title: Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review

text: We enhance the autonomy of the continuous active learning method shown by Cormack and Grossman (SIGIR 2014) to be effective for technology-assisted review, in which documents from a collection are retrieved and reviewed, using relevance feedback, until substantially all of the relevant documents have been reviewed. Autonomy is enhanced through the elimination of topic-specific and dataset-specific tuning parameters, so that the sole input required by the user is, at the outset, a short query, topic description, or single relevant document; and, throughout the review, ongoing relevance assessments of the retrieved documents. We show that our enhancements consistently yield superior results to Cormack and Grossman’s version of continuous active learning, and other methods, not only on average, but on the vast majority of topics from four separate sets of tasks: the legal datasets examined by Cormack and Grossman, the Reuters RCV1-v2 subject categories, the TREC 6 AdHoc task, and the construction of the TREC 2002 filtering test collection.


title: 06-025 Multi-agent reinforcement learning : A survey ∗

text: Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined. Keywords—multi-agent systems, reinforcement learning, game theory, distributed control


title: FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization

text: Probabilistic point-set registration methods have been gaining more attention for their robustness to noise, outliers and occlusions. However, these methods tend to be much slower than the popular iterative closest point (ICP) algorithms, which severely limits their usability. In this paper, we contribute a novel probabilistic registration method that achieves state-of-the-art robustness as well as substantially faster computational performance than modern ICP implementations. This is achieved using a rigorous yet computationally-efficient probabilistic formulation. Pointset registration is cast as a maximum likelihood estimation and solved using the EM algorithm. We show that with a simple augmentation, the E step can be formulated as a filtering problem, allowing us to leverage advances in efficient Gaussian filtering methods. We also propose a customized permutohedral filter [1] for improved efficiency while retaining sufficient accuracy for our task. Additionally, we present a simple and efficient twist parameterization that generalizes our method to the registration of articulated and deformable objects. For articulated objects, the complexity of our method is almost independent of the Degrees Of Freedom (DOFs), which makes it highly efficient even for high DOF systems. The results demonstrate the proposed method consistently outperforms many competitive baselines on a variety of registration tasks. The video demo and source code are available on our project page.


title: Taxonomy for description of cross-domain attacks on CPS

text: The pervasiveness of Cyber-Physical Systems (CPS) in various aspects of the modern society grows rapidly. This makes CPS to increasingly attractive targets for various kinds of attacks. We consider cyber-security as an integral part of CPS security. Additionally, the necessity exists to investigate the CPS-specific aspects which are out of scope of cyber-security. Most importantly, attacks capable to cross the cyber-physical domain boundary should be analyzed. The vulnerability of CPS to such cross-domain attacks has been practically proven by numerous examples, e.g., by the currently most famous Stuxnet attack. In this paper, we propose taxonomy for description of attacks on CPS. The proposed taxonomy is capable of representing both conventional cyber-attacks as well as cross-domain attacks on CPS. Furthermore, based on the proposed taxonomy, we define the attack categorization. Several possible application areas of the proposed taxonomy are extensively discussed. Among others, it can be used to establish a knowledge base about attacks on CPS known in the literature. Furthermore, the proposed description structure will foster the quantitative and qualitative analysis of these attacks, both of which are necessarily to improve CPS security.


title: Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach

text: Segmentation of lungs with (large) lung cancer regions is a nontrivial problem. We present a new fully automated approach for segmentation of lungs with such high-density pathologies. Our method consists of two main processing steps. First, a novel robust active shape model (RASM) matching method is utilized to roughly segment the outline of the lungs. The initial position of the RASM is found by means of a rib cage detection method. Second, an optimal surface finding approach is utilized to further adapt the initial segmentation result to the lung. Left and right lungs are segmented individually. An evaluation on 30 data sets with 40 abnormal (lung cancer) and 20 normal left/right lungs resulted in an average Dice coefficient of 0.975±0.006 and a mean absolute surface distance error of 0.84±0.23 mm, respectively. Experiments on the same 30 data sets showed that our methods delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches. In addition, our RASM approach is generally applicable and suitable for large shape models.


title: Detecting Code-Switching between Turkish-English Language Pair

text: Code-switching (usage of different languages within a single conversation context in an alternative manner) is a highly increasing phenomenon in social media and colloquial usage which poses different challenges for natural language processing. This paper introduces the first study for the detection of TurkishEnglish code-switching and also a small test data collected from social media in order to smooth the way for further studies. The proposed system using character level n-grams and conditional random fields (CRFs) obtains 95.6% micro-averaged F1-score on the introduced test data set.


title: Daily Mindful Responding Mediates the Effect of Meditation Practice on Stress and Mood: The Role of Practice Duration and Adherence.

text:
OBJECTIVE
Although meditation practice is an important component of many mindfulness-based interventions (MBIs), empirical findings of its effects on psychological functioning are mixed and the mechanisms for the effects remain unclear. Responding with mindfulness (i.e., returning one's attention back to a nonjudgmental, present-oriented awareness) is a fundamental skill practiced in meditations. With repeated meditation practice, this skill is thought to become internalized and be applied to one's daily life. We thus hypothesized that the extent to which individuals responded to daily events with mindfulness would mediate the effects of meditation practice (instance, duration, and adherence to instructions) on psychological well-being.

METHOD
Using a daily diary methodology, we tracked the meditation practice, use of mindful responding during the day, and psychological outcomes (perceived stress, negative and positive affect) of 117 mindfulness-based stress reduction program participants.

RESULTS
We found that on days when participants meditated, they responded with greater mindfulness to daily events, which accounted for the beneficial effects of meditating on psychological outcomes. Furthermore, findings suggest that on meditation days, longer and more closely adhered meditation practices were independently associated with increases in mindful responding, which in turn were associated with better psychological outcomes.

CONCLUSION
These results suggest that regular, longer, and more closely adhered meditation practice is an important component of MBIs, in part because it leads to responding more mindfully in daily life, which promotes well-being.


title: Social Norms and Community Enforcement

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.


title: Motion estimation with non-local total variation regularization

text: State-of-the-art motion estimation algorithms suffer from three major problems: Poorly textured regions, occlusions and small scale image structures. Based on the Gestalt principles of grouping we propose to incorporate a low level image segmentation process in order to tackle these problems. Our new motion estimation algorithm is based on non-local total variation regularization which allows us to integrate the low level image segmentation process in a unified variational framework. Numerical results on the Mid-dlebury optical flow benchmark data set demonstrate that we can cope with the aforementioned problems.


title: BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams

text: Supporting sequential pattern mining from data streams is nowadays a relevant problem in the area of data stream mining research. Actual proposals available in the literature are based on the well-known PrefixSpan approach and are, indeed, able to effectively bound the error of discovered patterns. This approach foresees the idea of dividing the target stream in a collection of manageable chunks, i.e., pieces of stream, in order to gain into effectiveness and efficiency. Unfortunately, mining patterns from stream chunks indeed introduce additional errors with respect to the basic application scenario where the target stream is mined continuously, in a non-batch manner. This is due to several reasons. First, since batches are processed individually, patterns that contain items from two consecutive batches are lost. Secondly, in most batch-based approaches, the decision about the frequency of a pattern is done locally inside a single batch. Thus, if a pattern is frequent in the stream but its items are scattered over different batches, it will be continuously pruned out and will never become frequent due to the algorithm’s lack of the “complete-picture” perspective. In order to address so-delineated pattern mining problems, this paper introduces and experimentally assesses BFSPMiner, a Batch-Free Sequential Pattern Miner algorithm for effectively and efficiently mining patterns in streams without being constrained to the traditional batch-based processing. This allows us, for instance, to discover frequent patterns that would be lost according to alternative batch-based stream mining processing models. We complement our analytical contributions by means of a comprehensive experimental campaign of BFSPMiner against real-world data stream sets and in comparison with current batch-based stream sequential pattern mining algorithms.


title: Treatment of keloids and hypertrophic scars.

