Query: Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning

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Return of Frustratingly Easy Domain Adaptation <=SEP=> Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being “frustratingly easy” to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple–it can be implemented in four lines of Matlab code–CORAL performs remarkably well in extensive evaluations on standard benchmark datasets. “Everything should be made as simple as possible, but not simpler.”

Cross-domain video concept detection using adaptive svms <=SEP=> Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.

Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit <=SEP=> This paper demonstrates theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous results, which require O(m2) measurements. The new results for OMP are comparable with recent results for another approach called basis pursuit (BP). In some settings, the OMP algorithm is faster and easier to implement, so it is an attractive alternative to BP for signal recovery problems.

Unsupervised Domain Adaptation by Backpropagation <=SEP=> Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled targetdomain data is necessary). As the training progresses, the approach promotes the emergence of “deep” features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation. Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-ofthe-art on Office datasets.

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition <=SEP=> We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.

Fall detection using ceiling-mounted 3D depth camera <=SEP=> This paper proposes an algorithm for fall detection using a ceiling-mounted 3D depth camera. The lying pose is separated from common daily activities by a k-NN classifier, which was trained on features expressing head-floor distance, person area and shape's major length to width. In order to distinguish between intentional lying postures and accidental falls the algorithm also employs motion between static postures. The experimental validation of the algorithm was conducted on realistic depth image sequences of daily activities and simulated falls. It was evaluated on more than 45000 depth images and gave 0% error. To reduce the processing overload an accelerometer was used to indicate the potential impact of the person and to start an analysis of depth images.

Improving Dermoscopic Image Segmentation With Enhanced Convolutional-Deconvolutional Networks <=SEP=> Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is further exacerbated when dealing with a large amount of image data. In this paper, we extended our previous work by developing a deeper network architecture with smaller kernels to enhance its discriminant capacity. In addition, we explicitly included color information from multiple color spaces to facilitate network training and thus to further improve the segmentation performance. We participated and extensively evaluated our method on the ISBI 2017 skin lesion segmentation challenge. By training with the 2000 challenge training images, our method achieved an average Jaccard Index of 0.765 on the 600 challenge testing images, which ranked itself in the first place among 21 final submissions in the challenge.

Retinal vessel segmentation via deep learning network and fully-connected conditional random fields <=SEP=> Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets.

Learning a non-linear knowledge transfer model for cross-view action recognition <=SEP=> This paper concerns action recognition from unseen and unknown views. We propose unsupervised learning of a non-linear model that transfers knowledge from multiple views to a canonical view. The proposed Non-linear Knowledge Transfer Model (NKTM) is a deep network, with weight decay and sparsity constraints, which finds a shared high-level virtual path from videos captured from different unknown viewpoints to the same canonical view. The strength of our technique is that we learn a single NKTM for all actions and all camera viewing directions. Thus, NKTM does not require action labels during learning and knowledge of the camera viewpoints during training or testing. NKTM is learned once only from dense trajectories of synthetic points fitted to mocap data and then applied to real video data. Trajectories are coded with a general codebook learned from the same mocap data. NKTM is scalable to new action classes and training data as it does not require re-learning. Experiments on the IXMAS and N-UCLA datasets show that NKTM outperforms existing state-of-the-art methods for cross-view action recognition.

