title: Emotion Detection in the Loop from Brain Signals and Facial Images

abstract: In this project, we intended to develop techniques for multimodal emotion detection, one modality being brain signals via fNIRS, the second modality being face video and the third modality being the scalp EEG signals. EEG and ::: fNIRS provided us with an “internal” look at the emotion generation processes, while video sequence gave us an “external” look on the “same” phenomenon. ::: Fusions of fNIRS with video and of EEG with fNIRS were considered. Fusion of all three modalities was not considered due to the extensive noise on the EEG signals caused by facial muscle movements, which are required for emotion detection from video sequences. ::: Besides the techniques mentioned above, peripheral signals, namely, respiration, cardiac rate, and galvanic skin resistance were also measured from the subjects during “fNIRS + EEG” recordings. These signals provided us with extra information about the emotional state of the subjects. ::: The critical point in the success of this project was to be able to build a “good” database. Good data acquisition means synchronous data and requires the definition of some specific experimental protocols for emotions elicitation. Thus, we devoted much of our time to data acquisition throughout the workshop, which resulted in a large enough database for ::: making the first analyses. Results presented in this report should be considered as preliminary. However, they are promising enough to extend the scope of the research.


title: Epigenetics of Reprogramming to Induced Pluripotency

abstract: Reprogramming to induced pluripotent stem cells (iPSCs) proceeds in a stepwise manner with reprogramming factor binding, transcription, and chromatin states changing during transitions. Evidence is emerging that epigenetic priming events early in the process may be critical for pluripotency induction later. Chromatin and its regulators are important controllers of reprogramming, and reprogramming factor levels, stoichiometry, and extracellular conditions influence the outcome. The rapid progress in characterizing reprogramming is benefiting applications of iPSCs and is already enabling the rational design of novel reprogramming factor cocktails. However, recent studies have also uncovered an epigenetic instability of the X chromosome in human iPSCs that warrants careful consideration.


title: Frustratingly Easy NBNN Domain Adaptation

abstract: Over the last years, several authors have signaled that state of the art categorization methods fail to perform well when trained and tested on data from different databases. The general consensus in the literature is that this issue, known as domain adaptation and/or dataset bias, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. The large majority of these works use BOW feature descriptors, and learning methods based on image-to-image distance functions. Following the seminal work of [6], in this paper we challenge these two assumptions. We experimentally show that using the NBNN classifier over existing domain adaptation databases achieves always very strong performances. We build on this result, and present an NBNN-based domain adaptation algorithm that learns iteratively a class metric while inducing, for each sample, a large margin separation among classes. To the best of our knowledge, this is the first work casting the domain adaptation problem within the NBNN framework. Experiments show that our method achieves the state of the art, both in the unsupervised and semi-supervised settings.


title: Inductive Conformal Prediction: Theory and Application to Neural Networks

abstract: Traditional machine learning algorithms for pattern recognition just output simple predictions, without any associated confidence values. Confidence values are an indication of how likely each prediction is of being correct. In the ideal case, a confidence of 99% or higher for all examples in a set, means that the percentage of erroneous predictions in that set will not exceed 1%. Knowing the likelihood of each prediction enables us to assess the extent to which we can rely on it. For this reason, predictions that are associated with some kind of confidence values are highly desirable in many risk-sensitive applications, such as those used for medical diagnosis or financial analysis. In fact, such information can benefit any application that requires human-computer interaction, as confidence values can be used to determine the way in which each prediction will be treated. For instance, a filtering mechanism can be employed so that only predictions which satisfy a certain level of confidence will be taken into account, while the rest can be discarded or passed on to a human for judgement. There are two main areas in mainstream machine learning that can be used in order to obtain some kind of confidence values; the Bayesian framework and the theory of Probably Approximately Correct learning (PAC theory). Quite often the Bayesian framework is used for producing algorithms that complement individual predictions with probabilistic measures of their quality. On the other hand, PAC theory can be used for producing upper bounds on the probability of error for a given algorithm with respect to some confidence level 1 − δ. Both of these approaches however, have their drawbacks. In order to apply the Bayesian framework one is required to have some prior knowledge about the distribution that generates the data. When the correct prior is known, Bayesian methods provide optimal decisions. For real world data sets though, as the required knowledge is not available, one has to assume the existence of an arbitrarily chosen prior. In this case, if the assumed prior is incorrect, the resulting confidence levels may also be “incorrect”; for example the predictive regions output for the 95% confidence level may contain the true label in much less than 95% of the cases. This signifies a major failure, as we would expect confidence levels to bound the percentage of expected errors. An experimental demonstration of how misleading Bayesian methods can become when their assumptions are violated can be found in (Melluish et al., 2001).


title: Collaboratively crowdsourcing workflows with turkomatic

abstract: Preparing complex jobs for crowdsourcing marketplaces requires careful attention to workflow design, the process of decomposing jobs into multiple tasks, which are solved by multiple workers. Can the crowd help design such workflows? This paper presents Turkomatic, a tool that recruits crowd workers to aid requesters in planning and solving complex jobs. While workers decompose and solve tasks, requesters can view the status of worker-designed workflows in real time; intervene to change tasks and solutions; and request new solutions to subtasks from the crowd. These features lower the threshold for crowd employers to request complex work. During two evaluations, we found that allowing the crowd to plan without requester supervision is partially successful, but that requester intervention during workflow planning and execution improves quality substantially. We argue that Turkomatic's collaborative approach can be more successful than the conventional workflow design process and discuss implications for the design of collaborative crowd planning systems.


title: Sherlock: Scalable Fact Learning in Images

abstract: We study scalable and uniform understanding of facts in images. Existing visual recognition systems are typically modeled differently for each fact type such as objects, actions, and interactions. We propose a setting where all these facts can be modeled simultaneously with a capacity to understand unbounded number of facts in a structured way. The training data comes as structured facts in images, including (1) objects (e.g., $ $), (2) attributes (e.g., $ $), (3) actions (e.g., $ $), and (4) interactions (e.g., $ $). Each fact has a semantic language view (e.g., $ $) and a visual view (an image with this fact). We show that learning visual facts in a structured way enables not only a uniform but also generalizable visual understanding. We propose and investigate recent and strong approaches from the multiview learning literature and also introduce two learning representation models as potential baselines. We applied the investigated methods on several datasets that we augmented with structured facts and a large scale dataset of more than 202,000 facts and 814,000 images. Our experiments show the advantage of relating facts by the structure by the proposed models compared to the designed baselines on bidirectional fact retrieval.


title: Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction

abstract: We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder models can be used for the generation of corrections, in addition to error identification, which is of interest for certain end-user applications. We show that a character-based encoder-decoder model is particularly effective, outperforming other results on the AESW Shared Task on its own, and showing gains over a word-based counterpart. Our final model--a combination of three character-based encoder-decoder models, one word-based encoder-decoder model, and a sentence-level CNN--is the highest performing system on the AESW 2016 binary prediction Shared Task.


title: A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations

abstract: Recent work has shown that neural network-based vision classifiers exhibit a significant vulnerability to misclassifications caused by imperceptible but adversarial perturbations of their inputs. These perturbations, however, are purely pixel-wise and built out of loss function gradients of either the attacked model or its surrogate. As a result, they tend to be contrived and look pretty artificial. This might suggest that such vulnerability to slight input perturbations can only arise in a truly adversarial setting and thus is unlikely to be an issue in more "natural" contexts. ::: In this paper, we provide evidence that such belief might be incorrect. We demonstrate that significantly simpler, and more likely to occur naturally, transformations of the input - namely, rotations and translations alone, suffice to significantly degrade the classification performance of neural network-based vision models across a spectrum of datasets. This remains to be the case even when these models are trained using appropriate data augmentation. Finding such "fooling" transformations does not require having any special access to the model - just trying out a small number of random rotation and translation combinations already has a significant effect. These findings suggest that our current neural network-based vision models might not be as reliable as we tend to assume. ::: Finally, we consider a new class of perturbations that combines rotations and translations with the standard pixel-wise attacks. We observe that these two types of input transformations are, in a sense, orthogonal to each other. Their effect on the performance of the model seems to be additive, while robustness to one type does not seem to affect the robustness to the other type. This suggests that this combined class of transformations is a more complete notion of similarity in the context of adversarial robustness of vision models.


