A Wideband Differential-Fed Slot Antenna Using Integrated Compact Balun With Matching Capability. This communication proposes a wideband differential-fed slot antenna that works in its dominant mode ( λ/2 mode). The antenna has a near omnidirectional radiation pattern with vertical polarization, which is designed for near-ground wireless sensor node applications. A compact integrated T-slot balun and a loop to T-slot feeding structure are introduced to realize the differential feeding; the bandwidth is expanded to 30.7% (2.2 GHz-3.0 GHz) with a 3.8 dB peak gain. A detailed matching method without using lumped elements is proposed, which can be applied to various types of RFIC chips. A prototype is built and measured. Its differential impedance, antenna patterns, and gain are consistent with the simulation results.

Question: What is a wideband differential-fed slot antenna?


An Effective CU Size Decision Method for HEVC Encoders. The emerging high efficiency video coding standard (HEVC) adopts the quadtree-structured coding unit (CU). Each CU allows recursive splitting into four equal sub-CUs. At each depth level (CU size), the test model of HEVC (HM) performs motion estimation (ME) with different sizes including 2N × 2N, 2N × N, N × 2N and N × N. ME process in HM is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency but requires a very high computational complexity. In this paper, we propose a fast CU size decision algorithm for HM. Since the optimal depth level is highly content-dependent, it is not efficient to use all levels. We can determine CU depth range (including the minimum depth level and the maximum depth level) and skip some specific depth levels rarely used in the previous frame and neighboring CUs. Besides, the proposed algorithm also introduces early termination methods based on motion homogeneity checking, RD cost checking and SKIP mode checking to skip ME on unnecessary CU sizes. Experimental results demonstrate that the proposed algorithm can significantly reduce computational complexity while maintaining almost the same RD performance as the original HEVC encoder.

Question: What is the CU size decision method for HEVC encoders?


Mendeley: Recommendations for Researchers. For a researcher, keeping up with what is going on in their research field can be a difficult and time-consuming task. For example, a fresh PhD student may want to know what are the relevant papers matching their research interests. An assistant professor may like to be up-to-date with what their colleagues are publishing. A professor might want to be notified about funding opportunities relevant to the work done in their research group. Since the volume of published research and research activity is constantly growing, it is becoming increasingly more difficult for researchers to be able to manage and filter through the research information flow. In this challenging context, Mendeley's mission is to become the world's "research operating system". We do this not only by providing our well-know reference management system, but also by providing discovery capabilities for researchers on different kinds of entities, such as articles and profiles. In our talk, we will share Mendeley's experiences with building our article and profile recommendation systems, the challenges that we have faced and the solutions that we have put in place. We will discuss how we address different users' needs with our data and algorithm infrastructure to achieve good user experience.

Question: What is the mission of Mendeley?


A Survey on Potential Applications of Honeypot Technology in Intrusion Detection Systems. Information security in the sense of personal and institutional has become a top priority in digitalized modern world in parallel to the new technological developments. Many methods, tools and technologies are used to provide the information security of IT systems. These are considered, encryption, authentication, firewall, and intrusion detection and prevention systems. Moreover, honeypot systems are proposed as complementary structures. This paper presents the overall view of the publications in IDS, IPS and honeypot systems. Recently, honeypot systems are anymore used in connection with intrusion detection systems. So this paper describes possible implementation of honeypot technologies combined with IDS/IPS in a network. Studies in the literature have shown intrusion detection systems cannot find the 0-day vulnerabilities. The system provided by the honeypots and intrusion detection systems in the network, might detect new exploit and hacker attempt. Index Terms – Information security, Intrusion detection system (IDS), Intrusion prevention system (IPS), Honeypot, Network Security.

Question: What is the difference between honeypot and honeypot?


Evaluation of a dynamic role-playing platform for simulations based on Octalysis gamification framework. The use of serious games (SG) in education and their pedagogical benefit is being widely recognized. However, effective integration of SGs in education depends on addressing two big challenges: successful incorporation of motivation and engagement that can lead to learning; and high cost and specialised skills associated with customised development to achieve the required pedagogical results. This paper presents a SG platform that offers tools to educators to dynamically create three dimensional (3D) scenes and verbal and non-verbal interaction with fully embodied conversational agents (ECA) that can be used to simulate numerous educational scenarios. We then evaluate the effectiveness of the platform in terms of supporting the creation of motivating and engaging educational simulations based on the Octalysis gamification framework. The latter includes a number of game design elements that can be used as appropriate in the optimization of the experience in each phase of the player's journey. We conclude with directions of the further extension to our platform to address the aforementioned gamification framework, as well as directions for further development of the Octalysis framework.

Question: How can we use gamification to motivate and engage learners?


Cubical Type Theory: A Constructive Interpretation of the Univalence Axiom. This paper presents a type theory in which it is possible to directly manipulate n-dimensional cubes (points, lines, squares, cubes, etc.) based on an interpretation of dependent type theory in a cubical set model. This enables new ways to reason about identity types, for instance, function extensionality is directly provable in the system. Further, Voevodsky's univalence axiom is provable in this system. We also explain an extension with some higher inductive types like the circle and propositional truncation. Finally we provide semantics for this cubical type theory in a constructive meta-theory. 1998 ACM Subject Classification F.3.2 Logics and Meanings of Programs: Semantics of Programming Languages, F.4.1 Mathematical Logic and Formal Languages: Mathematical Logic

Question: What is the meaning of the word "constructive" in this context?


A convolutional neural network approach for face verification. In this paper, we present a convolutional neural network (CNN) approach for the face verification task. We propose a "Siamese" architecture of two CNNs, with each CNN reduced to only four layers by fusing convolutional and subsampling layers. Network training is performed using the stochastic gradient descent algorithm with annealed global learning rate. Generalization ability of network is investigated via unique pairing of face images, and testing is done on AT&T face database. Experimental work shows that the proposed CNN system can classify a pair of 46×46 pixel face images in 0.6 milliseconds, which is significantly faster compared to equivalent network architecture with cascade of convolutional and subsampling layers. The verification accuracy achieved is 3.33% EER (equal error rate). Learning converges within 20 epochs, and the proposed technique can verify a test subject unseen in training. This work shows the viability of the "Siamese" CNN for face verification applications, and further improvements to the architecture are under construction to enhance its performance.

Question: What is the convolutional neural network approach for face verification?


Finger Sleeve : A Wearable Navigation Device. In this paper we present a novel wearable navigation system along with implicit HCI (iHCI) model, where interaction with technology is dissolved into a day-today activity. In this type of HCI model a computer takes the input and tries to output an action that is a proactive anticipation of next action of a user. Usually, in urban areas people use voice assisted navigation systems or navigation guidelines displayed on a mobile phone. Some navigation systems are already installed on car dashboard, which needs explicit attention in order to make driving decisions. A navigation system using haptic perception to guide a user throughout a journey is the key contribution of this paper. It does not ask for explicit user attention and demonstrates the indolent form of technological interaction. This wearable device is an index finger sleeve, which consists of vibrator modules, Bluetooth communication module and Microcontroller Unit (MCU). A working prototype has been built and tested.

Question: What is the difference between a wearable navigation device and a navigation device?


Competing Loyalty Programs : Impact of Market Saturation , Market Share , and Category Expandability. Loyalty programs have become an important component of firms' relationship management strategies. There are now some industries in which numerous rival loyalty programs are offered, inducing intense competition among these programs. However, existing research on loyalty programs has often studied such programs in a noncompetitive setting and has often focused on a single program in isolation. Addressing this gap, this research examines the effect of a firm's competitive positioning and market saturation on the performance of the firm's loyalty program. Based on the analysis of firmand individual-level data from the airline industry, the results indicate that larger firms tend to benefit more from their loyalty program offerings than smaller firms. Moreover, when the product category demand is rigid, the impact of an individual loyalty program decreases as the marketplace becomes more saturated with competing programs. However, when the product category is highly expandable, the saturation effect disappears. Under such situations, loyalty programs can help an industry gain competitive advantage over substitute offerings outside the industry, and multiple programs can effectively coexist even under a high level of market saturation.

Question: What is the impact of market saturation on the performance of a loyalty program?


