zeta-alpha-ai/NanoSCIDOCS huggingface dataset:
Matching unknown latent fingerprints lifted from crime scenes to full (rolled or plain) fingerprints in law enforcement databases is of critical importance for combating crime and fighting terrorism. Compared to good quality full fingerprints acquired using live-scan or inking methods during enrollment, latent fingerprints are often smudgy and blurred, capture only a small finger area, and have large nonlinear distortion. For this reason, features (minutiae and singular points) in latents are typically manually marked by trained latent examiners. However, this introduces an undesired interoperability problem between latent examiners and automatic fingerprint identification systems (AFIS); the features marked by examiners are not always compatible with those automatically extracted by AFIS, resulting in reduced matching accuracy. While the use of automatically extracted minutiae from latents can avoid interoperability problem, such minutiae tend to be very unreliable, because of the poor quality of latents. In this paper, we improve latent to full fingerprint matching accuracy by combining manually marked (ground truth) minutiae with automatically extracted minutiae. Experimental results on a public domain database, NIST SD27, demonstrate the effectiveness of the proposed algorithm.
The Internet of Things is currently getting significant interest from the scientific community. Academia and industry are both focused on moving ahead in attempts to enhance usability, maintainability, and security through standardization and development of best practices.We focus on security because of its impact as one of themost limiting factors to wider Internet ofThings adoption. Numerous research areas exist in the security domain, ranging from cryptography to network security to identitymanagement.This paper provides a survey of existing research applicable to the Internet of Things environment at the application layer in the areas of identity management, authentication, and authorization. We survey and analyze more than 200 articles, categorize them, and present current trends in the Internet of Things security domain.
This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arisingfrom diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached, to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those/rom an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accurately enough for some task and, i f not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees of freedom and provides a practical means of estimating the relationships (position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.
India is agriculture based nation. It is necessary to improve the productivity and quality of agro based products. The proposed design is an automatic system that aids the famers in irrigation process. It keeps notifying the farmer through an on-board LCD display and messages that is sent on the farmer's cellular number. This proposed design is also helpful for the farmers who are facing power failure issues to maintain a uniform water supply due to power failure or inadequate and non-uniform water supply. The automatic irrigation system also keeps the farmer updated with all the background activities through a SIM900 Module that sends messages on the registered number. This device can be a turning point for our society. The device is easily affordable by the farmers of the country. This proposed design is helpful for reducing the human labour. This is a low budget system with an essential social application.
An intelligent drip irrigation system optimizes water and fertilizer use for agricultural crops using wireless sensors and fuzzy logic. The wireless sensor networks consists of many sensor nodes, hub and control unit. The sensor collects real-time data such as temperature, soil humidity. This data is sent to the hub using the wireless technology. The hub processes the data using fuzzy logic and decides the time duration for keeping the valves open. Accordingly, the drip irrigation system is implemented for a particular amount of time. The whole system is powered by photovoltaic cells and has a communication link which allows the system to be monitored, controlled, and scheduled through cellular text messages. The system can quickly and accurately calculate water demand amount of crops, which can provide a scientific basis for water-saving irrigation, as well as a method to optimize the amount of fertilizer used.
Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.
This paper shows the design and fabrication of a Thermo-electric generator (TEG) and the implementation of an automated irrigation system using this TEG as a soil moisture detector. The TEG inserted in two heat exchangers is capable of finding the thermal difference between the air and the soil that establishes a relationship with the soil’s moisture condition. Being able to obtain the soil moisture level from the TEG’s output, a microcontroller is used to automate the irrigation system. The irrigation system adapts to the soil area’s condition it irrigates based from the moisture it detects via the TEG. The water consumption of the soil is controlled by the automated irrigation system based on the soil’s condition and therefore, promotes water conservation compared to that of the water consumption of manual irrigation system. It also optimizes plant growth in that it waters it to the correct moisture level at the right time.
At present, labor-saving and water-saving technology is a key issue in irrigation. A wireless solution for intelligent field irrigation system dedicated to Jew's-ear planting in Lishui, Zhejiang, China, based on ZigBee technology was proposed in this paper. Instead of conventional wired connection, the wireless design made the system easy installation and maintenance. The hardware architecture and software algorithm of wireless sensor/actuator node and portable controller, acting as the end device and coordinator in ZigBee wireless sensor network respectively, were elaborated in detail. The performance of the whole system was evaluated in the end. The long-time smooth and proper running of the system in the field proved its high reliability and practicability. As an explorative application of wireless sensor network in irrigation management, this paper offered a methodology to establish large-scale remote intelligent irrigation system.
