nils-herrmann/clean_scirepeval huggingface dataset:
Role of the amygdala in decision-making.

The somatic marker hypothesis proposes that both the amygdala and the orbitofrontal cortex are parts of a neural circuit critical for judgment and decision-making. Although both structures couple exteroceptive sensory information with interoceptive information concerning somatic/emotional states, they do so at different levels, thus making different contributions to the process. We define "primary inducers" as stimuli that unconditionally, or through learning (e.g., conditioning and semantic knowledge), can (perceptually or subliminally) produce states that are pleasurable or aversive. Encountering a fear object (e.g., a snake), a stimulus predictive of a snake, or semantic information such as winning or losing a large sum of money are all examples of primary inducers. "Secondary inducers" are entities generated by the recall of a personal or hypothetical emotional event or perceiving a primary inducer that generates "thoughts" and "memories" about the inducer, all of which, when they are brought to memory, elicit a somatic state. The episodic memory of encountering a snake, losing a large sum of money, imagining the gain of a large sum of money, or hearing or looking at primary inducers that bring to memory "thoughts" pertaining to an emotional event are all examples of secondary inducers. We present evidence in support of the hypothesis that the amygdala is a critical substrate in the neural system necessary for triggering somatic states from primary inducers. The ventromedial cortex is a critical substrate in the neural system necessary for the triggering of somatic states from secondary inducers. The amygdala system is a priori a necessary step for the normal development of the orbitofrontal system for triggering somatic states from secondary inducers. However, once this orbitofrontal system is developed, the induction of somatic states by secondary inducers via the orbitofrontal system is less dependent on the amygdala system. Perhaps the amygdala is equivalent to the hippocampus with regard to emotions, that is, necessary for acquiring new emotional attributes (anterograde emotions), but not for retrieving old emotional attributes (retrograde emotions). Given the numerous lesion and functional neuroimaging studies illustrating the involvement of the amygdala in complex cognitive and behavioral functions, including "social cognition," we suggest that this involvement is a manifestation of a more fundamental function mediated by the amygdala, which is to couple stimuli/entities with their emotional attributes, that is, the processing of somatic states from primary inducers.
A Wideband Circularly Polarized Cross-Dipole Antenna

This letter introduces a wideband circularly polarized (CP) cross-dipole antenna fabricated on a double-layered printed circuit board (PCB) substrate for 2.45-GHz ISM band wireless communications. Unlike conventional cross-dipole antennas, the proposed cross dipole is designed with wide open ends such that both impedance and axial-ratio (AR) bandwidths are enhanced. In addition, to excite the CP radiation effectively, a curved-delay line providing an orthogonal phase difference among the cross-dipole elements is attached at the corners of the sequentially rotated elements. By choosing a proper radius of the curved-delay line, a wide input impedance of the antenna can be realized. The antenna is center-fed by a 50- Ω coaxial cable and is placed above a square reflector to obtain a directional CP radiation pattern. With the advantage of centered feed, symmetric CP radiation patterns can be achieved across the entire operating bandwidth. Simulated and measured results confirm the proposed antenna produced good CP characteristics. An impedance bandwidth (for VSWR ≤ 2) of about 50.2% (1.99-3.22 GHz) and the 3-dB AR bandwidth of about 27% (2.30-2.9 GHz) around the center frequency of 2.45 GHz were measured. The antenna has an average CP gain of 6.2 dBic across the operating bandwidth and the maximum gain of 6.8 dBic at the center frequency. The size of antenna is 0.45λ×0.45λ×0.24λ.
Fast Radial Symmetry for Detecting Points of Interest

A new transform is presented that utilizes local radial symmetry to highlight points of interest within a scene. Its lowcomputational complexity and fast runtimes makes this method well-suited for real-time vision applications. The performance of the transform is demonstrated on a wide variety of images and compared with leading techniques from the literature. Both as a facial feature detector and as a generic region of interest detector the new transform is seen to offer equal or superior performance to contemporary techniques at a relatively low-computational cost. A real-time implementation of the transform is presented running at over 60 frames per second on a standard Pentium III PC.
S2E: a platform for in-vivo multi-path analysis of software systems

