nils-herrmann/clean_scirepeval huggingface dataset:
Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

Soft shadows, depth of field, and diffuse global illumination are common distribution effects, usually rendered by Monte Carlo ray tracing. Physically correct, noise-free images can require hundreds or thousands of ray samples per pixel, and take a long time to compute. Recent approaches have exploited sparse sampling and filtering; the filtering is either fast (axis-aligned), but requires more input samples, or needs fewer input samples but is very slow (sheared). We present a new approach for fast sheared filtering on the GPU. Our algorithm factors the 4D sheared filter into four 1D filters. We derive complexity bounds for our method, showing that the per-pixel complexity is reduced from O(n2 l2) to O(nl), where n is the linear filter width (filter size is O(n2)) and l is the (usually very small) number of samples for each dimension of the light or lens per pixel (spp is l2). We thus reduce sheared filtering overhead dramatically. We demonstrate rendering of depth of field, soft shadows and diffuse global illumination at interactive speeds. We reduce the number of samples needed by 5-8×, compared to axis-aligned filtering, and framerates are 4× faster for equal quality.
Hype and Dump Manipulation ∗

This paper introduces signaling in a standard market microstructure model so as to explore the economic circumstances under which hype and dump manipulation can be an equilibrium outcome. We consider a discrete time, multi-period model with stages of signaling and asset trading. A single informed trader contemplates whether or not to spread a (possibly dishonest) rumor on the asset payoff among uninformed traders. Dishonest rumor-mongering is costly due to regulatory enforcement, and the uninformed traders who access the rumor can besophisticatedor naive. The sophisticated traders correctly anticipate the relationship between the rumor and the asset payoff, whereas the naive ones take the rumor at its face value as if it truthfully reveals the asset payoff. The presence of sophisticated traders puts the informed trader off from rumor-mongering, because sophisticates fully infer the asset payoff from the rumor, reducing the informational rents enjoyed by the informed trader. Nevertheless we show that it can be optimal for an informed trader to create false hype among uninformed traders provided that there is at least one naive trader in the market and the cost of dishonest rumor-mongering is not too low. The false hype allows the informed trader to sell at an inflated price or buy at a deflated one. Intense regulatory enforcement, which makes dishonest rumor-mongering very costly, may not necessarily curb hype and dump schemes. Market depth and trading volume rise with “hype and dump” while market efficiency decreases.
Random Graphs and Complex Networks

In this chapter, we draw motivation from real-world networks, and formulate random graph models for them. We focus on some of the models that have received the most attention in the literature, namely, the Erdős-Rényi random graph, Inhomogeneous random graphs, the configuration model and preferential attachment models. We also discuss some of their extensions that have the potential to yield more realistic models for real-world networks. We follow van der Hofstad (2017), which we refer to as [Volume 1], both for the motivation as well as for the introduction of the random graph models involved. We then continue to discuss an important technique in this book, called local-weak convergence. Looking back, and ahead In Volume 1 of this pair of books, we have discussed various models having flexible degree sequences. The generalized random graph and the configuration model give us static flexible models for random graphs with various degree sequences. Preferential attachment models give us a convincing explanation of the abundance of power-law degree sequences in various applications. In [Volume 1, Chapters 6– 8], we have focussed on the properties of the degrees of such graphs. However, we have noted in [Volume 1, Chapter 1] that many real-world networks not only have degree sequences that are rather different from the ones of the Erdős-Rényi random graph, also many examples are small worlds and have a giant connected component. In Chapters 2–7, we shall return to the models discussed in [Volume 1, Chapters 6–8], and focus on their connected components as well as on the distances in these random graph models. Interestingly, a large chunk of the non-rigorous physics literature suggests that the behavior in various different random graph models can be described by only a few essential parameters. The key parameter of each of these models in the power-law degree exponent, and the physics literature predicts that the behavior in random graph models with similar degree sequences is similar. This is an example of the notion of universality, a notion which is central in statistical physics. Despite its importance, there are only few example of universality that can be rigorously proved. In Chapters 2–7, we investigate the level of universality present in random graph models. We will often refer to Volume 1. When we do, we write [Volume 1, Theorem 2.17] to mean that we refer to Theorem 2.17 in van der Hofstad (2017). Organisation of this chapter This chapter is organised as follows. In Section 1.1, we discuss real-world networks the inspiration that they provide. In Section 1.2, we then discuss how graph
Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.
Citizen adoption of electronic government initiatives