text: Clinicians always find it difficult to treat hypertrophic scars and keloids. Various treatment modalities are available. Intralesional corticosteroids, topical applications, cryotherapy, surgery, laser therapy, and silicone sheeting are the widely used options. Radiation therapy can also help in cases of recalcitrant keloids. Most recently, pulsed-dye laser has been successfully used to treat keloids and hypertrophic scars. There are no set guidelines for the treatment of keloids. Treatment has to be individualized depending upon the distribution, size, thickness, and consistency of the lesions and association of inflammation. A combination approach to therapy seems to be the best option.


title: Understanding Technology Adoption : Theory and Future Directions for Informal Learning

text: How and why individuals adopt innovations has motivated a great deal of research. This article examines individuals' computing adoption processes through the lenses of three adoption theories: Rogers's innovation diffusion theory, the Concerns-Based Adoption Model, the Technology Acceptance Model, and the United Theory of Acceptance and Use of Technology. Incorporating all three models, this article suggests technology adoption is a complex, inherently social, developmental process; individuals construct unique yet malleable perceptions of technology that influence their adoption decisions. Thus, successfully facilitating technology adoption must address cognitive, emotional, and contextual concerns. This article also focuses specific attention on adoption theory outside of a formal organization and the implications of adoption theory on informal environments. For a crime against the gods—the specifics of which are variously reported—he was condemned to an eternity at hard labor. And frustrating labor at that. For his assignment was to roll a great boulder to the top of a hill. Only every time Sisyphus, by the greatest of exertion and toil, attained the summit, the darn thing rolled back down again.


title: From natural language requirements to UML class diagrams

text: Unified Modeling Language (UML) is the most popular modeling language for analysis, design and development of the software system. There has been a lot of research interest in generating these UML models, especially class diagrams, automatically from Natural Language requirements. The interest in class diagrams can be attributed to the fact that classes represent the abstractions present in the system to be developed. However, automated generation of UML class diagrams is a challenging task as it involves lot of pre-processing or manual intervention at times. In this paper, we present dependency analysis based approach to derive UML class diagrams automatically from Natural Language requirements. We transform the requirements statements to an intermediary frame-based structured representation using dependency analysis of requirements statements and the Grammatical Knowledge Patterns. The knowledge stored in the frame-based structured representation is used to derive class diagrams using rule-based algorithm. Our approach has generated similar class diagrams as reported in earlier works based on linguistic analysis with either annotation or manual intervention. We present the effectiveness of our approach in terms of recall and precision for the case-studies presented in earlier works.


On the Construction of Huffman Trees


title: Health Effects Related to Wind Turbine Noise Exposure: A Systematic Review

text:
BACKGROUND
Wind turbine noise exposure and suspected health-related effects thereof have attracted substantial attention. Various symptoms such as sleep-related problems, headache, tinnitus and vertigo have been described by subjects suspected of having been exposed to wind turbine noise.

OBJECTIVE
This review was conducted systematically with the purpose of identifying any reported associations between wind turbine noise exposure and suspected health-related effects.

DATA SOURCES
A search of the scientific literature concerning the health-related effects of wind turbine noise was conducted on PubMed, Web of Science, Google Scholar and various other Internet sources.

STUDY ELIGIBILITY CRITERIA
All studies investigating suspected health-related outcomes associated with wind turbine noise exposure were included.

RESULTS
Wind turbines emit noise, including low-frequency noise, which decreases incrementally with increases in distance from the wind turbines. Likewise, evidence of a dose-response relationship between wind turbine noise linked to noise annoyance, sleep disturbance and possibly even psychological distress was present in the literature. Currently, there is no further existing statistically-significant evidence indicating any association between wind turbine noise exposure and tinnitus, hearing loss, vertigo or headache.

LIMITATIONS
Selection bias and information bias of differing magnitudes were found to be present in all current studies investigating wind turbine noise exposure and adverse health effects. Only articles published in English, German or Scandinavian languages were reviewed.

CONCLUSIONS
Exposure to wind turbines does seem to increase the risk of annoyance and self-reported sleep disturbance in a dose-response relationship. There appears, though, to be a tolerable level of around LAeq of 35 dB. Of the many other claimed health effects of wind turbine noise exposure reported in the literature, however, no conclusive evidence could be found. Future studies should focus on investigations aimed at objectively demonstrating whether or not measureable health-related outcomes can be proven to fluctuate depending on exposure to wind turbines.


title: A near linear time constant factor approximation for Euclidean bichromatic matching (cost)

text: We give an NlogO(1) N-time randomized O(1)-approximation algorithm for computing the cost of minimum bichromatic matching between two planar point-sets of size N.


title: I Can Has Cheezburger? A Nonparanormal Approach to Combining Textual and Visual Information for Predicting and Generating Popular Meme Descriptions

text: The advent of social media has brought Internet memes, a unique social phenomenon, to the front stage of the Web. Embodied in the form of images with text descriptions, little do we know about the “language of memes”. In this paper, we statistically study the correlations among popular memes and their wordings, and generate meme descriptions from raw images. To do this, we take a multimodal approach—we propose a robust nonparanormal model to learn the stochastic dependencies among the image, the candidate descriptions, and the popular votes. In experiments, we show that combining text and vision helps identifying popular meme descriptions; that our nonparanormal model is able to learn dense and continuous vision features jointly with sparse and discrete text features in a principled manner, outperforming various competitive baselines; that our system can generate meme descriptions using a simple pipeline.


title: Seeding Deep Learning using Wireless Localization

text: Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian and vehicle detection as examples, we demonstrate the feasibility, benefits, and challenges of an automatic image annotation system. Our work calls for new technical development on passive localization, mobile data analytics, and error-resilient ML models, as well as design issues in user privacy policies.


title: Mastering the game of Go with deep neural networks and tree search

text: The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.


title: Clinical spinal instability and low back pain.

text: Clinical instability is an important cause of low back pain. Although there is some controversy concerning its definition, it is most widely believed that the loss of normal pattern of spinal motion causes pain and/or neurologic dysfunction. The stabilizing system of the spine may be divided into three subsystems: (1) the spinal column; (2) the spinal muscles; and (3) the neural control unit. A large number of biomechanical studies of the spinal column have provided insight into the role of the various components of the spinal column in providing spinal stability. The neutral zone was found to be a more sensitive parameter than the range of motion in documenting the effects of mechanical destabilization of the spine caused by injury and restabilization of the spine by osteophyle formation, fusion or muscle stabilization. Clinical studies indicate that the application of an external fixator to the painful segment of the spine can significantly reduce the pain. Results of an in vitro simulation of the study found that it was most probably the decrease in the neutral zone, which was responsible for pain reduction. A hypothesis relating the neutral zone to pain has been presented. The spinal muscles provide significant stability to the spine as shown by both in vitro experiments and mathematical models. Concerning the role of neuromuscular control system, increased body sway has been found in patients with low back pain, indicating a less efficient muscle control system with decreased ability to provide the needed spinal stability.


title: A Study on Balancing Parallelism, Data Locality, and Recomputation in Existing PDE Solvers

text: Structured-grid PDE solver frameworks parallelize over boxes, which are rectangular domains of cells or faces in a structured grid. In the Chombo framework, the box sizes are typically 163 or 323, but larger box sizes such as 1283 would result in less surface area and therefore less storage, copying, and/or ghost cells communication overhead. Unfortunately, current on-node parallelization schemes perform poorly for these larger box sizes. In this paper, we investigate 30 different inter-loop optimization strategies and demonstrate the parallel scaling advantages of some of these variants on NUMA multicore nodes. Shifted, fused, and communication-avoiding variants for 1283 boxes result in close to ideal parallel scaling and come close to matching the performance of 163 boxes on three different multicore systems for a benchmark that is a proxy for program idioms found in Computational Fluid Dynamic (CFD) codes.


