enable any user to prove his identity and the authenticity of his messages to any other user without shared or public keys. The schemes are provably secure against any known or chosen message attack if factoring is difficult, and typical implementations require only 1% to 4% of the number of modular multiplications required by the RSA scheme. Due to their simplicity, security and speed, these schemes are ideally suited for microprocessor-based devices such as smart cards, personal computers, and remote control systems.

Question: How to prove yourself?


An ontology-based model to determine the automation level of an automated vehicle for co-driving. Full autonomy of ground vehicles is a major goal of the ITS (Intelligent Transportation Systems) community. However, reaching such highest autonomy level in all situations (weather, traffic, ...) may seem difficult in practice, despite recent results regarding driverless cars (e.g., Google Cars). In addition, an automated vehicle should also self-assess its own perception abilities, and not only perceive its environment. In this paper, we propose an intermediate approach towards full automation, by defining a spectrum of automation layers, from fully manual (the car is driven by a driver) to fully automated (the car is driven by a computer), based on an ontological model for representing knowledge. We also propose a second ontology for situation assessment (what does the automated car perceive?), including the sensors/actuators state, environmental conditions and driver's state. Finally, we also define inference rules to link the situation assessment ontology to the automation level one. Both ontological models have been built and first results are presented.

Question: What is an ontology?


Insights from Price's equation into evolutionary epidemiology. The basic reproduction number, denoted by R0, is one of the most important quantities in epidemiological theory ([11], [23]). It is defined as the expected number of new infections generated by an infected individual in an otherwise wholly susceptible population ([2], [12], [23]). Part of the reason why R0 plays such a central role in this body of theory undoubtedly stems from its relatively simple and intuitively sensible interpretation as a measure of pathogen reproduction. If R0 is less than unity then we expect the pathogen to die out since each infected individual fails to generate at least one other infection during the lifetime of the infection. Given that R0 is a measure of pathogen reproductive success, it is not surprising that this quantity has also come to form the basis of most evolutionary considerations of host-pathogen interactions ([1], [18]). For example, mathematical models for numerous epidemiological settings have been used to demonstrate that natural selection is often expected to favour the pathogen strain that results in the largest value of R0 ([6], [18]). In more complex epidemiological settings such optimization criteria typically cannot be derived and instead a game-theoretic approach is taken ([5]). In this context a measure of the fitness of a rare mutant pathogen strain is used to characterize the evolutionarily stable strain (i.e., the strain that, if present within the population in sufficient numbers, cannot be displaced by any mutant strain that arises). Typically R0 again plays a central role as the measure of mutant fitness in such invasion analyses ([10], [18], [30]). In this chapter we consider an alternative approach for developing theory in evolutionary epidemiology. Rather than using the total number of new infections generated by an infected individual (i.e., R0) as a measure of pathogen fitness we use the instantaneous rate of change of the number of infected h

Question: What is the basic reproduction number?


A Live-User Study of Opinionated Explanations for Recommender Systems. This paper describes an approach for generating rich and compelling explanations in recommender systems, based on opinions mined from user-generated reviews. The explanations highlight the features of a recommended item that matter most to the user and also relate them to other recommendation alternatives and the user's past activities to provide a context.

Question: What is a recommender system?


Gated End-to-End Memory Networks. Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks (MemN2N) have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact questionanswering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-toEnd trainable Memory Network architecture (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the Dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art. ∗work done as an Intern at XRCE

Question: What is the state of the art on the 20 bAbI dataset?


DLM: Decentralized Linearized Alternating Direction Method of Multipliers. This paper develops the Decentralized Linearized Alternating Direction Method of Multipliers (DLM) that minimizes a sum of local cost functions in a multiagent network. The algorithm mimics operation of the decentralized alternating direction method of multipliers (DADMM) except that it linearizes the optimization objective at each iteration. This results in iterations that, instead of successive minimizations, implement steps whose cost is akin to the much lower cost of the gradient descent step used in the distributed gradient method (DGM). The algorithm is proven to converge to the optimal solution when the local cost functions have Lipschitz continuous gradients. Its rate of convergence is shown to be linear if the local cost functions are further assumed to be strongly convex. Numerical experiments in least squares and logistic regression problems show that the number of iterations to achieve equivalent optimality gaps are similar for DLM and ADMM and both much smaller than those of DGM. In that sense, DLM combines the rapid convergence of ADMM with the low computational burden of DGM.

Question: What is the difference between DGM and DLM?


An improved wavelet based shock wave detector. In this paper, the detection of shock wave that generated by supersonic bullet is considered. A wavelet based multi-scale products method has been widely used for detection. However, the performance of method decreased at low signal-to-noise ratio (SNR). It is noted that the method does not consider the distribution of the signal and noise. Thus we analyze the method under the standard likelihood ratio test in this paper. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An improved detector under the standard likelihood ratio test is proposed. Monte Carlo simulations is conducted with simulated shock waves under additive white Gaussian noise. The result shows that this new detection algorithm outperforms the conventional detection algorithm.

Question: What is the difference between a wavelet and a wavelet transform?


Deep transfer learning with ontology for image classification. Purely, data-driven large scale image classification has been achieved using various feature descriptors like SIFT, HOG etc. Major milestone in this regards is Convolutional Neural Networks (CNN) based methods which learn optimal feature descriptors as filters. Little attention has been given to the use of domain knowledge. Ontology plays an important role in learning to categorize images into abstract classes where there may not be a clear visual connect between category and image, for example identifying image mood - happy, sad and neutral. Our algorithm combines CNN and ontology priors to infer abstract patterns in Indian Monument Images. We use a transfer learning based approach in which, knowledge of domain is transferred to CNN while training (top down transfer) and inference is made using CNN prediction and ontology tree/priors (bottom up transfer ). We classify images to categories like Tomb, Fort and Mosque. We demonstrate that our method improves remarkably over logistic classifier and other transfer learning approach. We conclude with a remark on possible applications of the model and note about scaling this to bigger ontology.

Question: What is the difference between transfer learning and deep learning?


Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach. Tax fraud is the intentional act of lying on a tax return form with intent to lower one's tax liability. Under-reporting is one of the most common types of tax fraud, it consists in filling a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment.  Detecting tax fraud is one of the main priorities of local tax authorities which are required to develop cost-efficient strategies to tackle this problem. Most of the recent works in tax fraud detection are based on supervised machine learning techniques that make use of labeled or audit-assisted data. Regrettably, auditing tax declarations is a slow and costly process, therefore access to labeled historical information is extremely limited. For this reason, the applicability of supervised machine learning techniques for tax fraud detection is severely hindered.  Such limitations motivate the contribution of this work. We present a novel approach for the detection of potential fraudulent tax payers using only unsupervised learning techniques and allowing the future use of supervised learning techniques. We demonstrate the ability of our model to identify under-reporting taxpayers on real tax payment declarations, reducing the number of potential fraudulent tax payers to audit. The obtained results demonstrate that our model doesn't miss on marking declarations as suspicious and labels previously undetected tax declarations as suspicious, increasing the operational efficiency in the tax supervision process without needing historic labeled data.

Question: How to detect tax fraud?


Physical Primitive Decomposition. Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact with the world. In this paper, we study physical primitive decomposition—understanding an object through its components, each with physical and geometric attributes. As annotated data for object parts and physics are rare, we propose a novel formulation that learns physical primitives by explaining both an object's appearance and its behaviors in physical events. Our model performs well on block towers and tools in both synthetic and real scenarios; we also demonstrate that visual and physical observations often provide complementary signals. We further present ablation and behavioral studies to better understand our model and contrast it with human performance.

