nils-herrmann/clean_scirepeval huggingface dataset:
Neuronal correlates of a perceptual decision

THE relationship between neuronal activity and psychophysical judgement has long been of interest to students of sensory processing. Previous analyses of this problem have compared the performance of human or animal observers in detection or discrimination tasks with the signals carried by individual neurons, but have been hampered because neuronal and perceptual data were not obtained at the same time and under the same conditions1–4. We have now measured the performance of monkeys and of visual cortical neurons while the animals performed a psychophysical task well matched to the properties of the neurons under study. Here we report that the reliability and sensitivity of most neurons on this task equalled or exceeded that of the monkeys. We therefore suggest that under our conditions, psychophysical judgements could be based on the activity of a relatively small number of neurons.
Adaptive Kalman Filtering for GPS-based Mobile Robot Localization

Kalman filters have been widely used for navigation in mobile robotics. One of the key problems associated with Kalman filter is how to assign suitable statistical properties to both the dynamic and the observational models. For GPS-based localization of a rough-terrain mobile robot, the maneuver of the vehicle and the level of measurement noise are environmental dependent, and hard to be predicted. This is particularly true when the vehicle experiences a sudden change of its state, which is typical on rugged terrain due, for example, to an obstacle or slippery slopes. Therefore to assign constant noise levels for such applications is not realistic. In this work we propose a real-time adaptive algorithm for GPS data processing based on the observation of residuals. Large value of residuals suggests poor performance of the filter that can be improved giving more weight to the measurements provided by the GPS using a fading memory factor. For a finer gradation of this parameter, we used a fuzzy logic inference system implementing our physical understanding of the phenomenon. The proposed approach was validated in experimental trials comparing the performance of the adaptive algorithm with a conventional Kalman filter for vehicle localization. The results demonstrate that the novel adaptive algorithm is much robust to the sudden changes of vehicle motion and measurement errors.
On the Duration, Addressability, and Capacity of Memory-Augmented Recurrent Neural Networks

Memory-augmented recurrent neural networks (M-RNNs) have demonstrated empirically that they are very attractive for many applications, but a good theoretical understanding of their behaviors is unclear yet. In this paper, three analytical indicators named duration, addressability, and capacity of general forms of the additional memory in M-RNNs are formalized. The analysis results of the interactions among these indicators reveal that it is hard for an M-RNN to simultaneously provide good performance on more than two out of three of indicators. Meanwhile, the duration, addressability, and capacity are applied to analyze and compare two M-RNNs: long short term memory and neural turing machine for different cases. The comparison results show that none of the models has better performance on one indicator than the other model all the time. Moreover, it is found that separating memory system into sub-memories can bring the increasing duration and addressability and the decreasing capacity for the whole memory system.
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying classification with respect to specific fairness metrics, modeled the corresponding fair classification problem as constrained optimization problems, and developed tailored algorithms to solve them. Despite this, there still remain important metrics for which there are no fair classifiers with theoretical guarantees; primarily because the resulting optimization problem is non-convex. The main contribution of this paper is a meta-algorithm for classification that can take as input a general class of fairness constraints with respect to multiple non-disjoint and multi-valued sensitive attributes, and which comes with provable guarantees. In particular, our algorithm can handle non-convex "linear fractional" constraints (which includes fairness constraints such as predictive parity) for which no prior algorithm was known. Key to our results is an algorithm for a family of classification problems with convex constraints along with a reduction from classification problems with linear fractional constraints to this family. Empirically, we observe that our algorithm is fast, can achieve near-perfect fairness with respect to various fairness metrics, and the loss in accuracy due to the imposed fairness constraints is often small.
Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network

We investigate the task of open domain opinion relation extraction. Given a large number of unlabelled texts, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is designed to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset, and show that the proposed model is able to achieve promising performances without any human annotations.
Surgical technique: development of an anatomic medial knee reconstruction.

The main static stabilizers of the medial knee are the superficial medial collateral and posterior oblique ligaments. A number of reconstructive techniques have been advocated including one we describe here. However, whether these reconstructions restore function and stability is unclear.



This anatomic reconstruction technique consisted of reconstruction of the proximal and distal divisions of the superficial medial collateral and the posterior oblique ligament using two separate grafts.



We prospectively followed all 28 patients (19 male, nine females) who had this new reconstruction between 2007 and 2009. The average age was 32.4 years (range, 16-56 years). There were eight acute and 20 chronic injuries. All patients presented with side-to-side instability with activities of daily living and other higher level activities. Minimum followup was 6 months (average, 1.5 years; range, 0.5-3 years). No patients were lost to followup.



