Query: Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller

Relevant passages:
Error-Related EEG Potentials Generated During Simulated Brain–Computer Interaction <=SEP=> Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: ldquoAre ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?rdquo We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively, using a classifier built with data recorded up to three months earlier.

ERP components on reaction errors and their functional significance: a tutorial <=SEP=> Some years ago we described a negative (Ne) and a later positive (Pe) deflection in the event-related brain potentials (ERPs) of incorrect choice reactions [Falkenstein, M., Hohnsbein, J., Hoormann, J., Blanke, L., 1990. In: Brunia, C.H.M., Gaillard, A.W.K., Kok, A. (Eds.), Psychophysiological Brain Research. Tilburg Univesity Press, Tilburg, pp. 192-195. Falkenstein, M., Hohnsbein, J., Hoormann, J., 1991. Electroencephalography and Clinical Neurophysiology, 78, 447-455]. Originally we assumed the Ne to represent a correlate of error detection in the sense of a mismatch signal when representations of the actual response and the required response are compared. This hypothesis was supported by the results of a variety of experiments from our own laboratory and that of Coles [Gehring, W. J., Goss, B., Coles, M.G.H., Meyer, D.E., Donchin, E., 1993. Psychological Science 4, 385-390. Bernstein, P.S., Scheffers, M.K., Coles, M.G.H., 1995. Journal of Experimental Psychology: Human Perception and Performance 21, 1312-1322. Scheffers, M.K., Coles, M. G.H., Bernstein, P., Gehring, W.J., Donchin, E., 1996. Psychophysiology 33, 42-54]. However, new data from our laboratory and that of Vidal et al. [Vidal, F., Hasbroucq, T., Bonnet, M., 1999. Biological Psychology, 2000] revealed a small negativity similar to the Ne also after correct responses. Since the above mentioned comparison process is also required after correct responses it is conceivable that the Ne reflects this comparison process itself rather than its outcome. As to the Pe, our results suggest that this is a further error-specific component, which is independent of the Ne, and hence associated with a later aspect of error processing or post-error processing. Our new results with different age groups argue against the hypotheses that the Pe reflects conscious error processing or the post-error adjustment of response strategies. Further research is necessary to specify the functional significance of the Pe.

Updating P300: An integrative theory of P3a and P3b <=SEP=> The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulus-driven frontal attention mechanisms during task processing, whereas P3b originates from temporal-parietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations.

Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects <=SEP=> Braincomputer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development has grown explosively over the past two decades. Efforts have begun recently to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this paper, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology and identify potential users and potential applications. Lastly, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.

Anterior cingulate cortex, error detection, and the online monitoring of performance. <=SEP=> An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.

QR code location based reverse car-searching route recommendation model <=SEP=> To deal with the reverse car-searching issue in large buildings and parking lots, a Quick Response Code (QR code) based reverse car-searching route recommendation model is designed. By scanning the deployed QR codes, a Smartphone can pinpoint the host location and parking location efficiently. Based on the submitted location information, the central control system can finally return the recommended routes, which facilitates a host to reach the parking location effectively. In our model, the reverse car-searching route is divided into two parts: choosing the optimal exports (elevator) and computing the shortest walking distance route. Based on the optimal export selection algorithm and regional shortest path algorithm, our model can choose the prior exports (elevator) effectively, and then recommend the optimal walking route in the buildings and parking lots. The simulation shows that this low-cost system can effectively solve the reverse car-searching problem in large buildings and parking lots, save the driver's car-searching time and improve the utilization rate of parking facilities.

Ant supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, applied to Traveling Salesman Problem <=SEP=> AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.

Using association rules for ontology extraction from a Quran corpus <=SEP=> Ontology can be seen as a formal representation of knowledge. They have been investigated in many arti ficial intelligence studies including semantic web, softwa re engineering, and information retrieval. The aim of ontology is t o develop knowledge representations that can be shared and re used. This research project is concerned with the use of assoc iati n rules to extract the Quran ontology. The manual acquisition of ontologies from Quran verses can be very costly; therefore, we ne d an intelligent system for Quran ontology construction using patternbased schemes and associations rules to discover Qu ran concepts and semantics relations from Quran verses. Our system i s based on the combination of statistics and linguistics methods t o extract concepts and conceptual relations from Quran. In particular, linguistic pattern-based approach is exploited to extract spec ific concepts from the Quran, while the conceptual relations are found based on association rules technique. The Quran ontology wil l offer a new and powerful representation of Quran knowledge, and the association rules will help to represent the relations between all classes of connected concepts in the Quran ontology. Keywords— Ontology; Concepts Extraction ; Quran; Association Rules; Text Mining.

