nils-herrmann/clean_scirepeval huggingface dataset:
Working memory contributions to reinforcement learning impairments in schizophrenia.

Previous research has shown that patients with schizophrenia are impaired in reinforcement learning tasks. However, behavioral learning curves in such tasks originate from the interaction of multiple neural processes, including the basal ganglia- and dopamine-dependent reinforcement learning (RL) system, but also prefrontal cortex-dependent cognitive strategies involving working memory (WM). Thus, it is unclear which specific system induces impairments in schizophrenia. We recently developed a task and computational model allowing us to separately assess the roles of RL (slow, cumulative learning) mechanisms versus WM (fast but capacity-limited) mechanisms in healthy adult human subjects. Here, we used this task to assess patients' specific sources of impairments in learning. In 15 separate blocks, subjects learned to pick one of three actions for stimuli. The number of stimuli to learn in each block varied from two to six, allowing us to separate influences of capacity-limited WM from the incremental RL system. As expected, both patients (n = 49) and healthy controls (n = 36) showed effects of set size and delay between stimulus repetitions, confirming the presence of working memory effects. Patients performed significantly worse than controls overall, but computational model fits and behavioral analyses indicate that these deficits could be entirely accounted for by changes in WM parameters (capacity and reliability), whereas RL processes were spared. These results suggest that the working memory system contributes strongly to learning impairments in schizophrenia.
Change Detection in Time Series Data Using Wavelet Footprints

Detecting changes in time series data is an important data analysis task with application in various scientific domains. In this paper, we propose a novel approach to address the problem of change detection in time series data, which can find both the amplitude and degree of changes. Our approach is based on wavelet footprints proposed originally by the signal processing community for signal compression. We, however, exploit the properties of footprints to efficiently capture discontinuities in a signal. We show that transforming time series data using footprint basis up to degree D generates nonzero coefficients only at the change points with degree up to D. Exploiting this property, we propose a novel change detection query processing scheme which employs footprint-transformed data to identify change points, their amplitudes, and degrees of change efficiently and accurately. We also present two methods for exact and approximate transformation of data. Our analytical and empirical results with both synthetic and real-world data show that our approach outperforms the best known change detection approach in terms of both performance and accuracy. Furthermore, unlike the state of the art approaches, our query response time is independent from the number of change points in the data and the user-defined change threshold.
Magnetic MIMO: how to charge your phone in your pocket

This paper bridges wireless communication with wireless power transfer. It shows that mobile phones can be charged remotely, while in the user's pocket by applying the concept of MIMO beamforming. However, unlike MIMO beamforming in communication systems which targets the radiated field, we transfer power by beamforming the non-radiated magnetic field and steering it toward the phone. We design MagMIMO, a new system for wireless charging of cell phones and portable devices. MagMIMO consumes as much power as existing solutions, yet it can charge a phone remotely without being removed from the user's pocket. Furthermore, the phone need not face the charging pad, and can charge independently of its orientation. We have built MagMIMO and demonstrated its ability to charge the iPhone and other smart phones, while in the user's pocket.
A meta-analysis of the Dark Triad and work behavior: a social exchange perspective.

We reviewed studies of the Dark Triad (DT) personality traits--Machiavellianism, narcissism, and psychopathy-and meta-analytically examined their implications for job performance and counterproductive work behavior (CWB). Relations among the DT traits and behaviors were extracted from original reports published between 1951 and 2011 of 245 independent samples (N = 43,907). We found that reductions in the quality of job performance were consistently associated with increases in Machiavellianism and psychopathy and that CWB was associated with increases in all 3 components of the DT, but that these associations were moderated by such contextual factors as authority and culture. Multivariate analyses demonstrated that the DT explains moderate amounts of the variance in counterproductivity, but not job performance. The results showed that the 3 traits are positively related to one another but are sufficiently distinctive to warrant theoretical and empirical partitioning.
Inductive reasoning and bounded rationality

