nils-herrmann/clean_scirepeval huggingface dataset:
Modality Effects in Deception Detection and Applications in Automatic-Deception-Detection

Modality is an important context factor in deception, which is context-dependent. In order to build a reliable and flexible tool for automatic-deception-detection (ADD), we investigated the characteristics of verbal cues to deceptive behavior in three modalities: text, audio and face-to-face communication. Seven categories of verbal cues (21 cues) were studied: quantity, complexity, diversity, verb nonimmediacy, uncertainty, specificity and affect. After testing the interaction effects between modality and condition (deception or truth), we found significance only with specificity and observed that differences between deception and truth were in general consistent across the three modalities. However, modality had strong effects on verbal cues. For example, messages delivered face-to-face were largest in quantity (number of words, verbs, and sentences), followed by the audio modality. Text had the sparsest examples. These modality effects are an important factor in building baselines in ADD tools, because they make it possible to use them to adjust the baseline for an unknown modality according to a known baseline, thereby simplifying the process of ADD. The paper discusses in detail the implications of these findings on modality effects in three modalities.
Lane detection and tracking using B-Snake

In this paper, we proposed a B-Snake based lane detection and tracking algorithm without any cameras’ parameters. Compared with other lane models, the B-Snake based lane model is able to describe a wider range of lane structures since B-Spline can form any arbitrary shape by a set of control points. The problems of detecting both sides of lane markings (or boundaries) have been merged here as the problem of detecting the mid-line of the lane, by using the knowledge of the perspective parallel lines. Furthermore, a robust algorithm, called CHEVP, is presented for providing a good initial position for the B-Snake. Also, a minimum error method by Minimum Mean Square Error (MMSE) is proposed to determine the control points of the B-Snake model by the overall image forces on two sides of lane. Experimental results show that the proposed method is robust against noise, shadows, and illumination variations in the captured road images. It is also applicable to the marked and the unmarked roads, as well as the dash and the solid paint line roads. q 2003 Elsevier B.V. All rights reserved.
Assessing the Effectiveness of Corpus-Based Methods in Solving SAT Sentence Completion Questions

SAT sentence completion questions are designed to assess knowledge of the English language. The ability to answer such types of questions has wide implications in optical character recognition, speech recognition, and word-suggestion programs. In our study, we analyze several statistical corpus-based methods through which to answer such questions, including normalized pointwise mutual information, co-occurrence frequencies, latent semantic analysis, and the word2vec neural net implementations of continuous bag of words (CBOW) and continuous skip-gram (CSKIP) models. We find that the co-occurrence frequency method has a strong performance with 52% correctness and that combining the co-occurrence frequency method with CBOW and CSKIP results in a state-of-the-art performance of 59%. The results of this study demonstrate that local context is a fairly strong measure in determining how well a word fits in a sentence and that exploration of non-similarity based methods may be required to further enhance the ability of computers to answer such questions.
Deep Reinforcement Learning for General Video Game AI

The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.
Design and Fabrication of Fine-Pitch Pixelated-Addressed Micro-LED Arrays on Printed Circuit Board for Display and Communication Applications

In this paper, we report the design and fabrication of fine-pitch and pixelated-addressed micro-light emitting diode (LED) arrays with emission wavelengths of red (R), green (G), blue (B), and infrared (IR). The arrays have a resolution of 8×8 with monochromatic LED chips directly bonded to custom-designed printed circuit boards to form pixelated matrix. R, G, B, and IR micro-LED arrays with a pixel pitch of 1 mm were demonstrated. Sequential row scanning and multiple columns programming is performed by two CMOS-based application specific integrated circuit chips providing constant-sinking current and forward voltage for each pixel of the micro-LED arrays. The uniformity of operating current and forward voltage of the micro-LED pixels was measured with 1.94% coefficient of variation. The micro-LED pixel has much shorter rising time and higher response frequency than those of regular packaged blue and white LEDs. The implemented R-G-B micro-LED arrays are the excellent candidate for high-definition fine-pitch LED displays and demonstrated its versatile functionalities such as visible light communications. The IR micro-LED arrays show great potential in health medical treatment, IR detection imaging and local positioning system.
The relationship between trauma and beliefs about hearing voices: a study of psychiatric and non-psychiatric voice hearers.