title: The elements of platform leadership

abstract: Nowhere is the growing interconnectedness of business more clear than in the information-technology industry, where the success of any one company's innovation is dependent on the activities of a complex web of partners. Michael Cusumano of the Sloan School and Annabelle Gawer of INSEAD offer a new framework for understanding what is going on and how to orchestrate the interactions. ::: ::: Building on their in-depth research at Intel, a company that excels in shaping its environment, the authors show how organizations can maximize innovation. First, they say, companies must decide if they are platform leaders (companies that drive industrywide innovation for an evolving system of separately developed pieces), wannabes (companies that want to be platform leaders) or complementors (companies that make ancillary products that expand the platform's market). ::: ::: Managers have two major challenges: coordinating internal units that play one or more of those roles and interacting effectively with outsiders playing the same roles at different times. The authors delineate four levers of platform leadership (including productive ways to work with complementors and techniques for building a supportive internal organization) and offer practical advice to complementors as well. They provide guidelines on when to show your cards to competitors and when to hold them. ::: ::: As the experiences of Intel, Microsoft, Cisco, NTT DoCoMo and others demonstrate, platform leaders need to have a vision that extends beyond their current business operations and the technical specifications of one product or one component. Complementors have an important role, but it is the platform leaders, with the decisions they make, that have the most influence over the innovations that complementary producers create ? and the future of the industry.


title: An empirical study of regression testing techniques incorporating context and lifetime factors and improved cost-benefit models

abstract: Regression testing is an important but expensive activity, and a great deal of research on regression testing methodologies has been performed. In recent years, much of this research has emphasized empirical studies, including evaluations of the effectiveness and efficiency of regression testing techniques. To date, however, most studies have been limited in terms of their consideration of testing context and system lifetime, and have used cost-benefit models that omit important factors and render some types of comparisons between techniques impossible. These limitations can cause studies to improperly assess the costs and benefits of regression testing techniques in practical settings. In this paper, we provide improved cost-benefit models for use in assessing regression testing methodologies, that incorporate context and lifetime factors not considered in prior studies, and we use these models to compare several common methodologies. Our results show that the factors we consider (in particular, time constraints and incremental resource availability) can affect assessments of the relative benefits of regression testing techniques, and suggest that particular classes of techniques may compare differently across different types of test suites.


title: Central Pattern Generator With Inertial Feedback for Stable Locomotion and Climbing in Unstructured Terrain

abstract: Inspired by the locomotor nervous system of vertebrates, central pattern generator (CPG) models can be used to design gaits for articulated robots, such as crawling, swimming or legged robots. Incorporating sensory feedback for gait adaptation in these models can improve the locomotive performance of such robots in challenging terrain. However, many CPG models to date have been developed exclusively for open-loop gait generation for traversing level terrain. In this paper, we present a novel approach for incorporating inertial feedback into the CPG framework for the control of body posture during legged locomotion on steep, unstructured terrain. That is, we adapt the limit cycle of each leg of the robot with time to simultaneously produce locomotion and body posture control. We experimentally validate our approach on a hexapod robot, locomoting in a variety of steep, challenging terrains (grass, rocky slide, stairs). We show how our approach can be used to level the robot's body, allowing it to locomote at a relatively constant speed, even as terrain steepness and complexity prevents the use of an open-loop control strategy.


title: OpenDaylight: Towards a Model-Driven SDN Controller architecture

abstract: This paper describes a novel Software-Defined Networking (SDN) Controller architecture that is built on Model-Driven Software Engineering (MDSE) principles. It supports both the “classic” OpenFlow-based approach to SDN and emerging model-driven network management/programmability technologies, such as NETCONF/YANG. The architecture was first implemented in the OpenDaylight Project Hydrogen release, and it is being further evolved in subsequent OpenDaylight releases.


title: D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images

abstract: In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and lightweighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed D3 model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.


title: A spatio-temporal representation for the orienteering problem with time-varying profits

abstract: We consider an orienteering problem (OP) where an agent needs to visit a series (possibly a subset) of depots, from which the maximal accumulated profits are desired within given limited time budget. Different from most existing works where the profits are assumed to be static, in this work we investigate a variant that has arbitrary time-dependent profits. Specifically, the profits to be collected change over time and they follow different (e.g., independent) time-varying functions. The problem is of inherent nonlinearity and difficult to solve by existing methods. To tackle the challenge, we present a simple and effective framework that incorporates time-variations into the fundamental planning process. Specifically, we propose a deterministic spatio-temporal representation where both spatial description and temporal logic are unified into one routing topology. By employing existing basic sorting and searching algorithms, the routing solutions can be computed in an extremely efficient way. The proposed method is easy to implement and extensive numerical results show that our approach is time efficient and generates near-optimal solutions.


title: Microplastic pollution is widely detected in US municipal wastewater treatment plant effluent.

abstract: Municipal wastewater effluent has been proposed as one pathway for microplastics to enter the aquatic environment. Here we present a broad study of municipal wastewater treatment plant effluent as a pathway for microplastic pollution to enter receiving waters. A total of 90 samples were analyzed from 17 different facilities across the United States. Averaging all facilities and sampling dates, 0.05 ± 0.024 microparticles were found per liter of effluent. Though a small value on a per liter basis, even minor municipal wastewater treatment facilities process millions of liters of wastewater each day, yielding daily discharges that ranged from ∼50,000 up to nearly 15 million particles. Averaging across the 17 facilities tested, our results indicate that wastewater treatment facilities are releasing over 4 million microparticles per facility per day. Fibers and fragments were found to be the most common type of particle within the effluent; however, some fibers may be derived from non-plastic sources. Considerable inter- and intra-facility variation in discharge concentrations, as well as the relative proportions of particle types, was observed. Statistical analysis suggested facilities serving larger populations discharged more particles. Results did not suggest tertiary filtration treatments were an effective means of reducing discharge. Assuming that fragments and pellets found in the effluent arise from the 'microbeads' found in many cosmetics and personal care products, it is estimated that between 3 and 23 billion (with an average of 13 billion) of these microplastic particles are being released into US waterways every day via municipal wastewater. This estimate can be used to evaluate the contribution of microbeads to microplastic pollution relative to other sources (e.g., plastic litter and debris) and pathways (e.g., stormwater) of discharge.


title: An Analysis of the Kerberos Authentication System

abstract: The first of this project’s two objectives was to review the current level of trustworthiness inherent to MIT’s implementation of the Kerberos authentication standard. The second objective was to consider how various probable abuses could be detected by host-based or network-based Intrusion Detection Systems and demonstrate that Kerberos produces enough auditable information to make intrusion detection a feasible and effective means of defense. Our vulnerability assessments of Kerberos predominantly used black box testing techniques and information from published security advisories. The most significant unresolved vulnerabilities we found in MIT’s implementation include password guessing attacks and (previously unpublished) Denial of Service (DoS) attacks invoked by disabling disk access required for caching tickets. We also addressed issues associated with the trustworthiness of two external subsystems (time synchronization and domain name services) which Kerberos depends on in very fundamental ways.


title: Aspect of Blame in Tweets: A Deep Recurrent Neural Network Approach

abstract: Twitter as an information dissemination tool has proved to be instrumental in generating user curated content in short spans of time. Tweeting usually occurs when reacting to events, speeches, about a service or product. This in some cases comes with its fair share of blame on varied aspects in reference to say an event. Our work in progress details how we plan to collect the informal texts, clean them and extract features for blame detection. We are interested in augmenting Recurrent Neural Networks (RNN) with self-developed association rules in getting the most out of the data for training and evaluation. We aim to test the performance of our approach using human-induced terror-related tweets corpus. It is possible tailoring the model to fit natural disaster scenarios.


title: Cognitive Informatics and Cognitive Computing in Year 10 and Beyond

abstract: Cognitive Informatics CI is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. The latest advances in CI leads to the establishment of cognitive computing theories and methodologies, as well as the development of Cognitive Computers CogC that perceive, infer, and learn. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC'11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world's renowned researchers and scholars in the field of cognitive informatics and cognitive computing.


title: Improved underwater image enhancement algorithms based on partial differential equations (PDEs)

abstract: The experimental results of improved underwater image enhancement algorithms based on partial differential equations (PDEs) are presented in this report. This second work extends the study of previous work and incorporating several improvements into the revised algorithm. Experiments show the evidence of the improvements when compared to previously proposed approaches and other conventional algorithms found in the literature.


title: Small size dropping cross dipole antenna with isoflux pattern in C band for LEO satellites

abstract: In the recent years, various antennas have been used for delivering the satellite data specially pictures such as Reflector, Helical, Microstrip Patch, Slot, Multi-Mode Horn (MMH) and Cross dipole antennas. In this paper, an isoflux pattern antenna with high resolution and data rate has been proposed for employing in the LEO satellites at C band which has dropping arms for attaining the best antenna performance characteristics. This small size dropping cross dipole antenna contains an appropriate −10 dB bandwidth of Su from 6.9 GHz to 7.8 GHz (approximately 0.9 GHz bandwidth with center frequency at 7.4 GHz) accompanied by VSWR<2. The simulated gain obtained by ANSYS HFSS using the Finite Element Method (FEM) is approximately 5 dB. It should be mentioned that the primary sense of the polarization in the proximity of the center of the band is near Left Hand Circular Polarization (LHCP) and the compact occupied size (height × radial) of antenna is 40mm × 80mm.


