nils-herrmann/clean_scirepeval huggingface dataset:
UIUC: A Knowledge-rich Approach to Identifying Semantic Relations between Nominals

This paper describes a state-of-the-art supervised, knowledge-intensive approach to the automatic identification of semantic relations between nominals in English sentences. The system employs a combination of rich and varied sets of new and previously used lexical, syntactic, and semantic features extracted from various knowledge sources such as WordNet and additional annotated corpora. The system ranked first at the third most popular SemEval 2007 Task – Classification of Semantic Relations between Nominals and achieved an F-measure of 72.4% and an accuracy of 76.3%. We also show that some semantic relations are better suited for WordNet-based models than other relations. Additionally, we make a distinction between out-of-context (regular) examples and those that require sentence context for relation identification and show that contextual data are important for the performance of a noun–noun semantic parser. Finally, learning curves show that the task difficulty varies across relations and that our learned WordNet-based representation is highly accurate so the performance results suggest the upper bound on what this representation can do.
CORD: Energy-Efficient Reliable Bulk Data Dissemination in Sensor Networks

This paper presents CORD, a reliable bulk data dissemination protocol for propagating a large data object to all the nodes in a large scale sensor network. Unlike well- known reliable data dissemination protocols such as Deluge whose primary design criterion is to reduce the latency of object propagation, CORD's primary goal is to minimize energy consumption. To achieve its goals CORD employs a two phase approach in which the object is delivered to a subset of nodes in the network that form a connected dominating set in the first phase, and to the remaining nodes in the second phase. Further, CORD installs a coordinated sleep schedule on the nodes in the network whereby nodes that are not involved in receiving or transmitting data can turn off their radios to reduce their energy consumption. We evaluated the performance of CORD experimentally on both an indoor and outdoor sensor network testbed and via extensive simulations. Our results show that in comparison to Deluge (the de facto network reprogramming protocol for TinyOS) CORD significantly reduces the energy consumption for reliable data dissemination while achieving a comparable latency.
Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors

In this paper, we propose a static scheduling algorithm for allocating task graphs to fullyconnected multiprocessors. We discuss six recently reported scheduling algorithms and show that they possess one drawback or the other which can lead to poor performance. The proposed algorithm, which is called the Dynamic Critical-Path (DCP) scheduling algorithm, is different from the previously proposed algorithms in a number of ways. First, it determines the critical path of the task graph and selects the next node to be scheduled in a dynamic fashion. Second, it rearranges the schedule on each processor dynamically in the sense that the positions of the nodes in the partial schedules are not fixed until all nodes have been considered. Third, it selects a suitable processor for a node by looking ahead the potential start times of the remaining nodes on that processor, and schedules relatively less important nodes to the processors already in use. A global as well as a pair-wise comparison is carried out for all seven algorithms under various scheduling conditions. The DCP algorithm outperforms the previous algorithms by a considerable margin. Despite having a number of new features, the DCP algorithm has admissible time complexity, is economical in terms of the number of processors used and is suitable for a wide range of graph structures.
A fuzzy logic approach for timely adaptive traffic light based on traffic load

In this research, an adaptive timely traffic light is proposed as solution for congestion in typical area in Indonesia.  City, particularly in the most complex junction (fly over, Pettarani, Reformasi highway and Urip S.) is observed for months using static cameras. The condition is mapped into fuzzy logic to have a better time transition of traffic light as opposed to the current conventional traffic light system. In preliminary result, fuzzy logic shows significant number of potential reduced in congestion. Each traffic line has 20-30% less congestion with future implementation of the proposed system.
Screening for Internet addiction: an empirical study on cut-off points for the Chen Internet Addiction Scale.

The aim of this study was to establish the optimal cut-off points of the Chen Internet Addiction Scale (CIAS), to screen for and diagnose Internet addiction among adolescents in the community by using the well-established diagnostic criteria of Internet addiction. This survey of 454 adolescents used screening (57/58) and diagnostic (63/64) cut-off points of the CIAS, a self-reported instrument, based on the results of systematic diagnostic interviews by psychiatrists. The area under the curve of the receiver operating characteristic curve revealed that CIAS has good diagnostic accuracy (89.6%). The screening cut-off point had high sensitivity (85.6%) and the diagnostic cut-off point had the highest diagnostic accuracy, classifying 87.6% of participants correctly. Accordingly, the screening point of the CIAS could provide a screening function in two-stage diagnosis, and the diagnostic point could serve as a diagnostic criterion in one-stage massive epidemiologic research.
High-Voltage Gain Boost Converter Based on Three-State Commutation Cell for Battery Charging Using PV Panels in a Single Conversion Stage