Cognitive models suggest that distress associated with auditory hallucinations is best understood in terms of beliefs about voices. What is less clear is what factors govern such beliefs. This study aimed to explore the way in which traumatic life events contribute towards beliefs about voices and any associated distress.



The difference in the nature and prevalence of traumatic life events and associated psychological sequelae was compared in two groups of voice hearers: psychiatric voice hearers with predominantly negative beliefs about voices (PVH) and non-psychiatric voice hearers with predominantly positive beliefs about voices (NPVH). The data from the two groups were then combined in order to examine which factors could significantly account for the variance in beliefs about voices and therefore levels of distress.



Both groups reported a high prevalence of traumatic life events although significantly more PVH reported trauma symptoms sufficient for a diagnosis of post-traumatic stress disorder (PTSD). Furthermore, significantly more PVH reported experiencing childhood sexual abuse. Current trauma symptoms (re-experiencing, avoidance and hyperarousal) were found to be a significant predictor of beliefs about voices. Trauma variables accounted for a significant proportion of the variance in anxiety and depression.



The results suggest that beliefs about voices may be at least partially understood in the context of traumatic life events.
Design and implementation of microcontroller based PWM technique for sine wave inverter

A microcontroller based advanced technique of generating sine wave with minimized harmonics is implemented in this paper. The proposed technique “impulse-sine product” aims to design and implement a voltage regulated inverter with ripple free and glitch free output sine wave that can operate electronic devices efficiently. The output voltage of the inverter is regulated by a feedback loop using analog to digital protocol of PIC16f877 microcontroller. The design is essentially focused upon low power electronic appliances such as personal computers, chargers, television sets. The inverter output is regulated from 16-volt rms to 220-volt rms for a variation of load between 1-Ω to 180-Ω loads. The designed inverter provides a range of operation from 25 Watts to 250 Watts. The design is mathematically modeled which is simulated in Matlab, Proteus and finally the results are practically verified.
A 60 V Auto-Zero and Chopper Operational Amplifier With 800 kHz Interleaved Clocks and Input Bias Current Trimming

An auto-zero and chopper operational amplifier with a 4.5-60 V supply voltage range is realized, using a 0.18 μm CMOS process augmented by 5 V CMOS and 60 V DMOS transistors. It achieves a maximum offset voltage drift of 0.02 μV/°C, a minimum CMRR of 145 dB, a noise PSD of 6.8 nV/√Hz, and a 3.1 MHz unity gain bandwidth, while dissipating 840 μA of current. Up-modulated chopper ripple is suppressed by auto- zeroing. Furthermore, glitches from the charge injection of the input switches are mitigated by employing six parallel input stages with 800 kHz interleaved clocks. This moves the majority of the glitch energy up to 4.8 MHz, while leaving little energy at 800 kHz. As a result, the requirements on an external low-pass glitch filter is relaxed, and a wider usable signal bandwidth can be obtained. Maximum input bias current due to charge injection mismatch is reduced from 1.5 nA to 150 pA by post production trimming with an on-chip charge mismatch compensation circuit.
Assessing the benefits of computational offloading in mobile-cloud applications

