Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating multi-touch and pen gestures for diagram editing on interactive surfaces

Creating and editing large graphs and node-link diagrams are crucial activities in many application areas. For them, we consider multi-touch and pen input on interactive surfaces as very promising. This fundamental work presents a user study investigating how people edit node-link diagrams on an interactive tabletop. The study covers a set of basic operations, such as creating, moving, and deleting diagram elements. Participants were asked to perform spontaneous gestures for 14 given tasks. They could interact in three different ways: using one hand, both hands, as well as pen and hand together. The subjects' activities were observed and recorded in various ways, analyzed and enriched with think-aloud data. As a result, we contribute a user-elicited collection of touch and pen gestures for editing node-link diagrams. The study provides valuable insight how people would interact on interactive surfaces for this as well as other tabletop domains.
Data fusion and switching function for MAV navigation system

In this work, data fusion and switching function based approach for MAV (Micro Aerial Vehicle) navigation system is proposed. Our approach applies monocular simultaneous localization and mapping (SLAM) system. Extended Kaiman Filter (EKF) algorithm is then used to fuse SLAM data with velocity and acceleration data, yielding metric-scaled position data. Moreover, GPS sensor is employed to retrieve the MAV position in global world frame coordinate. In addition, the switching function is added to deal with particular conditions. Some benefits from our approach are improvement of data accuracy when the MAV is moving indoor with global coordinate system as well as overcoming the problem of losing visual tracking.
Decentralized Energy and Power Estimation in Solar-Powered Wireless Sensor Networks

Solar powered wireless sensor networks are very adapted to smart city applications, since they can operate for extended durations with minimal installation costs. Nonetheless, they require energy management schemes to operate reliably, unlike their grid-powered counterparts. Such schemes require the forecasting of future solar power inputs for each wireless sensor node, over a time horizon. They also require the determination of battery energy parameters in real time. To address both requirements, we propose a collaborative solar power forecasting framework combined to a real time battery capacity estimation model, which can be used to optimize the node schedules over the corresponding horizon.
Reducing Employment Insecurity

The perception of job insecurity is known to be a stressful condition for employees. Less is known about employment insecurity and the ways employees and their families deal with it. This study investigates whether participation in further training is a strategy that employees adopt to reduce perceived employment insecurity. As participation in further training is often costly and time-consuming, we assume that the family context is of importance for the decision to take part in further training. To take account of possible self-selection, we apply a propensity score matching procedure on longitudinal data from the Swiss Household Panel (2004-2013). Three main findings can be emphasized: first, participation in further training is not a strategy adopted particularly by employees who perceive high employment insecurity as they are less likely to train than their secure counterparts. Second, even though further training is not a strategy that is actively adopted, employees who train subsequently report lower levels of perceived employment insecurity. Third, the family context indeed influences the likelihood to train: partnered employees are more likely to train and preschool-aged children act as a constraint on women’s but enhance men’s participation in further training. Yet, in the context of high perceived employment insecurity, children generally reduce their parents’ likelihood to train as the parents may turn to other strategies that reduce perceived employment insecurity.
An Operation Scenario Model for Energy Harvesting Embedded Systems and an Algorithm to Maximize the Operation Quality

Although energy harvesting embedded systems can work permanently when the chance to harvest energy is steady, it is necessary to adopt an appropriate operation strategy due to the limitation of harvested energy. In this paper, we propose an operation scenario model for energy harvesting embedded systems. The proposed model is defined as a set of operation points selected from periodical candidate points to execute the application. In order to ensure the stability of system operation, the application has to be executed at least once within a given maximum interval. In this work, an operation quality in the model is defined as execution frequency. We further propose an efficient algorithm to maximize the operation quality, i.e., To find a maximum set of operation points. The proposed algorithm selects as many execution points as possible while satisfying the constraints of the battery capacity and the system stability. We prove the optimality of proposed algorithm. Additionally, the experiments demonstrate that proposed algorithm can find one of the optimum solution quickly.
Transformation invariant sparse coding

Sparse coding is a well established principle for unsupervised learning. Traditionally, features are extracted in sparse coding in specific locations, however, often we would prefer invariant representation. This paper introduces a general transformation invariant sparse coding (TISC) model. The model decomposes images into features invariant to location and general transformation by a set of specified operators as well as a sparse coding matrix indicating where and to what degree in the original image these features are present. The TISC model is in general overcomplete and we therefore invoke sparse coding to estimate its parameters. We demonstrate how the model can correctly identify components of non-trivial artificial as well as real image data. Thus, the model is capable of reducing feature redundancies in terms of pre-specified transformations improving the component identification.
Predicting Subcutaneous Glucose Concentration in Humans: Data-Driven Glucose Modeling