Question: What is physical primitive decomposition?


Gravitational Search Algorithm and Recent Trends. A R T I C L E I N F O A B S T R A C T Article history: Received 20 March 2017 Received in revised form 22 March 2017 Accepted 28 March 2017 Available online 31March 2017 The inability of conventional optimization algorithms to handle exponential increase in the size of the search space has paved way towards capturing the behavior of natural organisms for solving optimization problems. Nature Inspired Algorithms have mostly been associated with the swarm behavior of organisms. A rather new idea in this respect was proposed by Rashedi et al in 2009 by the name 'Gravitational Search Algorithm'. The basis of their proposal, as clear from the name, is the Newtonian gravity and interactions between populations of masses. The algorithm has been widely used since then in many applications of science. Since its proposal, researches have been continuously headed in extending GSA for different scenarios and applications. The working of GSA and some such popular and recent proposals in the direction of extending GSA are discussed in this paper.

Question: What is the gravitational search algorithm?


The validity of the Hospital Anxiety and Depression Scale. An updated literature review.. OBJECTIVE To review the literature of the validity of the Hospital Anxiety and Depression Scale (HADS).   METHOD A review of the 747 identified papers that used HADS was performed to address the following questions: (I) How are the factor structure, discriminant validity and the internal consistency of HADS? (II) How does HADS perform as a case finder for anxiety disorders and depression? (III) How does HADS agree with other self-rating instruments used to rate anxiety and depression?   RESULTS Most factor analyses demonstrated a two-factor solution in good accordance with the HADS subscales for Anxiety (HADS-A) and Depression (HADS-D), respectively. The correlations between the two subscales varied from.40 to.74 (mean.56). Cronbach's alpha for HADS-A varied from.68 to.93 (mean.83) and for HADS-D from.67 to.90 (mean.82). In most studies an optimal balance between sensitivity and specificity was achieved when caseness was defined by a score of 8 or above on both HADS-A and HADS-D. The sensitivity and specificity for both HADS-A and HADS-D of approximately 0.80 were very similar to the sensitivity and specificity achieved by the General Health Questionnaire (GHQ). Correlations between HADS and other commonly used questionnaires were in the range.49 to.83.   CONCLUSIONS HADS was found to perform well in assessing the symptom severity and caseness of anxiety disorders and depression in both somatic, psychiatric and primary care patients and in the general population.

Question: How does HADS perform as a case finder for anxiety disorders and depression?


CPU and/or GPU: Revisiting the GPU Vs. CPU Myth. Parallel computing using accelerators has gained widespre ad research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. Howev er, accelerator based computing has significantly relegated the role of CPUs in computation. As CPUs evo lv and also offer matching computational resources, it is important to also include CPUs in the comput ation. We call this thehybrid computing model. Indeed, most computer systems of the present age offe r a d gree of heterogeneity and therefore such a model is quite natural. We reevaluate the claim of a recent paper by Lee et al.(ISCA 20 10). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+ GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, spars e matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-c ore Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CP U with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good ad vantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 9 0% resource efficient on average. Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale yst ms with significant performance gains and resource efficiency to the large scale user community.

Question: How to use a CPU+ GPU platform efficiently?


IL-17 and Th17 Cells.. CD4+ T cells, upon activation and expansion, develop into different T helper cell subsets with different cytokine profiles and distinct effector functions. Until recently, T cells were divided into Th1 or Th2 cells, depending on the cytokines they produce. A third subset of IL-17-producing effector T helper cells, called Th17 cells, has now been discovered and characterized. Here, we summarize the current information on the differentiation and effector functions of the Th17 lineage. Th17 cells produce IL-17, IL-17F, and IL-22, thereby inducing a massive tissue reaction owing to the broad distribution of the IL-17 and IL-22 receptors. Th17 cells also secrete IL-21 to communicate with the cells of the immune system. The differentiation factors (TGF-beta plus IL-6 or IL-21), the growth and stabilization factor (IL-23), and the transcription factors (STAT3, RORgammat, and RORalpha) involved in the development of Th17 cells have just been identified. The participation of TGF-beta in the differentiation of Th17 cells places the Th17 lineage in close relationship with CD4+CD25+Foxp3+ regulatory T cells (Tregs), as TGF-beta also induces differentiation of naive T cells into Foxp3+ Tregs in the peripheral immune compartment. The investigation of the differentiation, effector function, and regulation of Th17 cells has opened up a new framework for understanding T cell differentiation. Furthermore, we now appreciate the importance of Th17 cells in clearing pathogens during host defense reactions and in inducing tissue inflammation in autoimmune disease.

Question: What is the difference between Th1 and Th2 cells?


Mechanisms of emotional arousal and lasting declarative memory. Neuroscience is witnessing growing interest in understanding brain mechanisms of memory formation for emotionally arousing events, a development closely related to renewed interest in the concept of memory consolidation. Extensive research in animals implicates stress hormones and the amygdaloid complex as key, interacting modulators of memory consolidation for emotional events. Considerable evidence suggests that the amygdala is not a site of long-term explicit or declarative memory storage, but serves to influence memory-storage processes in other brain regions, such as the hippocampus, striatum and neocortex. Human-subject studies confirm the prediction of animal work that the amygdala is involved with the formation of enhanced declarative memory for emotionally arousing events.

Question: What is the amygdala?


Practical Techniques for Searches on Encrypted Data. It is desirable to store data on data storage servers such as mail servers and file servers in encrypted form to reduce security and privacy risks. But this usually implies that on e has to sacrifice functionality for security. For example, if a client wishes to retrieve only documents containing certai n words, it was not previously known how to let the data storage server perform the search and answer the query without loss of data confidentiality. In this paper, we describe our cryptographic schemes for the problem of searching on encrypted data and provide proofs of security for the resulting crypto systems. Ou r techniques have a number of crucial advantages. They are provably secure : they provideprovable secrecy for encryption, in the sense that the untrusted server cannot learn anything about the plaintext when only given the ciphertext; they providequery isolationfor searches, meaning that the untrusted server cannot learn anything more about the plaintext than the search result; they provide controlled searching, so that the untrusted server cannot search for an arbitrary word without the user's authorization; they also supporthidden queries , so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. The algorithms we present are simple, fast (for a document of length n, the encryption and search algorithms only need O(n) stream cipher and block cipher operations), and introduce almost no space and communication overhead, and hence are practical to use today. We gratefully acknowledge support for this research from se veral US government agencies. This research was suported in part by t he Defense Advanced Research Projects Agency under DARPA contract N66 01-9928913 (under supervision of the Space and Naval Warfare Syst ems Center San Diego), by the National Science foundation under grant F D99-79852, and by the United States Postal Service under

Question: What is the difference between a mail server and a file server?


The critical role of retrieval practice in long-term retention. Learning is usually thought to occur during episodes of studying, whereas retrieval of information on testing simply serves to assess what was learned. We review research that contradicts this traditional view by demonstrating that retrieval practice is actually a powerful mnemonic enhancer, often producing large gains in long-term retention relative to repeated studying. Retrieval practice is often effective even without feedback (i.e. giving the correct answer), but feedback enhances the benefits of testing. In addition, retrieval practice promotes the acquisition of knowledge that can be flexibly retrieved and transferred to different contexts. The power of retrieval practice in consolidating memories has important implications for both the study of memory and its application to educational practice.

Question: What is the role of retrieval practice in long-term retention?