Cooperative navigation (CN) enables a group of cooperative robots to reduce their individual navigation errors. For a general multi-robot (MR) measurement model that involves both inertial navigation data and other onboard sensor readings, taken at different time instances, the various sources of information become correlated. Thus, this correlation should be solved for in the process of information fusion to obtain consistent state estimation. The common approach for obtaining the correlation terms is to maintain an augmented covariance matrix. This method would work for relative pose measurements, but is impractical for a general MR measurement model, because the identities of the robots involved in generating the measurements, as well as the measurement time instances, are unknown a priori. In the current work, a new consistent information fusion method for a general MR measurement model is developed. The proposed approach relies on graph theory. It enables explicit on-demand calculation of the required correlation terms. The graph is locally maintained by every robot in the group, representing all the MR measurement updates. The developed method calculates the correlation terms in the most general scenarios of MR measurements while properly handling the involved process and measurement noise. A theoretical example and a statistical study are provided, demonstrating the performance of the method for vision-aided navigation based on a three-view measurement model. The method is compared, in a simulated environment, to a fixed-lag centralized smoothing approach. The method is also validated in an experiment that involved real imagery and navigation data. Computational complexity estimates show that the newly-developed method is computationally efficient.
Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We verify our understanding through comparing this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.
While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.
BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization is sample efficient by building a posterior distribution to capture the evidence and prior knowledge for the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.
Electrically non-contact ECG measurement system on a chair can be applied to a number of various fields for continuous health monitoring in daily life. However, the body is floated electrically for this system due to the capacitive electrodes and the floated body is very sensitive to the external noises or motion artifacts which affect the measurement system as the common mode noise. In this paper, the Driven-Seat-Ground circuit similar to the Driven-Right-Leg circuit is proposed to reduce the common mode noise. The analysis of this equivalent circuit is performed and the output signal waveforms are compared between with Driven-Seat-Ground and with capacitive ground. As the results, the Driven-Seat-Ground circuit improves significantly the properties of the fully capacitive ECG measurement system as the negative feedback.
0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.08.001 ⇑ Corresponding author. Tel.: +886 02 7734 3347; f E-mail address: joum@ntnu.edu.tw (M. Jou). Cloud computing technology has matured as it has been integrated with every kind of digitalization processes. It offers numerous advantages for data and software sharing, and thus making the management of complex IT systems much simpler. For education in engineering, cloud computing even provides students with versatile and ubiquitous access to software commonly used in the field without having to step into an actual computer lab. Our study analyzed learning attitudes and academic performances induced by the utilization of resources driven by cloud computing technologies. Comparisons were made between college students with high school and vocational high school backgrounds. One hundred and thirtytwo students who took the computer-aided designing (CAD) course participated in the study. Technology Acceptance Model (TAM) was used as the fundamental framework. Open-ended sets of questionnaires were designed to measure academic performance and causal attributions; the results indicated no significant differences in the cognitive domain between the two groups of students, though it is not so in both the psychomotor and the affective domains. College students with vocational high school background appeared to possess higher learning motivation in CAD applications. 2012 Elsevier Ltd. All rights reserved.
This chapter provides the overview of the state of the art in intrusion detection research. Intrusion detection systems are software and/or hardware components that monitor computer systems and analyze events occurring in them for signs of intrusions. Due to widespread diversity and complexity of computer infrastructures, it is difficult to provide a completely secure computer system. Therefore, there are numerous security systems and intrusion detection systems that address different aspects of computer security. This chapter first provides taxonomy of computer intrusions, along with brief descriptions of major computer attack categories. Second, a common architecture of intrusion detection systems and their basic characteristics are presented. Third, taxonomy of intrusion detection systems based on five criteria (information source, analysis strategy, time aspects, architecture, response) is given. Finally, intrusion detection systems are classified according to each of these categories and the most representative research prototypes are briefly described.