Learning Image Descriptors with Boosting <=SEP=> We propose a novel and general framework to learn compact but highly discriminative floating-point and binary local feature descriptors. By leveraging the boosting-trick we first show how to efficiently train a compact floating-point descriptor that is very robust to illumination and viewpoint changes. We then present the main contribution of this paper—a binary extension of the framework that demonstrates the real advantage of our approach and allows us to compress the descriptor even further. Each bit of the resulting binary descriptor, which we call BinBoost, is computed with a boosted binary hash function, and we show how to efficiently optimize the hash functions so that they are complementary, which is key to compactness and robustness. As we do not put any constraints on the weak learner configuration underlying each hash function, our general framework allows us to optimize the sampling patterns of recently proposed hand-crafted descriptors and significantly improve their performance. Moreover, our boosting scheme can easily adapt to new applications and generalize to other types of image data, such as faces, while providing state-of-the-art results at a fraction of the matching time and memory footprint.

SMART VOTING SYSTEM WITH FACE RECOGNITION <=SEP=> Manual voting system has been deployed for many yea rs in our country. However in many parts of our cou ntry people cannot attend the voting because of several r asons. In order to solve these problems there is a need of online election voting system in addition to manual voting system. The long-term goal of this project is to g reatly reduce the cost and complexity of running elections and increase th e accuracy of results by removing the direct involv ement of humans in gathering and counting of votes. The voters will be a le to give their votes at any field areas by usi ng the system if they prefer online voting In this system, the user votes using android applic ation which can be downloaded over internet. The us er has to login before giving his vote. Authentication will b e done using SMS confirmation and face recognition. Users can view parties, enter votes, can verify vote via SMS or email and can view result after result is declared.

The Nature, Importance, and Difficulty of Machine Ethics <=SEP=> The question of whether machine ethics exists or might exist in the future is difficult to answer if we can't agree on what counts as machine ethics. Some might argue that machine ethics obviously exists because humans are machines and humans have ethics. Others could argue that machine ethics obviously doesn't exist because ethics is simply emotional expression and machines can't have emotions. A wide range of positions on machine ethics are possible, and a discussion of the issue could rapidly propel us into deep and unsettled philosophical issues. Perhaps, understandably, few in the scientific arena pursue the issue of machine ethics. As we expand computers' decision-making roles in practical matters, such as computers driving cars, ethical considerations are inevitable. Computer scientists and engineers must examine the possibilities for machine ethics because, knowingly or not, they've already engaged in some form of it. Before we can discuss possible implementations of machine ethics, however, we need to be clear about what we're asserting or denying

Integrating Conflicting Data: The Role of Source Dependence <=SEP=> Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sourc es can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers pendencebetween data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources ( e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracyof data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.

Continental Airlines Flies High with Real-Time Business Intelligence <=SEP=> Real-time business intelligence (BI) is taking Continental Airlines to new heights. Powered by a real-time data warehouse, the company has dramatically changed all aspects of its business. Continental’s president and COO, Larry Kellner, describes the impact of real-time BI in the following way: “Real-time BI is critical to the accomplishment of our business strategy and has created significant business benefits." In fact, Continental has realized more than $500 million in cost savings and revenue generation over the past six years from its BI initiatives, producing an ROI of more than 1,000 percent.

Teenager ’ s Addiction to Smart Phones and Its Integrated Therapy Method <=SEP=> The fundamental cause of teenagers’ addiction to smart phone is due to stress arising from family and school. Smartphone can cause many problems including musculoskeletal symptom which can develop due to staying in one position for a long time. Furthermore, starring at a small screen for an extended time can cause decreased eye sight, along with amblyopia. Smart phone addiction causes depression and anxiety which will lead to severe mental problems. This will lead to problems associated to one’s social life and human network. Fundamental treatment of cognitive and emotional elements needed to become harmonized as a whole in order to increase the effectiveness of the treatment results. The therapy has been verified to cure the behavioral and emotional aspect related to depression, sense of control, and assimilation in school. From these integrated treatment approaches, this report compared the treatment’s upsides and downsides and also, assessed the possibility the integrated treatment process. We can presume that this report provided the opportunity and support for the creation of a fundamental treatment method that can help teenagers live a healthy life by overcoming addiction to smart phones.