The type of rationality we assume in economics--perfect, logical, deductive rationality--is extremely useful in generating solutions to theoretical problems. But it demands much of human behavior--much more in fact than it can usually deliver. If we were to imagine the vast collection of decision problems economic agents might conceivably deal with as a sea or an ocean, with the easier problems on top and more complicated ones at increasing depth, then deductive rationality would describe human behavior accurately only within a few feet of the surface. For example, the game Tic-Tac-Toe is simple, and we can readily find a perfectly rational, minimax solution to it. But we do not find rational "solutions" at the depth of Checkers; and certainly not at the still modest depths of Chess and Go.
Recognising Textual Entailment with Logical Inference

We use logical inference techniques for recognising textual entailment. As the performance of theorem proving turns out to be highly dependent on not readily available background knowledge, we incorporate model building, a technique borrowed from automated reasoning, and show that it is a useful robust method to approximate entailment. Finally, we use machine learning to combine these deep semantic analysis techniques with simple shallow word overlap; the resulting hybrid model achieves high accuracy on the RTE testset, given the state of the art. Our results also show that the different techniques that we employ perform very differently on some of the subsets of the RTE corpus and as a result, it is useful to use the nature of the dataset as a feature.
Learning normalized inputs for iterative estimation in medical image segmentation

In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data. In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. As in other fully convolutional approaches, our pipeline can be used off-the-shelf on different image modalities. We show that using this pipeline, we exhibit state-of-the-art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods. We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods. Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods. The obtained results illustrate the strong potential and versatility of the pipeline by achieving accurate segmentations on a variety of image modalities and different anatomical regions.
Structure-Sensitive Saliency Detection via Multilevel Rank Analysis in Intrinsic Feature Space

This paper advocates a novel multiscale, structure-sensitive saliency detection method, which can distinguish multilevel, reliable saliency from various natural pictures in a robust and versatile way. One key challenge for saliency detection is to guarantee the entire salient object being characterized differently from nonsalient background. To tackle this, our strategy is to design a structure-aware descriptor based on the intrinsic biharmonic distance metric. One benefit of introducing this descriptor is its ability to simultaneously integrate local and global structure information, which is extremely valuable for separating the salient object from nonsalient background in a multiscale sense. Upon devising such powerful shape descriptor, the remaining challenge is to capture the saliency to make sure that salient subparts actually stand out among all possible candidates. Toward this goal, we conduct multilevel low-rank and sparse analysis in the intrinsic feature space spanned by the shape descriptors defined on over-segmented super-pixels. Since the low-rank property emphasizes much more on stronger similarities among super-pixels, we naturally obtain a scale space along the rank dimension in this way. Multiscale saliency can be obtained by simply computing differences among the low-rank components across the rank scale. We conduct extensive experiments on some public benchmarks, and make comprehensive, quantitative evaluation between our method and existing state-of-the-art techniques. All the results demonstrate the superiority of our method in accuracy, reliability, robustness, and versatility.
Sparse Coding and Dictionary Learning with Linear Dynamical Systems

Linear Dynamical Systems (LDSs) are the fundamental tools for encoding spatio-temporal data in various disciplines. To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs. Rather than approximate the extended observability with a finite-order matrix, we represent the space of LDSs by an infinite Grassmannian consisting of the orthonormalized extended observability subspaces. Via a homeomorphic mapping, such Grassmannian is embedded into the space of symmetric matrices, where a tractable objective function can be derived for sparse coding. Then, we propose an efficient method to learn the system parameters of the dictionary atoms explicitly, by imposing the symmetric constraint to the transition matrices of the data and dictionary systems. Moreover, we combine the state covariance into the algorithm formulation, thus further promoting the performance of the models with symmetric transition matrices. Comparative experimental evaluations reveal the superior performance of proposed methods on various tasks including video classification and tactile recognition.
2D bipedal walking with knees and feet: A hybrid control approach

In this paper, we consider an anthropomorphically-inspired hybrid model of a bipedal robot with locking knees and feet in order to develop a control law that results in human-like walking. The presence of feet results in periods of full actuation and periods of underactuation during the course of a step. Properties of each of these phases of walking are utilized in order to achieve a stable walking gait. In particular, we will show that using controlled symmetries in the fully-actuated domains coupled with “partial” controlled symmetries and local ankle control laws in the underactuated domains yields stable walking; this result is possible due to the amount of time which the biped spends in the fully-actuated domains. The paper concludes with simulation results along with a comparison of these results to human walking data.
A Fast Asynchronous Huffman Decoder for Compressed-Code Embedded Processors