title: Communication chains and multitasking

abstract: There is a growing literature on managing multitasking and interruptions in the workplace. In an ethnographic study, we investigated the phenomenon of communication chains, the occurrence of interactions in quick succession. Focusing on chains enable us to better understand the role of communication in multitasking. Our results reveal that chains are prevalent in information workers, and that attributes such as the number of links, and the rate of media and organizational switching can be predicted from the first catalyzing link of the chain. When chains are triggered by external interruptions, they have more links, a trend for more media switches and more organizational switches. We also found that more switching of organizational contexts in communication is associated with higher levels of stress. We describe the role of communication chains as performing alignment in multitasking and discuss the implications of our results.


title: Human Shape Capture and Tracking at Home

abstract: Human body tracking typically requires specialized capture set-ups. Although pose tracking is available in consumer devices like Microsoft Kinect, it is restricted to stick figures visualizing body part detection. In this paper, we propose a method for full 3D human body shape and motion capture of arbitrary movements from the depth channel of a single Kinect, when the subject wears casual clothes. We do not use the RGB channel or an initialization procedure that requires the subject to move around in front of the camera. This makes our method applicable for arbitrary clothing textures and lighting environments, with minimal subject intervention. Our method consists of 3D surface feature detection and articulated motion tracking, which is regularized by a statistical human body model [26]. We also propose the idea of a Consensus Mesh (CMesh) which is the 3D template of a person created from a single view point. We demonstrate tracking results on challenging poses and argue that using CMesh along with statistical body models can improve tracking accuracies. Quantitative evaluation of our dense body tracking shows that our method has very little drift which is improved by the usage of CMesh.


title: A Coarse to Fine Minutiae-Based Latent Palmprint Matching

abstract: With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high-resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature-based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4 percent, which is significantly higher than the 60.8 percent identification accuracy of a state-of-the-art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141 ms for genuine match and 50 ms for impostor match, on a Windows XP desktop system with 2.2-GHz CPU and 1.00-GB RAM. The computation time of our algorithm is an order of magnitude faster than a previously published state-of-the-art-algorithm.


title: Object existence probability fusion using dempster-shafer theory in a high-level sensor data fusion architecture

abstract: Future driver assistance systems need to be more robust and reliable because these systems will react to increasingly complex situations. This requires increased performance in environment perception sensors and algorithms for detecting other relevant traffic participants and obstacles. An object's existence probability has proven to be a useful measure for determining the quality of an object. This paper presents a novel method for the fusion of the existence probability based on Dempster-Shafer evidence theory in the framework of a highlevel sensor data fusion architecture. The proposed method is able to take into consideration sensor reliability in the fusion process. The existence probability fusion algorithm is evaluated for redundant and partially overlapping sensor configurations.


title: Face synthesis and recognition from a single image under arbitrary unknown lighting using a spherical harmonic basis morphable model

abstract: Understanding and modifying the effects of arbitrary illumination on human faces in a realistic manner is a challenging problem both for face synthesis and recognition. Recent research demonstrates that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace using spherical harmonics representation. Morphable models are statistical ensembles of facial properties such as shape and texture. In this paper, we integrate spherical harmonics into the morphable model framework, by proposing a 3D spherical harmonic basis morphable model (SHBMM) and demonstrate that any face under arbitrary unknown lighting can be simply represented by three low-dimensional vectors: shape parameters, spherical harmonic basis parameters and illumination coefficients. We show that, with our SHBMM, given one single image under arbitrary unknown lighting, we can remove the illumination effects from the image (face "delighting") and synthesize new images under different illumination conditions (face "re-lighting"). Furthermore, we demonstrate that cast shadows can be detected and subsequently removed by using the image error between the input image and the corresponding rendered image. We also propose two illumination invariant face recognition methods based on the recovered SHBMM parameters and the de-lit images respectively. Experimental results show that using only a single image of a face under unknown lighting, we can achieve high recognition rates and generate photorealistic images of the face under a wide range of illumination conditions, including multiple sources of illumination.


title: Unmarked 'already': Aspectual expressions in two varieties of English

abstract: This paper examines variation in the expression of aspect both from a language-internal and a cross-linguistic perspective. We make the following proposals: (i) the morpheme already encodes the meaning of two contrasting phases; (ii) several types of event structures, including that of the Perfect, also encode a similar meaning of contrasting phases, but with particular orderings of phases specified. If in addition we assume Optimality Theory with partial ordering, we derive a range of expressions of a given aspectual meaning that is cross-linguistically supported, and show that already emerges as an unmarked aspectual operator in Colloquial Singapore English.


title: Function of yeast species and strains in wine flavour.

abstract: The diversity and the composition of the yeast micropopulation significantly contribute to the sensory characteristics of wine. The growth of each wine yeast species is characterized by a specific metabolic activity, which determines concentrations of flavour compounds in the final wine. However, it must be underlined that, within each species, significant strain variability has been recorded. The wide use of starter cultures, mainly applied to reduce the risk of spoilage and unpredictable changes of wine flavour, can ensure a balanced wine flavour, but it may also cause a loss of characteristic aroma and flavour determinants. Thus, the beneficial contribution from the yeast increases when starter cultures for winemaking are selected on the basis of scientifically verified characteristics and are able to complement and optimise grape quality and individual characteristics. Here we report the characterization of a large number of strains of different wine yeast species, isolated from spontaneous wine fermentations and included in the culture collection of the Basilicata University. D 2003 Elsevier B.V. All rights reserved.


title: Low-noise single Photon Avalanche Diodes in 0.15 μm CMOS technology

abstract: Two different Single-Photon Avalanche Diode (SPAD) structures in a standard 0.15-nm CMOS technology are presented. A characterization of the two detectors, having a 10-μm active-area diameter, and monolithically integrated with a passive quenching circuit and a fast comparator is presented. The two devices exhibit respectively a typical dark count rate of 230cps and 160cps, an afterpulsing probability of 2.1% and 1.3% at 30ns dead time, a Photon Detection Probability of 31% and 26 % at λ=470nm and a timing resolution of 170ps and 60ps. The adopted technology is therefore promising for the realization of SPAD-based image sensors with good overall performance.


title: Quantum First-Passage Time: Exact Solutions for a Class of Tight-Binding Hamiltonian Systems

abstract: The Schr\"odinger integral-equation approach for calculating the classical first-passage time (C-fpt) probability density is extended to the case of quantum first-passage time (Q-fpt). Using this extension, we have calculated analytically the Q-fpt probability density for a class of few-site/state tight-binding Hamiltonian systems, e.g., a qubit, as well as for an infinite 1D lattice. The defining feature of such a quantum system is that the passage across the boundary between a subspace (omega) and its complement (omega-bar) is through a unique pair of"door-way"sites such that the first departure from (arrival at) omega corresponds to the first arrival at (departure from) omega-bar. The Q-fpt probability density so derived remains positive over the time interval in which it also normalizes to unity. These conditions of positivity and normalization define the physical time domain for the Q-fpt problem. This time domain is found to remain finite for the few-site/state Hamiltonian systems considered here, which is quite unlike the case for the diffusive C-fpt problems. The door-way sites and the associated Q-fpt probability density derived here should be relevant to inter-biomolecular/nanostructural electron transport phenomena.


title: Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study

abstract: Neural sequence-to-sequence (seq2seq) approaches have proven to be successful in grammatical error correction (GEC). Based on the seq2seq framework, we propose a novel fluency boost learning and inference mechanism. Fluency boosting learning generates diverse error-corrected sentence pairs during training, enabling the error correction model to learn how to improve a sentence's fluency from more instances, while fluency boosting inference allows the model to correct a sentence incrementally with multiple inference steps. Combining fluency boost learning and inference with convolutional seq2seq models, our approach achieves the state-of-the-art performance: 75.72 (F_{0.5}) on CoNLL-2014 10 annotation dataset and 62.42 (GLEU) on JFLEG test set respectively, becoming the first GEC system that reaches human-level performance (72.58 for CoNLL and 62.37 for JFLEG) on both of the benchmarks.


title: Towards green data centers: A comparison of x86 and ARM architectures power efficiency

abstract: Servers and clusters are fundamental building blocks of high performance computing systems and the IT infrastructure of many companies and institutions. This paper analyzes the feasibility of building servers based on low power computers through an experimental comparison of server applications running on x86 and ARM computer architectures. The comparison executed on web and database servers includes power usage, CPU load, temperature, request latencies and the number of requests handled by each tested system. Floating point performance and power usage are also evaluated. The use of ARM based systems has shown to be a good choice when power efficiency is needed without losing performance.


title: Stable and Efficient Representation Learning with Nonnegativity Constraints

abstract: Orthogonal matching pursuit (OMP) is an efficient approximation algorithm for computing sparse representations. However, prior research has shown that the representations computed by OMP may be of inferior quality, as they deliver suboptimal classification accuracy on several image datasets. We have found that this problem is caused by OMP's relatively weak stability under data variations, which leads to unreliability in supervised classifier training. We show that by imposing a simple nonnegativity constraint, this nonnegative variant of OMP (NOMP) can mitigate OMP's stability issue and is resistant to noise overfitting. In this work, we provide extensive analysis and experimental results to examine and validate the stability advantage of NOMP. In our experiments, we use a multi-layer deep architecture for representation learning, where we use K-means for feature learning and NOMP for representation encoding. The resulting learning framework is not only efficient and scalable to large feature dictionaries, but also is robust against input noise. This framework achieves the state-of-the-art accuracy on the STL-10 dataset.