This paper presents a novel high-voltage gain boost converter topology based on the three-state commutation cell for battery charging using PV panels and a reduced number of conversion stages. The presented converter operates in zero-voltage switching (ZVS) mode for all switches. By using the new concept of single-stage approaches, the converter can generate a dc bus with a battery bank or a photovoltaic panel array, allowing the simultaneous charge of the batteries according to the radiation level. The operation principle, design specifications, and experimental results from a 500-W prototype are presented in order to validate the proposed structure.
A robust video based traffic light detection algorithm for intelligent vehicles

Recently, researches on intelligent vehicles which can drive in urban environment autonomously become more popular. Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, a robust and efficient algorithm to detect traffic lights based on video sequences captured by a low cost off-the-shelf video camera is proposed. The algorithm models the hue and saturation according to Gaussian distributions and learns their parameters with training images. From learned models, candidate regions of the traffic lights in the test images can be extracted. Post processing method which takes account of the shape information is applied to the candidate regions. Furthermore, detection results of the previous image frames are aggregated in order to provide a more robust result. Experimental results on several video sequences captured in typical urban environment prove the effectiveness of the proposed algorithm.
Comparative study of using different electric motors in the electric vehicles

In this paper, different electric motors are studied and compared to see the benefits of each motor and the one that is more suitable to be used in the electric vehicle (EV) applications. There are five main electric motor types, DC, induction, permanent magnet synchronous, switched reluctance and brushless DC motors are studied. It is concluded that although the induction motors technology is more mature than others, for the EV applications the brushless DC and permanent magnet motors are more suitable than others. The use of these motors will result in less pollution, less fuel consumption and higher power to volume ratio. The reducing prices of the permanent magnet materials and the trend of increasing efficiency in the permanent magnet and brushless DC motors make them more and more attractive for the EV applications.
Driver Fatigue Detection Based on Eye Tracking

Driver fatigue is a key factor of accident. To solve the problem, eye tracking method is used to alarm tired drivers. To finalize this procedure, we took driver face images first by using a digital camera, then the images are processed and segmented. Then the horizontal projections of the detected objects in the binary images are analyzed. In this way, eye locations are obtained based on the horizontal projection histogram (e.g. at high peak of projection charts). And horizontal boundaries of eyes are located at the valley points. To determine the valley points, an improved slope algorithm is presented in this paper. We use first order linear difference with five steps to calculate curve slope. As a result, boundaries of eyes can be obtained. And eye pixels can be counted in the eye region. Spirit status of drivers can be detected by analyzing the eye pixel numbers
Design considerations and results for an overlapped subarray radar antenna

Overlapped subarray networks produce flat-topped sector patterns with low sidelobes that suppress grating lobes outside of the main beam of the subarray pattern. They are typically used in limited scan applications, where it is desired to minimize the number of controls required to steer the beam. However, the architecture of an overlapped subarray antenna includes many signal crossovers and a wide variation in splitting/combining ratios, which make it difficult to maintain required error tolerances. This paper presents the design considerations and results for an overlapped subarray radar antenna, including a custom subarray weighting function and the corresponding circuit design and fabrication. Measured pattern results will be shown for a prototype design compared with desired patterns.
Experimental Verification of the Oscillating Paddling Gait for an ePaddle-EGM Amphibious Locomotion Mechanism

An eccentric paddle locomotion mechanism based on the epicyclic gear mechanism (ePaddle-EGM), which was proposed to enhance the mobility of amphibious robots in multiterrain tasks, can perform various terrestrial and aquatic gaits. Two of the feasible aquatic gaits are the rotational paddling gait and the oscillating paddling gait. The former one has been studied in our previous work, and a capacity of generating vectored thrust has been found. In this letter, we focus on the oscillating paddling gait by measuring the generated thrusts of the gait on an ePaddle-EGM prototype module. Experimental results verify that the oscillating paddling gait can generate vectored thrust by changing the location of the paddle shaft as well. Furthermore, we compare the oscillating paddling gait with the rotational paddling gait at the vectored thrusting property, magnitude of the thrust, and the gait efficiency.
Radio Over Fiber Link Design for Next Generation Wireless Systems

