nils-herrmann/clean_scirepeval huggingface dataset:
Towards adaptive classification for BCI.

Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.
Integrated 60-GHz Antenna on Multilayer Organic Package With Broadside and End-Fire Radiation

Existing antenna and array systems for 60-GHz wireless personal area network communications suffer from inherent poor radiation at grazing angles. This limitation is overcome in this work with a highly integrated antenna module that combines both broadside and end-fire radiators in a single multilayer organic package. Liquid crystal polymer and Rogers RO3003 are used to implement a small form factor (12.5 mm × 10 mm × 1.3 mm) antenna architecture. The co-designed broadside and end-fire antennas are characterized and measured for operation in the 57-66-GHz frequency range. Measured boresight gains of 8.7 and 10.9 dBi are achieved for the broadside and end-fire antennas while maintaining 35-45-dB isolation between both antennas. The numerically estimated radiation efficiency is found to be 92.5% and 78.5% for the broadside and end-fire elements. These antennas are orthogonally polarized and suitable for frequency reuse. Integrated circuits are mounted inside recessed cavities to realize a fully active antenna module with beam switching or simultaneous radiation. To the best of our knowledge, this is the first publication of a single package multilayer integration of millimeter-wave active antennas with both azimuth and elevation coverage.
Test Model for Rich Semantic Graph Representation for Hindi Text using Abstractive Method

In this paper we present a method for summarizing Hindi Text document by creating rich semantic graph(RSG) of original document and identifying substructures of graph that can extract meaningful sentences for generating a document summary. This paper contributes the idea to summarize Hindi text document using abstractive method. We extract a set of features from each sentence that helps identify its importance in the document. It uses Hindi WordNet to identify appropriate position of word for checking SOV (Subject-Object-Verb) qualification. Therefore to optimize the summary, we find similarity among the sentences and merge the sentence which represented using Rich Semantic Sub graph which in turn produces a summarized text document.
Privacy-Aware Framework for Matching Online Social Identities in Multiple Social Networking Services

With various emerging Social Networking Services (SNS), it is possible for users to join multiple SNS for social relationships with other users and to collect a large amount of information (e.g., statuses on Facebook and tweets on Twitter). However, these users have been facing difficulties in managing all the data collected from the multiple SNS. It is important to match social identities from the multiple SNS. In this study, we propose a privacy-aware framework for a social identity matching (SIM) method across these multiple SNS. It means that the proposed approach can protect user privacy, because only the public information (e.g., username and the social relationships of the users) is employed to find the best matches between social identities. As a result, we have shown by evaluation that the F-measure of the proposed SIM method is about 60%.
A Survey of Security and Privacy in Online Social Networks

This survey highlights the major issues concerning privacy and security in online social networks. Firstly, we discuss research that aims to protect user data from the various attack vantage points including other users, advertisers, third party application developers, and the online social network provider itself. Next we cover social network inference of user attributes, locating hubs, and link prediction. Because online social networks are so saturated with sensitive information, network inference plays a major privacy role. As a response to the issues brought forth by client-server architectures, distributed social networks are discussed. We then cover the challenges that providers face in maintaining the proper operation of an online social network including minimizing spam messages, and reducing the number of sybil accounts. Finally, we present research in anonymizing social network data. This area is of particular interest in order to continue research in this field both in academia and in industry.
Technological , Organizational , and Environmental Antecedents to Web Services Adoption

The proliferation of web services within the last two years enables organizations to assimilate software and services from different companies and locations into an integrated service capable of streamlining important processes. Widespread adoption of web services has not yet occurred across all industries. To better understand the key determinants of web services adoption at the firm level, a conceptual model of factors impacting web services adoption was developed. The conceptual model was grounded in the technology-organization-environment (TOE) framework (Tomatzky and Fleischer, 1990) to support the formulation of eleven propositions that may affect adoption and continued utilization of web services. Specifically, factors for each of the contexts within the TOE framework were formulated and supported including: (1) technological factors (security concerns; reliability; deployability); (2) organizational factors (firm size; firm scope; technological knowledge; perceived benefits); and, (3) environmental factors (competitive pressure; regulatory influence; dependent partner readiness; trust in the web service provider). A summary of the relationships between the key constructs in the model and recommendations for future research are provided.
Modular 50-kV IGBT Switch for Pulsed-Power Applications

In this paper, we describe the development of a modular semiconductor switch, based on the compact IGBT switch, presented at the 14th EML. Using a discrete 18-kV IGBT switching module, we tested first two and then three of these modules connected in series. The goal was to handle 30 kV with two switches and 50 kV with three switches. In order to have safe operating conditions during the experiments, the current was limited by the load resistor and should not exceed 500 A. All the tests were carried out as single-shot tests. To get a safe synchronous switching, it was necessary to have a trigger unit that is capable of creating a signal triggering all the modules at the same time. For this purpose, we used a trigger unit, which was inductively coupled by means of a ferrite core system to the modules, to create the gate signals for all the discrete IGBTs on the board simultaneously. With that experimental setup, we have shown that, by using the modular IGBT-based semiconductor switch, it is possible to handle 50 kV. Depending on the available load resistor, the current which could be handled amounted to 450 A at the maximum.
High-Performance Local Dimming Algorithm and Its Hardware Implementation for LCD Backlight

