nils-herrmann/clean_scirepeval huggingface dataset:
Exploring social influence via posterior effect of word-of-mouth recommendations

Word-of-mouth has proven an effective strategy for promoting products through social relations. Particularly, existing studies have convincingly demonstrated that word-of-mouth recommendations can boost users' prior expectation and hence encourage them to adopt a certain innovation, such as buying a book or watching a movie. However, less attention has been paid to studying the posterior effect of word-of-mouth recommendations, i.e., whether or not word-of-mouth recommendations can influence users' posterior evaluation on the products or services recommended to them, the answer to which is critical to estimating user satisfaction when proposing a word-of-mouth marketing strategy. In order to fill this gap, in this paper we empirically study the above issue and verify that word-of-mouth recommendations are strongly associated with users' posterior evaluation. Through elaborately designed statistical hypothesis tests we prove the causality that word-of-mouth recommendations directly prompt the posterior evaluation of receivers. Finally, we propose a method for investigating users' social influence, namely, their ability to affect followers' posterior evaluation via word-of-mouth recommendations, by examining the number of their followers and their sensitivity of discovering good items. The experimental results on real datasets show that our method can successfully identify 78% influential friends with strong social influence.
Active Learning with Multi-Label SVM Classification

Multi-label classification, where each instance is assigned to multiple categories, is a prevalent problem in data analysis. However, annotations of multi-label instances are typically more timeconsuming or expensive to obtain than annotations of single-label instances. Though active learning has been widely studied on reducing labeling effort for single-label problems, current research on multi-label active learning remains in a preliminary state. In this paper, we first propose two novel multi-label active learning strategies, a max-margin prediction uncertainty strategy and a label cardinality inconsistency strategy, and then integrate them into an adaptive framework of multi-label active learning. Our empirical results on multiple multilabel data sets demonstrate the efficacy of the proposed active instance selection strategies and the integrated active learning approach.
Quaternionic Manin symbols, Brandt matrices, and Hilbert modular forms

In this paper, we propose a generalization of the algorithm we developed previously. Along the way, we also develop a theory of quaternionic M symbols whose definition bears some resemblance to the classical M -symbols, except for their combinatorial nature. The theory gives a more efficient way to compute Hilbert modular forms over totally real number fields, especially quadratic fields, and we have illustrated it with several examples. Namely, we have computed all the newforms of prime levels of norm less than 100 over the quadratic fields Q( √ 29) and Q( √ 37), and whose Fourier coefficients are rational or are defined over a quadratic field.
C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems

Cyber-physical system (CPS) is a new trend in the Internet-of-Things related research works, where physical systems act as the sensors to collect real-world information and communicate them to the computation modules (i.e. cyber layer), which further analyze and notify the findings to the corresponding physical systems through a feedback loop. Contemporary researchers recommend integrating cloud technologies in the CPS cyber layer to ensure the scalability of storage, computation, and cross domain communication capabilities. Though there exist a few descriptive models of the cloud-based CPS architecture, it is important to analytically describe the key CPS properties: computation, control, and communication. In this paper, we present a digital twin architecture reference model for the cloud-based CPS, C2PS, where we analytically describe the key properties of the C2PS. The model helps in identifying various degrees of basic and hybrid computation-interaction modes in this paradigm. We have designed C2PS smart interaction controller using a Bayesian belief network, so that the system dynamically considers current contexts. The composition of fuzzy rule base with the Bayes network further enables the system with reconfiguration capability. We also describe analytically, how C2PS subsystem communications can generate even more complex system-of-systems. Later, we present a telematics-based prototype driving assistance application for the vehicular domain of C2PS, VCPS, to demonstrate the efficacy of the architecture reference model.
A 60 GHz single-chip 256-element wafer-scale phased array with EIRP of 45 dBm using sub-reticle stitching