The performance of radio over fiber (RoF) links using low cost optoelectronic components is assessed for distributed antenna applications in next generation wireless systems. Important design issues are discussed and an example link design is presented for a wireless system requiring the transmission of four radio channels per link direction, each with 100 MHz bandwidth, modulation complexity of 256-QAM and 2048 OFDM subcarriers. We show that the noise introduced by the RoF links does not have a significant impact on wireless range, provided the wireless system has uplink power control. Finally, we compare the cost and performance of RoF links for this application with alternative link types that use digitized radio transmission and show that RoF is the optimum choice from a cost perspective.
SIW Slotted Waveguide Array With Pillbox Transition for Mechanical Beam Scanning

A dual-layer pillbox antenna is proposed for mechanical beam-scanning applications at millimeter-wave frequencies. It consists of three main subsystems (multislot quasi-optical transition, input part, and radiating part). The input and radiating parts are placed in two different layers, and the quasi-optical transition behaves as a 180° E-plane coupler-type bent. The latter is made of an integrated parabolic reflector and several coupling slots etched in the metal layer located between the two substrates hosting the input and radiating parts. A physical optics analysis of the transition is proposed and compared to full-wave simulations. The radiating part is a planar slotted waveguide array. The input part is an H-plane integrated sectoral horn placed in the focal plane of the parabola and moved along a straight line to obtain the beam scanning. For the horn aligned with the parabola axis, the radiation patterns present half-power beamwidth equal to 6° and 15° in E- and H-plane, respectively. The scan range is ±35°. This mechanical scanning approach represents an attractive solution for those applications where a continuous steering of the main beam is required.
ComboGAN: Unrestrained Scalability for Image Domain Translation

The past year alone has seen unprecedented leaps in the area of learning-based image translation, namely Cycle-GAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs; whereas our model requires only 14 generator/discriminator pairs.
Learning a Recurrent Visual Representation for Image Caption Generation

In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. We propose learning this mapping using a recurrent neural network. Unlike previous approaches that map both sentences and images to a common embedding, we enable the generation of novel sentences given an image. Using the same model, we can also reconstruct the visual features associated with an image given its visual description. We use a novel recurrent visual memory that automatically learns to remember long-term visual concepts to aid in both sentence generation and visual feature reconstruction. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-ofthe-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are preferred by humans over 19.8% of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.
A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility

In this paper, we study a set of real-time scheduling problems whose objectives can be expressed as piecewise linear utility functions. This model has very wide applications in scheduling-related problems, such as mixed criticality, response time minimization, and tardiness analysis. Approximation schemes and matrix vectorization techniques are applied to transform scheduling problems into linear constraint optimization with a piecewise linear and concave objective; thus, a neural network-based optimization method can be adopted to solve such scheduling problems efficiently. This neural network model has a parallel structure, and can also be implemented on circuits, on which the converging time can be significantly limited to meet real-time requirements. Examples are provided to illustrate how to solve the optimization problem and to form a schedule. An approximation ratio bound of 0.5 is further provided. Experimental studies on a large number of randomly generated sets suggest that our algorithm is optimal when the set is nonoverloaded, and outperforms existing typical scheduling strategies when there is overload. Moreover, the number of steps for finding an approximate solution remains at the same level when the size of the problem (number of jobs within a set) increases.
What's in a Name: A Study of Names, Gender Inference, and Gender Behavior in Facebook

In this paper, by crawling Facebook public profile pages of a l arge and diverse user population in New York City, we create a comp rehensive and contemporary first name list, in which each name is annotated with a popularity estimate and a gender probability. First, we use the name list as part of a novel and powerful tech nique for inferring Facebook users’ gender. Our name-centric approach to gende r prediction partitions the users into two groups, A andB, and is able to accurately predict genders for users belonging toA. Applying our methodology to NYC users in Facebook, we are able to achieve an accuracy of 95.2% for groupA consisting of95.1% of the NYC users. This is a significant improvement over recent r sults of gender prediction , which achieved a maximum accuracy of 77.2% based on users’ group affiliations. Second, having inferred the gender of most users in our Faceb ook dataset, we learn several interesting gender characteristics and anal yze how males and females behave in Facebook. We find, for example, that females a nd m les exhibit contrasting behaviors while hiding their attributes, such as gender, age, and sexual preference, and that females are more conscious about th eir online privacy on Facebook.
Automatic cinematography and multilingual NLG for generating video documentaries

