Query: Semi-Supervised Image-to-Video Adaptation for Video Action Recognition

Relevant passages:
Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition <=SEP=> Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene or event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape or appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.

Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization <=SEP=> Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint `2,1-norm minimization on both loss function and regularization. The `2,1-norm based loss function is robust to outliers in data points and the `2,1norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence. Our regression based objective makes the feature selection process more efficient. Our method has been applied into both genomic and proteomic biomarkers discovery. Extensive empirical studies are performed on six data sets to demonstrate the performance of our feature selection method.

A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback <=SEP=> We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Action recognition in the premotor cortex. <=SEP=> We recorded electrical activity from 532 neurons in the rostral part of inferior area 6 (area F5) of two macaque monkeys. Previous data had shown that neurons of this area discharge during goal-directed hand and mouth movements. We describe here the properties of a newly discovered set of F5 neurons ("mirror neurons', n = 92) all of which became active both when the monkey performed a given action and when it observed a similar action performed by the experimenter. Mirror neurons, in order to be visually triggered, required an interaction between the agent of the action and the object of it. The sight of the agent alone or of the object alone (three-dimensional objects, food) were ineffective. Hand and the mouth were by far the most effective agents. The actions most represented among those activating mirror neurons were grasping, manipulating and placing. In most mirror neurons (92%) there was a clear relation between the visual action they responded to and the motor response they coded. In approximately 30% of mirror neurons the congruence was very strict and the effective observed and executed actions corresponded both in terms of general action (e.g. grasping) and in terms of the way in which that action was executed (e.g. precision grip). We conclude by proposing that mirror neurons form a system for matching observation and execution of motor actions. We discuss the possible role of this system in action recognition and, given the proposed homology between F5 and human Brocca's region, we posit that a matching system, similar to that of mirror neurons exists in humans and could be involved in recognition of actions as well as phonetic gestures.

Learning with Local and Global Consistency <=SEP=> We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

DC and RF breakdown voltage characteristics of SiGe HBTs for WiFi PA applications <=SEP=> Breakdown voltage and RF characteristics relevant for RF power amplifiers (PA) are presented in this paper. Typically, DC collector-to-emitter breakdown voltage with base open (BVCEO) or DC collector-to-base breakdown with emitter open (BVCBO) has been presented as the metric for voltage limit of PA devices. In practical PA circuits, the RF envelope voltage can swing well beyond BVCEO without causing a failure. An analysis of output power swing limitations and DC breakdown is presented with attention to biasing and temperature.

Cognitive Reduced Prefrontal Connectivity in Psychopathy <=SEP=> Julian C. Motzkin,1 Joseph P. Newman,2 Kent A. Kiehl,3,4 and Michael Koenigs1 1Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin 53719, 2Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin 53706, 3The MIND Research Network, Albuquerque, New Mexico 87131, and 4Departments of Psychology and Neuroscience, University of New Mexico, Albuquerque, New Mexico 87131

BVFG An Unbiased Detector of Curvilinear Structures <=SEP=> The extraction of curvilinear structures is an important low-level operation in computer vision that has many applications. Most existing operators use a simple model for the line that is to be extracted, i.e., they do not take into account the surroundings of a line. This leads to the undesired consequence that the line will be extracted in the wrong position whenever a line with different lateral contrast is extracted. In contrast, the algorithm proposed in this paper uses an explicit model for lines and their surroundings. By analyzing the scale-space behaviour of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed. Furthermore, the algorithm not only returns the precise sub-pixel line position, but also the width of the line for each line point, also with sub-pixel accuracy.

Design and implementation of an Android host-based intrusion prevention system <=SEP=> Android has a dominating share in the mobile market and there is a significant rise of mobile malware targeting Android devices. Android malware accounted for 97% of all mobile threats in 2013 [26]. To protect smartphones and prevent privacy leakage, companies have implemented various host-based intrusion prevention systems (HIPS) on their Android devices. In this paper, we first analyze the implementations, strengths and weaknesses of three popular HIPS architectures. We demonstrate a severe loophole and weakness of an existing popular HIPS product in which hackers can readily exploit. Then we present a design and implementation of a secure and extensible HIPS platform---"Patronus." Patronus not only provides intrusion prevention without the need to modify the Android system, it can also dynamically detect existing malware based on runtime information. We propose a two-phase dynamic detection algorithm for detecting running malware. Our experiments show that Patronus can prevent the intrusive behaviors efficiently and detect malware accurately with a very low performance overhead and power consumption.

Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments <=SEP=> 

Approximate queuing analysis for IEEE 802.15.4 sensor network <=SEP=> Wireless sensor networks (WSNs) have attracted much attention in recent years for their unique characteristics and wide use in many different applications. Especially, in military networks, all sensor motes are deployed randomly and densely. How can we optimize the number of deployed nodes (sensor node and sink) with a QoS guarantee (minimal end-to-end delay and packet drop)? In this paper, using the M/M/1 queuing model we propose a deployment optimization model for non-beacon-mode 802.15.4 sensor networks. The simulation results show that the proposed model is a promising approach for deploying the sensor network.

BeAware! - Situation awareness, the ontology-driven way <=SEP=> Available online 18 July 2010 Information overload is a severe problem for human operators of large-scale control systems as, for example, encountered in the domain of road traffic management. Operators of such systems are at risk to lack situation awareness, because existing systems focus on themere presentation of the available information on graphical user interfaces—thus endangering the timely and correct identification, resolution, and prevention of critical situations. In recent years, ontology-based approaches to situation awareness featuring a semantically richer knowledge model have emerged. However, current approaches are either highly domain-specific or have, in case they are domain-independent, shortcomings regarding their reusability. In this paper, we present our experience gained from the development of BeAware!, a framework for ontology-driven information systems aiming at increasing anoperator's situation awareness. In contrast to existing domain-independent approaches, BeAware!'s ontology introduces the concept of spatio-temporal primitive relations betweenobserved real-world objects thereby improving the reusability of the framework. To show its applicability, a prototype of BeAware! has been implemented in thedomain of road trafficmanagement. An overviewof this prototype and lessons learned for the development of ontology-driven information systems complete our contribution. © 2010 Elsevier B.V. All rights reserved.

Submaximal exercise testing: clinical application and interpretation. <=SEP=> Compared with maximal exercise testing, submaximal exercise testing appears to have greater applicability to physical therapists in their role as clinical exercise specialists. This review contrasts maximal and submaximal exercise testing. Two major categories of submaximal tests (ie, predictive and performance tests) and their relative merits are described. Predictive tests are submaximal tests that are used to predict maximal aerobic capacity. Performance tests involve measuring the responses to standardized physical activities that are typically encountered in everyday life. To maximize the validity and reliability of data obtained from submaximal tests, physical therapists are cautioned to apply the tests selectively based on their indications; to adhere to methods, including the requisite number of practice sessions; and to use measurements such as heart rate, blood pressure, exertion, and pain to evaluate test performance and to safely monitor patients.

Microfinance and Poverty : Evidence Using Panel Data from Bangladesh <=SEP=> Microfinance supports mainly informal activities that often have a low return and low market demand. It may therefore be hypothesized that the aggregate poverty impact of microfinance is modest or even nonexistent. If true, the poverty impact of microfinance observed at the participant level represents either income redistribution or short-run income generation from the microfinance intervention. This article examines the effects of microfinance on poverty reduction at both the participant and the aggregate levels using panel data from Bangladesh. The results suggest that access to microfinance contributes to poverty reduction, especially for female participants, and to overall poverty reduction at the village level. Microfinance thus helps not only poor participants but also the local economy.

Using sonification <=SEP=> The idea behind sonification is that synthetic non-verbal sounds can represent numerical data and provide support for information processing activities of many different kinds. This article describes some of the ways that sonification has been used in assistive technologies, remote collaboration, engineering analyses, scientific visualisations, emergency services and aircraft cockpits. Approaches for designing sonifications are surveyed, and issues raised by the existing approaches and applications are outlined. Relations are drawn to other areas of knowledge where similar issues have also arisen, such as human-computer interaction, scientific visualisation, and computer music. At the end is a list of resources that will help you delve further into the topic.