This paper presents the results of a formative study conducted to determine the effects of computation offloading in mobile applications by comparing “application performance” (chiefly energy consumption and response time). The study examined two general execution scenarios: (1) computation is performed locally on a mobile device, and (2) when it is offloaded entirely to the cloud. The study also carefully considered the underlying network characteristics as an important factor affecting the performance. More specifically, we refactored two mobile applications to offload their computationally intensive functionality to execute in the cloud. We then profiled these applications under different network conditions, and carefully measured “application performance” in each case. The results were not as conclusive as we had expected. On fast networks, offloading is almost always beneficial. However, on slower networks, the offloading cost-benefit analysis is not as clear cut. The characteristics of the data transferred between the mobile device and the cloud may be a deciding factor in determining whether offloading a computation would improve performance.
Pose-space subspace dynamics

We enrich character animations with secondary soft-tissue Finite Element Method (FEM) dynamics computed under arbitrary rigged or skeletal motion. Our method optionally incorporates pose-space deformation (PSD). It runs at milliseconds per frame for complex characters, and fits directly into standard character animation pipelines. Our simulation method does not require any skin data capture; hence, it can be applied to humans, animals, and arbitrary (real-world or fictional) characters. In standard model reduction of three-dimensional nonlinear solid elastic models, one builds a reduced model around a single pose, typically the rest configuration. We demonstrate how to perform multi-model reduction of Finite Element Method (FEM) nonlinear elasticity, where separate reduced models are precomputed around a representative set of object poses, and then combined at runtime into a single fast dynamic system, using subspace interpolation. While time-varying reduction has been demonstrated before for offline applications, our method is fast and suitable for hard real-time applications in games and virtual reality. Our method supports self-contact, which we achieve by computing linear modes and derivatives under contact constraints.
Analyzing the effects of disk-pointer corruption

The long-term availability of data stored in a file system depends on how well it safeguards on-disk pointers used to access the data. Ideally, a system would correct all pointer errors. In this paper, we examine how well corruption-handling techniques work in reality. We develop a new technique called type-aware pointer corruption to systematically explore how a file system reacts to corrupt pointers. This approach reduces the exploration space for corruption experiments and works without source code. We use type-aware pointer corruption to examine Windows NTFS and Linux ext3. We find that they rely on type and sanity checks to detect corruption, and NTFS recovers using replication in some instances. However, NTFS and ext3 do not recover from most corruptions, including many scenarios for which they possess sufficient redundant information, leading to further corruption, crashes, and unmountable file systems. We use our study to identify important lessons for handling corrupt pointers.
Deep Learning for Nonlinear Diffractive Imaging

Image reconstruction under multiple light scattering is crucial for a number of important applications in cell microscopy and tissue imaging. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and a regularizer is used to enforce the prior on the object. In this letter, We propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network (CNN) for inverting multiple scattering. Simulations show that the proposed formulation is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.
The future of seawater desalination: energy, technology, and the environment.

In recent years, numerous large-scale seawater desalination plants have been built in water-stressed countries to augment available water resources, and construction of new desalination plants is expected to increase in the near future. Despite major advancements in desalination technologies, seawater desalination is still more energy intensive compared to conventional technologies for the treatment of fresh water. There are also concerns about the potential environmental impacts of large-scale seawater desalination plants. Here, we review the possible reductions in energy demand by state-of-the-art seawater desalination technologies, the potential role of advanced materials and innovative technologies in improving performance, and the sustainability of desalination as a technological solution to global water shortages.
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead

ÐThe analysis of medical images has been woven into the fabric of the Pattern Analysis and Machine Intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come. Index TermsÐMedical imaging, medical image analysis, computer vision, image segmentation, image registration, nonrigid motion, deformable contours, image-guided surgery.
The CMU Pose, Illumination, and Expression (PIE) Database

Between October 2000 and December 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this database the CMU Pose, Illumination, and Expression (PIE) database. In this paper we describe the imaging hardware, the collection procedure, the organization of the database, several potential uses of the database, and how to obtain the database.
Illuminant Aware Gamut-Based Color Transfer

This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white-balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same ‘white axis’ and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant-aware strategy gives a better result than directly working with the original source and target image’s luminance channel as done by many previous methods. Afterwards, our method performs a full gamut-based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant-aware and gamut-based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.
Towards Design Theory and expandable rationality : The unfinished program of