Automatically constructing a complete documentary or educational film from scattered pieces of images and knowledge is a significant challenge. Even when this information is provided in an annotated format, the problems of ordering, structuring and animating sequences of images, and producing natural language descriptions that correspond to those images within multiple constraints, are each individually difficult tasks. This paper describes an approach for tackling these problems through a combination of rhetorical structures with narrative and film theory to produce movie-like visual animations from still images along with natural language generation techniques needed to produce text descriptions of what is being seen in the animations. The use of rhetorical structures from NLG is used to integrate separate components for video creation and script generation. We further describe an implementation, named GLAMOUR, that produces actual, short video documentaries, focusing on a cultural heritage domain, and that have been evaluated by professional filmmakers.
Design of a quasi-passive knee exoskeleton to assist running

In this paper we describe the design and preliminary evaluation of an energetically-autonomous powered knee exoskeleton to facilitate running. The device consists of a knee brace in which a motorized mechanism actively places and removes a spring in parallel with the knee joint. This mechanism is controlled such that the spring is in parallel with the knee joint from approximately heel-strike to toe-off, and is removed from this state during the swing phase of running. In this way, the spring is intended to store energy at heel-strike which is then released when the heel leaves the ground, reducing the effort required by the quadriceps to exert this energy, thereby reducing the metabolic cost of running.
Supporting business intelligence by providing ontology-based end-user information self-service

Business users need to analyse changing sets of information to effectively support their working tasks. Due to the complexity of enterprise systems and available tools, especially technically unskilled users face considerable challenges when trying to flexibly retrieve needed data in an ad-hoc manner. As a consequence, available data is limited to information artefacts like queries or reports which have been predefined for them by IT experts. To improve information self-service capabilities of business users, we present an ontology-based architecture and end-user tool, enabling easy data access and query creation for business users. Our approach is based on a semantic middleware integrating data from heterogeneous information systems and providing a comprehensible data model in the form of a business level ontology (BO). We show how our end-user tool Semantic Query Designer (SQD) enables convenient navigation and query building upon the BO, and illustrate its usage and the processing of data over all layers of our system architecture in detail, using a comprehensible use case example. As flexible query creation is a crucial precondition of leveraging the usage of enterprise data, we contribute to the enablement of business users of making better informed decisions, thus increasing effectiveness and efficiency of business processes.
FPGA-Based Speed Control IC for PMSM Drive With Adaptive Fuzzy Control

The new generation of field programmable gate array (FPGA) technologies enables an embedded processor intellectual property (IP) and an application IP to be integrated into a system-on-a-programmable-chip (SoPC) developing environment. Therefore, this study presents a speed control integrated circuit (IC) for permanent magnet synchronous motor (PMSM) drive under this SoPC environment. First, the mathematic model of PMSM is defined and the vector control used in the current loop of PMSM drive is explained. Then, an adaptive fuzzy controller adopted to cope with the dynamic uncertainty and external load effect in the speed loop of PMSM drive is proposed. After that, an FPGA-based speed control IC is designed to realize the controllers. The proposed speed control IC has two IPs, a Nios II embedded processor IP and an application IP. The Nios II processor is used to develop the adaptive fuzzy controller in software due to the complicated control algorithm and low sampling frequency control (speed control: 2 kHz). The designed application IP is utilized to implement the current vector controller in hardware owing to the requirement for high sampling frequency control (current loop: 16 kHz, pulsewidth modulation circuit: 4-8 MHz) but simple computation. Finally, an experimental system is set up and some experimental results are demonstrated.
Flipping the CMOS Switch

As CMOS technology is scaled down and adopted for many RF and millimeter-wave radio systems, design of T/R switches in CMOS has received considerable attention. Many T/R switches designed in 0.5 ¿m 65 nm CMOS processes have been reported. Table 4 summarizes these T/R switches. Some of them have become great candidates for WLAN and UWB radios. However, none of them met the requirements of mobile cellular and WPAN 60-GHz radios. CMOS device innovations and novel ideas such as artificial dielectric strips and bandgap structures may provide a comprehensive solution to the challenges of design of T/R switches for mobile cellular and 60-GHz radios.