Automatic code generation of convolutional neural networks in FPGA implementation. Convolutional neural networks (CNNs) have gained great success in various computer vision applications. However, state-of-the-art CNN models are computation-intensive and hence are mainly processed on high performance processors like server CPUs and GPUs. Owing to the advantages of high performance, energy efficiency and reconfigurability, Field-Programmable Gate Arrays (FPGAs) have been widely explored as CNN accelerators. In this paper, we propose parallel structures to exploit the inherent parallelism and efficient computation units to perform operations in convolutional and fully-connected layers. Further, an automatic generator is proposed to generate Verilog HDL source code automatically according to high-level hardware description language. Execution time, DSP consumption and performance are analytically modeled based on some critical design variables. We demonstrate the automatic methodology by implementing two representative CNNs (LeNet and AlexNet) and evaluate the execution time models by comparing estimated and measured values. Our results show that the proposed automatic methodology yields hardware design with good performance and saves much developing round time.

Question: How to generate convolutional neural network in FPGA?


Thorough static analysis of device drivers. Bugs in kernel-level device drivers cause 85% of the system crashes in the Windows XP operating system [44]. One of the sources of these errors is the complexity of the Windows driver API itself: programmers must master a complex set of rules about how to use the driver API in order to create drivers that are good clients of the kernel. We have built a static analysis engine that finds API usage errors in C programs. The Static Driver Verifier tool (SDV) uses this engine to find kernel API usage errors in a driver. SDV includes models of the OS and the environment of the device driver, and over sixty API usage rules. SDV is intended to be used by driver developers "out of the box." Thus, it has stringent requirements: (1) complete automation with no input from the user; (2) a low rate of false errors. We discuss the techniques used in SDV to meet these requirements, and empirical results from running SDV on over one hundred Windows device drivers.

Question: What is a static analysis engine?


A PASSIVE MILLIMETER-WAVE IMAGER USED FOR CONCEALED WEAPON DETECTION. An 8mm-band passive millimeter-wave imager BHU-2D has been developed by Beihang University. This imager is designed for detecting concealed weapons on human body. The imager adopts two-dimensional synthetic aperture interferometric radiometer (SAIR) technique which avoids the radiation risk to human body. Compared with scanning technique, SAIR technique could obtain images of a larger field of view (FOV) while achieving high imaging rate, which are necessary for security monitoring. In this paper, the imaging principle of SAIR is stated firstly. Secondly, background cancellation method designed for BHU-2D is interpreted. The technique is used to reduce the complexity as well as the dimensional requirements of the receiving elements. Thirdly, system configuration is illustrated in detail. Then external point source calibration method is introduced and discussed specifically for security check applications. Finally, imaging experiments on a person with concealed weapon are conducted by which the design and image calibration algorithms are verified. To conclude, experimental results prove that BHU-2D could be employed in security check applications.

Question: What is the design of the imager?


Wireless Measurement of RFID IC Impedance. Accurate knowledge of the input impedance of a radio-frequency identification (RFID) integrated circuit (IC) at its wake-up power is valuable as it enables the design of a performance-optimized tag for a specific IC. However, since the IC impedance is power dependent, few methods exist to measure it without advanced equipment. We propose and demonstrate a wireless method, based on electromagnetic simulation and threshold power measurement, applicable to fully assembled RFID tags, to determine the mounted IC's input impedance in the absorbing state, including any parasitics arising from the packaging and the antenna-IC connection. The proposed method can be extended to measure the IC's input impedance in the modulating state as well.

Question: How to measure the input impedance of an RFID IC?


SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine. We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.

Question: What is the size of the dataset?


Security and Privacy in Sensor Networks. As wireless sensor networks (WSN) continue to grow, so does the need for effective security mechanisms. Since sensor networks can interact with sensitive data and/or operate in hostile unattended environments, it is imperative that these security concerns be addressed from the beginning of the system design. This paper aims at describing security solutions for collecting and processing data in WSNs. Adequate security capabilities for medium and large scale WSNs are a hard but necessary goal to achieve to prepare these networks for the market. The paper includes an overview on WSNs space security solutioins and reliability challenges for

Question: What is the security of a sensor network?


Pachyonychia congenita type 2 (Jackson-Lawler syndrome) or PC-17: case report.. Pachyonychia congenita (PC) is a rare genodermatosis caused by mutations in any of the four genes KRT6A, KRT6B, KRT16, or KRT17, which can lead to dystrophic, thickened nails and focal palmoplantar keratoderma, among other manifestations. Although classically subdivided into two major variants, PC-1 (Jadassohn-Lewandowski syndrome) and PC-2 (Jackson-Lawler syndrome), according to the localization of the mutations in the KRT6A/KRT16 or KRT6B/KRT17 genes, respectively, a classification system based on the mutant gene (PC-6a, PC-6b, PC-16 and PC-17) has been recently proposed. We report a 2-year-old female patient with a history of thickened and discolored nails, small cystic papulonodules on the central face, dry, unruly and curly hair, slight palmoplantar hyperkeratosis, and natal teeth. Both her father and paternal grandfather presented onychodystrophy, palmoplantar keratoderma, and previous excision of "sebaceous" cysts. Molecular genetic analysis of the patient revealed a missense mutation (c.1163T>C) in heterozygosity in exon 6 of the KRT17 gene, confirming the diagnosis of PC-2 (Jackson-Lawler type), or PC-17. We conclude that PC is a relatively easy and consistent clinical diagnosis, but a high index of suspicion is required if the diagnosis is to be made correctly. With this case, the authors intend to draw attention to this condition and the role of the dermatologist in the diagnosis.

Question: What is the diagnosis of pachyonychia congenita?


Response and Habituation of the Human Amygdala during Visual Processing of Facial Expression. We measured amygdala activity in human volunteers during rapid visual presentations of fearful, happy, and neutral faces using functional magnetic resonance imaging (fMRI). The first experiment involved a fixed order of conditions both within and across runs, while the second one used a fully counterbalanced order in addition to a low level baseline of simple visual stimuli. In both experiments, the amygdala was preferentially activated in response to fearful versus neutral faces. In the counterbalanced experiment, the amygdala also responded preferentially to happy versus neutral faces, suggesting a possible generalized response to emotionally valenced stimuli. Rapid habituation effects were prominent in both experiments. Thus, the human amygdala responds preferentially to emotionally valenced faces and rapidly habituates to them.

Question: What is the amygdala?


Computational thinking (CT): on weaving it in. Paul Curzon Queen Mary University of London Mile End, London, E1 4NS UK +44 (20) 7882 5212 pc@dcs.qmul.ac.uk Joan Peckham Harriet Taylor NSF 4201 Wilson Blvd. Arlington, VA 22230, USA (703) 292-7344 jpeckham/htaylor@nsf.gov Amber Settle School of Computing DePaul University 243 S. Wabash Avenue Chicago, IL 60604, USA (312) 362-5324 asettle@cdm.depaul.edu Eric Roberts Computer Science Stanford University 202 Gates Stanford, CA 94305-901 650-723-3642 eroberts@cs.stanford.edu

Question: What is the difference between computational thinking and computational thinking?


A finger on the pulse: temporal rhythms and information seeking in medical work. Most cooperative work takes place in information-rich environments. However, studies of "information work" tend to focus on the decontextualized access and retrieval problems faced by individual information seekers. Our work is directed towards understanding how information management is seamlessly integrated into the course of everyday activities. Drawing on an ethnographic study of medical work, we explore the relationship between information and temporal coordination and discuss the role of temporal patterns or "rhythms" in providing individuals with the means to coordinate information and work.

Question: What is medical work?


mPHASiS: Mobile patient healthcare and sensor information system. Pervasive care and chronic disease management to reduce institutionalization is a priority for most western countries. The realization of next generation ubiquitous and pervasive healthcare systems will be a challenging task, as these systems are likely to involve a complex structure. Such systems will consist of various devices, ranging from resource-constrained sensors and actuators to complex multimedia devices, supporting time critical applications. This is further compounded by cultural and socio-economical factors that must be addressed for next generation healthcare systems to be widely diffused and used. In this study, the requirements for a vital sign monitoring solution space is derived and mPHASiS is developed based on these requirements. mPHASiS is an end to end solution not only providing sensor networking and vital sign monitoring but also closing the loop by signaling alert messages to the caregiver and allowing pervasive access to vital signs of a patient using smartphones over a heterogeneous network. A role based access control mechanism is developed to limit access to sensitive data. The end to end delay and delay variations for both vital sign data collection and pervasive access are analyzed. mPHASiS is developed as a complementary solution augmenting functionality of a hospital information system and can be loosely couple with the hospital information system using webservices. & 2010 Elsevier Ltd. All rights reserved.