Measuring the Impact of Network Performance on Cloud-Based Speech Recognition Applications. Cloud-based speech recognition systems enhance Web surfing, transportation, health care, etc. For example, using voice commands helps drivers search the Internet without affecting traffic safety risks. User frustration with network traffic problems can affect the usability of these applications. The performance of these type of applications should be robust in difficult network conditions. We evaluate the performance of several client-server speech recognition applications, under various network conditions. We measure transcription delay and accuracy of each application under different packet loss and jitter values. Results of our study show that performance of client-server speech recognition systems is affected by jitter and packet loss; which commonly occur in WiFi and cellular networks.

Question: How do you measure the performance of a speech recognition application?


Brain activities associated with gaming urge of online gaming addiction.. The aim of this study was to identify the neural substrates of online gaming addiction through evaluation of the brain areas associated with the cue-induced gaming urge. Ten participants with online gaming addiction and 10 control subjects without online gaming addiction were tested. They were presented with gaming pictures and the paired mosaic pictures while undergoing functional magnetic resonance imaging (fMRI) scanning. The contrast in blood-oxygen-level dependent (BOLD) signals when viewing gaming pictures and when viewing mosaic pictures was calculated with the SPM2 software to evaluate the brain activations. Right orbitofrontal cortex, right nucleus accumbens, bilateral anterior cingulate and medial frontal cortex, right dorsolateral prefrontal cortex, and right caudate nucleus were activated in the addicted group in contrast to the control group. The activation of the region-of-interest (ROI) defined by the above brain areas was positively correlated with self-reported gaming urge and recalling of gaming experience provoked by the WOW pictures. The results demonstrate that the neural substrate of cue-induced gaming urge/craving in online gaming addiction is similar to that of the cue-induced craving in substance dependence. The above-mentioned brain regions have been reported to contribute to the craving in substance dependence, and here we show that the same areas were involved in online gaming urge/craving. Thus, the results suggest that the gaming urge/craving in online gaming addiction and craving in substance dependence might share the same neurobiological mechanism.

Question: What brain areas are involved in online gaming urge/craving?


Sources of Customer Satisfaction and Dissatisfaction with Information Technology Help Desks. As the use, develophient, and control of information systems continues to become diffused throughout organizations and society, the information technology (IT) help desk function plays an increasingly important role in effective management and utilization of information resources. Variously referred to as information centers, software support centers, software hotlines, and PC help desks, such centers have been established tohelp end users resolve problems and obtain information about new functions in the information systems they use. This study investigates the determinants of customer satisfaction and dissatisfaction with service encounters involving information technologyhelp desks. The IS satisfaction research that has been done to datehas viewed satisfaction as an attitudinal, bipolar evaluative construct (Melone 1990; Heckman 1993). Satisfaction as viewed in this way is a relatively enduring, stable cognitive state. In the marketing literature, however, satisfaction has also been conceptualized as a less enduring post-consumption response. In this study, a conceptualization of the satisfaction construct based on the service encounter and consumer product satisfaction literatures (e.g; Bitner, Boon}s and Tetreault 1990) is adopted as a starting point. After responses to help desk service encounters have been analyzed from this perspective, an attempt is made to integrate these findings with attitudinal satisfaction constructs. The study employs the Critical Incident Technique (CIT), an inductive, qualitative methodology. It consists of a set of specifically defined procedures for collecting observations of human behavior and classifying them in such a way as to make them useful in addressing practical problems (Flanagan 1954). It is a I thod that is comparable to other inductive grouping procedures such as factor analysis, cluster analysis, and multidimensional scaling.

Question: What is the critical incident technique?


Policy Gradient Methods for Robotics. The acquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of high-dimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm

Question: What is the policy gradient method for robotics?


Safe Planes for Injection Rhinoplasty: A Histological Analysis of Midline Longitudinal Sections of the Asian Nose. Dorsal nasal augmentation is an essential part of injection rhinoplasty on the Asian nose. Aesthetic physicians require detailed knowledge of the nasal anatomy to accurately and safely inject filler. One hundred and thirty-five histological cross sections were examined from 45 longitudinal strips of soft tissue harvested from the midline of the nose, beginning from the glabella to the nasal tip. Muscles and nasal cartilage were used as landmarks for vascular identification. At the nasal tip, a midline longitudinal columellar artery with a diameter of 0.21 ± 0.09 mm was noted in 14 cadavers (31.1 %). At the infratip, subcutaneous tissue contained cavernous tissue similar to that of the nasal mucosa. The feeding arteries of these dilated veins formed arteriovenous shunts, into which retrograde injection of filler may be possible. All of the nasal arteries present were identified as subcutaneous arteries. They coursed mainly in the superficial layer of the subcutaneous tissues, with smaller branches forming subdermal plexuses. A substantial arterial anastomosis occurred at the supratip region, in which the artery lay in the middle of the subcutaneous tissue at the level of the major alar cartilages. These arteries had a diameter ranging between 0.4 and 0.9 mm and were found in 29 of 45 specimens (64.4 %). This was at the level midway between the rhinion above the supratip and the infratip. This anastomotic artery also crossed the midline at the rhinion superficial to the origin of the procerus on the lower end of the nasal bone. Here the arterial diameter ranged between 0.1 and 0.3 mm, which was not large enough to cause arterial emboli. Fascicular cross sections of the nasalis muscle directly covered the entire upper lateral cartilage. The subdermal tissue contained few layers of fat cells along with the occasional small artery. The procerus arose from th

Question: What is the diameter of the arteries in the nose?


Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery. Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features Remote Sens. 2015, 7 14681 from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the lowand mid-level features. Moreover, we tentatively combine features extracted from differen

Question: How to transfer features from pre-trained CNNs for high-resolution remote sensing scene classification?


Theodor Lipps and the shift from "sympathy" to "empathy".. In the course of extensive philosophical debates on aesthetics in nineteenth-century Germany, Robert Vischer introduced the concept of Einfühlung in relation to art. Theodor Lipps subsequently extended it from art to visual illusions and interpersonal understanding. While Lipps had regarded Einfühlung as basically similar to the old notion of sympathy, Edward Titchener in America believed it had a different meaning. Hence, he coined the term empathy as its translation. This term came to be increasingly widely adopted, first in psychology and then more generally. But the lack of agreement about the supposed difference between these concepts suggests that Lipps had probably been right.

Question: What is the difference between sympathy and empathy?


Design and Development of GPS-GSM Based Tracking System with Google Map Based Monitoring. GPS is one of the technologies that are used in a huge number of applications today. One of the applications is tracking your vehicle and keeps regular monitoring on them. This tracking system can inform you the location and route travelled by vehicle, and that information can be observed from any other remote location. It also includes the web application that provides you exact location of target. This system enables us to track target in any weather conditions. This system uses GPS and GSM technologies. The paper includes the hardware part which comprises of GPS, GSM, Atmega microcontroller MAX 232, 16x2 LCD and software part is used for interfacing all the required modules and a web application is also developed at the client side. Main objective is to design a system that can be easily installed and to provide platform for further enhancement.

Question: What is the main objective of the paper?


UAV borne real-time road mapping system. Road information is useful in many fields. In this paper, a real-time mapping system is presented to acquire the image and determine the geometry of the road. These include designs of platform and instruments, data transmission, processing and archiving. Compare with the traditional platforms, the UAV (Unmanned Aerial Vehicle) platforms offer greater flexibility, shorter response time and is able to generate very high resolution data. It is inexpensive to operate, and its operation is not limited by air traffic constraints. With autonomous flight control system, it is easy to navigate the aircraft along the planned lines strictly. In this system, an unmanned fixed-wing aircraft with double engine and double generator is used, which helps to improve the reliability and capability of the platform. A digital three-axis stabilized platform is developed, which performs an important role in camera attitude controlling. To ensure there is no coverage hole, a data manage system is adopted: after an instantaneous inspection of data integrity, the data will be archived as they are received. If any anomaly is detected, the mission planning will be adapted to re-image the concerned area. Vehicle information extraction from image sequence is also described in this paper. The experiment showed the presented system worked well in real-time road mapping and vehicle information extraction.