The combination of predictive data-driven models with frequent glucose measurements may provide for an early warning of impending glucose excursions and proactive regulatory interventions for diabetes patients. However, from a modeling perspective, before the benefits of such a strategy can be attained, we must first be able to quantitatively characterize the behavior of the model coefficients as well as the model predictions as a function of prediction horizon. We need to determine if the model coefficients reflect viable physiologic dependencies of the individual glycemic measurements and whether the model is stable with respect to small changes in noise levels, leading to accurate near-future predictions with negligible time lag. We assessed the behavior of linear autoregressive data-driven models developed under three possible modeling scenarios, using continuous glucose measurements of nine subjects collected on a minute-by-minute basis for approximately 5 days. Simulation results indicated that stable and accurate models for near-future glycemic predictions (<60 min) with clinically acceptable time lags are attained only when the raw glucose measurements are smoothed and the model coefficients are regularized. This study provides a starting point for further needed investigations before real-time deployment can be considered.
GoSpark : An In-Memory Distributed Computation Platform in Go

Computation tasks in Computer Vision research are data-intensive. Such tasks usually involve performing repeated single operations, such as image resizing and feature extraction, on thousands of images. They also require training iterative algorithms, such as K-means clustering or logistic regressions, on thousands of image feature vectors. To support the computation of such tasks, the CSAIL Computer Vision Group has been using 36 high-performance in-house computation servers1. However, currently there is no efficient way to distribute the processing of large data sets across these servers. Each user can only connect to one single server using Secure Shell (SSH) and perform computation task (under Matlab, Python, or C++) with data and intermediate results stored in the NFS space maintained by CSAIL TIG. The use of centralized NFS space as data storage has caused I/O to be the bottleneck for large scale data processing tasks. Thus, a distributed computation platform with distributed file system would highly improve the performance of computations in computer vision research.
Deep Neural Networks: A Case Study for Music Genre Classification

Music classification is a challenging problem with many applications in today's large-scale datasets with Gigabytes of music files and associated metadata and online streaming services. Recent success with deep neural network architectures on large-scale datasets has inspired numerous studies in the machine learning community for various pattern recognition and classification tasks such as automatic speech recognition, natural language processing, audio classification and computer vision. In this paper, we explore a two-layer neural network with manifold learning techniques for music genre classification. We compare the classification accuracy rate of deep neural networks with a set of well-known learning models including support vector machines (SVM and '1-SVM), logistic regression and '1-regression in combination with hand-crafted audio features for a genre classification task on a public dataset. Our experimental results show that neural networks are comparable with classic learning models when the data is represented in a rich feature space.
Towards Never Ending Language Learning for Morphologically Rich Languages

This work deals with ontology learning from unstructured Russian text. We implement one of components Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologicaly rich free-word-order language. We demonstrate that this method may be successfully applied to Russian data. In addition we perform several additional experiments comparing different settings of the training process. We demonstrate that utilizing of morphological features significantly improves the system precision while using of seed patterns helps to improve the coverage.
In-Vitro Helix Opening of M. tuberculosis oriC by DnaA Occurs at Precise Location and Is Inhibited by IciA Like Protein

Background Mycobacterium tuberculosis (M.tb), the pathogen that causes tuberculosis, is capable of staying asymptomatically in a latent form, persisting for years in very low replicating state, before getting reactivated to cause active infection. It is therefore important to study M.tb chromosome replication, specifically its initiation and regulation. While the region between dnaA and dnaN gene is capable of autonomous replication, little is known about the interaction between DnaA initiator protein, oriC origin of replication sequences and their negative effectors of replication. Methodology/Principal Findings By KMnO4 mapping assays the sequences involved in open complex formation within oriC, mediated by M.tb DnaA protein, were mapped to position −500 to −518 with respect to the dnaN gene. Contrary to E. coli, the M.tb DnaA in the presence of non-hydrolysable analogue of ATP (ATPγS) was unable to participate in helix opening thereby pointing to the importance of ATP hydrolysis. Interestingly, ATPase activity in the presence of supercoiled template was higher than that observed for DnaA box alone. M.tb rRv1985c, a homologue of E.coli IciA (Inhibitor of chromosomal initiation) protein, could inhibit DnaA-mediated in-vitro helix opening by specifically binding to A+T rich region of oriC, provided the open complex formation had not initiated. rIciA could also inhibit in-vitro replication of plasmid carrying the M.tb origin of replication. Conclusions/Significance These results have a bearing on the functional role of the important regulator of M.tb chromosomal replication belonging to the LysR family of bacterial regulatory proteins in the context of latency.
Measurements of contrast constancy across a wide range of luminance levels