Question: What is the difference between a hospital information system and a pervasive healthcare system?


10 Watts UHF broadband GaN based power amplifier for multi-band applications. The demand of broadband amplifier has been boosted to meet the requirements of multi-mode and multi-band wireless applications. GaN HEMT is the next generation of RF power transistor technology. In this paper, we have designed a 10W UHF broadband class-AB Power Amplifier (PA) based on GaN HEMT. The proposed amplifier has been designed and developed in the frequency range from 200-500 MHz. A maximum drain efficiency of 71% is achieved.

Question: What is the frequency range of the amplifier?


Word Embedding based Correlation Model for Question/Answer Matching. The large scale of Q&A archives accumulated in community based question answering (CQA) servivces are important information and knowledge resource on the web. Question and answer matching task has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, a Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding. Given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs, while it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method's promising

Question: What is the advantage of word embedding based correlation model?


Femtosecond-Long Pulse-Based Modulation for Terahertz Band Communication in Nanonetworks. Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. Nanonetworks are the enabling technology of long-awaited applications such as advanced health monitoring systems or high-performance distributed nano-computing architectures. The peculiarities of novel plasmonic nano-transceivers and nano-antennas, which operate in the Terahertz Band (0.1-10 THz), require the development of tailored communication schemes for nanonetworks. In this paper, a modulation and channel access scheme for nanonetworks in the Terahertz Band is developed. The proposed technique is based on the transmission of one-hundred-femtosecond-long pulses by following an asymmetric On-Off Keying modulation Spread in Time (TS-OOK). The performance of TS-OOK is evaluated in terms of the achievable information rate in the single-user and the multi-user cases. An accurate Terahertz Band channel model, validated by COMSOL simulation, is used, and novel stochastic models for the molecular absorption noise in the Terahertz Band and for the multi-user interference in TS-OOK are developed. The results show that the proposed modulation can support a very large number of nano-devices simultaneously transmitting at multiple Gigabits-per-second and up to Terabits-per-second, depending on the modulation parameters and the network conditions.

Question: What is the terahertz band?


Risk assessment method for cyber security of cyber physical systems. Cyber security is one of the most important risks for all types of cyber-physical systems (CPS). To evaluate the cyber security risk of CPS, a quantitative hierarchized assessment model consists of attack severity, attack success probability and attack consequence is proposed, which can assess the risk caused by an ongoing attack at host level and system level. Then the definitions and calculation methods of the three indexes are discussed in detail. Finally, this paper gives the risk assessment algorithm which describes the steps of implementation. Numerical example shows that the model can response to the attack timely and obtain the system security risk change curve. So that it can help users response to the risk timely. The risk change curve can also be used to predict the risk for the future time.

Question: What is the risk assessment method for cyber security of cyber physical systems?


Power Consumption Modeling of Skid-Steer Tracked Mobile Robots on Rigid Terrain. Power consumption is a key element in outdoor mobile robot autonomy. This issue is very relevant in skid-steer tracked vehicles on account of their large ground contact area. In this paper, the power losses due to dynamic friction have been modeled from two different perspectives: 1) the power drawn by the rigid terrain and 2) the power supplied by the motors. Comparison of both approaches has provided new insight on skid steering on hard flat terrains at walking speeds. Experimental power models, which also include traction resistance and other power losses, have been obtained for two different track widths over marble flooring and asphalt with Auriga- beta, which is a full-size mobile robot. To this end, various internal probes have been set at different points of the power stream. Furthermore, new energy implications for navigation of these kinds of vehicles have been deduced and tested.

Question: How much power does a skid-steer robot use?


Modeling wrinkles on smooth surfaces for footwear design. We describe two new shape operators that superimpose wrinkles on top of a smooth NURBS surface. Previous research studying wrinkles focused mostly on cloth modeling or in animations, which are driven more by visual realism, but allow large elastic deformations. Our operators generate wrinkle-shaped deformations in a region of a smooth surface along a given boundary based on a few basic parametric inputs such as wrinkle magnitude and extent (these terms will be defined in the paper). The essential geometric transformation to map the smooth surface to a wrinkled one will be defined purely in terms of the geometry of the surface and the input parameters. Our model is based on two surface properties: geodesic offsets and surface energy. Practical implementation of the operators is discussed, and examples presented. Finally, the motivation for the operators will be given through their application in the computer-aided design and manufacture of footwear.

Question: How to model wrinkles on a smooth surface?


KCoFI: Complete Control-Flow Integrity for Commodity Operating System Kernels. We present a new system, KCoFI, that is the first we know of to provide complete Control-Flow Integrity protection for commodity operating systems without using heavyweight complete memory safety. Unlike previous systems, KCoFI protects commodity operating systems from classical control-flow hijack attacks, return-to-user attacks, and code segment modification attacks. We formally verify a subset of KCoFI's design by modeling several features in small-step semantics and providing a partial proof that the semantics maintain control-flow integrity. The model and proof account for operations such as page table management, trap handlers, context switching, and signal delivery. Our evaluation shows that KCoFI prevents all the gadgets found by an open-source Return Oriented Programming (ROP) gadget-finding tool in the FreeBSD kernel from being used, it also reduces the number of indirect control-flow targets by 98.18%. Our evaluation also shows that the performance impact of KCoFI on web server bandwidth is negligible while file transfer bandwidth using OpenSSH is reduced by an average of 13%, and at worst 27%, across a wide range of file sizes. Postmark, an extremely file-system intensive benchmark, shows 2x overhead. Where comparable numbers are available, the overheads of KCoFI are far lower than heavyweight memory-safety techniques.

Question: What is the impact of KCoFI on web server bandwidth?


Secure Overlay Cloud Storage with Access Control and Assured Deletion. We can now outsource data backups off-site to third-party cloud storage services so as to reduce data management costs. However, we must provide security guarantees for the outsourced data, which is now maintained by third parties. We design and implement FADE, a secure overlay cloud storage system that achieves fine-grained, policy-based access control and file assured deletion. It associates outsourced files with file access policies, and assuredly deletes files to make them unrecoverable to anyone upon revocations of file access policies. To achieve such security goals, FADE is built upon a set of cryptographic key operations that are self-maintained by a quorum of key managers that are independent of third-party clouds. In particular, FADE acts as an overlay system that works seamlessly atop today's cloud storage services. We implement a proof-of-concept prototype of FADE atop Amazon S3, one of today's cloud storage services. We conduct extensive empirical studies, and demonstrate that FADE provides security protection for outsourced data, while introducing only minimal performance and monetary cost overhead. Our work provides insights of how to incorporate value-added security features into today's cloud storage services.

Question: What is the difference between secure overlay cloud storage and secure cloud storage?


Video summarisation: A conceptual framework and survey of the state of the art. Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users. 2007 Elsevier Inc. All rights reserved.

Question: What is a conceptual framework for video summarisation?


Using Computer Programming to Enhance Science Learning for 5th Graders in Taipei. The purpose of this study is to evaluate the effectiveness of using Scratch Programming in science learning for 5th graders in Taipei. A quasi-experimental design with a single group was used in this study. 96 fifth graders at an elementary school in Taipei participated in 15 weeks of Scratch Programming activities to enrich computer learning during their science learning. Three research instruments were used in this study, including a logical thinking test, a problem solving test, and a learning questionnaire. The results were subjected to t-test and frequency analysis. The findings of this study include (1) the outcomes of the logical thinking test and the problem solving test indicate that students had better performance in logical thinking and problem solving, (2) feedback from the study questionnaire indicates that more than 71.4% of students favored using Scratch Programming in their computer learning, and more than 58.4% of students favored designing Scratch Programming in their science learning, (3) more than 54.7% of students reported that they are willing to do more similar computer programming projects for other science units in the future. In view of the above findings, this study concludes that Scratch Programming is effective for 5th graders in Taipei.

Question: What is the purpose of this study?