This paper presents the architecture and design of a high-performance asynchronous Huffman decoder for compressed-code embedded processors. In such processors, embedded programs are stored in compressed form in instruction ROM, then are decompressed on demand during instruction cache refill. The Huffman decoder is used as a code decompression engine. The circuit is non-pipelined, and is implemented as an iterative self-timed ring. It achieves a high-speed decode rate with very low area overhead. Simulations using Lsim show an average throughput of 32 bits/25 ns on the output side (or 163 MBytes/sec, or 1303 Mbit/sec), corresponding to about 889 Mbit/sec on the input side. The area of the design is extremely small: under 1 mm in a 0.8 micron fullcustom layout. The decoder is estimated to have higher throughput than any comparable synchronous Huffman decoder (after normalizing for feature size and voltage), yet is much smaller than synchronous designs. Its performance is also 83% faster than a recently published asynchronous Huffman decoder using the same technology.
Integrated Anomaly Detection for Cyber Security of the Substations

Cyber intrusions to substations of a power grid are a source of vulnerability since most substations are unmanned and with limited protection of the physical security. In the worst case, simultaneous intrusions into multiple substations can lead to severe cascading events, causing catastrophic power outages. In this paper, an integrated Anomaly Detection System (ADS) is proposed which contains host- and network-based anomaly detection systems for the substations, and simultaneous anomaly detection for multiple substations. Potential scenarios of simultaneous intrusions into the substations have been simulated using a substation automation testbed. The host-based anomaly detection considers temporal anomalies in the substation facilities, e.g., user-interfaces, Intelligent Electronic Devices (IEDs) and circuit breakers. The malicious behaviors of substation automation based on multicast messages, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Measured Value (SMV), are incorporated in the proposed network-based anomaly detection. The proposed simultaneous intrusion detection method is able to identify the same type of attacks at multiple substations and their locations. The result is a new integrated tool for detection and mitigation of cyber intrusions at a single substation or multiple substations of a power grid.
Piecewise Planar Modeling for Step Detection using Stereo Vision

A mobility aid for the visually impaired should be able to detect and warn about nearby obstacles. Reliable detection of curbs and steps is critical to meet this goal. This paper describes a stereo-vision based algorithm that estimates the underlying planar geometry of the 3D scene to generate hypotheses for the presence of steps. Tensor voting is used to calculate globally consistent normals at each data point and a clustering algorithm is described to generate a piecewise planar model of the scene. Results demonstrate the improvement in plane clustering using tensor voting and the ability of the algorithm to find sufficient evidence for the presence of curbs and steps.
Understanding online purchase intentions: contributions from technology and trust perspectives

12 July 2000 Revised: 20 August 2001 : 30 July 2002  15 October 2002  This paper explores factors that influence consumer’s intentions to purchase online at an electronic commerce website. Specifically, we investigate online purchase intention using two different perspectives: a technology-oriented perspective and a trust-oriented perspective. We summarise and review the antecedents of online purchase intention that have been developed within these two perspectives. An empirical study in which the contributions of both perspectives are investigated is reported. We study the perceptions of 228 potential online shoppers regarding trust and technology and their attitudes and intentions to shop online at particular websites. In terms of relative contributions, we found that the trust-antecedent ‘perceived risk’ and the technology-antecedent ‘perceived ease-of-use’ directly influenced the attitude towards purchasing online.  (2003) 12, 41–48. doi:10.1057/ palgrave.ejis.3000445
ATM Location Problem and Cash Management in Automated Teller Machines

Automated Teller Machines (ATMs) can be considered among one of the most important service facilities in the banking industry. The investment in ATMs and the impact on the banking industry is growing steadily in every part of the world. The banks take into consideration many factors like safety, convenience, visibility, and cost in order to determine the optimum locations of ATMs. Today, ATMs are not only available in bank branches but also at retail locations. Another important factor is the cash management in ATMs. A cash demand model for every ATM is needed in order to have an efficient cash management system. This forecasting model is based on historical cash demand data which is highly related to the ATMs location. So, the location and the cash management problem should be considered together. This paper provides a general review on studies, efforts and development in ATMs location and cash management problem. ATM location problem, cash management problem, ATM cash replenishment problem, literature review in ATMs.
Skin lesion segmentation in clinical images using deep learning

