OpenRaiser/Intern-Atlas huggingface dataset:
Computational Anatomy in Theano
Venue: arXiv cs.OH 2017
Year: 2017

To model deformation of anatomical shapes, non-linear statistics are required to take into account the non-linear structure of the data space. Computer implementations of non-linear statistics and differential geometry algorithms often lead to long and complex code sequences. The aim of the paper is to show how the Theano framework can be used for simple and concise implementation of complex differential geometry algorithms while being able to handle complex and high-dimensional data structures. We show how the Theano framework meets both of these requirements. The framework provides a symbolic language that allows mathematical equations to be directly translated into Theano code, and it is able to perform both fast CPU and GPU computations on high-dimensional data. We show how different concepts from non-linear statistics and differential geometry can be implemented in Theano, and give examples of the implemented theory visualized on landmark representations of Corpus Callosum shapes.
Generalization of a reduced Trefftz type approach
Venue: arXiv cs.CE 2017
Year: 2017

Summary This work presents variational concepts associated with reduced Trefftz type approaches and discusses the interrelationship between various concepts of the displacement, hybrid and Trefftz methods. The basic concept of the displacement version of the reduced Trefftz method operates on the natural boundary conditions enforced in an integral form whereas the stress version of the reduced Trefftz type approach operates on the essential boundary conditions enforced in an integral sense. The application of the method proposed in the framework of the finite element method is briefly outlined. The methods used by the reduced Trefftz type approach for enforcing conformity and interelement continuity between neighboured elements are also discussed. Comparisons with other known methods for the same purpose are performed. General strategy for developing finite elements of general geometric form such as quadrilateral elements with invariance properties is presented. The basic idea of this strategy consists in using the natural coordinate system only for defining the element geometry and performing the element integration in the biunit interval. For defining the approximation functions a local coordinate system defined from the directions of the covariant base vectors and the perpendicular contravariant base vectors computed in the geometric centre of the element is used. This strategy can also be used to implement other versions of finite elements and other forms of finite elements. Different numerical calculations and comparisons in the linear statics and kinetics are performed in order to assess the convergence and the numerical performance of finite elements developed by applying the reduced Trefftz type approach.
Formation Maneuvering Control of Multiple Nonholonomic Robotic Vehicles: Theory and Experimentation
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we present a new leader-follower type solution to the formation maneuvering problem for multiple, nonholonomic wheeled mobile robots. The solution is based on the graph that models the coordination among the robots being a spanning tree. Our decentralized control law ensures, in the least squares sense, that the robots globally acquire a given planar formation while the formation as a whole globally tracks a desired trajectory. The control law is first designed at the kinematic level and then extended to the dynamic level. In the latter, we consider that parametric uncertainty exists in the equations of motion. These uncertainties are accounted for by employing an adaptive control scheme. The proposed formation maneuvering controls are demonstrated experimentally and numerically.
Time Stretch Inspired Computational Imaging
Venue: arXiv cs.CV 2017
Year: 2017

We show that dispersive propagation of light followed by phase detection has properties that can be exploited for extracting features from the waveforms. This discovery is spearheading development of a new class of physics-inspired algorithms for feature extraction from digital images with unique properties and superior dynamic range compared to conventional algorithms. In certain cases, these algorithms have the potential to be an energy efficient and scalable substitute to synthetically fashioned computational techniques in practice today.
A practical fpt algorithm for Flow Decomposition and transcript assembly
Venue: arXiv cs.DS 2017
Year: 2017

The Flow Decomposition problem, which asks for the smallest set of weighted paths that "covers" a flow on a DAG, has recently been used as an important computational step in transcript assembly. We prove the problem is in FPT when parameterized by the number of paths by giving a practical linear fpt algorithm. Further, we implement and engineer a Flow Decomposition solver based on this algorithm, and evaluate its performance on RNA-sequence data. Crucially, our solver finds exact solutions while achieving runtimes competitive with a state-of-the-art heuristic. Finally, we contextualize our design choices with two hardness results related to preprocessing and weight recovery. Specifically, $k$-Flow Decomposition does not admit polynomial kernels under standard complexity assumptions, and the related problem of assigning (known) weights to a given set of paths is NP-hard.
Encoder-Decoder Shift-Reduce Syntactic Parsing
Venue: arXiv cs.CL 2017
Year: 2017