title: A Survey of the Functional Splits Proposed for 5 G Mobile Crosshaul Networks Line

text: Pacing the way towards 5G has lead researchers and industry in the direction of centralized processing known from Cloud-Radio Access Networks (C-RAN). In C-RAN research, a variety of different functional splits is presented by different names and focusing on different directions. The functional split determines how many Base Station (BS) functions to leave locally, close to the user, with the benefit of relaxing fronthaul network bitrate and delay requirements, and how many functions to centralize with the possibility of achieving greater processing benefits. This work presents for the first time a comprehensive overview systematizing the different work directions for both research and industry, while providing a detailed description of each functional split option and an assessment of the advantages and disadvantages. This work gives an overview of where the most effort has been directed in terms of functional splits, and where there is room for further studies. The standardization currently taking place is also considered and mapped into the research directions. It is investigated how the fronthaul network will be affected by the choice of functional split, both in terms of bitrates and latency, and as the different functional splits provide different advantages and disadvantages, the option of flexible functional splits is also looked into. Keywords—Functional Split; Crosshaul; X-haul; C-RAN; Fronthaul; standardization; industry; network architecture; Please refer to the list of acronyms provided in the end of the paper, right before the references.


title: A Prototype Navigation System for Guiding Blind People Indoors using NXT Mindstorms

text: People with visual impairment face enormous difficulties in terms of their mobility as they do not have enough information about their location and orientation with respect to traffic and obstacles on their route. Visually impaired people can navigate unknown areas by relying on the assistance of canes, other people, or specially trained guide dogs. The traditional ways of using guide dogs and long-cane only help to avoid obstacles, not to know where they are. The research presented in this paper introduces a mobile assistant navigation prototype to locate and direct blind people indoors. Since most of the existing navigation systems developed so far for blind people employ a complex conjunction of positioning systems, video cameras, locationbased and image processing algorithms, we designed an affordable low-cost prototype navigation system for orienting and tracking the position of blind people in complex environments. The prototype system is based on the inertial navigation system and experiments have been performed on NXT Mindstorms platform.


title: Online Learning of Weighted Relational Rules for Complex Event Recognition

text: Systems for symbolic complex event recognition detect occurrences of events in time using a set of event definitions in the form of logical rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, connections to techniques for learning such rules from data. We advance the state-of-the-art by combining an existing online algorithm for learning crisp relational structure with an online method for weight learning in Markov Logic Networks (MLN). The result is an algorithm that learns complex event patterns in the form of Event Calculus theories in the MLN semantics. We evaluate our approach on a challenging real-world application for activity recognition and show that it outperforms both its crisp predecessor and competing online MLN learners in terms of predictive performance, at the price of a small increase in training time.


title: Deep Reinforcement Learning for Robotic Manipulation

text: Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on offpolicy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.


title: Gradient and Curvature from Photometric Stereo Including Local Confidence Estimation

text: Photometric stereo is one technique for 3D shape determination that has been implemented in a variety of experimental settings and that has produced consistently good results. The idea is to use intensity values recorded from multiple images obtained from the same viewpoint but under different conditions of illumination. The resulting radiometric constraint makes it possible to obtain local estimates of both surface orientation and surface curvature without requiring global smoothness assumptions and without requiring prior image segmentation. This paper moves photometric stereo one step closer to practical viability by describing an experimental setting in which surface gradient estimation is achieved on full frame video data at near video frame rates (i.e., 15Hz). The implementation uses commercially available hardware. Reflectance is modeled empirically using measurements obtained from a calibration sphere. Estimation of the gradient, (p, q), requires only simple table lookup. Curvature estimation uses, in addition, the reflectance map, R(p, q). The required lookup table and reflectance maps are derived during calibration. Because reflectance is modeled empirically, no prior physical model of the reflectance characteristics of the objects to be analyzed is assumed. At the same time, if a good physical model is available, it can be retrofit to the method for implementation purposes. Photometric stereo is subject to error in the presence of cast shadows and interreflection. No purely local technique can succeed since these phenomena are inherently non-local. Nevertheless, this paper demonstrates that one can exploit the redundancy in three light source photometric stereo to, in most cases, locally detect the presence of cast shadows and interreflection. Detection is facilitated by explicitly including a local confidence estimate in the lookup table used for gradient estimation.


title: Anatomisation with slicing: a new privacy preservation approach for multiple sensitive attributes

text: An enormous quantity of personal health information is available in recent decades and tampering of any part of this information imposes a great risk to the health care field. Existing anonymization methods are only apt for single sensitive and low dimensional data to keep up with privacy specifically like generalization and bucketization. In this paper, an anonymization technique is proposed that is a combination of the benefits of anatomization, and enhanced slicing approach adhering to the principle of k-anonymity and l-diversity for the purpose of dealing with high dimensional data along with multiple sensitive data. The anatomization approach dissociates the correlation observed between the quasi identifier attributes and sensitive attributes (SA) and yields two separate tables with non-overlapping attributes. In the enhanced slicing algorithm, vertical partitioning does the grouping of the correlated SA in ST together and thereby minimizes the dimensionality by employing the advanced clustering algorithm. In order to get the optimal size of buckets, tuple partitioning is conducted by MFA. The experimental outcomes indicate that the proposed method can preserve privacy of data with numerous SA. The anatomization approach minimizes the loss of information and slicing algorithm helps in the preservation of correlation and utility which in turn results in reducing the data dimensionality and information loss. The advanced clustering algorithms prove its efficiency by minimizing the time and complexity. Furthermore, this work sticks to the principle of k-anonymity, l-diversity and thus avoids privacy threats like membership, identity and attributes disclosure.


title: Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation

text: Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerical feature vectors in a latent space such that the distance between two semantically-related entities is minimized. Compared with the state-ofthe-art relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains 19.4% and 24.5% reductions in entity disambiguation errors on two publicly available datasets respectively.


title: Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century.

text: Dengue fever/dengue hemorrhagic fever is now one of the most important public health problems in tropical developing countries and also has major economic and societal consequences.


title: AN AUTOMATIC SYSTEM FOR DETECTING AND COUNTING RBC AND WBC USING FUZZY LOGIC

text: Blood cell detection and counting is the initial process for detecting and diagnosing diseases. Several image processing algorithms are there for the blood cell classification and counting. The processed image helps to detect different blood related diseases. In all those algorithms several pre-processing steps are there for the process of detection and counting. Though all the algorithms give accurate results, the pre-processing steps are complex and time-consuming. This paper discusses about the RBC and WBC detection using fuzzy logic. Fuzzy logic toolbox software in MATLAB is used to develop the model on virtual platform.


title: Evoked-potential correlates of stimulus uncertainty.

text: The average evoked-potential waveforms to sound and light stimuli recorded from scalp in awake human subjects show differences as a function of the subject's degree of uncertainty with respect to the sensory modality of the stimulus to be presented. Differences are also found in the evoked potential as a function of whether or not the sensorymodality of the stimulus was anticipated correctly. The major waveform alteration is in the amplitude of a positive-going component which reaches peak amplitude at about 300 milliseconds.


title: Development of DC to Single-Phase AC Voltage Source Inverter With Active Power Decoupling Based on Flying Capacitor DC/DC Converter

text: In the present, a power decoupling method without additional component is proposed for a dc to single-phase ac converter, which consists of a flying capacitor dc/dc converter (FCC) and the voltage source inverter (VSI). In particular, a small flying capacitor in the FCC is used for both a boost operation and a double-line-frequency power ripple reduction. Thus, the dc-link capacitor value can be minimized in order to avoid the use of a large electrolytic capacitor. In addition, component design, of, e.g., the boost inductor and the flying capacitor, is clarified when the proposed control is applied. Experiments were carried out using a 1.5-kW prototype in order to verify the validity of the proposed control. The experimental results revealed that the use of the proposed control reduced the dc-link voltage ripple by 74.5%, and the total harmonic distortion (THD) of the inverter output current was less than 5%. Moreover, a maximum system efficiency of 95.4% was achieved at a load of 1.1 kW. Finally, the high power density design is evaluated by the Pareto front optimization. The power densities of three power decoupling topologies, such as a boost topology, a buck topology, and the proposed topology are compared. As a result, the proposed topology achieves the highest power density (5.3 kW/dm3) among the topologies considered herein.