Detecting and Tracking The Real-time Hot Topics: A Study on Computational Neuroscience. In this study, following the idea of our previous paper (Wang, et al., 2013a), we improve the method to detect and track hot topics in a specific field by using the real-time article usage data. With the "usage count" data provided by Web of Science, we take the field of computational neuroscience as an example to make analysis. About 10 thousand articles in the field of Computational Neuroscience are queried in Web of Science, when the records, including the usage count data of each paper, have been harvested and updated weekly from October 19, 2015 to March 21, 2016. The hot topics are defined by the most frequently used keywords aggregated from the articles. The analysis reveals that hot topics in Computational Neuroscience are related to the key technologies, like "fmri", "eeg", "erp", etc. Furthermore, using the weekly updated data, we track the dynamical changes of the topics. The characteristic of immediacy of usage data makes it possible to track the "heat" of hot topics timely and dynamically.

Question: What is the real-time hot topics in Computational Neuroscience?


Maximum Entropy Deep Inverse Reinforcement Learning. This paper presents a general framework for employing deep architectures in particular neural networks to solve the inverse reinforcement learning (IRL) problem. Specifically, we propose to exploit the representational capacity and favourable computational complexity of deep networks to approximate complex, nonlinear reward functions. We show that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. At test time, the approach leads to a computational complexity independent of the number of demonstrations. This makes it especially well-suited for applications in life-long learning scenarios commonly encountered in robotics. We demonstrate that our approach achieves performance commensurate to the state-of-the-art on existing benchmarks already with simple, comparatively shallow network architectures while significantly outperforming the stateof-the-art on an alternative benchmark based on more complex, highly varying reward structures representing strong interactions between features. Furthermore, we extend the approach to include convolutional layers in order to eliminate the dependency on precomputed features of current algorithms and to underline the substantial gain in flexibility in framing IRL in the context of deep learning.

Question: What is the maximum entropy deep inverse reinforcement learning?


A 10-nW 12-bit accurate analog storage cell with 10-aA leakage. Medium-term analog storage offers a compact, accurate, and low-power method of implementing temporary local memory that can be useful in adaptive circuit applications. The performance of these cells is characterized by the sampling accuracy and voltage droop that can be achieved with a given level of die area and power. Hand calculations suggest past implementations have not achieved minimum voltage droop due to uncompensated MOS leakage mechanisms. In this paper, the dominant sources of MOS leakage are experimentally characterized in a standard 1.5-/spl mu/m CMOS process using an on-chip current integration technique, focusing specifically on the 1 fA to 1 aA current range. These measurements reveal an accumulation-mode source-drain coupling mechanism that can easily dominate diode leakage under certain bias conditions and may have limited previous designs. A simple rule-of-thumb is offered for avoiding this leakage effect, leading to a novel ultra-low leakage switch topology. A differential storage cell incorporating this new switch achieves an average leakage of 10 aA at room temperature, an 8/spl times/ reduction over past designs. The cell loses one bit of voltage accuracy, 700 /spl mu/V on a 12-bit scale and 11.3 mV on an 8-bit scale, in 3.3 and 54 min, respectively. This represents a 15/spl times/ increase in hold time at these voltage accuracies over the lowest leakage cell to date, in only 92% of the area. Since the leakage is independent of amplifier bias, the cell can operate on as little as 10 nW of power. Initial measurements also indicate the switch's leakage decreases with the square of process feature size.

Question: What is the leakage of a storage cell?


STUDENTS ACCEPTANCE OF MOBILE LEARNING FOR HIGHER EDUCATION IN SAUDI ARABIA. Mobile learning is the next step in the development of distance learning. Widespread access to mobile devices and the opportunity to learn regardless of time and place make the mobile learning an important tool for lifelong learning. The research objectives are to examine the possibility of acceptance in mobile learning (m-Learning) and study main factors that affect using mLearning that focus on higher education students in Saudi Arabia. The researcher used a quantitative approach survey of 80 students. The modified acceptance framework that based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted to determine the factors that influence the students' intention to use m-Learning. The results from statistical analysis show that the acceptance level of students on m-Learning is in the high level.

Question: What is the acceptance level of students on m-Learning?


Adaptive Courseware: A Literature Review. This paper gives a detailed description of how adaptivity is realized in e-learning systems, with a focus on adaptivity of courseware. The review aims to answer three basic questions on adaptive courseware: what has been adapted, how and why. Researchers have tried to adapt according to these different elements: student knowledge, behaviour, interests, preferences, learning style, cognitive style, goals, courseware presentation, navigation, annotation, sequencing and knowledge testing. An historical background of adaptivity is described: from word definition, through adaptive instructions and adaptive systems, all the way to adaptive courseware. The biggest challenge was to find the most prominent representatives of adaptive systems in courseware generation that were selected after an exhaustive search through relevant scientific paper databases. Each prevailing system is briefly described and the student features, to which the system adapts, are highlighted, together with the level of adaptation. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from elearning systems in general to intelligent tutoring systems in particular. Such an analysis has not been done so far, especially in a way that adaptive systems are described uniformly. Finally, a comparative analysis of those systems and conclusions and demands about the ultimate adaptive system is given. This paper can be used as a guide for making decisions about what to adapt, how and why, while designing an adaptive e-learning system.

Question: What is adaptive courseware?


Real Time Localization and 3D Reconstruction. In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

Question: How to estimate the motion of a camera?


Optimizing orthodontic treatment in patients taking bisphosphonates for osteoporosis.. Bisphosphonates have unique pharmacological characteristics unlike those of any other drug group. Millions of adults take oral bisphosphonates for long-term treatment of osteoporosis and osteopenia; some of these people will most likely also seek orthodontic treatment. Adverse dental effects from bisphosphonates have been reported, including decreased tooth movement, impaired bone healing, and osteonecrosis in the mandible and the maxilla. Osteonecrosis has been rarely observed after bisphosphonate use for osteoporosis. However, adverse drug effects might occur more frequently in orthodontic patients, and they would probably be noted before the end-stage pathology of osteonecrosis. Adverse effects during orthodontic treatment, including decreased tooth movement, could last for years after the drug therapy is stopped. Successful orthodontic treatment requires optimal bone healing to prevent excessive tooth mobility. Bisphosphonates appear to have 2 bone elimination rates--a fast elimination of weeks from the bone surface and a slow elimination of years after incorporation into the bone structure. This article presents methods to clinically and radiographically monitor orthodontic patients who are taking oral bisphosphonates. Efforts to minimize adverse effects and optimize orthodontic procedures with physician-approved drug holidays are discussed. The orthodontic treatment results of 3 patients who received bisphosphonate therapy are reported.

Question: What are the adverse effects of bisphosphonates on orthodontic treatment?


LOCI: Fast Outlier Detection Using the Local Correlation Integral. Outlier detection is an integral part of data mining and has attracted much attention recently [BKNS00, JTH01, KNT00]. In this paper, we propose a new method for evaluating outlier-ness, which we call theLocal Correlation Integral(LOCI). As with the best previous methods, LOCI is highly effective for detecting outliers and groups of outliers ( a.k.a.micro-clusters). In addition, it offers the following advantages and novelties: (a) It provides an automatic, data-dictated cut-off to determine whether a point is an outlier—in contrast, previous methods force users to pick cut-offs, without any hints as to what cut-off value is best for a given dataset. (b) It can provide a LOCI plot for each point; this plot summarizes a wealth of information about the data in the vicinity of the point, determining clusters, micro-clusters, their diameters and their inter-cluster distances. None of the existing outlier-detection methods can match this feature, because they output only a single number for each point: its outlierness score.(c) Our LOCI method can be computed as quickly as the best previous methods. (d) Moreover, LOCI leads to a practically linear approximate method, aLOCI (for approximate LOCI ), which provides fast highly-accurate outlier detection. To the best of our knowledge, this is the first work to use approximate computations to speed up outlier detection. Experiments on synthetic and real world data sets show that LOCI and aLOCI can automatically detect outliers and micro-clusters, without user-required cut-offs, and that they quickly spot both expected and unexpected outliers.

Question: What is the local correlation integral?


