nils-herrmann/clean_scirepeval huggingface dataset:
Brain–machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm

Real-time functional MRI (rtfMRI) has been used as a basis for brain-computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain-machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects' thought processes was possible using the rtfMRI-based BMI trials.
Penetration state transition analysis: A rule-based intrusion detection approach

| This paper presents a new approach to representing and detecting computer penetrations in real-time. The approach, called state transition analysis, models penetrations as a series of state changes that lead from an initial secure state to a target compromised state. State transition diagrams, the graphical representation of penetrations, identify precisely the requirements for and the compromise of a penetration and present only the critical events that must occur for the successful completion of the penetration. State transition diagrams are written to correspond to the states of an actual computer system, and these diagrams form the basis of a rule-based expert system for detecting penetrations, called the State Transition Analysis Tool (STAT). The design and implementation of a UNIX-speci c prototype of this expert system, called USTAT, is also presented. This prototype provides a further illustration of the overall design and functionality of this intrusion detection approach. Lastly, STAT is compared to the functionality of comparable intrusion detection tools. Keywords|Security, Intrusion Detection, Expert Systems
The Pendubot: a mechatronic system for control research and education

1 In this paper we describe the Pendubot, a mechatronic device for use in control engineering education and for research in nonlinear control and robotics. This device is a two{link planar robot with an actuator at the shoulder but no actuator at the elbow. With this system , a number of fundamental concepts in nonlinear dynamics and control theory may be illustrated. The pendubot complements previous mechatronic systems, such as the Acrobot 3] and the inverted pendulum of Furuta 4].
Who is Retweeting the Tweeters? Modeling, Originating, and Promoting Behaviors in the Twitter Network

Real-time microblogging systems such as Twitter offer users an easy and lightweight means to exchange information. Instead of writing formal and lengthy messages, microbloggers prefer to frequently broadcast several short messages to be read by other users. Only when messages are interesting, are they propagated further by the readers. In this article, we examine user behavior relevant to information propagation through microblogging. We specifically use retweeting activities among Twitter users to define and model originating and promoting behavior. We propose a basic model for measuring the two behaviors, a mutual dependency model, which considers the mutual relationships between the two behaviors, and a range-based model, which considers the depth and reach of users’ original tweets. Next, we compare the three behavior models and contrast them with the existing work on modeling influential Twitter users. Last, to demonstrate their applicability, we further employ the behavior models to detect interesting events from sudden changes in aggregated information propagation behavior of Twitter users. The results will show that the proposed behavior models can be effectively applied to detect interesting events in the Twitter stream, compared to the baseline tweet-based approaches.
Architectural Support for Mitigating Row Hammering in DRAM Memories

DRAM scaling has been the prime driver of increasing capacity of main memory systems. Unfortunately, lower technology nodes worsen the cell reliability as it increases the coupling between adjacent DRAM cells, thereby exacerbating different failure modes. This paper investigates the reliability problem due to Row Hammering, whereby frequent activations of a given row can cause data loss for its neighboring rows. As DRAM scales to lower technology nodes, the threshold for the number of row activations that causes data loss for the neighboring rows reduces, making Row Hammering a challenging problem for future DRAM chips. To overcome Row Hammering, we propose two architectural solutions: First, Counter-Based Row Activation (CRA), which uses a counter with each row to count the number of row activations. If the count exceeds the row hammering threshold, a dummy activation is sent to neighboring rows proactively to refresh the data. Second, Probabilistic Row Activation (PRA), which obviates storage overhead of tracking and simply allows the memory controller to proactively issue dummy activations to neighboring rows with a small probability for all memory access. Our evaluations show that these solutions are effective at mitigating Row hammering while causing negligible performance loss (<; 1 percent).
Singularity-robust task-priority redundancy resolution for real-time kinematic control of robot manipulators

Practical application of the task-priority redundancy resolution technique must deal with the occurrence of kinematic and algorithmic singularities. The aim of this paper is twofold. First, the application of existing singularity-robust methods to the case of kinematically redundant arms is studied. Then, a new task-priority redundancy resolution technique is developed that overcomes the effects of algorithmic singularities. Computational aspects of the solutions are also considered in view of real-time implementation of a kinematic control algorithm. The method is applied to a seven-degree-of-freedom manipulator in numerical case studies to demonstrate its effectiveness.
Iterative Procedures for Nonlinear Integral Equations