title: Deadline‐constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

abstract: Summary ::: The cloud infrastructures provide a suitable environment for the execution of large-scale scientific workflow application. However, it raises new challenges to efficiently allocate resources for the workflow application and also to meet the user's quality of service requirements. In this paper, we propose an adaptive penalty function for the strict constraints compared with other genetic algorithms. Moreover, the coevolution approach is used to adjust the crossover and mutation probability, which is able to accelerate the convergence and prevent the prematurity. We also compare our algorithm with baselines such as Random, particle swarm optimization, Heterogeneous Earliest Finish Time, and genetic algorithm in a WorkflowSim simulator on 4 representative scientific workflows. The results show that it performs better than the other state-of-the-art algorithms in the criterion of both the deadline-constraint meeting probability and the total execution cost.


title: A Fast Sparse Recovery Algorithm via Resolution Approximation for LASAR 3D Imaging

abstract: Compressed sensing (CS) algorithms are used for linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging. However, it is difficult to obtain imaging results with both high computational efficiency and promising imaging quality. Because of the high-dimensional matrix-operations, the computational complexity of several CS algorithms is huge such as the iterative adaptive approach (IAA), bayesian compressed sensing (BCS), and sparsity bayesian recovery via iterative minimum (SBRIM) algorithm. Besides, the greedy pursuit algorithms such as the orthogonal matching pursuit (OMP) algorithm cannot acquire ideal imaging results on account of the preset sparsity of the imaging scene. To solve the problem, we present a fast sparse recovery algorithm via resolution approximation (FSRARA) in this paper. Firstly, the whole imaging scene is divided into 3D scattering units with large spacing, and SBRIM algorithm is used to obtain its low-resolution imaging results quickly. Secondly, the low-resolution imaging results are conducted image segmentation by the fuzzy c-means (FCM) clustering algorithm to extract the possible targets’ areas coarsely. Then we re-divide the imaging scene by higher imaging resolution and extract the possible targets’ areas according to the coarsely possible targets’ areas. FSRARA achieves improved computational efficiency with low-dimensional matrix-operations on the possible targets’ areas instead of the high-dimensional one on the whole imaging scene. Meanwhile, FSRARA performs better in suppressing the false targets and sidelobe interference and improves the imaging quality than the SBRIM algorithm. Simulation and experimental results prove that FSRARA improves the computational efficiency by hundreds of times at most than SBRIM algorithm and its computational efficiency is higher than smoothed $L_{0} $ norm (SL0), IAA, and BCS algorithm. Besides, FSRARA improves the imaging quality compared with OMP, IAA, SL0, BCS, and SBRIM algorithms.


Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks


title: Associative Embedding: End-to-End Learning for Joint Detection and Grouping

abstract: We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping. A number of computer vision problems can be framed in this manner including multi-person pose estimation, instance segmentation, and multi-object tracking. Usually the grouping of detections is achieved with multi-stage pipelines, instead we propose an approach that teaches a network to simultaneously output detections and group assignments. This technique can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions. We show how to apply this method to multi-person pose estimation and report state-of-the-art performance on the MPII and MS-COCO datasets.


title: Some Theoretical Considerations in Off-the-Shelf Text Analysis Software

abstract: This paper is concerned with theoretical considerations of commercial content analysis software, namely Linguistic Inquiry and Word Count (LIWC), developed by social psychologists at the University of Texas. LIWC is widely cited and forms the basis of many research papers from a range of disciplines. Here, LIWC is taken as an example of a context-independent, word-counting approach to text analysis, and the strengths and potential pitfalls of such a methodology are discussed. It is shown that text analysis software is constrained not only by its functions, but also by its underlying theoretical assumptions. The paper offers recommendations for good practice in software commercialisation and application, stressing the importance of transparency and acknowledgement of biases.


title: Stable Camera Motion Estimation Using Convex Programming

abstract: We study the inverse problem of estimating n locations $t_1, ..., t_n$ (up to global scale, translation and negation) in $R^d$ from noisy measurements of a subset of the (unsigned) pairwise lines that connect them, that is, from noisy measurements of $\pm (t_i - t_j)/\|t_i - t_j\|$ for some pairs (i,j) (where the signs are unknown). This problem is at the core of the structure from motion (SfM) problem in computer vision, where the $t_i$'s represent camera locations in $R^3$. The noiseless version of the problem, with exact line measurements, has been considered previously under the general title of parallel rigidity theory, mainly in order to characterize the conditions for unique realization of locations. For noisy pairwise line measurements, current methods tend to produce spurious solutions that are clustered around a few locations. This sensitivity of the location estimates is a well-known problem in SfM, especially for large, irregular collections of images. In this paper we introduce a semidefinite programming (SDP) formulation, specially tailored to overcome the clustering phenomenon. We further identify the implications of parallel rigidity theory for the location estimation problem to be well-posed, and prove exact (in the noiseless case) and stable location recovery results. We also formulate an alternating direction method to solve the resulting semidefinite program, and provide a distributed version of our formulation for large numbers of locations. Specifically for the camera location estimation problem, we formulate a pairwise line estimation method based on robust camera orientation and subspace estimation. Lastly, we demonstrate the utility of our algorithm through experiments on real images.


title: Human Factors For The Design Of Force-Reflecting Haptic Interfaces

abstract: This paper discusses the design criteria imposed by the capabilities of the human user on the design of force reflecting controllers for hands and arms. A framework of issues regarding human capabilities is presented that maps directly to mechanical design requirements. The state of knowledge for each capability is briefly summarized along with presentation of new experimental measurements. Finally, the implications of the human factors data to haptic interface design are discussed.


title: D.: User perspectives on relevance criteria: a comparison among relevant, partially relevant, and not-relevant judgements

abstract: This study investigates the use of criteria to assess relevant, partially relevant, and not-relevant documents. Study participants identified passages within 20 document representations that they used to make relevance judgments; judged each document representation as a whole to be relevant, partially relevant, or not relevant to their information need; and explained their decisions in an interview. Analysis revealed 29 criteria, discussed positively and negatively, that were used by the participants when selecting passages that contributed or detracted from a document's relevance. These criteria can be grouped into six categories: abstract (e.g., citability, informativeness), author (e.g., novelty, discipline, affiliation, perceived status), content (e.g., accuracy/validity, background, novelty, contrast, depth/scope, domain, citations, links, relevant to other interests, rarity, subject matter, thought catalyst), full text (e.g., audience, novelty, type, possible content, utility), journal/publisher (e.g., novelty, main focus, perceived quality), and personal (e.g., competition, time requirements). Results further indicate that multiple criteria are used when making relevant, partially relevant, and not-relevant judgments, and that most criteria can have either a positive or negative contribution to the relevance of a document. The criteria most frequently mentioned by study participants were content, followed by criteria characterizing the full text document. These findings may have implications for relevance feedback in information retrieval systems, suggesting that systems accept and utilize multiple positive and negative relevance criteria from users. Systems designers may want to focus on supporting content criteria followed by full text criteria as these may provide the greatest cost benefit.


title: USING PV MODULE AND LINE FREQUENCY RESPONSE DATA TO CREATE ROBUST ARC FAULT DETECTORS.

abstract: Photovoltaic (PV) systems have caused residential and commercial building fires when an electrical arc fault initiates in the conduction path. Article 690.11 in the United States 2011 National Electrical Code requires new photovoltaic systems on or penetrating a building to include a listed arc fault protection device to prevent additional fires. In response, manufacturers are creating arc fault circuit interrupters (AFCIs) using electrical frequencies for detection, but their operation is not fully characterized. Sandia National Labs has undergone a major effort to identify detection difficulties and establish tests for PV AFCI manufacturers to ensure their product can robustly detect arcing conditions while avoiding false trips from noise sources. In previous studies, arc fault signatures have been compared to string noise and frequency-dependant attenuation through PV modules has been quantified. In this paper, a frequency response analyzer was used to measure radio frequency (RF) propagation through arrays of varying irradiance and size. Irradiance did not affect module frequency response, but the length of unshielded wiring significantly affected the frequency response of the system above 100 kHz due to RF effects. Based on the RF affects in PV systems, it is recommended that arc fault circuit interrupter manufacturers select detection frequencies below 100 kHz.


