nils-herrmann/clean_scirepeval huggingface dataset:
Modeling Dynamic Relationships Between Characters in Literary Novels

Studying characters plays a vital role in computationally representing and interpreting narratives. Unlike previous work, which has focused on inferring character roles, we focus on the problem of modeling their relationships. Rather than assuming a fixed relationship for a character pair, we hypothesize that relationships are dynamic and temporally evolve with the progress of the narrative, and formulate the problem of relationship modeling as a structured prediction problem. We propose a semisupervised framework to learn relationship sequences from fully as well as partially labeled data. We present a Markovian model capable of accumulating historical beliefs about the relationship and status changes. We use a set of rich linguistic and semantically motivated features that incorporate world knowledge to investigate the textual content of narrative. We empirically demonstrate that such a framework outperforms competitive baselines.
Public Skepticism of Psychology Why Many People Perceive the Study of Human Behavior as Unscientific

Data indicate that large percentages of the general public regard psychology’s scientific status with considerable skepticism. I examine 6 criticisms commonly directed at the scientific basis of psychology (e.g., psychology is merely common sense, psychology does not use scientific methods, psychology is not useful to society) and offer 6 rebuttals. I then address 8 potential sources of public skepticism toward psychology and argue that although some of these sources reflect cognitive errors (e.g., hindsight bias) or misunderstandings of psychological science (e.g., failure to distinguish basic from applied research), others (e.g., psychology’s failure to police itself, psychology’s problematic public face) reflect the failure of professional psychology to get its own house in order. I offer several individual and institutional recommendations for enhancing psychology’s image and contend that public skepticism toward psychology may, paradoxically, be one of our field’s strongest allies.
An Overview of Cloud Services Adoption Challenges in Higher Education Institutions

Information Technology (IT) plays an important role in enabling education services be delivered to users. Most education online services in universities have been run on the cloud to provide services to support students, lecturers, researchers and administration staff. These are enabled with the emergence of cloud computing in the world of IT. Cloud computing offers on demand Internet-based computing services. This paper presents an overview of cloud computing adoption in higher education, mainly tertiary institutions and universities. The focus of the paper is the challenges of cloud computing in higher education. It introduces the background to cloud computing and reviews research on adoption challenges in higher education institutions. These challenges are important as they provide an overview of the adoption of cloud in higher education. The authors proposed an integrated reference model based on the challenges in the literature integrated with TAM model to investigate the factors influence the users’ attitudes and behaviours toward using cloud education services in universities ICT provision.
Adaptive controller for motion control of an omni-directional walker

Walking is a vital exercise for health promotion and a fundamental ability necessary for everyday life. In the authors' previous studies, an omni-directional walker was developed for walking rehabilitation. Walking training programs are stored in the walker and the walker must precisely follow the paths defined in the walking training programs to guarantee the effectiveness of rehabilitation. In the previous study, an adaptive control method has been proposed for path tracking of the walker considering a center of gravity shift and load change. In this paper simulations and running experiments are carried out to verify the proposed adaptive control method. First, the kinematics and the kinetics of the omni-directional walker motion are described. Second, the adaptive control strategy is presented. Finally, path tracking simulations and experiments are carried out using the proposed method. Comparing with the proportional-integral-derivative control (PID control), the simulation and experiment results demonstrate the feasibility and effectiveness of the adaptive control method.
A very low-power CMOS mixed-signal IC for implantable pacemaker applications

Low power consumption is crucial for medical implant devices. A single-chip, very-low-power interface IC used in implantable pacemaker systems is presented. It contains amplifiers, filters, ADCs, battery management system, voltage multipliers, high voltage pulse generators, programmable logic and timing control. A few circuit techniques are proposed to achieve nanopower circuit operations within submicron CMOS process. Subthreshold transistor designs and switched-capacitor circuits are widely used. The 200 k transistor IC occupies 49 mm/sup 2/, is fabricated in a 0.5-/spl mu/m two-poly three-metal multi-V/sub t/ process, and consumes 8 /spl mu/W.
Culture and Causal Cognition