Communication-efficient algorithms for statistical optimization <=SEP=> We study two communication-efficient algorithms for distributed statistical optimization on large-scale data. The first algorithm is an averaging method that distributes the N data samples evenly to m machines, performs separate minimization on each subset, and then averages the estimates. We provide a sharp analysis of this average mixture algorithm, showing that under a reasonable set of conditions, the combined parameter achieves mean-squared error that decays as O(N-1 + (N/m)-2). Whenever m ≤ √N, this guarantee matches the best possible rate achievable by a centralized algorithm with access to all N samples. The second algorithm is a novel method, based on an appropriate form of bootstrap. Requiring only a single round of communication, it has mean-squared error that decays as O(N-1 + (N/m)-3), and so is more robust to the amount of parallelization.

Reinforcement Learning of Coordination in Cooperative Multi-Agent Systems <=SEP=> We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such <i>independent </i> agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.

Characterization of log periodic planar dipole array antenna <=SEP=> This paper proposes a analysis approach for characterizing the broadband Log Periodic Planar Dipole Array (LPPDA) antenna useful for C-band applications. A simplest geometry of the edge fed Log Periodic Planar Dipole Array is designed and developed. Parameters like length, width of the elements, and spacing between elements are the varied using defined scale factor (τ) and a spacing factor (σ), keeping others constant and their impact was noted down. A parametric analysis is performed. using CST software. A transmission line equivalent circuit of the proposed LPPDA is constructed. Log periodicity behaviour of the proposed antenna is checked by plotting input impedance with respect to logarithm of frequency. An antenna is fabricated and measured in laboratory.

Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus <=SEP=> Bigrams and trigrams are commonly used in statistical natural language processing; this paper will describe techniques for working with much longer n-grams. Suffix arrays (Manber and Myers 1990) were first introduced to compute the frequency and location of a substring (n-gram) in a sequence (corpus) of length N. To compute frequencies over all N(N+1)/2 substrings in a corpus, the substrings are grouped into a manageable number of equivalence classes. In this way, a prohibitive computation over substrings is reduced to a manageable computation over classes. This paper presents both the algorithms and the code that were used to compute term frequency (tf) and document frequency (df) for all n-grams in two large corpora, an English corpus of 50 million words of Wall Street Journal and a Japanese corpus of 216 million characters of Mainichi Shimbun. The second half of the paper uses these frequencies to find interesting substrings. Lexicographers have been interested in n-grams with high mutual information (MI) where the joint term frequency is higher than what would be expected by chance, assuming that the parts of the n-gram combine independently. Residual inverse document frequency (RIDF) compares document frequency to another model of chance where terms with a particular term frequency are distributed randomly throughout the collection. MI tends to pick out phrases with noncompositional semantics (which often violate the independence assumption) whereas RIDF tends to highlight technical terminology, names, and good keywords for information retrieval (which tend to exhibit nonrandom distributions over documents). The combination of both MI and RIDF is better than either by itself in a Japanese word extraction task.

QDFS: A quality-aware distributed file storage service based on HDFS <=SEP=> On the basis of the Hadoop distributed file system (HDFS), this paper presents the design and implementation of QDFS, a distributed file storage service system that employs a backup policy based on recovery volumes and a quality-aware data distribution strategy. The experimental results show that QDFS increases the storage utilization ratio and improves the storage performance by being aware of the quality of service of the DataNodes. The improvements of QDFS compared with HDFS make the Hadoop distributed computing model more suitable to unstable wide area network environments.

