nils-herrmann/clean_scirepeval huggingface dataset:
Robotic Forceps Manipulator With a Novel Bending Mechanism

This paper proposes a new bending technique with a screwdrive mechanism that allows for omnidirectional bending motion by rotating two linkages, each consisting of a right-handed screw, a universal joint, and a left-handed screw. The new screwdrive mechanism, termed double-screw-drive (DSD) mechanism, is utilized in a robotic forceps manipulator for laparoscopic surgery. A robotic forceps manipulator incorporating the DSD mechanism (DSD forceps) can bend without using wires. Without wires, it has high rigidity, and can bend at 90° in any arbitrary direction. In addition, the gripper of the DSD forceps can perform rotational motion, which is achieved by rotating a third linkage in the DSD mechanism. Opening and closing motions of the gripper are attained by wire actuation. Fundamental experiments examining the bending force and the accuracy of the DSD forceps were conducted, and an analysis of the accuracy was performed. Control of the DSD forceps through a teleoperation system was achieved via a joystick-type manipulator. A servo system was constructed for each linkage and the wire actuation mechanism, and tracking control experiments as well as a suturing experiment were conducted. The results of the experiments showed that the required design specifications were fulfilled. Thus, the validity of the DSD forceps was demonstrated.
Multiparametric Decision Support System for the Prediction of Oral Cancer Reoccurrence

Oral squamous cell carcinoma (OSCC) constitutes the predominant neoplasm of the head and neck region, featuring particularly aggressive nature, associated with quite unfavorable prognosis. In this paper, we formulate a decision support system that integrates a multitude of heterogeneous data (clinical, imaging, and genomic), thus, framing all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses (local or metastatic) of the disease. The discrimination potential of each source of data is initially explored separately, and afterward the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse.
SAR automatic target recognition based on a visual cortical system

Human Vision system is the most complex and accurate system. In order to extract better features about Synthetic Aperture Radar (SAR) targets, a SAR automatic target recognition (ATR) algorithm based on human visual cortical system is proposed. This algorithm contains three stages: (1) Image preprocessing (we use a Kuan filter to do the enhancement and an adaptive Intersecting Cortical Model (ICM) to do the segmentation) (2) Feature extraction using a sparse autoencoder. (3) Classification using a softmax regression classifier. Experiment result of MSTAR public data shows a better performance of recognition.
The Influence of Perceived Value and Trust on Online Buying Intention

This research focuses on the factors influencing online buying intention. The aim of this research is to find out perceived value and trust’s influence on online buying intention, which combined several pervious researches’ result. Perceived value is measured by perceived value and perceived product sacrifice, trust is measured by ability, benevolence and integrity. Based on survey to 238 college students, the author analyzes the questionnaire data with SPSS 15.0 and LISREL 8.8. The results show that perceived value and perceived product sacrifice have significantly influence on online shopper’s buying intention. On the other hand, ability and benevolence also have positive effect on online shopper’s buying intention. But integrity, as a part of trust, has no significantly influence on online shopper’s
DOLPHIN: An Autonomous Indoor Positioning System in Ubiquitous Computing Environment

Determining physical location of indoor objects is one of the key issues in development of context-aware applications in ubiquitous computing environment. This is mainly because context information obtained from sensor network is meaningful only when the physical location of context information source is determined. Recently, several indoor location information systems, such as Active Bat[1] and Cricket[2], have been developed for precise indoor object localization. However, to provide accurate physical location tracking in large-scale space, those systems requires a lot of manual configuration for all the ultrasonic sensor nodes. To reduce configuration costs, we developed a new indoor positioning system called DOLPHIN. The DOLPIN system consists of distributed wireless sensor nodes which are capable of sending and receiving RF and ultrasonic signals. These nodes are attached to various indoor objects. And using a novel distributed positioning algorithm in the nodes, DOLPHIN enable autonomous positioning of the objects with minimal manual configuration. This paper describes the design and implementation of the DOLPHIN system, and evaluates basic performance through several experiments in indoor environment.
Enhanced cardiac perception is associated with benefits in decision-making.