This paper presents a 60 GHz wafer-scale transmit phased-array with 256-elements spaced λ/2 apart in the x and y directions, and occupying an area of 4.14×4.2 cm2 (1740 mm2). The phased array is built using independent RF, transmission-line and control circuit blocks which are stitched together to form an aggregate chip which is much larger than a standard reticle (22×22 mm2). This method allows for a wafer-scale design and can be extended to any size and any shape (rectangular, hexagonal, etc.) up to the edge of the wafer. The blocks include high-efficiency on-wafer antennas, phased-array channels with 3-bits amplitude and 5-bits phase control together with an amplifier having an output power of +3 dBm at 60 GHz. Also, a highly redundant RF distribution network is synthesized from several stitched blocks for improved yield, and the control blocks have redundant SPI control and power strips, also for improved yield. The 256-element array results in a half-power beamwidth of 6° in the E- and H-planes, a directivity of 29 dB, and scans to +/- 55° in the E- and H-planes with near-ideal patterns and a cross-polarization level of <;-25 dB. The measured EIRP is 45 dBm at 61 GHz and with a 3-dB bandwidth from 58 to 64 GHz. To our knowledge, this is the largest single-chip phased-array ever developed and allows the construction of large-scale (1000+ elements) phased-array systems, either on a single wafer or by assembling several of these chips together.
Adult attachment and the suppression of unwanted thoughts.

Dismissing-avoidant adults are characterized by expressing relatively low levels of attachment-related distress. However, it is unclear whether this reflects a relative absence of covert distress or an attempt to conceal covert distress. Two experiments were conducted to distinguish between these competing explanations. In Experiment 1, participants were instructed to suppression resulted in a decrease in the accessibility of abandonment-related thoughts for dismissing-avoidant adults. Experiment 2 demonstrated that attempts to suppress the attachment system resulted in decreases in physiological arousal for dismissing-avoidant adults. These experiments indicate that dismissing-avoidant adults are capable of suppressing the latent activation of their attachment system and are not simply concealing latent distress. The discussion focuses on development, cognitive, and social factors that may promote detachment.
Implicit feedback for inferring user preference: a bibliography

Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f. . Relevance feedback is typically used for query expansion during short-term modeling of a user’s immediate information need and for user profiling during long-term modeling of a user’s persistent interests and preferences. Traditional relevance feedback methods require that users explicitly give feedback by, for example, specifying keywords, selecting and marking documents, or answering questions about their interests. Such relevance feedback methods force users to engage in additional activities beyond their normal searching behavior. Since the cost to the user is high and the benefits are not always apparent, it can be difficult to collect the necessary data and the effectiveness of explicit techniques can be limited.
Quantitative phase imaging via Fourier ptychographic microscopy.

Fourier ptychographic microscopy (FPM) is a recently developed imaging modality that uses angularly varying illumination to extend a system's performance beyond the limit defined by its optical components. The FPM technique applies a novel phase-retrieval procedure to achieve resolution enhancement and complex image recovery. In this Letter, we compare FPM data to theoretical prediction and phase-shifting digital holography measurement to show that its acquired phase maps are quantitative and artifact-free. We additionally explore the relationship between the achievable spatial and optical thickness resolution offered by a reconstructed FPM phase image. We conclude by demonstrating enhanced visualization and the collection of otherwise unobservable sample information using FPM's quantitative phase.
Improving new user recommendations with rule-based induction on cold user data

With recommender systems, users receive items recommended on the basis of their profile. New users experience the cold start problem: as their profile is very poor, the system performs very poorly. In this paper, classical new user cold start techniques are improved by exploiting the cold user data, i.e. the user data that is readily available (e.g. age, occupation, location, etc.), in order to automatically associate the new user with a better first profile. Relying on the existing α-community spaces model, a rule-based induction process is used and a recommendation process based on the "level of agreement" principle is defined. The experiments show that the quality of recommendations compares to that obtained after a classical new user technique, while the new user effort is smaller as no initial ratings are asked.
Majority Vote of Diverse Classifiers for Late Fusion