Effects of psychological and social factors on organic disease: a critical assessment of research on coronary heart disease. <=SEP=> An extensive research literature in the behavioral sciences and medicine suggests that psychological and social factors may play a direct role in organic coronary artery disease (CAD) pathology. However, many in the medical and scientific community regard this evidence with skepticism. This chapter critically examines research on the impact of psychological and psychosocial factors on the development and outcome of coronary heart disease, with particular emphasis on studies employing verifiable outcomes of CAD morbidity or mortality. Five key variables identified as possible psychosocial risk factors for CAD are addressed: acute and chronic stress, hostility, depression, social support, and socioeconomic status. Evidence regarding the efficacy of psychosocial interventions is also presented. It is suggested that, taken as a whole, evidence for a psychological and social impact on CAD morbidity and mortality is convincing. However, continued progress in this area requires multidisciplinary research integrating expertise in cardiology and the behavioral sciences, and more effective efforts to communicate research findings to a biomedical audience.

Learning Theory and Instructional Design <=SEP=> Designing effective instruction goes beyond systematically executing various steps within an instructional design model. Among a host of considerations, effective instructional design should take into consideration the theoretical bases in which it is grounded. This is not to say that learning theory offers instructional designers answers to design problems but instead, offers clarity, direction and focus throughout the instructional design process. Merrill (2001, p. 294) explains that a “theoretical tool, in and of itself, is not an instructional design theory but defines instructional components that can be used to define instructional prescriptions more precisely.” Likewise, Merriam and Caffarella (1999, p. 250) make the point that “[learning] theories do not give us solutions, but they do direct our attention to those variables that are crucial in finding solutions.” Thus, understanding theoretical frameworks and properly incorporating them within the scope of instructional design is important for designers to effectively prepare and present instruction as well as for organizational entities to more precisely and efficiently address training-appropriate issues.

Oil content, tocopherol composition and fatty acid patterns of the seeds of 51 Cannabis sativa L. genotypes <=SEP=> The oil content, the tocopherol composition, the plastochromanol-8 (P-8) content and the fatty acid composition (19 fatty acids) of the seed of 51 hemp (Cannabis sativa L.) genotypes were studied in the 2000 and 2001 seasons. The oil content of the hemp seed ranged from 26.25% (w/w) to 37.50%. Analysis of variance revealed significant effects of genotype, year and of the interaction (genotype × year) on the oil content. The oil contents of the 51 genotypes in 2000 and 2001 were correlated (r = 0.37**) and averaged 33.19 ± 1.45% in 2000 and 31.21 ± 0.96% in 2001. The γ-tocopherol, α-tocopherol, δ-tocopherol, P-8- and β-tocopherol contents of the 51 genotypes averaged 21.68 ± 3.19, 1.82 ± 0.49, 1.20 ± 0.40, 0.18 ± 0.07 and 0.16 ± 0.04 mg 100g−1 of seeds, respectively (2000 and 2001 data pooled). Hierarchical clustering of the fatty acid data did not group the hemp genotypes according to their geographic origin. The γ-linolenic acid yield of hemp (3–30 kg ha−1) was similar to the γ-linolenic acid yield of plant species that are currently used as sources of γ-linolenic acid (borage (19–30 kg ha−1), evening primrose (7–30 kg ha−1)). The linoleic acid yield of hemp (129–326 kg ha−1) was similar to flax (102–250 kg ha−1), but less than in sunflower (868–1320 kg ha−1). Significant positive correlations were detected between some fatty acids and some tocopherols. Even though the average content of P-8 in hemp seeds was only 1/120th of the average γ-tocopherol content, P-8 content was more closely correlated with the unsaturated fatty acid content than γ-tocopherol or any other tocopherol fraction. The average broad-sense heritabilities of the oil content, the antioxidants (tocopherols and P-8) and the fatty acids were 0.53, 0.14 and 0.23, respectively. The genotypes Fibrimon 56, P57, Juso 31, GB29, Beniko, P60, FxT, Félina 34, Ramo and GB18 were capable of producing the largest amounts of high quality hemp oil.

Learning to Remember Translation History with a Continuous Cache <=SEP=> Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated online depending on the translation history retrieved from the memory, endowing NMT models with the capability to dynamically adapt over time. Experiments on multiple domains with different topics and styles show the effectiveness of the proposed approach with negligible impact on the computational cost.

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles <=SEP=> Magnetic anomaly detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.