The value system of a developmental robot signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. In the work reported here, a low level value system is modeled and implemented. It simulates the non-associative animal learning mechanism known as habituation e ect. Reinforcement learning is also integrated with novelty. Experimental results show that the proposed value system works as designed in a study of robot viewing angle selection.
Internet technology provides a new means of recalling and sharing personal memories in the digital age. What is the mnemonic consequence of posting personal memories online? Theories of transactive memory and autobiographical memory would make contrasting predictions. In the present study, college students completed a daily diary for a week, listing at the end of each day all the events that happened to them on that day. They also reported whether they posted any of the events online. Participants received a surprise memory test after the completion of the diary recording and then another test a week later. At both tests, events posted online were significantly more likely than those not posted online to be recalled. It appears that sharing memories online may provide unique opportunities for rehearsal and meaning-making that facilitate memory retention.
Identifying the function of problem behavior can lead to the development of more effective interventions. One way to identify the function is through functional behavior assessment (FBA). Teachers conduct FBA in schools. However, the task load of recording the data manually is high, and the challenge of accurately identifying antecedents and consequences is significant while interacting with students. These issues often result in imperfect information capture. CareLog allows teachers more easily to conduct FBAs and enhances the capture of relevant information. In this paper, we describe the design process that led to five design principles that governed the development of CareLog. We present results from a five-month, quasi-controlled study aimed at validating those design principles. We reflect on how various constraints imposed by special education settings impact the design and evaluation process for HCI practitioners and researchers.
This article explores the relationship between Internet use and the individual-level production of social capital. To do so, the authors adopt a motivational perspective to distinguish among types of Internet use when examining the factors predicting civic engagement, interpersonal trust, and life contentment. The predictive power of new media use is then analyzed relative to key demographic, contextual, and traditional media use variables using the 1999 DDB Life Style Study. Although the size of associations is generally small, the data suggest that informational uses of the Internet are positively related to individual differences in the production of social capital, whereas social-recreational uses are negatively related to these civic indicators. Analyses within subsamples defined by generational age breaks further suggest that social capital production is related to Internet use among Generation X, while it is tied to television use among Baby Boomers and newspaper use among members of the Civic Generation. The possibility of life cycle and cohort effects is discussed.
Identity-based encryption (IBE) is an exciting alternative to public-key encryption, as IBE eliminates the need for a Public Key Infrastructure (PKI). The senders using an IBE do not need to look up the public keys and the corresponding certificates of the receivers, the identities (e.g. emails or IP addresses) of the latter are sufficient to encrypt. Any setting, PKI- or identity-based, must provide a means to revoke users from the system. Efficient revocation is a well-studied problem in the traditional PKI setting. However in the setting of IBE, there has been little work on studying the revocation mechanisms. The most practical solution requires the senders to also use time periods when encrypting, and all the receivers (regardless of whether their keys have been compromised or not) to update their private keys regularly by contacting the trusted authority. We note that this solution does not scale well -- as the number of users increases, the work on key updates becomes a bottleneck. We propose an IBE scheme that significantly improves key-update efficiency on the side of the trusted party (from linear to logarithmic in the number of users), while staying efficient for the users. Our scheme builds on the ideas of the Fuzzy IBE primitive and binary tree data structure, and is provably secure.
A frequently proposed method of reducing unsolicited bulk email (“spam”) is for senders to pay for each email they send. Proof-ofwork schemes avoid charging real money by requiring senders to demonstrate that they have expended processing time in solving a cryptographic puzzle. We attempt to determine how difficult that puzzle should be so as to be effective in preventing spam. We analyse this both from an economic perspective, “how can we stop it being cost-effective to send spam”, and from a security perspective, “spammers can access insecure end-user machines and will steal processing cycles to solve puzzles”. Both analyses lead to similar values of puzzle difficulty. Unfortunately, realworld data from a large ISP shows that these difficulty levels would mean that significant numbers of senders of legitimate email would be unable to continue their current levels of activity. We conclude that proof-of-work will not be a solution to the problem of spam.
1. SUMMARY Automatic grading of programming assignments has been a feature of computer science courses for almost as long as there have been computer science courses [1]. However, contemporary autograding systems in computer science courses have extended their scope far beyond performing automated assessment to include gamification [2], test coverage analysis [3], managing human-authored feedback, contest adjudication [4], secure remote code execution [5], and more. Many of these individual features have been described and evaluated in the computer science education literature, but little attention has been given to the practical benefits and challenges of using the systems that implement these features in computer science courses.