In the present study we provide the first empirical evidence that viscero-sensory feedback from an internal organ is associated with decision-making processes. Participants with accurate vs. poor perception of their heart activity were compared with regard to their performance in the Iowa Gambling Task. During this task, participants have to choose between four card decks. Decks A and B yield high gains and high losses, and if played continuously, result in net loss. In contrast, decks C and D yield small gains and also small losses, but result in net profit if they are selected continuously. Accordingly, participants have to learn to avoid the net loss options in favor of the net gain options. In our study, participants with good cardiac perception chose significantly more of the net gain and fewer of the net loss options. Our findings document the substantial role of visceral feedback in decision-making processes in complex situations.
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.
Mindfulness meditation improves cognition: Evidence of brief mental training

Although research has found that long-term mindfulness meditation practice promotes executive functioning and the ability to sustain attention, the effects of brief mindfulness meditation training have not been fully explored. We examined whether brief meditation training affects cognition and mood when compared to an active control group. After four sessions of either meditation training or listening to a recorded book, participants with no prior meditation experience were assessed with measures of mood, verbal fluency, visual coding, and working memory. Both interventions were effective at improving mood but only brief meditation training reduced fatigue, anxiety, and increased mindfulness. Moreover, brief mindfulness training significantly improved visuo-spatial processing, working memory, and executive functioning. Our findings suggest that 4days of meditation training can enhance the ability to sustain attention; benefits that have previously been reported with long-term meditators.
Online Trajectory Generation: Basic Concepts for Instantaneous Reactions to Unforeseen Events

This paper introduces a new method for motion-trajectory generation of mechanical systems with multiple degrees of freedom (DOFs). The key feature of this new concept is that motion trajectories are generated online, i.e., within every control cycle, typically every millisecond. This enables systems to react instantaneously to unforeseen and unpredictable (sensor) events at any time instant and in any state of motion. As a consequence, (multi)sensor integration in robotics, in particular the development of control systems enabling sensor-guided and sensor-guarded motions, becomes greatly simplified. We introduce a class of online trajectory-generation algorithms and present the mathematical basics of this new approach. The algorithms presented here consist of three steps: calculation of the minimum synchronization time for all DOFs, synchronization of all DOFs, and calculation of output values. The theory is followed by real-world experimental results indicating new possibilities in robot-motion control.
IoT-fog-cloud based architecture for smart city: Prototype of a smart building

Here, we present a prototype of a smart building using newly surfacing technologies like IoT (Internet of Things), fog and cloud for the smart city. The demand for everything smart is increasing daily, but the main stumbling block is its high price. So, our aim is to improve the standard of living in home and in office with newly improved working facilities where the whole system will be automatic, efficient and will be under the control of the user via his/her smartphone or computer but the cost will stay within the budget of a common man. All these are done by the incorporation of IoT, fog and cloud. The assimilation is done using open source hardwares and softwares to reduce the cost dramatically than the other existing solutions and implement it in an impressive and ingenious way without compromising QoS (Quality of Service) of any of the functionalities provided by other existing solutions.
Bicycle dynamics and control: adapted bicycles for education and research

In this paper, the dynamics of bicycles is analyzed from the perspective of control. Models of different complexity are presented, starting with simple ones and ending with more realistic models generated from multibody software. Models that capture essential behavior such as self-stabilization as well as models that demonstrate difficulties with rear wheel steering are considered. Experiences using bicycles in control education along with suggestions for fun and thought-provoking experiments with proven student attraction are presented. Finally, bicycles and clinical programs designed for children with disabilities are described.
5GNOW: non-orthogonal, asynchronous waveforms for future mobile applications

This article provides some fundamental indications about wireless communications beyond LTE/LTE-A (5G), representing the key findings of the European research project 5GNOW. We start with identifying the drivers for making the transition to 5G networks. Just to name one, the advent of the Internet of Things and its integration with conventional human-initiated transmissions creates a need for a fundamental system redesign. Then we make clear that the strict paradigm of synchronism and orthogonality as applied in LTE prevents efficiency and scalability. We challenge this paradigm and propose new key PHY layer technology components such as a unified frame structure, multicarrier waveform design including a filtering functionality, sparse signal processing mechanisms, a robustness framework, and transmissions with very short latency. These components enable indeed an efficient and scalable air interface supporting the highly varying set of requirements originating from the 5G drivers.
Control Strategy to Naturally Balance Hybrid Converter for Variable-Speed Medium-Voltage Drive Applications