Question: What is a UAV?


Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping. The manipulator workspace mapping is an important problem in robotics and has attracted significant attention in the community. However, most of the pre-existing algorithms have expensive time complexity due to the reliance on sophisticated kinematic equations. To solve this problem, this paper introduces subspace learning (SL), a variant of subspace embedding, where a set of robot and scope parameters is mapped to the corresponding workspace by a deep neural network (DNN). Trained on a large dataset of around 6× 10 samples obtained from a MATLAB R © implementation of a classical method and sampling of designed uniform distributions, the experiments demonstrate that the embedding significantly reduces run-time from 5.23× 10 s of traditional discretization method to 0.224 s, with high accuracies (average F-measure is 0.9665 with batch gradient descent and resilient backpropagation).

Question: What is the time complexity of the traditional method?


Humpectomy and spreader flaps.. In a primary rhinoplasty that requires a humpectomy, the dorsal aspect of the upper lateral cartilages is commonly discarded. Many of these patients need spreader grafts to reconstruct the middle third of the nose. However, it is possible to reconstruct the upper lateral cartilages into "spreader flaps" that act much like spreader grafts. In the process of making spreader flaps, an incremental humpectomy is performed on the dorsal septum and bony hump. This humpectomy procedure is more accurate than the conventional humpectomy that involves resection of the bone, and septum as a single unit. The open and closed approaches of this technique are discussed in this article.

Question: How to make spreader flaps?


Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning. Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of

Question: What is the difference between a manifold and a manifold learning?


MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation. Person detection and pose estimation is a key requirement to develop intelligent context-aware assistance systems. To foster the development of human pose estimation methods and their applications in the Operating Room (OR), we release the Multi-View Operating Room (MVOR) dataset, the first public dataset recorded during real clinical interventions. It consists of 732 synchronized multi-view frames recorded by three RGB-D cameras in a hybrid OR. It also includes the visual challenges present in such environments, such as occlusions and clutter. We provide camera calibration parameters, color and depth frames, human bounding boxes, and 2D/3D pose annotations. In this paper, we present the dataset, its annotations, as well as baseline results from several recent person detection and 2D/3D pose estimation methods. Since we need to blur some parts of the images to hide identity and nudity in the released dataset, we also present a comparative study of how the baselines have been impacted by the blurring. Results show a large margin for improvement and suggest that the MVOR dataset can be useful to compare the performance of the different methods.

Question: What is the first large-scale Canadian peacekeeping mission?


Unsupervised Learning for Physical Interaction through Video Prediction. A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment. Many existing methods for learning the dynamics of physical interactions require labeled object information. However, to scale real-world interaction learning to a variety of scenes and objects, acquiring labeled data becomes increasingly impractical. To learn about physical object motion without labels, we develop an action-conditioned video prediction model that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames. Because our model explicitly predicts motion, it is partially invariant to object appearance, enabling it to generalize to previously unseen objects. To explore video prediction for real-world interactive agents, we also introduce a dataset of 50, 000 robot interactions involving pushing motions, including a test set with novel objects. In this dataset, accurate prediction of videos conditioned on the robot's future actions amounts to learning a "visual imagination" of different futures based on different courses of action. Our experiments show that our proposed method not only produces more accurate video predictions, but also more accurately predicts object motion, when compared to prior methods.

Question: How to predict the future of a robot?


Speech emotion classification using tree-structured sparse logistic regression. The extraction and selection of acoustic features are crucial steps in the development of a system for classifying emotions in speech. Most works in the field use some kind of prosodic features, often in combination with spectral and glottal features, and select appropriate features in classifying emotions. In the methods, feature choices are mostly made regardless of existing relationships and structures between features. However, considering them can be beneficial, potentially both for interpretability and to improve classification performance. To this end, a structured sparse logistic regression model incorporated with the hierarchical structure of features derived from prosody, spectral envelope, and glottal information is proposed in this paper. The proposed model simultaneously addresses tree-structured sparse feature selection and emotion classification. Evaluation of the proposed model on Berlin emotional database showed substantial improvement over the conventional sparse logistic regression model.

Question: How to classify emotions in speech?


Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson's Disease. Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson's disease. FoG is associated with falls and negatively impact the patient's quality of life. Wearable systems that detect FoG have been developed to help patients resume walking by means of auditory cueing. However, current methods for automated detection are not yet ideal. In this paper, we first compare feature learning approaches based on time-domain and statistical features to unsupervised ones based on principal components analysis. The latter systematically outperforms the former and also the standard in the field – Freezing Index by up to 8.1% in terms of F1-measure for FoG detection. We go a step further by analyzing FoG prediction, i.e., identification of patterns (pre-FoG) occurring before FoG episodes, based only on motion data. Until now this was only attempted using electroencephalography. With respect to the three-class problem (FoG vs. pre-FoG vs. normal locomotion), we show that FoG prediction performance is highly patient-dependent, reaching an F1-measure of 56% in the pre-FoG class for patients who exhibit enough gait degradation before FoG.

Question: How do we detect freezing of gait?


Dynamic modeling and inverse dynamic control of mobile robot. This paper presents a dynamic modeling and control studies of mobile robot. The robot is mainly built all around three wheels and platform. A mathematical model of unicycle mobile robot is determined by kinematic and dynamic model. The dynamic modelling equations are based on Lagrangian formulation. The motion control strategy is based on the inverse of dynamic control. This leads to accurate tracking of trajectory. The goal to reach in this paper is to improve the performances of inverse dynamic control of mobile robot. The validity of the proposed controller is demonstrated by the simulation of two wheels mobile robots case.

Question: How to improve the performance of inverse dynamic control of mobile robot?


Smartphone physics – a smart approach to practical work in science education ?. In the form of teacher didactical design research, this work addresses a didactical issue encountered during physics teaching in a Swedish upper secondary school. A need for renewed practical laboratory work related to Newtonian mechanics is met by proposing and designing an activity based on highspeed photography using the nowadays omnipresent smartphone, thus bringing new technology into the classroom. The activity – video analysis of the collision physics of football kicks – is designed and evaluated by following a didactical design cycle. The work elaborates on how the proposed laboratory activity relates to the potential and complications of experimental activities in science education, as described in the vast literature on the topic. It is argued that the use of smartphones constitutes an interesting use of new technology for addressing known problems of practical work. Of particular interest is that smartphones offer a way to bridge the gap between the everyday life of students and the world of physics experiments (smartphones are powerful pocket laboratories). The use of smartphones also avoids using unfamiliar laboratory equipment that is known to hinder focus on intended content, while at the same time exploring a powerful tool for data acquisition and analysis. Overall, the use of smartphones (and computers) in this manner can be seen as the result of applying Occam's razor to didactics: only familiar and readily available instrumentation is used, and skills learned (movie handling and image analysis) are all educationally worthwhile. Although the activity was judged successful, a systematic investigation of learning outcome was out of scope. This means that no strong conclusions can be drawn based on this limited work. Nonetheless, the smartphone activity was well received by the students and should constitute a useful addition to the set of instructional approaches, especia

Question: What is the physics of a football kick?


