nils-herrmann/clean_scirepeval huggingface dataset:
Bandwidth Enhancement of the Small-Size Internal Laptop Computer Antenna Using a Parasitic Open Slot for Penta-Band WWAN Operation

By embedding a parasitic open slot in the antenna ground of a small-size internal laptop computer antenna, enhanced bandwidth for the antenna's lower band to cover the GSM850/900 operation (824-960 MHz) can be achieved. The internal laptop computer antenna in this study is a small-size coupled-fed shorted T-monopole, whose length along the top edge of the display ground of the laptop computer is 43 mm only. The antenna can also provide a wide upper band to cover the GSM1800/1900/UMTS operation (1710-2170 MHz). With the inclusion of the parasitic open slot, the total antenna length along the top edge of the display ground is 48 mm only, and the occupied antenna volume is 10 × 3.5 × 48 mm3 above the top edge of the display ground, which is the smallest for the internal penta-band WWAN laptop computer antenna that have been reported for the present. Detailed results of the proposed parasitic open slot on bandwidth enhancement of the internal WWAN laptop computer antenna are presented.
Self-Adaptive Anytime Stream Clustering

Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. Moreover, we are capable of detecting concept drift, novelty and outliers in the stream. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.
IP Lookup using Two-level Indexing and B-Trees

Networks are expanding very fast and the number of clients is increasing dramatically, this causes the router forwarding table to become very large and present more demand on faster router operations. In this paper, we address the problem of packet forwarding in the routers aiming to increase the speed of address lookup and minimize the memory required for storing the forwarding table. We propose a new algorithm that makes use of two-level indexing and Btrees. We test the approach and compare it to other famous IP lookup approaches. The preliminary simulations show 20% less memory requirements and the lookup speed scaling linearly with increasing table size.
A radio resource management framework for TVWS exploitation under an auction-based approach

This paper elaborates on the design, implementation and performance evaluation of a prototype Radio Resource Management (RRM) framework for TV white spaces (TVWS) exploitation, under an auction-based approach. The proposed RRM framework is applied in a centralised Cognitive Radio (CR) network architecture, where exploitation of the available TVWS by Secondary Systems is orchestrated via a Spectrum Broker. Efficient RRM framework performance, as a matter of maximum-possible resources utilization and benefit of Spectrum Broker, is achieved by proposing and evaluating an auction-based algorithm. This auction-based algorithm considers both frequency and time domain during TVWS allocation process which was defined as an optimization problem, where maximum payoff of Spectrum Broker is the optimization goal. Experimental tests that were carried-out under controlled conditions environment, verified the validity of the proposed framework, besides identifying fields for further research.
Center feed single layer slotted waveguide array

A center feed configuration is newly introduced to enhance the bandwidth as well as to suppress the frequency dependent beam squinting in single layer slotted waveguide arrays. A multiple-way power divider consisting of cross-junctions is installed at the center of the aperture in an alternating-phase fed waveguide slot array to reduce the long line effect in the radiating waveguide. Each radiating slot is equipped with an inductive wall for reflection canceling, which dispenses with the beam tilting technique for suppressing cumulative reflection and assures the boresight beam. All these are equipped in single layer and are potential for low cost mass-production as is usual the case with alternating phase-fed arrays. The test antenna is fabricated at 26GHz band for FWA system. A 30.5 dBi gain with 46% efficiency with the main beam staying in the boresight is obtained. The sidelobes of -9.5 dB associated with the aperture blockage for the center feed is suppressed to below -14.7 dB at the design frequency, by applying a genetic algorithm for controlling the slot excitation
Adaptive Protocols for Information Dissemination in Wireless Sensor Networks

In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called metadata. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of two specific SPIN protocols, comparing them to other possible approaches and a theoretically optimal protocol. We find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches. We also find that, in terms of dissemination rate and energy usage, the SPIN protocols perform close to the theoretical optimum.
Monte Carlo localization in hand-drawn maps

Robot localization is one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we can successfully identify the room in which the robot is located in 80% of the tests.
On the security evaluation of the ARM TrustZone extension in a heterogeneous SoC

As the complexity of System-on-Chip (SoC) and the reuse of third party IP continues to grow, the security of a heterogeneous SoC has become a critical issue. In order to increase the software security of such SoC, the TrustZone technology has been proposed by ARM to enforce software security. Nevertheless, many SoC embed non-trusted third party Intellectual Property (IP) trying to take the benefits of this technology. In such case, is the security guaranteed by the ARM TrustZone technology reduced by the heterogeneity of SoC? In order to answer to this question, this paper presents relevant attack scenarios based on third party IP to exploit some security failures of the TrustZone extension through the all SoC. At the end, this article proposes to SoC designers to consider some design solutions to limit the impact of a malicious IP.
WiFi-Nano: reclaiming WiFi efficiency through 800 ns slots