Comparison of Different Classification Techniques on PIMA Indian Diabetes Data <=SEP=> The development of data-mining applications such as classification and clustering has been applied to large scale data. In this research, we present comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. The algorithm or classifiers tested are Multilayer Perceptron, Bayes Network, J48graft (c4.5), Fuzzy Lattice Reasoning (FLR), NaiveBayes, JRip (RIPPER), Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference Systems(ANFIS). A fundamental review on the selected technique is presented for introduction purposes. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.

SSD-Assisted Backup and Recovery for Database Systems <=SEP=> Backup and recovery is an important feature of database systems since it protects data against unexpected hardware and software failures. Database systems can provide data safety and reliability by creating a backup and restoring the backup from a failure. Database administrators can use backup/recovery tools that are provided with database systems or backup/recovery methods with operating systems. However, the existing tools perform time-consuming jobs and the existing methods may negatively affect run-time performance during normal operation even though high-performance SSDs are used. In this paper, we present an SSD-assisted backup/recovery scheme for database systems. In our scheme, we extend the out-of-place update characteristics of flash-based SSDs for backup/recovery operations. To this end, we exploit the resources (e.g., flash translation layer and DRAM cache with supercapacitors) inside SSDs, and we call our SSD with new backup/recovery features BR-SSD. We design and implement the backup/recovery functionality in the Samsung enterprise-class SSD (i.e., SM843Tn) for more realistic systems. Furthermore, we conduct a case study of BR-SSDs in replicated database systems and modify MySQL with replication to integrate BR-SSDs. The experimental result demonstrates that our scheme provides fast recovery while it does not negatively affect the run-time performance during normal operation.

Multimodal analysis of the implicit affective channel in computer-mediated textual communication <=SEP=> Computer-mediated textual communication has become ubiquitous in recent years. Compared to face-to-face interactions, there is decreased bandwidth in affective information, yet studies show that interactions in this medium still produce rich and fulfilling affective outcomes. While overt communication (e.g., emoticons or explicit discussion of emotion) can explain some aspects of affect conveyed through textual dialogue, there may also be an underlying implicit affective channel through which participants perceive additional emotional information. To investigate this phenomenon, computer-mediated tutoring sessions were recorded with Kinect video and depth images and processed with novel tracking techniques for posture and hand-to-face gestures. Analyses demonstrated that tutors implicitly perceived students' focused attention, physical demand, and frustration. Additionally, bodily expressions of posture and gesture correlated with student cognitive-affective states that were perceived by tutors through the implicit affective channel. Finally, posture and gesture complement each other in multimodal predictive models of student cognitive-affective states, explaining greater variance than either modality alone. This approach of empirically studying the implicit affective channel may identify details of human behavior that can inform the design of future textual dialogue systems modeled on naturalistic interaction.

phishGILLNET — phishing detection methodology using probabilistic latent semantic analysis , AdaBoost , and co-training <=SEP=> Identity theft is one of the most profitable crimes committed by felons. In the cyber space, this is commonly achieved using phishing. We propose here robust server side methodology to detect phishing attacks, called phishGILLNET, which incorporates the power of natural language processing and machine learning techniques. phishGILLNET is a multi-layered approach to detect phishing attacks. The first layer (phishGILLNET1) employs Probabilistic Latent Semantic Analysis (PLSA) to build a topic model. The topic model handles synonym (multiple words with similar meaning), polysemy (words with multiple meanings), and other linguistic variations found in phishing. Intentional misspelled words found in phishing are handled using Levenshtein editing and Google APIs for correction. Based on term document frequency matrix as input PLSA finds phishing and non-phishing topics using tempered expectation maximization. The performance of phishGILLNET1 is evaluated using PLSA fold in technique and the classification is achieved using Fisher similarity. The second layer of phishGILLNET (phishGILLNET2) employs AdaBoost to build a robust classifier. Using probability distributions of the best PLSA topics as features the classifier is built using AdaBoost. The third layer (phishGILLNET3) further expands phishGILLNET2 by building a classifier from labeled and unlabeled examples by employing Co-Training. Experiments were conducted using one of the largest public corpus of email data containing 400,000 emails. Results show that phishGILLNET3 outperforms state of the art phishing detection methods and achieves F-measure of 100%. Moreover, phishGILLNET3 requires only a small percentage (10%) of data be annotated thus saving significant time, labor, and avoiding errors incurred in human annotation.