The numerical solution of nonlinear integral equations involves the iterative soIutioon of finite systems of nonlinear algebraic or transcendental equations. Certain corwent i o n a l techniqucs for treating such systems are reviewed in the context of a particular class of n o n l i n e a r equations. A procedure is synthesized to offset some of the disadvantages of these t e c h n i q u e s in this context; however, the procedure is not restricted to this pt~rticular class of s y s t e m s of nonlinear equations.
A preliminary experiment to assess the fear value of preselected sound parameters in a survival horror game

This paper presents an experiment testing which sound parameters, in a survival horror game context, most warrant further investigation as a means to control the level of fear in such games. The experiment is part of a long-term study ultimately designed to support the development of a biofeedback procedural audio engine for computer games. By this means, it is hoped to provide an enhanced gaming experience whereby sound synthesis and audio processing is conducted in real-time according to the player's affect responses and emotional state. Results indicate that coarse manipulation of audio parameters has the potential to influence the intensity of the player's fear response whilst playing a survival horror game. Evidence is also presented that supports the integration of event logging and realtime participant vocal response into an experimental design to gather unbiased, quantitative data that can be associated with qualitative emotional response.
Hypertension and the brain: vulnerability of the prefrontal regions and executive functions.

Untreated hypertension negatively affects brain anatomy and cognitive functions, but the effects of medically treated hypertension are unclear. The authors compared 40 middle-age and older adults diagnosed with essential hypertension to demographically matched normotensive peers. Volumes of 7 brain regions and deep and periventricular white-matter hyperintensities (WMH) were measured on magnetic resonance imaging scans. Performance in 4 cognitive domains (perseveration, working memory, fluid reasoning, and vocabulary knowledge) was evaluated. Persons with hypertension had smaller prefrontal cortex and underlying white matter volumes and increased frontal WMH. No group differences were found in other examined brain regions. Among examined cognitive variables, hypertensive patients committed significantly more perseverative errors. Thus, even controlled hypertension may be associated with deficits in brain structure and cognition, warranting further study.
Topographically specific hippocampal projections target functionally distinct prefrontal areas in the rhesus monkey.

The sources of ipsilateral projections from the hippocampal formation, the presubiculum, area 29a-c, and parasubiculum to medial, orbital, and lateral prefrontal cortices were studied with retrograde tracers in 27 rhesus monkeys. Labeled neurons within the hippocampal formation (CA1, CA1', prosubiculum, and subiculum) were found rostrally, although some were noted throughout the entire rostrocaudal extent of the hippocampal formation. Most labeled neurons in the hippocampal formation projected to medial prefrontal cortices, followed by orbital areas. In addition, there were differences in the topography of afferent neurons projecting to medial when compared with orbital cortices. Labeled neurons innervating medial cortices were found mainly in the CA1' and CA1 fields rostrally, but originated in the subicular fields caudally. In contrast, labeled neurons which innervated orbital cortices were considerably more focal, emanating from the same relative position within a field throughout the rostrocaudal extent of the hippocampal formation. In marked contrast to the pattern of projection to medial and orbital prefrontal cortices, lateral prefrontal areas received projections from only a few labeled neurons found mostly in the subicular fields. Lateral prefrontal cortices received the most robust projections from the presubiculum and the supracallosal area 29a-c. Orbital, and to a lesser extent medial, prefrontal areas received projections from a smaller but significant number of neurons from the presubiculum and area 29a-c. Only a few labeled neurons were found in the parasubiculum, and most projected to medial prefrontal areas. The results suggest that functionally distinct prefrontal cortices receive projections from different components of the hippocampal region. Medial and orbital prefrontal cortices may have a role in long-term mnemonic processes similar to those associated with the hippocampal formation with which they are linked. Moreover, the preponderance of projection neurons from the hippocampal formation innervating medial when compared with orbital prefrontal areas followed the opposite trend from what we had observed previously for the amygdala (Barbas and De Olmos [1990] (J Comp Neurol 301:1-23). Thus, the hippocampal formation, associated with mnemonic processes, targets predominantly medial prefrontal cortices, whereas the amygdala, associated with emotional aspects of memory, issues robust projections to orbital limbic cortices. Lateral prefrontal cortices receive robust projections from the presubiculum and area 29a-c and sparse projections from the hippocampal formation. These findings are consistent with the idea that the role of lateral prefrontal cortices in memory is distinct from that of either medial or orbital cortices. The results suggest that signals from functionally distinct limbic structures to some extent follow parallel pathways to functionally distinct prefrontal cortices.
Understanding the Adoption of Mobile Banking Services: An Empirical Assessment