Electronic government, or e-government, increases the convenience and accessibility of government services and information to citizens. Despite the benefits of e-government - increased government accountability to citizens, greater public access to information, and a more efficient, cost-effective government - the success and acceptance of e-government initiatives, such as online voting and license renewal, are contingent upon citizens' willingness to adopt this innovation. In order to develop "citizen-centered" e-government services that provide citizens with accessible, relevant information and quality services that are more expedient than traditional "brick and mortar" transactions, government agencies must first understand the factors that influence citizen adoption of this innovation. This study integrates constructs from the technology acceptance model (TAM), diffusions of innovation theory (DOI) and Web trust model to form a parsimonious, yet comprehensive model of factors that influence citizen adoption of electronic government initiatives. The findings and implications of this study are discussed in the paper.
Black SDN for the Internet of Things

In this paper, we present Black SDN, a Software Defined Networking (SDN) architecture for secure Internet of Things (IoT) networking and communications. SDN architectures were developed to provide improved routing and networking performance for broadband networks by separating the control plain from the data plain. This basic SDN concept is amenable to IoT networks, however, the common SDN implementations designed for wired networks are not directly amenable to the distributed, ad hoc, low-power, mesh networks commonly found in IoT systems. SDN promises to improve the overall lifespan and performance of IoT networks. However, the SDN architecture changes the IoT network's communication patterns, allowing new types of attacks, and necessitating a new approach to securing the IoT network. Black SDN is a novel SDN-based secure networking architecture that secures both the meta-data and the payload within each layer of an IoT communication packet while utilizing the SDN centralized controller as a trusted third party for secure routing and optimized system performance management. We demonstrate through simulation the feasibility of Black SDN in networks where nodes are asleep most of their lives, and specifically examine a Black SDN IoT network based upon the IEEE 802.15.4 LR WPAN (Low Rate - Wireless Personal Area Network) protocol.
Development of a variable parallelogram tracked mobile robot

Based on the characteristics of wheeled, tracked and legged movements, a variable parallelogram tracked mobile robot(VPTMR) is proposed and developed to enhance its adaptability and stability in the complex environment. This VPTMR robot consists of two variable parallelogram structures, which are composed of one main tracked arm, two lower tracked arms and a chasis. The variable parallelogram structure is actuated by a DC motor. And another DC motor actuates the track rotation, which enables VPTMR robot to move in wheeled, tracked and legged mode that makes the robot to adapt to all rugged environments. The prototype(VPTMR) is developed to verify its performance on environmental adaptability, obstacle crossing ability and stability.
Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data

Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.
Task complexity and contingent processing in decision making: An information search and protocol analysis

Two process tracing techniques, explicit information search and verbal protocols, were used to examine the information processing strategies subjects use in reaching a decision. Subjects indicated preferences among apartments. The number of alternatives available and number of dimensions of information available was varied across sets of apartments. When faced with a two alternative situation, the subjects employed search strategies consistent with a compensatory decision process. In contrast, when faced with a more complex (multialternative) decision task, the subjects employed decision strategies designed to eliminate some of the available alternatives as quickly as possible and on the basis of a limited amount of information search and evaluation. The results demonstrate that the information processing leading to choice will vary as a function of task complexity. An integration of research in decision behavior with the methodology and theory of more established areas of cognitive psychology, such as human problem solving, is advocated.
Solutions to Security and Privacy Issues in Mobile Social Networking

Social network information is now being used in ways for which it may have not been originally intended. In particular, increased use of smartphones capable ofrunning applications which access social network information enable applications to be aware of a user's location and preferences. However, current models forexchange of this information require users to compromise their privacy and security. We present several of these privacy and security issues, along withour design and implementation of solutions for these issues. Our work allows location-based services to query local mobile devices for users' social network information, without disclosing user identity or compromising users' privacy and security. We contend that it is important that such solutions be acceptedas mobile social networks continue to grow exponentially.
Probabilistic CFG with Latent Annotations