Circle grid fractal pattern for calibration at different camera zoom levels <=SEP=> Camera calibration patterns and fiducial markers are basic technique for 3D measurement to create Computer Graphics (CG) and Augmented Reality (AR). A checkerboard is widely used as a calibration pattern and a circle or ring grid provides higher precision [Datta et al. 2009], and matrix codes are common as AR markers.

Biomedical Image Segmentation Using Fully Convolutional Networks on TrueNorth <=SEP=> With the rapid growth of medical and biomedical image data, energy-efficient solutions for analyzing such image data that can be processed fast and accurately on platforms with low power budget are highly desirable. This paper uses segmenting glial cells in brain microscopy images as a case study to demonstrate how to achieve biomedical image segmentation with significant energy saving and minimal comprise in accuracy. Specifically, we design, train, implement, and evaluate Fully Convolutional Networks (FCNs) for biomedical image segmentation on IBM's neurosynaptic DNN processor — TrueNorth (TN). Comparisons in terms of accuracy and energy dissipation of TN with that of a low power NVIDIA TX2 mobile GPU platform have been conducted. Experimental results show that TN can offer at least two orders of magnitude improvement in energy efficiency when compared to TX2 GPU for the same workload.

McRT-STM: a high performance software transactional memory system for a multi-core runtime <=SEP=> Applications need to become more concurrent to take advantage of the increased computational power provided by chip level multiprocessing. Programmers have traditionally managed this concurrency using locks (mutex based synchronization). Unfortunately, lock based synchronization often leads to deadlocks, makes fine-grained synchronization difficult, hinders composition of atomic primitives, and provides no support for error recovery. Transactions avoid many of these problems, and therefore, promise to ease concurrent programming.We describe a software transactional memory (STM) system that is part of McRT, an experimental Multi-Core RunTime. The McRT-STM implementation uses a number of novel algorithms, and supports advanced features such as nested transactions with partial aborts, conditional signaling within a transaction, and object based conflict detection for C/C++ applications. The McRT-STM exports interfaces that can be used from C/C++ programs directly or as a target for compilers translating higher level linguistic constructs.We present a detailed performance analysis of various STM design tradeoffs such as pessimistic versus optimistic concurrency, undo logging versus write buffering, and cache line based versus object based conflict detection. We also show a MCAS implementation that works on arbitrary values, coexists with the STM, and can be used as a more efficient form of transactional memory. To provide a baseline we compare the performance of the STM with that of fine-grained and coarse-grained locking using a number of concurrent data structures on a 16-processor SMP system. We also show our STM performance on a non-synthetic workload -- the Linux sendmail application.

Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization <=SEP=> In this paper we use a Non-negative Matrix Factorization (NMF) based approach to analyze the strokes of the mridangam, a South Indian hand drum, in terms of the normal modes of the instrument. Using NMF, a dictionary of spectral basis vectors are first created for each of the modes of the mridangam. The composition of the strokes are then studied by projecting them along the direction of the modes using NMF. We then extend this knowledge of each stroke in terms of its basic modes to transcribe audio recordings. Hidden Markov Models are adopted to learn the modal activations for each of the strokes of the mridangam, yielding up to 88.40% accuracy during transcription.

Blind image quality assessment by relative gradient statistics and adaboosting neural network <=SEP=> The image gradient is a commonly computed image feature and a potentially predictive factor for image quality assessment (IQA). Indeed, it has been successfully used for both fulland noreference image quality prediction. However, the gradient orientation has not been deeply explored as a predictive source of information for image quality assessment. Here we seek to amend this by studying the quality relevance of the relative gradient orientation, viz., the gradient orientation relative to the surround. We also deploy a relative gradient magnitude feature which accounts for perceptual masking and utilize an AdaBoosting back-propagation (BP) neural network to map the image features to image quality. The generalization of the AdaBoosting BP neural network results in an effective and robust quality prediction model. The new model, called Oriented Gradients Image Quality Assessment (OG-IQA), is shown to deliver highly competitive image quality prediction performance as compared with the most popular IQA approaches. Furthermore, we show that OG-IQA has good database independence properties and a low complexity. & 2015 Elsevier B.V. All rights reserved.