title: Influencing Human Behavior by Means of Subliminal Stimuli using Scent, Light and Brain Computer Interfaces

abstract: We present a proof-of-concept system that integrates ambient lights with a custom made olfactory display that releases subtle burst of scent triggered by brain signals and micro face-gestures such as blinks or jaw clenches. We non-invasively monitor real-time brain activity data using EEG and reflect this information using scent and subtle changes in the ambient light of the environment. This paper discusses different applications for this type of system as well as the implementation of the olfactory display and the supporting software that connects the ambient lights and the EEG sensor. We also discuss the use of subliminal cues like tuning the lighting setup and varying the scent for different kind of behavior changes.


title: Supporting real-time applications in an Integrated Services Packet Network: architecture and mechanism

abstract: This paper considers the support of real-time applications in an Integrated Services Packet Network (ISPN). We first review the characteristics of real-time applications. We observe that, contrary to the popular view that real-time applications necessarily require a fixed delay bound, some real-time applications are more flexible and can adapt to current network conditions. We then propose an ISPN architecture that supports two distinct kinds of real-time service: guaranteed service, which is the traditional form of real-time service discussed in most of the literature and involves pre-computed worst-case delay bounds, and predicted service which uses the measure performance of the network in computing delay bounds. We then propose a packet scheduling mechanism that can support both of these real-time services as well as accommodate datagram traffic. We also discuss two other aspects of an overall ISPN architecture: the service interface and the admission control criteria.


title: Child physical abuse and adult mental health: A national study

abstract: This study characterizes adults who report being physically abused during childhood, and examines associations of reported type and frequency of abuse with adult mental health. Data were derived from the 2000–2001 and 2004–2005 National Epidemiologic Survey on Alcohol and Related Conditions, a large cross-sectional survey of a representative sample (N = 43,093) of the U.S. population. Weighted means, frequencies, and odds ratios of sociodemographic correlates and prevalence of psychiatric disorders were computed. Logistic regression models were used to examine the strength of associations between child physical abuse and adult psychiatric disorders adjusted for sociodemographic characteristics, other childhood adversities, and comorbid psychiatric disorders. Child physical abuse was reported by 8% of the sample and was frequently accompanied by other childhood adversities. Child physical abuse was associated with significantly increased adjusted odds ratios (AORs) of a broad range of DSM-IV psychiatric disorders (AOR = 1.16–2.28), especially attention-deficit hyperactivity disorder, posttraumatic stress disorder, and bipolar disorder. A dose-response relationship was observed between frequency of abuse and several adult psychiatric disorder groups; higher frequencies of assault were significantly associated with increasing adjusted odds. The long-lasting deleterious effects of child physical abuse underscore the urgency of developing public health policies aimed at early recognition and prevention.


title: Induction of TNF Receptor I-Mediated Apoptosis via Two Sequential Signaling Complexes

abstract: Apoptosis induced by TNF-receptor I (TNFR1) is thought to proceed via recruitment of the adaptor FADD and caspase-8 to the receptor complex. TNFR1 signaling is also known to activate the transcription factor NF-kappa B and promote survival. The mechanism by which this decision between cell death and survival is arbitrated is not clear. We report that TNFR1-induced apoptosis involves two sequential signaling complexes. The initial plasma membrane bound complex (complex I) consists of TNFR1, the adaptor TRADD, the kinase RIP1, and TRAF2 and rapidly signals activation of NF-kappa B. In a second step, TRADD and RIP1 associate with FADD and caspase-8, forming a cytoplasmic complex (complex II). When NF-kappa B is activated by complex I, complex II harbors the caspase-8 inhibitor FLIP(L) and the cell survives. Thus, TNFR1-mediated-signal transduction includes a checkpoint, resulting in cell death (via complex II) in instances where the initial signal (via complex I, NF-kappa B) fails to be activated.


title: Attitudes and beliefs of an albino population toward sun avoidance: advice and services provided by an outreach albino clinic in Tanzania.

abstract: OBJECTIVE ::: To determine an albino population's expectations from an outreach albino clinic, understanding of skin cancer risk, and attitudes toward sun protection behavior. ::: ::: ::: DESIGN ::: Survey, June 1, 1997, to September 30, 1997. ::: ::: ::: SETTING ::: Outreach albino clinics in Tanzania. ::: ::: ::: PARTICIPANTS ::: All albinos 13 years and older and accompanying adults of younger children attending clinics. Unaccompanied children younger than 13 years and those too sick to answer questions were excluded. Ninety-four questionnaires were completed in 5 villages, with a 100% response rate. ::: ::: ::: INTERVENTIONS ::: Interview-based questionnaire with scoring system for pictures depicting poorly sun-protected albinos. ::: ::: ::: RESULTS ::: The most common reasons for attending the clinic were health education and skin examination. Thirteen respondents (14%) believed albinism was inherited; it was more common to believe in superstitious causes of albinism than inheritance. Seventy-three respondents (78%) believed skin cancer was preventable, and 60 (63%) believed skin cancer was related to the sun. Seventy-two subjects (77%) thought sunscreen provided protection from the sun; 9 (10%) also applied it at night. Reasons for not wearing sun-protective clothing included fashion, culture, and heat. The hats provided were thought to have too soft a brim, to shrink, and to be ridiculed. Suggestions for additional clinic services centered on education and employment. Albinos who had read the educational booklet had no better understanding of sun avoidance than those who had not (P =.49). ::: ::: ::: CONCLUSIONS ::: There was a reasonable understanding of risks of skin cancer and sun-avoidance methods. Clinical advice was often not followed for cultural reasons. The hats provided were unsuitable, and there was some confusion about the use of sunscreen. A lack of understanding of the cause of albinism led to many superstitions.


Fusion of Electrocardiogram and Arterial Blood Pressure Signals for Authentication in Wearable Medical Systems


title: Melanoproteins. I. Reactions between enzyme-generated quinones and amino acids.

abstract: Abstract 1. 1. A systematic survey of the reactions of compounds containing the functional groups of proteins with melanogenic systems comprised of o-diphenol oxidase and catechol, 4-methylcatechol, 3,4-dihydroxyphenylalanine, or 5,6-dihydroxyindole, has been carried out with an automatic recording rapid-scanning spectrophotometer. 2. 2. The 3-substituted indole ring, the amide group, the ureido and guanidino groupings, seryl hydroxyl, and 4-substituted imidazole groups do not react with any of the quinones derived by enzymic oxidation of the catecholic melanogens, according to spectroscopic criteria. 3. 3. N-terminal primary amino groups, aliphatic amino groups, secondary amines in amino acids, and thiol-containing amino acids react with o-benzoquinone and 4-methyl-o-benzoquinone to give intermediates with characteristic absorption spectra. 4. 4. Only thiol-containing compounds (and aromatic amines) react with melanogenic quinones derived from 3,4-dihydroxyphenylalanine to produce intermediates with characteristic absorption spectra. These reactions appear to involve “indole-5,6-quinone” only. 5. 5. A new melanochrome or leucomelanochrome which forms during the polymerization of indole-5,6-quinone to melanin, has been observed.


title: Tracking modularity in citation networks

abstract: Citation network analysis is an effective tool to analyze the structure of scientific research. Clustering is often used to visualize scientific domain and to detect emerging research front there. While we often set arbitrarily clustering threshold, there is few guide to set appropriate threshold. This study analyzed basic process how clustering of citation network proceeds by tracking size and modularity change during clustering. We found that there are three stages in clustering of citation networks and it is universal across our case studies. In the first stage, core clusters in the domain are formed. In the second stage, peripheral clusters are formed, while core clusters continue to grow. In the third stage, core clusters grow again. We found the minimum corpus size around one hundred assuring the clustering. When the corpus size is less than one hundred, clustered network structure tends to be more random. In addition even for the corpus whose size is larger than it, the clustering quality for some clusters formed in the later stage is low. These results give a fundamental guidance to the user of citation network analysis.


title: Can good news be bad? The role of brand communication strategy and brand commitment in the announcement of product improvements

abstract: Although product improvements are usually good news, this research reveals that brands can be affected negatively depending on the level of brand commitment and on the type of communication strategy chosen by the brand. More specifically, this research examines how high-commitment consumers react to product improvements, and how these consumers respond to a communication strategy in which a brand admits that the new product version improves upon a previous inferior product (herein referred to as ‘honesty strategy’). Results of an online study reveal that product improvement is perceived more negatively by high- (compared to low-) commitment consumers, and that these consumers react more negatively and attribute manipulative intent when the brand uses an honesty strategy. This research shows that brands need to be cautious in communicating product improvements to consumers, particularly to its most committed ones. An admission that a product improvement significantly enhances the quality of a previous (inf...


title: A Review of Ethical Theory in the ‘Upper Echelons ’ of Information Systems Research

abstract: Despite some notable and rare exceptions and after many years of relatively neglect (particularly in the ‘upper echelons’ of IS research), there appears to be some renewed interest in Information Systems Ethics (ISE). This paper reflects on the development of ISE by assessing the use and development of ethical theory in contemporary IS research with a specific focus on the ‘leading’ IS journals (according to the Association of Information Systems). The focus of this research is to evaluate if previous calls for more theoretically informed work are permeating the ‘upper echelons’ of IS research and if so, how (Walsham 1996; Smith and Hasnas 1999; Bell and Adam 2004). For the purposes of scope, this paper follows on from those previous studies and presents a detailed review of the leading IS publications between 2005 to 2007 inclusive. After several processes, a total of 32 papers are evaluated. This review highlights that whilst ethical topics are becoming increasingly popular in such influential media, most of the research continues to neglect considerations of ethical theory with preferences for a range of alternative approaches. Finally, this research focuses on some of the papers produced and considers how the use of ethical theory could contribute.