This thesis presents WiTrack, a system that tracks the 3D motion of a user from the radio signals reflected off her body. It works even if the person is occluded from the WiTrack device or in a different room. WiTrack does not require the user to carry any wireless device, yet its accuracy exceeds current RF localization systems, which require the user to hold a transceiver. Empirical measurements with a WiTrack prototype show that, on average, it localizes the center of a human body to within a median of 10 to 13 cm in the x and y dimensions, and 21 cm in the z dimension. It also provides coarse tracking of body parts, identifying the direction of a pointing hand with a median of 11.20. WiTrack bridges a gap between RF-based localization systems which locate a user through walls and occlusions, and human-computer interaction systems like Kinect, which can track a user without instrumenting her body, but require the user to stay within the direct line of sight of the device. Thesis Supervisor: Dina Katabi Title: Professor of Computer Science and Engineering
Internet search rankings have a significant impact on consumer choices, mainly because users trust and choose higher-ranked results more than lower-ranked results. Given the apparent power of search rankings, we asked whether they could be manipulated to alter the preferences of undecided voters in democratic elections. Here we report the results of five relevant double-blind, randomized controlled experiments, using a total of 4,556 undecided voters representing diverse demographic characteristics of the voting populations of the United States and India. The fifth experiment is especially notable in that it was conducted with eligible voters throughout India in the midst of India's 2014 Lok Sabha elections just before the final votes were cast. The results of these experiments demonstrate that (i) biased search rankings can shift the voting preferences of undecided voters by 20% or more, (ii) the shift can be much higher in some demographic groups, and (iii) search ranking bias can be masked so that people show no awareness of the manipulation. We call this type of influence, which might be applicable to a variety of attitudes and beliefs, the search engine manipulation effect. Given that many elections are won by small margins, our results suggest that a search engine company has the power to influence the results of a substantial number of elections with impunity. The impact of such manipulations would be especially large in countries dominated by a single search engine company.
This paper describes the construction of a system that recognizes hand-printed digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low error rate on the remaining examples. The system compares favorably with other state-of-the art recognizers. While some of the methods are specific to this task, it is hoped that many of the techniques will be applicable to a wide range of recognition tasks.
To many people, home is a sanctuary. For those people who need special medical care, they may need to be pulled out of their home to meet their medical needs. As the population ages, the percentage of people in this group is increasing and the effects are expensive as well as unsatisfying. We hypothesize that many people with disabilities can lead independent lives in their own homes with the aid of athome automated assistance and health monitoring. In order to accomplish this, robust methods must be developed to collect relevant data and process it dynamically and adaptively to detect and/or predict threatening long-term trends or immediate crises. The main objective of this paper is to investigate techniques for using agent-based smart home technologies to provide this at-home health monitoring and assistance. To this end, we have developed novel inhabitant modeling and automation algorithms that provide remote health monitoring for caregivers. Specifically, we address the following technological challenges: 1) identifying lifestyle trends, 2) detecting anomalies in current data, and 3) designing a reminder assistance system. Our solution approaches are being tested in simulation and with volunteers at the UTA’s MavHome site, an agent-based
Green supply chain management (GSCM) has gained increasing attention within both academia and industry. As the literature grows, finding new directions by critically evaluating the research and identifying future directions becomes important in advancing knowledge for the field. Using organizational theories to help categorize the literature provides opportunities to address both the objectives of understanding where the field currently stands and identifying research opportunities and directions. After providing a background discussion on GSCM we categorize and review recent GSCM literature under nine broad organizational theories. Within this review framework, we also identify GSCM research questions that are worthy of investigation. Additional organizational theories which are considered valuable for future GSCM research are also identified with a conclusion for this review.