A fast method of fog and haze removal <=SEP=> Fog and haze degrade the quality of preview and captured image by reducing the contrast and saturation. As a result the visibility of scene or object degrades. The objective of the present work is to enhance the visibility, saturation, contrast and reduce the noise in a foggy image. We propose a method that uses single frame for enhancing foggy images using multilevel transmission map. The method is fast and free from noise or artifacts that generally arise in such enhancement techniques. A comparison with existing methods shows that the proposed method performs better in terms of both processing time and quality. The proposed method works in real time for VGA resolution. The proposed work also presents a scheme to remove fog, rain and snow in real-time.

Snapshots in a flash with ioSnap <=SEP=> Snapshots are a common and heavily relied upon feature in storage systems. The high performance of flash-based storage systems brings new, more stringent, requirements for this classic capability. We present ioSnap, a flash optimized snapshot system. Through careful design exploiting common snapshot usage patterns and flash oriented optimizations, including leveraging native characteristics of Flash Translation Layers, ioSnap delivers low-overhead snapshots with minimal disruption to foreground traffic. Through our evaluation, we show that ioSnap incurs negligible performance overhead during normal operation, and that common-case operations such as snapshot creation and deletion incur little cost. We also demonstrate techniques to mitigate the performance impact on foreground I/O during intensive snapshot operations such as activation. Overall, ioSnap represents a case study of how to integrate snapshots into a modern, well-engineered flash-based storage system.

Analysis of back-end flash in a 1.5b/stage pipelined ADC <=SEP=> An analysis of the impact of last stage flash in a conventional pipeline ADC is performed in this paper. The performance of a pipeline ADC can be altered significantly by calibrating the comparators in the back-end flash. Also, realizing that the input to the back-end flash (in a pipeline ADC) is not uniformly distributed, this paper proposes alternative back-end flash references to improve the overall performance of the ADC. An analysis of the performance of the pipeline with large offsets in the MDAC stages is also presented in this paper.

A Preliminary Taxonomy of Crowdsourcing <=SEP=> Many firms are now asking how they can benefit from the new form of outsourcing labelled “crowdsourcing”. Like many other forms of outsourcing, crowdsourcing is now being “talked up” by a somewhat credulous trade press. However, the term crowdsourcing has been used to describe several related, but different phenomena, and what might be successful with one form of crowdsourcing may not be with another. In this paper the notion of crowdsourcing is decomposed to create a taxonomy that expands our understanding of what is meant by the term. This taxonomy focuses on the different capability levels of crowdsourcing suppliers; different motivations; and different allocation of benefits. The management implications of these distinctions are then considered in light of what we know about other forms of outsourcing.

Being John Malkovich <=SEP=> Given a photo of person A, we seek a photo of person B with similar pose and expression. Solving this problem enables a form of puppetry, in which one person appears to control the face of another. When deployed on a webcam-equipped computer, our approach enables a user to control another person’s face in real-time. This image-retrievalinspired approach employs a fully-automated pipeline of face analysis techniques, and is extremely general—we can puppet anyone directly from their photo collection or videos in which they appear. We show several examples using images and videos of celebrities from the Internet.

Common-Duty-Ratio Control of Input-Parallel Output-Parallel (IPOP) Connected DC–DC Converter Modules With Automatic Sharing of Currents <=SEP=> Input-parallel output-parallel (IPOP) connected converter systems allow the use of low-power converter modules for high-power applications, with interleaving control scheme resulting in smaller filter size, better dynamic performance, and higher power density. In this paper, a new IPOP converter system is proposed, which consists of multiple dual-active half-bridge (DAHB) dc-dc converter modules. Moreover, by applying a common-duty-ratio control scheme, without a dedicated current-sharing controller, the automatic sharing of input currents or load currents is achieved in the IPOP converter even in the presence of substantial differences of 10% in various module parameters. The current-sharing performance of the proposed control method is analyzed using both a small-signal model and a steady-state dc model of the IPOP system. It is concluded that the equal sharing of currents among modules can be achieved by reducing the mismatches in various module parameters, which is achievable in practice. The current-sharing performance of the IPOP converter is also verified by Saber simulation and a 400-W experimental prototype consisting of two DAHB modules. The common-duty-ratio control method can be extended to any IPOP system that consists of three or more converter modules, including traditional dual-active bridge dc-dc converters, which have a characteristic of current source.