title: Applications of Artificial Intelligence to Network Security

abstract: Attacks to networks are becoming more complex and sophisticated every day. Beyond the so-called script-kiddies and hacking newbies, there is a myriad of professional attackers seeking to make serious profits infiltrating in corporate networks. Either hostile governments, big corporations or mafias are constantly increasing their resources and skills in cybercrime in order to spy, steal or cause damage more effectively. traditional approaches to Network Security seem to start hitting their limits and it is being recognized the need for a smarter approach to threat detections. This paper provides an introduction on the need for evolution of Cyber Security techniques and how Artificial Intelligence could be of application to help solving some of the problems. It provides also, a high-level overview of some state of the art AI Network Security techniques, to finish analysing what is the foreseeable future of the application of AI to Network Security.


title: Variational Approaches for Auto-Encoding Generative Adversarial Networks

abstract: Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an auto-encoder. Such models aim to prevent mode collapse in the learned generative model by ensuring that it is grounded in all the available training data. In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model. The underlying principle shows that variational inference can be used a basic tool for learning, but with the in- tractable likelihood replaced by a synthetic likelihood, and the unknown posterior distribution replaced by an implicit distribution; both synthetic likelihoods and implicit posterior distributions can be learned using discriminators. This allows us to develop a natural fusion of variational auto-encoders and generative adversarial networks, combining the best of both these methods. We describe a unified objective for optimization, discuss the constraints needed to guide learning, connect to the wide range of existing work, and use a battery of tests to systematically and quantitatively assess the performance of our method.


title: Position-normalized click prediction in search advertising

abstract: Click-through rate (CTR) prediction plays a central role in search advertising. One needs CTR estimates unbiased by positional effect in order for ad ranking, allocation, and pricing to be based upon ad relevance or quality in terms of click propensity. However, the observed click-through data has been confounded by positional bias, that is, users tend to click more on ads shown in higher positions than lower ones, regardless of the ad relevance. We describe a probabilistic factor model as a general principled approach to studying these exogenous and often overwhelming phenomena. The model is simple and linear in nature, while empirically justified by the advertising domain. Our experimental results with artificial and real-world sponsored search data show the soundness of the underlying model assumption, which in turn yields superior prediction accuracy.


title: Modeling and Simulation of Off-Grid Power Generation System Using Photovoltaic

abstract: Off-Grid is a part of the power distribution system which uses renewable energy based of power generation connected to the grid system. Multi energy power generation is composed of renewable energy systems including photovoltaic, wind turbine, energy storage and local loads. Test bed of an Off-Grid system is the technique to ensure stable operation during faults and various network disturbances in grid and islanding connected mode. In this paper the Off-Grid using renewable energy consist of a 3 kW photovoltaic, with 30 pieces of 12V, DC/DC converter, charge controller for battery, single phase DC/AC inverter and various loads (resistor, capacitor, inductor) are develop. The AC buses 240V voltage include with isolation transformer to simulate the grid voltage level by Matlab/Simulink software. DOI: http://dx.doi.org/10.11591/telkomnika.v13i3.7061


title: Greatly Enhanced Eccentricity Oscillations in Quadruple Systems Composed of Two Binaries: Implications for Stars, Planets, and Transients

abstract: We study the orbital evolution of hierarchical quadruple systems composed of two binaries on a long mutual orbit, where each binary acts as a Kozai-Lidov (KL) perturber on the other. We find that the coupling between the two binaries qualitatively changes the behavior of their KL cycles. The binaries can experience coherent eccentricity oscillations as well as excursions to very high eccentricity that occur over a much larger fraction of the parameter space than in triple systems. For a ratio of outer to inner semi-major axes of 10 to 20, about 30 to 50% of equal-mass quadruples reach eccentricity 1-e < 10^{-3} in one of the binaries. This is about 4 to 12 times more than for triples with equivalent parameters. Orbital "flips" and collisions without previous tidal interaction are similarly enhanced in quadruples relative to triples. We argue that the frequency of evolutionary paths influenced by KL cycles is comparable in the triple and quadruple populations even though field quadruples are a factor of ~5 less frequent than triples. Additionally, quadruples might be a non-negligible source of triples and provide fundamentally new evolutionary outcomes involving close binaries, mergers, collisions, and associated transients, which occur without any fine tuning of parameters. Finally, we study the perturbations to a planetary orbit due to a distant binary and we find that the fraction of orbital flips is a factor of 3 to 4 higher than for the corresponding triple system given our fiducial parameters with implications for hot Jupiters and star-planet collisions.


title: Instability of default mode network connectivity in major depression: a two-sample confirmation study

abstract: Major depression is associated with altered static functional connectivity in various brain networks, particularly the default mode network (DMN). Dynamic functional connectivity is a novel tool with little application in affective disorders to date, and holds the potential to unravel fluctuations in connectivity strength over time in major depression. We assessed stability of connectivity in major depression between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), key nodes in the DMN that are implicated in ruminative cognitions. Functional connectivity stability between the mPFC and PCC over the course of a resting-state functional magnetic resonance imaging (fMRI) scan was compared between medication-free patients with major depression and healthy controls matched for age, sex and handedness. We tested replicability of the results in an independent sample using multi-echo resting-state fMRI. The primary sample included 20 patients and 19 controls, while the validation sample included 19 patients and 19 controls. Greater connectivity variability was detected in major depression between mPFC and PCC. This was demonstrated in both samples indicating that the results were reliable and were not influenced by the fMRI acquisition approach used. Our results demonstrate that alterations within the DMN in major depression go beyond changes in connectivity strength and extend to reduced connectivity stability within key DMN regions. Findings were robustly replicated across two independent samples. Further research is necessary to better understand the nature of these fluctuations in connectivity and their relationship to the aetiology of major depression.


title: Face Inpainting with Local Linear Representations

abstract: A numberof investigatorshave had success usingdomainspecific priorknowledgeto produce improved superresolution images of faces (“hallucinating faces”). These efforts address the scenario where a face image is obtained from a low-resolutioncamera. A related but less studied problem occurs when the missing information is the result of occlusion rather than low camera resolution, as in the case when a person is wearing sunglasses. Recently Hwang and Lee [14] introduced the first algorithm for solving this reconstruction “inpainting” problem. In the current work we report results of a psychological study that provides independent evidence regarding the validity of the face reconstruction task, and we demonstrate an improved reconstruction approach using a positive, local linear representation. The positive, local mixture operates on real-world images without manual intervention in many cases, and provides demonstrably lower reconstruction error than is obtainable with a global representation.


Enhanced Antimicrobial Activity of Biofunctionalized Zirconia Nanoparticles


title: Soft, Rotating Pneumatic Actuator

abstract: This article describes a soft pneumatic actuator that generates cyclical motion. The actuator consists of several (three, four, or five) chambers (arranged around the circumference of a circle surrounding a central rod) that can be actuated independently using negative pressure (or partial vacuum). Sequential actuation of the four-chamber device using reduced pressure moves the central rod cyclically in an approximately square path. We characterize the trajectory of the actuator and the force exerted by it, as we vary the material used for fabrication, the number of chambers, and the size of the actuator. We demonstrate two applications of this actuator: to deliver fluid while stirring (by replacing the central rod with a needle) and for locomotion that mimics a reptilian gait (by combining four actuators together).


title: Realtime Data Processing at Facebook

abstract: Realtime data processing powers many use cases at Facebook, including realtime reporting of the aggregated, anonymized voice of Facebook users, analytics for mobile applications, and insights for Facebook page administrators. Many companies have developed their own systems; we have a realtime data processing ecosystem at Facebook that handles hundreds of Gigabytes per second across hundreds of data pipelines. Many decisions must be made while designing a realtime stream processing system. In this paper, we identify five important design decisions that affect their ease of use, performance, fault tolerance, scalability, and correctness. We compare the alternative choices for each decision and contrast what we built at Facebook to other published systems. Our main decision was targeting seconds of latency, not milliseconds. Seconds is fast enough for all of the use cases we support and it allows us to use a persistent message bus for data transport. This data transport mechanism then paved the way for fault tolerance, scalability, and multiple options for correctness in our stream processing systems Puma, Swift, and Stylus. We then illustrate how our decisions and systems satisfy our requirements for multiple use cases at Facebook. Finally, we reflect on the lessons we learned as we built and operated these systems.