title: Efficient Exploration in Monte Carlo Tree Search using Human Action Abstractions

text: Monte Carlo Tree Search (MCTS) is a family of methods for planning in large domains. It focuses on finding a good action for a particular state, making its complexity independent of the size of the state space. However such methods are exponential with respect to the branching factor. Effective application of MCTS requires good heuristics to arbitrate action selection during learning. In this paper we present a policy-guided approach that utilizes action abstractions, derived from human input, with MCTS to facilitate efficient exploration. We draw from existing work in hierarchical reinforcement learning, interactive machine learning and show how multi-step actions, represented as stochastic policies, can serve as good action selection heuristics. We demonstrate the efficacy of our approach in the PacMan domain and highlight its advantages over traditional MCTS.


title: A Survey of Music Recommendation Systems and Future Perspectives

text: Along with the rapid expansion of digital music formats, managing and searching for songs has become significant. Though music information retrieval (MIR) techniques have been made successfully in last ten years, the development of music recommender systems is still at a very early stage. Therefore, this paper surveys a general framework and state-of-art approaches in recommending music. Two popular algorithms: collaborative filtering (CF) and content-based model (CBM), have been found to perform well. Due to the relatively poor experience in finding songs in long tail and the powerful emotional meanings in music, two user-centric approaches: context-based model and emotion-based model, have been paid increasing attention. In this paper, three key components in music recommender user modelling, item profiling, and match algorithms are discussed. Six recommendation models and four potential issues towards user experience, are explained. However, subjective music recommendation system has not been fully investigated. To this end, we propose a motivation-based model using the empirical studies of human behaviour, sports education, music psychology.


title: Image super-resolution using gradient profile prior

text: In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.


title: DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

text: We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.1


title: Adversarial Geometry and Lighting using a Differentiable Renderer

text: Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according to a measurement norm. This evaluation, however, has limited practical utility since perturbations in the pixel space do not correspond to underlying real-world phenomena of image formation that lead to them and has no security motivation attached. Pixels in natural images are measurements of light that has interacted with the geometry of a physical scene. As such, we propose a novel evaluation measure, parametric normballs, by directly perturbing physical parameters that underly image formation. One enabling contribution we present is a physically-based differentiable renderer that allows us to propagate pixel gradients to the parametric space of lighting and geometry. Our approach enables physically-based adversarial attacks, and our differentiable renderer leverages models from the interactive rendering literature to balance the performance and accuracy trade-offs necessary for a memory-efficient and scalable adversarial data augmentation workflow.


title: A framework for the design and integration of collaborative classroom games

text: The progress registered in the use of video games as educational tools has not yet been successfully transferred to the classroom. In an attempt to close this gap, a framework was developed that assists in the design and classroom integration of educational games. The framework addresses both the educational dimension and the ludic dimension. The educational dimension employs Bloom’s revised taxonomy to define learning objectives and applies the classroom multiplayer presential game (CMPG) pedagogical model while the ludic dimension determines the gaming elements subject to constraints imposed by the educational dimension. With a view to validating the framework, a game for teaching electrostatics was designed and experimentally implemented in a classroom context. An evaluation based on pre/post testing found that the game increased the average number of correct answers by students participating in the experiment from 6.11 to 10.00, a result found to be statistically significant. Thus validated, the framework offers a promising basis for further exploration through the development of other games and fine-tuning of its components. 2011 Elsevier Ltd. All rights reserved.


title: Effects of growth mode and pyruvate carboxylase on succinic acid production by metabolically engineered strains of Escherichia coli.

text: Escherichia coli NZN111, which lacks activities for pyruvate-formate lyase and lactate dehydrogenase, and AFP111, a derivative which contains an additional mutation in ptsG (a gene encoding an enzyme of the glucose phophotransferase system), accumulate significant levels of succinic acid (succinate) under anaerobic conditions. Plasmid pTrc99A-pyc, which expresses the Rhizobium etli pyruvate carboxylase enzyme, was introduced into both strains. We compared growth, substrate consumption, product formation, and activities of seven key enzymes (acetate kinase, fumarate reductase, glucokinase, isocitrate dehydrogenase, isocitrate lyase, phosphoenolpyruvate carboxylase, and pyruvate carboxylase) from glucose for NZN111, NZN111/pTrc99A-pyc, AFP111, and AFP111/pTrc99A-pyc under both exclusively anaerobic and dual-phase conditions (an aerobic growth phase followed by an anaerobic production phase). The highest succinate mass yield was attained with AFP111/pTrc99A-pyc under dual-phase conditions with low pyruvate carboxylase activity. Dual-phase conditions led to significant isocitrate lyase activity in both NZN111 and AFP111, while under exclusively anaerobic conditions, an absence of isocitrate lyase activity resulted in significant pyruvate accumulation. Enzyme assays indicated that under dual-phase conditions, carbon flows not only through the reductive arm of the tricarboxylic acid cycle for succinate generation but also through the glyoxylate shunt and thus provides the cells with metabolic flexibility in the formation of succinate. Significant glucokinase activity in AFP111 compared to NZN111 similarly permits increased metabolic flexibility of AFP111. The differences between the strains and the benefit of pyruvate carboxylase under both exclusively anaerobic and dual-phase conditions are discussed in light of the cellular constraint for a redox balance.


title: Cloning Cryptographic RFID

text: We develop a new, custom-built hardware for emulating contactless smartcards compliant to ISO 14443. The device is based on a modern low-cost microcontroller and can support basically all relevant (cryptographic) protocols used by contactless smartcards today, e.g., those based on AES or Triple-DES. As a proof of concept, we present a full emulation of Mifare Classic cards on the basis of our highly optimized implementation of the stream cipher Crypto1. The implementation enables the creation of exact clones of such cards, including the UID. We furthermore reverse-engineered the protocol of DESFire EV1 and realize the first emulation of DESFire and DESFire EV1 cards in the literature. We practically demonstrate the capabilities of our emulator by spoofing several real-world systems, e.g., creating a contactless payment card which allows an attacker to set the stored credit balance as desired and hence make an infinite amount of payments.


title: Spoken Language Recognition: From Fundamentals to Practice

text: Spoken language recognition refers to the automatic process through which we determine or verify the identity of the language spoken in a speech sample. We study a computational framework that allows such a decision to be made in a quantitative manner. In recent decades, we have made tremendous progress in spoken language recognition, which benefited from technological breakthroughs in related areas, such as signal processing, pattern recognition, cognitive science, and machine learning. In this paper, we attempt to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions, from both phonological and computational aspects. We also give a comprehensive review of current trends and future research directions using the language recognition evaluation (LRE) formulated by the National Institute of Standards and Technology (NIST) as the case studies.


title: Entailment above the word level in distributional semantics

text: We introduce two ways to detect entailment using distributional semantic representations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns (big cat |= cat), once represented as semantic vector pairs, generalizes to lexical entailment among nouns (dog |= animal). Our second experiment shows that a classifier fed semantic vector pairs can similarly generalize the entailment relation among quantifier phrases (many dogs|=some dogs) to entailment involving unseen quantifiers (all cats|=several cats). Moreover, nominal and quantifier phrase entailment appears to be cued by different distributional correlates, as predicted by the type-based view of entailment in formal semantics.


title: Do Supervised Distributional Methods Really Learn Lexical Inference Relations?

text: Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations between word pairs, such as hypernymy and entailment. We investigate a collection of these state-of-the-art methods, and show that they do not actually learn a relation between two words. Instead, they learn an independent property of a single word in the pair: whether that word is a “prototypical hypernym”.


title: Hierarchical Clustering Algorithms for Document Datasets

text: Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularity. This paper focuses on document clustering algorithms that build such hierarchical solutions and (i) presents a comprehensive study of partitional and agglomerative algorithms that use different criterion functions and merging schemes, and (ii) presents a new class of clustering algorithms called constrained agglomerative algorithms, which combine features from both partitional and agglomerative approaches that allows them to reduce the early-stage errors made by agglomerative methods and hence improve the quality of clustering solutions. The experimental evaluation shows that, contrary to the common belief, partitional algorithms always lead to better solutions than agglomerative algorithms; making them ideal for clustering large document collections due to not only their relatively low computational requirements, but also higher clustering quality. Furthermore, the constrained agglomerative methods consistently lead to better solutions than agglomerative methods alone and for many cases they outperform partitional methods, as well.