The local-dimming backlight has recently been presented for use in LCD TVs. However, the image resolution is low, particularly at weak edges. In this work, a local-dimming backlight is developed to improve the image contrast and reduce power dissipation. The algorithm enhances low-level edge information to improve the perceived image resolution. Based on the algorithm, a 42-in backlight module with white light-emitting diode (LED) devices was driven by a local dimming control core. The block-wise register approach substantially reduced the number of required line-buffers and shortened the latency time. The measurements made in the laboratory indicate that the backlight system reduces power dissipation by an average of 48% and exhibits no visible distortion compared relative to the fixed backlighting system. The system was successfully demonstrated in a 42-in LCD TV, and the contrast ratio was greatly improved by a factor of 100.
Domain Invariant Transfer Kernel Learning

Domain transfer learning generalizes a learning model across training data and testing data with different distributions. A general principle to tackle this problem is reducing the distribution difference between training data and testing data such that the generalization error can be bounded. Current methods typically model the sample distributions in input feature space, which depends on nonlinear feature mapping to embody the distribution discrepancy. However, this nonlinear feature space may not be optimal for the kernel-based learning machines. To this end, we propose a transfer kernel learning (TKL) approach to learn a domain-invariant kernel by directly matching source and target distributions in the reproducing kernel Hilbert space (RKHS). Specifically, we design a family of spectral kernels by extrapolating target eigensystem on source samples with Mercer's theorem. The spectral kernel minimizing the approximation error to the ground truth kernel is selected to construct domain-invariant kernel machines. Comprehensive experimental evidence on a large number of text categorization, image classification, and video event recognition datasets verifies the effectiveness and efficiency of the proposed TKL approach over several state-of-the-art methods.
Shape-Up: Shaping Discrete Geometry with Projections

We introduce a unified optimization framework for geometry processing based on shape constraints. These constraints preserve or prescribe the shape of subsets of the points of a geometric data set, such as polygons, one-ring cells, volume elements, or feature curves. Our method is based on two key concepts: a shape proximity function and shape projection operators. The proximity function encodes the distance of a desired least-squares fitted elementary target shape to the corresponding vertices of the 3D model. Projection operators are employed to minimize the proximity function by relocating vertices in a minimal way to match the imposed shape constraints. We demonstrate that this approach leads to a simple, robust, and efficient algorithm that allows implementing a variety of geometry processing applications, simply by combining suitable projection operators. We show examples for computing planar and circular meshes, shape space exploration, mesh quality improvement, shape-preserving deformation, and conformal parametrization. Our optimization framework provides a systematic way of building new solvers for geometry processing and produces similar or better results than state-of-the-art methods.
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing

In this paper, we explored a learning approach which combines di erent learning methods in inductive logic programming (ILP) to allow a learner to produce more expressive hypotheses than that of each individual learner. Such a learning approach may be useful when the performance of the task depends on solving a large amount of classication problems and each has its own characteristics which may or may not t a particular learning method. The task of semantic parser acquisition in two di erent domains was attempted and preliminary results demonstrated that such an approach is promising.
Cognitive Radio-based Wireless Sensor Networks: Conceptual design and open issues

Traditional static spectrum allocation policies have been to grant each wireless service exclusive usage of certain frequency bands, leaving several spectrum bands unlicensed for industrial, scientific and medical purposes. The rapid proliferation of low-cost wireless applications in unlicensed spectrum bands has resulted in spectrum scarcity among those bands. Since most applications in Wireless Sensor Networks (WSNs) utilize the unlicensed spectrum, network-wide performance of WSNs will inevitably degrade as their popularity increases. Sharing of under-utilized licensed spectrum among unlicensed devices is a promising solution to the spectrum scarcity issue. Cognitive Radio (CR) is a new paradigm in wireless communication that allows sensor nodes as the unlicensed users or Secondary Users (SUs) to detect and use the under-utilized licensed spectrum temporarily. Given that the licensed or Primary Users (PUs) are oblivious to the presence of SUs, the SUs access the licensed spectrum opportunistically without interfering the PUs, while improving their own performance. In this paper, we propose an approach to build Cognitive Radio-based Wireless Sensor Networks (CR-WSNs). We believe that CR-WSN is the next-generation WSN. Realizing that both WSNs and CR present unique challenges to the design of CR-WSNs, we provide an overview and conceptual design of WSNs from the perspective of CR. The open issues are discussed to motivate new research interests in this field. We also present our method to achieving context-awareness and intelligence, which are the key components in CR networks, to address an open issue in CR-WSN.
Occupancy-driven energy management for smart building automation

Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.
Analysis of human behavior recognition algorithms based on acceleration data

The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.
Reading behavior in the digital environment: Changes in reading behavior over the past ten years

This study attempts to investigate reading behavior in the digital environment by analyzing how people’s reading behavior has changed over the past ten years.  Survey and analysis methods are employed.  With an increasing amount of time spent reading electronic documents, a screen-based reading behavior is emerging. The screen-based reading behavior is characterized by more time spent on browsing and scanning, keyword spotting, one-time reading, non-linear reading, and reading more selectively, while less time is spent on in-depth reading, and concentrated reading. Decreasing sustained attention is also noted. Annotating and highlighting while reading is a common activity in the printed environment. However, this “traditional” pattern has not yet migrated to the digital environment when people read electronic documents.  Implications for the changes in reading behavior are discussed, and directions for future research are suggested.
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution

Current machine learning systems operate, almost exclusively, in a statistical, or model-blind mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal inference.
A Wideband CP Crossed Slot Antenna Using 1- $\lambda $ Resonant Mode With Single Feeding

This communication proposes to use the 1-<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> resonant mode of slot radiator with single feeding for bandwidth enhancement. The impedance of a simple slot radiator is studied using computer simulation. Since operating the slot radiator in the 1-<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> resonant mode requires a longer slot length, a large square slot is used at both ends of the slot to reduce the required slot length. The results of the study are subsequently used to design a wideband circularly polarized (CP) crossed slot antenna (CSA) with single feeding. The CSA consists of two slot radiators having the same length and placed in a crossed shape, i.e., orthogonal positions. A single microstrip line with a via at the end is used to feed the CSA. By loading reactive elements with appropriate values on each slot near to the feed line, the two slots generate two linearly polarized electric fields with 90°-phase difference for CP operation. The CSA is studied and designed using simulation and measurement. Measured results show that the CSA has a wide impedance bandwidth of 1.52–3.44 GHz (1.92 GHz, 77.4%) and axial-ratio bandwidth of 1.73–3.01 GHz (1.28 GHz, 54%), much wider than the previous directional and bidirectional CSAs using single feeding.
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem

Given a large overcomplete dictionary of basis vectors, the goal is to simultaneously represent L>1 signal vectors using coefficient expansions marked by a common sparsity profile. This generalizes the standard sparse representation problem to the case where multiple responses exist that were putatively generated by the same small subset of features. Ideally, the associated sparse generating weights should be recovered, which can have physical significance in many applications (e.g., source localization). The generic solution to this problem is intractable and, therefore, approximate procedures are sought. Based on the concept of automatic relevance determination, this paper uses an empirical Bayesian prior to estimate a convenient posterior distribution over candidate basis vectors. This particular approximation enforces a common sparsity profile and consistently places its prominent posterior mass on the appropriate region of weight-space necessary for simultaneous sparse recovery. The resultant algorithm is then compared with multiple response extensions of matching pursuit, basis pursuit, FOCUSS, and Jeffreys prior-based Bayesian methods, finding that it often outperforms the others. Additional motivation for this particular choice of cost function is also provided, including the analysis of global and local minima and a variational derivation that highlights the similarities and differences between the proposed algorithm and previous approaches.
Investigation of Methods for Data Communication and Power Delivery Through Metals

When a sensor is located within a metallic housing, conveying data back to a controller or data logger is not trivial. To maintain structural integrity, or simply for ease of installation, it is often not desirable to break the wall of the vessel, preventing any hard-wired solution. The conducting nature of the structure prevents the effective use of radio communications due to skin effect. Applications involving sealed containers also have a requirement for power delivery, as the periodic changing of batteries is impractical. In this paper, a number of systems are presented for through-metal communications and power delivery. A novel noncontact electromagnetic acoustic transducer communication system is demonstrated, which is capable of data rates in excess of 1 Mb/s. Furthermore, a power transfer efficiency of approximately 4% is shown to be achievable through 20-mm-thick stainless steel using inductive coupling. These novel solutions are critically compared to previous systems based on piezoelectric transducers.
Multi-Modal Curriculum Learning for Semi-Supervised Image Classification

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.
ABOUT THE CONTROL OF HIGH SPEED MOBILE INDOOR ROBOTS

This paper describes the control algorithms of the high speed mobile robot Kurt3D. Kurt3D drives up to 4 m/s autonomously and reliably in an unknown office environment. We present the reliable hardware, fast control cycle algorithms and a novel set value computation scheme for achieving these velocities. In addition we sketch a real-time capable laser based position tracking method that is well suited for driving with these velocities.