Realtime ray tracing and interactive global illumination <=SEP=> Interaktive 3D-Computergraphik basiert heutzutage fast ausschliesslich auf dem Rasterisierungsverfahren. Dieses ist heute sehr effizient und günstig realisierbar, stösst aber zunehmend an seine Grenzen hinsichtlich unterstützter Szenenkomplexität und erreichbarer Darstellungsqualität. Eine Alternative zum Rasterisierungsverfahren ist das Ray Tracing Verfahren, welches zwar allgemein für bessere Bildqualität bekannt ist, aber aufgrund hoher Rechenanforderungen bisher als unvereinbar mit interaktiver Performanz galt. In dieser Arbeit geht es um die Entwicklung der Echtzeit Ray Tracing Technologie, mit der das Ray Tracing Verfahren auch für interaktive Anwendungen ermöglicht wird. Die im Rahmen dieser Dissertation entwickelten Techniken ermöglichen nun erstmalig interaktive Ray Tracing Performanz selbst auf Standard PCs. Die dazu entwickelten Methoden beinhalten unter anderem einen extrem schnellen Ray Tracing Kern, ein effizientes Parallelisierungsframework, die Unterstützung dynamischer und extrem komplexer Modelle, sowie eine geeignete Programmierschnittstelle. Darauf aufbauend wurden dann weiterhin Verfahren entwickelt, die es erstmalig ermöglichen, auch die globale Lichtausbreitung interaktiv zu simulieren. Die im Rahmen der Dissertation entwickelten Techniken formen ein komplettes Framework für Echtzeit-Ray Tracing und interaktive Beleuchtungssimulation, welches interaktive Performanz selbst für massiv komplexe Szenen liefert, sowie erstmalig physikalisch basierte Computergraphik unter Echtzeitbedingungen ermöglicht.

Supervised clustering of streaming data for email batch detection <=SEP=> We address the problem of detecting batches of emails that have been created according to the same template. This problem is motivated by the desire to filter spam more effectively by exploiting collective information about entire batches of jointly generated messages. The application matches the problem setting of supervised clustering, because examples of correct clusterings can be collected. Known decoding procedures for supervised clustering are cubic in the number of instances. When decisions cannot be reconsidered once they have been made --- owing to the streaming nature of the data --- then the decoding problem can be solved in linear time. We devise a sequential decoding procedure and derive the corresponding optimization problem of supervised clustering. We study the impact of collective attributes of email batches on the effectiveness of recognizing spam emails.

VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals <=SEP=> Enabling touch gesture sensing on all surfaces of the mobile device, not limited to the touchscreen area, leads to new user interaction experiences. In this paper, we propose VSkin, a system that supports fine-grained gesture-sensing on the back of mobile devices based on acoustic signals. VSkin utilizes both the structure-borne sounds, i.e., sounds propagating through the structure of the device, and the air-borne sounds, i.e., sounds propagating through the air, to sense finger tapping and movements. By measuring both the amplitude and the phase of each path of sound signals, VSkin detects tapping events with an accuracy of 99.65% and captures finger movements with an accuracy of 3.59mm.

An Acoustic ECHO Suppressor Based on a Frequency-Domain Model of Highly Nonlinear Residual ECHO <=SEP=> This paper proposes a new acoustic echo suppressor based on a frequency-domain model of highly nonlinear residual echo. The proposed echo suppressor controls the gain in each frequency bin based on the new model where the highly nonlinear residual echo is approximated as a product of a regression coefficient and the echo replica in the frequency domain. To reduce annoying modulation by the error of the model, a flooring operation of estimated near-end signal level is introduced to the gain control. Simulation results with speech data recorded by a hands-free cellphone show that the proposed echo suppressor reduces the highly nonlinear residual echo to an almost inaudible level

W ORDS OR C HARACTERS ? F INE-GRAINED <=SEP=> Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children’s Book Test and Who Did What datasets. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task.1