In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle this problem, we investigate a recent and elegant well-founded quadratic program named MinCq coming from the machine learning PAC-Bayesian theory. MinCq looks for the weighted combination, over a set of real-valued functions seen as voters, leading to the lowest misclassification rate, while maximizing the voters’ diversity. We propose an extension of MinCq tailored to multimedia indexing. Our method is based on an order-preserving pairwise loss adapted to ranking that allows us to improve Mean Averaged Precision measure while taking into account the diversity of the voters that we want to fuse. We provide evidence that this method is naturally adapted to late fusion procedures and confirm the good behavior of our approach on the challenging PASCAL VOC’07 benchmark.
The Stability of Hedonic Coalition Structures

We consider the partitioning of a society into coalitions in purely hedonic settings; i.e., where each player's payo is completely determined by the identity of other members of her coalition. We rst discuss how hedonic and non-hedonic settings di er and some su cient conditions for the existence of core stable coalition partitions in hedonic settings. We then focus on a weaker stability condition: individual stability, where no player can bene t from moving to another coalition while not hurting the members of that new coalition. We show that if coalitions can be ordered according to some characteristic over which players have single-peaked preferences, or where players have symmetric and additively separable preferences, then there exists an individually stable coalition partition. Examples show that without these conditions, individually stable coalition partitions may not exist. We also discuss some other stability concepts, and the incompatibility of stability with other normative properties.
Predicting Learning Behavior of Students using Classification Techniques

The main objective of any educational organization is to provide quality education and improve the overall performance of an institution by looking at individual performances. One way to analyze learners' performances is to identify the areas of weakness and guide their students to a better future. Although data mining has been successful in many areas, its use in student performance analysis is still relatively new, i.e. the knowledge is hidden in educational data set and it is extracted using data mining techniques. This paper discusses about a learning model for predicting student performance using classification techniques. Also the paper shows the comparative performance analysis of J48, Naïve Bayesian classifier and Random forest algorithm.
Scaling for edge inference of deep neural networks

Such accomplishments can be largely credited to everincreasing computing power and a growing abundance of data. As larger clusters of faster computing nodes become available at lower cost and in smaller form factors, more data (assuming abundant data is readily available for the application of interest) can be used to train DNNs with more layers and neurons. This should translate to higher inference accuracy10. Network sizes have increased drastically over time, reaching beyond the petascale for some applications. Figure 1 shows, for example, how the number of parameters in popular deep neural networks has increased over the last twenty years, and clearly illustrates an exponential growth. For applications where powerful computing resources are easily accessible through network connections, large networks such as these may not present substantial challenges. However, for edge computation on embedded hardware platforms, where security, privacy and/or latency are critical considerations (such as smart sensors, wearable devices, autonomous driving and unmanned aerial vehicle tracking), inference must be performed locally or at the edge of the network, and such computation is subject to stringent area and power constraints due to the limited resources available. To address the computational demands of ever-increasing network sizes, hardware architects have begun exploring techniques to compress DNNs for efficient edge inference. The ultimate judgment of such techniques is that lower power and area overheads can be achieved with minimal loss in inference accuracy. As many data scientists are focusing on increasing inference accuracy through designing more complex DNNs, there is a race between data scientists and hardware architects. Complementary metal–oxide semiconductor (CMOS) technology scaling based on Moore’s law has provided hardware designers with a relatively easy path towards accommodating increasing network sizes. However, with the slowdown of CMOS scaling trends, novel architectures specifically designed for neural networks have emerged. In this Perspective, we demonstrate that gaps exist between Moore’s-law-based CMOS scaling and the scaling of DNNs for edge inference, and discuss various architecture and algorithm innovations that could help to bridge these gaps.
Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data

It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.
Sequence segmentation using joint RNN and structured prediction models