title: MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints

abstract: We consider applying computer vision to video on cloud-backed mobile devices using Deep Neural Networks (DNNs). The computational demands of DNNs are high enough that, without careful resource management, such applications strain device battery, wireless data, and cloud cost budgets. We pose the corresponding resource management problem, which we call Approximate Model Scheduling, as one of serving a stream of heterogeneous (i.e., solving multiple classification problems) requests under resource constraints. We present the design and implementation of an optimizing compiler and runtime scheduler to address this problem. Going beyond traditional resource allocators, we allow each request to be served approximately, by systematically trading off DNN classification accuracy for resource use, and remotely, by reasoning about on-device/cloud execution trade-offs. To inform the resource allocator, we characterize how several common DNNs, when subjected to state-of-the art optimizations, trade off accuracy for resource use such as memory, computation, and energy. The heterogeneous streaming setting is a novel one for DNN execution, and we introduce two new and powerful DNN optimizations that exploit it. Using the challenging continuous mobile vision domain as a case study, we show that our techniques yield significant reductions in resource usage and perform effectively over a broad range of operating conditions.


title: A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer

abstract: When creating a forgery, a forger can modify an image using many different image editing operations. Since a forensic examiner must test for each of these, significant interest has arisen in the development of universal forensic algorithms capable of detecting many different image editing operations and manipulations. In this paper, we propose a universal forensic approach to performing manipulation detection using deep learning. Specifically, we propose a new convolutional network architecture capable of automatically learning manipulation detection features directly from training data. In their current form, convolutional neural networks will learn features that capture an image's content as opposed to manipulation detection features. To overcome this issue, we develop a new form of convolutional layer that is specifically designed to suppress an image's content and adaptively learn manipulation detection features. Through a series of experiments, we demonstrate that our proposed approach can automatically learn how to detect multiple image manipulations without relying on pre-selected features or any preprocessing. The results of these experiments show that our proposed approach can automatically detect several different manipulations with an average accuracy of 99.10%.


title: A study of the effect of JPG compression on adversarial images

abstract: Neural network image classifiers are known to be vulnerable to adversarial images, i.e., natural images which have been modified by an adversarial perturbation specifically designed to be imperceptible to humans yet fool the classifier. Not only can adversarial images be generated easily, but these images will often be adversarial for networks trained on disjoint subsets of data or with different architectures. Adversarial images represent a potential security risk as well as a serious machine learning challenge---it is clear that vulnerable neural networks perceive images very differently from humans. Noting that virtually every image classification data set is composed of JPG images, we evaluate the effect of JPG compression on the classification of adversarial images. For Fast-Gradient-Sign perturbations of small magnitude, we found that JPG compression often reverses the drop in classification accuracy to a large extent, but not always. As the magnitude of the perturbations increases, JPG recompression alone is insufficient to reverse the effect.


title: Collaborative Experience Prototyping of Automotive Interior in VR with 3D Sketching and Haptic Helpers

abstract: Technological advances and socioeconomic disruptions such as self-driving cars, car-sharing services and artificial intelligence assistance may fundamentally alter interactions inside the future car. However, existing design tools and processes geared toward static physical authoring are ill-equipped for such interaction design. We propose a new design workflow that combines experience prototyping methods typically used by the user interface and product design communities with 3D sketching and haptic helper techniques to help automotive designers ideate, prototype, experience and evaluate multi-sensory interactions in a collaborative manner. Using our workflow, designers use 3D sketching to quickly and expressively author 3D shape and motion ideas in space; augment them with tactile and other sensory feedback through physical proxies and other available gadgets; and immediately enact and immersively experience them to progressively explore and develop them.


title: A pragmatic approach to managing enterprise IT infrastructures in the era of consumerization and individualization of IT

abstract: IT managers take one of the passive, reactive, and pragmatic approaches in the face of important sociotechnical changes.With the rise of IT consumerization, the management of IT infrastructure requires a more pragmatic and holistic approach.A pragmatic approach directs our attention to interdependencies among technologies, people, and their work practices.This article presents seven principles that can serve as general guidelines for a pragmatic approach. Historically, organizations owned and controlled the information technologies (IT) their employees used: telephone, inter-office memos, mainframes and timesharing systems. Today, employees often want to use their own IT: not only personal smart phones and tablets, but also Twitter and Google Docs. This new trend can diversify and extend enterprise IT infrastructure, but leaves organizations struggling with technology uses that they cannot control. With the emergence of new technological paradigms in consumer markets and organizations, the management of IT infrastructure requires a more pragmatic and holistic approach that goes beyond simple technological considerations. In this paper, we present a three-part frameworktechnology, people and practicethat helps managers understand and mitigate these tensions. Drawing on two empirical studies of European executives and consultants form multiple management consulting firms, the paper further outlines changes taking place along the three aspects of the framework. It concludes by discussing three distinct approaches to the management of organizational IT infrastructure (passive, reactive, and pragmatic), and by offering greater insight regarding a pragmatic approach.


title: Contact-Force Distribution Optimization and Control for Quadruped Robots Using Both Gradient and Adaptive Neural Networks

abstract: This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.


title: Permanent magnetic system design for the wall-climbing robot

abstract: This paper presents the design and analysis of the permanent magnetic system for a wall-climbing robot with permanent magnetic tracks. Based on the behaviour of gecko lizards, the architecture of the robot was designed and built, including the structure of the adhesion mechanism, the mechanical architecture and the anti-toppling mechanism. The permanent magnetic adhesion mechanism and the tracked locomotion mechanism were employed in this kind of wall-climbing robot. Through static and dynamic force analysis of the robot under different situations, design requirements for the adhesion mechanism were derived. Two different types of structures were put forward for the permanent magnetic units and are further discussed in this paper. These two types of structures are also analysed in detail. In addition, a finite-element method was used to verify the results of magnetic units. Finally, two wall-climbing robots, equipped with different magnetic systems described previously, are explained and their applications are discussed in this paper.


title: An object group-based component model

abstract: Dynamic reconfiguration, i.e. changing at runtime the communication pattern of a program is challenging for most programs as it is generally impossible to ensure that such modifications won't disrupt current computations. In this paper, we propose a new approach for the integration of components in an object-oriented language that allows safe dynamic reconfiguration. Our approach is built upon futures and object-groups to which we add: i) output ports to represent variability points, ii) critical sections to control when updates of the software can be made and iii) hierarchy to model locations and distribution. These different notions work together to allow dynamic and safe update of a system. We illustrate our approach with a few examples.


Deep Fusion LSTMs for Text Semantic Matching


title: Sinonasal and nasopharyngeal adenoidcystic carcinoma: report of four cases.

abstract: Four cases of sinonasal and nasopharyngeal adenoidcystic carcinoma that came to the RIMS Hospital, Imphal during the period of July, 2002 to March, 2003 are reported in this article. The age incidence ranged from 30 to 80 years and the mean age was 47.5 years; with male to female ratio of 1:1. The average duration from first sympton in the patient to the date of his medical consultation was 17.8 months. Nasal obstruction was the first symptom two cases; lump sensation in the throat and headache in the third and fourth cases respectively. Despite the evidence of rapid and extensive local spread there were definitely delays from the patients' end to consult a clinician. Whereas the tumour itself is not common, the usual sites, if it occurs, are palate and minor salivary glands and rarely mucous and seromucinous glands elsewhere.


title: Association between CXCR2 and IL-22BP expression indicate a poor outcome for gastric adenocarcinoma progression

abstract: C-X-C motif chemokine receptor type 2 (CXCR2), a key regulatory protein, has been associated with multiple roles in the progression of numerous tumors, including gastric adenocarcinoma (GA). However, the mechanism of CXCR2 in the development of tumors remains controversial and unclear. In a previous study, the expression of CXCR2 and interleukin-22 receptor 2 (IL-22BP) was observed in GA. This promoted the present study, which aimed to explore the association between the two proteins, and to further analyze their roles in GA. CXCR2 and IL-22BP protein expression was analyzed by immunohistochemistry and reverse transcription-quantitative polymerase chain reaction assays in gastric cancer (GC) tissue, additionally confirmed via western blotting and immunocytochemical analysis in the MKN-45, BGC-823 and SGC-7901 cell lines. The association between expression levels and clinicopathological characteristics was evaluated by the Mann-Whitney U and Kruskal-Wallis tests. Using Kaplan-Meier plots and Cox proportional hazard models, overall survival (OS) was analyzed. Compared with non-cancerous tissue, CXCR2 and IL-22BP were over expressed (P<0.001 and P<0.001, respectively), and were observed mainly in the cytoplasm (P=0.022 and P=0.014, respectively) in GA. The associated protein and messenger RNA levels were analyzed, and coexpression was identified. Increased expression and more positive cases of CXCR2 and IL-22BP were observed with advanced pathological tumor-node-metastasis (p-TNM) stage in GC (P<0.001 and P<0.001, respectively), as well as the presence and absence of lymph node metastasis (LNM) (P=0.003 and P=0.041, respectively) and deep or superficial muscular invasion (P=0.002 and P=0.004, respectively). In addition, an association between IL-22BP and tumor diameter was indicated (P=0.021). In a Kaplan-Meier analysis, compared with negative expression, the two proteins identified a group of patients with the shortest OS. Cox proportional hazard models revealed that the two proteins, in addition to p-TNM stage, LNM and depth of invasion, predicted a short time to OS. The coexpression of CXCR2 and IL-22BP was demonstrated in GA, which may indicate that CXCR2 is involved in more complex mechanisms and roles, and indicate a poor outcome in GA progression.


Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis


title: Planning Fitness Training Sessions Using the Bat Algorithm

abstract: Over fairly recent years the concept of an ar- tificial sport trainer has been proposed in literature. This concept is based on computational intelligence algorithms. In this paper, we try to extend the artificial sports trainer by planning fitness training sessions that are suitable for athletes, especially during idle seasons when no competi- tion takes place (e.g., winter). The bat algorithm was used for planning fitness training sessions and results showed promise for the proposed solution. Future directions for development are also outlined in the paper.


title: Value-gradient learning

abstract: We describe an Adaptive Dynamic Programming algorithm VGL(λ) for learning a critic function over a large continuous state space. The algorithm, which requires a learned model of the environment, extends Dual Heuristic Dynamic Programming to include a bootstrapping parameter analogous to that used in the reinforcement learning algorithm TD(λ). We provide on-line and batch mode implementations of the algorithm, and summarise the theoretical relationships and motivations of using this method over its precursor algorithms Dual Heuristic Dynamic Programming and TD(λ). Experiments for control problems using a neural network and greedy policy are provided.


title: A Bayesian Approach to Learning Bayesian Networks with Local Structure

abstract: Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distributions (CPDs) stored at each node. The majority of this work has concentrated on using decision-tree representations for the CPDs. In addition, researchers typically apply non-Bayesian (or asymptotically Bayesian) scoring functions such as MDL to evaluate the goodness-of-fit of networks to the data. ::: ::: In this paper we investigate a Bayesian approach to learning Bayesian networks that contain the more general decision-graph representations of the CPDs. First, we describe how to evaluate the posterior probability-- that is, the Bayesian score--of such a network, given a database of observed cases. Second, we describe various search spaces that can be used, in conjunction with a scoring function and a search procedure, to identify one or more high-scoring networks. Finally, we present an experimentd evaluation of the search spaces, using a greedy algorithm and a Bayesian scoring function.


title: Effective distinction of transparent and specular reflective objects in point clouds of a multi-echo laser scanner

abstract: A favoured sensor for mapping is a 3D laser scanner since it allows a wide scanning range, precise measurements, and is usable indoor and outdoor. Hence, a mapping module delivers detailed and high resolution maps which makes it possible to navigate safely. Difficulties result from transparent and specular reflective objects which cause erroneous and dubious measurements. At such objects, based on the incident angle, measurements result from the object surface, an object behind the transparent surface, or an object mirrored with respect to the reflective surface. This paper describes an enhanced Pre-Filter-Module to distinguish between these cases. Two experiments demonstrate the usability and show that for single scans the identification of mentioned objects in 3D is possible. The first experiment was made in an empty room with a mirror. The second experiment was made in a stairway which contains a glass door. Further, results show that a discrimination between a specular reflective and a transparent object is possible. Especially for transparent objects the detected size is restricted to the incident angle. That is why future work concentrates on implementing a post-filter module. Gained experience shows that collecting the data of multiple scans and postprocess them as soon as the object was bypassed will improve the map.


title: Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization

abstract: In this paper we study stochastic quasi-Newton methods for nonconvex stochastic optimization, where we assume that only stochastic information of the gradients of the objective function is available via a stochastic first-order oracle (SFO). Firstly, we propose a general framework of stochastic quasi-Newton methods for solving nonconvex stochastic optimization. The proposed framework extends the classic quasi-Newton methods working in deterministic settings to stochastic settings, and we prove its almost sure convergence to stationary points. Secondly, we propose a general framework for a class of randomized stochastic quasi-Newton methods, in which the number of iterations conducted by the algorithm is a random variable. The worst-case SFO-calls complexities of this class of methods are analyzed. Thirdly, we present two specific methods that fall into this framework, namely stochastic damped-BFGS method and stochastic cyclic Barzilai-Borwein method. Finally, we report numerical results to demonstrate the efficiency of the proposed methods.


title: Frequency Reconfigurable and Miniaturized Substrate Integrated Waveguide Interdigital Capacitor (SIW-IDC) Antenna

abstract: A novel frequency reconfigurable substrate integrated waveguide-interdigital capacitor (SIW-IDC) antenna is presented. The antenna is designed based on a composite right/left-handed (CRLH) resonator, which is realized by the SIW-IDC. Thanks to CRLH characteristics, the antenna size is reduced. Since the IDC determines the resonant frequency, a varactor diode is embedded on the interdigital slot of the short-ended SIW. Its zeroth-order resonance (ZOR) frequency is varied from 4.13 GHz to 4.50 GHz by changing the bias voltage from 0 V to 36 V while keeping good impedance matching better than 15 dB. The radiation performance is numerically and experimentally investigated. Very good agreements between the simulated and measured S-parameters and radiation patterns are observed.


title: Adaptive clip limit for contrast limited adaptive histogram equalization (CLAHE) of medical images using least mean square algorithm

abstract: Automated detection of abnormalities in human body is a critical application medical image computing. Preprocessing of medical image is of great need as the image quality of the captured images is highly attenuated due to several factors. In this work, we propose a novel idea of using the concepts of adaptive processing (LMS algorithm) for the estimation of clip limit for CLAHE. Statistical metrics like AMBE and PSNR are used for optimum window size selection for CLAHE. The algorithm proposed in this paper has been implemented on TMS320C6713T DSP for real time application and 60 images from STARE, DDSM and OASIS databases have been tested and analyzed. All the tested images show a better result in terms of SSIM index.


title: Professional Development and Teacher Change

abstract: This article describes a model of teacher change originally presented nearly two decades ago (Guskey, 1986) that began my long and warm friendship with Michael Huberman. The model portrays the temporal sequence of events from professional development experiences to enduring change in teachers' attitudes and perceptions. Research evidence supporting the model is summarized and the conditions under which change might be facilitated are described. The development and presentation of this model initiated a series of professional collaborations between Michael and myself, and led to the development of our co-edited book, Professional Development in Education: new paradigms and practices (Guskey & Huberman, 1995), which was named 'Book of the Year' by the National Staff Development Council in 1996.


Yelp Restaurant Photo Classification


title: Practical design considerations for a LLC multi-resonant DC-DC converter in battery charging applications

abstract: In this paper, resonant tank design procedure and practical design considerations are presented for a high performance LLC multi-resonant dc-dc converter in a two-stage smart battery charger for neighborhood electric vehicle applications. The multi-resonant converter has been analyzed and its performance characteristics are presented. It eliminates both low and high frequency current ripple on the battery, thus maximizing battery life without penalizing the volume of the charger. Simulation and experimental results are presented for a prototype unit converting 390 V from the input dc link to an output voltage range of 48 V to 72 V dc at 650 W. The prototype achieves a peak efficiency of 96 %.


title: Channel Coordination in Green Supply Chain Management

abstract: Environmental consciousness has become increasingly important in everyday life and business practice. The effort to reduce the impact of business activities on the environment has been labelled as green supply chain management. Any major greening project would require efforts on the part of the entire supply chain. However, very few studies have addressed the issue of coordinating the green supply chain. We consider the problem of coordination of a manufacturer and a retailer in a vertical supply chain, who put in efforts for ‘greening’ their operations. We address some pertinent questions in this regard such as extent of effort in greening of operations by manufacturer or retailer, level of cooperation between the two parties, and how to coordinate their operations in a supply chain. The greening efforts by the manufacturer and retailer result in demand expansion at the retail end. The decision variables of the manufacturer are wholesale price and greening effort, while those of the retailer are retail price and its greening effort. We find that the ratio of the optimal greening efforts put in by the manufacturer and retailer is equal to the ratio of their green sensitivity ratios and greening cost ratios. Further, profits and efforts are higher in the integrated channel as compared to the case of the decentralized channel. Finally, a two-part tariff contract is found to produce channel coordination in this problem. A numerical example illustrates the results.


title: The Probabilistic Production Simulation Incorporating the Correlation between PV Power and Loads

abstract: Photovoltaic (PV) is a crucial power source in future clean power system, which must be included in the system planning. But the correlation between variable PV power and loads is not involved in traditional planning method. This paper proposed a modified probabilistic production simulation (PPS) method considering the joint probability of PV output and load, especially the conditional dependence of them. The new PPS contains three parts of photovoltaic and load correlation characteristics modeling, photovoltaic probability calculation, and verification of production simulation method. Numerical study is finally proceeded, and the results verified the effectiveness and accuracy of the designed algorithm.