title: Exploring Various Knowledge in Relation Extraction

text: Extracting semantic relationships between entities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.


title: Learning concepts and categories: is spacing the "enemy of induction"?

text: Inductive learning -- that is, learning a new concept or category by observing exemplars -- happens constantly, for example, when a baby learns a new word or a doctor classifies x-rays. What influence does the spacing of exemplars have on induction? Compared with massing, spacing enhances long-term recall, but we expected spacing to hamper induction by making the commonalities that define a concept or category less apparent. We asked participants to study multiple paintings by different artists, with a given artist's paintings presented consecutively (massed) or interleaved with other artists' paintings (spaced). We then tested induction by asking participants to indicate which studied artist (Experiments 1a and 1b) or whether any studied artist (Experiment 2) painted each of a series of new paintings. Surprisingly, induction profited from spacing, even though massing apparently created a sense of fluent learning: Participants rated massing as more effective than spacing, even after their own test performance had demonstrated the opposite.


title: Using Lexical Resources for Irony and Sarcasm Classification

text: The paper presents a language dependent model for classification of statements into ironic and non-ironic. The model uses various language resources: morphological dictionaries, sentiment lexicon, lexicon of markers and a WordNet based ontology. This approach uses various features: antonymous pairs obtained using the reasoning rules over the Serbian WordNet ontology (R), antonymous pairs in which one member has positive sentiment polarity (PPR), polarity of positive sentiment words (PSP), ordered sequence of sentiment tags (OSA), Part-of-Speech tags of words (POS) and irony markers (M). The evaluation was performed on two collections of tweets that had been manually annotated according to irony. These collections of tweets as well as the used language resources are in the Serbian language (or one of closely related languages --Bosnian/Croatian/Montenegrin). The best accuracy of the developed classifier was achieved for irony with a set of 5 features -- (PPR, PSP, POS, OSA, M) -- acc = 86.1%, while for sarcasm the best results were achieved with the set (R, PSP, POS, OSA, M) -- acc = 72.8.


title: Robust Non-Rigid Motion Tracking and Surface Reconstruction Using $L_0$ Regularization

text: We present a new motion tracking technique to robustly reconstruct non-rigid geometries and motions from a single view depth input recorded by a consumer depth sensor. The idea is based on the observation that most non-rigid motions (especially human-related motions) are intrinsically involved in articulate motion subspace. To take this advantage, we propose a novel <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math><alternatives> <inline-graphic xlink:href="xu-ieq2-2688331.gif"/></alternatives></inline-formula> based motion regularizer with an iterative solver that implicitly constrains local deformations with articulate structures, leading to reduced solution space and physical plausible deformations. The <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math> <alternatives><inline-graphic xlink:href="xu-ieq3-2688331.gif"/></alternatives></inline-formula> strategy is integrated into the available non-rigid motion tracking pipeline, and gradually extracts articulate joints information online with the tracking, which corrects the tracking errors in the results. The information of the articulate joints is used in the following tracking procedure to further improve the tracking accuracy and prevent tracking failures. Extensive experiments over complex human body motions with occlusions, facial and hand motions demonstrate that our approach substantially improves the robustness and accuracy in motion tracking.


title: Grid interfaced solar PV based water pumping using brushless DC motor drive

text: This paper proposes a solar photovoltaic (PV) fed water pumping system driven by a brushless DC (BLDC) motor. A promising case of interruption in the water pumping due to the intermittency of PV power generation is resolved by using a power grid as an external power backup. The power is drawn from the grid in case the PV array is unable to meet the required power demand, otherwise the PV array is preferably used. A unidirectional power flow control for the same is developed and realized through a power factor corrected (PFC) boost converter. The proposed system and control enable a consumer to get a full volume of water delivery throughout the day and night regardless of the climatic condition. The utilization of motor-pump at best is thus made possible in addition to an enhanced reliability of the pumping system. The power quality standards required at the utility grid are met by the developed control. The MATLAB/Simulink based simulations and the performance analysis are carried out to demonstrate the applicability of the system.


title: Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features

text: Viewpoint invariant pedestrian recognition is an important yet under-addressed problem in computer vision. This is likely due to the difficulty in matching two objects with unknown viewpoint and pose. This paper presents a method of performing viewpoint invariant pedestrian recognition using an efficiently and intelligently designed object representation, the ensemble of localized features (ELF). Instead of designing a specific feature by hand to solve the problem, we define a feature space using our intuition about the problem and let a machine learning algorithm find the best representation. We show how both an object class specific representation and a discriminative recognition model can be learned using the AdaBoost algorithm. This approach allows many different kinds of simple features to be combined into a single similarity function. The method is evaluated using a viewpoint invariant pedestrian recognition dataset and the results are shown to be superior to all previous benchmarks for both recognition and reacquisition of pedestrians.


title: OpenCV on TI ’ s DSP + ARM ® platforms : Mitigating the challenges of porting OpenCV to embedded platforms Introduction

text: In today’s advancing market, the growing performance and decreasing price of embedded processors are opening many doors for developers to design highly sophisticated solutions for different end applications. The complexities of these systems can create bottlenecks for developers in the form of longer development times, more complicated development environments and issues with application stability and quality. Developers can address these problems using sophisticated software packages such as OpenCV, but migrating this software to embedded platforms poses its own set of challenges. This paper will review how to mitigate some of these issues, including C++ implementation, memory constraints, floating-point support and opportunities to maximize performance using vendor-optimized libraries and integrated accelerators or co-processors. Finally, we will introduce a new effort by Texas Instruments (TI) to optimize vision systems by running OpenCV on the C6000TM digital signal processor (DSP) architecture. Benchmarks will show the advantage of using the DSP by comparing the performance of a DSP+ARM® system-on-chip (SoC) processor against an ARM-only device. OpenCV on TI’s DSP+ARM platforms: Mitigating the challenges of porting OpenCV to embedded platforms Introduction OpenCV is a free and open-source computer vision library that offers a broad range of functionality under the permissive Berkeley Software Distribution (BSD) license. The library itself is written in C++ and is also usable through C or Python language applications. Thousands of developers use OpenCV to power their own specialized applications, making it the most widely-used library of its kind. The OpenCV project is under active development, with regular updates to eliminate bugs and add new functionality. The mainline development effort targets the x86 architecture and supports acceleration via Intel’s proprietary Integrated Performance Primitives (IPP) library. A recent release also added support for graphics processing unit (GPU) acceleration using NVIDIA’s Compute Unifi ed Device Architecture (CUDA) standard. OpenCV’s greatest asset is the sheer breadth of algorithms included in its standard distribution. Figure 1 on page 2 shows an incomplete list of some of the key function categories included in OpenCV. These range from low-level image fi ltering and transformation to sophisticated feature analysis and machine learning functionality. A complete listing of every function and use case is beyond the scope of this article, but we will consider the unique requirements of developers in the embedded vision space. For these developers, OpenCV represents an attractively comprehensive toolbox of useful, well-tested algorithms that can serve as building blocks for their own specialized applications. The question then becomes whether or not OpenCV can be used directly in their embedded systems. Despite its original development focus for use with PC workstations, OpenCV can also be a useful tool for embedded development. There are vendor-specifi c libraries that offer OpenCV-like capabilities on various embedded systems, but few can match OpenCV’s ubiquity in the computer vision fi eld or the sheer breadth of its included algorithms. It should come as no surprise that OpenCV has already been ported to the ARM® architecture, a popular CPU choice for embedded processors. It’s certainly possible to cross-compile the OpenCV source code as-is and use the result with embedded devices, but memory constraints and other architectural considerations may pose a problem. This white paper will examine some of the specifi c obstacles that must be overcome for OpenCV to achieve acceptable performance on Joseph Coombs, Applications engineering, Texas Instruments Rahul Prabhu, Applications engineering, Texas Instruments W H I T E P A P E R


title: Model-based control of a 6-dof electrohydraulic Stewart – Gough platform

text: In this paper, a novel model-based controller for a six Degree-of-Freedom (dof) electrohydraulic Stewart–Gough platform is developed. Dynamic models of low complexity are employed that describe the salient dynamics of the main electrohydraulic components. Rigid body equations of motion and hydraulics dynamics, including friction and servovalve models are used. The developed feedback controller uses the system dynamic and hydraulic model to yield servovalve currents, so that the error dynamics converge asymptotically to zero, independent of load variations. In this approach, force, pressure or acceleration feedback is not required. Simulations with typical desired trajectory inputs are presented and a good performance of the controller is obtained. The proposed methodology can be extended to electrohydraulic serial or closed-chain manipulators and simulators. 2007 Elsevier Ltd. All rights reserved.