We describe and analyze a simple and effective algorithm for sequence segmentation applied to speech processing tasks. We propose a neural architecture that is composed of two modules trained jointly: a recurrent neural network (RNN) module and a structured prediction model. The RNN outputs are considered as feature functions to the structured model. The overall model is trained with a structured loss function which can be designed to the given segmentation task. We demonstrate the effectiveness of our method by applying it to two simple tasks commonly used in phonetic studies: word segmentation and voice onset time segmentation. Results suggest the proposed model is superior to previous methods, obtaining state-of-the-art results on the tested datasets.
Low loss H-shape SIW hybrid coupler for millimeter-wave phased arrays antenna systems

A simple low loss H-shape hybrid coupler based on the substrate integrated waveguide technology is presented for millimeter-wave applications. The coupler operation is based on the excitation of two different modes, TE10 and TE20. The coupler S-matrix is calculated by using a full-wave solver that uses the even/odd mode (symmetry) analysis to minimize the computational time and provides more physical insight. The simulated return and insertion losses are better than -20 dB and -3.90 dB, respectively over the operating frequency bandwidth of 39-40.50 GHz.
Interactome Networks and Human Disease

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.
Corporate Governance and Firm Performance Corporate Governance and Firm Performance

How is corporate governance measured? What is the relationship between corporate governance and performance? This paper sheds light on these questions while taking into account the endogeneity of the relationships among corporate governance, corporate performance, corporate capital structure, and corporate ownership structure. We make three additional contributions to the literature: First, we find that better governance as measured by the Gompers, Ishii, and Metrick (GIM, 2003) and Bebchuk, Cohen and Ferrell (BCF, 2004) indices, stock ownership of board members, and CEO-Chair separation is significantly positively correlated with better contemporaneous and subsequent operating performance. Second, contrary to claims in GIM and BCF, none of the governance measures are correlated with future stock market performance. In several instances inferences regarding the (stock market) performance and governance relationship do depend on whether or not one takes into account the endogenous nature of the relationship between governance and (stock market) performance. Third, given poor firm performance, the probability of disciplinary management turnover is positively correlated with stock ownership of board members, and board independence. However, better governed firms as measured by the GIM and BCF indices are less likely to experience disciplinary management turnover in spite of their poor performance. The above results highlight the strategic importance of board incentives. Our recommendations on board incentives are consistent with the implications of Hermalin and
Estimating blood pressure using Windkessel model on photoplethysmogram

Simple and non-invasive methods to estimate vital signs are very important for preventive healthcare. In this paper, we present a methodology to estimate Blood Pressure (BP) using Photoplethysmography (PPG). Instead of directly relating systolic and diastolic BP values with PPG features, our proposed methodology initially maps PPG features with some person specific intermediate latent parameters and later derives BP values from them. The 2-Element Windkessel model has been considered in the current context to estimate total peripheral resistance and arterial compliance of a person using PPG features, followed by linear regression for simulating arterial blood pressure. Experimental results, performed on a standard hospital dataset yielded absolute errors of 0.78±13.1 mmHg and 0.59 ± 10.23 mmHg for systolic and diastolic BP values respectively. Results also indicate that the methodology is more robust than the standard methodologies that directly estimate BP values from PPG signal.
Deep Convolutional Neural Networks for Hyperspectral Image Classification

Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
Efficient Additive Kernels via Explicit Feature Maps

Large scale nonlinear support vector machines (SVMs) can be approximated by linear ones using a suitable feature map. The linear SVMs are in general much faster to learn and evaluate (test) than the original nonlinear SVMs. This work introduces explicit feature maps for the additive class of kernels, such as the intersection, Hellinger's, and χ2 kernels, commonly used in computer vision, and enables their use in large scale problems. In particular, we: 1) provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels; 2) derive corresponding approximate finite-dimensional feature maps based on a spectral analysis; and 3) quantify the error of the approximation, showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as χ2. We demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train/test times of SVMs. We also compare with two other approximation methods: Nystrom's approximation of Perronnin et al. , which is data dependent, and the explicit map of Maji and Berg  for the intersection kernel, which, as in the case of our approximations, is data independent. The approximations are evaluated on a number of standard data sets, including Caltech-101 , Daimler-Chrysler pedestrians [4], and INRIA pedestrians .