The human visual system (HVS) is robust to changes in viewing conditions, such as variations in illumination and viewing distance. Not all elements of our perception stay constant, for example the way we perceive brightness is highly dependent on the background luminance and threshold contrast strongly depends on signal frequency. However, suprathreshold contrast is considered to be constant across viewing conditions and stimuli, the property known as contrast constancy. We intend to verify whether this holds true for a wide range of luminance levels. Results of such measurements could be used to better reproduce images on displays of varying brightness.
The Impact of Cluster-Based Web Systems Design on User-Perceived Performance

As the Internet has become more pervasive, many research works have to relay on it. Mostly, an individual access for information is only required and a best effort way of providing such service may be quite enough. However in several computing projects that have been established nowadays, there is urgent need for good and scalable Internet performance as perceived by end users. Web users perceive good Internet performance as low latency, high throughput and high availability. Almost 40% latency, as perceived by user s, is causing on a Web site side, therefore, there is a general consensus that intensive research in Web site design is needed. This paper discusses the impact of cluster-based Web systems design on user-perceived performance and presents results from empirical performance studies carried out for cluster-based Web systems.
Recognition by Matching Dense , Oriented Edge

This paper describes techniques to perform eecient and accurate recognition in diicult domains by matching dense, oriented edge pixels. We model three-dimensional objects as the set of two-dimensional views of the object. Translation, rotation, and scaling of the views are allowed to approximate full three-dimensional motion. A modiied Hausdorr measure is used to determine which transformations of each object model are reported as matches. The use of dense, oriented edge pixels allows us to achieve a low rate of false positives. Techniques to prune the search space are used to obtain a system that is eecient in practice. We give results of the system recognizing object views in intensity and infrared images.
Acid Deposition: Unraveling a Regional Phenomenon

Because sources of sulfur and nitrogen oxides distributed broadly across eastern North America have greatly overlapping zones of influence, it is difficult to determine detailed relations between emissions and the resulting acid deposition. Although substantial progress has been made in the past decade in understanding the pertinent atmospheric processes and in describing them in numerical models, because of the complexities of these processes and the wide range of the time and space scales involved, credible source-receptor relations for regional-scale acid deposition are not yet at hand. Consequently, near-term strategies for reducing acid deposition should be based on considerations other than detailed atmospheric source-receptor relations, but with confidence that regional deposition will be reduced equivalently to any reduction in regional emissions.
Disturbance observer-based motion control of paramagnetic microparticles against time-varying flow rates

We control the motion of paramagnetic microparticles with average diameter of 100 μm inside microfluidic channels against and along time-varying flow rates. The drag force and the force due to time-varying flow rate are modeled as time-varying disturbance force. This force is estimated using a disturbance force observer and compensated using a magnetic-based closed-loop control system. The closed-loop control with disturbance compensation decreases the steady-state error by 81.4%, 70.2%, and 70.3% at flow rates of 6 ml.hr-1, 17 ml.hr-1, and 35 ml.hr-1, respectively. The proposed control system is essential to translate these paramagnetic microparticles and magnetic drug carriers into in vivo applications.
Performance and Disruption Tolerance of Transport Protocols for Airborne Telemetry Networks

The airborne telemetry environment presents unique challenges to end-to-end communications due to the highly dynamic topology and time-varying connectivity of high-velocity wireless nodes. The AeroTP transport protocol uses multiple reliability modes to trade off end-to-end reliability and efficiency as appropriate for different categories of telemetry data. Based on the architecture previously presented, we have further developed the design of this protocol, as well as performing preliminary simulations of AeroTP using the ns-3 simulation platform. In this paper we present the operational modes of AeroTP in greater detail, as well as comparing the performance of TCP with the AeroTP domain-specific transport protocol. INTRODUCTION AND MOTIVATION Telemetry for airborne test and evaluation is an application that poses unique challenges. Traditionally, telemetry communication has consisted primarily of point-to-point links from multiple sources to a single sink. More recently, with the increasing number of sources in the typical telemetry test scenario, there is a need to move to networked systems in order to meet the demands of bandwidth and connectivity. This need has been recognized by various groups, including the Integrated Network Enhanced Telemetry (iNET) program for Major Range and Test Facility Bases (MRTFB) across United States [1]. The current TCP/IP-based Internet architecture is not designed to address the needs of telemetry applications [2] and there remain a number of issues to be solved at the network and transport layers [3]. Given the constraints and requirements of the aeronautical environment, the current Internet protocols are not suitable in a number of respects. These constraints include the physical network characteristics such as topology and mobility that present severe challenges to reliable end-to-end communication. In order to build a resilient network infrastructure, we need cross-layer enabled protocols at the transport, network, and MAC layers that are particularly suited for the airborne telemetry networks. At the same time, there is a need to be compatible with both TCP/IP-based devices located on the airborne nodes as well as with the control applications. Therefore, a new protocol suite, while being specific to the aeronautical telemetry
BEER: BEtter Evaluation as Ranking