Are Metamorphic Viruses Really Invincible? <=SEP=> Metamorphic viruses enjoy the apparent invincibility because a virus writer has the advantage of knowing the weak spots of AV technologies. We could turn the tables if we can identify similar weak spots in a metamorphic virus. Indeed, Lakhotia and Singh close their otherwise gloomy article with one bright spot: " The good news is that a virus writer is confronted with the same theoretical limit as anti-virus technologies… It may be worth contemplating how this could be used to the advantage of anti-virus technologies. " SUMMARY In the game of " hide and seek, " where a virus tries to hide and the AV scanners tries to seek, the winner is the one that can take advantage of the other's weak spot. So far a virus writer has enjoyed the upper hand for she could exploit the limitations of AV technologies. Metamorphic viruses are particularly insidious in taking such advantage. A metamorphic virus thwarts detection by signature-based (static) AV technologies by morphing its code as it propagates. A virus can also thwart detection by emulation-based (dynamic) technologies. To do so it needs to detect whether it is running in an emulator and change its behavior. So are metamorphic viruses invincible? This paper investigates the above remark and identifies what promises to be the Achilles' heel of a metamorphic virus. The key observation is that in order to mutate its code, generations after generations, a metamorphic virus must analyze its own code. Thus, it too must face the limits of static and dynamic analyses. Beyond that a metamorphic virus has another constraint: it must be able to re-analyze the mutated code that it generates. Thus, the analysis within the virus, of how to transform the code in current generation, depends upon the complexity of transformations in the previous generation. To overcome the challenges of static and dynamic analyses, the virus has the following options: do not obfuscate the transformed code in any generation; use some coding conventions that can aid it in detecting its own obfuscations; or develop smart algorithms to detect its specific obfuscations. This paper uncovers the Achilles' heel of a metamorphic virus.

MapReduce network enabled algorithms for classification based on association rules <=SEP=> 

Optimizing memory transactions <=SEP=> Atomic blocks allow programmers to delimit sections of code as 'atomic', leaving the language's implementation to enforce atomicity. Existing work has shown how to implement atomic blocks over word-based transactional memory that provides scalable multi-processor performance without requiring changes to the basic structure of objects in the heap. However, these implementations perform poorly because they interpose on all accesses to shared memory in the atomic block, redirecting updates to a thread-private log which must be searched by reads in the block and later reconciled with the heap when leaving the block.This paper takes a four-pronged approach to improving performance: (1) we introduce a new 'direct access' implementation that avoids searching thread-private logs, (2) we develop compiler optimizations to reduce the amount of logging (e.g. when a thread accesses the same data repeatedly in an atomic block), (3) we use runtime filtering to detect duplicate log entries that are missed statically, and (4) we present a series of GC-time techniques to compact the logs generated by long-running atomic blocks.Our implementation supports short-running scalable concurrent benchmarks with less than 50\% overhead over a non-thread-safe baseline. We support long atomic blocks containing millions of shared memory accesses with a 2.5-4.5x slowdown.

Contextual gaps: privacy issues on Facebook <=SEP=> Social networking sites like Facebook are rapidly gaining in popularity. At the same time, they seem to present significant privacy issues for their users. We analyze two of Facebooks’s more recent features, Applications and News Feed, from the perspective enabled by Helen Nissenbaum’s treatment of privacy as “contextual integrity.” Offline, privacy is mediated by highly granular social contexts. Online contexts, including social networking sites, lack much of this granularity. These contextual gaps are at the root of many of the sites’ privacy issues. Applications, which nearly invisibly shares not just a users’, but a user’s friends’ information with third parties, clearly violates standard norms of information flow. News Feed is a more complex case, because it involves not just questions of privacy, but also of program interface and of the meaning of “friendship” online. In both cases, many of the privacy issues on Facebook are primarily design issues, which could be ameliorated by an interface that made the flows of information more transparent to users.

Map-based navigation in mobile robots:: II. A review of map-learning and path-planning strategies <=SEP=> This article reviews map-learning and path-planning strategies within the context of mapbased navigation in mobile robots. Concerning map-learning, it distinguishes metric maps from topological maps and describes procedures that help maintain the coherency of these maps. Concerning path-planning, it distinguishes continuous from discretized spaces and describes procedures applicable when the execution of a plan fails. It insists on the need for an integrated conception of such procedures, that must be tightly tailored to the specific robot that is used notably to the capacities and limitations of its sensory-motor equipment and to the specific environment that is experienced. A hierarchy of navigation strategies is outlined in the discussion, together with the sort of adaptive capacities each affords to cope with unexpected obstacles or dangers encountered on an animat or robot’s way to its goal.