A particle swarm optimization algorithm for flexible job shop scheduling problem. The classical job shop scheduling problem (JSP) is the most popular machine scheduling model in practice and is well known as NP-hard. The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed. Flexible job shop scheduling problem (FJSP) is an extension of the JSP, which allows an operation to be processed by any machine from a given set. Since FJSP requires an additional decision of machine allocation during scheduling, therefore it is much more complex problem than JSP. To solve such NP-hard problems, heuristic approaches are commonly preferred over the traditional mathematical techniques. This paper proposes a particle swarm optimization (PSO) based heuristic for solving the FJSP for minimum makespan time criterion. The performance of the proposed PSO is evaluated by comparing its results with the results obtained using ILOG Solver, a constraint-programming tool. The comparison of the results proves the effectiveness of the proposed PSO for solving FJSP instances.

Question: What is a particle swarm optimization algorithm?


A framework for designing cloud forensic-enabled services (CFeS). Cloud computing is used by consumers to access cloud services. Malicious actors exploit vulnerabilities of cloud services to attack consumers. The link between these two assumptions is the cloud service. Although cloud forensics assists in the direction of investigating and solving cloud-based cyber-crimes, in many cases the design and implementation of cloud services fall back. Software designers and engineers should focus their attention on the design and implementation of cloud services that can be investigated in a forensic sound manner. This paper presents a methodology that aims on assisting designers to design cloud forensic-enabled services. The methodology supports the design of cloud services by implementing a number of steps to make the services cloud forensic enabled. It consists of a set of cloud forensic constraints, a modeling language expressed through a conceptual model and a process based on the concepts identified and presented in the model. The main advantage of the proposed methodology is the correlation of cloud services' characteristics with the cloud investigation while providing software engineers the ability to design and implement cloud forensic-enabled services via the use of a set of predefined forensic-related tasks.

Question: What is a cloud forensic-enabled service?


My science tutor: A conversational multimedia virtual tutor for elementary school science. This article describes My Science Tutor (MyST), an intelligent tutoring system designed to improve science learning by students in 3rd, 4th, and 5th grades (7 to 11 years old) through conversational dialogs with a virtual science tutor. In our study, individual students engage in spoken dialogs with the virtual tutor Marni during 15 to 20 minute sessions following classroom science investigations to discuss and extend concepts embedded in the investigations. The spoken dialogs in MyST are designed to scaffold learning by presenting open-ended questions accompanied by illustrations or animations related to the classroom investigations and the science concepts being learned. The focus of the interactions is to elicit self-expression from students. To this end, Marni applies some of the principles of Questioning the Author, a proven approach to classroom conversations, to challenge students to think about and integrate new concepts with prior knowledge to construct enriched mental models that can be used to explain and predict scientific phenomena. In this article, we describe how spoken dialogs using Automatic Speech Recognition (ASR) and natural language processing were developed to stimulate students' thinking, reasoning and self explanations. We describe the MyST system architecture and Wizard of Oz procedure that was used to collect data from tutorial sessions with elementary school students. Using data collected with the procedure, we present evaluations of the ASR and semantic parsing components. A formal evaluation of learning gains resulting from system use is currently being conducted. This paper presents survey results of teachers' and children's impressions of MyST.

Question: How does My Science Tutor work?


Near-optimal hybrid analog and digital precoding for downlink mmWave massive MIMO systems. Millimeter wave (mmWave) massive MIMO can achieve orders of magnitude increase in spectral and energy efficiency, and it usually exploits the hybrid analog and digital precoding to overcome the serious signal attenuation induced by mmWave frequencies. However, most of hybrid precoding schemes focus on the full-array structure, which involves a high complexity. In this paper, we propose a near-optimal iterative hybrid precoding scheme based on the more realistic subarray structure with low complexity. We first decompose the complicated capacity optimization problem into a series of ones easier to be handled by considering each antenna array one by one. Then we optimize the achievable capacity of each antenna array from the first one to the last one by utilizing the idea of successive interference cancelation (SIC), which is realized in an iterative procedure that is easy to be parallelized. It is shown that the proposed hybrid precoding scheme can achieve better performance than other recently proposed hybrid precoding schemes, while it also enjoys an acceptable computational complexity.

Question: What is the complexity of the proposed hybrid precoding scheme?


Priors for people tracking from small training sets. We advocate the use of scaled Gaussian process latent variable models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a low-dimensional embedding of the high-dimensional pose data and a density function that both gives higher probability to points close to training data and provides a nonlinear probabilistic mapping from the low-dimensional latent space to the full-dimensional pose space. The SGPLVM is a natural choice when only small amounts of training data are available. We demonstrate our approach with two distinct motions, golfing and walking. We show that the SGPLVM sufficiently constrains the problem such that tracking can be accomplished with straightforward deterministic optimization.

Question: What is a nonlinear probabilistic mapping?


A Flexible AC Distribution System Device for a Microgrid. This paper presents a flexible ac distribution system device for microgrid applications. The device aims to improve the power quality and reliability of the overall power distribution system that the microgrid is connected to. The control design employs a new model predictive control algorithm which allows faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. Extended Kalman filters are also employed for frequency tracking and to extract the harmonic spectra of the grid voltage and the load currents in the microgrid. The design concept is verified through different test case scenarios to demonstrate the capability of the proposed device and the results obtained are discussed.

Question: What is a microgrid?


Research on Chinese text classification based on Word2vec. The set of features which the traditional feature selection algorithm of chi-square selected is not complete. This causes the low performance for the final text classification. Therefore, this paper proposes a method. The method utilizes word2vec to generate word vector to improve feature selection algorithm of the chi square. The algorithm applies the word vector generated by word2vec to the process of the traditional feature selection and uses these words to supplement the set of features as appropriate. Ultimately, the set of features obtained by this method has better discriminatory power. Because, the feature words with the better discriminatory power has the strong ability of distinguishing categories as its semantically similar words. On this base, multiple experiments have been carried out in this paper. The experimental results show that the performance of text classification can increase after extension of feature words.

Question: How to improve the performance of text classification?


When does Consumer Empowerment Lead to Satisfied Customers? Some Mediating and Moderating Effects of the Empowerment-Satisfaction Link. Technological advances increasingly provide marketers with the opportunity to empower consumers. Consumer empowerment is a positive subjective state evoked by consumer perceptions of increasing control. As a positive state, increasing consumer empowerment should be associated with increasing consumer satisfaction. If such a relationship exists, it may be influenced by a number of contextual variables. Knowing in what contexts empowerment has a greater impact on satisfaction would help marketers decide when they could more effectively use such a strategy. This study has two purposes: 1) to investigate the relationship between consumer empowerment and satisfaction and 2) to investigate a set of potential influences on that relationship. Marketers will be in a better position to decide when to empower consumers if they have guidance on the relationships between these variables.

Question: How does empowerment affect satisfaction?


Integrating the 3+1 SysML view model with safety engineering. System safety is the property of the system that characterizes its ability to prevent from hazards, which may lead to accidents or losses. Traditionally, system developers are not familiar with system safety analysis processes which are performed by safety engineers. One reason for this is the gap that exists between the traditional development processes, methodologies, notations and tools and the ones used in safety engineering. This gap makes the development of safety aware systems a very complicated task. Several approaches based on UML have been proposed to address this gap. In this paper, an approach to integrate safety engineering with a SysML based development process that is expressed in the form of the V-model, is presented. Preliminary hazard analysis is adopted and applied to a SysML based requirements specification of the mechatronic system that exploits essential use cases. A case study from the railway domain is used to illustrate the proposed approach.

Question: What is the difference between a safety engineer and a system engineer?