In this paper, a novel control strategy to naturally balance a hybrid converter is presented for medium-voltage induction motor drive applications. Fundamental current of the motor is produced by a three-level quasi-square-wave converter. Several single-phase full-bridge converters are connected in series with this quasi-square-wave converter to eliminate harmonic voltages from the net output voltage of this hybrid converter. Various novel modulation strategies for these series cells are proposed. These lead to a naturally regulated dc-bus voltage of series cells. These also optimize the number of series cells required. Variable fundamental voltage and frequency control of induction motor is adopted with a constant dc-bus voltage of the three-level quasi-square-wave converter. Experimental results are also produced here to validate the proposed modulation strategies for induction motor drives using low-voltage protomodel. Based on the aforementioned, a 2.2-kV induction motor drive has been built, and results are also produced in this paper.
Investigating the impact of video games on high school students' engagement and learning about genetics

0360-1315/$ see front matter 2009  Corresponding author.  fax E-mail address: len_annetta@ncsu.edu (L.A. Annet The popularity of video games has transcended entertainment crossing into the world of education. While the literature base on educational gaming is growing, there is still a lack of systematic study of this emerging technology’s efficacy. This quasi-experimental study evaluated a teacher created video game on genetics in terms of its affective and cognitive impact on student users. While statistical results indicated no differences (p > .05) in student learning as measured by our instrument, there were significant differences (p < .05) found in the participants’ level of engagement while interfacing with the video game. Implications on this emerging line of inquiry are discussed.
Chirp Sensing Codes : Deterministic Compressed Sensing Measurements for Fast Recovery

Compressed sensing is a novel technique to acquire sparse signals with few measurements. Normally, compressed sensing uses random projections as measurements. Here we design deterministic measurements and an algorithm to accomplish signal recovery with computational efficiently. A measurement matrix is designed with chirp sequences forming the columns. Chirps are used since an efficient method using FFTs can recover the parameters of a small superposition. We show empirically that this type of matrix is valid as compressed sensing measurements. This is done by a comparison with random projections and a modified reduced isometry property. Further, by implementing our algorithm, simulations show successful recovery of signals with sparsity levels similar to those possible by Matching Pursuit with random measurements. For sufficiently sparse signals, our algorithm recovers the signal with computational complexity O(K log K) for K measurements. This is a significant improvement over existing algorithms.
Adaptive Kalman Filtering for anomaly detection in software appliances

Availability and reliability are often important features of key software appliances such as firewalls, web servers, etc. In this paper we seek to go beyond the simple heartbeat monitoring that is widely used for failover control. We do this by integrating more fine grained measurements that are readily available on most platforms to detect possible faults or the onset of failures. In particular, we evaluate the use of adaptive Kalman Filtering for automated CPU usage prediction that is then used to detect abnormal behaviour. Examples from experimental tests are given.
A 27-mW 3.6-gb/s I/O transceiver

This paper describes a 3.6-Gb/s 27-mW transceiver for chip-to-chip applications. A voltage-mode transmitter is proposed that equalizes the channel while maintaining impedance matching. A comparator is proposed that achieves sampling bandwidth control and offset compensation. A novel timing recovery circuit controls the phase by mismatching the current in the charge pump. The architecture maintains high signal integrity while each port consumes only 7.5 mW/Gb/s. The entire design occupies 0.2 mm/sup 2/ in a 0.18-/spl mu/m 1.8-V CMOS technology.
Cannabis, a complex plant: different compounds and different effects on individuals.

Cannabis is a complex plant, with major compounds such as delta-9-tetrahydrocannabinol and cannabidiol, which have opposing effects. The discovery of its compounds has led to the further discovery of an important neurotransmitter system called the endocannabinoid system. This system is widely distributed in the brain and in the body, and is considered to be responsible for numerous significant functions. There has been a recent and consistent worldwide increase in cannabis potency, with increasing associated health concerns. A number of epidemiological research projects have shown links between dose-related cannabis use and an increased risk of development of an enduring psychotic illness. However, it is also known that not everyone who uses cannabis is affected adversely in the same way. What makes someone more susceptible to its negative effects is not yet known, however there are some emerging vulnerability factors, ranging from certain genes to personality characteristics. In this article we first provide an overview of the biochemical basis of cannabis research by examining the different effects of the two main compounds of the plant and the endocannabinoid system, and then go on to review available information on the possible factors explaining variation of its effects upon different individuals.
Efficient Transfer Learning Method for Automatic Hyperparameter Tuning