Starting from NMT, encoder-decoder neu- ral networks have been used for many NLP problems. Graph-based models and transition-based models borrowing the en- coder components achieve state-of-the-art performance on dependency parsing and constituent parsing, respectively. How- ever, there has not been work empirically studying the encoder-decoder neural net- works for transition-based parsing. We apply a simple encoder-decoder to this end, achieving comparable results to the parser of Dyer et al. (2015) on standard de- pendency parsing, and outperforming the parser of Vinyals et al. (2015) on con- stituent parsing.
Web Vulnerability Scanners: A Case Study
Venue: arXiv cs.CR 2017
Year: 2017

Cloud security is one of the biggest concerns for many companies. The growth in the number and size of websites increases the need for better securing those websites. Manual testing and detection of web vulnerabilities can be very time consuming. Automated Web Vulnerability Scanners (WVS) help with the detection of vulnerabilities in web applications. Acunetix is one of the widely used vulnerability scanners. Acunetix is also easy to implement and to use. The scan results not only provide the details of the vulnerabilities, but also give information about fixing the vulnerabilities. AcuSensor and AcuMonitor (technologies used by Acunetix) help generate more accurate potential vulnerability results. One of the purposes of this paper is to orient current students of computer security with using vulnerability scanners. Secondly, this paper provides a literature review related to the topic of security vulnerability scanners. Finally, web vulnerabilities are addressed from the mobile device and browser perspectives.
FAIR: A Hadoop-based Hybrid Model for Faculty Information Retrieval System
Venue: arXiv cs.DC 2017
Year: 2017

In era of ever-expanding data and knowledge, we lack a centralized system that maps all the faculties to their research works. This problem has not been addressed in the past and it becomes challenging for students to connect with the right faculty of their domain. Since we have so many colleges and faculties this lies in the category of big data problem. In this paper, we present a model which works on the distributed computing environment to tackle big data. The proposed model uses apache spark as an execution engine and hive as database. The results are visualized with the help of Tableau that is connected to Apache Hive to achieve distributed computing.
An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles (Extended)
Venue: arXiv cs.MA 2017
Year: 2017

Before reaching full autonomy, vehicles will gradually be equipped with more and more advanced driver assistance systems (ADAS), effectively rendering them semi-autonomous. However, current ADAS technologies seem unable to handle complex traffic situations, notably when dealing with vehicles arriving from the sides, either at intersections or when merging on highways. The high rate of accidents in these settings prove that they constitute difficult driving situations. Moreover, intersections and merging lanes are often the source of important traffic congestion and, sometimes, deadlocks. In this article, we propose a cooperative framework to safely coordinate semi-autonomous vehicles in such settings, removing the risk of collision or deadlocks while remaining compatible with human driving. More specifically, we present a supervised coordination scheme that overrides control inputs from human drivers when they would result in an unsafe or blocked situation. To avoid unnecessary intervention and remain compatible with human driving, overriding only occurs when collisions or deadlocks are imminent. In this case, safe overriding controls are chosen while ensuring they deviate minimally from those originally requested by the drivers. Simulation results based on a realistic physics simulator show that our approach is scalable to real-world scenarios, and computations can be performed in real-time on a standard computer for up to a dozen simultaneous vehicles.
Detekcja upadku i wybranych akcji na sekwencjach obrazów cyfrowych
Venue: arXiv cs.CV 2017
Year: 2017

In recent years a growing interest on action recognition is observed, including detection of fall accident for the elderly. However, despite many efforts undertaken, the existing technology is not widely used by elderly, mainly because of its flaws like low precision, large number of false alarms, inadequate privacy preserving during data acquisition and processing. This research work meets these expectations. The work is empirical and it is situated in the field of computer vision systems. The main part of the work situates itself in the area of action and behavior recognition. Efficient algorithms for fall detection were developed, tested and implemented using image sequences and wireless inertial sensor worn by a monitored person. A set of descriptors for depth maps has been elaborated to permit classification of pose as well as the action of a person. Experimental research was carried out based on the prepared data repository consisting of synchronized depth and accelerometric data. The study was carried out in the scenario with a static camera facing the scene and an active camera observing the scene from above. The experimental results showed that the developed algorithms for fall detection have high sensitivity and specificity. The algorithm were designed with regard to low computational demands and possibility to run on ARM platforms. Several experiments including person detection, tracking and fall detection in real-time were carried out to show efficiency and reliability of the proposed solutions.
A Security Framework for Wireless Sensor Networks: Theory and Practice
Venue: arXiv cs.DC 2017
Year: 2017