Sustainability rests on the principle that we must meet the needs of the present without compromising the ability of future generations to meet their own needs. Starving people in poor nations, obesity in rich nations, increasing food prices, on-going climate changes, increasing fuel and transportation costs, flaws of the global market, worldwide pesticide pollution, pest adaptation and resistance, loss of soil fertility and organic carbon, soil erosion, decreasing biodiversity, desertification, and so on. Despite unprecedented advances in sciences allowing us to visit planets and disclose subatomic particles, serious terrestrial issues about food show clearly that conventional agriculture is no longer suited to feeding humans and preserving ecosystems. Sustainable agriculture is an alternative for solving fundamental and applied issues related to food production in an ecological way (Lal (2008) Agron. Sustain. Dev. 28, 57–64.). While conventional agriculture is driven almost solely by productivity and profit, sustainable agriculture integrates biological, chemical, physical, ecological, economic and social sciences in a comprehensive way to develop new farming practices that are safe and do not degrade our environment. To address current agronomical issues and to promote worldwide discussions and cooperation we implemented sharp changes at the journal Agronomy for Sustainable Development from 2003 to 2006. Here we report (1) the results of the renovation of the journal and (2) a short overview of current concepts of agronomical research for sustainable agriculture. Considered for a long time as a soft, side science, agronomy is rising fast as a central science because current issues are about food, and humans eat food. This report is the introductory article of the book Sustainable Agriculture, volume 1, published by EDP Sciences and Springer (Lichtfouse et al. (2009) Sustainable Agriculture, Vol. 1, Springer, EDP Sciences, in press).
With advances in data collection and generation technologies, organizations and researchers are faced with the ever growing problem of how to manage and analyze large dynamic datasets. Environments that produce streaming sources of data are becoming common place. Examples include stock market, sensor, web click stream, and network data. In many instances, these environments are also equipped with multiple distributed computing nodes that are often located near the data sources. Analyzing and monitoring data in such environments requires data mining technology that is cognizant of the mining task, the distributed nature of the data, and the data influx rate. In this chapter, we survey the current state of the field and identify potential directions of future research.
Die Untersuchung der Akzeptanz von Technologien im Allgemeinen und Software im Besonderen ist ein fruchtbares Feld in der angloamerikanischen Forschung der Disziplinen (Management) Information Systems und der deutschen Wirtschaftsinformatik. Trotz zahlreicher Untersuchungen, die ihren Ursprung in dem Technologie-Akzeptanzmodell und verwandten Theorien haben, mehren sich Beiträge, welche die Defizite bisherigerer Studien und Forschungsansätze herausstellen. Eine wesentliche Ursache ist Fokussierung auf quantitative Forschungsmethoden, wie wir anhand von Metastudien und einer eigenen Literaturrecherche aufzeigen. Während quantitative Verfahren sich in der Regel gut für eine Überprüfung gegebener Theorien eignen, ist ihr Beitrag für die Bildung neuer Theorien begrenzt. Im vorliegenden Beitrag wird aufgezeigt, wie ein qualitatives Verfahren zur besseren Theoriebildung genutzt werden kann. Am Beispiel der Untersuchung der Akzeptanz von Projektmanagement-Software (PMS) kann aufgezeigt werden, dass dieses Vorgehen zu neuen Konstrukten führt, während einige Konstrukte bestehender Akzeptanz-Theorien nicht bestätigt werden konnten.
Ahsfract-Using the notion of fading memory we prove very strong versions of two folk theorems. The first is that any time-inuariant (TZ) con~inuou.r nonlinear operator can be approximated by a Volterra series operator, and the second is that the approximating operator can be realized as a finiiedimensional linear dynamical system with a nonlinear readout map. While previous approximation results are valid over finite time inlero& and for signals in compact sets, the approximations presented here hold for all time and for signals in useful (noncompact) sets. The discretetime analog of the second theorem asserts that nny TZ operator with fading memory can be approximated (in our strong sense) by a nonlinear movingaverage operator. Some further discussion of the notion of fading memory is given.
Three new classes of miniaturized Marchand balun are defined based on the synthesis of filter prototypes. They are suitable for mixed lumped-distributed planar realizations with small size resulting from transmission-line resonators being a quarter-wavelength long at frequencies higher than the passband center frequency. Each class corresponds to an S-plane bandpass prototype derived from the specification of transmission zero locations. A tunable 50:100-/spl Omega/ balun is realized at 1 GHz to demonstrate the advantages of the approach presented here.