This paper presents S2E, a platform for analyzing the properties and behavior of software systems. We demonstrate S2E's use in developing practical tools for comprehensive performance profiling, reverse engineering of proprietary software, and bug finding for both kernel-mode and user-mode binaries. Building these tools on top of S2E took less than 770 LOC and 40 person-hours each.
 S2E's novelty consists of its ability to scale to large real systems, such as a full Windows stack. S2E is based on two new ideas: selective symbolic execution, a way to automatically minimize the amount of code that has to be executed symbolically given a target analysis, and relaxed execution consistency models, a way to make principled performance/accuracy trade-offs in complex analyses. These techniques give S2E three key abilities: to simultaneously analyze entire families of execution paths, instead of just one execution at a time; to perform the analyses in-vivo within a real software stack--user programs, libraries, kernel, drivers, etc.--instead of using abstract models of these layers; and to operate directly on binaries, thus being able to analyze even proprietary software.
 Conceptually, S2E is an automated path explorer with modular path analyzers: the explorer drives the target system down all execution paths of interest, while analyzers check properties of each such path (e.g., to look for bugs) or simply collect information (e.g., count page faults). Desired paths can be specified in multiple ways, and S2E users can either combine existing analyzers to build a custom analysis tool, or write new analyzers using the S2E API.
Attributed scattering centers for SAR ATR

High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.
Motivational theory of human robot teamwork

Human‒robot teams, teams of humans and robots, are being deployed across many work  This is reflected in the increased use of robots in the workforce, where they are expected to replace as much as half the workforce in 10‒20 years.2‒4 The ability to effectively integrate robots into teams can either hinder or facilitate successful teamwork.5 However, advances in technology have far outpaced our theorizing with regard to the relationships between humans and robots.5‒7 Motivational theories of teamwork highlight the role of effort or a lack of effort when it comes to promoting effective teamwork. Motivation is defined as an individual or team’s intention of achieving a goal, which is followed by an allocation of effort to achieve this goal.8 In this paper, more effective teamwork is assumed to lead to better performance, team satisfaction and viability. All three outcomes are widely recognized as important measures of successful teamwork.7 Motivational theories have been used to explain both individual and team performance.9‒12 Working with robots often involves both physical and mental activities. This implies that motivation might be particularly important to the success of these teams. Unfortunately, despite the potential importance there is much we do not know with regard to the role of motivation in effective teamwork with robots.13‒15 With this in mind, our goal in this paper is to advance our understanding of teamwork with robots through a motivational view . In this paper we propose the “Motivational Theory of Human‒Robot Teamwork” to better understand teamwork in human‒robot teams. In doing so, we leverage the research on robot personality. Personality has consistently demonstrated an impact on the actions and outcomes of teamwork among humans and with robots.16 In this paper we present a theory that allows us to better understand motivation in human‒robot teamwork. Here we make three key contributions. First, we theoretically integrate the literature on human‒robot personality with theories of individual and team motivation. In doing so, we put forth a more coherent explanation of the relationship between prior HRI research on personality and future HRI research on motivation. Second, we identify the robot’s competence as a key theoretical mechanism linking robot personality and human motivation in human‒robot teams. Finally, the proposed theory goes beyond the individual level of analysis to explicate the mechanisms driving motivation at the team level as well linking both individual and team outcomes. The following section presents the general relationships between a robot’s personality traits, motivation, and individual and team outcomes. This first set of propositions presents the impacts of the robot’s personality on a human’s perceptions of its competence. The next section discusses the effects of the robot’s competence on human motivation. This is followed by propositions related to the impact of motivation on individual and team outcomes. Finally, we conclude the paper.
Distance-based hoax detection system

Hoaxes are non malicious viruses. They live on deceiving human's perception by conveying false claims as truth. Throughout history, hoaxes have actually able to influence a lot of people to the extent of tarnishing the victim's image and credibility. Moreover, wrong and misleading information has always been a distortion to a human's growth. Some hoaxes were created in a way that they can even obtain personal data by convincing the victims that those data were required for official purposes. Hoaxes are different from spams in a way that they masquerade themselves through the address of those related either directly or indirectly to us. Most of the time, they appear as a forwarded message and sometimes from legit companies. This paper addresses this issue by developing a hoax detection system by incorporating text matching method using Levenshtein Distance measure. The proposed model is used to identify text-based hoax emails. Sensitivity and specificity are used to evaluate the accuracy of the system identifying hoax emails.
Image based Static Facial Expression Recognition with Multiple Deep Network Learning