Unconstrained Monocular 3D Human Pose Estimation by Action Detection and Cross-Modality Regression Forest <=SEP=> This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts.

0-Day Vulnerabilities and Cybercrime <=SEP=> This study analyzes 0-day vulnerabilities in the broader context of cybercrime and economic markets. The work is based on the interviews of several leading experts and on a field research of the authors. In particular, cybercrime is considered when involving traditional criminal activities or when military operations are involved. A description of different 0-day vulnerability markets - White, Black and Government markets - is provided, as well as the characteristics of malware factories and their major customers are discussed.

PreFix: Switch Failure Prediction in Datacenter Networks <=SEP=> In modern datacenter networks (DCNs), failures of network devices are the norm rather than the exception, and many research efforts have focused on dealing with failures after they happen. In this paper, we take a different approach by predicting failures, thus the operators can intervene and "fix" the potential failures before they happen. Specifically, in our proposed system, named PreFix, we aim to determine during runtime whether a switch failure will happen in the near future. The prediction is based on the measurements of the current switch system status and historical switch hardware failure cases that have been carefully labelled by network operators. Our key observation is that failures of the same switch model share some common syslog patterns before failures occur, and we can apply machine learning methods to extract the common patterns for predicting switch failures. Our novel set of features (message template sequence, frequency, seasonality and surge) for machine learning can efficiently deal with the challenges of noises, sample imbalance, and computation overhead. We evaluated PreFix on a data set collected from 9397 switches (3 different switch models) deployed in more than 20 datacenters owned by a top global search engine in a 2-year period. PreFix achieved an average of 61.81% recall and 1.84 * 10^-5 false positive ratio. It outperforms the other failure prediction methods for computers and ISP devices.

Semantic Feature Selection for Text with Application to Phishing Email Detection <=SEP=> In a phishing attack, an unsuspecting victim is lured, typically via an email, to a web site designed to steal sensitive information such as bank/credit card account numbers, login information for accounts, etc. Each year Internet users lose billions of dollars to this scourge. In this paper, we present a general semantic feature selection method for text problems based on the statistical t-test and WordNet, and we show its effectiveness on phishing email detection by designing classifiers that combine semantics and statistics in analyzing the text in the email. Our feature selection method is general and useful for other applications involving text-based analysis as well. Our email body-text-only classifier achieves more than 95% accuracy on detecting phishing emails with a false positive rate of 2.24%. Due to its use of semantics, our feature selection method is robust against adaptive attacks and avoids the problem of frequent retraining needed by machine learning classifiers.

Mining Parallel Corpora from Sina Weibo and Twitter <=SEP=> Microblogs such as Twitter, Facebook, and Sina Weibo (China's equivalent of Twitter) are a remarkable linguistic resource. In contrast to content from edited genres such as newswire, microblogs contain discussions of virtually every topic by numerous individuals in different languages and dialects and in different styles. In this work, we show that some microblog users post “self-translated” messages targeting audiences who speak different languages, either by writing the same message in multiple languages or by retweeting translations of their original posts in a second language. We introduce a method for finding and extracting this naturally occurring parallel data. Identifying the parallel content requires solving an alignment problem, and we give an optimally efficient dynamic programming algorithm for this. Using our method, we extract nearly 3M Chinese–English parallel segments from Sina Weibo using a targeted crawl of Weibo users who post in multiple languages. Additionally, from a random sample of Twitter, we obtain substantial amounts of parallel data in multiple language pairs. Evaluation is performed by assessing the accuracy of our extraction approach relative to a manual annotation as well as in terms of utility as training data for a Chinese–English machine translation system. Relative to traditional parallel data resources, the automatically extracted parallel data yield substantial translation quality improvements in translating microblog text and modest improvements in translating edited news content.

Synthetic aperture radar interferometry-Invited paper <=SEP=>