Web 2.0 technologies have enabled more and more people to freely comment on different kinds of entities (e.g. sellers, products, services). The large scale of information poses the need and challenge of automatic summarization. In many cases, each of the user-generated short comments comes with an overall rating. In this paper, we study the problem of generating a ``rated aspect summary'' of short comments, which is a decomposed view of the overall ratings for the major aspects so that a user could gain different perspectives towards the target entity. We formally define the problem and decompose the solution into three steps. We demonstrate the effectiveness of our methods by using eBay sellers' feedback comments. We also quantitatively evaluate each step of our methods and study how well human agree on such a summarization task. The proposed methods are quite general and can be used to generate rated aspect summary automatically given any collection of short comments each associated with an overall rating.
This work demonstrates the design of an adaptive reconfigurable rectifier to address the issue of early breakdown voltage in a conventional rectifier and extends the rectifier's operation for wide dynamic input power range. A depletion-mode field-effect transistor has been introduced to operate as a switch and compensate at low and high input power levels for rectifier. This design accomplishes 40% of RF-DC power conversion efficiency over a wide dynamic input power range from -10 dBm to 27 dBm, while exhibiting 78% of peak power efficiency at 22 dBm. The power harvester is designed to operate in the 900 MHz ISM band and suitable for Wireless Power Transfer applications.
Cryptocurrencies like Bitcoin have proven to be a phenomenal success. Bitcoin-like systems use proofof-work mechanism which is therefore considered as 1-hop blockchain, and their security holds if the majority of the computing power is under the control of honest players. However, this assumption has been seriously challenged recently and Bitcoin-like systems will fail when this assumption is broken. We propose the rst provably secure 2-hop blockchain by combining proof-of-work (rst hop) and proof-of-stake (second hop) mechanisms. On top of Bitcoin’s brilliant ideas of utilizing the power of the honest miners, via their computing resources, to secure the blockchain, we further leverage the power of the honest users/stakeholders, via their coins/stake, to achieve this goal. e security of our blockchain holds if the honest players control majority of the collective resources (which consists of both computing power and stake). at said, even if the adversary controls more than 50% computing power, the honest players still have the chance to defend the blockchain via honest stake. ∗An early version with title “Securing Bitcoin-like Blockchains against a Malicious Majority of Computing Power” appeared in ePrint Archive in July 2016. e current version shares the same motivation. But the construction idea and modeling approach have been completely revised. †Virginia Commonwealth University. Email: duong3@vcu.edu. ‡Shanghai Jiao Tong University. Most work done while visiting Cryptography Lab at Virginia Commonwealth University. Email: fanlei@sjtu.edu.cn. §Virginia Commonwealth University. Email: hszhou@vcu.edu.
Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of moves for the sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialized population of ISAc lists while the other initializes populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.
The cortical, cerebellar and brainstem BOLD-signal changes have been identified with fMRI in humans during mental imagery of walking. In this study the whole brain activation and deactivation pattern during real locomotion was investigated by [(18)F]-FDG-PET and compared to BOLD-signal changes during imagined locomotion in the same subjects using fMRI. Sixteen healthy subjects were scanned at locomotion and rest with [(18)F]-FDG-PET. In the locomotion paradigm subjects walked at constant velocity for 10 min. Then [(18)F]-FDG was injected intravenously while subjects continued walking for another 10 min. For comparison fMRI was performed in the same subjects during imagined walking. During real and imagined locomotion a basic locomotion network including activations in the frontal cortex, cerebellum, pontomesencephalic tegmentum, parahippocampal, fusiform and occipital gyri, and deactivations in the multisensory vestibular cortices (esp. superior temporal gyrus, inferior parietal lobule) was shown. As a difference, the primary motor and somatosensory cortices were activated during real locomotion as distinct to the supplementary motor cortex and basal ganglia during imagined locomotion. Activations of the brainstem locomotor centers were more prominent in imagined locomotion. In conclusion, basic activation and deactivation patterns of real locomotion correspond to that of imagined locomotion. The differences may be due to distinct patterns of locomotion tested. Contrary to constant velocity real locomotion (10 min) in [(18)F]-FDG-PET, mental imagery of locomotion over repeated 20-s periods includes gait initiation and velocity changes. Real steady-state locomotion seems to use a direct pathway via the primary motor cortex, whereas imagined modulatory locomotion an indirect pathway via a supplementary motor cortex and basal ganglia loop.