We report our image based static facial expression recognition method for the Emotion Recognition in the Wild Challenge (EmotiW) 2015. We focus on the sub-challenge of the SFEW 2.0 dataset, where one seeks to automatically classify a set of static images into 7 basic emotions. The proposed method contains a face detection module based on the ensemble of three state-of-the-art face detectors, followed by a classification module with the ensemble of multiple deep convolutional neural networks (CNN). Each CNN model is initialized randomly and pre-trained on a larger dataset provided by the Facial Expression Recognition (FER) Challenge 2013. The pre-trained models are then fine-tuned on the training set of SFEW 2.0. To combine multiple CNN models, we present two schemes for learning the ensemble weights of the network responses: by minimizing the log likelihood loss, and by minimizing the hinge loss. Our proposed method generates state-of-the-art result on the FER dataset. It also achieves 55.96% and 61.29% respectively on the validation and test set of SFEW 2.0, surpassing the challenge baseline of 35.96% and 39.13% with significant gains.
An Offset Linear-Array-Fed Ku/Ka Dual-Band Reflectarray for Planet Cloud/Precipitation Radar

A Ku/Ka band dual-frequency offset array-fed microstrip reflectarray antenna using thin membranes has been developed. This antenna is a demonstration model for the next generation titan cloud precipitation radar and altimeter (TCPRA) and is intended to enhance the capability of the future cloud and precipitation remote sensing system for Earth and other planets. The reflectarray has a dimension of 0.5-m square and a flat aperture that emulates a cylindrical reflector antenna. Two sets of linear arrays with linearly polarized microstrip elements and having low cross polarization and low sidelobe levels (SLL) are placed along the focal line to illuminate the reflectarray. One set is for Ku-band, while the other is for Ka-band. Within each set, the wide-swath scanning capability of the antenna is provided by the two fixed-beam feed arrays with one for broadside beam and the other one for a 20deg tilted beam. The unique feature of this paper is that this is the first offset-fed reflectarray that has ever been practically developed to emulate a cylindrical/parabolic type of reflector antenna. In addition, new dual-concentric-ring elements and ring-patch elements are uniquely applied here to achieve the desirable wide phase variation characteristics. Tests of feed arrays and the reflectarray in the anechoic chamber of Texas A&M University result in good pattern performance and the successful demonstration of beam scanning at both frequency bands.
Environmental Sensor Networks : A revolution in the earth system science ?

Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into ‘intelligent’ sensor networks that comprise a network of automatic sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other environmental datasets. The sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. Biosensor Networks use emerging biotechnologies to monitor environmental processes as well as developing proxies for immediate use. In the future, sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a ‘virtual’ connection with the environment, they allow new field and conceptual approaches to the study of environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them.
Argument Mining: Extracting Arguments from Online Dialogue

Online forums are now one of the primary venues for public dialogue on current social and political issues. The related corpora are often huge, covering any topic imaginable. Our aim is to use these dialogue corpora to automatically discover the semantic aspects of arguments that conversants are making across multiple dialogues on a topic. We frame this goal as consisting of two tasks: argument extraction and argument facet similarity. We focus here on the argument extraction task, and show that we can train regressors to predict the quality of extracted arguments with RRSE values as low as .73 for some topics. A secondary goal is to develop regressors that are topic independent: we report results of cross-domain training and domain-adaptation with RRSE values for several topics as low as .72, when trained on topic independent features.
MPPT performance improvement of PV system using hybrid-PI controller

This paper proposes the hybrid-PI hybrid-PI control for MPPT control of photovoltaic system. The conventional constant voltage(CV), perturbation and observation(PO) and incremental conductance(IC) are the method which finding maximum power point(MPP) by the continued self-excitation vibration, and uses the fixed step size. If the fixed step size is a large, the tracking speed of maximum power point is faster, but the tracking accuracy in the steady state is decreased. On the contrary, when the fixed step size is a small, the tracking accuracy is increased and the tracking speed is slower. Therefore, in order to solve these problems, this paper proposes hybrid-PI controller that is adjusted gain of conventional PI control using fuzzy control, and the maximum power point tracks using this controller. The validity of the controller proposed in this paper proves through the results of the comparisons.
Application-centric security policies on unmodified Android