The increase in WiFi physical layer transmission speeds from 1~Mbps to 1 Gbps has reduced transmission times for a 1500 byte packet from 12 ms to 12 us. However, WiFi MAC overheads such as channel access and acks have not seen similar reductions and cumulatively contribute about 150 us on average per packet. Thus, the efficiency of WiFi has deteriorated from over 80% at 1 Mbps to under 10% at 1 Gbps.
 In this paper, we propose WiFi-Nano, a system that uses 800 ns slots} to significantly improve WiFi efficiency. Reducing slot time from 9 us to 800 ns makes backoffs efficient, but clear channel assessment can no longer be completed in one slot since preamble detection can now take multiple slots. Instead of waiting for multiple slots for detecting preambles, nodes speculatively transmit preambles as their backoff counters expire, while continuing to detect premables using self-interference cancellation. Upon detection of preambles from other transmitters, nodes simply abort their own preamble transmissions, thereby allowing the earliest transmitter to succeed. Further, receivers speculatively transmit their ack preambles at the end of packet reception, thereby reducing ack overhead. We validate the effectiveness of WiFi-Nano through implementation on an FPGA-based software defined radio platform, and through extensive simulations, demonstrate efficiency gains of up to 100%.
Re-place-ing Space: The Roles of Place and Space in Collaborative Systems

Many collaborative and communicative environments use notions of “space” and spatial organisation to facilitate and structure interaction. We argue that a focus on spatial models is misplaced. Drawing on understandings from architecture and urban design, as well as from our own research findings, we highlight the critical distinction between “space” and “place”. While designers use spatial models to support interaction, we show how it is actually a notion of “place” which frames interactive behaviour. This leads us to re-evaluate spatial systems, and discuss how “place”, rather than “space”, can support CSCW design.
Arabic Tokenization, Part-of-Speech Tagging and Morphological Disambiguation in One Fell Swoop

We present an approach to using a morphological analyzer for tokenizing and morphologically tagging (including partof-speech tagging) Arabic words in one process. We learn classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer. We obtain accuracy rates on all tasks in the high nineties.
Cognition in Friedreich's ataxia: a behavioral and multimodal imaging study

Friedreich's ataxia (FRDA) is a spinocerebellar degenerative disorder, in which cognitive deficits are sparsely explored. In this behavioral and multimodal magnetic resonance imaging (MRI) study, we investigated the neurocognitive profile and cortico-cerebellar dysfunctions underlying executive functioning in individuals with FRDA.



22 FRDA patients and 22 controls were clinically and neuropsychologically examined. Fifteen of each underwent structural and functional MRI using a verbal-fluency task with phonemic and semantic conditions. Gray (GM) and white matter (WM) alterations were assessed by means of voxel-based morphometry and diffusion-tensor imaging.



The neuropsychological profile demonstrated deficits in verbal fluency, working memory and social cognition. Functional MRI data showed most pronounced group-differences in phonemic fluency with patients exhibiting enhanced activity in the cerebellum (VI, Crus I), fronto-insular, premotor and temporo-occipital regions. The semantic condition only revealed reduced activity in the anterior cerebellum; for overt speech, we found increased activity in the motor cortex. Functional connectivity-analysis showed higher co-activation within cerebellar and cortical regions, respectively, and impaired interregional coupling between the cerebellum and fronto-insular cortex for phonemic processing, which was also related to poorer task performance. GM reduction in FRDA was mainly found in lobule VI, whereas WM degeneration was more pronounced including brainstem, cerebellum, and cortex. Decreased cerebellar GM was associated with enhanced activity in the fronto-insular cortex, while loss of WM integrity may translate cortico-cerebellar pathway disruptions.



The pattern of increased neural response with both cerebellar and cortical involvement underlying executive functioning indicates functional reorganization driven by disease-related structural damage in FRDA.
Dynamic Modeling and Performance Analysis of a Grid-Connected Current-Source Inverter-Based Photovoltaic System

Voltage-source inverter (VSI) topology is widely used for grid interfacing of distributed generation (DG) systems. However, when employed as the power conditioning unit in photovoltaic (PV) systems, VSI normally requires another power electronic converter stage to step up the voltage, thus adding to the cost and complexity of the system. To make the proliferation of grid-connected PV systems a successful business option, the cost, performance, and life expectancy of the power electronic interface need to be improved. The current-source inverter (CSI) offers advantages over VSI in terms of inherent boosting and short-circuit protection capabilities, direct output current controllability, and ac-side simpler filter structure. Research on CSI-based DG is still in its infancy. This paper focuses on modeling, control, and steady-state and transient performances of a PV system based on CSI. It also performs a comparative performance evaluation of VSI-based and CSI-based PV systems under transient and fault conditions. Analytical expectations are verified using simulations in the Power System Computer Aided Design/Electromagnetic Transient Including DC (PSCAD/EMTDC) environment, based on a detailed system model.
Cauchy's Theorem and Edge Lengths of Convex Polyhedra