We propose a fast and effective algorithm for automatic hyperparameter tuning that can generalize across datasets. Our method is an instance of sequential model-based optimization (SMBO) that transfers information by constructing a common response surface for all datasets, similar to Bardenet et al. (2013). The time complexity of reconstructing the response surface at every SMBO iteration in our method is linear in the number of trials (significantly less than previous work with comparable performance), allowing the method to realistically scale to many more datasets. Specifically, we use deviations from the per-dataset mean as the response values. We empirically show the superiority of our method on a large number of synthetic and real-world datasets for tuning hyperparameters of logistic regression and ensembles of classifiers.
Measures of retaining digital evidence to prosecute computer-based cyber-crimes

With the rapid growth of computer and network systems in recent years, there has also been a corresponding increase in cyber-crime. Cybercrime takes many forms and has garnered much attention in the media, making information security a more urgent and important priority. In order to fight cyber-crime, criminal evidence must be gathered from these computer-based systems. This is quite different from the collection of conventional criminal evidence and can confuse investigators attempting to deal with the forensics of cyber-crime, highlighting the importance of computer forensics. In this paper, we offer solutions to guard against cyber-crime through the implementation of software toolkits for computerbased systems. In this way, those who engage in criminal acts in cyber-space can be more easily apprehended.
Technological impact of magnetic hard disk drives on storage systems

Magnetic hard disk drives have undergone vast technological improvements since their introduction as storage devices over 45 years ago, and these improvements have had a marked influence on how disk drives are applied and what they can do. Areal density increases have exceeded the traditional semiconductor development trajectory and have yielded higher-capacity, higher-performance, and smaller-form-factor disk drives, enabling desktop and mobile computers to store multi-gigabytes of data easily. Server systems containing large numbers of drives have achieved unparalleled reliability, performance, and storage capacity. All of these characteristics have been achieved at rapidly declining disk costs. This paper relates advances in disk drives to corresponding trends in storage systems and projects where these trends may lead in the future. Magnetic hard disk drives are used as the primary storage device for a wide range of applications, including desktop, mobile, and server systems. In 2002, nearly 200 million disk drives were manufactured worldwide, 1 with the total capacity to store more than 10 19 bytes. Since the first disk drive was introduced in 1956, drives have undergone a rapid evolution, thanks to the application of new magnetic, electronic, and mechanical technologies. These developments have yielded storage devices with very significant capacity and performance increases. Early disk drives were specialized and very expensive, whereas today's drives are nearly commodity items and are universally available. Although the fundamental architecture of disk drives has changed very little in the years since their introduction, the geometric size of drives has been reduced almost to the point of micro-min-iaturization, and these smaller sizes have resulted in storage system characteristics that offer new horizons in data retention and availability. The trends characterizing storage systems in which large numbers of drives participate as a single storage unit have followed the evolutionary behavior of their principal component, the hard disk drive (HDD). Storage system characteristics are also influenced somewhat by components other than disk drives, including DRAM (dynamic random-access memory) caches and buffers, cooling systems, frames and cases, and system software. Areal/volumetric densities Areal density, a traditional measurement for disk drives, determines capacity, internal (media) data rate, and ultimately price per unit of capacity. 2 Figure 1 shows the areal density improvement for hard disk drives since 1956. Significant trend changes have occurred when new technologies have been adopted, so that today's CGR (compound growth rate) is essentially 100 percent or doubling every year, and a 35-million-times increase in this …
Human Face Detection by using Skin Color Segmentation, Face Features and Regions Properties

The goal of face detection is to lo­cate all regions that contain a face. This pa­per has a simple face detection procedure which has two major steps, first to segment skin region from an image, and second, to decide these regions contain human face or not. Our procedure is based on skin color segmentation and human face features (knowledge-based approach). In this paper, we used RGB, YCbCr, CEILAB (L*a*b) and HSV color models for skin color segmentation. These color models with thresholds, help to remove non skin like pixels from an image. We tested each skin region, that skin region is actually represents a human face or not, by using human face features based on knowledge of geometrical properties of human face. The experiment result shows that, the algorithm gives hopeful results. At last, we concluded this paper and proposed future work.