Services offered through the mobile channel are increasing rapidly. However, limited understanding exists on the variation in adoption of these services. This study provides insights on this area by examining factors that influence the adoption of mobile banking services. Based on UTAUT, innovation diffusion, and ubiquitous computing framework, we identity accessibility, alertness, personalization, ease of navigation, service compatibility, device compatibility, and facilitating conditions as key drivers of mobile banking services adoption. Empirical assessment based on discriminant analysis shows that individual perceptions regarding accessibility, alertness, and ease of navigation have a significant influence on the adoption decision. Interestingly, we found that accessibility and ease of navigation had a positive association with the adoption of mobile banking services, while non-adopters perceived alertness to be a more compelling value proposition. Overall, the results offer guidance on how to increase adoption rates and open new avenues for research in the area of mobile services.
Phishing Detection Taxonomy for Mobile Device

Phishing is one of the social engineering attacks and currently hit on mobile devices. Based on security report by Lookout , 30% of Lookout users clicking on an unsafe link per year by using mobile device. Few phishing detection techniques have been applied on mobile device. However, review on phishing detection technique on the detection technique redundant is still need. This paper addresses the current trend phishing detection for mobile device and identifies significant criterion to improve phishing detection techniques on mobile device. Thus, existing research on phishing detection technique for computer and mobile device will be compared and analysed. Hence, outcome of the analysis becomes a guideline in proposing generic phishing detection taxonomy for mobile device.
Stress sensitivity of fault seismicity : a comparison between limited-offset oblique and major strike-slip faults

We present a new three-dimensional inventory of the southern San Francisco Bay area faults and use it to calculate stress applied principally by the 1989 M=7.1 Loma Prieta earthquake, and to compare fault seismicity rates before and after 1989. The major high-angle right-lateral faults exhibit a different response to the stress change than do minor oblique (rightlateral/thrust) faults. Seismicity on oblique-slip faults in the southern Santa Clara Valley thrust belt increased where the faults were unclamped. The strong dependence of seismicity change on normal stress change implies a high coefficient of static friction. In contrast, we observe that faults with significant offset (> 50-100 km) behave differently; microseismicity on the Hayward fault diminished where right-lateral shear stress was reduced, and where it was unclamped by the Loma Prieta earthquake. We observe a similar response on the San Andreas fault zone in southern California after the Landers earthquake sequence. Additionally, the offshore San Gregorio fault shows a seismicity rate increase where right-lateral/oblique shear stress was increased by the Loma Prieta earthquake despite also being clamped by it. These responses are consistent with either a low coefficient of static friction or high pore fluid pressures within the fault zones. We can explain the different behavior of the two styles of faults if those with large cumulative offset become impermeable through gouge buildup; coseismically pressurized pore fluids could be trapped and negate imposed normal stress changes, whereas in more limited offset faults fluids could rapidly escape. The difference in behavior between minor and major faults may explain why frictional failure criteria that apply intermediate coefficients of static friction can be effective in describing the broad distributions of aftershocks that follow large earthquakes, since many of these events occur both inside and outside major fault zones.
Empirical Study on Deep Learning Models for QA

In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation , Neural Turing Machine , and Memory Networks  for a simulated QA data set . This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.
Augmented Reality with Hololens: Experiential Architectures Embedded in the Real World

Early hands-on experiences with the Microsoft Hololens augmented/mixed reality device are reported and discussed, with a general aim of exploring basic 3D visualization. A range of usage cases are tested, including data visualization and immersive data spaces, in-situ visualization of 3D models and full scale architectural form visualization. Ultimately, the Hololens is found to provide a remarkable tool for moving from traditional visualization of 3D objects on a 2D screen, to fully experiential 3D visualizations embedded in the real world.
Gated convolutional networks based hybrid acoustic models for low resource speech recognition

In acoustic modeling for large vocabulary speech recognition, recurrent neural networks (RNN) have shown great abilities to model temporal dependencies. However, the performance of RNN is not prominent in resource limited tasks, even worse than the traditional feedforward neural networks (FNN). Furthermore, training time for RNN is much more than that for FNN. In recent years, some novel models are provided. They use non-recurrent architectures to model long term dependencies. In these architectures, they show that using gate mechanism is an effective method to construct acoustic models. On the other hand, it has been proved that using convolution operation is a good method to learn acoustic features. We hope to take advantages of both these two methods. In this paper we present a gated convolutional approach to low resource speech recognition tasks. The gated convolutional networks use convolutional architectures to learn input features and a gate to control information. Experiments are conducted on the OpenKWS, a series of low resource keyword search evaluations. From the results, the gated convolutional networks relatively decrease the WER about 6% over the baseline LSTM models, 5% over the DNN models and 3% over the BLSTM models. In addition, the new models accelerate the learning speed by more than 1.8 and 3.2 times compared to that of the baseline LSTM and BLSTM models.
Multiple-resonator-based bandpass filters