In this paper we explore, from an algorithmic point of view, the extent to which the facial angles and combinatorial structure of a convex polyhedron determine the polyhedron—in particular the edge lengths and dihedral angles of the polyhedron. Cauchy’s rigidity theorem of 1813 states that the dihedral angles are uniquely determined. Finding them is a significant algorithmic problem which we express as a spherical graph drawing problem. Our main result is that the edge lengths, although not uniquely determined, can be found via linear programming. We make use of significant mathematics on convex polyhedra by Stoker, Van Heijenoort, Gale, and Shepherd.
Automatically Constructing a Dictionary for Information Extraction Tasks

Knowledge-based natural language processing systems have achieved good success with certain tasks but they are often criticized because they depend on a domain-specific dictionary that requires a great deal of manual knowledge engineering. This knowledge engineering bottleneck makes knowledge-based NLP systems impractical for real-world applications because they cannot be easily scaled up or ported to new domains. In response to this problem, we developed a system called AutoSlog that automatically builds a domain-specific dictionary of concepts for extracting information from text. Using AutoSlog, we constructed a dictionary for the domain of terrorist event descriptions in only 5 person-hours. We then compared the AutoSlog dictionary with a hand-crafted dictionary that was built by two highly skilled graduate students and required approximately 1500 person-hours of effort. We evaluated the two dictionaries using two blind test sets of 100 texts each. Overall, the AutoSlog dictionary achieved 98% of the performance of the hand-crafted dictionary. On the first test set, the AutoSlog dictionary obtained 96.3% of the performance of the hand-crafted dictionary. On the second test set, the overall scores were virtually indistinguishable with the AutoSlog dictionary achieving 99.7% of the performance of the hand-
Collective Classification of Network Data

Numerous real-world applications produce networked data such as web data (hypertext documents connected via hyperlinks) and communication networks (people connected via communication links). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such data. In this report, we attempt to provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
On happiness and human potentials: a review of research on hedonic and eudaimonic well-being.

Well-being is a complex construct that concerns optimal experience and functioning. Current research on well-being has been derived from two general perspectives: the hedonic approach, which focuses on happiness and defines well-being in terms of pleasure attainment and pain avoidance; and the eudaimonic approach, which focuses on meaning and self-realization and defines well-being in terms of the degree to which a person is fully functioning. These two views have given rise to different research foci and a body of knowledge that is in some areas divergent and in others complementary. New methodological developments concerning multilevel modeling and construct comparisons are also allowing researchers to formulate new questions for the field. This review considers research from both perspectives concerning the nature of well-being, its antecedents, and its stability across time and culture.
Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.
Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals. That men are machines (whatever else they may be) has long been suspected; but not till our generation have men fairly felt in concrete just what wonderful psycho-neuro-physical mechanisms they are. William James (1893; 1842 – 1910) A. Fred,  BIOSTEC 2009, CCIS 52, pp. 21–47, 2010.
Simple Customization of Recursive Neural Networks for Semantic Relation Classification

In this paper, we present a recursive neural network (RNN) model that works on a syntactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting of important phrases for the target task. We also propose to average parameters in training. Our experimental results on semantic relation classification show that both phrase categories and task-specific weighting significantly improve the prediction accuracy of the model. We also show that averaging the model parameters is effective in stabilizing the learning and improves generalization capacity. The proposed model marks scores competitive with state-of-the-art RNN-based models.
Hybrid distribution transformer: Concept development and field demonstration

Today's distribution system is expected to supply power to loads for which it was not designed. Moreover, high penetration of distributed generation units is redefining the requirements for the design, control and operation of the electric distribution system. A Hybrid Distribution Transformer is a potential cost-effective alternative solution to various distribution grid control devices. The Hybrid Distribution Transformer is realized by augmenting a regular transformer with a fractionally rated power electronic converter, which provides the transformer with additional control capabilities. The Hybrid Distribution Transformer concept can provide dynamic ac voltage regulation, reactive power compensation and, in future designs, form an interface with energy storage devices. Other potential functionalities that can be realized from the Hybrid Distribution Transformer include voltage phase angle control, harmonic compensation and voltage sag compensation. This paper presents the concept of a Hybrid Distribution Transformer and the status of our efforts towards a 500 kVA, 12.47 kV/480 V field demonstrator.
A self-powered ultra-fast DC solid state circuit breaker using a normally-on SiC JFET

This paper introduces a new self-powered solid state circuit breaker (SSCB) concept using a normally-on SiC JFET as the main static switch and a fast-starting isolated DC/DC converter as the protection driver. The new SSCB detects short circuit faults by sensing its drain-source voltage rise, and draws power from the fault condition to turn and hold off the SiC JFET. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or additional wiring. A unique low power isolated DC/DC converter is designed and optimized to provide a fast reaction to a short circuit event. The SSCB prototypes have experimentally demonstrated a fault current interruption capability up to 180 amperes at a DC bus voltage of 400 volts within 0.8 microseconds. DC circuit protection applications provide a unique market opportunity for wide bandgap power semiconductor devices outside the conventional focus on power electronic converter applications.