Effective Defense Schemes for Phishing Attacks on Mobile Computing Platforms <=SEP=> Recent years have witnessed the increasing threat of phishing attacks on mobile computing platforms. In fact, mobile phishing is particularly dangerous due to the hardware limitations of mobile devices and the habits of mobile users. In this paper, we did a comprehensive study on the security vulnerabilities caused by mobile phishing attacks, including web page phishing attacks, application phishing attacks, and account registry phishing attacks. Existing schemes designed for web phishing attacks on personal computers (PCs) cannot effectively address the various phishing attacks on mobile devices. Hence, we propose MobiFish, which is a novel automated lightweight antiphishing scheme for mobile platforms. MobiFish verifies the validity of web pages, applications, and persistent accounts by comparing the actual identity to the claimed identity. MobiFish has been implemented on a Nexus 4 smartphone running the Android 4.2 operating system. We experimentally evaluate the performance of MobiFish with 100 phishing URLs and corresponding legitimate URLs, as well as phishing apps. The results show that MobiFish is very effective in detecting phishing attacks on mobile phones.

Knowledge actionability: satisfying technical and business interestingness <=SEP=> Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.

Results of syndactyly release using a modification of the Flatt technique. <=SEP=> The results of 144 congenital syndactyly releases over a 12-year period by a single surgeon using a modified Flatt technique (dorsal hourglass flap, interdigitating zigzag flaps, and full-thickness skin grafts) are analyzed considering the association of skin grafts and web creep. The mean follow-up was 5 years. There were seven cases of graft failure, only one of which developed web creep. Web creep occurred in 4.2% of web releases. The results suggest that avoiding longitudinal straight-line scars across the web space may be an important factor in avoiding web creep when performing the modified Flatt technique described.

Measuring the Similarity between Automatically Generated Topics <=SEP=> Previous approaches to the problem of measuring similarity between automatically generated topics have been based on comparison of the topics’ word probability distributions. This paper presents alternative approaches, including ones based on distributional semantics and knowledgebased measures, evaluated by comparison with human judgements. The best performing methods provide reliable estimates of topic similarity comparable with human performance and should be used in preference to the word probability distribution measures used previously.

A hybrid medium access control protocol for underwater wireless networks <=SEP=> Underwater networks allow investigation of many areas of the world not easily accessed by humans, but offer interesting challenges to device and protocol designers due to the unique channel conditions present when using acoustic communications. The high transmit power of acoustic transceivers makes the medium access protocol a primary focus point for reducing energy consumption in energy limited underwater devices. Scheduled protocols use very little power by eliminating collisions, but cannot adapt to changing traffic conditions in the same way as random protocols. We attempt to bridge these two ideas by dividing time into scheduled and unscheduled access periods in order to yield the benefits of each protocol. We show that this technique increases the bits delivered per energy unit in many cases of interest. Additionally, the hybrid technique provides low latency for a wider range of traffic rates than either of the two protocols when considered individually. We also investigate some of the design tradeoffs to consider when using a hybrid protocol.

NativeGuard: protecting android applications from third-party native libraries <=SEP=> Android applications often include third-party libraries written in native code. However, current native components are not well managed by Android's security architecture. We present NativeGuard, a security framework that isolates native libraries from other components in Android applications. Leveraging the process-based protection in Android, NativeGuard isolates native libraries of an Android application into a second application where unnecessary privileges are eliminated. NativeGuard requires neither modifications to Android nor access to the source code of an application. It addresses multiple technical issues to support various interfaces that Android provides to the native world. Experimental results demonstrate that our framework works well with a set of real-world applications, and incurs only modest overhead on benchmark programs.