Mindfulness-based psychotherapies: a review of conceptual foundations, empirical evidence and practical considerations.. OBJECTIVE This paper, composed by an interest group of clinicians and researchers based in Melbourne, presents some background to the practice of mindfulness-based therapies as relevant to the general professional reader. We address the empirical evidence for these therapies, the principles through which they might operate, some practical questions facing those wishing to commence practice in this area or to refer patients into mindfulness-based therapies, and some considerations relevant to the conduct and interpretation of research into the therapeutic application of mindfulness.   METHOD Databases (e.g. PsycINFO, MEDLINE) were searched for literature on the impact of mindfulness interventions, and the psychological and biological mechanisms that underpin the effects of mindfulness practice. This paper also draws upon the clinical experience of the author group.   RESULTS Mindfulness practice and principles have their origins in many contemplative and philosophical traditions but individuals can effectively adopt the training and practice of mindfulness in the absence of such traditions or vocabulary. A recent surge of interest regarding mindfulness in therapeutic techniques can be attributed to the publication of some well-designed empirical evaluations of mindfulness-based cognitive therapy. Arising from this as well as a broader history of clinical integration of mindfulness and Western psychotherapies, a growing number of clinicians have interest and enthusiasm to learn the techniques of mindfulness and to integrate them into their therapeutic work. This review highlights the importance of accurate professional awareness and understanding of mindfulness and its therapeutic applications.   CONCLUSIONS The theoretical and empirical literatures on therapeutic applications of mindfulness are in states of significant growth and development. This

Question: What is the difference between mindfulness and meditation?


Compaction Management in Distributed Key-Value Datastores. Compactions are a vital maintenance mechanism used by datastores based on the log-structured merge-tree to counter the continuous buildup of data files under update-intensive workloads. While compactions help keep read latencies in check over the long run, this comes at the cost of significantly degraded read performance over the course of the compaction itself. In this paper, we offer an in-depth analysis of compaction-related performance overheads and propose techniques for their mitigation. We offload large, expensive compactions to a dedicated compaction server to allow the datastore server to better utilize its resources towards serving the actual workload. Moreover, since the newly compacted data is already cached in the compaction server's main memory, we fetch this data over the network directly into the datastore server's local cache, thereby avoiding the performance penalty of reading it back from the filesystem. In fact, pre-fetching the compacted data from the remote cache prior to switching the workload over to it can eliminate local cache misses altogether. Therefore, we implement a smarter warmup algorithm that ensures that all incoming read requests are served from the datastore server's local cache even as it is warming up. We have integrated our solution into HBase, and using the YCSB and TPC-C benchmarks, we show that our approach significantly mitigates compaction-related performance problems. We also demonstrate the scalability of our solution by distributing compactions across multiple compaction servers.

Question: What is the difference between a datastore and a database?


A critical survey of state-of-the-art image inpainting quality assessment metrics. 7 Image inpainting is the process of restoring missing pixels in digital images in a plausible way. Research in image inpainting has received considerable attention in di erent areas, including restoration of old and damaged documents, removal of undesirable objects, computational photography, retouching applications, etc. The challenge is that the recovery processes themselves introduce noticeable artifacts within and around the restored image regions. As an alternative to subjective evaluation by humans, a number of approaches have been introduced to quantify inpainting processes objectively. Unfortunately, existing objective metrics have their own strengths and weaknesses as they use di erent criteria. This paper provides a thorough insight into existing metrics related to image inpainting quality assessment, developed during the last few years. The paper provides, under a new framework, a comprehensive description of existing metrics, their strengths, their weaknesses, and a detailed performance analysis on real images from public image inpainting database. The paper also outlines future research directions and applications of inpainting and inpainting-related quality assessment measures.

Question: What is the difference between a metric and a quality assessment metric?


Radio Interface Technologies for Cooperative Transmission in 3GPP LTE-Advanced. This paper presents an overview of radio interface technologies for cooperative transmission in 3GPP LTE-Advanced, i.e., coordinated multi-point (CoMP) transmission, enhanced inter-cell interference coordination (eICIC) for heterogeneous deployments, and relay transmission techniques. This paper covers not only the technical components in the 3GPP specifications that have already been released, but also those that were discussed in the Study Item phase of LTE-Advanced, and those that are currently being discussed in 3GPP for potential specification in future LTE releases. key words: LTE-Advanced, CoMP, eICIC, relay

Question: What is the difference between LTE-Advanced and LTE-Advanced?


Hyperbolic Graph Generator. Networks representing many complex systems in nature and society share some common structural properties like heterogeneous degree distributions and strong clustering. Recent research on network geometry has shown that those real networks can be adequately modeled as random geometric graphs in hyperbolic spaces. In this paper, we present a computer program to generate such graphs. Besides real-world-like networks, the program can generate random graphs from other well-known graph ensembles, such as the soft configuration model, random geometric graphs on a circle, or Erdős-Rényi random graphs. The simulations show a good match between the expected values of different network structural properties and the corresponding empirical values measured in generated graphs, confirming the accurate behavior of the program.

Question: What is a random geometric graph?


On fast surface reconstruction methods for large and noisy point clouds. In this paper we present a method for fast surface reconstruction from large noisy datasets. Given an unorganized 3D point cloud, our algorithm recreates the underlying surface's geometrical properties using data resampling and a robust triangulation algorithm in near realtime. For resulting smooth surfaces, the data is resampled with variable densities according to previously estimated surface curvatures. Incremental scans are easily incorporated into an existing surface mesh, by determining the respective overlapping area and reconstructing only the updated part of the surface mesh. The proposed framework is flexible enough to be integrated with additional point label information, where groups of points sharing the same label are clustered together and can be reconstructed separately, thus allowing fast updates via triangular mesh decoupling. To validate our approach, we present results obtained from laser scans acquired in both indoor and outdoor environments.

Question: What is the difference between a point cloud and a mesh?


The Logic of Plausible Reasoning: A Core Theory. 1. o formal representation of plausible inference patterns; such OS deductions, inductions, ond analogies, thot ore frequently employed in answering every doy questions: 2. a set of parameters, such OS conditional likelihood, typicality, and similarity, that affect the certainty of people's onswers to such questions; ond 3. a system relating the different plorisible inference patterns and the different certainty parameters.

Question: What is the logic of plausible reasoning?


Unsupervised speech representation learning using WaveNet autoencoders. We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. The behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE (VQVAE). We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQVAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.

Question: What is the zero speech 2017 unsupervised acoustic unit discovery task?


A fast nearest neighbor search algorithm by nonlinear embedding. We propose an efficient algorithm to find the exact nearest neighbor based on the Euclidean distance for large-scale computer vision problems. We embed data points nonlinearly onto a low-dimensional space by simple computations and prove that the distance between two points in the embedded space is bounded by the distance in the original space. Instead of computing the distances in the high-dimensional original space to find the nearest neighbor, a lot of candidates are to be rejected based on the distances in the low-dimensional embedded space; due to this property, our algorithm is well-suited for high-dimensional and large-scale problems. We also show that our algorithm is improved further by partitioning input vectors recursively. Contrary to most of existing fast nearest neighbor search algorithms, our technique reports the exact nearest neighbor - not an approximate one - and requires a very simple preprocessing with no sophisticated data structures. We provide the theoretical analysis of our algorithm and evaluate its performance in synthetic and real data.

Question: What is the nearest neighbor search algorithm?


Myanmar Spell Checker. Natural Language Processing (NLP) is one of the most important research area carried out in the world of Human Language. For every language, spell checker is an essential component of many of Office Automation Systems and Machine Translation Systems. In this paper, we develop a Myanmar Spell Checker System which can handle Typographic errors, Sequence errors, Phonetic errors, and Context errors. A Myanmar Text Corpus is created for developing Myanmar Spell checker. To check Typographic Errors, corpus look up approach is applied. Myanmar3 Unicode is applied in this system so that it can automatically reorder the character sequence. A compound misused word detection algorithm is proposed for Phonetic Errors checking and Bayesian Classifier is applied for Context Errors checking. In this system, Levenshtein Distance Algorithm is applied to improve users' efficiency by providing a suggestion list for misspelled Myanmar Words. We provide evaluation results of the system and our approach can handle various types of Myanmar spell errors.

Question: How to check Myanmar spell errors?


Discrete Wigner Function Derivation of the Aaronson-Gottesman Tableau Algorithm. The Gottesman–Knill theorem established that stabilizer states and Clifford operations can be efficiently simulated classically. For qudits with odd dimension three and greater, stabilizer states and Clifford operations have been found to correspond to positive discrete Wigner functions and dynamics. We present a discrete Wigner function-based simulation algorithm for odd-d qudits that has the same time and space complexity as the Aaronson–Gottesman algorithm for qubits. We show that the efficiency of both algorithms is due to harmonic evolution in the symplectic structure of discrete phase space. The differences between the Wigner function algorithm for odd-d and the Aaronson–Gottesman algorithm for qubits are likely due only to the fact that the Weyl–Heisenberg group is not in SU(d) for d = 2 and that qubits exhibit state-independent contextuality. This may provide a guide for extending the discrete Wigner function approach to qubits.