title: Model Selection Management Systems: The Next Frontier of Advanced Analytics

text:
John Boyd recognized in the 1960's the importance of situation awareness for military operations and introduced the notion of the OODA loop (Observe, Orient, Decide, and Act). Today we realize that many applications have to deal with situation awareness: Customer Relationship Management, Human Capital Management, Supply Chain Management, patient care, power grid management, and cloud services management, as well as any IoT (Internet of Things) related application; the list seems to be endless. Situation awareness requires applications to support the management of data, knowledge, processes, and other services such as social networking in an integrated way. These applications additionally require high personalization as well as rapid and continuous evolution. They must provide a wide variety of operational and functional requirements, including real time processing.
 Handcrafting these applications is an almost impossible task requiring exhaustive resources for development and maintenance. Due to the resources and time involved in their development, these applications typically fall way short of the desired functionality, operational characteristics, and ease and speed of evolution. We - the authors - have developed a model enabling the development and maintenance of situation-aware applications in a declarative and therefore economical manner; we call this model KIDS - Knowledge Intensive Data-processing System.


title: Training Sources 3 D Normalized Pose Space 2 D Normalized Pose Space KD-Tree Input Image 2 D Pose Estimation 3 D Pose Reconstruction

text: In this work we address the challenging problem of 3D human pose estimation from single images. Recent approaches learn deep neural networks to regress 3D pose directly from images. One major challenge for such methods, however, is the collection of training data. Specifically, collecting large amounts of training data containing unconstrained images annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of accurate 3D motion capture data, and the second source consists of unconstrained images with annotated 2D poses. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient 3D pose retrieval. To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data. During inference, we estimate the 2D pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a mapping from the 3D pose space to the image and reconstruct the 3D pose. We provide a comprehensive evaluation of the proposed method and experimentally demonstrate the effectiveness of our approach, even when the skeleton structures of the two sources differ substantially.


title: Accurate and dense wide-baseline stereo matching using SW-POC

text: This paper proposes an accurate and dense wide-baseline stereo matching method using Scaled Window Phase-Only Correlation (SW-POC). The wide-baseline setting of the stereo camera can improve the accuracy of the 3D reconstruction compared with the short-baseline setting. However, it is difficult to find accurate and dense correspondence from wide-baseline stereo images due to its large perspective distortion. Addressing this problem, we employ the SW-POC, which is a correspondence matching method using 1D POC with the concept of Scale Window Matching (SWM). The use of SW-POC makes it possible to find the accurate and dense correspondence from a wide-baseline stereo image pair with low computational cost. We also apply the proposed method to 3D reconstruction using a moving and uncalibrated consumer digital camera.


title: Assessment of chronic pain coping strategies

text: Introduction. We made an adaptation of the Coping Strategies Questionnaire (CSQ) to the Spanish population. This measure, the most used in its scope, was developed by Rosenstiel and Keefe in 1983. Method. 205 participants coming from Primary Health Care and pain clinics made up the sample. More than half suffered migraine and chronic tension-type headache; the rest, fibromyalgia, low back pain, arthrosis or arthritis. Results. Factor analyses explained 59% of the total variance, on an 8-factor structure that converged into a 2-factor structure. In the 8-factor solution the novelty was the diversification of mental-non-mental distraction strategies, and religious-non-religious hope strategies. In the 2-factor solution the novelty was the grouping according to the efficacy of the coping. All the CSQ factors showed inner consistency and construct validity. Thus, unadaptive coping strategies were related to negative, anxious and depressed self-talk, related to lack of control and perceived self-efficacy, and related to many pain behaviors. On the contrary it happened with adaptive coping strategies. In addition, the diagnosis of pain was related to the utilization and effectiveness of coping strategies. Conclussions. CSQ is shown to be a reliable and valid measure of coping strategies in chronic pain in the Spanish population, showing the difference between theoretical and empirical factor structures again.


title: Heart rate variability is associated with emotion recognition: direct evidence for a relationship between the autonomic nervous system and social cognition.

text: It is well established that heart rate variability (HRV) plays an important role in social communication. Polyvagal theory suggests that HRV may provide a sensitive marker of one's ability to respond and recognize social cues. The aim of the present study was to directly test this hypothesis. Resting-state HRV was collected and performance on the Reading the Mind in the Eyes Test was assessed in 65 volunteers. HRV was positively associated with performance on this emotion recognition task confirming our hypothesis and these findings were retained after controlling for a variety of confounding variables known to influence HRV - sex, BMI, smoking habits, physical activity levels, depression, anxiety, and stress. Our data suggests that increased HRV may provide a novel marker of one's ability to recognize emotions in humans. Implications for understanding the biological basis of emotion recognition, and social impairment in humans are discussed.


title: Medusa: a programming framework for crowd-sensing applications

text: The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.


title: The Cultural Evolution of Religion

text: After almost a century of dwelling in two “non-overlapping magisteria,” as Steven Jay Gould once put it, religion and science are coming together again. Long the exclusive province of the humanities and left outside of the mainstream of psychology and the behavioural sciences, religion is gaining scientific attention at a rapid pace. The dismantling of the taboos that have kept religion out of the scientific spotlight will take time (Dennett, 2006). Nevertheless, these are exciting times, and we can now safely say that religion—to paraphrase Chomsky about language—has been upgraded from scientific mystery to scientific puzzle (Boyer, 2001). This growing scientific interest promises to offer a naturalistic account for a deeply affecting aspect of human lives that is widespread in all known cultures in the world.


title: Probabilistic routing in intermittently connected networks

text: We consider the problem of routing in intermittently connected networks. In such networks there is no guarantee that a fully connected path between source and destination exist at any time, rendering traditional routing protocols unable to deliver messages between hosts. We propose a probabilistic routing protocol for such networks.


title: DIAC+: a Professional Diacritics Recovering System

text: In languages that use diacritical characters, if these special signs are stripped-off from a word, the resulted string of characters may not exist in the language, and therefore its normative form is, in general, easy to recover. However, this is not always the case, as presence or absence of a diacritical sign attached to a base letter of a word which exists in both variants, may change its grammatical properties or even the meaning, making the recovery of the missing diacritics a difficult task, not only for a program but sometimes even for a human reader. We describe and evaluate an accurate knowledge-based system for automatic recovering the missing diacritics in MSOffice documents written in Romanian. For the rare cases when the system is not able to reliably make a decision, it either provides the user a list of words with their recovery suggestions, or probabilistically choose one of the possible changes, but leaves a trace (a highlighted comment) on each word the modification of which was uncertain.


title: A Performance Comparison of Open-Source Stream Processing Platforms

text: Distributed stream processing platforms is a new class of real-time monitoring systems that analyze and extracts knowledge from large continuous streams of data. This type of systems is crucial for providing high throughput and low latency required by Big Data or Internet of Things monitoring applications. This paper describes and analyzes three main open-source distributed stream- processing platforms: Storm Flink, and Spark Streaming. We analyze the system architectures and we compare their main features. We carry out two experiments concerning anomaly detection on network traffic to evaluate the throughput efficiency and the resilience to node failures. Results show that the performance of native stream processing systems, Storm and Flink, is up to 15 times higher than the micro-batch processing system, Spark Streaming. On the other hand, Spark Streaming is more robust to node failures and provides recovery without losses.