Wireless sensor networks are often deployed in public or otherwise untrusted and even hostile environments, which prompts a number of security issues. Although security is a necessity in other types of networks, it is much more so in sensor networks due to the resource-constraint, susceptibility to physical capture, and wireless nature. In this work we emphasize two security issues: (1) secure communication infrastructure and (2) secure nodes scheduling algorithm. Due to resource constraints, specific strategies are often necessary to preserve the network's lifetime and its quality of service. For instance, to reduce communication costs nodes can go to sleep mode periodically (nodes scheduling). These strategies must be proven as secure, but protocols used to guarantee this security must be compatible with the resource preservation requirement. To achieve this goal, secure communications in such networks will be defined, together with the notions of secure scheduling. Finally, some of these security properties will be evaluated in concrete case studies.
Exceptions in Business Processes in Relation to Operational Performance
Venue: arXiv cs.SE 2017
Year: 2017

Business process models describe the way of working in an organization. Typically, business process models distinguish between the normal flow of work and exceptions to that normal flow. However, they often present an idealized view. This means that unexpected exceptions - exceptions that are not modelled in the business process model - can also occur in practice. This has an effect on the efficiency of the organization, because information systems are not developed to handle unexpected exceptions. This paper studies the relation between the occurrence of exceptions and operational performance. It does this by analyzing the execution logs of business processes from five organizations, classifying execution paths as normal or exceptional. Subsequently, it analyzes the differences between normal and exceptional paths. The results show that exceptions are related to worse operational performance in terms of a longer throughput time and that unexpected exceptions relate to a stronger increase in throughput time than expected exceptions.
$μ$Nap: Practical Micro-Sleeps for 802.11 WLANs
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we revisit the idea of putting interfaces to sleep during 'packet overhearing' (i.e., when there are ongoing transmissions addressed to other stations) from a practical standpoint. To this aim, we perform a robust experimental characterisation of the timing and consumption behaviour of a commercial 802.11 card. We design $μ$Nap, a local standard-compliant energy-saving mechanism that leverages micro-sleep opportunities inherent to the CSMA operation of 802.11 WLANs. This mechanism is backwards compatible and incrementally deployable, and takes into account the timing limitations of existing hardware, as well as practical CSMA-related issues (e.g., capture effect). According to the performance assessment carried out through trace-based simulation, the use of our scheme would result in a 57% reduction in the time spent in overhearing, thus leading to an energy saving of 15.8% of the activity time.
A simple method for shifting local dq impedance models to a global reference frame for stability analysis
Venue: arXiv cs.SY 2017
Year: 2017

Impedance-based stability analysis in the dq-domain is a widely applied method for power electronic dominated systems. An inconvenient property with this method is that impedance models are normally referred to their own local reference frame, and need to be recalculated when referring to a global reference frame in a given network. This letter presents a simple method for translating impedance sub-models within a complex network, from their own reference frames to any given point in the network. What distinguishes this method is that by using a simple rotational matrix, it only needs impedance models in their own local reference frames, to be translated to a global reference in the network. By way of this method, standard circuit analysis rules for series and parallel connection are applicable, as proven in the letter. The method is defined and validated for impedances in the dq and modified sequence domains, and it is shown that the dependency on reference frame is marginal in the latter. An additional finding from the application of this method is that components or subsystems with a certain symmetry property called Mirror Frequency Decoupling are invariant to the choice of reference frame.
Optimal choice: new machine learning problem and its solution
Venue: arXiv cs.AI 2017
Year: 2017