Question: What is the discrete Wigner function?


Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition. RECENTLY , several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches. Firstly, this dissertation will present a new framework for multi-target tracking in a single camera. We proposed a novel data association technique using Dominant set clustering (DCS) framework. We formulate the tracking task as finding dominant sets on the constructed undirected edge weighted graph. Unlike most techniques which are limited in temporal locality (i.e. few frames are considered), we utilized a pairwise relationships (in appearance and position) between different detections across the whole temporal span of the video for data association in a global manner. Meanwhile, temporal sliding window technique is utilized to find tracklets and perform further merging on them. Our robust tracklet merging step renders our tracker to long term occlusions with more robustness. DSC leads us to a more accurate approach to multi-object tracking by considering all the pairwise relationships in a batch of frames; however, it has some limitations. Firstly, it finds target trajectories(clusters) one-by-one (peel off strategy), causing change in the scale of the problem. Secondly, the algorithm used to enumerate dominant sets, i.e., replicator dynamics, have quadratic computational complexity, which makes it impractical on larger graphs. To address these problems and extend tracking problem to multiple non-overlapping camer

Question: What is the difference between a clustering framework and a clustering algorithm?


Work strain, health, and absenteeism: a meta-analysis.. Work strain has been argued to be a significant cause of absenteeism in the popular and academic press. However, definitive evidence for associations between absenteeism and strain is currently lacking. A theory focused meta-analysis of 275 effects from 153 studies revealed positive but small associations between absenteeism and work strain, psychological illness, and physical illness. Structural equation modeling results suggested that the strain-absence connection may be mediated by psychological and physical symptoms. Little support was received for the purported volitional distinction between absence frequency and time lost absence measures on the basis of illness. Among the moderators examined, common measurement, midterm and stable sources of variance, and publication year received support.

Question: What is the difference between work strain and absenteeism?


The role of emotions for moral judgments depends on the type of emotion and moral scenario.. Emotions seem to play a critical role in moral judgment. However, the way in which emotions exert their influence on moral judgments is still poorly understood. This study proposes a novel theoretical approach suggesting that emotions influence moral judgments based on their motivational dimension. We tested the effects of two types of induced emotions with equal valence but with different motivational implications (anger and disgust), and four types of moral scenarios (disgust-related, impersonal, personal, and beliefs) on moral judgments. We hypothesized and found that approach motivation associated with anger would make moral judgments more permissible, while disgust, associated with withdrawal motivation, would make them less permissible. Moreover, these effects varied as a function of the type of scenario: the induced emotions only affected moral judgments concerning impersonal and personal scenarios, while we observed no effects for the other scenarios. These findings suggest that emotions can play an important role in moral judgment, but that their specific effects depend upon the type of emotion induced. Furthermore, induced emotion effects were more prevalent for moral decisions in personal and impersonal scenarios, possibly because these require the performance of an action rather than making an abstract judgment. We conclude that the effects of induced emotions on moral judgments can be predicted by taking their motivational dimension into account. This finding has important implications for moral psychology, as it points toward a previously overlooked mechanism linking emotions to moral judgments.

Question: What is the difference between moral judgments and moral decisions?


A Machine Learning Approach to Software Requirements Prioritization. Deciding which, among a set of requirements, are to be considered first and in which order is a strategic process in software development. This task is commonly referred to as requirements prioritization. This paper describes a requirements prioritization method called Case-Based Ranking (CBRank), which combines project's stakeholders preferences with requirements ordering approximations computed through machine learning techniques, bringing promising advantages. First, the human effort to input preference information can be reduced, while preserving the accuracy of the final ranking estimates. Second, domain knowledge encoded as partial order relations defined over the requirement attributes can be exploited, thus supporting an adaptive elicitation process. The techniques CBRank rests on and the associated prioritization process are detailed. Empirical evaluations of properties of CBRank are performed on simulated data and compared with a state-of-the-art prioritization method, providing evidence of the method ability to support the management of the tradeoff between elicitation effort and ranking accuracy and to exploit domain knowledge. A case study on a real software project complements these experimental measurements. Finally, a positioning of CBRank with respect to state-of-the-art requirements prioritization methods is proposed, together with a discussion of benefits and limits of the method.

Question: What is a requirements prioritization method?


Classification of watermelon leaf diseases using neural network analysis. This paper mainly discussed the process to classify Anthracnose and Downey Mildew, watermelon leaf diseases using neural network analysis. A few of infected leaf samples were collected and they were captured using a digital camera with specific calibration procedure under controlled environment. The classification on the watermelon's leaf diseases is based on color feature extraction from RGB color model where the RGB pixel color indices have been extracted from the identified Regions of Interest (ROI). The proposed automated classification model involved the process of diseases classification using Statistical Package for the Social Sciences (SPSS) and Neural Network Pattern Recognition Toolbox in MATLAB. Determinations in this work have shown that the type of leaf diseases achieved 75.9% of accuracy based on its RGB mean color component.

Question: What is the process to classify watermelon leaf diseases using neural network analysis?


AutoISES: Automatically Inferring Security Specification and Detecting Violations. The importance of software security cannot be overstated. In the past, researchers have applied program analysis techniques to automatically detect security vulnerabilities and verify security properties. However, such techniques have limited success in reality because they require manually provided code-level security specifications. Manually writing and generating these code-level security specifications are tedious and error-prone. Additionally, they seldom exist in production software. In this paper, we propose a novel method and tool, called AutoISES, which Automatically Infers Security Specifications by statically analyzing source code, and then directly use these specifications to automatically detect security violations. Our experiments with the Linux kernel and Xen demonstrated the effectiveness of this approach – AutoISES automatically generated 84 security specifications and detected 8 vulnerabilities in the Linux kernel and Xen, 7 of which have already been confirmed by the corresponding developers.

Question: What is the importance of software security?


A Hierarchical Game Framework for Resource Management in Fog Computing. Supporting real-time and mobile data services, fog computing has been considered as a promising technology to overcome long and unpredicted delay in cloud computing. However, as resources in FNs are owned by independent users or infrastructure providers, the ADSSs cannot connect and access data services from the FNs directly, but can only request data service from the DSOs in the cloud. Accordingly, in fog computing, the DSOs are required to communicate with FNs and allocate resources from the FNs to the ADSSs. The DSOs provide virtualized data services to the ADSSs, and the FNs, motivated by the DSOs, provide data services in the physical network. Nevertheless, with fog computing added as the intermediate layer between the cloud and users, there are challenges such as the resource allocation in the virtualized network between the DSOs and ADSSs, the asymmetric information problem between DSOs and ADSSs, and the resource matching from the FNs to the ADSSs in the physical network. In this article, we propose a three-layer hierarchical game framework to solve the challenges in fog computing networks. In the proposed framework, we apply the Stackelberg sub-game for the interaction between DSOs and ADSSs, moral hazard modeling for the interaction between DSOs and FNs, and the student project allocation matching sub-game for the interaction between FNs and ADSSs. The purpose is to obtain stable and optimal utilities for each DSO, FN, and ADSS in a distributed fashion.

Question: What is fog computing?


Continuous N-gram Representations for Authorship Attribution. This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.

Question: What is the difference between discrete and continuous representations?


LIBSVM: a library for support vector machines.

Question: What is a support vector machine?


Glaucoma-Deep : Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning Detection of Glaucoma by Abbas. Detection of glaucoma eye disease is still a challenging task for computer-aided diagnostics (CADx) systems. During eye screening process, the ophthalmologists measures the glaucoma by structure changes in optic disc (OD), loss of nerve fibres (LNF) and atrophy of the peripapillary region (APR). In retinal images, the automated CADx systems are developed to assess this eye disease through segmentation-based hand-crafted features. Therefore in this paper, the convolutional neural network (CNN) unsupervised architecture was used to extract the features through multilayer from raw pixel intensities. Afterwards, the deep-belief network (DBN) model was used to select the most discriminative deep features based on the annotated training dataset. At last, the final decision is performed by softmax linear classifier to differentiate between glaucoma and non-glaucoma retinal fundus image. This proposed system is known as Glaucoma-Deep and tested on 1200 retinal images obtained from publically and privately available datasets. To evaluate the performance of Glaucoma-Deep system, the sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRC) statistical measures were utilized. On average, the SE of 84.50%, SP of 98.01%, ACC of 99% and PRC of 84% values were achieved. Comparing to state-of-the-art systems, the Nodular-Deep system accomplished significant higher results. Consequently, the Glaucoma-Deep system can easily recognize the glaucoma eye disease to solve the problem of clinical experts during eye-screening process on large-scale environments. Keywords—Fundus imaging; glaucoma; diabetic retinopathy; deep learning; convolutional neural networks; deep belief network

Question: What is the sensitivity of the system?