Google’s Android platform uses a fairly standard resource-centric permission model to protect resources such as the camera, GPS, and Internet connection. We claim that a much better permission model for developers and users would be application-centric, with a vocabulary that directly relates to application-level functionality, e.g., one permission could allow camera use, but only for barcode scanning; another could allow Internet access, but only to certain domains. Despite the large apparent gap between resourceand application-centric permissions, we argue that Android already provides the necessary mechanisms to support an expressive and practical form of application-centric policies. Specifically, each application-centric permission can be represented by a new Android permission and can be enforced by coupling the permission with a trusted service running in its own process. We present a survey of the top 24 free Android apps and show that a small vocabulary of application-centric permissions covers much of the functionality of those apps. We also describe a prototype implementation of our approach.
An early fire-detection method based on image processing

The paper presents an early fire-alarm raising method based on video processing. The basic idea of the proposed of fire-detection is to adopt a RGB (red, green, blue) model based chromatic and disorder measurement for extracting fire-pixels and smoke-pixels. The decision function of fire-pixels is mainly deduced by the intensity and saturation of R component. The extracted fire-pixels will be verified if it is a real fire by both dynamics of growth and disorder, and further smoke. Based on iterative checking on the growing ratio of flames, a fire-alarm is given when the alarm-raising condition is met. Experimental results show that the developed technique can achieve fully automatic surveillance of fire accident with a lower false alarm rate and thus is very attractive for the important military, social security, commercial applications, and so on, at a general cost.
Semi-global matching based disparity estimate using fast Census transform

The Census transform is very useful in stereo matching algorithms. But the traditional Census transform cost too much computation, and its accuracy and robustness are not satisfying. In this paper, a modified semi-global matching (SGM) algorithm based on the fast Census transform is proposed to improve the quality of the range map and decrease the computation. In addition, we modify the disparity refinement to enhance the quality of the estimated range map. Our experiments show that the proposed method performs better than the original SGM algorithm in terms of the accuracy and the anti-noise capability.
End-Task Oriented Textual Entailment via Deep Exploring Inter-Sentence Interactions

This work deals with SCITAIL, a natural entailment problem derived from a multi-choice question answering task. The premises and hypotheses in SCITAIL were authored independent of each other and independent of the entailment task, which makes it more challenging than other entailment tasks as linguistic variations in it are not limited by the coverage of hand-designed rules or the creativity of crowd-workers. We propose DEISTE (deep exploring inter-sentence interactions for textual entailment) for this entailment task. Given word-to-word interactions between the premise-hypothesis pair (P , H), DEISTE consists of: (i) A parameter-dynamic convolution to make important words in P and H play a dominant role in learnt representations; (ii) A position-aware attentive convolution to encode the representation and position information of the aligned word pairs. Experiments show DEISTE gets ∼ 5% improvement over prior state of the art.1
(sp)iPhone: decoding vibrations from nearby keyboards using mobile phone accelerometers

Mobile phones are increasingly equipped with a range of highly responsive sensors. From cameras and GPS receivers to three-axis accelerometers, applications running on these devices are able to experience rich interactions with their environment. Unfortunately, some applications may be able to use such sensors to monitor their surroundings in unintended ways. In this paper, we demonstrate that an application with access to accelerometer readings on a modern mobile phone can use such information to recover text entered on a nearby keyboard. Note that unlike previous emanation recovery papers, the accelerometers on such devices sample at near the Nyquist rate, making previous techniques unworkable. Our application instead detects and decodes keystrokes by measuring the relative physical position and distance between each vibration. We then match abstracted words against candidate dictionaries and record word recovery rates as high as 80%. In so doing, we demonstrate the potential to recover significant information from the vicinity of a mobile device without gaining access to resources generally considered to be the most likely sources of leakage (e.g., microphone, camera).