Touch, Taste, & Smell User Interfaces: The Future of Multisensory HCI. The senses we call upon when interacting with technology are very restricted. We mostly rely on vision and audition, increasingly harnessing touch, whilst taste and smell remain largely underexploited. In spite of our current knowledge about sensory systems and sensory devices, the biggest stumbling block for progress concerns the need for a deeper understanding of people's multisensory experiences in HCI. It is essential to determine what tactile, gustatory, and olfactory experiences we can design for, and how we can meaningfully stimulate such experiences when interacting with technology. Importantly, we need to determine the contribution of the different senses along with their interactions in order to design more effective and engaging digital multisensory experiences. Finally, it is vital to understand what the limitations are that come into play when users need to monitor more than one sense at a time. The aim of this workshop is to deepen and expand the discussion on touch, taste, and smell within the CHI community and promote the relevance of multisensory experience design and research in HCI.

Question: What is the future of multisensory HCI?


Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation. We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods. We apply the proposed approach to a challenging partially observable robot navigation task. The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera. We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network. In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task.

Question: How to integrate algorithmic planning and deep learning in a principled manner?


Towards Instrument Segmentation for Music Content Description: a Critical Review of Instrument Classification Techniques. A system capable of describing the musical content of any kind of soundfile or soundstream, as it is supposed to be done in MPEG7-compliant applications, should provide an account of the different moments where a certain instrument can be listened to. This segmentation according to instrument taxonomies must be solved with different strategies than segmentation according to perceptual features. In this paper we concentrate on reviewing the different techniques that have been so far proposed for automatic classification of musical instruments. Although the ultimate goal should be the segmentation of complex sonic mixtures, it is still far from being solved. Therefore, the practical approach is to reduce the scope of the classification systems to only deal with isolated, and out-of-context, sounds. There is an obvious tradeoff in endorsing this strategy: we gain simplicity and tractability, but we lose contextual and time-dependent cues that can be exploited as relevant features for classifying the sounds.

Question: What is the scope of the paper?


LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization. AdaBoost.MH is a boosting algorithm that is considered to be one of the most accurate algorithms for multilabel classification. It works by iteratively building a committee of weak hypotheses of decision stumps. To build the weak hypotheses, in each iteration, AdaBoost.MH obtains the whole extracted features and examines them one by one to check their ability to characterize the appropriate category. Using Bag-Of-Words for text representation dramatically increases the computational time of AdaBoost.MH learning, especially for large-scale datasets. In this paper we demonstrate how to improve the efficiency and effectiveness of AdaBoost.MH using latent topics, rather than words. A well-known probabilistic topic modelling method, Latent Dirichlet Allocation, is used to estimate the latent topics in the corpus as features for AdaBoost.MH. To evaluate LDA-AdaBoost.MH, the following four datasets have been used: Reuters-21578-ModApte, WebKB, 20-Newsgroups and a collection of Arabic news. The experimental results confirmed that representing the texts as a small number of latent topics, rather than a large number of words, significantly decreased the computational time of AdaBoost.MH learning and improved its performance for text categorization.

Question: How to improve the efficiency and effectiveness of AdaBoost.MH using latent topics, rather than words?


Design, Analysis, and Test of a Novel 2-DOF Nanopositioning System Driven by Dual Mode. Piezodriven flexure-based motion stages, with a large workspace and high positioning precision, are really attractive for the realization of high-performance atomic force microscope (AFM) scanning. In this paper, a modified lever displacement amplifier is proposed for the mechanism design of a novel compliant two-degree-of-freedom (2-DOF) nanopositioning stage, which can be selected to drive in dual modes. Besides, the modified double four-bar parallelogram, P (P denotes prismatic) joints are adopted in designing the flexure limbs. The established models for the mechanical performance evaluation of the stage, in terms of kinetostatics, dynamics, and workspace, are validated by the finite-element analysis. After a series of dimension optimizations carried out through the particle swarm optimization algorithm, a novel active disturbance rejection controller, including the nonlinearity tracking differentiator, the extended state observer, and the nonlinear state error feedback, is proposed to automatically estimate and suppress plant uncertainties arising from the hysteresis nonlinearity, creep effect, sensor noises, and unknown disturbances. The simulation and prototype test results indicate that the first natural frequency of the proposed stage is approximated to be 831 Hz, the amplification ratio in two axes is about 4.2, and the workspace is 119.7 μm × 121.4 μm, while the cross coupling between the two axes is kept within 2%. All the results prove that the developed stage possesses a good property for high-performance AFM scanning.

Question: How large is the canadian military?


Differential Cryptanalysis of the Data Encryption Standard.

Question: What is the differential cryptanalysis of the Data Encryption Standard?


Deep Constrained Clustering - Algorithms and Advances. The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. We explore a deep learning formulation of constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our formulation can not only handle standard together/apart constraints without the well documented negative effects reported but can also model instance level constraints (level-of-difficulty), cluster level constraints (balancing cluster size) and triplet constraints. The first two are new ways for domain experts to enforce guidance whilst the later importantly allows generating ordering constraints from continuous side-information.

Question: What is deep constrained clustering?


Fast Exact Inference with a Factored Model for Natural Language Parsing. We presenta novel generati ve model for natural languagetree structuresin whichsemantic(lexical dependenc y) andsyntacticstructuresare scoredwith separatemodels.Thisfactorizationprovidesconceptual simplicity, straightforwardopportunitiesfor separatelyimproving the componentmodels,anda level of performancealreadycloseto thatof similar, non-factoredmodels.Most importantly, unlikeothermodernparsing models,thefactoredmodeladmitsanextremelyeffectiveA parsingalgorithm,which makesefficient,exactinferencefeasible.

Question: What is the name of the algorithm?


BreakDancer: An algorithm for high resolution mapping of genomic structural variation. Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods with which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including insertion-deletions (indels), inversions and translocations. We examined BreakDancer's performance in simulation, in comparison with other methods and in analyses of a sample from an individual with acute myeloid leukemia and of samples from the 1,000 Genomes trio individuals. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.

Question: What is the difference between a breakdancer and a breakdancer?


Discriminating IT Governance. T information technology (IT) governance literature predominantly explains firms' IT governance choices, but not their strategic consequences. We develop the idea that a firm's IT governance choices induce adeptness at strategically exploiting IT only when they are discriminatingly aligned with its departments' knowledge outside their specialty. Discriminating means that governing the two undertheorized classes of IT assets—apps and infrastructure—requires "peripheral" knowledge in different departments. Analyses of data from 105 firms support our middle-range theory.

Question: What is the middle-range theory?


Low Profile Fully Planar Folded Dipole Antenna on a High Impedance Surface. A fully planar antenna design incorporating a high impedance surface (HIS) is presented. The HIS is composed by a periodic array of subwavelength dogbone-shaped conductors printed on top of a thin dielectric substrate and backed by a metallic ground plane. First, the characteristics of a dipole over PEC or PMC layers, a dielectric slab, and the HIS are compared and studied in detail, highlighting the advantages provided by the use of the HIS. Then, the design of a low profile folded dipole antenna working at 5.5 GHz on top of the HIS is described. The surface provides close to 6% antenna impedance bandwidth and increased gain up to 7 dBi, while shielding the lower half space from radiation. The antenna structure comprises three metal layers without any vias between them, and its overall thickness is 0.059λ0. The dipole is fed by a balanced twin lead line through a balun transformer integrated in the same antenna layer. A prototype has been built and measurements confirming simulation results are provided.

Question: What is a balun transformer?


LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing. Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study seven supervoxel algorithms, including both off-line and streaming methods, in the context of what we consider to be a good supervoxel: namely, spatiotemporal uniformity, object/region boundary detection, region compression and parsimony. For the evaluation we propose a comprehensive suite of seven quality metrics to measure these desirable supervoxel characteristics. In addition, we evaluate the methods in a supervoxel classification task as a proxy for subsequent high-level uses of the supervoxels in video analysis. We use six existing benchmark video datasets with a variety of content-types and dense human annotations. Our findings have led us to conclusive evidence that the hierarchical graph-based (GBH), segmentation by weighted aggregation (SWA) and temporal superpixels (TSP) methods are the top-performers among the seven methods. They all perform well in terms of segmentation accuracy, but vary in regard to the other desiderata: GBH captures object boundaries best; SWA has the best potential for region compression; and TSP achieves the best undersegmentation error.

Question: What is the best supervoxel algorithm?


A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format. Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.

Question: What is a de-identification pipeline for ultrasound medical images in dicom format?


Smart music player integrating facial emotion recognition and music mood recommendation. Songs, as a medium of expression, have always been a popular choice to depict and understand human emotions. Reliable emotion based classification systems can go a long way in helping us parse their meaning. However, research in the field of emotion-based music classification has not yielded optimal results. In this paper, we present an affective cross-platform music player, EMP, which recommends music based on the real-time mood of the user. EMP provides smart mood based music recommendation by incorporating the capabilities of emotion context reasoning within our adaptive music recommendation system. Our music player contains three modules: Emotion Module, Music Classification Module and Recommendation Module. The Emotion Module takes an image of the user's face as an input and makes use of deep learning algorithms to identify their mood with an accuracy of 90.23%. The Music Classification Module makes use of audio features to achieve a remarkable result of 97.69% while classifying songs into 4 different mood classes. The Recommendation Module suggests songs to the user by mapping their emotions to the mood type of the song, taking into consideration the preferences of the user.

Question: What is the accuracy of emotion based music classification?


Flocks , Herds , and Schools : A Distributed Behavioral Model. The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the n a t u r a l world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.

Question: How does a flock of birds navigate?


A framework based on RSA and AES encryption algorithms for cloud computing services. Cloud computing is an emerging computing model in which resources of the computing communications are provided as services over the Internet. Privacy and security of cloud storage services are very important and become a challenge in cloud computing due to loss of control over data and its dependence on the cloud computing provider. While there is a huge amount of transferring data in cloud system, the risk of accessing data by attackers raises. Considering the problem of building a secure cloud storage service, current scheme is proposed which is based on combination of RSA and AES encryption methods to share the data among users in a secure cloud system. The proposed method allows providing difficulty for attackers as well as reducing the time of information transmission between user and cloud data storage.

Question: What is a framework based on RSA and AES encryption algorithms for cloud computing services?


Sequential Query Expansion using Concept Graph. Manually and automatically constructed concept graphs (or semantic networks), in which the nodes correspond to words or phrases and the typed edges designate semantic relationships between words and phrases, have been previously shown to be rich sources of effective latent concepts for query expansion. However, finding good expansion concepts for a given query in large and dense concept graphs is a challenging problem, since the number of candidate concepts that are related to query terms and phrases and need to be examined increases exponentially with the distance from the original query concepts. In this paper, we propose a two-stage feature-based method for sequential selection of the most effective concepts for query expansion from a concept graph. In the first stage, the proposed method weighs the concepts according to different types of computationally inexpensive features, including collection and concept graph statistics. In the second stage, a sequential concept selection algorithm utilizing more expensive features is applied to find the most effective expansion concepts at different distances from the original query concepts. Experiments on TREC datasets of different type indicate that the proposed method achieves significant improvement in retrieval accuracy over state-of-the-art methods for query expansion using concept graphs.

Question: What is the canadian military?


Improving Movie Gross Prediction through News Analysis. Traditional movie gross predictions are based on numerical and categorical movie data from The Internet Movie Database (IMDB). In this paper, we use the quantitative news data generated by Lydia, our system for large-scale news analysis, to help people to predict movie grosses. By analyzing two different models (regression and k-nearest neighbor models), we find models using only news data can achieve similar performance to those using IMDB data. Moreover, we can achieve better performance by using the combination of IMDB data and news data. Further, the improvement is statistically significant.

Question: How to improve movie gross prediction?


Fear of being laughed at and social anxiety : A preliminary psychometric study. The present study examines the relationship between questionnaire measures of social phobia and gelotophobia. A sample of 211 Colombian adults filled in Spanish versions of the Social Anxiety and Distress scale (SAD; Watson & Friend, 1969), the Fear of Negative Evaluation scale (FNE; Watson & Friend, 1969) and the GELOPH<15> (Ruch & Proyer, 2008). Results confirmed that both Social Anxiety and Distress and Fear of Negative Evaluation scale overlapped with the fear of being laughed at without being identical with it. The SAD and FNE correlated highly with the GELOPH<15> but not all high scorers in these scales expressed a fear of being laughed at. Furthermore, an item factor analysis yielded three factors that were mostly loaded by items of the respective scales. This three-factor structure was verified using confirmatory factor analysis. A measure model where one general factor of social anxiety was specified, or another one where two different factors were defined (gelotophobia vs. social anxiety assessed by SAD and FNE) showed a very poor fit to the data. It is concluded that the fear of being laughed cannot fully be accounted for by these measures of social phobia.

Question: What is the difference between social anxiety and gelotophobia?


Innovation : Mapping the winds of creative destruction *. This paper develops a framework for analyzing the competitive implications of innovation. The framework is based on the concept of transilience the capacity of an innovation to influence the established systems of production and marketing. Application of the concept results in a categorization of innovation into four types. Examples from the technical history of the US auto industry are used to illustrate the concepts and their applicability. The analysis shows that the categories of innovation are closely linked to different patterns of evolution and to different managerial environments. Special emphasis is placed on the role of incremental technical change in shaping competition and on the possibilities for a technology based reversal in the process of industrial maturity.

Question: What is the difference between incremental and disruptive innovation?


The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms. Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle taxis to passengers in need. To precisely balance the supply and the demand of taxis, online taxicab platforms need to predict the Unit Original Taxi Demand (UOTD), which refers to the number of taxi-calling requirements submitted per unit time (e.g., every hour) and per unit region (e.g., each POI). Predicting UOTD is non-trivial for large-scale industrial online taxicab platforms because both accuracy and flexibility are essential. Complex non-linear models such as GBRT and deep learning are generally accurate, yet require labor-intensive model redesign after scenario changes (e.g., extra constraints due to new regulations). To accurately predict UOTD while remaining flexible to scenario changes, we propose LinUOTD, a unified linear regression model with more than 200 million dimensions of features. The simple model structure eliminates the need of repeated model redesign, while the high-dimensional features contribute to accurate UOTD prediction. We further design a series of optimization techniques for efficient model training and updating. Evaluations on two large-scale datasets from an industrial online taxicab platform verify that LinUOTD outperforms popular non-linear models in accuracy. We envision our experiences to adopt simple linear models with high-dimensional features in UOTD prediction as a pilot study and can shed insights upon other industrial large-scale spatio-temporal prediction problems.

Question: How to predict taxi demand?


Scatterplots: Tasks, Data, and Designs. Traditional scatterplots fail to scale as the complexity and amount of data increases. In response, there exist many design options that modify or expand the traditional scatterplot design to meet these larger scales. This breadth of design options creates challenges for designers and practitioners who must select appropriate designs for particular analysis goals. In this paper, we help designers in making design choices for scatterplot visualizations. We survey the literature to catalog scatterplot-specific analysis tasks. We look at how data characteristics influence design decisions. We then survey scatterplot-like designs to understand the range of design options. Building upon these three organizations, we connect data characteristics, analysis tasks, and design choices in order to generate challenges, open questions, and example best practices for the effective design of scatterplots.