Primed for Violence : The Role of Gender Inequality in Predicting Internal Conflict. We know, most notably through Ted Gurr's research, that ethnic discrimination can lead to ethnopolitical rebellion–intrastate conflict. I seek to discover what impact, if any, gender inequality has on intrastate conflict. Although democratic peace scholars and others highlight the role of peaceful domestic behavior in predicting state behavior, many scholars have argued that a domestic environment of inequality and violenceFstructural and cultural violenceFresults in a greater likelihood of violence at the state and the international level. This project contributes to this line of inquiry and further tests the grievance theory of intrastate conflict by examining the norms of violence that facilitate a call to arms. And in many ways, I provide an alternative explanation for the significance of some of the typical economic measuresFthe greed theoryFbased on the link between discrimination, inequality, and violence. I test whether states characterized by higher levels of gender inequality are more likely to experience intrastate conflict. Ultimately, the basic link between gender inequality and intrastate conflict is confirmedFstates characterized by gender inequality are more likely to experience intrastate conflict, 1960–2001.

Question: What is the gender inequality theory of intrastate conflict?


A survey on policy search algorithms for learning robot controllers in a handful of trials. Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.

Question: How can a robot adapt with only a handful of trials (a dozen) and a few minutes?


Platelet-Rich Plasma: Applications in Dermatology.

Question: What is platelet-rich plasma?


Simultaneous video defogging and stereo reconstruction. We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We first improve the photo-consistency term to explicitly model the appearance change due to the scattering effects. The prior matting Laplacian constraint on fog transmission imposes a detail-preserving smoothness constraint on the scene depth. We further enforce the ordering consistency between scene depth and fog transmission at neighboring points. These novel constraints are formulated together in an MRF framework, which is optimized iteratively by introducing auxiliary variables. The experiment results on real videos demonstrate the strength of our method.

Question: How to reconstruct a clear image from a foggy image?


RoXSum: Leveraging Data Aggregation and Batch Processing for XML Routing. Content-based routing is the primary form of communication within publish/subscribe systems. In those systems data transmission is performed by sophisticated overlay networks of content-based routers, which match data messages against registered subscriptions and forward them based on this matching. Despite their inherent complexities, such systems are expected to deliver information in a timely and scalable fashion. As a result, their successful deployment is a strenuous task. Relevant efforts have so far focused on the construction of the overlay network and the filtering of messages at each broker. However, the efficient transmission of messages has received less attention. In this work, we propose a solution that gracefully handles the transmission task, while providing performance benefits for the matching task as well. Along those lines, we design RoXSum, a message representation scheme that aggregates the routing information from multiple documents in a way that permits subscription matching directly on the aggregated content. Our performance study shows that RoXSum is a viable and effective technique, as it speeds up message routing for more than an order of magnitude.

Question: What is the difference between a content-based router and a content-based routing system?


Two-factor authentication for the Bitcoin protocol. We show how to realize two-factor authentication for a Bitcoin wallet. To do so, we explain how to employ an ECDSA adaption of the two-party signature protocol by MacKenzie and Reiter (Int J Inf Secur 2(3–4):218–239, 2004. doi: 10.1007/s10207-004-0041-0 ) in the context of Bitcoin and present a prototypic implementation of a Bitcoin wallet that offers both: two-factor authentication and verification over a separate channel. Since we use a smart phone as the second authentication factor, our solution can be used with hardware already available to most users and the user experience is quite similar to the existing online banking authentication methods.

Question: How to realize two-factor authentication for the bitcoin protocol?


FLAP: An End-to-End Event Log Analysis Platform for System Management. Many systems, such as distributed operating systems, complex networks, and high throughput web-based applications, are continuously generating large volume of event logs. These logs contain useful information to help system administrators to understand the system running status and to pinpoint the system failures. Generally, due to the scale and complexity of modern systems, the generated logs are beyond the analytic power of human beings. Therefore, it is imperative to develop a comprehensive log analysis system to support effective system management. Although a number of log mining techniques have been proposed to address specific log analysis use cases, few research and industrial efforts have been paid on providing integrated systems with an end-to-end solution to facilitate the log analysis routines.  In this paper, we design and implement an integrated system, called FIU Log Analysis Platform (a.k.a. FLAP), that aims to facilitate the data analytics for system event logs. FLAP provides an end-to-end solution that utilizes advanced data mining techniques to assist log analysts to conveniently, timely, and accurately conduct event log knowledge discovery, system status investigation, and system failure diagnosis. Specifically, in FLAP, state-of-the-art template learning techniques are used to extract useful information from unstructured raw logs; advanced data transformation techniques are proposed and leveraged for event transformation and storage; effective event pattern mining, event summarization, event querying, and failure prediction techniques are designed and integrated for log analytics; and user-friendly interfaces are utilized to present the informative analysis results intuitively and vividly. Since 2016, FLAP has been used by Huawei Technologies Co. Ltd for internal event log analysis, and has provided effective support in its system operation and workflow optimization.

Question: What is the end-to-end solution for log analysis?


Using PMU Data to Increase Situational Awareness Final Project Report.

Question: How to use PMU data to increase situational awareness?


Estimation of IMU and MARG orientation using a gradient descent algorithm. This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.

Question: How to estimate the orientation of an IMU using a gradient descent algorithm?


Process Mining in IT Service Management: A Case Study. The explosion of process-related data in nowadays organizations has raised the interest to exploit the data in order to know in deep how the business processes are being carried out. To face this need, process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from process related data. In this paper, we present a case study of process mining in a real IT service management scenario. We describe an exploratory analysis of real life event logs generated between 2012 and 2015, in four different processes designed within an IT platform. More specifically, we analyze the way of handling the different requests and incidents registered in an organization.

Question: What is process mining?


Development and initial validation of a short three-dimensional inventory of character strengths. Character strength is described as a positive and organized pattern of emotions, thoughts, and behaviors. It serves as a schema that organizes categories of information toward the self, others, and the world, and provides the self-aware knowledge that facilitates the pursuit of goals, values, and ethical principles. Recent research has suggested that three reliable factors emerge from the measures of character strengths: caring, inquisitiveness, and self-control. The goal of this paper is to develop a psychometrically sound short measure of character strength. The questions were addressed in two studies using two independent samples: a cross-cultural (i.e., 518 Asians and 556 Westerners) sample, and a cross-population (i.e., 175 community participants and 171 inpatients) sample in China. Findings from the exploratory and confirmatory factor analysis suggested a cross-cultural three-factor model of character strength that could be measured by the Three-dimensional Inventory of Character Strengths (TICS). A multigroup confirmatory factor analysis further indicated that the number of factors and factor loadings was invariant in the medical and community samples. This result indicated that the brief inventory could be applied to a medical context. Internal reliability, content validity, and predictive validity were good, although the predictive validity of the three character strengths for psychological symptoms in the medical sample was more modest than that in the community sample. TICS is expected to be used for screening populations at risk, and a tool to aid mental health professionals in group-based treatment/intervention planning. It also should be noted that this short inventory should be used with caution for individual decision making.

Question: What is the difference between a character strength and a virtue?


Long-term stability of anterior open-bite treatment by intrusion of maxillary posterior teeth.. INTRODUCTION Anterior open bite results from the combined influences of skeletal, dental, functional, and habitual factors. The long-term stability of anterior open bite corrected with absolute anchorage has not been thoroughly investigated. The purpose of this study was to examine the long-term stability of anterior open-bite correction with intrusion of the maxillary posterior teeth.   METHODS Nine adults with anterior open bite were treated by intrusion of the maxillary posterior teeth. Lateral cephalographs were taken immediately before and after treatment, 1 year posttreatment, and 3 years posttreatment to evaluate the postintrusion stability of the maxillary posterior teeth.   RESULTS On average, the maxillary first molars were intruded by 2.39 mm (P<0.01) during treatment and erupted by 0.45 mm (P<0.05) at the 3-year follow-up, for a relapse rate of 22.88%. Eighty percent of the total relapse of the intruded maxillary first molars occurred during the first year of retention. Incisal overbite increased by a mean of 5.56 mm (P<0.001) during treatment and decreased by a mean of 1.20 mm (P<0.05) by the end of the 3-year follow-up period, for a relapse rate of 17.00%. Incisal overbite significantly relapsed during the first year of retention (P<0.05) but did not exhibit significant recurrence between the 1-year and 3-year follow-ups.   CONCLUSIONS Most relapse occurred during the first year of retention. Thus, it is reasonable to conclude that the application of an appropriate retention method during this period clearly enhances the long-term stability of the treatment.