Melanoma is the most aggressive form of skin cancer and is on rise. There exists a research trend for computerized analysis of suspicious skin lesions for malignancy using images captured by digital cameras. Analysis of these images is usually challenging due to existence of disturbing factors such as illumination variations and light reflections from skin surface. One important stage in diagnosis of melanoma is segmentation of lesion region from normal skin. In this paper, a method for accurate extraction of lesion region is proposed that is based on deep learning approaches. The input image, after being preprocessed to reduce noisy artifacts, is applied to a deep convolutional neural network (CNN). The CNN combines local and global contextual information and outputs a label for each pixel, producing a segmentation mask that shows the lesion region. This mask will be further refined by some post processing operations. The experimental results show that our proposed method can outperform the existing state-of-the-art algorithms in terms of segmentation accuracy.
Learning Program Embeddings to Propagate Feedback on Student Code

Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code.org Hour of Code and Stanford University’s CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions.
Real-time end-to-end secure voice communications over GSM voice channel

GSM is the most wide spread mobile communications system in the world. However the security of the GSM voice traffic is not guaranteed especially over the core network. It is highly desirable to have end-to-end secure communications over the GSM voice channel. In order to achieve end-to-end security, speech must be encrypted before it enters the GSM network. A modulation scheme that enables the transmission of encrypted voice and data over the GSM voice channel was designed1. A real-time prototype is implemented demonstrating the end-to-end secure voice communications over the GSM voice channel. The modem technology presented facilitates the transmission of encrypted data and an encryption algorithm is not specified. The users may choose an algorithm and a hardware platform as necessary.
On Enhancing Lane Estimation Using Contextual Cues

Vision-based lane detection is a critical component of modern automotive active safety systems. Although a number of robust and accurate lane estimation (LE) algorithms have been proposed, computationally efficient systems that can be realized on embedded platforms have been less explored and addressed. This paper presents a framework that incorporates contextual cues for LE to further enhance the performance in terms of both computational efficiency and accuracy. The proposed context-aware LE framework considers the state of the ego vehicle, its surroundings, and the system-level requirements to adapt and scale the LE process resulting in substantial computational savings. This is accomplished by synergistically fusing data from multiple sensors along with the visual data to define the context around the ego vehicle. The context is then incorporated as an input to the LE process to scale it depending on the contextual requirements. A detailed evaluation of the proposed framework on real-world driving conditions shows that the dynamic and static configuration of the lane detection process results in computation savings as high as 90%, without compromising on the accuracy of LE.
A Feasibility Study on Autonomous Online Condition Monitoring of High-Voltage Overhead Power Lines

For electric power transmission, high-voltage overhead power lines play an important role, as the costs for power transmission are comparatively low. However, the sag of the conductors (e.g., due to temperature variations, aging, or icing of conductors as a result of extreme weather conditions) may increase safety margins and limit the operability of these power lines. Furthermore, heavy loads due to icing or vibrations excited by wind streams increase the risk of line breakage. With online condition monitoring of power lines, critical states can be detected early, and appropriate countermeasures can be applied. In this paper, we investigate possibilities for monitoring devices that are directly mounted onto a conductor. The feasibility of powering the device from the electric field, protection of electronic circuitry from strong electric fields, and data transmission by means of a wireless link is demonstrated, as well as its operability during the presence of strong magnetic fields due to high currents and transient signals due to partial and spark discharge events.
On extracting feature models from product descriptions

In product line engineering, domain analysis is the process of analyzing related products to identify their common and variable features. This process is generally carried out by experts on the basis of existing product descriptions, which are expressed in a more or less structured way. Modeling and reasoning about product descriptions are error-prone and time consuming tasks. Feature models (FMs) constitute popular means to specify product commonalities and variabilities in a compact way, and to provide automated support to the domain analysis process. This paper aims at easing the transition from product descriptions expressed in a tabular format to FMs accurately representing them. This process is parameterized through a dedicated language and high-level directives (e.g., products/features scoping). We guarantee that the resulting FM represents the set of legal feature combinations supported by the considered products and has a readable tree hierarchy together with variability information. We report on our experiments based on public data and characterize the properties of the derived FMs.