Challenges during the transition to Agile Methodologies : A Holistic Overview


title: An Introduction to Active Shape Models ∗

text: Biomedical images usually contain complex objects, which will vary in appearance significantly from one image to another. Attempting to measure or detect the presence of particular structures in such images can be a daunting task. The inherent variability will thwart naive schemes. However, by using models which can cope with the variability it is possible to successfully analyse complex images. Here we will consider a number of methods where the model represents the expected shape and local grey-level structure of a target object in an image. Model-based methods make use of a prior model of what is expected in the image, and typically attempt to find the best match of the model to the data in a new image. Having matched the model, one can then make measurements or test whether the target is actually present. This approach is a ‘top-down’ strategy, and differs significantly from ‘bottomup’ methods. In the latter the image data is examined at a low level, looking for local structures such as edges or regions, which are assembled into groups in an attempt to identify objects of interest. Without a global model of what to expect, this approach is difficult and prone to failure. A wide variety of model based approaches have been explored (see the review below). This chapter will concentrate on a statistical approach, in which a model is built from analysing the appearance of a set of labelled examples. Where structures vary in shape or texture, it is possible to learn what are plausible variations and what are not. A new image can be interpreted by finding the best plausible match of the model to the image data. The advantages of such a method are that


title: Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech

text: Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tremendous advancement of the smartphone technology, it can be a powerful medium for speech emotion recognition in the outside laboratory natural environment, which is likely to incorporate background noise in the speech. We capitalize on the current breakthrough of Recurrent Neural Network (RNN) and seek to investigate its performance for emotion classification from noisy speech. We particularly focus on the recently proposed Gated Recurrent Unit (GRU), which is yet to be explored for emotion recognition from speech. Experiments conducted with speech compounded with eight different types of noises reveal that GRU incurs an 18.16% smaller run-time while performing quite comparably to the Long Short-Term Memory (LSTM), which is the most popular Recurrent Neural Network proposed to date. This result is promising for any embedded platform in general and will initiate further studies to utilize GRU to its full potential for emotion recognition on smartphones.


title: Class-Conditional Superresolution with GANs

text: ● Super resolution enhances the resolution of a low-res image. ● Many deep learning models today work fairly well with an upscaling factor of 4x but use only the downscaled image as input. ● We demonstrate two class-conditional GAN models that outperform state-of-the-art GANs for superresolution: ○ Explicitly passes in a known class parameter as input ○ Uses a separately trained classifier to inform a ResNet about character attributes ● Both improve the quality of the super-resolved output.


title: Learning Structured Output Representation using Deep Conditional Generative Models

text: Supervised deep learning has been successfully applied to many recognition problems. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional generative model for structured output prediction using Gaussian latent variables. The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies to build robust structured prediction algorithms, such as input noise-injection and multi-scale prediction objective at training. In experiments, we demonstrate the effectiveness of our proposed algorithm in comparison to the deterministic deep neural network counterparts in generating diverse but realistic structured output predictions using stochastic inference. Furthermore, the proposed training methods are complimentary, which leads to strong pixel-level object segmentation and semantic labeling performance on Caltech-UCSD Birds 200 and the subset of Labeled Faces in the Wild dataset.


title: Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition

text:
Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk.
 In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.


title: Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition

text: In this paper we show that misclassification attacks against face-recognition systems based on deep neural networks (DNNs) are more dangerous than previously demonstrated, even in contexts where the adversary can manipulate only her physical appearance (versus directly manipulating the image input to the DNN). Specifically, we show how to create eyeglasses that, when worn, can succeed in targeted (impersonation) or untargeted (dodging) attacks while improving on previous work in one or more of three facets: (i) inconspicuousness to onlooking observers, which we test through a user study; (ii) robustness of the attack against proposed defenses; and (iii) scalability in the sense of decoupling eyeglass creation from the subject who will wear them, i.e., by creating “universal” sets of eyeglasses that facilitate misclassification. Central to these improvements are adversarial generative nets, a method we propose to generate physically realizable attack artifacts (here, eyeglasses) automatically.


title: Learning with a Strong Adversary

text: The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, learning with a strong adversary, that learns robust classifiers from supervised data by generating adversarial examples as an intermediate step. A new and simple way of finding adversarial examples is presented that is empirically stronger than existing approaches in terms of the accuracy reduction as a function of perturbation magnitude. Experimental results demonstrate that resulting learning method greatly improves the robustness of the classification models produced.


title: Deep learning from temporal coherence in video

text: This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.


title: Software Re-Modularization Based on Structural and Semantic Metrics

text: The structure of a software system has a major impact on its maintainability. To improve maintainability, software systems are usually organized into subsystems using the constructs of packages or modules. However, during software evolution the structure of the system undergoes continuous modifications, drifting away from its original design, often reducing its quality. In this paper we propose an approach for helping maintainers to improve the quality of software modularization. The proposed approach analyzes the (structural and semantic) relationships between classes in a package identifying chains of strongly related classes. The identified chains are used to define new packages with higher cohesion than the original package. The proposed approach has been empirical evaluated through a case study. The context of the study is represented by an open source system, JHotDraw, and two software systems developed by teams of students at the University of Salerno. The analysis of the results reveals that the proposed approach generates meaningful re-modularization of the studied systems, which can lead to higher quality.


title: Towards a Welsh Semantic Annotation System

text: Automatic semantic annotation of natural language data is an important task in Natural Language Processing, and a variety of semantic taggers have been developed for this task, particularly for English. However, for many languages, particularly for low-resource languages, such tools are yet to be developed. In this paper, we report on the development of an automatic Welsh semantic annotation tool (named CySemTagger) in the CorCenCC Project, which will facilitate semantic-level analysis of Welsh language data on a large scale. Based on Lancaster’s USAS semantic tagger framework, this tool tags words in Welsh texts with semantic tags from a semantic classification scheme, and is designed to be compatible with multiple Welsh POS taggers and POS tagsets by mapping different tagsets into a core shared POS tagset that is used internally by CySemTagger. Our initial evaluation shows that the tagger can cover up to 91.78% of words in Welsh text. This tagger is under continuous development, and will provide a critical tool for Welsh language corpus and information processing at semantic level.


title: Two (Note) Heads Are Better Than One: Pen-Based Multimodal Interaction with Music Scores

text: Digitizing early music sources requires new ways of dealing with musical documents. Assuming that current technologies cannot guarantee a perfect automatic transcription, our intention is to develop an interactive system in which user and software collaborate to complete the task. Since conventional score post-editing might be tedious, the user is allowed to interact using an electronic pen. Although this provides a more ergonomic interface, this interaction must be decoded as well. In our framework, the user traces the symbols using the electronic pen over a digital surface, which provides both the underlying image (offline data) and the drawing made by the e-pen (online data) to improve classification. Applying this methodology over 70 scores of the target musical archive, a dataset of 10 230 bimodal samples of 30 different symbols was obtained and made available for research purposes. This paper presents experimental results on classification over this dataset, in which symbols are recognized by combining the two modalities. This combination of modes has demonstrated its good performance, decreasing the error rate of using each modality separately and achieving an almost error-free performance.


title: Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses

text: A simulation procedure is described for making feasible large-scale simulations of recurrent neural networks of spiking neurons and plastic synapses. The procedure is applicable if the dynamic variables of both neurons and synapses evolve deterministically between any two successive spikes. Spikes introduce jumps in these variables, and since spike trains are typically noisy, spikes introduce stochasticity into both dynamics. Since all events in the simulation are guided by the arrival of spikes, at neurons or synapses, we name this procedure event-driven. The procedure is described in detail, and its logic and performance are compared with conventional (synchronous) simulations. The main impact of the new approach is a drastic reduction of the computational load incurred upon introduction of dynamic synaptic efficacies, which vary organically as a function of the activities of the pre- and postsynaptic neurons. In fact, the computational load per neuron in the presence of the synaptic dynamics grows linearly with the number of neurons and is only about 6 more than the load with fixed synapses. Even the latter is handled quite efficiently by the algorithm. We illustrate the operation of the algorithm in a specific case with integrate-and-fire neurons and specific spike-driven synaptic dynamics. Both dynamical elements have been found to be naturally implementable in VLSI. This network is simulated to show the effects on the synaptic structure of the presentation of stimuli, as well as the stability of the generated matrix to the neural activity it induces.