Preoperative International Knee Documentation Committee subjective outcome scores averaged 43.5 (range, 14-66) and final postoperative values averaged 76.2 (range, 54-88). Preoperative valgus stress radiographs averaged 6.2 mm of medial compartment gapping compared with the contralateral normal knee, whereas postoperative stress radiographs averaged 1.3 mm.



Early observations suggest this anatomic reconstruction technique improves overall patient function and restores valgus instability.
The mirror neuron system and its function in humans

Mirror neurons are a particular type of neurons that discharge when an individual performs an action, as well as when he/she observes a similar action done by another individual. Mirror neurons have been described originally in the premotor cortex (area F5) of the monkey. Subsequent studies have shown that they are present also in the monkey inferior parietal lobule (Rizzolatti et al. 2001). In the human brain, evidence for mirror neurons is indirect, but, although there is no single-neuron study showing the existence of mirror neurons, functional imaging studies revealed activation of the likely homo-logue of monkey area F5 (area 44 and the adjacent ventral area 6) during action observation (see Rizzolatti and Craighero 2004). Furthermore, magnetoencepha-lography (Hari et al. 1998) and EEG (Cochin et al. 1999) have shown activation of motor cortex during observation of finger movements. Very recently, alpha rhythm desynchronization in functionally delimited language and hand motor areas was demonstrated during execution and observation of finger movements in a patient with implanted subdural electrodes (Tremblay et al. 2004). What is the functional role of the mirror neurons? Various hypotheses have advanced: action understanding , imitation, intention understanding, and empathy (see Rizzolatti and Craighero 2004; Gallese et al. 2004). In addition, it has been suggested that mirror-neuron system is the basic neural mechanism from which language developed (Rizzolatti and Arbib 1998). It is my opinion that the question of which is the function of the mirror neurons or of the mirror-neuron system is ill posed. Mirror neurons do not have a specific functional role. The properties of mirror neurons indicate that primate brain is endowed with a mechanism mapping the pictorial description of actions, carried out in the higher order visual areas onto their motor counterpart. This matching mechanism may underlie a variety of functions, depending on what aspect of the observed action is coded, the species considered, the circuit in which mirror neurons are included, and the connectivity of the mirror-neuron system with other systems. Let us examine first action understanding, the original hypothesis that has been proposed for explaining the functional role of the mirror system (Gallese et al. 1996; Rizzolatti et al. 1996). It might sound bizarre that in order to recognize an action, one should activate the motor system. As a matter of fact, this is not so strange. A mere visual perception, without involvement of the motor system would only provide a description …
Reasoning about Categories in Conceptual Spaces

Understanding the process of categorization is a primary research goal in artificial intelligence. The conceptual space framework provides a flexible approach to modeling context-sensitive categorization via a geometrical representation designed for modeling and managing concepts. In this paper we show how algorithms developed in computational geometry, and the Region Connection Calculus can be used to model important aspects of categorization in conceptual spaces. In particular, we demonstrate the feasibility of using existing geometric algorithms to build and manage categories in conceptual spaces, and we show how the Region Connection Calculus can be used to reason about categories and other conceptual regions.
Efficient Combining Keypoints and Correlation Filter for Long-Term Tracking

I72 this paper, we propose a tracker (CKCF) to tackle the challenges of long-term tracking where the target encounter occlusion, deformation. For these problems, we decompose the task of the long-term tracking into translation filter and re-detector, where the re-detector consists of point tracking, a simple method for estimating location and GMS (Grid-based Motion Statistics) filter and it is activated in case of tracking failure, which is detected by computing a score. Finally, the CKCF was evaluated on OTB2015 datasets and the experimental results show that our tracker outperforms favorably many state-of-art trackers in term of speed (∼50 FPS), accuracy and robustness, especially occlusion is encountered.
Design and evaluation of voluntary opening and voluntary closing prosthetic terminal device.