East Asian and American causal reasoning differs significantly. East Asians understand behavior in terms of complex interactions between disposi-tions of the person or other object and contextual factors, whereas Americans often view social behavior primarily as the direct unfolding of disposi-tions. These culturally differing causal theories seem to be rooted in more pervasive, culture specific mentalities in East Asia and the West. The Western mentality is analytic, focus-ing attention on the object, categorizing it by reference to its attributes, and ascribing cau-sality based on rules about it. The East Asian mentality is ho-listic, focusing attention on the field in which the object is located and ascribing causality by reference to the relationship between the object and the field. Psychologists within the cogni-tive science tradition have long believed that fundamental reasoning processes such as causal attribution are the same in all cultures (Gard-ner, 1985). Although recognizing that the content of causal beliefs can differ widely across cultures, psychologists have assumed that the ways in which people come to make their causal judgments are essentially the same, and therefore that they tend to make the same sorts of inferential errors. A case in point is the fundamental attribu-tion error, or FAE (Ross, 1977), a phenomenon that is of central importance to social psychology and until recently was held to be invariable across cultures. The FAE refers to people's inclination to see behavior as the result of dispositions corresponding to the apparent nature of the behavior. This tendency often results in error when there are obvious situ-ational constraints that leave little or no role for dispositions in producing the behavior. The classic example of the FAE was demonstrated in a study by Jones and Harris (1967) in which participants read a speech or essay that a target person had allegedly been required to produce by a debate coach or psychology experimenter. The speech or essay favored a particular position on an issue, for example , the legalization of marijuana. Participants' estimates of the target's actual views on the issue reflected to a substantial extent the views expressed in the speech or essay, even when they knew that the target had been explicitly instructed to defend a particular position. Thus, participants inferred an attitude that corresponded to the target person's apparent behavior , without taking into account the situational constraints operating on the behavior. Since that classic study, the FAE has been found in …
An MOS transistor model for analog circuit design

This paper presents a physically based model for the metal–oxide–semiconductor (MOS) transistor suitable for analysis and design of analog integrated circuits. Static and dynamic characteristics of the MOS field-effect transistor are accurately described by single-piece functions of two saturation currents in all regions of operation. Simple expressions for the transconductance-to-current ratio, the drain-to-source saturation voltage, and the cutoff frequency in terms of the inversion level are given. The design of a common-source amplifier illustrates the application of the proposed model.
Integrated switch current sensor for shortcircuit protection and current control of 1.7-kV SiC MOSFET modules

This paper presents design and implementations of a switch current sensor based on Rogowski coils. The current sensor is designed to address the issue of using desaturation circuit to protect the SiC MOSFET during shortcircuit. Specifications are given to meet the application requirement for SiC MOSFETs. It is also designed for high accuracy and high bandwidth for converter current control. PCB-based winding and shielding layout is proposed to minimize the noises caused by the high dv/dt at switching. The coil on PCB are modeled by impedance measurement, thus the bandwidth of coil is calculated. At the end, various test results are demonstrated to validate the great performance of the switch current sensor.
Metaphor comprehension : What makes a metaphor difficult to understand ?

Comprehension difficulty was rated for metaphors of the form Noun1-is-aNoun2; in addition, participants completed frames of the form Noun1-is-________ with their literal interpretation of the metaphor. Metaphor comprehension was simulated with a computational model based on Latent Semantic Analysis. The model matched participants’ interpretations for both easy and difficult metaphors. When interpreting easy metaphors, both the participants and the model generated highly consistent responses. When interpreting difficult metaphors, both the participants and the model generated disparate responses.
An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software

Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML as an implementation approach has on ISO 26262 safety lifecycle and ask what could be done to address them. We then provide a set of recommendations on how to adapt the standard to accommodate ML.
Health care monitoring system in Internet of Things (IoT) by using RFID