This article describes a class of recently developed multiple-mode-resonator-based bandpass filters for ultra-wide-band (UWB) transmission systems. These filters have many attractive features, including a simple design, compact size, low loss and good linearity in the UWB, enhanced out-of-band rejection, and easy integration with other circuits/antennas. In this article, we present a variety of multiple-mode resonators with stepped-impedance or stub-loaded nonuniform configurations and analyze their properties based on the transmission line theory. Along with the frequency dispersion of parallel-coupled transmission lines, we design and implement various filter structures on planar, uniplanar, and hybrid transmission line geometries.
Data Approximation Using Biarcs

. An algorithm for data approximation with biarcs is presented. The method uses a specific formulation of biarcs appropriate for parametric curves in Bézier or NURBS formulation. A base curve is applied to obtain tangents and anchor points for the individual arcs joining in G 1 continuity. Data sampled from circular arcs or straight line segments is represented precisely by one biarc. The method is most useful in numerical control to drive the cutter along straight line or circular paths.
Hemihyperplasia-multiple lipomatosis syndrome (HHML): a challenge in spinal care.

A 15-year-old girl developed a progressive paraparesis over a period of six months, secondary to spinal cord compression by a lipomatous mass and anomalies of the vertebral column. Clinically, a right hemihyperplasia affecting the trunk and lower limb was evident, as well as a right convex lumbar scoliosis. CT and MRI demonstrated severe spinal cord compression resulting from intraspinal lipomatosis, overgrowth of right facet joints (T8 to L5), and kyphoscoliosis. Surgical decompression was undertaken. A lumbar scoliosis of 48 degrees was partially corrected by means of dual-rod instrumentation. The neurological deficit improved significantly, and ambulation was progressively restored. The patient carried the diagnosis of Proteus syndrome for several years, but reevaluation of clinical features prompted the diagnosis of Hemihyperplasia Multiple Lipomatosis syndrome (HHML). This rare sporadic disorder is often confused with Proteus syndrome. As in Proteus syndrome, spinal cord compression in patients with HHML can result from lipomatous infiltration and/or significant spinal abnormalities including kyphoscoliosis and overgrowth. HHML and Proteus syndrome are discussed and compared with special emphasis on spinal and orthopaedic pathologies.
Real-time approaching vehicle detection in blind-spot area

Detecting objects (including all kinds of vehicles, bicycles, and pedestrians) accurately and efficiently is an essential issue in blind-spot information system (BLIS). To meet these requirements, this paper presents an image-based method to detect approaching objects in blind-spot area and proposes a verification method by using the recorded video database from real traffic environment. By taking video frames and converting the images into one dimensional information, the image entropy of the road scene in the near lane are estimated. Thus, by analysis the lane information, an object will be detected and located in a constant time. This idea has been realized and implemented on low-cost DSP platform developed by Automotive Research and Testing Center (ARTC, Taiwan). The accurate rate of this blind-spot detection system (BDS) is 91% and the frame rate is more than 20 frames per sec (fps), in day and night and all weather conditions. The BDS has been applied for general vehicles and heavy truck vehicles nowadays.
Approximate 32-bit floating-point unit design with 53% power-area product reduction

The floating-point unit is one of the most common building block in any computing system and is used for a huge number of applications. By combining two state-of-the-art techniques of imprecise hardware, namely Gate-Level Pruning and Inexact Speculative Adder, and by introducing a novel Inexact Speculative Multiplier architecture, three different approximate FPUs and one reference IEEE-754 compliant FPU have been integrated in a 65 nm CMOS process within a low-power multi-core processor. Silicon measurements show up to 27% power, 36% area and 53 % power-area product savings compared to the IEEE-754 single-precision FPU. Accuracy loss has been evaluated with a high-dynamic-range image tone-mapping algorithm, resulting in small but non-visible errors with image PSNR value of 90 dB.
New Word Detection in Ancient Chinese Literature

Mining Ancient Chinese corpus is not as convenient as modern Chinese, because there is no complete dictionary of ancient Chinese words which leads to the bad performance of tokenizers. So finding new words in ancient Chinese texts is significant. In this paper, the Apriori algorithm is improved and used to produce candidate character sequences. And a long short-term memory (LSTM) neural network is used to identify the boundaries of the word. Furthermore, we design word confidence feature to measure the confidence score of new words. The experimental results demonstrate that the improved Apriori-like algorithm can greatly improve the recall rate of valid candidate character sequences, and the average accuracy of our method on new word detection raise to 89.7%.