Question: How long does it take for the maxillary first molars to erupt after intrusion?


Arabic Optical Character Recognition Software : A Review 1. This paper provides a thorough evaluation of a set of six important Arabic OCR systems available in the market; namely: Abbyy FineReader, Leadtools, Readiris, Sakhr, Tesseract and NovoVerus. We test the OCR systems using a randomly selected images from the well known Arabic Printed Text Image database (250 images from the APTI database) and using a set of 8 images from an Arabic book. The APTI database contains 45.313.600 of both decomposable and non-decomposable word images. In the evaluation, we conduct two tests. The first test is based on usual metrics used in the literature. In the second test, we provide a novel measure for Arabic language, which can be used for other non-Latin languages.

Question: What is the arabic optical character recognition software?


Multi-agent Path Planning and Network Flow. This paper connects multi-agent path planning on graphs (roadmaps) to network flow problems, showing that the former can be reduced to the latter, therefore enabling the application of combinatorial network flow algorithms, as well as general linear program techniques, to multi-agent path planning problems on graphs. Exploiting this connecti on, we show that when the goals are permutation invariant, the problem always has a feasible solution path set with a longes t finish time of no more than n+V − 1 steps, in which n is the number of agents andV is the number of vertices of the underlying graph. We then give a complete algorithm that finds such a solution in O(nVE) time, with E being the number of edges of the graph. Taking a further step, we study time and distance optimality of the feasible solutions, show that th ey have a pairwise Pareto optimal structure, and again provide efficient algorithms for optimizing two of these practical objectives.

Question: What is the longest path in a graph?


Person-Independent 3D Gaze Estimation Using Face Frontalization. Person-independent and pose-invariant estimation of eye-gaze is important for situation analysis and for automated video annotation. We propose a fast cascade regression based method that first estimates the location of a dense set of markers and their visibility, then reconstructs face shape by fitting a part-based 3D model. Next, the reconstructed 3D shape is used to estimate a canonical view of the eyes for 3D gaze estimation. The model operates in a feature space that naturally encodes local ordinal properties of pixel intensities leading to photometric invariant estimation of gaze. To evaluate the algorithm in comparison with alternative approaches, three publicly-available databases were used, Boston University Head Tracking, Multi-View Gaze and CAVE Gaze datasets. Precision for head pose and gaze averaged 4 degrees or less for pitch, yaw, and roll. The algorithm outperformed alternative methods in both datasets.

Question: How to estimate the location of a dense set of markers and their visibility?


D-AMINO ACID OXIDASE: A REVIEW. Over many years, D-serine and glycine were found to be the endogenous ligands for glycine-binding site for N-methyl-D-aspartate receptors. D-serine before being used up by the cells undergoes oxidative deamination by the enzyme D-amino acid oxidase (DAAO) and makes D-serine levels reduced in the brain, thereby affecting brain functions. In this review, we will discuss about the synthesis, location, therapeutic potential of DAAO function, role in cognition, and neuropathic pain.

Question: What is the role of DAAO in cognition?


ActiveStereoNet: End-to-End Self-supervised Learning for Active Stereo Systems. In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of 1/30th of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation is edge-preserving and smooths the loss function, which is key to allow the network to reach compelling results. Finally we show how the task of predicting invalid regions, such as occlusions, can be trained end-toend without ground-truth. This component is crucial to reduce blur and particularly improves predictions along depth discontinuities. Extensive quantitatively and qualitatively evaluations on real and synthetic data demonstrate state of the art results in many challenging scenes.

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


A Language and Program for Complex Bayesian Modelling. http://www.jstor.org A Language and Program for Complex Bayesian Modelling Author(s): W. R. Gilks, A. Thomas and D. J. Spiegelhalter Source: Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 43, No. 1, Special Issue: Conference on Practical Bayesian Statistics, 1992 (3) (1994), pp. 169-177 Published by: for the Wiley Royal Statistical Society Stable URL: http://www.jstor.org/stable/2348941 Accessed: 19-08-2014 17:40 UTC

Question: What is the title of the paper?


Deep visual foresight for planning robot motion. A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation — pushing objects — and can handle novel objects not seen during training.

Question: How can we predict the effects of our actions?


Planning for decentralized control of multiple robots under uncertainty. This paper presents a probabilistic framework for synthesizing control policies for general multi-robot systems that is based on decentralized partially observable Markov decision processes (Dec-POMDPs). Dec-POMDPs are a general model of decision-making where a team of agents must cooperate to optimize a shared objective in the presence of uncertainty. Dec-POMDPs also consider communication limitations, so execution is decentralized. While Dec-POMDPs are typically intractable to solve for real-world problems, recent research on the use of macro-actions in Dec-POMDPs has significantly increased the size of problem that can be practically solved. We show that, in contrast to most existing methods that are specialized to a particular problem class, our approach can synthesize control policies that exploit any opportunities for coordination that are present in the problem, while balancing uncertainty, sensor information, and information about other agents. We use three variants of a warehouse task to show that a single planner of this type can generate cooperative behavior using task allocation, direct communication, and signaling, as appropriate. This demonstrates that our algorithmic framework can automatically optimize control and communication policies for complex multi-robot systems.

Question: What is a warehouse task?


Strong intuitionistic fuzzy graphs. We introduce the notion of strong intuitionistic fuzzy graphs and investigate some of their properties. We discuss some propositions of self complementary and self weak complementary strong intuitionistic fuzzy graphs. We introduce the concept of intuitionistic fuzzy line graphs.

Question: What is intuitionistic fuzzy graph?


Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System. Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path.

Question: How accurate is the heading estimation using a quaternion-based unscented Kalman filter?


Lessons learned from the deployment of a smartphone-based access-control system. Grey is a smartphone-based system by which a user can exercise her authority to gain access to rooms in our university building, and by which she can delegate that authority to other users. We present findings from a trial of Grey, with emphasis on how common usability principles manifest themselves in a smartphone-based security application. In particular, we demonstrate aspects of the system that gave rise to failures, misunderstandings, misperceptions, and unintended uses; network effects and new flexibility enabled by Grey; and the implications of these for user behavior. We argue that the manner in which usability principles emerged in the context of Grey can inform the design of other such applications.

Question: What are the implications of the design of the access control system?


Highly Sensitive, Flexible, and Wearable Pressure Sensor Based on a Giant Piezocapacitive Effect of Three-Dimensional Microporous Elastomeric Dielectric Layer.. We report a flexible and wearable pressure sensor based on the giant piezocapacitive effect of a three-dimensional (3-D) microporous dielectric elastomer, which is capable of highly sensitive and stable pressure sensing over a large tactile pressure range. Due to the presence of micropores within the elastomeric dielectric layer, our piezocapacitive pressure sensor is highly deformable by even very small amounts of pressure, leading to a dramatic increase in its sensitivity. Moreover, the gradual closure of micropores under compression increases the effective dielectric constant, thereby further enhancing the sensitivity of the sensor. The 3-D microporous dielectric layer with serially stacked springs of elastomer bridges can cover a much wider pressure range than those of previously reported micro-/nanostructured sensing materials. We also investigate the applicability of our sensor to wearable pressure-sensing devices as an electronic pressure-sensing skin in robotic fingers as well as a bandage-type pressure-sensing device for pulse monitoring at the human wrist. Finally, we demonstrate a pressure sensor array pad for the recognition of spatially distributed pressure information on a plane. Our sensor, with its excellent pressure-sensing performance, marks the realization of a true tactile pressure sensor presenting highly sensitive responses to the entire tactile pressure range, from ultralow-force detection to high weights generated by human activity.

Question: What is the difference between a piezocapacitive pressure sensor and a piezoresistive pressure sensor?