Body-powered prostheses use a cable-operated system to generate forces and move prosthetic joints. However, this control system can only generate forces in one direction, so current body-powered prehensor designs allow the user either to voluntarily open or voluntarily close the tongs. Both voluntary opening (VO) and voluntary closing (VC) modes of operation have advantages for certain tasks, and many end-users desire a terminal device that can switch between the two modes. However, such a terminal device must maintain the same thumb position (i.e., point of Bowden cable attachment) and movement direction in both modes in order to avoid the need to readjust the harness after every mode switch. In this study, we demonstrate a simple design that fulfills these requirements while allowing the user to switch easily between modes. We describe the design concept, describe a rugged split-hook prototype, provide specifications (size, weight, efficiency, etc.), and present a pilot study in which five subjects with intact arms and two subjects with amputation used the VO and VC split-hook prehensor to perform the Southampton Hand Assessment Procedure. Subjects performed an average of 4 to 7 (+/- 0.2) points better when they could choose to switch between modes on a task-by-task basis than when they were constrained to using only VO or VC modes.
An intelligent system for user behavior detection in Internet Banking

Security and making trust is the first step toward development in both real and virtual societies. Internet-based development is inevitable. Increasing penetration of technology in the internet banking and its effectiveness in contributing to banking profitability and prosperity requires that satisfied customers turn into loyal customers. Currently, a large number of cyber attacks have been focused on online banking systems, and these attacks are considered as a significant security threat. Banks or customers might become the victim of the most complicated financial crime, namely internet fraud. This study has developed an intelligent system that enables detecting the user's abnormal behavior in online banking. Since the user's behavior is associated with uncertainty, the system has been developed based on the fuzzy theory, This enables it to identify user behaviors and categorize suspicious behaviors with various levels of intensity. The performance of the fuzzy expert system has been evaluated using an receiver operating characteristic curve, which provides the accuracy of 94%. This expert system is optimistic to be used for improving e-banking services security and quality.
Split-parallel through-the-road hybrid electric vehicle: Operation, power flow and control modes

A split-parallel hybrid electric vehicle (HEV) is a particular sub-type of the HEV in which the internal combustion engine (ICE) and electric motor provide propulsion power to different axles. Due to the coupling of power through the vehicle itself, its wheels and the road on which it moves - rather than through some mechanical device - the vehicle is referred to as a `through-the-road' (TTR) hybrid. A variant of the TTR hybrid has the motors in the hub of the otherwise non-driven wheels, called in-wheel motors (IWM). This configuration enables an existing vehicle to be converted into a hybrid vehicle with minimal modification. The present work identifies specific operating modes and power flow of the TTR-IWM hybrid, specifically a retrofit-conversion hybrid, in comparison to the ordinary parallel hybrid vehicle. It discusses control architecture, control objectives and system parameters, along with specific control issues of the retrofit TTR-IWM hybrid vehicle.
Development of folded dual-polarization dividers for broadband ortho-mode transducers

A waveguide divider with folded lateral arms is presented for separating dual orthogonal linear polarizations in broadband ortho-mode transducers. The structure is based on a well-known double symmetry junction, where the metallic pins have been eliminated and the lateral outputs have been folded to achieve a combined effect: matching for the vertical polarization and a very significant size reduction. In addition, since the path for the lateral branches has been reduced, the insertion losses for the different polarizations are balanced. The isolation between orthogonal polarizations is kept because of the double-symmetry of the junction. From the mechanical point of view, the proposed junction allows a more simple manufacture and assembly of the ortho-mode transducer parts, which has been shown with a Ku-band design, covering the full Ku-band from 12.6 to 18.25 GHz. The experimental prototype has demonstrated a measured return loss better than 28 dB in the design band and insertion loss smaller than 0.15 dB for both polarizations.
DeSVi : An Architecture for Detecting SLA Violations in Cloud Computing Infrastructures

Cloud computing is a promising paradigm for the implementation of scalable on-demand computing infrastructures. Self-manageable Cloud infrastructures are required in order to comply with users’ requirements specified by Service Level Agreements (SLAs) on one hand and to minimize user interactions with the system on the other hand. Adequate SLA monitoring strategies and timely detection of possible SLA violations represent challenging research issues. In this paper we present DeSVi—an architecture for detecting SLA violations through resource monitoring in Cloud computing infrastructures. Based on the user requests DeSVi allocates necessary resources for a requested service and arranges its deployment on a virtualized environment. Resources are monitored using an efficient framework that is also capable of mapping low-level resources metrics to user-defined SLAs. The detection of possible SLA violations is based on the predefined service level objectives and we utilize knowledge databases to manage the SLA violations. Knowledge databases are implemented using techniques like case-based reasoning, where reactive actions are defined based on the past system experience. For the evaluation of our approach, we developed image rendering services, which exhibit heterogeneous workloads for investigating the optimal monitoring interval of SLA parameters. The achieved results show that our architecture is able to monitor and prevent SLA violations considering different costs, measurement intervals, and heterogeneous workloads.
Using video games to combine learning and assessment in mathematics education