This paper defines a generative probabilistic model of parse trees, which we call PCFG-LA. This model is an extension of PCFG in which non-terminal symbols are augmented with latent variables. Finegrained CFG rules are automatically induced from a parsed corpus by training a PCFG-LA model using an EM-algorithm. Because exact parsing with a PCFG-LA is NP-hard, several approximations are described and empirically compared. In experiments using the Penn WSJ corpus, our automatically trained model gave a performance of 86.6% (F , sentences 40 words), which is comparable to that of an unlexicalized PCFG parser created using extensive manual feature selection.
Linear Phase Low-Pass IIR Digital Differentiators

A novel approach to designing approximately linear phase infinite-impulse-response (IIR) digital filters in the passband region is introduced. The proposed approach yields digital IIR filters whose numerators represent linear phase finite-impulse-response (FIR) filters. As an example, low-pass IIR differentiators are introduced. The range and high-frequency suppression of the proposed low-pass differentiators are comparable to those obtained by higher order FIR low-pass differentiators. In addition, the differentiators exhibit almost linear phases in the passband regions
Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex

Recently, several pattern recognition methods have been proposed to automatically discriminate between patients with and without Alzheimer's disease using different imaging modalities: sMRI, fMRI, PET and SPECT. Classical approaches in visual information retrieval have been successfully used for analysis of structural MRI brain images. In this paper, we use the visual indexing framework and pattern recognition analysis based on structural MRI data to discriminate three classes of subjects: normal controls (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD). The approach uses the circular harmonic functions (CHFs) to extract local features from the most involved areas in the disease: hippocampus and posterior cingulate cortex (PCC) in each slice in all three brain projections. The features are quantized using the Bag-of-Visual-Words approach to build one signature by brain (subject). This yields a transformation of a full 3D image of brain ROIs into a 1D signature, a histogram of quantized features. To reduce the dimensionality of the signature, we use the PCA technique. Support vector machines classifiers are then applied to classify groups. The experiments were conducted on a subset of ADNI dataset and applied to the "Bordeaux-3City" dataset. The results showed that our approach achieves respectively for ADNI dataset and "Bordeaux-3City" dataset; for AD vs NC classification, an accuracy of 83.77% and 78%, a specificity of 88.2% and 80.4% and a sensitivity of 79.09% and 74.7%. For NC vs MCI classification we achieved for the ADNI datasets an accuracy of 69.45%, a specificity of 74.8% and a sensitivity of 62.52%. For the most challenging classification task (AD vs MCI), we reached an accuracy of 62.07%, a specificity of 75.15% and a sensitivity of 49.02%. The use of PCC visual features description improves classification results by more than 5% compared to the use of hippocampus features only. Our approach is automatic, less time-consuming and does not require the intervention of the clinician during the disease diagnosis.
Agricultural Production System Based on IoT

There has been much research and various attempts to apply new IoT technology to agricultural areas. However, IoT for the agriculture should be considered differently against the same areas such as industrial, logistics. This paper presents the IoT-based agricultural production system for stabilizing supply and demand of agricultural products while developing the environment sensors and prediction system for the growth and production amount of crops by gathering its environmental information. Currently, the demand by consumption of agricultural products could be predicted quantitatively, however, the variation of harvest and production by the change of farm's cultivated area, weather change, disease and insect damage etc. could not be predicted, so that the supply and demand of agricultural products has not been controlled properly. To overcome it, this paper designed the IoT-based monitoring system to analyze crop environment, and the method to improve the efficiency of decision making by analyzing harvest statistics. Therefore, this paper developed the decision support system to forecast agricultural production using IoT sensors. This system was also a unified system that supports the processes sowing seeds through selling agricultural products to consumers. 3 Corresponding author The IoT-based agricultural production system through correlation analysis between the crop statistical information and agricultural environment information has enhanced the ability of farmers, researchers, and government officials to analyze current conditions and predict future harvest. Additionally, agricultural products quality can be improved because farmers observe whole cycle from seeding to selling using this IoT-based decision support system.
A Low Power Consumption Algorithm for Efficient Energy Consumption in ZigBee Motes