title: The Human Brain in Numbers: A Linearly Scaled-up Primate Brain

text: The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10x more glial cells. Such uniqueness was seemingly necessary to justify the superior cognitive abilities of humans over larger-brained mammals such as elephants and whales. However, our recent studies using a novel method to determine the cellular composition of the brain of humans and other primates as well as of rodents and insectivores show that, since different cellular scaling rules apply to the brains within these orders, brain size can no longer be considered a proxy for the number of neurons in the brain. These studies also showed that the human brain is not exceptional in its cellular composition, as it was found to contain as many neuronal and non-neuronal cells as would be expected of a primate brain of its size. Additionally, the so-called overdeveloped human cerebral cortex holds only 19% of all brain neurons, a fraction that is similar to that found in other mammals. In what regards absolute numbers of neurons, however, the human brain does have two advantages compared to other mammalian brains: compared to rodents, and probably to whales and elephants as well, it is built according to the very economical, space-saving scaling rules that apply to other primates; and, among economically built primate brains, it is the largest, hence containing the most neurons. These findings argue in favor of a view of cognitive abilities that is centered on absolute numbers of neurons, rather than on body size or encephalization, and call for a re-examination of several concepts related to the exceptionality of the human brain.


title: Detecting Review Spammer Groups via Bipartite Graph Projection

text: Online product reviews play an important role in E-commerce websites because most customers read and rely on them when making purchases. For the sake of profit or reputation, review spammers deliberately write fake reviews to promote or demote target products, some even fraudulently work in groups to try and control the sentiment about a product. To detect such spammer groups, previous work exploits frequent itemset mining (FIM) to generate candidate spammer groups, which can only find tightly coupled groups, i.e. each reviewer in the group reviews every target product. In this paper, we present the loose spammer group detection problem, i.e. each group member is not required to review every target product. We solve this problem using bipartite graph projection. We propose a set of group spam indicators to measure the spamicity of a loose spammer group, and design a novel algorithm to identify highly suspicious loose spammer groups in a divide and conquer manner. Experimental results show that our method not only can find loose spammer groups with high precision and recall, but also can generate more meaningful candidate spammer groups than FIM, thus it can also be used as an alternative preprocessing tool for existing FIM-based approaches.


title: WAIS-IV visual puzzles in a mixed clinical sample.

text: Little is known about which cognitive functions underlie the new Visual Puzzles subtest of the Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV). The purpose of this study was to investigate relationships between Visual Puzzles and common neuropsychological measures in a mixed clinical sample. A total of 44 veterans (75% men) were administered the WAIS-IV as part of a neuropsychological evaluation. Average age was 47.4 years (SD = 11.8), and average education was 13.8 years (SD = 2.3). Correlations were conducted to examine relationships between Visual Puzzles, demographic variables, and neuropsychological measures. Hierarchical regression analysis was used to determine which measures contributed the most variance to Visual Puzzles. Visual Puzzles correlated significantly with measures of visuospatial reasoning, verbal learning and recall, mental flexibility, processing speed, and naming, which accounted for 50% of the variance in Visual Puzzles performance. The results indicate that Visual Puzzles is not a pure measure of visuoperceptual reasoning, at least in a mixed clinical sample, because memory, mental flexibility, processing speed, and language abilities also contribute to successful performance of the task. Thus it may be important to consider other aspects of cognitive functioning when interpreting Visual Puzzles performance.


The Great Grevy ’ s Rally : The Need , Methods , Findings , Implications and Next Steps


title: Recent and robust query auto-completion

text: Query auto-completion (QAC) is a common interactive feature that assists users in formulating queries by providing completion suggestions as they type. In order for QAC to minimise the user's cognitive and physical effort, it must: (i) suggest the user's intended query after minimal input keystrokes, and (ii) rank the user's intended query highly in completion suggestions. Typically, QAC approaches rank completion suggestions by their past popularity. Accordingly, QAC is usually very effective for previously seen and consistently popular queries. Users are increasingly turning to search engines to find out about unpredictable emerging and ongoing events and phenomena, often using previously unseen or unpopular queries. Consequently, QAC must be both robust and time-sensitive -- that is, able to sufficiently rank both consistently and recently popular queries in completion suggestions. To address this trade-off, we propose several practical completion suggestion ranking approaches, including: (i) a sliding window of query popularity evidence from the past 2-28 days, (ii) the query popularity distribution in the last N queries observed with a given prefix, and (iii) short-range query popularity prediction based on recently observed trends. Using real-time simulation experiments, we extensively investigated the parameters necessary to maximise QAC effectiveness for three openly available query log datasets with prefixes of 2-5 characters: MSN and AOL (both English), and Sogou 2008 (Chinese). Optimal parameters vary for each query log, capturing the differing temporal dynamics and querying distributions. Results demonstrate consistent and language-independent improvements of up to 9.2% over a non-temporal QAC baseline for all query logs with prefix lengths of 2-3 characters. This work is an important step towards more effective QAC approaches.


title: How Does Disagreement Benefit Co-teaching?

text: Learning with noisy labels is one of the most important question in weakly-supervised learning domain. Classical approaches focus on adding the regularization or estimating the noise transition matrix. However, either a regularization bias is permanently introduced, or the noise transition matrix is hard to be estimated accurately. In this paper, following a novel path to train on small-loss samples, we propose a robust learning paradigm called Co-teaching+. This paradigm naturally bridges “Update by Disagreement” strategy with Co-teaching that trains two deep neural networks, thus consists of disagreement-update step and cross-update step. In disagreement-update step, two networks predicts all data first, and feeds forward prediction disagreement data only. Then, in cross-update step, each network selects its small-loss data from such disagreement data, but back propagates the small-loss data by its peer network and updates itself parameters. Empirical results on noisy versions of MNIST, CIFAR10 and NEWS demonstrate that Co-teaching+ is much superior to the state-of-the-art methods in the robustness of trained deep models.


title: Reinforcement Learning for Humanoid Robotics

text: Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, ‘vanilla’ policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. ‘Vanilla’ policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot [3]. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade [10] is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.


title: Experimental Analysis of a UAV-Based Wireless Power Transfer Localization System

text: Sensors deployed in remote locations provide unprecedented amounts of data, but powering these sensors over long periods remains a challenge. In this paper, we develop and present a UAV-based wireless power transfer system. We discuss design considerations and present our system that allows a UAV to fly to remote locations to charge hard to access sensors. We analyze the impact of different materials on the wireless power transfer system. Since GPS does not provide sufficient accuracy, we develop and experimentally characterize a relative localization algorithm based on sensing the magnetic field of the power transfer system and optical flow that allows the UAV to localize the sensor with an average error of 15 cm to enable the transfer of on average 4.2 W . These results overcome some of the practical challenges associated with wirelessly charging sensors with a UAV and show that UAVs with wireless power transfer systems can greatly extend the life of remotely deployed sensors.


title: Cruciform: Solving Crosswords with Natural Language Processing

text: Crossword puzzles are popular word games that require not only a large vocabulary, but also a broad knowledge of topics. Answering each clue is a natural language task on its own as many clues contain nuances, puns, or counter-intuitive word definitions. Additionally, it can be extremely difficult to ascertain definitive answers without the constraints of the crossword grid itself. This task is challenging for both humans and computers. We describe here a new crossword solving system, Cruciform. We employ a group of natural language components, each of which returns a list of candidate words with scores when given a clue. These lists are used in conjunction with the fill intersections in the puzzle grid to formulate a constraint satisfaction problem, in a manner similar to the one used in the Dr. Fill system[4]. We describe the results of several of our experiments with the system.