Question: How to design scatterplots for large data sets?


Model based control of fiber reinforced elastofluidic enclosures. Fiber-Reinforced Elastofluidic Enclosures (FREEs), are a subset of pneumatic soft robots with an asymmetric continuously deformable skin that are able to generate a wide range of deformations and forces, including rotation and screw motions. Though these soft robots are able to generate a variety of motions, simultaneously controlling their end effector rotation and position has remained challenging due to the lack of a simple model. This paper presents a model that establishes a relationship between the pressure, torque due to axial loading, and axial rotation to enable a model-driven open-loop control for FREEs. The modeling technique relies on describing force equilibrium between the fiber, fluid, and an elastomer model which is computed via system identification. The model is experimentally tested as these variables are changed, illustrating that it provides good agreement with the real system. To further illustrate the potential of the model, a precision open-loop control experiment of opening a rotational combination lock is presented2.

Question: How do you model a soft robot?


On the Analysis and Design of Low-Loss Single-Pole Double-Throw W-Band Switches Utilizing Saturated SiGe HBTs. This paper describes the analysis and design of saturated silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) switches for millimeter-wave applications. A switch optimization procedure is developed based on detailed theoretical analysis and is then used to design multiple switch variants. The switches utilize IBM's 90-nm 9HP technology, which features SiGe HBTs with peak f T/ fmax of 300/350 GHz. Using a reverse-saturated configuration, a single-pole double-throw switch with a measured insertion loss of 1.05 dB and isolation of 22 dB is achieved at 94 GHz after de-embedding pad losses. The switch draws 5.2 mA from a 1.1-V supply, limiting power consumption to less than 6 mW. The switching speed is analyzed and the simulated turn-on and turn-off times are found to be less than 200 ps. A technique is also introduced to significantly increase the power-handling capabilities of saturated SiGe switches up to an input-referred 1-dB compression point of 22 dBm. Finally, the impact of RF stress on this novel configuration is investigated and initial measurements over a 48-h period show little performance degradation. These results demonstrate that SiGe-based switches may provide significant benefits to millimeter-wave systems.

Question: What is the input referred 1-dB compression point of a saturated SiGe HBT?


ICE: Item Concept Embedding via Textual Information. This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information. Specifically, in the proposed framework there are two stages: graph construction and embedding learning. In the first stage, we propose a generalized network construction method to build a network involving heterogeneous nodes and a mixture of both homogeneous and heterogeneous relations. The second stage leverages the concept of neighborhood proximity to learn the embeddings of both items and words. With the proposed carefully designed ICE networks, the resulting embedding facilitates both homogeneous and heterogeneous retrieval, including item-to-item and word-to-item retrieval. Moreover, as a distributed embedding approach, the proposed ICE approach not only generates related retrieval results but also delivers more diverse results than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, ICE encodes useful textual information and thus outperforms traditional methods in various item classification and retrieval tasks.

Question: What is the item concept embedding?


Map API - scalable decentralized map building for robots. Large scale, long-term, distributed mapping is a core challenge to modern field robotics. Using the sensory output of multiple robots and fusing it in an efficient way enables the creation of globally accurate and consistent metric maps. To combine data from multiple agents into a global map, most existing approaches use a central entity that collects and manages the information from all agents. Often, the raw sensor data of one robot needs to be made available to processing algorithms on other agents due to the lack of computational resources on that robot. Unfortunately, network latency and low bandwidth in the field limit the generality of such an approach and make multi-robot map building a tedious task. In this paper, we present a distributed and decentralized back-end for concurrent and consistent robotic mapping. We propose a set of novel approaches that reduce the bandwidth usage and increase the effectiveness of inter-robot communication for distributed mapping. Instead of locking access to the map during operations, we define a version control system which allows concurrent and consistent access to the map data. Updates to the map are then shared asynchronously with agents which previously registered notifications. A technique for data lookup is provided by state-of-the-art algorithms from distributed computing. We validate our approach on real-world datasets and demonstrate the effectiveness of the proposed algorithms.

Question: What is a scalable decentralized map building for robots?


Design and development of a heart rate measuring device using fingertip. In this paper, we presented the design and development of a new integrated device for measuring heart rate using fingertip to improve estimating the heart rate. As heart related diseases are increasing day by day, the need for an accurate and affordable heart rate measuring device or heart monitor is essential to ensure quality of health. However, most heart rate measuring tools and environments are expensive and do not follow ergonomics. Our proposed Heart Rate Measuring (HRM) device is economical and user friendly and uses optical technology to detect the flow of blood through index finger. Three phases are used to detect pulses on the fingertip that include pulse detection, signal extraction, and pulse amplification. Qualitative and quantitative performance evaluation of the device on real signals shows accuracy in heart rate estimation, even under intense of physical activity. We compared the performance of HRM device with Electrocardiogram reports and manual pulse measurement of heartbeat of 90 human subjects of different ages. The results showed that the error rate of the device is negligible.

Question: What is the difference between a heart rate measuring device and a heart rate monitor?


A swarm optimization algorithm inspired in the behavior of the social-spider. Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.

Question: What is a swarm optimization algorithm?


DaTuM: Dynamic tone mapping technique for OLED display power saving based on video classification. The adoption of the latest OLED (organic light emitting diode) technology does not change the fact that screen is still one of the most energy-consuming modules in modern smartphones. In this work, we found that video streams from the same video category share many common power consumption features on OLED screens. Therefore, we are able to build a Hidden Markov Model (HMM) classifier to categorize videos based on OLED screen power characteristics. Using this HMM classifier, we propose a video classification based dynamic tone mapping (DTM) scheme, namely, DaTuM, to remap output color range and minimize the power-hungry color compositions on OLED screens for power saving. Experiment shows that DaTuM scheme averagely reduces OLED screen power by 17.8% with minimum display quality degradation. Compared to DTM scheme based on official category info provided by the video sources and one state-of-the-art scheme, DaTuM substantially enhances OLED screens' power efficiency and display quality controllability.

Question: What is the difference between OLED and LCD?


CipherXRay: Exposing Cryptographic Operations and Transient Secrets from Monitored Binary Execution. Malwares are becoming increasingly stealthy, more and more malwares are using cryptographic algorithms (e.g., packing, encrypting C&C communication) to protect themselves from being analyzed. The use of cryptographic algorithms and truly transient cryptographic secrets inside the malware binary imposes a key obstacle to effective malware analysis and defense. To enable more effective malware analysis, forensics, and reverse engineering, we have developed CipherXRay - a novel binary analysis framework that can automatically identify and recover the cryptographic operations and transient secrets from the execution of potentially obfuscated binary executables. Based on the avalanche effect of cryptographic functions, CipherXRay is able to accurately pinpoint the boundary of cryptographic operation and recover truly transient cryptographic secrets that only exist in memory for one instant in between multiple nested cryptographic operations. CipherXRay can further identify certain operation modes (e.g., ECB, CBC, CFB) of the identified block cipher and tell whether the identified block cipher operation is encryption or decryption in certain cases. We have empirically validated CipherXRay with OpenSSL, popular password safe KeePassX, the ciphers used by malware Stuxnet, Kraken and Agobot, and a number of third party softwares with built-in compression and checksum. CipherXRay is able to identify various cryptographic operations and recover cryptographic secrets that exist in memory for only a few microseconds. Our results demonstrate that current software implementations of cryptographic algorithms hardly achieve any secrecy if their execution can be monitored.

Question: What is the name of the algorithm used by the malware?