We present the UvA-ILLC submission of the BEER metric to WMT 14 metrics task. BEER is a sentence level metric that can incorporate a large number of features combined in a linear model. Novel contributions are (1) efficient tuning of a large number of features for maximizing correlation with human system ranking, and (2) novel features that give smoother sentence level scores.
Object recognition in assembly assisted by augmented reality system

Assembly processes for simple toys or complex machines require instructions to be executed. Traditionally, these instructions are written in the form of paper or digital manuals. These manuals contain descriptive text, photos or diagrams to guide the assembly sequence from the beginning to the final state. To change this paradigm, an augmented reality system is proposed to guide users in assembly tasks. The system recognizes each piece to be assembled through image processing techniques and guides the piece placement with graphic signs. Still, the system checks if the pieces are properly assembled and alerts the user when the assembly has been finished. In the field of assembly assisted by augmented reality systems, many works use some kind of customized device, such as head mounted displays (HMD). Additionally, markers are commonly used to track camera position and to identify assembly parts. These two features restrict the spread of the technology whence, in this work, customized devices and markers to track and identify parts are not used and all the processing is executed on embedded software in an off-the-shelf device without the need of communication with other computers to offload image processing. In this work we present the current results of the proposed system's object recognition process. The implemented object recognition subsystem is part of a system called A3R (Assembly Assisted by Augmented Reality).
Automated Reasoning on TBoxes with Qualified Number Restrictions via SMT

The problem of reasoning with qualified number restrictions in Description Logics (DLs) has been investigated since the very first research steps in automated reasoning for DLs. Moreover, developing techniques for optimized reasoning with qualified number restrictions and has gained further importance since qualified number restrictions have been added to the forthcoming standard OWL 2 for Semantic Web applications. On the one hand, however, actual DL reasoning techniques, often lack of efficiency in handling those features, especially when the number of restrictions or the values involved are high. On the other hand the manifold problem of reasoning including numerical constraints is a well-established and thoroughly investigate problem in the SMT community, in which a lot of effort is continuously spent in order to enhance the efficiency of reasoning techniques for such kind of problems. In this paper we propose and investigate a novel approach for concept satisfiability in acyclic ALCQ ontologies. The idea is to encode an ALCQ ontology into a formula in Satisfiability Modulo the Theory of Costs (SMT(C)), which is a specific and computationally much cheaper subcase of Linear Arithmetic under the Integers (LA(Z)), and to exploit the power of modern SMT solvers to compute every concept-satisfiability query on a given ontology. We have implemented and tested our approach (called ALCQ2SMT_C) and some optimizations on a wide set of synthesized benchmark formulas, comparing the approach with the main state-of-the-art tools available. Our empirical evaluation confirms the potential of the approach.
Multihour Design of Multi-Hop Virtual Path based Wide-Area ATM Networks

The cost eecient design of ATM networks is considered to be a challenging problem due to the heterogenity of services, the statistical muliplexing gain resulting from the resource sharing of variable bitrate (VBR) connections and the possibility of resource separation through virtual paths (VPs). Most proposals in literature are well suited for small problems only. For that before developing a new algorithm we take a look at large scale design techniques known from telephone network dimensioning. It turns out that the well known Uniied Algorithm (UA) of G. Ash 1] is a good basis for an ATM network design method. So we extend and improve the UA in order to cope with the following multihour multiservice ATM network design problem: Find the minimum cost VP network structure on top of a given physical network and the design hour individual optimal VC routing sequences according to end-to-end call blocking constraints. Beside transmission costs also virtual path/virtual channel switching costs are included in our problem formulation. Moreover a simpliied procedure for single hour design is presented. The new design procedures have been applied successfully to several network examples including a German and an American wide-area ATM network carrying up to ve traac classes.