One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from {image, question, answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. Our method thus learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine [10]. The core of our proposed method is a new co-attention model. In addition, the proposed approach generates human-readable reasons for its decision, and can still be trained end-to-end without ground truth reasons being given. We demonstrate the effectiveness on two publicly available datasets, Visual Genome and VQA, and show that it produces the state-of-the-art results in both cases.
Rogue devices are an increasingly dangerous reality in the insider threat problem domain. Industry, government, and academia need to be aware of this problem and promote state-of-the-art detection methods.
We have designed two end-fire antennas on LTCC with horizontal and vertical polarizations respectively. The antennas operate at 38GHz, a potential frequency for 5G applications. The horizontally-polarized antenna provide a broadband performance about 27% and 6dB end-fire gain and the vertically-polarized one provide a 12.5% bandwidth and 5dB gain. Both antenna are integrated under a compact substrate. Excellent isolation is achieved between the nearby elements making these antennas suitable for corner elements in 5G mobile system.
Deep belief networks (DBN) are generative models with many layers of hidden causal variables, recently introduced by Hinton, Osindero, and Teh (2006), along with a greedy layer-wise unsupervised learning algorithm. Building on Le Roux and Bengio (2008) and Sutskever and Hinton (2008), we show that deep but narrow generative networks do not require more parameters than shallow ones to achieve universal approximation. Exploiting the proof technique, we prove that deep but narrow feedforward neural networks with sigmoidal units can represent any Boolean expression.
In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus will be on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying the most prominent theoretical results of RO over the past decade, we will also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we will highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.
We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural “oracle”. We evaluate NDPS on the task of learning to drive a simulated car in the TORCS carracing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.
This document presents the state-of-the-art of TeS Ku band antennas for mobile satellite communications on board high speed trains and ground vehicles, and his evolution in terms of Ku band antenna performances improvement and upgrade to Ka band terminals.
The general issue of this letter deals with the design of a phased array antenna for high-data-rate SATCOM. A final demonstrator antenna could be installed on an unmanned aerial vehicle (UAV) to communicate with a satellite in Ka-band. First, a compact reflection-type phase shifter is designed and realized. Second, the conception of a phased array antenna prototype is detailed. Third, a new calibration method is involved that can provide the bias voltage to be applied to each phase shifter in order to scan the beam in the desired direction.
A 2D-periodic leaky-wave antenna for a Ka-band satcom-on-the-move ground user terminal is presented. The antenna panel operates at the 20 GHz downlink as well as the 30 GHz uplink bands with the respective circular polarizations, using a common radiation aperture and a common phase centre. The dual-band performance is achieved by a carefully designed stacked dual-layer frequency selective surface, with one layer operating at 20 GHz and being transparent at 30 GHz, and the second layer acting vice versa. The paper describes the design of the circularly polarized primary feed, the dual-layer structures, and the complete compact leaky-wave antenna panel. The measured radiation performance reveals realized-gain values above 22 dBi and efficiencies above 60%. The cross-polarization discrimination and sidelobe level are suitable to meet the power spectral requirements for satellite communications at Ka-band.
Previous neural machine translation models used some heuristic search algorithms (e.g., beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test phase. In this paper, we propose the GumbelGreedy Decoding which trains a generative network to predict translation under a trained model. We solve such a problem using the Gumbel-Softmax reparameterization, which makes our generative network differentiable and trainable through standard stochastic gradient methods. We empirically demonstrate that our proposed model is effective for generating sequences of discrete words.
This article discusses a new idea called cognitive radar. Three ingredients are basic to the constitution of cognitive radar: 1) intelligent signal processing, which builds on learning through interactions of the radar with the surrounding environment; 2) feedback from the receiver to the transmitter, which is a facilitator of intelligence; and 3) preservation of the information content of radar returns, which is realized by the Bayesian approach to target detection through tracking. All three of these ingredients feature in the echo-location system of a bat, which may be viewed as a physical realization (albeit in neurobiological terms) of cognitive radar. Radar is a remote-sensing system that is widely used for surveillance, tracking, and imaging applications, for both civilian and military needs. In this article, we focus on future possibilities of radar with particular emphasis on the issue of cognition. As an illustrative case study along the way, we consider the problem of radar surveillance applied to an ocean environment.