It is said that Herbert Simon would have described himself as follows : «I am a monomaniac. What I am a monomaniac about is decision making ». In spite of its shares of legend and humour, this self-portrait deeply reflects the main logic of Herbert Simon’s works. From his early papers on administrative behaviour to his last investigations on thought and learning, Simon kept a same goal : to explain complex and mysterious human behaviour by simple and constrained, yet informed, decision rules. « Bounded rationality » was the name he gave to a research orientation wich rejected the maximizing behaviour assumed by classic economics. But beyond this critical aim, Simon attempted to build an empirically grounded theory of human problem solving. A theory that was intended to settle the foundation stone of « behavioural economics ».
Analysis of the Electromagnetic Signature of Reinforced Concrete Structures for Nondestructive Evaluation of Corrosion Damage

This paper presents a nondestructive corrosion damage detection method for reinforced concrete structures based on the analysis of the electromagnetic signature of the steel rebar corrosion. The signature of the corrosion on the scattered field upon microwave illumination is first numerically analyzed. First-order quality factor parameters, the energy and the mean propagation delay, are proposed to quantify the corrosion amount in the structure. To validate the model, low-profile ultra-wide-band antennas (3-12 GHz) are fabricated and measured. Measurements on 12 reinforced concrete samples with induced corrosion are performed, using three different antenna setups. The experiments demonstrate a good correlation between an increase in the corrosion amount with a decrease in the energy and an increase in the time delay of the propagated signal.
Biologically Plausible, Human-Scale Knowledge Representation

Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony (Shastri & Ajjanagadde, ), "mesh" binding (van der Velde & de Kamps, ), and conjunctive binding (Smolensky, ). Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate that the biologically plausible structured representations employed in the Semantic Pointer Architecture (SPA) approach to modeling cognition (Eliasmith, ) do scale appropriately. Specifically, we construct a spiking neural network of about 2.5 million neurons that employs semantic pointers to successfully encode and decode the main lexical relations in WordNet, which has over 100,000 terms. In addition, we show that the same representations can be employed to construct recursively structured sentences consisting of arbitrary WordNet concepts, while preserving the original lexical structure. We argue that these results suggest that semantic pointers are uniquely well-suited to providing a biologically plausible account of the structured representations that underwrite human cognition.
Results of syndactyly release using a modification of the Flatt technique.

The results of 144 congenital syndactyly releases over a 12-year period by a single surgeon using a modified Flatt technique (dorsal hourglass flap, interdigitating zigzag flaps, and full-thickness skin grafts) are analyzed considering the association of skin grafts and web creep. The mean follow-up was 5 years. There were seven cases of graft failure, only one of which developed web creep. Web creep occurred in 4.2% of web releases. The results suggest that avoiding longitudinal straight-line scars across the web space may be an important factor in avoiding web creep when performing the modified Flatt technique described.
An online chess game designed for people with dyslexia

Teaching chess to students with learning disabilities has been shown to benefit their school performance in unrelated domains. At the same time, chess involves skills that are highly correlated with dyslexia, such as visuospatial and calculation abilities. In this paper, we created a online chess game designed for people with dyslexia and seek to understand whether people with dyslexia learn and play chess online in ways that differ from other students and whether such differences may be leveraged to improve classroom performance. To test how people with dyslexia learn to play chess we carried out a within-subject experiment with 62 participants, 31 of them with diagnosed dyslexia. Participants used an instrumented web-based chess learning platform that we developed to (i) complete lessons on how to play chess and about chess theory, (ii) work through exercises designed to test and reaffirm their skills, and (iii) play chess against a computer opponent. We could not find significant differences on four dependent measures out of the twelve measures we collected. Therefore, dyslexia might have an impact on how people learn and play chess using a computer, suggesting that chess may be useful as a fun way to help people with dyslexia improve their abilities.