One problem with most education systems is that learning and (summative) assessment are generally treated as quite separate things in schools. We argue that video games can provide an opportunity to combine these processes in an engaging and effective way. The present study focuses on investigating the effectiveness and the assessment power of two different mathematics video games, Semideus and Wuzzit Trouble. In the current study, we validated the Semideus game as a rational number test instrument. We used it as a preand a post-test for a three-hour intervention in which we studied the effectiveness of Wuzzit Trouble, a game built on whole number arithmetic and designed to enhance mathematical thinking and problem solving skills. The results showed that (1) games can be used to assess mathematical knowledge validly, and (2) even short game-based interventions can be very effective. Based on the results, we argue that game-based assessment can create a more complete picture of mathematical knowledge than simply measuring students' accuracy, providing indicators of student misconceptions and conceptual
Design and control of underactuated tendon-driven mechanisms

Many robotic hands or prosthetic hands have been developed in the last several decades, and many use tendon-driven mechanisms for their transmissions. Robotic hands are now built with underactuated mechanisms, which have fewer actuators than degrees of freedom, to reduce mechanical complexity or to realize a biomimetic motion such as flexion of an index finger. The design is heuristic and it is useful to develop design methods for the underactuated mechanisms. This paper classifies mechanisms driven by tendons into three classes, and proposes a design method for them. The two classes are related to underactuated tendon-driven mechanisms, and these have been used without distinction so far. An index finger robot, which has four active tendons and two passive tendons, is developed and controlled with the proposed method.
Plane Detection in Point Cloud Data

Plane detection is a prerequisite to a wide variety of vision tasks. RANdom SAmple Consensus (RANSAC) algorithm is widely used for plane detection in point cloud data. Minimum description length (MDL) principle is used to deal with several competing hypothesis. This paper presents a new approach to the plane detection by integrating RANSAC and MDL. The method could avoid detecting wrong planes due to the complex geometry of the 3D data. The paper tests the performance of proposed method on both synthetic and real data.
Compact Rep-Rate GW Pulsed Generator Based on Forming Line With Built-In High-Coupling Transformer

In this paper, a compact rep-rate GW pulsed power generator is developed. First, its three key subsystems are theoretically analyzed, engineering designed, and experimentally investigated, respectively. The emphases are put on these four problems: the theoretical analysis of the voltage distribution across the conical secondary windings of the high-coupling transformer, the investigation of the high energy storage density dielectric used in the pulse forming line, the choice of the gas flow velocity of the gas blowing system, and theoretical analysis of the passive stability of the pulsed power generator operated in rep-rate mode. Second, the developed pulsed power generator is described in detail. It has a 0.2-m diameter, a 1.0-m length, and a 20- Ω wave impedance. Across a 100- Ω resistive dummy load, it can steadily operate at a 300-kV output voltage in 50-Hz rep-rate and 250 kV in 150 Hz without gas blowing system. The average power is ~ 1 kW. Finally, the pulsed power generator is applied to drive a relativistic backward-wave oscillator, generating a high-power microwave with peak output power of 200 MW and duration (full-width at half-maximum) of 5 ns in 150-Hz rep-rate. These efforts set a good foundation for the development of a compact rep-rate pulsed power generator and show a promising application for the future.
Design of biped robot inspired by cats for fast running

ELECT A novel design of a biped robot inspired by domestic cats for fast running is introduced. The skeletomuscular system of a cat is analysed and applied to determine the link parameters and the linkage structure of the proposed mechanism. The linkage design of the leg mechanism is explained and a kinematic analysis based on vector loop equations is performed. The effectiveness of the proposed mechanism is verified experimentally. The biped robot runs at an average speed of 2 m/s at a step frequency of 4 Hz. This leg mechanism can facilitate the development of fast running robot systems.
The 5C categorization of social media tools

Social media can offer potential business benefits in the company context. However, successful social media initiative calls for careful strategic planning and approaching social media tools as enablers for reaching a purpose. A categorization of social media tools is called for to help the strategic choices and evaluation between the tools. To date, no coherent classification exists, and those suggested approach social media tools from a substantival perspective of what a tool is. From strategical planning viewpoint it is more practical to take the functional perspective of what a tool does. In this paper, we present a framework for categorizing social media tools by the actions they enable. The 5C categorization is based on the action s enabled by the social media tools: communicating, collaborating, connecting, completing, and combining. In order to demonstrate how the 5C categorization can be used as a framework to assess social media tools we conducted an empirical study of social media based crowdsourcing platforms in business-to-business setting. The 5C categorization can be utilized in planning a social media strategy as it forwards the understanding of which tools are suitable for certain purpose and provides a scheme to evaluate and compare different social media tools and applications.