Healthcare industry has perpetually been on the forefront in the adoption and utilization of information and communication technologies (ICT) for the efficient healthcare administration and treatment. Recent developments in ICT and the emergence of Internet of Things (IoT) have opened up new avenues for research and exploration in the all fields including medical and healthcare industry. Hospitals have started using the cell instruments for communication intent and for this intent internet of things (IoT) has been used and fused with wi-fi sensor node reminiscent of RFID, NFC tag and small sensor nodes. The usage of a cellular agent in healthcare procedure underneath wi-fi community environment gives a chance to explore improved services for patients and staffs reminiscent of medical professionals and nurses given that of its mobility. In this paper novel method to utilize it IoT within the field of scientific and crafty wellness care are presented. The majority of the survey exist about the different healthcare approaches used in the IoT, similar to, wireless well-being monitoring, U-healthcare, E-healthcare, Age-friendly healthcare techniques. This paper describes and proposes a complete monitoring existence cycle and effective healthcare monitoring system designed by using the IoT and RFID tags. The experimental results in this paper show the robust output against various medical emergencies. In this system to get the veracious evaluation results, supervising and weighing the health status of patient and to increase the power of IoT, the combination of microcontroller with sensors is presented.
A 116nW multi-band wake-up receiver with 31-bit correlator and interference rejection

This paper presents a 116nW wake-up radio complete with crystal reference, interference compensation, and baseband processing, such that a selectable 31-bit code is required to toggle a wake-up signal. The front-end operates over a broad frequency range, tuned by an off-chip band-select filter and matching network, and is demonstrated in the 402-405MHz MICS band and the 915MHz and 2.4GHz ISM bands with sensitivities of -45.5dBm, -43.4dBm, and -43.2dBm, respectively. Additionally, the baseband processor implements automatic threshold feedback to detect the presence of interferers and dynamically adjust the receiver's sensitivity, mitigating the jamming problem inherent to previous energy-detection wake-up radios. The wake-up radio has a raw OOK chip-rate of 12.5kbps, an active area of 0.35mm2 and operates using a 1.2V supply for the crystal reference and RF demodulation, and a 0.5V supply for subthreshold baseband processing.
Journal as Markers of Intellectual Space: Journal Co-Citation Analysis of Information Retrieval Area, 1987–1997

A journal co-citation analysis of fifty journals and other publications in the information retrieval (IR) discipline was conducted over three periods spanning the years of 1987 to 1997. Relevant data retrieved from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) are analysed according to the highly cited journals in various disciplines, especially in the Library & Information Science area. The results are compared with previous research that covered the data only from the Social Science Citation Index (SSCI). The analysis reveals that there is no distinct difference between these two sets of results. The results of current study show that IR speciality is multi-disciplinary with broad relations with other specialities. The field of IR is a mature field, as the journals used for research communication remained quite stable during the study period.
Bounding and Counting Linear Regions of Deep Neural Networks

We investigate the complexity of deep neural networks (DNN) that represent piecewise linear (PWL) functions. In particular, we study the number of linear regions, i.e. pieces, that a PWL function represented by a DNN can attain, both theoretically and empirically. We present (i) tighter upper and lower bounds for the maximum number of linear regions on rectifier networks, which are exact for inputs of dimension one; (ii) a first upper bound for multi-layer maxout networks; and (iii) a first method to perform exact enumeration or counting of the number of regions by modeling the DNN with a mixed-integer linear formulation. These bounds come from leveraging the dimension of the space defining each linear region. The results also indicate that a deep rectifier network can only have more linear regions than every shallow counterpart with same number of neurons if that number exceeds the dimension of the input.
A systematic study of the class imbalance problem in convolutional neural networks

In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that completely eliminates the imbalance, whereas the optimal undersampling ratio depends on the extent of imbalance; (iv) as opposed to some classical machine learning models, oversampling does not cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.
Cross-Domain Recommendation: An Embedding and Mapping Approach

Data sparsity is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation, i.e., leveraging feedbacks or ratings from multiple domains to improve recommendation performance in a collective manner. In this paper, we propose an Embedding and Mapping framework for Cross-Domain Recommendation, called EMCDR. The proposed EMCDR framework distinguishes itself from existing crossdomain recommendation models in two aspects. First, a multi-layer perceptron is used to capture the nonlinear mapping function across domains, which offers high flexibility for learning domain-specific features of entities in each domain. Second, only the entities with sufficient data are used to learn the mapping function, guaranteeing its robustness to noise caused by data sparsity in single domain. Extensive experiments on two cross-domain recommendation scenarios demonstrate that EMCDR significantly outperforms stateof-the-art cross-domain recommendation methods.
Compact Mixed-Mode Filter Based on ${\rm{TE}}_{\mathrm {101}}$ Cavity Mode and ${\rm{TE}}_{\mathrm {01\delta }}$ Dielectric Mode