Working memory deficits in adults with attention-deficit/hyperactivity disorder (ADHD): an examination of central executive and storage/rehearsal processes. <=SEP=> The current study was the first to use a regression approach to examine the unique contributions of central executive (CE) and storage/rehearsal processes to working memory (WM) deficits in adults with ADHD. Thirty-seven adults (ADHD = 21, HC = 16) completed phonological (PH) and visuospatial (VS) working memory tasks. While both groups performed significantly better during the PH task relative to the VS task, adults with ADHD exhibited significant deficits across both working memory modalities. Further, the ADHD group recalled disproportionately fewer PH and VS stimuli as set-size demands increased. Overall, the CE and PH storage/rehearsal processes of adults with ADHD were both significantly impaired relative to those of the healthy control adults; however, the magnitude of the CE effect size was much smaller compared to previous studies of children with the disorder. Collectively, results provide support for a lifelong trajectory of WM deficits in ADHD.

Line-of-sight guidance for formation control of quadrotor <=SEP=> In the broad class of unmanned aerial vehicles (UAV), Quadrotor is an important member. Cooperative control of UAVs are currently being researched for a wide range of applications. An active and important research area of cooperative control of multiple UAVs is the formation control. Formation flying is the disciplined flight of two or more aircraft under the command of a flight leader. The main goal is to coordinate a group of UAVs to achieve a desired formation pattern and avoid inter-vehicle collisions at the same time. Formation flight of a team of UAVs has broad applications such as aerial mapping, traffic monitoring, terrain and utilities inspection, search and rescue operations, perimeter planning, disaster monitoring, reconnaissance mission, surveillance etc. Here we propose a Line Of Sight (LOS) guidance based flight formation control strategy for multiple quad rotor UAVs in leader-follower framework. The guidance laws for 2D formation are implemented with the help of LOS angles, between adjacent quadrotors to keep the current quadrotor's position in formation. The full dynamic model of quadrotor incorporating LOS guidance law is considered for designing PID controller and the simulation analysis was done using MATLAB/ Simulink.

Hand Gesture Recognition With Multiscale Weighted Histogram of Contour Direction Normalization for Wearable Applications <=SEP=> This paper proposes a static hand gesture recognition method with low computation and memory consumption for wearable applications. The hand contour is chosen as the hand gesture feature and support vector machine is used to classify the feature. A multiscale weighted histogram of contour direction-based direction normalization is proposed to ensure good recognition performance. In order to improve efficiency, the proposed histogram only counts the direction of the contour point to focus on the most significant hand feature in the first-person view of wearable devices. Based on the hand’s anatomy, the proposed histogram is weighted by considering each contour point’s position and direction jointly using the direction-angle map, to ensure robustness. Experimental results show that the proposed method can give a recognition accuracy of 97.1% with a frame rate of 30 fps on a PC.

Prototype of automatic accident detection and management in vehicular environment using VANET and IoT <=SEP=> The increase in the population of cities and the number of vehicles leads to increase in congestion on roads and accidents. The lack of quick medical service on road accidents is the major cause of death. In such situation, an automatic accident detection can help to save the loss of life. In this paper, a prototype is designed for an automatic accident detection using Vehicular Adhoc Network (VANET) and Internet of Things (IoT). The application is able to detect accident and severity of the emergency level with the help of mechanical and medical sensors deployed in the vehicle. In case of emergency, the message is sent to a hospital using VANET, where our central server is able to find out the location of the accident and nearest medical center. It sends a message for an ambulance after detecting basic information. In order to clear the path on the way to accident's location, the client application on the ambulance generates alert messages.

Author ' s personal copy Narcissism and recollections of early life experiences <=SEP=> Recent studies have found associations between narcissistic personality features and retrospective accounts of early experiences. The current study sought to extend these previous findings by examining whether adaptive and maladaptive features of narcissism were associated with recollections of early life experiences in a non-clinical sample of undergraduate students (N = 334). Results revealed that the Entitlement/Exploitativeness feature of narcissism was associated with low security, high parental discipline, and high threats of separation. Narcissistic Grandiosity was positively associated with peer affectional support and parental discipline, whereas Narcissistic Vulnerability was not uniquely associated with memories of early life experiences. The results provide partial support for models of narcissism in which parents are recalled as failing to provide a secure base while inducing threats of separation and discipline. 2011 Elsevier Ltd. All rights reserved.