Wireless Sensor Networks (WSNs) are becoming increasingly popular since they can gather information from different locations without wires. This advantage is exploited in applications such as robotic systems, telecare, domotic or smart cities, among others. To gain independence from the electricity grid, WSNs devices are equipped with batteries, therefore their operational time is determined by the time that the batteries can power on the device. As a consequence, engineers must consider low energy consumption as a critical objective to design WSNs. Several approaches can be taken to make efficient use of energy in WSNs, for instance low-duty-cycling sensor networks (LDC-WSN). Based on the LDC-WSNs, we present LOKA, a LOw power Konsumption Algorithm to minimize WSNs energy consumption using different power modes in a sensor mote. The contribution of the work is a novel algorithm called LOKA that implements two duty-cycling mechanisms using the end-device of the ZigBee protocol (of the Application Support Sublayer) and an external microcontroller (Cortex M0+) in order to minimize the energy consumption of a delay tolerant networking. Experiments show that using LOKA, the energy required by the sensor device is reduced to half with respect to the same sensor device without using LOKA.
Internet Over-Users' Psychological Profiles: A Behavior Sampling Analysis on Internet Addiction

What kinds of psychological features do people have when they are overly involved in usage of the internet? Internet users in Korea were investigated in terms of internet over-use and related psychological profiles by the level of internet use. We used a modified Young's Internet Addiction Scale, and 13,588 users (7,878 males, 5,710 females), out of 20 million from a major portal site in Korea, participated in this study. Among the sample, 3.5% had been diagnosed as internet addicts (IA), while 18.4% of them were classified as possible internet addicts (PA). The Internet Addiction Scale showed a strong relationship with dysfunctional social behaviors. More IA tried to escape from reality than PA and Non-addicts (NA). When they got stressed out by work or were just depressed, IA showed a high tendency to access the internet. The IA group also reported the highest degree of loneliness, depressed mood, and compulsivity compared to the other groups. The IA group seemed to be more vulnerable to interpersonal dangers than others, showing an unusually close feeling for strangers. Further study is needed to investigate the direct relationship between psychological well-being and internet dependency.
Individual Voltage Balancing Strategy for PWM Cascaded H-Bridge Converter-Based STATCOM

This paper presents a new control method for cascaded connected H-bridge converter-based static compensators. These converters have classically been commutated at fundamental line frequencies, but the evolution of power semiconductors has allowed the increase of switching frequencies and power ratings of these devices, permitting the use of pulsewidth modulation techniques. This paper mainly focuses on dc-bus voltage balancing problems and proposes a new control technique (individual voltage balancing strategy), which solves these balancing problems, maintaining the delivered reactive power equally distributed among all the H-bridges of the converter.
Random Redundant Soft-In Soft-Out Decoding of Linear Block Codes

A number of authors have recently considered iterative soft-in soft-out (SISO) decoding algorithms for classical linear block codes that utilize redundant Tanner graphs. Jiang and Narayanan presented a practically realizable algorithm that applies only to cyclic codes while Kothiyal et al. presented an algorithm that, while applicable to arbitrary linear block codes, does not imply a low-complexity implementation. This work first presents the aforementioned algorithms in a common framework and then presents a related algorithm - random redundant iterative decoding - that is both practically realizable and applicable to arbitrary linear block codes. Simulation results illustrate the successful application of the random redundant iterative decoding algorithm to the extended binary Golay code. Additionally, the proposed algorithm is shown to outperform Jiang and Narayanan's algorithm for a number of Bose-Chaudhuri-Hocquenghem (BCH) codes
Language Models for Image Captioning: The Quirks and What Works

Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits of these different language modeling approaches for the first time by using the same state-ofthe-art CNN as input. We examine issues in the different approaches, including linguistic irregularities, caption repetition, and data set overlap. By combining key aspects of the ME and RNN methods, we achieve a new record performance over previously published results on the benchmark COCO dataset. However, the gains we see in BLEU do not translate to human judgments.