A new class of compact in-line filters with pseudoelliptic responses is presented in this paper. The proposed filters employ a new type of mixed-mode resonator. Such a resonator consists of a cavity loaded with a suspended high permittivity dielectric puck, so that both cavity TE101 mode and dielectric TE01δ mode are exploited within the same volume. This structure realizes the transverse doublet topology and it is therefore capable of generating a transmission zero (TZ) that can be either located above or below the passband. Multiple mixedmode resonators can be used as basic building blocks to obtain higher order filters by cascading them through nonresonating nodes. These filters are capable of implementing TZs that are very close to the passband edges, thus realizing an extreme closein rejection. As a result of the dielectric loading, the proposed solution leads to a very compact structure with improved temperature stability. To validate the proposed class of filters, a second-order filter with 2.0% fractional bandwidth (FBW) and a fourth-order filter with 2.5% FBW have been designed and manufactured.
Training an Active Random Field for Real-Time Image Denoising

Many computer vision problems can be formulated in a Bayesian framework based on Markov random fields (MRF) or conditional random fields (CRF). Generally, the MRF/CRF model is learned independently of the inference algorithm that is used to obtain the final result. In this paper, we observe considerable gains in speed and accuracy by training the MRF/CRF model together with a fast and suboptimal inference algorithm. An active random field (ARF) is defined as a combination of a MRF/CRF based model and a fast inference algorithm for the MRF/CRF model. This combination is trained through an optimization of a loss function and a training set consisting of pairs of input images and desired outputs. We apply the ARF concept to image denoising, using the Fields of Experts MRF together with a 1-4 iteration gradient descent algorithm for inference. Experimental validation on unseen data shows that the ARF approach obtains an improved benchmark performance as well as a 1000-3000 times speedup compared to the Fields of Experts MRF. Using the ARF approach, image denoising can be performed in real-time, at 8 fps on a single CPU for a 256times256 image sequence, with close to state-of-the-art accuracy.
High Performance and Energy-Efficient On-Chip Cache Using Dual Port (1R/1W) Spin-Orbit Torque MRAM

This paper proposes a dual (1R/1W) port spin-orbit torque magnetic random access memory (1R/1W SOT-MRAM) for energy efficient on-chip cache applications. Our proposed dual port memory can alleviate the impact of write latency on system performance by supporting simultaneous read and write accesses. The spin-orbit device leverages the high spin current injection efficiency of spin Hall metal to achieve low critical switching current to program a magnetic tunnel junction. The low write current reduces the write power consumption, and the size of the access transistors, leading to higher integration density. Furthermore, the decoupled read and write current paths of the spin-orbit device improves oxide barrier reliability, because the write current does not flow through the oxide barrier. Device, circuit, and system level co-simulations show that a 1R/1W SOT-MRAM based L2 cache can improve the performance and energy-efficiency of the computing systems compared to SRAM and standard STT-MRAM based L2 caches.
Enhancing product search by best-selling prediction in e-commerce

With the rapid growth of E-Commerce on the Internet, online product search service has emerged as a popular and effective paradigm for customers to find desired products and select transactions. Most product search engines today are based on adaptations of relevance models devised for information retrieval. However, there is still a big gap between the mechanism of finding products that customers really desire to purchase and that of retrieving products of high relevance to customers' query. In this paper, we address this problem by proposing a new ranking framework for enhancing product search based on dynamic best-selling prediction in E-Commerce. Specifically, we first develop an effective algorithm to predict the dynamic best-selling, i.e. the volume of sales, for each product item based on its transaction history. By incorporating such best-selling prediction with relevance, we propose a new ranking model for product search, in which we rank higher the product items that are not only relevant to the customer's need but with higher probability to be purchased by the customer. Results of a large scale evaluation, conducted over the dataset from a commercial product search engine, demonstrate that our new ranking method is more effective for locating those product items that customers really desire to buy at higher rank positions without hurting the search relevance.