The task of learning to pick a single preferred example out a finite set of examples, an "optimal choice problem", is a supervised machine learning problem with complex, structured input. Problems of optimal choice emerge often in various practical applications. We formalize the problem, show that it does not satisfy the assumptions of statistical learning theory, yet it can be solved efficiently in some cases. We propose two approaches to solve the problem. Both of them reach good solutions on real life data from a signal processing application.
Learning to Map Vehicles into Bird's Eye View
Venue: arXiv cs.CV 2017
Year: 2017

Awareness of the road scene is an essential component for both autonomous vehicles and Advances Driver Assistance Systems and is gaining importance both for the academia and car companies. This paper presents a way to learn a semantic-aware transformation which maps detections from a dashboard camera view onto a broader bird's eye occupancy map of the scene. To this end, a huge synthetic dataset featuring 1M couples of frames, taken from both car dashboard and bird's eye view, has been collected and automatically annotated. A deep-network is then trained to warp detections from the first to the second view. We demonstrate the effectiveness of our model against several baselines and observe that is able to generalize on real-world data despite having been trained solely on synthetic ones.
Towards Modern Inclusive Factories: A Methodology for the Development of Smart Adaptive Human-Machine Interfaces
Venue: arXiv cs.HC 2017
Year: 2017

Modern manufacturing systems typically require high degrees of flexibility, in terms of ability to customize the production lines to the constantly changing market requests. For this purpose, manufacturing systems are required to be able to cope with changes in the types of products, and in the size of the production batches. As a consequence, the human-machine interfaces (HMIs) are typically very complex, and include a wide range of possible operational modes and commands. This generally implies an unsustainable cognitive workload for the human operators, in addition to a non-negligible training effort. To overcome this issue, in this paper we present a methodology for the design of adaptive human-centred HMIs for industrial machines and robots. The proposed approach relies on three pillars: measurement of user's capabilities, adaptation of the information presented in the HMI, and training of the user. The results expected from the application of the proposed methodology are investigated in terms of increased customization and productivity of manufacturing processes, and wider acceptance of automation technologies. The proposed approach has been devised in the framework of the European project INCLUSIVE.
Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability
Venue: arXiv cs.CL 2017
Year: 2017

Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.
Skill Learning by Autonomous Robotic Playing using Active Learning and Creativity
Venue: arXiv cs.RO 2017
Year: 2017

We treat the problem of autonomous acquisition of manipulation skills where problem-solving strategies are initially available only for a narrow range of situations. We propose to extend the range of solvable situations by autonomous playing with the object. By applying previously-trained skills and behaviours, the robot learns how to prepare situations for which a successful strategy is already known. The information gathered during autonomous play is additionally used to learn an environment model. This model is exploited for active learning and the creative generation of novel preparatory behaviours. We apply our approach on a wide range of different manipulation tasks, e.g. book grasping, grasping of objects of different sizes by selecting different grasping strategies, placement on shelves, and tower disassembly. We show that the creative behaviour generation mechanism enables the robot to solve previously-unsolvable tasks, e.g. tower disassembly. We use success statistics gained during real-world experiments to simulate the convergence behaviour of our system. Experiments show that active improves the learning speed by around 9 percent in the book grasping scenario.
Neural Question Answering at BioASQ 5B
Venue: arXiv cs.CL 2017
Year: 2017

This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning
Venue: arXiv cs.CV 2017
Year: 2017

Prevalent techniques in zero-shot learning do not generalize well to other related problem scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero-shot and few-shot learning problems. Our approach is based on a novel Class Adapting Principal Directions (CAPD) concept that allows multiple embeddings of image features into a semantic space. Given an image, our method produces one principal direction for each seen class. Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class, and opposite to the other classes. This allows efficient and class-adaptive information transfer from seen to unseen classes. In addition, we propose an automatic process for selection of the most useful seen classes for each unseen class to achieve robustness in zero-shot learning. Our method can update the unseen CAPD taking the advantages of few unseen images to work in a few-shot learning scenario. Furthermore, our method can generalize the seen CAPDs by estimating seen-unseen diversity that significantly improves the performance of generalized zero-shot learning. Our extensive evaluations demonstrate that the proposed approach consistently achieves superior performance in zero-shot, generalized zero-shot and few/one-shot learning problems.